



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							:  Optional[Any]  ,	__SCREAMING_SNAKE_CASE							:  Optional[int]  ,	__SCREAMING_SNAKE_CASE							:  Any=13  ,	__SCREAMING_SNAKE_CASE							:  Tuple=30  ,	__SCREAMING_SNAKE_CASE							:  str=2  ,	__SCREAMING_SNAKE_CASE							:  List[Any]=3  ,	__SCREAMING_SNAKE_CASE							:  Any=True  ,	__SCREAMING_SNAKE_CASE							:  Optional[Any]=True  ,	__SCREAMING_SNAKE_CASE							:  Optional[Any]=32  ,	__SCREAMING_SNAKE_CASE							:  Any=2  ,	__SCREAMING_SNAKE_CASE							:  List[Any]=4  ,	__SCREAMING_SNAKE_CASE							:  Optional[Any]=37  ,	__SCREAMING_SNAKE_CASE							:  int="gelu"  ,	__SCREAMING_SNAKE_CASE							:  Dict=0.1  ,	__SCREAMING_SNAKE_CASE							:  Tuple=0.1  ,	__SCREAMING_SNAKE_CASE							:  Dict=10  ,	__SCREAMING_SNAKE_CASE							:  Optional[Any]=0.02  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any]=3  ,	__SCREAMING_SNAKE_CASE							:  Tuple=None  ,	__SCREAMING_SNAKE_CASE							:  List[str]=2  ,	):


           '''simple docstring'''

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

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



       def 	_lowerCamelCase    (	self							:  str):


           '''simple docstring'''

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

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

           __a		 =       self.get_config()

           return config, pixel_values, labels



       def 	_lowerCamelCase    (	self							:  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=__SCREAMING_SNAKE_CASE  ,	initializer_range=self.initializer_range  ,	encoder_stride=self.encoder_stride  ,	)



       def 	_lowerCamelCase    (	self							:  int  ,	__SCREAMING_SNAKE_CASE							:  Any  ,	__SCREAMING_SNAKE_CASE							:  str  ,	__SCREAMING_SNAKE_CASE							:  List[str]):


           '''simple docstring'''

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



       def 	_lowerCamelCase    (	self							:  Optional[Any]  ,	__SCREAMING_SNAKE_CASE							:  str  ,	__SCREAMING_SNAKE_CASE							:  str  ,	__SCREAMING_SNAKE_CASE							:  Optional[Any]):


           '''simple docstring'''

           __a		 =       TFDeiTForMaskedImageModeling(config=__SCREAMING_SNAKE_CASE)
           __a		 =       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
           __a		 =       1
           __a		 =       TFDeiTForMaskedImageModeling(__SCREAMING_SNAKE_CASE)

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



       def 	_lowerCamelCase    (	self							:  Optional[Any]  ,	__SCREAMING_SNAKE_CASE							:  Optional[int]  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any]  ,	__SCREAMING_SNAKE_CASE							:  Tuple):


           '''simple docstring'''

           __a		 =       self.type_sequence_label_size
           __a		 =       TFDeiTForImageClassification(__SCREAMING_SNAKE_CASE)
           __a		 =       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
           __a		 =       1
           __a		 =       TFDeiTForImageClassification(__SCREAMING_SNAKE_CASE)

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





       def 	_lowerCamelCase    (	self							:  List[Any]):


           '''simple docstring'''

           __a		 =       self.prepare_config_and_inputs()
           __a ,     __a ,     __a		 =       config_and_inputs
           __a		 =       {'''pixel_values''': pixel_values}
           return config, inputs_dict


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

       UpperCamelCase__						:					Optional[int]		      =		False
       UpperCamelCase__						:					Optional[Any]		      =		False
       UpperCamelCase__						:					str		      =		False
       UpperCamelCase__						:					Dict		      =		False



       def 	_lowerCamelCase    (	self							:  Optional[Any]):


           '''simple docstring'''

           __a		 =       TFDeiTModelTester(self)
           __a		 =       ConfigTester(self  ,	config_class=__SCREAMING_SNAKE_CASE  ,	has_text_modality=__SCREAMING_SNAKE_CASE  ,	hidden_size=37)



       def 	_lowerCamelCase    (	self							:  Optional[Any]):


           '''simple docstring'''

           self.config_tester.run_common_tests()



       @unittest.skip(reason='''DeiT does not use inputs_embeds''')
       def 	_lowerCamelCase    (	self							:  int):


           '''simple docstring'''

           pass



       def 	_lowerCamelCase    (	self							:  Optional[int]):


           '''simple docstring'''

           __a ,     __a		 =       self.model_tester.prepare_config_and_inputs_for_common()

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



       def 	_lowerCamelCase    (	self							:  List[Any]):


           '''simple docstring'''

           __a ,     __a		 =       self.model_tester.prepare_config_and_inputs_for_common()

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

               __a		 =       ['''pixel_values''']
               self.assertListEqual(arg_names[:1]  ,	__SCREAMING_SNAKE_CASE)



       def 	_lowerCamelCase    (	self							:  str):


           '''simple docstring'''

           __a		 =       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE)



       def 	_lowerCamelCase    (	self							:  int):


           '''simple docstring'''

           __a		 =       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_image_modeling(*__SCREAMING_SNAKE_CASE)



       def 	_lowerCamelCase    (	self							:  Optional[int]):


           '''simple docstring'''

           __a		 =       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*__SCREAMING_SNAKE_CASE)



       def 	_lowerCamelCase    (	self							:  int  ,	__SCREAMING_SNAKE_CASE							:  Optional[Any]  ,	__SCREAMING_SNAKE_CASE							:  Any  ,	__SCREAMING_SNAKE_CASE							:  Tuple=False):


           '''simple docstring'''

           __a		 =       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 	_lowerCamelCase    (	self							:  List[Any]):


           '''simple docstring'''

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





def   __snake_case			(    ):
    __a		 =       Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       )
    return image

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



       @cached_property
       def 	_lowerCamelCase    (	self							:  Tuple):


           '''simple docstring'''

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





       @slow
       def 	_lowerCamelCase    (	self							:  List[str]):


           '''simple docstring'''

           __a		 =       TFDeiTForImageClassificationWithTeacher.from_pretrained('''facebook/deit-base-distilled-patch16-224''')

           __a		 =       self.default_image_processor
           __a		 =       prepare_img()
           __a		 =       image_processor(images=__SCREAMING_SNAKE_CASE  ,	return_tensors='''tf''')

           # forward pass
           __a		 =       model(**__SCREAMING_SNAKE_CASE)

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

           __a		 =       tf.constant([-1.02_66, 0.19_12, -1.28_61])

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










def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase = False       ):

    if not isinstance(_UpperCAmelCase     ,				_UpperCAmelCase       ):
        __a		 =       f'Expected string as input, found {type(_UpperCAmelCase       )}'
        raise ValueError(_UpperCAmelCase       )
    if not isinstance(_UpperCAmelCase     ,				_UpperCAmelCase       ):
        __a		 =       f'Expected boolean as use_pascal parameter, found {type(_UpperCAmelCase       )}'
        raise ValueError(_UpperCAmelCase       )

    __a		 =       input_str.split('''_'''       )

    __a		 =       0 if use_pascal else 1

    __a		 =       words[start_index:]

    __a		 =       [word[0].upper() + word[1:] for word in words_to_capitalize]

    __a		 =       '''''' if use_pascal else words[0]

    return "".join([initial_word, *capitalized_words]       )


if __name__ == "__main__":
 from doctest import testmod

 testmod()





