code: 
  
  
  
  
  
  '''simple docstring'''
  
  import argparse
  import glob
  import inspect
  import os
  import re
  import unittest
  
  from get_test_info import get_test_classes
  
  from tests.test_pipeline_mixin import pipeline_test_mapping
  
  
  lowercase_			       =				{}
  for task, _ in pipeline_test_mapping.items():
        lowercase_			       =				{"""pt""": None, """tf""": None}
  
  
  # DO **NOT** add item to this set (unless the reason is approved)
  lowercase_			       =				{
      """tests/models/esm/test_modeling_esmfold.py""",  # The pipeline test mapping is added to `test_modeling_esm.py`
  }
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					Any					)		->int:
  
      if "ModelTesterMixin" in [x.__name__ for x in test_class.__bases__]:
          return "pt"
      elif "TFModelTesterMixin" in [x.__name__ for x in test_class.__bases__]:
          return "tf"
      elif "FlaxModelTesterMixin" in [x.__name__ for x in test_class.__bases__]:
          return "flax"
      else:
          return None
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					Tuple      , __lowerCamelCase				:					List[str]					)		->Union[str, Any]:
      # Use the cached results
      if PIPELINE_TEST_MAPPING[task].get(__lowerCamelCase      , __lowerCamelCase					) is not None:
          return PIPELINE_TEST_MAPPING[task][framework]
  
      _SCREAMING_SNAKE_CASE             =    pipeline_test_mapping[task]["""test"""]
      _SCREAMING_SNAKE_CASE             =    None
  
      if framework == "pt":
          _SCREAMING_SNAKE_CASE             =    getattr(__lowerCamelCase      , """model_mapping"""      , __lowerCamelCase					)
      elif framework == "tf":
          _SCREAMING_SNAKE_CASE             =    getattr(__lowerCamelCase      , """tf_model_mapping"""      , __lowerCamelCase					)
  
      if mapping is not None:
          _SCREAMING_SNAKE_CASE             =    dict(mapping.items()					)
  
      # cache the results
      _SCREAMING_SNAKE_CASE             =    mapping
      return mapping
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					List[str]      , __lowerCamelCase				:					Optional[Any]					)		->Optional[Any]:
      _SCREAMING_SNAKE_CASE             =    get_framework(__lowerCamelCase					)
      if framework is None:
          return None
      _SCREAMING_SNAKE_CASE             =    get_mapping_for_task(__lowerCamelCase      , __lowerCamelCase					)
      if mapping is None:
          return None
  
      _SCREAMING_SNAKE_CASE             =    list({model_class.config_class for model_class in test_class.all_model_classes}					)
      if len(__lowerCamelCase					) != 1:
          raise ValueError("""There should be exactly one configuration class from `test_class.all_model_classes`."""					)
  
      # This could be a list/tuple of model classes, but it's rare.
      _SCREAMING_SNAKE_CASE             =    mapping.get(config_classes[0]      , __lowerCamelCase					)
      if isinstance(__lowerCamelCase      , (tuple, list)					):
          _SCREAMING_SNAKE_CASE             =    sorted(__lowerCamelCase      , key=lambda __lowerCamelCase					: x.__name__					)
  
      return model_class
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					int					)		->Tuple:
      _SCREAMING_SNAKE_CASE             =    [(task, get_model_for_pipeline_test(__lowerCamelCase      , __lowerCamelCase					)) for task in pipeline_test_mapping]
      _SCREAMING_SNAKE_CASE             =    sorted([(task, model) for task, model in mapping if model is not None]      , key=lambda __lowerCamelCase					: x[0]					)
  
      return dict(__lowerCamelCase					)
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					Any					)		->str:
      _SCREAMING_SNAKE_CASE             =    get_framework(__lowerCamelCase					)
      if framework == "pt":
          _SCREAMING_SNAKE_CASE             =    """torch"""
      _SCREAMING_SNAKE_CASE             =    """{}"""
  
