


import collections
import importlib.util
import os
import re
from pathlib import Path


__lowerCAmelCase		      =							'''src/transformers'''


# Matches is_xxx_available()
__lowerCAmelCase		      =							re.compile(r'''is\_([a-z_]*)_available()''')
# Catches a one-line _import_struct = {xxx}
__lowerCAmelCase		      =							re.compile(r'''^_import_structure\s+=\s+\{([^\}]+)\}''')
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
__lowerCAmelCase		      =							re.compile(r'''\s+"\S*":\s+\[([^\]]*)\]''')
# Catches a line if not is_foo_available
__lowerCAmelCase		      =							re.compile(r'''^\s*if\s+not\s+is\_[a-z_]*\_available\(\)''')
# Catches a line _import_struct["bla"].append("foo")
__lowerCAmelCase		      =							re.compile(r'''^\s*_import_structure\["\S*"\]\.append\("(\S*)"\)''')
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
__lowerCAmelCase		      =							re.compile(r'''^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]''')
# Catches a line with an object between quotes and a comma:     "MyModel",
__lowerCAmelCase		      =							re.compile('''^\s+"([^"]+)",''')
# Catches a line with objects between brackets only:    ["foo", "bar"],
__lowerCAmelCase		      =							re.compile('''^\s+\[([^\]]+)\]''')
# Catches a line with from foo import bar, bla, boo
__lowerCAmelCase		      =							re.compile(r'''\s+from\s+\S*\s+import\s+([^\(\s].*)\n''')
# Catches a line with try:
__lowerCAmelCase		      =							re.compile(r'''^\s*try:''')
# Catches a line with else:
__lowerCAmelCase		      =							re.compile(r'''^\s*else:''')




def        snake_case_					(  snake_case					)  ->    Dict:
       if _re_test_backend.search(snake_case					) is None:
              return None
       lowercase__:       List[str]        =       [b[0] for b in _re_backend.findall(snake_case					)]
       backends.sort()
       return "_and_".join(snake_case					)




def        snake_case_					(  snake_case					)  ->    Dict:
       with open(snake_case      ,							'r'      ,							encoding='utf-8'      ,							newline='\n'					) as f:
              lowercase__:       Optional[Any]        =       f.readlines()

       lowercase__:       Optional[int]        =       0
       while line_index < len(snake_case					) and not lines[line_index].startswith('_import_structure = {'					):
              line_index += 1

       # If this is a traditional init, just return.
       if line_index >= len(snake_case					):
              return None

       # First grab the objects without a specific backend in _import_structure
       lowercase__:       Dict        =       []
       while not lines[line_index].startswith('if TYPE_CHECKING'					) and find_backend(lines[line_index]					) is None:
              lowercase__:       int        =       lines[line_index]
              # If we have everything on a single line, let's deal with it.
              if _re_one_line_import_struct.search(snake_case					):
                     lowercase__:       int        =       _re_one_line_import_struct.search(snake_case					).groups()[0]
                     lowercase__:       List[Any]        =       re.findall('\[([^\]]+)\]'      ,							snake_case					)
                     for imp in imports:
                            objects.extend([obj[1:-1] for obj in imp.split(', '					)]					)
                     line_index += 1
                     continue
              lowercase__:       List[Any]        =       _re_import_struct_key_value.search(snake_case					)
              if single_line_import_search is not None:
                     lowercase__:       Any        =       [obj[1:-1] for obj in single_line_import_search.groups()[0].split(', '					) if len(snake_case					) > 0]
                     objects.extend(snake_case					)
              elif line.startswith(' ' * 8 + '"'					):
                     objects.append(line[9:-3]					)
              line_index += 1

       lowercase__:       Union[str, Any]        =       {'none': objects}
       # Let's continue with backend-specific objects in _import_structure
       while not lines[line_index].startswith('if TYPE_CHECKING'					):
              # If the line is an if not is_backend_available, we grab all objects associated.
              lowercase__:       List[str]        =       find_backend(lines[line_index]					)
              # Check if the backend declaration is inside a try block:
              if _re_try.search(lines[line_index - 1]					) is None:
                     lowercase__:       Union[str, Any]        =       None

              if backend is not None:
                     line_index += 1

                     # Scroll until we hit the else block of try-except-else
                     while _re_else.search(lines[line_index]					) is None:
                            line_index += 1

