






'''simple docstring'''
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        __SCREAMING_SNAKE_CASE :




 def __init__( self		:							List[Any]   , __lowercase		:							List[Any]   , __lowercase		:							Optional[int]=13   , __lowercase		:							Optional[int]=30   , __lowercase		:							Tuple=2   , __lowercase		:							str=3   , __lowercase		:							List[Any]=True   , __lowercase		:							Tuple=True   , __lowercase		:							List[str]=32   , __lowercase		:							List[Any]=2   , __lowercase		:							str=4   , __lowercase		:							Optional[int]=37   , __lowercase		:							Tuple="gelu"   , __lowercase		:							List[str]=0.1   , __lowercase		:							Union[str, Any]=0.1   , __lowercase		:							Dict=10   , __lowercase		:							Optional[int]=0.02   , __lowercase		:							List[str]=3   , __lowercase		:							Optional[Any]=None   , __lowercase		:							str=2   , )		->  List[Any]:
       SCREAMING_SNAKE_CASE__       :			List[Any]													=parent
       SCREAMING_SNAKE_CASE__       :			str													=batch_size
       SCREAMING_SNAKE_CASE__       :			Dict													=image_size
       SCREAMING_SNAKE_CASE__       :			int													=patch_size
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=num_channels
       SCREAMING_SNAKE_CASE__       :			Any													=is_training
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=use_labels
       SCREAMING_SNAKE_CASE__       :			List[str]													=hidden_size
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=num_hidden_layers
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=num_attention_heads
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=intermediate_size
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=hidden_act
       SCREAMING_SNAKE_CASE__       :			Any													=hidden_dropout_prob
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE__       :			Tuple													=type_sequence_label_size
       SCREAMING_SNAKE_CASE__       :			Dict													=initializer_range
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=scope
       SCREAMING_SNAKE_CASE__       :			Dict													=encoder_stride

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




 def        __magic_name__		( self		:							Union[str, Any] )		->  Any:
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

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

       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=self.get_config()

       return config, pixel_values, labels




 def        __magic_name__		( self		:							Optional[int] )		->  List[str]:
       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=__lowercase   , initializer_range=self.initializer_range   , encoder_stride=self.encoder_stride   , )




 def        __magic_name__		( self		:							List[str]   , __lowercase		:							List[str]   , __lowercase		:							Dict   , __lowercase		:							Optional[Any] )		->  Tuple:
       SCREAMING_SNAKE_CASE__       :			List[str]													=TFDeiTModel(config=__lowercase )
       SCREAMING_SNAKE_CASE__       :			List[Any]													=model(__lowercase )
       self.parent.assertEqual(result.last_hidden_state.shape   , (self.batch_size, self.seq_length, self.hidden_size) )




 def        __magic_name__		( self		:							int   , __lowercase		:							List[str]   , __lowercase		:							List[Any]   , __lowercase		:							Any )		->  Optional[Any]:
       SCREAMING_SNAKE_CASE__       :			int													=TFDeiTForMaskedImageModeling(config=__lowercase )
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=model(__lowercase )
       self.parent.assertEqual(
           result.reconstruction.shape   , (self.batch_size, self.num_channels, self.image_size, self.image_size) )

       # test greyscale images
       SCREAMING_SNAKE_CASE__       :			Tuple													=1
       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=TFDeiTForMaskedImageModeling(__lowercase )

       SCREAMING_SNAKE_CASE__       :			List[Any]													=floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
       SCREAMING_SNAKE_CASE__       :			str													=model(__lowercase )
       self.parent.assertEqual(result.reconstruction.shape   , (self.batch_size, 1, self.image_size, self.image_size) )




 def        __magic_name__		( self		:							Optional[int]   , __lowercase		:							str   , __lowercase		:							Union[str, Any]   , __lowercase		:							Optional[int] )		->  Dict:
       SCREAMING_SNAKE_CASE__       :			int													=self.type_sequence_label_size
       SCREAMING_SNAKE_CASE__       :			str													=TFDeiTForImageClassification(__lowercase )
       SCREAMING_SNAKE_CASE__       :			Dict													=model(__lowercase   , labels=__lowercase )
       self.parent.assertEqual(result.logits.shape   , (self.batch_size, self.type_sequence_label_size) )

