





"""simple docstring"""
import inspect
import unittest
import warnings
from typing import List

from transformers import EfficientFormerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import (
      MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
      MODEL_MAPPING,
      EfficientFormerForImageClassification,
      EfficientFormerForImageClassificationWithTeacher,
      EfficientFormerModel,
  )
  from transformers.models.efficientformer.modeling_efficientformer import (
      EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
  )


if is_vision_available():
  from PIL import Image

  from transformers import EfficientFormerImageProcessor






class       _SCREAMING_SNAKE_CASE:




       def __init__(		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__ = 13	,SCREAMING_SNAKE_CASE__ = 64	,SCREAMING_SNAKE_CASE__ = 2	,SCREAMING_SNAKE_CASE__ = 3	,SCREAMING_SNAKE_CASE__ = 3	,SCREAMING_SNAKE_CASE__ = True	,SCREAMING_SNAKE_CASE__ = True	,SCREAMING_SNAKE_CASE__ = 1_28	,SCREAMING_SNAKE_CASE__=[16, 32, 64, 1_28]	,SCREAMING_SNAKE_CASE__ = 7	,SCREAMING_SNAKE_CASE__ = 4	,SCREAMING_SNAKE_CASE__ = 37	,SCREAMING_SNAKE_CASE__ = "gelu"	,SCREAMING_SNAKE_CASE__ = 0.1	,SCREAMING_SNAKE_CASE__ = 0.1	,SCREAMING_SNAKE_CASE__ = 10	,SCREAMING_SNAKE_CASE__ = 0.0_2	,SCREAMING_SNAKE_CASE__ = 2	,SCREAMING_SNAKE_CASE__ = 1	,SCREAMING_SNAKE_CASE__ = 1_28	,SCREAMING_SNAKE_CASE__ = [2, 2, 2, 2]	,SCREAMING_SNAKE_CASE__ = 2	,SCREAMING_SNAKE_CASE__ = 2	,)    ->   Union[str, Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Any	       =						parent
             __SCREAMING_SNAKE_CASE     :int	       =						batch_size
             __SCREAMING_SNAKE_CASE     :List[str]	       =						image_size
             __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						patch_size
             __SCREAMING_SNAKE_CASE     :Any	       =						num_channels
             __SCREAMING_SNAKE_CASE     :Optional[int]	       =						is_training
             __SCREAMING_SNAKE_CASE     :List[str]	       =						use_labels
             __SCREAMING_SNAKE_CASE     :int	       =						hidden_size
             __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						num_hidden_layers
             __SCREAMING_SNAKE_CASE     :Optional[int]	       =						num_attention_heads
             __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						intermediate_size
             __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						hidden_act
             __SCREAMING_SNAKE_CASE     :List[Any]	       =						hidden_dropout_prob
             __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						attention_probs_dropout_prob
             __SCREAMING_SNAKE_CASE     :Tuple	       =						type_sequence_label_size
             __SCREAMING_SNAKE_CASE     :int	       =						initializer_range
             __SCREAMING_SNAKE_CASE     :List[str]	       =						encoder_stride
             __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						num_attention_outputs
             __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						embed_dim
             __SCREAMING_SNAKE_CASE     :int	       =						embed_dim + 1
             __SCREAMING_SNAKE_CASE     :Optional[int]	       =						resolution
             __SCREAMING_SNAKE_CASE     :Dict	       =						depths
             __SCREAMING_SNAKE_CASE     :List[str]	       =						hidden_sizes
             __SCREAMING_SNAKE_CASE     :Any	       =						dim
             __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						mlp_expansion_ratio




       def 					_UpperCamelCase  (		self							)    ->   Optional[int]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Dict	       =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

             __SCREAMING_SNAKE_CASE     :Tuple	       =						None
             if self.use_labels:
                   __SCREAMING_SNAKE_CASE     :Dict	       =						ids_tensor([self.batch_size]	,self.type_sequence_label_size							)

             __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						self.get_config()
             return config, pixel_values, labels




       def 					_UpperCamelCase  (		self							)    ->   Dict:





             """simple docstring"""






             return EfficientFormerConfig(
                 image_size=self.image_size	,patch_size=self.patch_size	,num_channels=self.num_channels	,hidden_size=self.hidden_size	,num_hidden_layers=self.num_hidden_layers	,num_attention_heads=self.num_attention_heads	,intermediate_size=self.intermediate_size	,hidden_act=self.hidden_act	,hidden_dropout_prob=self.hidden_dropout_prob	,attention_probs_dropout_prob=self.attention_probs_dropout_prob	,is_decoder=SCREAMING_SNAKE_CASE__	,initializer_range=self.initializer_range	,encoder_stride=self.encoder_stride	,resolution=self.resolution	,depths=self.depths	,hidden_sizes=self.hidden_sizes	,dim=self.dim	,mlp_expansion_ratio=self.mlp_expansion_ratio	,)




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							)    ->   str:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Tuple	       =						EfficientFormerModel(config=SCREAMING_SNAKE_CASE__							)
             model.to(SCREAMING_SNAKE_CASE__							)
             model.eval()
             __SCREAMING_SNAKE_CASE     :List[Any]	       =						model(SCREAMING_SNAKE_CASE__							)
             self.parent.assertEqual(result.last_hidden_state.shape	,(self.batch_size, self.seq_length, self.hidden_size)							)




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							)    ->   Tuple:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						self.type_sequence_label_size
             __SCREAMING_SNAKE_CASE     :str	       =						EfficientFormerForImageClassification(SCREAMING_SNAKE_CASE__							)
             model.to(SCREAMING_SNAKE_CASE__							)
             model.eval()
             __SCREAMING_SNAKE_CASE     :Dict	       =						model(SCREAMING_SNAKE_CASE__	,labels=SCREAMING_SNAKE_CASE__							)
             self.parent.assertEqual(result.logits.shape	,(self.batch_size, self.type_sequence_label_size)							)

             # test greyscale images
             __SCREAMING_SNAKE_CASE     :Dict	       =						1
             __SCREAMING_SNAKE_CASE     :List[Any]	       =						EfficientFormerForImageClassification(SCREAMING_SNAKE_CASE__							)
             model.to(SCREAMING_SNAKE_CASE__							)
             model.eval()

             __SCREAMING_SNAKE_CASE     :Optional[int]	       =						floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
             __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						model(SCREAMING_SNAKE_CASE__							)
             self.parent.assertEqual(result.logits.shape	,(self.batch_size, self.type_sequence_label_size)							)




       def 					_UpperCamelCase  (		self							)    ->   Union[str, Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Optional[int]	       =						self.prepare_config_and_inputs()
             (
                 (
                 __SCREAMING_SNAKE_CASE
             )       ,(
                 __SCREAMING_SNAKE_CASE
             )       ,(
                 __SCREAMING_SNAKE_CASE
             )       ,
             )     :int	       =						config_and_inputs
             __SCREAMING_SNAKE_CASE     :str	       =						{'''pixel_values''': pixel_values}
             return config, inputs_dict






@require_torch
class       _SCREAMING_SNAKE_CASE(     A					,			A					,			unittest.TestCase						):
       SCREAMING_SNAKE_CASE_     :   Tuple					=						(
           (
               EfficientFormerModel,
               EfficientFormerForImageClassificationWithTeacher,
               EfficientFormerForImageClassification,
           )
           if is_torch_available()
           else ()
       )
       SCREAMING_SNAKE_CASE_     :   Optional[Any]					=						(
           {
               '''feature-extraction''': EfficientFormerModel,
               '''image-classification''': (
                   EfficientFormerForImageClassification,
                   EfficientFormerForImageClassificationWithTeacher,
               ),
           }
           if is_torch_available()
           else {}
       )
       SCREAMING_SNAKE_CASE_     :   Tuple					=						False

       SCREAMING_SNAKE_CASE_     :   Union[str, Any]					=						False
       SCREAMING_SNAKE_CASE_     :   Union[str, Any]					=						False
       SCREAMING_SNAKE_CASE_     :   List[str]					=						False




       def 					_UpperCamelCase  (		self							)    ->   Union[str, Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :str	       =						EfficientFormerModelTester(self							)
             __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						ConfigTester(
                 self	,config_class=SCREAMING_SNAKE_CASE__	,has_text_modality=SCREAMING_SNAKE_CASE__	,hidden_size=37							)




       def 					_UpperCamelCase  (		self							)    ->   List[str]:





             """simple docstring"""






             self.config_tester.run_common_tests()




       @unittest.skip(reason='''EfficientFormer does not use inputs_embeds'''							)
       def 					_UpperCamelCase  (		self							)    ->   int:





             """simple docstring"""






             pass




       @unittest.skip(reason='''EfficientFormer does not support input and output embeddings'''							)
       def 					_UpperCamelCase  (		self							)    ->   Any:





             """simple docstring"""






             pass




       def 					_UpperCamelCase  (		self							)    ->   Optional[Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                   __SCREAMING_SNAKE_CASE     :Any	       =						model_class(SCREAMING_SNAKE_CASE__							)
                   __SCREAMING_SNAKE_CASE     :str	       =						inspect.signature(model.forward							)
                   # signature.parameters is an OrderedDict => so arg_names order is deterministic
                   __SCREAMING_SNAKE_CASE     :List[Any]	       =						[*signature.parameters.keys()]

                   __SCREAMING_SNAKE_CASE     :List[Any]	       =						['''pixel_values''']
                   self.assertListEqual(arg_names[:1]	,SCREAMING_SNAKE_CASE__							)




       def 					_UpperCamelCase  (		self							)    ->   List[Any]:





             """simple docstring"""






             def check_hidden_states_output(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							):
                   __SCREAMING_SNAKE_CASE     :Tuple	       =						model_class(SCREAMING_SNAKE_CASE__							)
                   model.to(SCREAMING_SNAKE_CASE__							)
                   model.eval()

                   with torch.no_grad():
                         __SCREAMING_SNAKE_CASE     :List[Any]	       =						model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							)							)

                   __SCREAMING_SNAKE_CASE     :List[Any]	       =						outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

                   __SCREAMING_SNAKE_CASE     :int	       =						getattr(
                       self.model_tester	,'''expected_num_hidden_layers'''	,self.model_tester.num_hidden_layers + 1							)
                   self.assertEqual(len(SCREAMING_SNAKE_CASE__							)	,SCREAMING_SNAKE_CASE__							)

                   if hasattr(self.model_tester	,'''encoder_seq_length'''							):
                         __SCREAMING_SNAKE_CASE     :Any	       =						self.model_tester.encoder_seq_length
                         if hasattr(self.model_tester	,'''chunk_length'''							) and self.model_tester.chunk_length > 1:
                               __SCREAMING_SNAKE_CASE     :Dict	       =						seq_length * self.model_tester.chunk_length
                   else:
                         __SCREAMING_SNAKE_CASE     :Optional[int]	       =						self.model_tester.seq_length

                   self.assertListEqual(
                       list(hidden_states[-1].shape[-2:]							)	,[seq_length, self.model_tester.hidden_size]	,)

                   if config.is_encoder_decoder:
                         __SCREAMING_SNAKE_CASE     :List[str]	       =						outputs.decoder_hidden_states

                         self.assertIsInstance(SCREAMING_SNAKE_CASE__	,(list, tuple)							)
                         self.assertEqual(len(SCREAMING_SNAKE_CASE__							)	,SCREAMING_SNAKE_CASE__							)
                         __SCREAMING_SNAKE_CASE     :List[str]	       =						getattr(self.model_tester	,'''seq_length'''	,SCREAMING_SNAKE_CASE__							)
                         __SCREAMING_SNAKE_CASE     :Tuple	       =						getattr(self.model_tester	,'''decoder_seq_length'''	,SCREAMING_SNAKE_CASE__							)

                         self.assertListEqual(
                             list(hidden_states[-1].shape[-2:]							)	,[decoder_seq_length, self.model_tester.hidden_size]	,)

             __SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE     :List[Any]	       =						self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                   __SCREAMING_SNAKE_CASE     :List[str]	       =						True
                   check_hidden_states_output(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							)

                   # check that output_hidden_states also work using config
                   del inputs_dict["output_hidden_states"]
                   __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						True

                   check_hidden_states_output(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							)




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__=False							)    ->   List[str]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Optional[int]	       =						super()._prepare_for_class(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,return_labels=SCREAMING_SNAKE_CASE__							)

             if return_labels:
                   if model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher":
                         del inputs_dict["labels"]

             return inputs_dict




       def 					_UpperCamelCase  (		self							)    ->   int:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__							)




       @unittest.skip(reason='''EfficientFormer does not implement masked image modeling yet'''							)
       def 					_UpperCamelCase  (		self							)    ->   Optional[int]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Dict	       =						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_masked_image_modeling(*SCREAMING_SNAKE_CASE__							)




       def 					_UpperCamelCase  (		self							)    ->   List[Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE__							)




       def 					_UpperCamelCase  (		self							)    ->   Union[str, Any]:





             """simple docstring"""






             if not self.model_tester.is_training:
                   return

             __SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE     :str	       =						self.model_tester.prepare_config_and_inputs_for_common()
             __SCREAMING_SNAKE_CASE     :int	       =						True

             for model_class in self.all_model_classes:
                   # EfficientFormerForImageClassificationWithTeacher supports inference-only
                   if (
                       model_class in get_values(SCREAMING_SNAKE_CASE__							)
                       or model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher"
                   ):
                         continue
                   __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						model_class(SCREAMING_SNAKE_CASE__							)
                   model.to(SCREAMING_SNAKE_CASE__							)
                   model.train()
                   __SCREAMING_SNAKE_CASE     :Optional[int]	       =						self._prepare_for_class(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,return_labels=SCREAMING_SNAKE_CASE__							)
                   __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						model(**SCREAMING_SNAKE_CASE__							).loss
                   loss.backward()




       def 					_UpperCamelCase  (		self							)    ->   Dict:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE     :Tuple	       =						self.model_tester.prepare_config_and_inputs_for_common()

             __SCREAMING_SNAKE_CASE     :str	       =						[
                 {'''title''': '''multi_label_classification''', '''num_labels''': 2, '''dtype''': torch.float},
                 {'''title''': '''single_label_classification''', '''num_labels''': 1, '''dtype''': torch.long},
                 {'''title''': '''regression''', '''num_labels''': 1, '''dtype''': torch.float},
             ]

             for model_class in self.all_model_classes:
                   if (
                       model_class
                       not in [
                           *get_values(SCREAMING_SNAKE_CASE__							),
                       ]
                       or model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher"
                   ):
                         continue

                   for problem_type in problem_types:
                         with self.subTest(msg=f'''Testing {model_class} with {problem_type["title"]}'''							):
                               __SCREAMING_SNAKE_CASE     :str	       =						problem_type['''title''']
                               __SCREAMING_SNAKE_CASE     :Tuple	       =						problem_type['''num_labels''']

                               __SCREAMING_SNAKE_CASE     :List[str]	       =						model_class(SCREAMING_SNAKE_CASE__							)
                               model.to(SCREAMING_SNAKE_CASE__							)
                               model.train()

                               __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						self._prepare_for_class(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,return_labels=SCREAMING_SNAKE_CASE__							)

                               if problem_type["num_labels"] > 1:
                                     __SCREAMING_SNAKE_CASE     :List[Any]	       =						inputs['''labels'''].unsqueeze(1							).repeat(1	,problem_type['''num_labels''']							)

                               __SCREAMING_SNAKE_CASE     :List[Any]	       =						inputs['''labels'''].to(problem_type['''dtype''']							)

                               # This tests that we do not trigger the warning form PyTorch "Using a target size that is different
                               # to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
                               # they have the same size." which is a symptom something in wrong for the regression problem.
                               # See https://github.com/huggingface/transformers/issues/11780
                               with warnings.catch_warnings(record=SCREAMING_SNAKE_CASE__							) as warning_list:
                                     __SCREAMING_SNAKE_CASE     :Tuple	       =						model(**SCREAMING_SNAKE_CASE__							).loss
                               for w in warning_list:
                                     if "Using a target size that is different to the input size" in str(w.message							):
                                           raise ValueError(
                                               f'''Something is going wrong in the regression problem: intercepted {w.message}'''							)

                               loss.backward()




       @slow
       def 					_UpperCamelCase  (		self							)    ->   List[Any]:





             """simple docstring"""






             for model_name in EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						EfficientFormerModel.from_pretrained(SCREAMING_SNAKE_CASE__							)
                   self.assertIsNotNone(SCREAMING_SNAKE_CASE__							)




       def 					_UpperCamelCase  (		self							)    ->   str:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE     :Any	       =						self.model_tester.prepare_config_and_inputs_for_common()
             __SCREAMING_SNAKE_CASE     :List[str]	       =						True

             __SCREAMING_SNAKE_CASE     :List[Any]	       =						getattr(self.model_tester	,'''seq_length'''	,SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE     :Tuple	       =						getattr(self.model_tester	,'''encoder_seq_length'''	,SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE     :Optional[int]	       =						getattr(self.model_tester	,'''key_length'''	,SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						getattr(self.model_tester	,'''chunk_length'''	,SCREAMING_SNAKE_CASE__							)

             if chunk_length is not None and hasattr(self.model_tester	,'''num_hashes'''							):
                   __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						encoder_seq_length * self.model_tester.num_hashes

             for model_class in self.all_model_classes:
                   __SCREAMING_SNAKE_CASE     :int	       =						True
                   __SCREAMING_SNAKE_CASE     :List[str]	       =						False
                   __SCREAMING_SNAKE_CASE     :str	       =						True
                   __SCREAMING_SNAKE_CASE     :Optional[int]	       =						model_class(SCREAMING_SNAKE_CASE__							)
                   model.to(SCREAMING_SNAKE_CASE__							)
                   model.eval()
                   with torch.no_grad():
                         __SCREAMING_SNAKE_CASE     :Dict	       =						model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							)							)
                   __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
                   self.assertEqual(len(SCREAMING_SNAKE_CASE__							)	,self.model_tester.num_attention_outputs							)

