


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__lowerCAmelCase		      =							{
    '''configuration_tapas''': ['''TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TapasConfig'''],
    '''tokenization_tapas''': ['''TapasTokenizer'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __lowerCAmelCase		      =							[
     '''TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''TapasForMaskedLM''',
     '''TapasForQuestionAnswering''',
     '''TapasForSequenceClassification''',
     '''TapasModel''',
     '''TapasPreTrainedModel''',
     '''load_tf_weights_in_tapas''',
 ]
try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __lowerCAmelCase		      =							[
     '''TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''TFTapasForMaskedLM''',
     '''TFTapasForQuestionAnswering''',
     '''TFTapasForSequenceClassification''',
     '''TFTapasModel''',
     '''TFTapasPreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
 from .tokenization_tapas import TapasTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tapas import (
      TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
      TapasForMaskedLM,
      TapasForQuestionAnswering,
      TapasForSequenceClassification,
      TapasModel,
      TapasPreTrainedModel,
      load_tf_weights_in_tapas,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_tapas import (
      TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFTapasForMaskedLM,
      TFTapasForQuestionAnswering,
      TFTapasForSequenceClassification,
      TFTapasModel,
      TFTapasPreTrainedModel,
  )


else:
 import sys

 __lowerCAmelCase		      =							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






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			)			)


