




'''simple docstring'''


import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin







class     SCREAMING_SNAKE_CASE						(	snake_case_							):
      __magic_name__     :					int             =				'''AutoFeatureExtractor'''
      __magic_name__     :					int             =				'''Speech2Text2Tokenizer'''






      def __init__(   self      :     Optional[Any]	,	lowercase__      :     Optional[Any]	,	lowercase__      :     str					):


           '''simple docstring'''

           super().__init__(lowercase__	,	lowercase__					)
           a_    :       Tuple    	=     self.feature_extractor
           a_    :       Tuple    	=     False






      def __call__(   self      :     Dict	,	*lowercase__      :     Optional[Any]	,	**lowercase__      :     Union[str, Any]					):


           '''simple docstring'''

           if self._in_target_context_manager:
                return self.current_processor(*lowercase__	,	**lowercase__					)

           if "raw_speech" in kwargs:
                warnings.warn("""Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."""					)
                a_    :       str    	=     kwargs.pop("""raw_speech"""					)
           else:
                a_    :       Dict    	=     kwargs.pop("""audio"""	,	lowercase__					)
           a_    :       Optional[Any]    	=     kwargs.pop("""sampling_rate"""	,	lowercase__					)
           a_    :       Any    	=     kwargs.pop("""text"""	,	lowercase__					)
           if len(lowercase__					) > 0:
                a_    :       str    	=     args[0]
                a_    :       int    	=     args[1:]

           if audio is None and text is None:
                raise ValueError("""You need to specify either an `audio` or `text` input to process."""					)

           if audio is not None:
                a_    :       Tuple    	=     self.feature_extractor(lowercase__	,	*lowercase__	,	sampling_rate=lowercase__	,	**lowercase__					)
           if text is not None:
                a_    :       Optional[int]    	=     self.tokenizer(lowercase__	,	**lowercase__					)

           if text is None:
                return inputs
           elif audio is None:
                return encodings
           else:
                a_    :       Optional[int]    	=     encodings["""input_ids"""]
                return inputs






      def        lowercase_	(   self      :     Optional[int]	,	*lowercase__      :     Optional[Any]	,	**lowercase__      :     str					):


           '''simple docstring'''

           return self.tokenizer.batch_decode(*lowercase__	,	**lowercase__					)






      def        lowercase_	(   self      :     Tuple	,	*lowercase__      :     Optional[Any]	,	**lowercase__      :     int					):


           '''simple docstring'''

           return self.tokenizer.decode(*lowercase__	,	**lowercase__					)






      @contextmanager
      def        lowercase_	(   self      :     Union[str, Any]					):


           '''simple docstring'''

           warnings.warn(
               """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
               """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
               """your audio inputs, or in a separate call."""					)
           a_    :       int    	=     True
           a_    :       Optional[Any]    	=     self.tokenizer
           yield
           a_    :       Any    	=     self.feature_extractor
           a_    :       Tuple    	=     False






'''simple docstring'''


import inspect
import unittest

from transformers import EfficientNetConfig
from transformers.testing_utils import is_pipeline_test, 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 EfficientNetForImageClassification, EfficientNetModel
 from transformers.models.efficientnet.modeling_efficientnet import EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor







class     SCREAMING_SNAKE_CASE						:






      def __init__(   self      :     Dict	,	lowercase__      :     Any	,	lowercase__      :     Tuple=13	,	lowercase__      :     Any=32	,	lowercase__      :     List[str]=3	,	lowercase__      :     Any=[3, 3, 5]	,	lowercase__      :     int=[32, 16, 24]	,	lowercase__      :     str=[16, 24, 40]	,	lowercase__      :     Optional[Any]=[1, 1, 2]	,	lowercase__      :     int=[1, 1, 2]	,	lowercase__      :     Tuple=[1, 6, 6]	,	lowercase__      :     Optional[Any]=True	,	lowercase__      :     Union[str, Any]=True	,	lowercase__      :     List[str]=37	,	lowercase__      :     Optional[int]="gelu"	,	lowercase__      :     str=10	,	):


           '''simple docstring'''

           a_    :       Dict    	=     parent
           a_    :       Any    	=     batch_size
           a_    :       str    	=     image_size
           a_    :       Tuple    	=     num_channels
           a_    :       Dict    	=     kernel_sizes
           a_    :       List[Any]    	=     in_channels
           a_    :       int    	=     out_channels
           a_    :       Union[str, Any]    	=     strides
           a_    :       List[Any]    	=     num_block_repeats
           a_    :       List[Any]    	=     expand_ratios
           a_    :       Dict    	=     is_training
           a_    :       Optional[int]    	=     hidden_act
           a_    :       List[Any]    	=     num_labels
           a_    :       List[str]    	=     use_labels






      def        lowercase_	(   self      :     Optional[int]					):