                   # check that output_attentions also work using config
                   del inputs_dict["output_attentions"]
                   __SCREAMING_SNAKE_CASE     :List[Any]	       =						True
                   __SCREAMING_SNAKE_CASE     :str	       =						model_class(SCREAMING_SNAKE_CASE__							)
                   model.to(SCREAMING_SNAKE_CASE__							)
                   model.eval()
                   with torch.no_grad():
                         __SCREAMING_SNAKE_CASE     :List[Any]	       =						model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							)							)
                   __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
                   self.assertEqual(len(SCREAMING_SNAKE_CASE__							)	,self.model_tester.num_attention_outputs							)

                   if chunk_length is not None:
                         self.assertListEqual(
                             list(attentions[0].shape[-4:]							)	,[self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length]	,)
                   else:
                         self.assertListEqual(
                             list(attentions[0].shape[-3:]							)	,[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]	,)




def   __lowerCamelCase     (							)    ->   List[Any]:
      __SCREAMING_SNAKE_CASE     :Any	       =						Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
      return image







@require_torch
@require_vision
class       _SCREAMING_SNAKE_CASE(     unittest.TestCase						):




       @cached_property
       def 					_UpperCamelCase  (		self							)    ->   Dict:





             """simple docstring"""






             return (
                 EfficientFormerImageProcessor.from_pretrained('''snap-research/efficientformer-l1-300'''							)
                 if is_vision_available()
                 else None
             )




       @slow
       def 					_UpperCamelCase  (		self							)    ->   Tuple:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Dict	       =						EfficientFormerForImageClassification.from_pretrained('''snap-research/efficientformer-l1-300'''							).to(
                 SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE     :int	       =						self.default_image_processor
             __SCREAMING_SNAKE_CASE     :Optional[int]	       =						prepare_img()
             __SCREAMING_SNAKE_CASE     :Any	       =						image_processor(images=SCREAMING_SNAKE_CASE__	,return_tensors='''pt'''							).to(SCREAMING_SNAKE_CASE__							)

             # forward pass
             with torch.no_grad():
                   __SCREAMING_SNAKE_CASE     :int	       =						model(**SCREAMING_SNAKE_CASE__							)

             # verify the logits
             __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						(1, 10_00)
             self.assertEqual(outputs.logits.shape	,SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE     :Any	       =						torch.tensor([-0.0_5_5_5, 0.4_8_2_5, -0.0_8_5_2]							).to(SCREAMING_SNAKE_CASE__							)
             self.assertTrue(torch.allclose(outputs.logits[0][:3]	,SCREAMING_SNAKE_CASE__	,atol=1E-4							)							)




       @slow
       def 					_UpperCamelCase  (		self							)    ->   Optional[Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :str	       =						EfficientFormerForImageClassificationWithTeacher.from_pretrained(
                 '''snap-research/efficientformer-l1-300'''							).to(SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE     :List[Any]	       =						self.default_image_processor
             __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						prepare_img()
             __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						image_processor(images=SCREAMING_SNAKE_CASE__	,return_tensors='''pt'''							).to(SCREAMING_SNAKE_CASE__							)

             # forward pass
             with torch.no_grad():
                   __SCREAMING_SNAKE_CASE     :str	       =						model(**SCREAMING_SNAKE_CASE__							)

             # verify the logits
             __SCREAMING_SNAKE_CASE     :str	       =						(1, 10_00)
             self.assertEqual(outputs.logits.shape	,SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE     :List[str]	       =						torch.tensor([-0.1_3_1_2, 0.4_3_5_3, -1.0_4_9_9]							).to(SCREAMING_SNAKE_CASE__							)
             self.assertTrue(torch.allclose(outputs.logits[0][:3]	,SCREAMING_SNAKE_CASE__	,atol=1E-4							)							)






