





"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase_          =	{
    "configuration_switch_transformers": [
        "SWITCH_TRANSFORMERS_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "SwitchTransformersConfig",
        "SwitchTransformersOnnxConfig",
    ]
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  lowerCamelCase_          =	[
      "SWITCH_TRANSFORMERS_PRETRAINED_MODEL_ARCHIVE_LIST",
      "SwitchTransformersEncoderModel",
      "SwitchTransformersForConditionalGeneration",
      "SwitchTransformersModel",
      "SwitchTransformersPreTrainedModel",
      "SwitchTransformersTop1Router",
      "SwitchTransformersSparseMLP",
  ]


if TYPE_CHECKING:
  from .configuration_switch_transformers import (
      SWITCH_TRANSFORMERS_PRETRAINED_CONFIG_ARCHIVE_MAP,
      SwitchTransformersConfig,
      SwitchTransformersOnnxConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_switch_transformers import (
        SWITCH_TRANSFORMERS_PRETRAINED_MODEL_ARCHIVE_LIST,
        SwitchTransformersEncoderModel,
        SwitchTransformersForConditionalGeneration,
        SwitchTransformersModel,
        SwitchTransformersPreTrainedModel,
        SwitchTransformersSparseMLP,
        SwitchTransformersTopaRouter,
    )


else:
  import sys

  lowerCamelCase_          =	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






"""simple docstring"""
import inspect
import unittest

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

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_MAPPING, GLPNConfig, GLPNForDepthEstimation, GLPNModel
  from transformers.models.glpn.modeling_glpn import GLPN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import GLPNImageProcessor






class       _SCREAMING_SNAKE_CASE(     A						):




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





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Any	       =						self.config_class(**self.inputs_dict							)
             self.parent.assertTrue(hasattr(SCREAMING_SNAKE_CASE__	,'''hidden_sizes'''							)							)
             self.parent.assertTrue(hasattr(SCREAMING_SNAKE_CASE__	,'''num_attention_heads'''							)							)
             self.parent.assertTrue(hasattr(SCREAMING_SNAKE_CASE__	,'''num_encoder_blocks'''							)							)






class       _SCREAMING_SNAKE_CASE:




       def __init__(		self	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__=13	,SCREAMING_SNAKE_CASE__=64	,SCREAMING_SNAKE_CASE__=3	,SCREAMING_SNAKE_CASE__=4	,SCREAMING_SNAKE_CASE__=[2, 2, 2, 2]	,SCREAMING_SNAKE_CASE__=[8, 4, 2, 1]	,SCREAMING_SNAKE_CASE__=[16, 32, 64, 1_28]	,SCREAMING_SNAKE_CASE__=[1, 4, 8, 16]	,SCREAMING_SNAKE_CASE__=[1, 2, 4, 8]	,SCREAMING_SNAKE_CASE__=True	,SCREAMING_SNAKE_CASE__=True	,SCREAMING_SNAKE_CASE__="gelu"	,SCREAMING_SNAKE_CASE__=0.1	,SCREAMING_SNAKE_CASE__=0.1	,SCREAMING_SNAKE_CASE__=0.0_2	,SCREAMING_SNAKE_CASE__=16	,SCREAMING_SNAKE_CASE__=3	,SCREAMING_SNAKE_CASE__=None	,)    ->   List[Any]:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Tuple	       =						parent
             __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						batch_size
             __SCREAMING_SNAKE_CASE     :str	       =						image_size
             __SCREAMING_SNAKE_CASE     :Dict	       =						num_channels
             __SCREAMING_SNAKE_CASE     :str	       =						num_encoder_blocks
             __SCREAMING_SNAKE_CASE     :Any	       =						sr_ratios
             __SCREAMING_SNAKE_CASE     :Tuple	       =						depths
             __SCREAMING_SNAKE_CASE     :Dict	       =						hidden_sizes
             __SCREAMING_SNAKE_CASE     :Any	       =						downsampling_rates
             __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						num_attention_heads
             __SCREAMING_SNAKE_CASE     :Dict	       =						is_training
             __SCREAMING_SNAKE_CASE     :Optional[int]	       =						use_labels
             __SCREAMING_SNAKE_CASE     :List[str]	       =						hidden_act
             __SCREAMING_SNAKE_CASE     :List[Any]	       =						hidden_dropout_prob
             __SCREAMING_SNAKE_CASE     :Optional[int]	       =						attention_probs_dropout_prob
             __SCREAMING_SNAKE_CASE     :int	       =						initializer_range
             __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						decoder_hidden_size
             __SCREAMING_SNAKE_CASE     :Any	       =						num_labels
             __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						scope




