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 			UpperCAmelCase__    :



     """simple docstring"""






     def __init__(				self:			List[str]				,				__lowerCAmelCase:			List[Any]				,				__lowerCAmelCase:			int=13				,				__lowerCAmelCase:			Union[str, Any]=30				,				__lowerCAmelCase:			Dict=2				,				__lowerCAmelCase:			Optional[int]=3				,				__lowerCAmelCase:			Dict=True				,				__lowerCAmelCase:			Tuple=True				,				__lowerCAmelCase:			int=32				,				__lowerCAmelCase:			Optional[Any]=2				,				__lowerCAmelCase:			Tuple=4				,				__lowerCAmelCase:			Any=37				,				__lowerCAmelCase:			List[Any]="gelu"				,				__lowerCAmelCase:			Dict=0.1				,				__lowerCAmelCase:			Optional[int]=0.1				,				__lowerCAmelCase:			Union[str, Any]=10				,				__lowerCAmelCase:			List[str]=0.02				,				__lowerCAmelCase:			List[str]=3				,				__lowerCAmelCase:			Optional[int]=None				,				__lowerCAmelCase:			List[Any]=2				,				) -> List[str]:




       '''simple docstring'''




       __UpperCAmelCase						    =       parent
       __UpperCAmelCase						    =       batch_size
       __UpperCAmelCase						    =       image_size
       __UpperCAmelCase						    =       patch_size
       __UpperCAmelCase						    =       num_channels
       __UpperCAmelCase						    =       is_training
       __UpperCAmelCase						    =       use_labels
       __UpperCAmelCase						    =       hidden_size
       __UpperCAmelCase						    =       num_hidden_layers
       __UpperCAmelCase						    =       num_attention_heads
       __UpperCAmelCase						    =       intermediate_size
       __UpperCAmelCase						    =       hidden_act
       __UpperCAmelCase						    =       hidden_dropout_prob
       __UpperCAmelCase						    =       attention_probs_dropout_prob
       __UpperCAmelCase						    =       type_sequence_label_size
       __UpperCAmelCase						    =       initializer_range
       __UpperCAmelCase						    =       scope
       __UpperCAmelCase						    =       encoder_stride

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






     def 	_UpperCAmelCase				(				self:			Tuple   ) -> Dict:




       '''simple docstring'''




       __UpperCAmelCase						    =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

       __UpperCAmelCase						    =       None
       if self.use_labels:
         __UpperCAmelCase						    =       ids_tensor([self.batch_size]				,				self.type_sequence_label_size   )

       __UpperCAmelCase						    =       self.get_config()

       return config, pixel_values, labels






     def 	_UpperCAmelCase				(				self:			Any   ) -> str:




       '''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=_SCREAMING_SNAKE_CASE				,				initializer_range=self.initializer_range				,				encoder_stride=self.encoder_stride				,				)






     def 	_UpperCAmelCase				(				self:			List[Any]				,				__lowerCAmelCase:			int				,				__lowerCAmelCase:			Dict				,				__lowerCAmelCase:			Optional[Any]   ) -> Tuple:




       '''simple docstring'''




       __UpperCAmelCase						    =       TFDeiTModel(config=_SCREAMING_SNAKE_CASE   )
       __UpperCAmelCase						    =       model(_SCREAMING_SNAKE_CASE   )
       self.parent.assertEqual(result.last_hidden_state.shape				,				(self.batch_size, self.seq_length, self.hidden_size)   )






     def 	_UpperCAmelCase				(				self:			List[str]				,				__lowerCAmelCase:			Tuple				,				__lowerCAmelCase:			Any				,				__lowerCAmelCase:			Dict   ) -> Optional[Any]:




       '''simple docstring'''




       __UpperCAmelCase						    =       TFDeiTForMaskedImageModeling(config=_SCREAMING_SNAKE_CASE   )
       __UpperCAmelCase						    =       model(_SCREAMING_SNAKE_CASE   )
       self.parent.assertEqual(
           result.reconstruction.shape				,				(self.batch_size, self.num_channels, self.image_size, self.image_size)   )