           '''simple docstring'''

           a_    :       List[Any]    	=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

           a_    :       int    	=     None
           if self.use_labels:
                a_    :       int    	=     ids_tensor([self.batch_size]	,	self.num_labels					)

           a_    :       List[Any]    	=     self.get_config()
           return config, pixel_values, labels






      def        lowercase_	(   self      :     Dict					):


           '''simple docstring'''

           return EfficientNetConfig(
               num_channels=self.num_channels	,	kernel_sizes=self.kernel_sizes	,	in_channels=self.in_channels	,	out_channels=self.out_channels	,	strides=self.strides	,	num_block_repeats=self.num_block_repeats	,	expand_ratios=self.expand_ratios	,	hidden_act=self.hidden_act	,	num_labels=self.num_labels	,	)






      def        lowercase_	(   self      :     int	,	lowercase__      :     Any	,	lowercase__      :     Any	,	lowercase__      :     Optional[int]					):


           '''simple docstring'''

           a_    :       Tuple    	=     EfficientNetModel(config=lowercase__					)
           model.to(lowercase__					)
           model.eval()
           a_    :       Optional[int]    	=     model(lowercase__					)
           # expected last hidden states: B, C, H // 4, W // 4
           self.parent.assertEqual(
               result.last_hidden_state.shape	,	(self.batch_size, config.hidden_dim, self.image_size // 4, self.image_size // 4)	,	)






      def        lowercase_	(   self      :     Optional[Any]	,	lowercase__      :     Union[str, Any]	,	lowercase__      :     Any	,	lowercase__      :     str					):


           '''simple docstring'''

           a_    :       Dict    	=     EfficientNetForImageClassification(lowercase__					)
           model.to(lowercase__					)
           model.eval()
           a_    :       str    	=     model(lowercase__	,	labels=lowercase__					)
           self.parent.assertEqual(result.logits.shape	,	(self.batch_size, self.num_labels)					)






      def        lowercase_	(   self      :     int					):


           '''simple docstring'''

           a_    :       Union[str, Any]    	=     self.prepare_config_and_inputs()
           a_	,    a_	,    a_    :       Optional[Any]    	=     config_and_inputs
           a_    :       str    	=     {"""pixel_values""": pixel_values}
           return config, inputs_dict







@require_torch
class     SCREAMING_SNAKE_CASE						(	snake_case_       ,			snake_case_       ,			unittest.TestCase							):
      __magic_name__     :					Any             =				(EfficientNetModel, EfficientNetForImageClassification) if is_torch_available() else ()
      __magic_name__     :					List[Any]             =				(
          {'''feature-extraction''': EfficientNetModel, '''image-classification''': EfficientNetForImageClassification}
          if is_torch_available()
          else {}
      )

      __magic_name__     :					int             =				False
      __magic_name__     :					Dict             =				False
      __magic_name__     :					Dict             =				False
      __magic_name__     :					Union[str, Any]             =				False
      __magic_name__     :					Optional[int]             =				False






      def        lowercase_	(   self      :     List[Any]					):


           '''simple docstring'''

           a_    :       Optional[int]    	=     EfficientNetModelTester(self					)
           a_    :       Union[str, Any]    	=     ConfigTester(
               self	,	config_class=lowercase__	,	has_text_modality=lowercase__	,	hidden_size=37					)






      def        lowercase_	(   self      :     List[str]					):


           '''simple docstring'''

           self.create_and_test_config_common_properties()
           self.config_tester.create_and_test_config_to_json_string()
           self.config_tester.create_and_test_config_to_json_file()
           self.config_tester.create_and_test_config_from_and_save_pretrained()
           self.config_tester.create_and_test_config_with_num_labels()
           self.config_tester.check_config_can_be_init_without_params()
           self.config_tester.check_config_arguments_init()






      def        lowercase_	(   self      :     Tuple					):


           '''simple docstring'''

           return






      @unittest.skip(reason="""EfficientNet does not use inputs_embeds"""					)
      def        lowercase_	(   self      :     Union[str, Any]					):