"""simple docstring"""
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowerCamelCase_          =	logging.get_logger(__name__)


lowerCamelCase_          =	{"vocab_file": "vocab.json", "merges_file": "merges.txt"}

# See all MVP models at https://huggingface.co/models?filter=mvp
lowerCamelCase_          =	{
    "vocab_file": {
        "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json",
    },
    "added_tokens.json": {
        "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json",
    },
    "merges_file": {
        "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt",
    },
}

lowerCamelCase_          =	{
    "RUCAIBox/mvp": 1_0_2_4,
}



@lru_cache()
def   __lowerCamelCase     (							)    ->   List[Any]:
      __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						(
          list(range(ord('''!'''							)				,     ord('''~'''							) + 1							)							) + list(range(ord('''¡'''							)				,     ord('''¬'''							) + 1							)							) + list(range(ord('''®'''							)				,     ord('''ÿ'''							) + 1							)							)
      )
      __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						bs[:]
      __SCREAMING_SNAKE_CASE     :str	       =						0
      for b in range(2**8							):
            if b not in bs:
                  bs.append(a_							)
                  cs.append(2**8 + n							)
                  n += 1
      __SCREAMING_SNAKE_CASE     :int	       =						[chr(a_							) for n in cs]
      return dict(zip(a_				,     a_							)							)



def   __lowerCamelCase     (							a_  :	Dict							)    ->   List[str]:
      __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						set()
      __SCREAMING_SNAKE_CASE     :Dict	       =						word[0]
      for char in word[1:]:
            pairs.add((prev_char, char)							)
            __SCREAMING_SNAKE_CASE     :List[str]	       =						char
      return pairs







class       _SCREAMING_SNAKE_CASE(     A						):
       SCREAMING_SNAKE_CASE_     :   str					=						VOCAB_FILES_NAMES
       SCREAMING_SNAKE_CASE_     :   Union[str, Any]					=						PRETRAINED_VOCAB_FILES_MAP
       SCREAMING_SNAKE_CASE_     :   Tuple					=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       SCREAMING_SNAKE_CASE_     :   List[Any]					=						['''input_ids''', '''attention_mask''']




       def __init__(		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__="replace"	,SCREAMING_SNAKE_CASE__="<s>"	,SCREAMING_SNAKE_CASE__="</s>"	,SCREAMING_SNAKE_CASE__="</s>"	,SCREAMING_SNAKE_CASE__="<s>"	,SCREAMING_SNAKE_CASE__="<unk>"	,SCREAMING_SNAKE_CASE__="<pad>"	,SCREAMING_SNAKE_CASE__="<mask>"	,SCREAMING_SNAKE_CASE__=False	,**SCREAMING_SNAKE_CASE__	,)    ->   Dict:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Dict	       =						AddedToken(SCREAMING_SNAKE_CASE__	,lstrip=SCREAMING_SNAKE_CASE__	,rstrip=SCREAMING_SNAKE_CASE__							) if isinstance(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							) else bos_token
             __SCREAMING_SNAKE_CASE     :List[Any]	       =						AddedToken(SCREAMING_SNAKE_CASE__	,lstrip=SCREAMING_SNAKE_CASE__	,rstrip=SCREAMING_SNAKE_CASE__							) if isinstance(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							) else eos_token
             __SCREAMING_SNAKE_CASE     :str	       =						AddedToken(SCREAMING_SNAKE_CASE__	,lstrip=SCREAMING_SNAKE_CASE__	,rstrip=SCREAMING_SNAKE_CASE__							) if isinstance(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							) else sep_token
             __SCREAMING_SNAKE_CASE     :Dict	       =						AddedToken(SCREAMING_SNAKE_CASE__	,lstrip=SCREAMING_SNAKE_CASE__	,rstrip=SCREAMING_SNAKE_CASE__							) if isinstance(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							) else cls_token
             __SCREAMING_SNAKE_CASE     :str	       =						AddedToken(SCREAMING_SNAKE_CASE__	,lstrip=SCREAMING_SNAKE_CASE__	,rstrip=SCREAMING_SNAKE_CASE__							) if isinstance(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							) else unk_token
             __SCREAMING_SNAKE_CASE     :Optional[int]	       =						AddedToken(SCREAMING_SNAKE_CASE__	,lstrip=SCREAMING_SNAKE_CASE__	,rstrip=SCREAMING_SNAKE_CASE__							) if isinstance(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							) else pad_token