                     line_index += 1

                     lowercase__:       Optional[int]        =       []
                     # Until we unindent, add backend objects to the list
                     while len(lines[line_index]					) <= 1 or lines[line_index].startswith(' ' * 4					):
                            lowercase__:       Any        =       lines[line_index]
                            if _re_import_struct_add_one.search(snake_case					) is not None:
                                   objects.append(_re_import_struct_add_one.search(snake_case					).groups()[0]					)
                            elif _re_import_struct_add_many.search(snake_case					) is not None:
                                   lowercase__:       Optional[Any]        =       _re_import_struct_add_many.search(snake_case					).groups()[0].split(', '					)
                                   lowercase__:       Tuple        =       [obj[1:-1] for obj in imports if len(snake_case					) > 0]
                                   objects.extend(snake_case					)
                            elif _re_between_brackets.search(snake_case					) is not None:
                                   lowercase__:       Optional[int]        =       _re_between_brackets.search(snake_case					).groups()[0].split(', '					)
                                   lowercase__:       Optional[int]        =       [obj[1:-1] for obj in imports if len(snake_case					) > 0]
                                   objects.extend(snake_case					)
                            elif _re_quote_object.search(snake_case					) is not None:
                                   objects.append(_re_quote_object.search(snake_case					).groups()[0]					)
                            elif line.startswith(' ' * 8 + '"'					):
                                   objects.append(line[9:-3]					)
                            elif line.startswith(' ' * 12 + '"'					):
                                   objects.append(line[13:-3]					)
                            line_index += 1

                     lowercase__:       List[Any]        =       objects
              else:
                     line_index += 1

    # At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
       lowercase__:       Dict        =       []
       while (
           line_index < len(snake_case					)
           and find_backend(lines[line_index]					) is None
           and not lines[line_index].startswith('else'					)
       ):
              lowercase__:       Union[str, Any]        =       lines[line_index]
              lowercase__:       Dict        =       _re_import.search(snake_case					)
              if single_line_import_search is not None:
                     objects.extend(single_line_import_search.groups()[0].split(', '					)					)
              elif line.startswith(' ' * 8					):
                     objects.append(line[8:-2]					)
              line_index += 1

       lowercase__:       Tuple        =       {'none': objects}
       # Let's continue with backend-specific objects
       while line_index < len(snake_case					):
              # If the line is an if is_backend_available, we grab all objects associated.
              lowercase__:       Optional[Any]        =       find_backend(lines[line_index]					)
              # Check if the backend declaration is inside a try block:
              if _re_try.search(lines[line_index - 1]					) is None:
                     lowercase__:       Any        =       None

              if backend is not None:
                     line_index += 1

                     # Scroll until we hit the else block of try-except-else
                     while _re_else.search(lines[line_index]					) is None:
                            line_index += 1

                     line_index += 1

                     lowercase__:       str        =       []
                     # Until we unindent, add backend objects to the list
                     while len(lines[line_index]					) <= 1 or lines[line_index].startswith(' ' * 8					):
                            lowercase__:       str        =       lines[line_index]
                            lowercase__:       Optional[int]        =       _re_import.search(snake_case					)
                            if single_line_import_search is not None:
                                   objects.extend(single_line_import_search.groups()[0].split(', '					)					)
                            elif line.startswith(' ' * 12					):
                                   objects.append(line[12:-2]					)
                            line_index += 1

                     lowercase__:       Optional[Any]        =       objects
              else:
                     line_index += 1

       return import_dict_objects, type_hint_objects




def        snake_case_					(  snake_case      ,							snake_case					)  ->    str:

       def find_duplicates(snake_case					):
              return [k for k, v in collections.Counter(snake_case					).items() if v > 1]

       if list(import_dict_objects.keys()					) != list(type_hint_objects.keys()					):
              return ["Both sides of the init do not have the same backends!"]

       lowercase__:       str        =       []
       for key in import_dict_objects.keys():
              lowercase__:       str        =       find_duplicates(import_dict_objects[key]					)
              if duplicate_imports:
                     errors.append(f'Duplicate _import_structure definitions for: {duplicate_imports}'					)
              lowercase__:       int        =       find_duplicates(type_hint_objects[key]					)
              if duplicate_type_hints:
                     errors.append(f'Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}'					)