       # test greyscale images
       __UpperCAmelCase						    =       1
       __UpperCAmelCase						    =       TFDeiTForMaskedImageModeling(_SCREAMING_SNAKE_CASE   )

       __UpperCAmelCase						    =       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]   )
       __UpperCAmelCase						    =       model(_SCREAMING_SNAKE_CASE   )
       self.parent.assertEqual(result.reconstruction.shape				,				(self.batch_size, 1, self.image_size, self.image_size)   )






     def 	_UpperCAmelCase				(				self:			int				,				__lowerCAmelCase:			str				,				__lowerCAmelCase:			Any				,				__lowerCAmelCase:			List[str]   ) -> int:




       '''simple docstring'''




       __UpperCAmelCase						    =       self.type_sequence_label_size
       __UpperCAmelCase						    =       TFDeiTForImageClassification(_SCREAMING_SNAKE_CASE   )
       __UpperCAmelCase						    =       model(_SCREAMING_SNAKE_CASE				,				labels=_SCREAMING_SNAKE_CASE   )
       self.parent.assertEqual(result.logits.shape				,				(self.batch_size, self.type_sequence_label_size)   )

       # test greyscale images
       __UpperCAmelCase						    =       1
       __UpperCAmelCase						    =       TFDeiTForImageClassification(_SCREAMING_SNAKE_CASE   )

       __UpperCAmelCase						    =       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]   )
       __UpperCAmelCase						    =       model(_SCREAMING_SNAKE_CASE				,				labels=_SCREAMING_SNAKE_CASE   )
       self.parent.assertEqual(result.logits.shape				,				(self.batch_size, self.type_sequence_label_size)   )






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




       '''simple docstring'''




       __UpperCAmelCase						    =       self.prepare_config_and_inputs()
       __UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase						    =       config_and_inputs
       __UpperCAmelCase						    =       {"pixel_values": pixel_values}
       return config, inputs_dict






@require_tf
class 			UpperCAmelCase__    (		lowerCAmelCase__		,	lowerCAmelCase__		,	unittest.TestCase					):



     """simple docstring"""






     lowerCAmelCase__		:	List[str]    		= (
         (
             TFDeiTModel,
             TFDeiTForImageClassification,
             TFDeiTForImageClassificationWithTeacher,
             TFDeiTForMaskedImageModeling,
         )
         if is_tf_available()
         else ()
     )
     lowerCAmelCase__		:	int    		= (
         {
             "feature-extraction": TFDeiTModel,
             "image-classification": (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
         }
         if is_tf_available()
         else {}
     )

     lowerCAmelCase__		:	Optional[int]    		= False
     lowerCAmelCase__		:	Any    		= False
     lowerCAmelCase__		:	List[Any]    		= False
     lowerCAmelCase__		:	Union[str, Any]    		= False






     def 	_UpperCAmelCase				(				self:			int   ) -> Optional[int]:




       '''simple docstring'''




       __UpperCAmelCase						    =       TFDeiTModelTester(self   )
       __UpperCAmelCase						    =       ConfigTester(self				,				config_class=_SCREAMING_SNAKE_CASE				,				has_text_modality=_SCREAMING_SNAKE_CASE				,				hidden_size=37   )






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




       '''simple docstring'''




       self.config_tester.run_common_tests()






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




       '''simple docstring'''




       pass






     def 	_UpperCAmelCase				(				self:			List[Any]   ) -> int:




       '''simple docstring'''




       __UpperCAmelCase    ,		__UpperCAmelCase						    =       self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
         __UpperCAmelCase						    =       model_class(_SCREAMING_SNAKE_CASE   )
         self.assertIsInstance(model.get_input_embeddings()				,				(tf.keras.layers.Layer)   )
         __UpperCAmelCase						    =       model.get_output_embeddings()
         self.assertTrue(x is None or isinstance(_SCREAMING_SNAKE_CASE				,				tf.keras.layers.Dense   )   )






     def 	_UpperCAmelCase				(				self:			Tuple   ) -> List[str]:




       '''simple docstring'''