       # test greyscale images
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=1
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=TFDeiTForImageClassification(__lowercase )

       SCREAMING_SNAKE_CASE__       :			Tuple													=floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
       SCREAMING_SNAKE_CASE__       :			Any													=model(__lowercase   , labels=__lowercase )
       self.parent.assertEqual(result.logits.shape   , (self.batch_size, self.type_sequence_label_size) )




 def        __magic_name__		( self		:							Optional[int] )		->  Optional[Any]:
       SCREAMING_SNAKE_CASE__       :			Optional[int]													=self.prepare_config_and_inputs()
       SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__       :			List[Any]													=config_and_inputs
       SCREAMING_SNAKE_CASE__       :			Dict													={'''pixel_values''': pixel_values}
       return config, inputs_dict






@require_tf
class        __SCREAMING_SNAKE_CASE (     lowerCamelCase		,			lowerCamelCase		,			unittest.TestCase			):
 snake_case_												=							(
     (
         TFDeiTModel,
         TFDeiTForImageClassification,
         TFDeiTForImageClassificationWithTeacher,
         TFDeiTForMaskedImageModeling,
     )
     if is_tf_available()
     else ()
 )
 snake_case_												=							(
     {
         """feature-extraction""": TFDeiTModel,
         """image-classification""": (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
     }
     if is_tf_available()
     else {}
 )

 snake_case_												=							False
 snake_case_												=							False
 snake_case_												=							False
 snake_case_												=							False




 def        __magic_name__		( self		:							Tuple )		->  str:
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=TFDeiTModelTester(self )
       SCREAMING_SNAKE_CASE__       :			Any													=ConfigTester(self   , config_class=__lowercase   , has_text_modality=__lowercase   , hidden_size=37 )




 def        __magic_name__		( self		:							Dict )		->  int:
       self.config_tester.run_common_tests()




 @unittest.skip(reason='''DeiT does not use inputs_embeds''' )
 def        __magic_name__		( self		:							Dict )		->  Tuple:
       pass




 def        __magic_name__		( self		:							List[Any] )		->  List[str]:
       SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__       :			List[str]													=self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
             SCREAMING_SNAKE_CASE__       :			Tuple													=model_class(__lowercase )
             self.assertIsInstance(model.get_input_embeddings()   , (tf.keras.layers.Layer) )
             SCREAMING_SNAKE_CASE__       :			List[Any]													=model.get_output_embeddings()
             self.assertTrue(x is None or isinstance(__lowercase   , tf.keras.layers.Dense ) )




 def        __magic_name__		( self		:							Optional[Any] )		->  Any:
       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__       :			Union[str, Any]													=model_class(__lowercase )
             SCREAMING_SNAKE_CASE__       :			List[str]													=inspect.signature(model.call )
             # signature.parameters is an OrderedDict => so arg_names order is deterministic
             SCREAMING_SNAKE_CASE__       :			Optional[Any]													=[*signature.parameters.keys()]

             SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=['''pixel_values''']
             self.assertListEqual(arg_names[:1]   , __lowercase )




 def        __magic_name__		( self		:							Tuple )		->  List[Any]:
       SCREAMING_SNAKE_CASE__       :			List[str]													=self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*__lowercase )




 def        __magic_name__		( self		:							Any )		->  Tuple:
       SCREAMING_SNAKE_CASE__       :			Dict													=self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_image_modeling(*__lowercase )




 def        __magic_name__		( self		:							Tuple )		->  str:
       SCREAMING_SNAKE_CASE__       :			str													=self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*__lowercase )




 def        __magic_name__		( self		:							List[str]   , __lowercase		:							Tuple   , __lowercase		:							List[Any]   , __lowercase		:							Union[str, Any]=False )		->  str:
       SCREAMING_SNAKE_CASE__       :			int													=super()._prepare_for_class(__lowercase   , __lowercase   , return_labels=__lowercase )

