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        lowerCamelCase__				:





			def __init__(       self	:	int  ,  lowercase__	:	str  ,  lowercase__	:	Tuple=13  ,  lowercase__	:	List[Any]=30  ,  lowercase__	:	List[str]=2  ,  lowercase__	:	Any=3  ,  lowercase__	:	List[str]=True  ,  lowercase__	:	Tuple=True  ,  lowercase__	:	Tuple=32  ,  lowercase__	:	Optional[Any]=2  ,  lowercase__	:	Any=4  ,  lowercase__	:	int=37  ,  lowercase__	:	Optional[int]="gelu"  ,  lowercase__	:	Union[str, Any]=0.1  ,  lowercase__	:	List[Any]=0.1  ,  lowercase__	:	Optional[int]=10  ,  lowercase__	:	Tuple=0.0_2  ,  lowercase__	:	Dict=3  ,  lowercase__	:	Optional[Any]=None  ,  lowercase__	:	Any=2  ,  ):
										_lowerCAmelCase							=							parent
										_lowerCAmelCase							=							batch_size
										_lowerCAmelCase							=							image_size
										_lowerCAmelCase							=							patch_size
										_lowerCAmelCase							=							num_channels
										_lowerCAmelCase							=							is_training
										_lowerCAmelCase							=							use_labels
										_lowerCAmelCase							=							hidden_size
										_lowerCAmelCase							=							num_hidden_layers
										_lowerCAmelCase							=							num_attention_heads
										_lowerCAmelCase							=							intermediate_size
										_lowerCAmelCase							=							hidden_act
										_lowerCAmelCase							=							hidden_dropout_prob
										_lowerCAmelCase							=							attention_probs_dropout_prob
										_lowerCAmelCase							=							type_sequence_label_size
										_lowerCAmelCase							=							initializer_range
										_lowerCAmelCase							=							scope
										_lowerCAmelCase							=							encoder_stride

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





			def   SCREAMING_SNAKE_CASE__     (       self	:	List[str]						):
										_lowerCAmelCase							=							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

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

										_lowerCAmelCase							=							self.get_config()

										return config, pixel_values, labels





			def   SCREAMING_SNAKE_CASE__     (       self	:	Any						):
										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   SCREAMING_SNAKE_CASE__     (       self	:	Dict  ,  lowercase__	:	Optional[int]  ,  lowercase__	:	Dict  ,  lowercase__	:	Dict						):
										_lowerCAmelCase							=							TFDeiTModel(config=lowercase__						)
										_lowerCAmelCase							=							model(lowercase__						)
										self.parent.assertEqual(result.last_hidden_state.shape  ,  (self.batch_size, self.seq_length, self.hidden_size)						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Optional[int]  ,  lowercase__	:	Tuple  ,  lowercase__	:	List[str]  ,  lowercase__	:	Optional[int]						):
										_lowerCAmelCase							=							TFDeiTForMaskedImageModeling(config=lowercase__						)
										_lowerCAmelCase							=							model(lowercase__						)
										self.parent.assertEqual(
										    result.reconstruction.shape  ,  (self.batch_size, self.num_channels, self.image_size, self.image_size)						)

										# test greyscale images
										_lowerCAmelCase							=							1
										_lowerCAmelCase							=							TFDeiTForMaskedImageModeling(lowercase__						)

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





			def   SCREAMING_SNAKE_CASE__     (       self	:	Tuple  ,  lowercase__	:	int  ,  lowercase__	:	Optional[int]  ,  lowercase__	:	Optional[int]						):
										_lowerCAmelCase							=							self.type_sequence_label_size
										_lowerCAmelCase							=							TFDeiTForImageClassification(lowercase__						)
										_lowerCAmelCase							=							model(lowercase__  ,  labels=lowercase__						)
										self.parent.assertEqual(result.logits.shape  ,  (self.batch_size, self.type_sequence_label_size)						)

										# test greyscale images
										_lowerCAmelCase							=							1
										_lowerCAmelCase							=							TFDeiTForImageClassification(lowercase__						)