      _SCREAMING_SNAKE_CASE             =    get_pipeline_model_mapping(__lowerCamelCase					)
      if len(__lowerCamelCase					) == 0:
          return ""
  
      _SCREAMING_SNAKE_CASE             =    []
      for task, model_classes in mapping.items():
          if isinstance(__lowerCamelCase      , (tuple, list)					):
              # A list/tuple of model classes
              _SCREAMING_SNAKE_CASE             =    """(""" + """, """.join([x.__name__ for x in model_classes]					) + """)"""
          else:
              # A single model class
              _SCREAMING_SNAKE_CASE             =    model_classes.__name__
          texts.append(F'"{task}": {value}'					)
      _SCREAMING_SNAKE_CASE             =    """{""" + """, """.join(__lowerCamelCase					) + """}"""
      _SCREAMING_SNAKE_CASE             =    F'pipeline_model_mapping = {text} if is_{framework}_available() else {default_value}'
  
      return text
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					Optional[Any]					)		->List[str]:
      _SCREAMING_SNAKE_CASE             =    {"""ModelTesterMixin""", """TFModelTesterMixin""", """FlaxModelTesterMixin"""}
      if not issubclass(__lowerCamelCase      , unittest.TestCase					):
          return False
      return len(base_class_names.intersection([x.__name__ for x in test_class.__bases__]					)					) > 0
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					int					)		->Optional[int]:
      _SCREAMING_SNAKE_CASE             =    [x for x in get_test_classes(__lowerCamelCase					) if is_valid_test_class(__lowerCamelCase					)]
  
      _SCREAMING_SNAKE_CASE             =    None
      for test_class in test_classes:
          # If a test class has defined `pipeline_model_mapping`, let's take it
          if getattr(__lowerCamelCase      , """pipeline_model_mapping"""      , __lowerCamelCase					) is not None:
              _SCREAMING_SNAKE_CASE             =    test_class
              break
      # Take the test class with the shortest name (just a heuristic)
      if target_test_class is None and len(__lowerCamelCase					) > 0:
          _SCREAMING_SNAKE_CASE             =    sorted(__lowerCamelCase      , key=lambda __lowerCamelCase					: (len(x.__name__					), x.__name__)					)[0]
  
      return target_test_class
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					Union[str, Any]      , __lowerCamelCase				:					Dict      , __lowerCamelCase				:					Optional[int]					)		->List[Any]:
      _SCREAMING_SNAKE_CASE             =    start_idx
      for idx, line in enumerate(lines[start_idx:]					):
          _SCREAMING_SNAKE_CASE             =    len(__lowerCamelCase					) - len(line.lstrip()					)
          if idx == 0 or indent > indent_level or (indent == indent_level and line.strip() == ")"):
              _SCREAMING_SNAKE_CASE             =    start_idx + idx
          elif idx > 0 and indent <= indent_level:
              # Outside the definition block of `pipeline_model_mapping`
              break
  
      return end_idx
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					Any      , __lowerCamelCase				:					int=False					)		->int:
      if getattr(__lowerCamelCase      , """pipeline_model_mapping"""      , __lowerCamelCase					) is not None:
          if not overwrite:
              return "", -1
  
      _SCREAMING_SNAKE_CASE             =    get_pipeline_model_mapping_string(__lowerCamelCase					)
      if len(__lowerCamelCase					) == 0:
          return "", -1
      _SCREAMING_SNAKE_CASE             =    line_to_add + """\n"""
  
      # The code defined the class `test_class`
      _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    inspect.getsourcelines(__lowerCamelCase					)
      # `inspect` gives the code for an object, including decorator(s) if any.
      # We (only) need the exact line of the class definition.
      for idx, line in enumerate(__lowerCamelCase					):
          if line.lstrip().startswith("""class """					):
              _SCREAMING_SNAKE_CASE             =    class_lines[idx:]
              class_start_line_no += idx
              break
      _SCREAMING_SNAKE_CASE             =    class_start_line_no + len(__lowerCamelCase					) - 1
  