       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        __magic_name__		( self		:							Any )		->  Any:
       for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             SCREAMING_SNAKE_CASE__       :			str													=TFDeiTModel.from_pretrained(__lowercase )
             self.assertIsNotNone(__lowercase )





def 	_a(					):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			Dict													=Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
      return image







@require_tf
@require_vision
class        __SCREAMING_SNAKE_CASE (     unittest.TestCase			):




 @cached_property
 def        __magic_name__		( self		:							int )		->  List[Any]:
       return (
           DeiTImageProcessor.from_pretrained('''facebook/deit-base-distilled-patch16-224''' )
           if is_vision_available()
           else None
       )




 @slow
 def        __magic_name__		( self		:							List[str] )		->  str:
       SCREAMING_SNAKE_CASE__       :			Tuple													=TFDeiTForImageClassificationWithTeacher.from_pretrained('''facebook/deit-base-distilled-patch16-224''' )

       SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=self.default_image_processor
       SCREAMING_SNAKE_CASE__       :			List[str]													=prepare_img()
       SCREAMING_SNAKE_CASE__       :			int													=image_processor(images=__lowercase   , return_tensors='''tf''' )

       # forward pass
       SCREAMING_SNAKE_CASE__       :			Optional[Any]													=model(**__lowercase )

       # verify the logits
       SCREAMING_SNAKE_CASE__       :			int													=tf.TensorShape((1, 10_00) )
       self.assertEqual(outputs.logits.shape   , __lowercase )

       SCREAMING_SNAKE_CASE__       :			Tuple													=tf.constant([-1.0266, 0.1912, -1.2861] )

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







'''simple docstring'''
import argparse
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
a_					=	'src/diffusers'

# Matches is_xxx_available()
a_					=	re.compile(R'is\_([a-z_]*)_available\(\)')
# Matches from xxx import bla
a_					=	re.compile(R'\s+from\s+\S*\s+import\s+([^\(\s].*)\n')


a_					=	'\n{0} = None\n'

a_					=	'\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, {1})\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, {1})\n'


a_					=	'\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n'





def 	_a(					UpperCamelCase__			:  Any							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			Dict													=_re_backend.findall(UpperCamelCase__							)
      if len(UpperCamelCase__							) == 0:
            return None

      return "_and_".join(UpperCamelCase__							)





def 	_a(					):




      '''simple docstring'''






      with open(os.path.join(UpperCamelCase__,		'''__init__.py'''							),		'''r''',		encoding='''utf-8''',		newline='''\n'''							) as f:
            SCREAMING_SNAKE_CASE__       :			List[str]													=f.readlines()

      # Get to the point we do the actual imports for type checking
      SCREAMING_SNAKE_CASE__       :			Optional[int]													=0
      SCREAMING_SNAKE_CASE__       :			List[str]													={}
      # Go through the end of the file
      while line_index < len(UpperCamelCase__							):
            # If the line contains is_backend_available, we grab all objects associated with the `else` block
            SCREAMING_SNAKE_CASE__       :			List[Any]													=find_backend(lines[line_index]							)
            if backend is not None:
                  while not lines[line_index].startswith('''else:'''							):
                        line_index += 1
                  line_index += 1
                  SCREAMING_SNAKE_CASE__       :			List[Any]													=[]
                  # Until we unindent, add backend objects to the list
                  while line_index < len(UpperCamelCase__							) and len(lines[line_index]							) > 1:
                        SCREAMING_SNAKE_CASE__       :			Optional[Any]													=lines[line_index]
                        SCREAMING_SNAKE_CASE__       :			Optional[Any]													=_re_single_line_import.search(UpperCamelCase__							)
                        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

                  if len(UpperCamelCase__							) > 0:
                        SCREAMING_SNAKE_CASE__       :			Any													=objects
            else:
                  line_index += 1

      return backend_specific_objects





def 	_a(					UpperCamelCase__			:  Union[str, Any],		UpperCamelCase__			:  List[Any]							):