             # Mask token behave like a normal word, i.e. include the space before it
             __SCREAMING_SNAKE_CASE     :str	       =						AddedToken(SCREAMING_SNAKE_CASE__	,lstrip=SCREAMING_SNAKE_CASE__	,rstrip=SCREAMING_SNAKE_CASE__							) if isinstance(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							) else mask_token

             super().__init__(
                 errors=SCREAMING_SNAKE_CASE__	,bos_token=SCREAMING_SNAKE_CASE__	,eos_token=SCREAMING_SNAKE_CASE__	,unk_token=SCREAMING_SNAKE_CASE__	,sep_token=SCREAMING_SNAKE_CASE__	,cls_token=SCREAMING_SNAKE_CASE__	,pad_token=SCREAMING_SNAKE_CASE__	,mask_token=SCREAMING_SNAKE_CASE__	,add_prefix_space=SCREAMING_SNAKE_CASE__	,**SCREAMING_SNAKE_CASE__	,)
             with open(SCREAMING_SNAKE_CASE__	,encoding='''utf-8'''							) as vocab_handle:
                   __SCREAMING_SNAKE_CASE     :List[str]	       =						json.load(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE     :Any	       =						{v: k for k, v in self.encoder.items()}
             __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						errors  # how to handle errors in decoding
             __SCREAMING_SNAKE_CASE     :Dict	       =						bytes_to_unicode()
             __SCREAMING_SNAKE_CASE     :Dict	       =						{v: k for k, v in self.byte_encoder.items()}
             with open(SCREAMING_SNAKE_CASE__	,encoding='''utf-8'''							) as merges_handle:
                   __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						merges_handle.read().split('''\n'''							)[1:-1]
             __SCREAMING_SNAKE_CASE     :Optional[int]	       =						[tuple(merge.split()							) for merge in bpe_merges]
             __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						dict(zip(SCREAMING_SNAKE_CASE__	,range(len(SCREAMING_SNAKE_CASE__							)							)							)							)
             __SCREAMING_SNAKE_CASE     :Any	       =						{}
             __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						add_prefix_space

             # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
             __SCREAMING_SNAKE_CASE     :int	       =						re.compile(R'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'''							)




       @property
       def 					_UpperCamelCase  (		self							)    ->   Union[str, Any]:





             """simple docstring"""






             return len(self.encoder							)




       def 					_UpperCamelCase  (		self							)    ->   Dict:





             """simple docstring"""






             return dict(self.encoder	,**self.added_tokens_encoder							)




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__							)    ->   Tuple:





             """simple docstring"""






             if token in self.cache:
                   return self.cache[token]
             __SCREAMING_SNAKE_CASE     :Tuple	       =						tuple(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE     :List[str]	       =						get_pairs(SCREAMING_SNAKE_CASE__							)

             if not pairs:
                   return token

             while True:
                   __SCREAMING_SNAKE_CASE     :List[Any]	       =						min(SCREAMING_SNAKE_CASE__	,key=lambda SCREAMING_SNAKE_CASE__							: self.bpe_ranks.get(SCREAMING_SNAKE_CASE__	,float('''inf'''							)							)							)
                   if bigram not in self.bpe_ranks:
                         break
                   __SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE     :str	       =						bigram
                   __SCREAMING_SNAKE_CASE     :Any	       =						[]
                   __SCREAMING_SNAKE_CASE     :List[Any]	       =						0
                   while i < len(SCREAMING_SNAKE_CASE__							):
                         try:
                               __SCREAMING_SNAKE_CASE     :Dict	       =						word.index(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							)
                         except ValueError:
                               new_word.extend(word[i:]							)
                               break
                         else:
                               new_word.extend(word[i:j]							)
                               __SCREAMING_SNAKE_CASE     :Any	       =						j

                         if word[i] == first and i < len(SCREAMING_SNAKE_CASE__							) - 1 and word[i + 1] == second:
                               new_word.append(first + second							)
                               i += 2
                         else:
                               new_word.append(word[i]							)
                               i += 1
                   __SCREAMING_SNAKE_CASE     :Optional[int]	       =						tuple(SCREAMING_SNAKE_CASE__							)
                   __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						new_word
                   if len(SCREAMING_SNAKE_CASE__							) == 1:
                         break
                   else:
                         __SCREAMING_SNAKE_CASE     :Tuple	       =						get_pairs(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						''' '''.join(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE     :int	       =						word
             return word