       def 					_UpperCamelCase  (		self							)    ->   str:





             """simple docstring"""






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

             __SCREAMING_SNAKE_CASE     :Any	       =						None
             if self.use_labels:
                   __SCREAMING_SNAKE_CASE     :Optional[int]	       =						ids_tensor([self.batch_size, self.image_size, self.image_size]	,self.num_labels							)

             __SCREAMING_SNAKE_CASE     :int	       =						self.get_config()
             return config, pixel_values, labels




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





             """simple docstring"""






             return GLPNConfig(
                 image_size=self.image_size	,num_channels=self.num_channels	,num_encoder_blocks=self.num_encoder_blocks	,depths=self.depths	,hidden_sizes=self.hidden_sizes	,num_attention_heads=self.num_attention_heads	,hidden_act=self.hidden_act	,hidden_dropout_prob=self.hidden_dropout_prob	,attention_probs_dropout_prob=self.attention_probs_dropout_prob	,initializer_range=self.initializer_range	,decoder_hidden_size=self.decoder_hidden_size	,)




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





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						GLPNModel(config=SCREAMING_SNAKE_CASE__							)
             model.to(SCREAMING_SNAKE_CASE__							)
             model.eval()
             __SCREAMING_SNAKE_CASE     :int	       =						model(SCREAMING_SNAKE_CASE__							)
             __SCREAMING_SNAKE_CASE     :Dict	       =						self.image_size // (self.downsampling_rates[-1] * 2)
             self.parent.assertEqual(
                 result.last_hidden_state.shape	,(self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)							)




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





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Any	       =						self.num_labels
             __SCREAMING_SNAKE_CASE     :List[str]	       =						GLPNForDepthEstimation(SCREAMING_SNAKE_CASE__							)
             model.to(SCREAMING_SNAKE_CASE__							)
             model.eval()
             __SCREAMING_SNAKE_CASE     :Optional[int]	       =						model(SCREAMING_SNAKE_CASE__							)
             self.parent.assertEqual(result.predicted_depth.shape	,(self.batch_size, self.image_size, self.image_size)							)
             __SCREAMING_SNAKE_CASE     :List[Any]	       =						model(SCREAMING_SNAKE_CASE__	,labels=SCREAMING_SNAKE_CASE__							)
             self.parent.assertEqual(result.predicted_depth.shape	,(self.batch_size, self.image_size, self.image_size)							)




       def 					_UpperCamelCase  (		self							)    ->   Any:





             """simple docstring"""






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






@require_torch
class       _SCREAMING_SNAKE_CASE(     A					,			A					,			unittest.TestCase						):
       SCREAMING_SNAKE_CASE_     :   Optional[Any]					=						(GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
       SCREAMING_SNAKE_CASE_     :   int					=						(
           {'''depth-estimation''': GLPNForDepthEstimation, '''feature-extraction''': GLPNModel} if is_torch_available() else {}
       )

       SCREAMING_SNAKE_CASE_     :   Optional[Any]					=						False
       SCREAMING_SNAKE_CASE_     :   str					=						False
       SCREAMING_SNAKE_CASE_     :   Dict					=						False




       def 					_UpperCamelCase  (		self							)    ->   int:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Tuple	       =						GLPNModelTester(self							)
             __SCREAMING_SNAKE_CASE     :List[str]	       =						GLPNConfigTester(self	,config_class=SCREAMING_SNAKE_CASE__							)




       def 					_UpperCamelCase  (		self							)    ->   Any:





             """simple docstring"""






             self.config_tester.run_common_tests()




       def 					_UpperCamelCase  (		self							)    ->   Tuple:





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :str	       =						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__							)




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





             """simple docstring"""






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




       @unittest.skip('''GLPN does not use inputs_embeds'''							)
       def 					_UpperCamelCase  (		self							)    ->   Tuple:





             """simple docstring"""






             pass




       @unittest.skip('''GLPN does not have get_input_embeddings method and get_output_embeddings methods'''							)
       def 					_UpperCamelCase  (		self							)    ->   str:





             """simple docstring"""






             pass




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





             """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     :Optional[Any]	       =						model_class(SCREAMING_SNAKE_CASE__							)
                   __SCREAMING_SNAKE_CASE     :Any	       =						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     :Optional[int]	       =						['''pixel_values''']
                   self.assertListEqual(arg_names[:1]	,SCREAMING_SNAKE_CASE__							)




       def 					_UpperCamelCase  (		self							)    ->   str:





             """simple docstring"""






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

             for model_class in self.all_model_classes:
                   __SCREAMING_SNAKE_CASE     :str	       =						True
                   __SCREAMING_SNAKE_CASE     :List[str]	       =						False
                   __SCREAMING_SNAKE_CASE     :int	       =						True
                   __SCREAMING_SNAKE_CASE     :Dict	       =						model_class(SCREAMING_SNAKE_CASE__							)
                   model.to(SCREAMING_SNAKE_CASE__							)
                   model.eval()
                   with torch.no_grad():
                         __SCREAMING_SNAKE_CASE     :str	       =						model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							)							)
                   __SCREAMING_SNAKE_CASE     :List[Any]	       =						outputs.attentions

                   __SCREAMING_SNAKE_CASE     :List[str]	       =						sum(self.model_tester.depths							)
                   self.assertEqual(len(SCREAMING_SNAKE_CASE__							)	,SCREAMING_SNAKE_CASE__							)

                   # check that output_attentions also work using config
                   del inputs_dict["output_attentions"]
                   __SCREAMING_SNAKE_CASE     :Dict	       =						True
                   __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						model_class(SCREAMING_SNAKE_CASE__							)
                   model.to(SCREAMING_SNAKE_CASE__							)
                   model.eval()
                   with torch.no_grad():
                         __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							)							)
                   __SCREAMING_SNAKE_CASE     :str	       =						outputs.attentions

                   self.assertEqual(len(SCREAMING_SNAKE_CASE__							)	,SCREAMING_SNAKE_CASE__							)

                   # verify the first attentions (first block, first layer)
                   __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						(self.model_tester.image_size // 4) ** 2
                   __SCREAMING_SNAKE_CASE     :Any	       =						(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
                   self.assertListEqual(
                       list(attentions[0].shape[-3:]							)	,[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]	,)

                   # verify the last attentions (last block, last layer)
                   __SCREAMING_SNAKE_CASE     :str	       =						(self.model_tester.image_size // 32) ** 2
                   __SCREAMING_SNAKE_CASE     :Any	       =						(self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
                   self.assertListEqual(
                       list(attentions[-1].shape[-3:]							)	,[self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len]	,)
                   __SCREAMING_SNAKE_CASE     :Optional[int]	       =						len(SCREAMING_SNAKE_CASE__							)

                   # Check attention is always last and order is fine
                   __SCREAMING_SNAKE_CASE     :Any	       =						True
                   __SCREAMING_SNAKE_CASE     :Tuple	       =						True
                   __SCREAMING_SNAKE_CASE     :Any	       =						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__							)							)

                   self.assertEqual(out_len + 1	,len(SCREAMING_SNAKE_CASE__							)							)