              if sorted(set(import_dict_objects[key]					)					) != sorted(set(type_hint_objects[key]					)					):
                     lowercase__:       Optional[Any]        =       'base imports' if key == 'none' else f'{key} backend'
                     errors.append(f'Differences for {name}:'					)
                     for a in type_hint_objects[key]:
                            if a not in import_dict_objects[key]:
                                   errors.append(f'  {a} in TYPE_HINT but not in _import_structure.'					)
                     for a in import_dict_objects[key]:
                            if a not in type_hint_objects[key]:
                                   errors.append(f'  {a} in _import_structure but not in TYPE_HINT.'					)
       return errors




def        snake_case_					(  )  ->    List[str]:
       lowercase__:       str        =       []
       for root, _, files in os.walk(snake_case					):
              if "__init__.py" in files:
                     lowercase__:       List[str]        =       os.path.join(snake_case      ,							'__init__.py'					)
                     lowercase__:       str        =       parse_init(snake_case					)
                     if objects is not None:
                            lowercase__:       Union[str, Any]        =       analyze_results(*snake_case					)
                            if len(snake_case					) > 0:
                                   lowercase__:       Dict        =       f'Problem in {fname}, both halves do not define the same objects.\n{errors[0]}'
                                   failures.append('\n'.join(snake_case					)					)
       if len(snake_case					) > 0:
              raise ValueError('\n\n'.join(snake_case					)					)




def        snake_case_					(  )  ->    Optional[Any]:
       lowercase__:       Dict        =       []
       for path, directories, files in os.walk(snake_case					):
              for folder in directories:
                     # Ignore private modules
                     if folder.startswith('_'					):
                            directories.remove(snake_case					)
                            continue
                     # Ignore leftovers from branches (empty folders apart from pycache)
                     if len(list((Path(snake_case					) / folder).glob('*.py'					)					)					) == 0:
                            continue
                     lowercase__:       Optional[int]        =       str((Path(snake_case					) / folder).relative_to(snake_case					)					)
                     lowercase__:       Optional[int]        =       short_path.replace(os.path.sep      ,							'.'					)
                     submodules.append(snake_case					)
              for fname in files:
                     if fname == "__init__.py":
                            continue
                     lowercase__:       List[Any]        =       str((Path(snake_case					) / fname).relative_to(snake_case					)					)
                     lowercase__:       List[str]        =       short_path.replace('.py'      ,							''					).replace(os.path.sep      ,							'.'					)
                     if len(submodule.split('.'					)					) == 1:
                            submodules.append(snake_case					)
       return submodules


__lowerCAmelCase		      =							[
    '''convert_pytorch_checkpoint_to_tf2''',
    '''modeling_flax_pytorch_utils''',
]




def        snake_case_					(  )  ->    Dict:
       # This is to make sure the transformers module imported is the one in the repo.
       lowercase__:       Optional[Any]        =       importlib.util.spec_from_file_location(
           'transformers'      ,							os.path.join(snake_case      ,							'__init__.py'					)      ,							submodule_search_locations=[PATH_TO_TRANSFORMERS]      ,							)
       lowercase__:       str        =       spec.loader.load_module()

       lowercase__:       Union[str, Any]        =       [
           module
           for module in get_transformers_submodules()
           if module not in IGNORE_SUBMODULES and module not in transformers._import_structure.keys()
       ]
       if len(snake_case					) > 0:
              lowercase__:       List[Any]        =       '\n'.join(f'- {module}' for module in module_not_registered					)
              raise ValueError(
                  'The following submodules are not properly registered in the main init of Transformers:\n'
                  f'{list_of_modules}\n'
                  'Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value.'					)


if __name__ == "__main__":
 check_all_inits()
 check_submodules()






from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers import (
     TFDeiTForImageClassification,
     TFDeiTForImageClassificationWithTeacher,
     TFDeiTForMaskedImageModeling,
     TFDeiTModel,
 )
 from transformers.models.deit.modeling_tf_deit import TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import DeiTImageProcessor





class   __a      :
 def __init__(       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__=13    ,			lowerCAmelCase__=30    ,			lowerCAmelCase__=2    ,			lowerCAmelCase__=3    ,			lowerCAmelCase__=True    ,			lowerCAmelCase__=True    ,			lowerCAmelCase__=32    ,			lowerCAmelCase__=2    ,			lowerCAmelCase__=4    ,			lowerCAmelCase__=37    ,			lowerCAmelCase__="gelu"    ,			lowerCAmelCase__=0.1    ,			lowerCAmelCase__=0.1    ,			lowerCAmelCase__=10    ,			lowerCAmelCase__=0.0_2    ,			lowerCAmelCase__=3    ,			lowerCAmelCase__=None    ,			lowerCAmelCase__=2    ,			) ->    List[str]:




        '''simple docstring'''
        lowercase__:       List[str]        =       parent
        lowercase__:       Tuple        =       batch_size
        lowercase__:       Optional[Any]        =       image_size
        lowercase__:       int        =       patch_size
        lowercase__:       Union[str, Any]        =       num_channels
        lowercase__:       Dict        =       is_training
        lowercase__:       Any        =       use_labels
        lowercase__:       Optional[int]        =       hidden_size
        lowercase__:       Tuple        =       num_hidden_layers
        lowercase__:       List[str]        =       num_attention_heads
        lowercase__:       Any        =       intermediate_size
        lowercase__:       Union[str, Any]        =       hidden_act
        lowercase__:       Union[str, Any]        =       hidden_dropout_prob
        lowercase__:       Optional[Any]        =       attention_probs_dropout_prob
        lowercase__:       str        =       type_sequence_label_size
        lowercase__:       List[str]        =       initializer_range
        lowercase__:       Optional[Any]        =       scope
        lowercase__:       Dict        =       encoder_stride

        # in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
        lowercase__:       List[str]        =       (image_size // patch_size) ** 2
        lowercase__:       Tuple        =       num_patches + 2
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Optional[int]:




        '''simple docstring'''
        lowercase__:       List[Any]        =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

        lowercase__:       int        =       None
        if self.use_labels:
               lowercase__:       str        =       ids_tensor([self.batch_size]    ,			self.type_sequence_label_size			)

        lowercase__:       List[str]        =       self.get_config()

        return config, pixel_values, labels
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Optional[Any]:




        '''simple docstring'''
        return DeiTConfig(
            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=lowerCAmelCase__    ,			initializer_range=self.initializer_range    ,			encoder_stride=self.encoder_stride    ,			)
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__			) ->    int:




        '''simple docstring'''
        lowercase__:       List[str]        =       TFDeiTModel(config=lowerCAmelCase__			)
        lowercase__:       Any        =       model(lowerCAmelCase__			)
        self.parent.assertEqual(result.last_hidden_state.shape    ,			(self.batch_size, self.seq_length, self.hidden_size)			)
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__			) ->    Union[str, Any]:




        '''simple docstring'''
        lowercase__:       Any        =       TFDeiTForMaskedImageModeling(config=lowerCAmelCase__			)
        lowercase__:       int        =       model(lowerCAmelCase__			)
        self.parent.assertEqual(
            result.reconstruction.shape    ,			(self.batch_size, self.num_channels, self.image_size, self.image_size)			)

        # test greyscale images
        lowercase__:       Tuple        =       1
        lowercase__:       Optional[int]        =       TFDeiTForMaskedImageModeling(lowerCAmelCase__			)

        lowercase__:       int        =       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
        lowercase__:       str        =       model(lowerCAmelCase__			)
        self.parent.assertEqual(result.reconstruction.shape    ,			(self.batch_size, 1, self.image_size, self.image_size)			)
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__			) ->    List[Any]:




        '''simple docstring'''
        lowercase__:       Any        =       self.type_sequence_label_size
        lowercase__:       Optional[Any]        =       TFDeiTForImageClassification(lowerCAmelCase__			)
        lowercase__:       List[Any]        =       model(lowerCAmelCase__    ,			labels=lowerCAmelCase__			)
        self.parent.assertEqual(result.logits.shape    ,			(self.batch_size, self.type_sequence_label_size)			)

        # test greyscale images
        lowercase__:       Optional[Any]        =       1
        lowercase__:       Union[str, Any]        =       TFDeiTForImageClassification(lowerCAmelCase__			)

        lowercase__:       List[str]        =       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
        lowercase__:       str        =       model(lowerCAmelCase__    ,			labels=lowerCAmelCase__			)
        self.parent.assertEqual(result.logits.shape    ,			(self.batch_size, self.type_sequence_label_size)			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Tuple:




        '''simple docstring'''
        lowercase__:       Dict        =       self.prepare_config_and_inputs()
        lowercase__      ,						lowercase__      ,						lowercase__:       Dict        =       config_and_inputs
        lowercase__:       Optional[Any]        =       {'pixel_values': pixel_values}
        return config, inputs_dict