      # The index in `class_lines` that starts the definition of `all_model_classes`, `all_generative_model_classes` or
      # `pipeline_model_mapping`. This assumes they are defined in such order, and we take the start index of the last
      # block that appears in a `test_class`.
      _SCREAMING_SNAKE_CASE             =    None
      # The indent level of the line at `class_lines[start_idx]` (if defined)
      _SCREAMING_SNAKE_CASE             =    0
      # To record if `pipeline_model_mapping` is found in `test_class`.
      _SCREAMING_SNAKE_CASE             =    None
      for idx, line in enumerate(__lowerCamelCase					):
          if line.strip().startswith("""all_model_classes = """					):
              _SCREAMING_SNAKE_CASE             =    len(__lowerCamelCase					) - len(line.lstrip()					)
              _SCREAMING_SNAKE_CASE             =    idx
          elif line.strip().startswith("""all_generative_model_classes = """					):
              _SCREAMING_SNAKE_CASE             =    len(__lowerCamelCase					) - len(line.lstrip()					)
              _SCREAMING_SNAKE_CASE             =    idx
          elif line.strip().startswith("""pipeline_model_mapping = """					):
              _SCREAMING_SNAKE_CASE             =    len(__lowerCamelCase					) - len(line.lstrip()					)
              _SCREAMING_SNAKE_CASE             =    idx
              _SCREAMING_SNAKE_CASE             =    line
              break
  
      if start_idx is None:
          return "", -1
      # Find the ending index (inclusive) of the above found block.
      _SCREAMING_SNAKE_CASE             =    find_block_ending(__lowerCamelCase      , __lowerCamelCase      , __lowerCamelCase					)
  
      # Extract `is_xxx_available()` from existing blocks: some models require specific libraries like `timm` and use
      # `is_timm_available()` instead of `is_torch_available()`.
      # Keep leading and trailing whitespaces
      _SCREAMING_SNAKE_CASE             =    re.compile(R"""\s(is_\S+?_available\(\))\s"""					)
      for line in class_lines[start_idx : end_idx + 1]:
          _SCREAMING_SNAKE_CASE             =    r.search(__lowerCamelCase					)
          if backend_condition is not None:
              # replace the leading and trailing whitespaces to the space character " ".
              _SCREAMING_SNAKE_CASE             =    """ """ + backend_condition[0][1:-1] + """ """
              _SCREAMING_SNAKE_CASE             =    r.sub(__lowerCamelCase      , __lowerCamelCase					)
              break
  
      if def_line is None:
          # `pipeline_model_mapping` is not defined. The target index is set to the ending index (inclusive) of
          # `all_model_classes` or `all_generative_model_classes`.
          _SCREAMING_SNAKE_CASE             =    end_idx
      else:
          # `pipeline_model_mapping` is defined. The target index is set to be one **BEFORE** its start index.
          _SCREAMING_SNAKE_CASE             =    start_idx - 1
          # mark the lines of the currently existing `pipeline_model_mapping` to be removed.
          for idx in range(__lowerCamelCase      , end_idx + 1					):
              # These lines are going to be removed before writing to the test file.
              _SCREAMING_SNAKE_CASE             =    None  # noqa
  
      # Make sure the test class is a subclass of `PipelineTesterMixin`.
      _SCREAMING_SNAKE_CASE             =    [x.__name__ for x in test_class.__bases__]
      if "PipelineTesterMixin" not in parent_classes:
          # Put `PipelineTesterMixin` just before `unittest.TestCase`
          _SCREAMING_SNAKE_CASE             =    [x for x in parent_classes if x != """TestCase"""] + ["""PipelineTesterMixin"""]
          if "TestCase" in parent_classes:
              # Here we **assume** the original string is always with `unittest.TestCase`.
              _parent_classes.append("""unittest.TestCase"""					)
          _SCREAMING_SNAKE_CASE             =    """, """.join(_parent_classes					)
          for idx, line in enumerate(__lowerCamelCase					):
              # Find the ending of the declaration of `test_class`
              if line.strip().endswith("""):"""					):
                  # mark the lines of the declaration of `test_class` to be removed
                  for _idx in range(idx + 1					):
                      _SCREAMING_SNAKE_CASE             =    None  # noqa
                  break
          # Add the new, one-line, class declaration for `test_class`
          _SCREAMING_SNAKE_CASE             =    F'class {test_class.__name__}({parent_classes}):\n'
  