                   __SCREAMING_SNAKE_CASE     :int	       =						outputs.attentions

                   self.assertEqual(len(SCREAMING_SNAKE_CASE__							)	,SCREAMING_SNAKE_CASE__							)
                   # verify the first attentions (first block, first layer)
                   __SCREAMING_SNAKE_CASE     :str	       =						(self.model_tester.image_size // 4) ** 2
                   __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
                   self.assertListEqual(
                       list(self_attentions[0].shape[-3:]							)	,[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]	,)




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





             """simple docstring"""






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

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

                   __SCREAMING_SNAKE_CASE     :List[Any]	       =						outputs.hidden_states

                   __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						self.model_tester.num_encoder_blocks
                   self.assertEqual(len(SCREAMING_SNAKE_CASE__							)	,SCREAMING_SNAKE_CASE__							)

                   # verify the first hidden states (first block)
                   self.assertListEqual(
                       list(hidden_states[0].shape[-3:]							)	,[
                           self.model_tester.hidden_sizes[0],
                           self.model_tester.image_size // 4,
                           self.model_tester.image_size // 4,
                       ]	,)

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

             for model_class in self.all_model_classes:
                   __SCREAMING_SNAKE_CASE     :Tuple	       =						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     :int	       =						True

                   check_hidden_states_output(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__							)




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





             """simple docstring"""






             if not self.model_tester.is_training:
                   return

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

             for model_class in self.all_model_classes:
                   if model_class in get_values(SCREAMING_SNAKE_CASE__							):
                         continue
                   # TODO: remove the following 3 lines once we have a MODEL_FOR_DEPTH_ESTIMATION_MAPPING
                   # this can then be incorporated into _prepare_for_class in test_modeling_common.py
                   if model_class.__name__ == "GLPNForDepthEstimation":
                         __SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE     :Any	       =						inputs_dict['''pixel_values'''].shape
                         __SCREAMING_SNAKE_CASE     :Optional[Any]	       =						torch.zeros(
                             [self.model_tester.batch_size, height, width]	,device=SCREAMING_SNAKE_CASE__							).long()
                   __SCREAMING_SNAKE_CASE     :Tuple	       =						model_class(SCREAMING_SNAKE_CASE__							)
                   model.to(SCREAMING_SNAKE_CASE__							)
                   model.train()
                   __SCREAMING_SNAKE_CASE     :List[str]	       =						self._prepare_for_class(SCREAMING_SNAKE_CASE__	,SCREAMING_SNAKE_CASE__	,return_labels=SCREAMING_SNAKE_CASE__							)
                   __SCREAMING_SNAKE_CASE     :str	       =						model(**SCREAMING_SNAKE_CASE__							).loss
                   loss.backward()




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





             """simple docstring"""






             for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                   __SCREAMING_SNAKE_CASE     :str	       =						GLPNModel.from_pretrained(SCREAMING_SNAKE_CASE__							)
                   self.assertIsNotNone(SCREAMING_SNAKE_CASE__							)




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







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




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





             """simple docstring"""






             __SCREAMING_SNAKE_CASE     :Any	       =						GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]							)
             __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]							).to(SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE     :str	       =						prepare_img()
             __SCREAMING_SNAKE_CASE     :int	       =						image_processor(images=SCREAMING_SNAKE_CASE__	,return_tensors='''pt'''							).to(SCREAMING_SNAKE_CASE__							)

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

             # verify the predicted depth
             __SCREAMING_SNAKE_CASE     :Union[str, Any]	       =						torch.Size([1, 4_80, 6_40]							)
             self.assertEqual(outputs.predicted_depth.shape	,SCREAMING_SNAKE_CASE__							)

             __SCREAMING_SNAKE_CASE     :str	       =						torch.tensor(
                 [[3.4_2_9_1, 2.7_8_6_5, 2.5_1_5_1], [3.2_8_4_1, 2.7_0_2_1, 2.3_5_0_2], [3.1_1_4_7, 2.4_6_2_5, 2.2_4_8_1]]							).to(SCREAMING_SNAKE_CASE__							)

             self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3]	,SCREAMING_SNAKE_CASE__	,atol=1E-4							)							)