      '''simple docstring'''






      if name.isupper():
            return DUMMY_CONSTANT.format(UpperCamelCase__							)
      elif name.islower():
            return DUMMY_FUNCTION.format(UpperCamelCase__,		UpperCamelCase__							)
      else:
            return DUMMY_CLASS.format(UpperCamelCase__,		UpperCamelCase__							)





def 	_a(					UpperCamelCase__			:  Any=None							):




      '''simple docstring'''






      if backend_specific_objects is None:
            SCREAMING_SNAKE_CASE__       :			int													=read_init()
      # For special correspondence backend to module name as used in the function requires_modulename
      SCREAMING_SNAKE_CASE__       :			Optional[int]													={}

      for backend, objects in backend_specific_objects.items():
            SCREAMING_SNAKE_CASE__       :			Tuple													='''[''' + ''', '''.join(f"\"{b}\"" for b in backend.split('''_and_'''							)							) + ''']'''
            SCREAMING_SNAKE_CASE__       :			List[str]													='''# This file is autogenerated by the command `make fix-copies`, do not edit.\n'''
            dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
            dummy_file += "\n".join([create_dummy_object(UpperCamelCase__,		UpperCamelCase__							) for o in objects]							)
            SCREAMING_SNAKE_CASE__       :			Tuple													=dummy_file

      return dummy_files





def 	_a(					UpperCamelCase__			:  Any=False							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			Tuple													=create_dummy_files()
      # For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
      SCREAMING_SNAKE_CASE__       :			List[str]													={'''torch''': '''pt'''}

      # Locate actual dummy modules and read their content.
      SCREAMING_SNAKE_CASE__       :			List[str]													=os.path.join(UpperCamelCase__,		'''utils'''							)
      SCREAMING_SNAKE_CASE__       :			Union[str, Any]													={
          backend: os.path.join(UpperCamelCase__,		f"dummy_{short_names.get(UpperCamelCase__,		UpperCamelCase__							)}_objects.py"							)
          for backend in dummy_files.keys()
      }

      SCREAMING_SNAKE_CASE__       :			str													={}
      for backend, file_path in dummy_file_paths.items():
            if os.path.isfile(UpperCamelCase__							):
                  with open(UpperCamelCase__,		'''r''',		encoding='''utf-8''',		newline='''\n'''							) as f:
                        SCREAMING_SNAKE_CASE__       :			List[Any]													=f.read()
            else:
                  SCREAMING_SNAKE_CASE__       :			int													=''''''

      for backend in dummy_files.keys():
            if dummy_files[backend] != actual_dummies[backend]:
                  if overwrite:
                        print(
                            f"Updating diffusers.utils.dummy_{short_names.get(UpperCamelCase__,		UpperCamelCase__							)}_objects.py as the main "
                            '''__init__ has new objects.'''							)
                        with open(dummy_file_paths[backend],		'''w''',		encoding='''utf-8''',		newline='''\n'''							) as f:
                              f.write(dummy_files[backend]							)
                  else:
                        raise ValueError(
                            '''The main __init__ has objects that are not present in '''
                            f"diffusers.utils.dummy_{short_names.get(UpperCamelCase__,		UpperCamelCase__							)}_objects.py. Run `make fix-copies` "
                            '''to fix this.'''							)


if __name__ == "__main__":
  a_					=	argparse.ArgumentParser()
  parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
  a_					=	parser.parse_args()

  check_dummies(args.fix_and_overwrite)