      # Add indentation
      _SCREAMING_SNAKE_CASE             =    """ """ * indent_level + line_to_add
      # Insert `pipeline_model_mapping` to `class_lines`.
      # (The line at `target_idx` should be kept by definition!)
      _SCREAMING_SNAKE_CASE             =    class_lines[: target_idx + 1] + [line_to_add] + class_lines[target_idx + 1 :]
      # Remove the lines that are marked to be removed
      _SCREAMING_SNAKE_CASE             =    [x for x in class_lines if x is not None]
  
      # Move from test class to module (in order to write to the test file)
      _SCREAMING_SNAKE_CASE             =    inspect.getsourcelines(inspect.getmodule(__lowerCamelCase					)					)[0]
      # Be careful with the 1-off between line numbers and array indices
      _SCREAMING_SNAKE_CASE             =    module_lines[: class_start_line_no - 1] + class_lines + module_lines[class_end_line_no:]
      _SCREAMING_SNAKE_CASE             =    """""".join(__lowerCamelCase					)
  
      _SCREAMING_SNAKE_CASE             =    inspect.getsourcefile(__lowerCamelCase					)
      with open(__lowerCamelCase      , """w"""      , encoding="""UTF-8"""      , newline="""\n"""					) as fp:
          fp.write(__lowerCamelCase					)
  
      return line_to_add
  
  
  
  
  
  def lowerCamelCase		(  __lowerCamelCase				:					Optional[int]      , __lowerCamelCase				:					str=False					)		->str:
      _SCREAMING_SNAKE_CASE             =    find_test_class(__lowerCamelCase					)
      if test_class:
          add_pipeline_model_mapping(__lowerCamelCase      , overwrite=__lowerCamelCase					)
  
  
  if __name__ == "__main__":
        lowercase_			       =				argparse.ArgumentParser()
        parser.add_argument(
            """--test_file""", type=str, help="""A path to the test file, starting with the repository's `tests` directory."""
        )
        parser.add_argument(
            """--all""",
            action="""store_true""",
            help="""If to check and modify all test files.""",
        )
        parser.add_argument(
            """--overwrite""",
            action="""store_true""",
            help="""If to overwrite a test class if it has already defined `pipeline_model_mapping`.""",
        )
        lowercase_			       =				parser.parse_args()
  
        if not args.all and not args.test_file:
              raise ValueError("""Please specify either `test_file` or pass `--all` to check/modify all test files.""")
        elif args.all and args.test_file:
              raise ValueError("""Only one of `--test_file` and `--all` could be specified.""")
  
        lowercase_			       =				[]
        if args.test_file:
              lowercase_			       =				[args.test_file]
        else:
              lowercase_			       =				os.path.join("""tests""", """models""", """**""", """test_modeling_*.py""")
              for test_file in glob.glob(pattern):
                    # `Flax` is not concerned at this moment
                    if not test_file.startswith("""test_modeling_flax_"""):
                          test_files.append(test_file)
  
        for test_file in test_files:
              if test_file in TEST_FILE_TO_IGNORE:
                    print(f"""[SKIPPED] {test_file} is skipped as it is in `TEST_FILE_TO_IGNORE` in the file {__file__}.""")
                    continue
              add_pipeline_model_mapping_to_test_file(test_file, overwrite=args.overwrite)
  
  
  
  
style_context: 
  
  
  
  
  
  '''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       a_  (       snake_case_ ):
  