       __UpperCAmelCase    ,		__UpperCAmelCase						    =       self.model_tester.prepare_config_and_inputs_for_common()

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

         __UpperCAmelCase						    =       ["pixel_values"]
         self.assertListEqual(arg_names[:1]				,				_SCREAMING_SNAKE_CASE   )






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




       '''simple docstring'''




       __UpperCAmelCase						    =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE   )






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




       '''simple docstring'''




       __UpperCAmelCase						    =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_image_modeling(*_SCREAMING_SNAKE_CASE   )






     def 	_UpperCAmelCase				(				self:			str   ) -> Dict:




       '''simple docstring'''




       __UpperCAmelCase						    =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE   )






     def 	_UpperCAmelCase				(				self:			Optional[int]				,				__lowerCAmelCase:			int				,				__lowerCAmelCase:			Any				,				__lowerCAmelCase:			List[Any]=False   ) -> str:




       '''simple docstring'''




       __UpperCAmelCase						    =       super()._prepare_for_class(_SCREAMING_SNAKE_CASE				,				_SCREAMING_SNAKE_CASE				,				return_labels=_SCREAMING_SNAKE_CASE   )

       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 	_UpperCAmelCase				(				self:			Optional[Any]   ) -> Dict:




       '''simple docstring'''




       for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         __UpperCAmelCase						    =       TFDeiTModel.from_pretrained(_SCREAMING_SNAKE_CASE   )
         self.assertIsNotNone(_SCREAMING_SNAKE_CASE   )






def       __lowerCAmelCase      (						)    ->  Optional[int]:
  __UpperCAmelCase						    =       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"					)
  return image





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



     """simple docstring"""






     @cached_property
     def 	_UpperCAmelCase				(				self:			str   ) -> Dict:




       '''simple docstring'''




       return (
           DeiTImageProcessor.from_pretrained("facebook/deit-base-distilled-patch16-224"   )
           if is_vision_available()
           else None
       )






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




       '''simple docstring'''




       __UpperCAmelCase						    =       TFDeiTForImageClassificationWithTeacher.from_pretrained("facebook/deit-base-distilled-patch16-224"   )

       __UpperCAmelCase						    =       self.default_image_processor
       __UpperCAmelCase						    =       prepare_img()
       __UpperCAmelCase						    =       image_processor(images=_SCREAMING_SNAKE_CASE				,				return_tensors="tf"   )

       # forward pass
       __UpperCAmelCase						    =       model(**_SCREAMING_SNAKE_CASE   )

       # verify the logits
       __UpperCAmelCase						    =       tf.TensorShape((1, 1_000)   )
       self.assertEqual(outputs.logits.shape				,				_SCREAMING_SNAKE_CASE   )

       __UpperCAmelCase						    =       tf.constant([-1.0266, 0.1912, -1.2861]   )

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




import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union


@dataclass
class 			UpperCAmelCase__    :



  """simple docstring"""






  lowerCAmelCase__		:	Optional[Union[str, Path]]    		= None
  lowerCAmelCase__		:	bool    		= False
  lowerCAmelCase__		:	bool    		= False
  lowerCAmelCase__		:	bool    		= False
  lowerCAmelCase__		:	Optional[Dict]    		= None
  lowerCAmelCase__		:	Optional[str]    		= None
  lowerCAmelCase__		:	bool    		= False
  lowerCAmelCase__		:	bool    		= False
  lowerCAmelCase__		:	bool    		= False
  lowerCAmelCase__		:	bool    		= True
  lowerCAmelCase__		:	Optional[int]    		= None
  lowerCAmelCase__		:	int    		= 1
  lowerCAmelCase__		:	Optional[Union[str, bool]]    		= None
  lowerCAmelCase__		:	bool    		= False
  lowerCAmelCase__		:	Optional[Dict]    		= None
  lowerCAmelCase__		:	Optional[str]    		= None






  def 	_UpperCAmelCase				(				self:			Any   ) -> "DownloadConfig":




    '''simple docstring'''




    return self.__class__(**{k: copy.deepcopy(__lowerCAmelCase   ) for k, v in self.__dict__.items()}   )