           '''simple docstring'''

           pass






      @unittest.skip(reason="""EfficientNet does not support input and output embeddings"""					)
      def        lowercase_	(   self      :     Optional[Any]					):


           '''simple docstring'''

           pass






      @unittest.skip(reason="""EfficientNet does not use feedforward chunking"""					)
      def        lowercase_	(   self      :     str					):


           '''simple docstring'''

           pass






      def        lowercase_	(   self      :     Union[str, Any]					):


           '''simple docstring'''

           a_	,    a_    :       Dict    	=     self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                a_    :       Tuple    	=     model_class(lowercase__					)
                a_    :       int    	=     inspect.signature(model.forward					)
                # signature.parameters is an OrderedDict => so arg_names order is deterministic
                a_    :       Any    	=     [*signature.parameters.keys()]

                a_    :       Dict    	=     ["""pixel_values"""]
                self.assertListEqual(arg_names[:1]	,	lowercase__					)






      def        lowercase_	(   self      :     Optional[int]					):


           '''simple docstring'''

           a_    :       Tuple    	=     self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*lowercase__					)






      def        lowercase_	(   self      :     Tuple					):


           '''simple docstring'''

           def check_hidden_states_output(lowercase__      :     Optional[int]	,	lowercase__      :     List[Any]	,	lowercase__      :     Tuple					):
                a_    :       str    	=     model_class(lowercase__					)
                model.to(lowercase__					)
                model.eval()

                with torch.no_grad():
                     a_    :       Tuple    	=     model(**self._prepare_for_class(lowercase__	,	lowercase__					)					)

                a_    :       Tuple    	=     outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
                a_    :       Tuple    	=     sum(config.num_block_repeats					) * 4
                self.assertEqual(len(lowercase__					)	,	lowercase__					)

                # EfficientNet's feature maps are of shape (batch_size, num_channels, height, width)
                self.assertListEqual(
                    list(hidden_states[0].shape[-2:]					)	,	[self.model_tester.image_size // 2, self.model_tester.image_size // 2]	,	)

           a_	,    a_    :       Any    	=     self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                a_    :       Any    	=     True
                check_hidden_states_output(lowercase__	,	lowercase__	,	lowercase__					)

                # check that output_hidden_states also work using config
                del inputs_dict["output_hidden_states"]
                a_    :       Any    	=     True

                check_hidden_states_output(lowercase__	,	lowercase__	,	lowercase__					)






      def        lowercase_	(   self      :     Union[str, Any]					):


           '''simple docstring'''

           a_    :       Optional[int]    	=     self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*lowercase__					)






      @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""					)
      def        lowercase_	(   self      :     List[Any]					):


           '''simple docstring'''

           pass






      @slow
      def        lowercase_	(   self      :     Dict					):


           '''simple docstring'''

           for model_name in EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                a_    :       List[str]    	=     EfficientNetModel.from_pretrained(lowercase__					)
                self.assertIsNotNone(lowercase__					)






      @is_pipeline_test
      @require_vision
      @slow
      def        lowercase_	(   self      :     Tuple					):


           '''simple docstring'''

           super().test_pipeline_image_classification()



def    _SCREAMING_SNAKE_CASE    (			):




     """simple docstring"""


     a_    :       List[str]    	=     Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""							)
     return image







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






      @cached_property
      def        lowercase_	(   self      :     Dict					):


           '''simple docstring'''

           return AutoImageProcessor.from_pretrained("""google/efficientnet-b7"""					) if is_vision_available() else None






      @slow
      def        lowercase_	(   self      :     Any					):


           '''simple docstring'''

           a_    :       Optional[Any]    	=     EfficientNetForImageClassification.from_pretrained("""google/efficientnet-b7"""					).to(lowercase__					)

           a_    :       Any    	=     self.default_image_processor
           a_    :       List[str]    	=     prepare_img()
           a_    :       str    	=     image_processor(images=lowercase__	,	return_tensors="""pt"""					).to(lowercase__					)

           # forward pass
           with torch.no_grad():
                a_    :       Tuple    	=     model(**lowercase__					)

           # verify the logits
           a_    :       Dict    	=     torch.Size((1, 1000)					)
           self.assertEqual(outputs.logits.shape	,	lowercase__					)

           a_    :       List[Any]    	=     torch.tensor([0.0001, 0.0002, 0.0002]					).to(lowercase__					)
           self.assertTrue(torch.allclose(outputs.logits[0, :3]	,	lowercase__	,	atol=1e-4					)					)