      '''simple docstring'''
  
  
      def     snake_case_(	self )	->     int:
          _SCREAMING_SNAKE_CASE             =    self.config_class(**self.inputs_dict )
          self.parent.assertTrue(hasattr(A				,	"""hidden_sizes""" ) )
          self.parent.assertTrue(hasattr(A				,	"""num_attention_heads""" ) )
          self.parent.assertTrue(hasattr(A				,	"""num_encoder_blocks""" ) )
  
  
  
  
  class       a_  :
  
      '''simple docstring'''
  
  
      def __init__(	self				,	A				,	A=13				,	A=64				,	A=3				,	A=4				,	A=[2, 2, 2, 2]				,	A=[8, 4, 2, 1]				,	A=[16, 32, 64, 128]				,	A=[1, 4, 8, 16]				,	A=[1, 2, 4, 8]				,	A=True				,	A=True				,	A="gelu"				,	A=0.1				,	A=0.1				,	A=0.02				,	A=16				,	A=3				,	A=None				,	)	->     List[str]:
          _SCREAMING_SNAKE_CASE             =    parent
          _SCREAMING_SNAKE_CASE             =    batch_size
          _SCREAMING_SNAKE_CASE             =    image_size
          _SCREAMING_SNAKE_CASE             =    num_channels
          _SCREAMING_SNAKE_CASE             =    num_encoder_blocks
          _SCREAMING_SNAKE_CASE             =    sr_ratios
          _SCREAMING_SNAKE_CASE             =    depths
          _SCREAMING_SNAKE_CASE             =    hidden_sizes
          _SCREAMING_SNAKE_CASE             =    downsampling_rates
          _SCREAMING_SNAKE_CASE             =    num_attention_heads
          _SCREAMING_SNAKE_CASE             =    is_training
          _SCREAMING_SNAKE_CASE             =    use_labels
          _SCREAMING_SNAKE_CASE             =    hidden_act
          _SCREAMING_SNAKE_CASE             =    hidden_dropout_prob
          _SCREAMING_SNAKE_CASE             =    attention_probs_dropout_prob
          _SCREAMING_SNAKE_CASE             =    initializer_range
          _SCREAMING_SNAKE_CASE             =    decoder_hidden_size
          _SCREAMING_SNAKE_CASE             =    num_labels
          _SCREAMING_SNAKE_CASE             =    scope
  
  
      def     snake_case_(	self )	->     Optional[int]:
          _SCREAMING_SNAKE_CASE             =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
  
          _SCREAMING_SNAKE_CASE             =    None
          if self.use_labels:
              _SCREAMING_SNAKE_CASE             =    ids_tensor([self.batch_size, self.image_size, self.image_size]				,	self.num_labels )
  
          _SCREAMING_SNAKE_CASE             =    self.get_config()
          return config, pixel_values, labels
  
  
      def     snake_case_(	self )	->     Optional[int]:
          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     snake_case_(	self				,	A				,	A				,	A )	->     str:
          _SCREAMING_SNAKE_CASE             =    GLPNModel(config=A )
          model.to(A )
          model.eval()
          _SCREAMING_SNAKE_CASE             =    model(A )
          _SCREAMING_SNAKE_CASE             =    _SCREAMING_SNAKE_CASE             =    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     snake_case_(	self				,	A				,	A				,	A )	->     Any:
          _SCREAMING_SNAKE_CASE             =    self.num_labels
          _SCREAMING_SNAKE_CASE             =    GLPNForDepthEstimation(A )
          model.to(A )
          model.eval()
          _SCREAMING_SNAKE_CASE             =    model(A )
          self.parent.assertEqual(result.predicted_depth.shape				,	(self.batch_size, self.image_size, self.image_size) )
          _SCREAMING_SNAKE_CASE             =    model(A				,	labels=A )
          self.parent.assertEqual(result.predicted_depth.shape				,	(self.batch_size, self.image_size, self.image_size) )
  
  
  
  
  
      def     snake_case_(	self )	->     Any:
          _SCREAMING_SNAKE_CASE             =    self.prepare_config_and_inputs()
          _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    config_and_inputs
          _SCREAMING_SNAKE_CASE             =    {"""pixel_values""": pixel_values}
          return config, inputs_dict
  