@require_tf
class   __a      (   __UpperCamelCase	,	__UpperCamelCase	,	unittest.TestCase				):
 __lowercase			:							Union[str, Any]       					=			(
     (
         TFDeiTModel,
         TFDeiTForImageClassification,
         TFDeiTForImageClassificationWithTeacher,
         TFDeiTForMaskedImageModeling,
     )
     if is_tf_available()
     else ()
 )
 __lowercase			:							Union[str, Any]       					=			(
     {
         'feature-extraction': TFDeiTModel,
         'image-classification': (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
     }
     if is_tf_available()
     else {}
 )

 __lowercase			:							Union[str, Any]       					=			False
 __lowercase			:							Tuple       					=			False
 __lowercase			:							int       					=			False
 __lowercase			:							Optional[int]       					=			False
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[Any]:




        '''simple docstring'''
        lowercase__:       List[Any]        =       TFDeiTModelTester(self			)
        lowercase__:       int        =       ConfigTester(self    ,			config_class=lowerCAmelCase__    ,			has_text_modality=lowerCAmelCase__    ,			hidden_size=37			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[str]:




        '''simple docstring'''
        self.config_tester.run_common_tests()
 @unittest.skip(reason='DeiT does not use inputs_embeds'			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[str]:




        '''simple docstring'''
        pass
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Dict:




        '''simple docstring'''
        lowercase__      ,						lowercase__:       int        =       self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               lowercase__:       Tuple        =       model_class(lowerCAmelCase__			)
               self.assertIsInstance(model.get_input_embeddings()    ,			(tf.keras.layers.Layer)			)
               lowercase__:       Union[str, Any]        =       model.get_output_embeddings()
               self.assertTrue(x is None or isinstance(lowerCAmelCase__    ,			tf.keras.layers.Dense			)			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Union[str, Any]:




        '''simple docstring'''
        lowercase__      ,						lowercase__:       Union[str, Any]        =       self.model_tester.prepare_config_and_inputs_for_common()

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

               lowercase__:       Any        =       ['pixel_values']
               self.assertListEqual(arg_names[:1]    ,			lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Dict:




        '''simple docstring'''
        lowercase__:       Dict        =       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[str]:




        '''simple docstring'''
        lowercase__:       Tuple        =       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_image_modeling(*lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    int:




        '''simple docstring'''
        lowercase__:       List[Any]        =       self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__    ,			lowerCAmelCase__    ,			lowerCAmelCase__=False			) ->    List[Any]:




        '''simple docstring'''
        lowercase__:       Union[str, Any]        =       super()._prepare_for_class(lowerCAmelCase__    ,			lowerCAmelCase__    ,			return_labels=lowerCAmelCase__			)

        if return_labels:
               if "labels" in inputs_dict and "labels" not in inspect.signature(model_class.call			).parameters:
                      del inputs_dict["labels"]

        return inputs_dict
 @slow
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Tuple:




        '''simple docstring'''
        for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               lowercase__:       Union[str, Any]        =       TFDeiTModel.from_pretrained(lowerCAmelCase__			)
               self.assertIsNotNone(lowerCAmelCase__			)





def        snake_case_					(  )  ->    Tuple:
       lowercase__:       Any        =       Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
       return image





@require_tf
@require_vision
class   __a      (   unittest.TestCase				):
 @cached_property
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Tuple:




        '''simple docstring'''
        return (
            DeiTImageProcessor.from_pretrained('facebook/deit-base-distilled-patch16-224'			)
            if is_vision_available()
            else None
        )
 @slow
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Dict:




        '''simple docstring'''
        lowercase__:       str        =       TFDeiTForImageClassificationWithTeacher.from_pretrained('facebook/deit-base-distilled-patch16-224'			)

        lowercase__:       str        =       self.default_image_processor
        lowercase__:       Dict        =       prepare_img()
        lowercase__:       Optional[int]        =       image_processor(images=lowerCAmelCase__    ,			return_tensors='tf'			)

        # forward pass
        lowercase__:       int        =       model(**lowerCAmelCase__			)

        # verify the logits
        lowercase__:       Tuple        =       tf.TensorShape((1, 1_000)			)
        self.assertEqual(outputs.logits.shape    ,			lowerCAmelCase__			)

        lowercase__:       List[Any]        =       tf.constant([-1.0_2_6_6, 0.1_9_1_2, -1.2_8_6_1]			)

        self.assertTrue(np.allclose(outputs.logits[0, :3]    ,			lowerCAmelCase__    ,			atol=1E-4			)			)