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__							)    ->   Optional[Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :str	       =						[]
             for token in re.findall(self.pat	,SCREAMING_SNAKE_CASE__							):
                   __SCREAMING_SNAKE_CASE     :List[Any]	       =						''''''.join(
                       self.byte_encoder[b] for b in token.encode('''utf-8'''							)							)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
                   bpe_tokens.extend(bpe_token for bpe_token in self.bpe(SCREAMING_SNAKE_CASE__							).split(''' '''							)							)
             return bpe_tokens




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__							)    ->   List[Any]:





             """simple docstring"""






             return self.encoder.get(SCREAMING_SNAKE_CASE__	,self.encoder.get(self.unk_token							)							)




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__							)    ->   Optional[Any]:





             """simple docstring"""






             return self.decoder.get(SCREAMING_SNAKE_CASE__							)




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__							)    ->   int:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						''''''.join(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE     :List[Any]	       =						bytearray([self.byte_decoder[c] for c in text]							).decode('''utf-8'''	,errors=self.errors							)
             return text




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__ = None							)    ->   Tuple[str]:





             """simple docstring"""






             if not os.path.isdir(SCREAMING_SNAKE_CASE__							):
                   logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''							)
                   return
             __SCREAMING_SNAKE_CASE     :str	       =						os.path.join(
                 SCREAMING_SNAKE_CASE__	,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']							)
             __SCREAMING_SNAKE_CASE     :Any	       =						os.path.join(
                 SCREAMING_SNAKE_CASE__	,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']							)

             with open(SCREAMING_SNAKE_CASE__	,'''w'''	,encoding='''utf-8'''							) as f:
                   f.write(json.dumps(self.encoder	,indent=2	,sort_keys=SCREAMING_SNAKE_CASE__	,ensure_ascii=SCREAMING_SNAKE_CASE__							) + '''\n'''							)

             __SCREAMING_SNAKE_CASE     :Optional[int]	       =						0
             with open(SCREAMING_SNAKE_CASE__	,'''w'''	,encoding='''utf-8'''							) as writer:
                   writer.write('''#version: 0.2\n'''							)
                   for bpe_tokens, token_index in sorted(self.bpe_ranks.items()	,key=lambda SCREAMING_SNAKE_CASE__							: kv[1]							):
                         if index != token_index:
                               logger.warning(
                                   f'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
                                   ''' Please check that the tokenizer is not corrupted!'''							)
                               __SCREAMING_SNAKE_CASE     :int	       =						token_index
                         writer.write(''' '''.join(SCREAMING_SNAKE_CASE__							) + '''\n'''							)
                         index += 1

             return vocab_file, merge_file




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__ = None							)    ->   List[int]:





             """simple docstring"""






             if token_ids_a is None:
                   return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
             __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						[self.cls_token_id]
             __SCREAMING_SNAKE_CASE     :Optional[int]	       =						[self.sep_token_id]
             return cls + token_ids_a + sep + sep + token_ids_a + sep




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__ = None	,SCREAMING_SNAKE_CASE__ = False							)    ->   List[int]:





             """simple docstring"""






             if already_has_special_tokens:
                   return super().get_special_tokens_mask(
                       token_ids_a=SCREAMING_SNAKE_CASE__	,token_ids_a=SCREAMING_SNAKE_CASE__	,already_has_special_tokens=SCREAMING_SNAKE_CASE__							)

             if token_ids_a is None:
                   return [1] + ([0] * len(SCREAMING_SNAKE_CASE__							)) + [1]
             return [1] + ([0] * len(SCREAMING_SNAKE_CASE__							)) + [1, 1] + ([0] * len(SCREAMING_SNAKE_CASE__							)) + [1]




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__ = None							)    ->   List[int]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :List[Any]	       =						[self.sep_token_id]
             __SCREAMING_SNAKE_CASE     :str	       =						[self.cls_token_id]

             if token_ids_a is None:
                   return len(cls + token_ids_a + sep							) * [0]
             return len(cls + token_ids_a + sep + sep + token_ids_a + sep							) * [0]




       def 					_UpperCamelCase  (		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__=False	,**SCREAMING_SNAKE_CASE__							)    ->   Union[str, Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Tuple	       =						kwargs.pop('''add_prefix_space'''	,self.add_prefix_space							)
             if (is_split_into_words or add_prefix_space) and (len(SCREAMING_SNAKE_CASE__							) > 0 and not text[0].isspace()):
                   __SCREAMING_SNAKE_CASE     :Any	       =						''' ''' + text
             return (text, kwargs)