  
  
  
  @require_torch
  class       a_  (       snake_case_   ,					snake_case_   ,					unittest.TestCase ):
  
      '''simple docstring'''
      UpperCamelCase									=				(GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
      UpperCamelCase									=				(
          {'''depth-estimation''': GLPNForDepthEstimation, '''feature-extraction''': GLPNModel} if is_torch_available() else {}
      )
  
      UpperCamelCase									=				False
      UpperCamelCase									=				False
      UpperCamelCase									=				False
  
  
      def     snake_case_(	self )	->     str:
          _SCREAMING_SNAKE_CASE             =    GLPNModelTester(self )
          _SCREAMING_SNAKE_CASE             =    GLPNConfigTester(self				,	config_class=A )
  
  
      def     snake_case_(	self )	->     Optional[Any]:
          self.config_tester.run_common_tests()
  
  
      def     snake_case_(	self )	->     Tuple:
          _SCREAMING_SNAKE_CASE             =    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*A )
  
  
      def     snake_case_(	self )	->     List[Any]:
          _SCREAMING_SNAKE_CASE             =    self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_depth_estimation(*A )
  
  
      @unittest.skip("""GLPN does not use inputs_embeds""" )
      def     snake_case_(	self )	->     List[Any]:
          pass
  
  
      @unittest.skip("""GLPN does not have get_input_embeddings method and get_output_embeddings methods""" )
      def     snake_case_(	self )	->     Any:
          pass
  
  
      def     snake_case_(	self )	->     str:
          _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    self.model_tester.prepare_config_and_inputs_for_common()
  
          for model_class in self.all_model_classes:
              _SCREAMING_SNAKE_CASE             =    model_class(A )
              _SCREAMING_SNAKE_CASE             =    inspect.signature(model.forward )
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              _SCREAMING_SNAKE_CASE             =    [*signature.parameters.keys()]
  
              _SCREAMING_SNAKE_CASE             =    ["""pixel_values"""]
              self.assertListEqual(arg_names[:1]				,	A )
  
  
      def     snake_case_(	self )	->     List[str]:
          _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    self.model_tester.prepare_config_and_inputs_for_common()
          _SCREAMING_SNAKE_CASE             =    True
  
          for model_class in self.all_model_classes:
              _SCREAMING_SNAKE_CASE             =    True
              _SCREAMING_SNAKE_CASE             =    False
              _SCREAMING_SNAKE_CASE             =    True
              _SCREAMING_SNAKE_CASE             =    model_class(A )
              model.to(A )
              model.eval()
              with torch.no_grad():
                  _SCREAMING_SNAKE_CASE             =    model(**self._prepare_for_class(A				,	A ) )
              _SCREAMING_SNAKE_CASE             =    outputs.attentions
  
              _SCREAMING_SNAKE_CASE             =    sum(self.model_tester.depths )
              self.assertEqual(len(A )				,	A )
  
              # check that output_attentions also work using config
              del inputs_dict["output_attentions"]
              _SCREAMING_SNAKE_CASE             =    True
              _SCREAMING_SNAKE_CASE             =    model_class(A )
              model.to(A )
              model.eval()
              with torch.no_grad():
                  _SCREAMING_SNAKE_CASE             =    model(**self._prepare_for_class(A				,	A ) )
              _SCREAMING_SNAKE_CASE             =    outputs.attentions
  
              self.assertEqual(len(A )				,	A )
  
              # verify the first attentions (first block, first layer)
              _SCREAMING_SNAKE_CASE             =    (self.model_tester.image_size // 4) ** 2
              _SCREAMING_SNAKE_CASE             =    (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             =    (self.model_tester.image_size // 32) ** 2
              _SCREAMING_SNAKE_CASE             =    (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             =    len(A )
  