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





			def   SCREAMING_SNAKE_CASE__     (       self	:	Any						):
										_lowerCAmelCase							=							self.prepare_config_and_inputs()
										_lowerCAmelCase			,		_lowerCAmelCase			,		_lowerCAmelCase							=							config_and_inputs
										_lowerCAmelCase							=							{'pixel_values': pixel_values}
										return config, inputs_dict







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

			UpperCamelCase__    							=False
			UpperCamelCase__    							=False
			UpperCamelCase__    							=False
			UpperCamelCase__    							=False





			def   SCREAMING_SNAKE_CASE__     (       self	:	Union[str, Any]						):
										_lowerCAmelCase							=							TFDeiTModelTester(self						)
										_lowerCAmelCase							=							ConfigTester(self  ,  config_class=lowercase__  ,  has_text_modality=lowercase__  ,  hidden_size=37						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Any						):
										self.config_tester.run_common_tests()





			@unittest.skip(reason='DeiT does not use inputs_embeds'						)
			def   SCREAMING_SNAKE_CASE__     (       self	:	Optional[int]						):
										pass





			def   SCREAMING_SNAKE_CASE__     (       self	:	List[Any]						):
										_lowerCAmelCase			,		_lowerCAmelCase							=							self.model_tester.prepare_config_and_inputs_for_common()

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





			def   SCREAMING_SNAKE_CASE__     (       self	:	Optional[Any]						):
										_lowerCAmelCase			,		_lowerCAmelCase							=							self.model_tester.prepare_config_and_inputs_for_common()

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

																	_lowerCAmelCase							=							['pixel_values']
																	self.assertListEqual(arg_names[:1]  ,  lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Tuple						):
										_lowerCAmelCase							=							self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Tuple						):
										_lowerCAmelCase							=							self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_image_modeling(*lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Dict						):
										_lowerCAmelCase							=							self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*lowercase__						)





			def   SCREAMING_SNAKE_CASE__     (       self	:	Any  ,  lowercase__	:	Dict  ,  lowercase__	:	Optional[int]  ,  lowercase__	:	List[str]=False						):
										_lowerCAmelCase							=							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   SCREAMING_SNAKE_CASE__     (       self	:	str						):
										for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	_lowerCAmelCase							=							TFDeiTModel.from_pretrained(lowercase__						)
																	self.assertIsNotNone(lowercase__						)







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



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





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





			@slow
			def   SCREAMING_SNAKE_CASE__     (       self	:	Optional[int]						):
										_lowerCAmelCase							=							TFDeiTForImageClassificationWithTeacher.from_pretrained('facebook/deit-base-distilled-patch16-224'						)

										_lowerCAmelCase							=							self.default_image_processor
										_lowerCAmelCase							=							prepare_img()
										_lowerCAmelCase							=							image_processor(images=lowercase__  ,  return_tensors='tf'						)

										# forward pass
										_lowerCAmelCase							=							model(**lowercase__						)

										# verify the logits
										_lowerCAmelCase							=							tf.TensorShape((1, 10_00)						)
										self.assertEqual(outputs.logits.shape  ,  lowercase__						)

										_lowerCAmelCase							=							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]  ,  lowercase__  ,  atol=1e-4						)						)







from __future__ import annotations

from random import choice






def _lowerCamelCase  (		snake_case  ):
							return choice(snake_case  )






def _lowerCamelCase  (		snake_case     , snake_case  ):
							_lowerCAmelCase							=							random_pivot(snake_case  )

							# partition based on pivot
							# linear time
							_lowerCAmelCase							=							[e for e in lst if e < pivot]
							_lowerCAmelCase							=							[e for e in lst if e > pivot]

							# if we get lucky, pivot might be the element we want.
							# we can easily see this:
							# small (elements smaller than k)
							# + pivot (kth element)
							# + big (elements larger than k)
							if len(snake_case  ) == k - 1:
														return pivot
							# pivot is in elements bigger than k
							elif len(snake_case  ) < k - 1:
														return kth_number(snake_case     , k - len(snake_case  ) - 1  )
							# pivot is in elements smaller than k
							else:
														return kth_number(snake_case     , snake_case  )


if __name__ == "__main__":
				import doctest

				doctest.testmod()