              # Check attention is always last and order is fine
              _SCREAMING_SNAKE_CASE             =    True
              _SCREAMING_SNAKE_CASE             =    True
              _SCREAMING_SNAKE_CASE             =    model_class(A )
              model.to(A )
              model.eval()
              with torch.no_grad():
                  _SCREAMING_SNAKE_CASE             =    model(**self._prepare_for_class(A				,	A ) )
  
              self.assertEqual(out_len + 1				,	len(A ) )
  
              _SCREAMING_SNAKE_CASE             =    outputs.attentions
  
              self.assertEqual(len(A )				,	A )
              # verify the first attentions (first block, first layer)
              _SCREAMING_SNAKE_CASE             =    (self.model_tester.image_size // 4) ** 2
              _SCREAMING_SNAKE_CASE             =    (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     snake_case_(	self )	->     List[Any]:
          def check_hidden_states_output(A				,	A				,	A ):
              _SCREAMING_SNAKE_CASE             =    model_class(A )
              model.to(A )
              model.eval()
  
              with torch.no_grad():
                  _SCREAMING_SNAKE_CASE             =    model(**self._prepare_for_class(A				,	A ) )
  
              _SCREAMING_SNAKE_CASE             =    outputs.hidden_states
  
              _SCREAMING_SNAKE_CASE             =    self.model_tester.num_encoder_blocks
              self.assertEqual(len(A )				,	A )
  
              # 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             =    self.model_tester.prepare_config_and_inputs_for_common()
  
          for model_class in self.all_model_classes:
              _SCREAMING_SNAKE_CASE             =    True
              check_hidden_states_output(A				,	A				,	A )
  
              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              _SCREAMING_SNAKE_CASE             =    True
  
              check_hidden_states_output(A				,	A				,	A )
  
  
      def     snake_case_(	self )	->     int:
          if not self.model_tester.is_training:
              return
  
          _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE             =    self.model_tester.prepare_config_and_inputs_for_common()
          _SCREAMING_SNAKE_CASE             =    True
  
          for model_class in self.all_model_classes:
              if model_class in get_values(A ):
                  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             =    inputs_dict["""pixel_values"""].shape
                  _SCREAMING_SNAKE_CASE             =    torch.zeros(
                      [self.model_tester.batch_size, height, width]				,	device=A ).long()
              _SCREAMING_SNAKE_CASE             =    model_class(A )
              model.to(A )
              model.train()
              _SCREAMING_SNAKE_CASE             =    self._prepare_for_class(A				,	A				,	return_labels=A )
              _SCREAMING_SNAKE_CASE             =    model(**A ).loss
              loss.backward()
  
  
  
  
  
      @slow
      def     snake_case_(	self )	->     str:
          for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              _SCREAMING_SNAKE_CASE             =    GLPNModel.from_pretrained(A )
              self.assertIsNotNone(A )
  
  
  
  
  
  def lowerCamelCase		(  )		->Union[str, Any]:
      _SCREAMING_SNAKE_CASE             =    Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""					)
      return image
  
  
  
  
  
  
  @require_torch
  @require_vision
  @slow
  class       a_  (       unittest.TestCase ):
  
      '''simple docstring'''
  
  
      @slow
      def     snake_case_(	self )	->     Optional[Any]:
          _SCREAMING_SNAKE_CASE             =    GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
          _SCREAMING_SNAKE_CASE             =    GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(A )
  
          _SCREAMING_SNAKE_CASE             =    prepare_img()
          _SCREAMING_SNAKE_CASE             =    image_processor(images=A				,	return_tensors="""pt""" ).to(A )
  
          # forward pass
          with torch.no_grad():
              _SCREAMING_SNAKE_CASE             =    model(**A )
  
          # verify the predicted depth
          _SCREAMING_SNAKE_CASE             =    torch.Size([1, 480, 640] )
          self.assertEqual(outputs.predicted_depth.shape				,	A )
  
          _SCREAMING_SNAKE_CASE             =    torch.tensor(
              [[3.4291, 2.7865, 2.5151], [3.2841, 2.7021, 2.3502], [3.1147, 2.4625, 2.2481]] ).to(A )
  
          self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3]				,	A				,	atol=1e-4 ) )
  
  
  
  