


"""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 snake_case      :







		def __init__(					self      :       Union[str, Any]    ,   a__      :       List[str]    ,   a__      :       Optional[Any]=13    ,   a__      :       List[Any]=30    ,   a__      :       List[str]=2    ,   a__      :       Optional[int]=3    ,   a__      :       Any=True    ,   a__      :       Optional[int]=True    ,   a__      :       Optional[Any]=32    ,   a__      :       List[Any]=2    ,   a__      :       Tuple=4    ,   a__      :       Dict=37    ,   a__      :       Any="gelu"    ,   a__      :       List[str]=0.1    ,   a__      :       Optional[Any]=0.1    ,   a__      :       List[Any]=10    ,   a__      :       List[str]=0.0_2    ,   a__      :       int=3    ,   a__      :       Optional[Any]=None    ,   a__      :       str=2    ,   )       ->						int:





						'''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  a_ (					self      :       str		)       ->						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  a_ (					self      :       Tuple		)       ->						int:





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







		def  a_ (					self      :       Tuple    ,   a__      :       int    ,   a__      :       Union[str, Any]    ,   a__      :       int		)       ->						Any:





						'''simple docstring'''







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







		def  a_ (					self      :       List[Any]    ,   a__      :       Union[str, Any]    ,   a__      :       Dict    ,   a__      :       Optional[int]		)       ->						str:





						'''simple docstring'''







						_A					  =			TFDeiTForMaskedImageModeling(config=a__		)
						_A					  =			model(a__		)
						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(a__		)

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







		def  a_ (					self      :       int    ,   a__      :       List[str]    ,   a__      :       str    ,   a__      :       List[str]		)       ->						Any:





						'''simple docstring'''







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

						# test greyscale images
						_A					  =			1
						_A					  =			TFDeiTForImageClassification(a__		)

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







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





						'''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 snake_case      (  _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  a_ (					self      :       Dict		)       ->						Union[str, Any]:





						'''simple docstring'''







						_A					  =			TFDeiTModelTester(self		)
						_A					  =			ConfigTester(self    ,   config_class=a__    ,   has_text_modality=a__    ,   hidden_size=37		)







		def  a_ (					self      :       Optional[int]		)       ->						Any:





						'''simple docstring'''







						self.config_tester.run_common_tests()







		@unittest.skip(reason="DeiT does not use inputs_embeds"		)
		def  a_ (					self      :       Union[str, Any]		)       ->						Dict:





						'''simple docstring'''







						pass







		def  a_ (					self      :       Optional[Any]		)       ->						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(a__		)
										self.assertIsInstance(model.get_input_embeddings()    ,   (tf.keras.layers.Layer)		)
										_A					  =			model.get_output_embeddings()
										self.assertTrue(x is None or isinstance(a__    ,   tf.keras.layers.Dense		)		)







		def  a_ (					self      :       Dict		)       ->						List[str]:





						'''simple docstring'''







						_A  ,		_A					  =			self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
										_A					  =			model_class(a__		)
										_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]    ,   a__		)







		def  a_ (					self      :       Union[str, Any]		)       ->						Dict:





						'''simple docstring'''







						_A					  =			self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*a__		)







		def  a_ (					self      :       int		)       ->						Union[str, Any]:





						'''simple docstring'''







						_A					  =			self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_masked_image_modeling(*a__		)







		def  a_ (					self      :       List[str]		)       ->						Optional[Any]:





						'''simple docstring'''







						_A					  =			self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*a__		)







		def  a_ (					self      :       int    ,   a__      :       List[Any]    ,   a__      :       List[str]    ,   a__      :       str=False		)       ->						str:





						'''simple docstring'''







						_A					  =			super()._prepare_for_class(a__    ,   a__    ,   return_labels=a__		)

						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  a_ (					self      :       Optional[Any]		)       ->						Any:





						'''simple docstring'''







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






def      a__ (				)	->	Union[str, Any]:
				_A					  =			Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"   )
				return image




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







		@cached_property
		def  a_ (					self      :       List[str]		)       ->						Optional[Any]:





						'''simple docstring'''







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







		@slow
		def  a_ (					self      :       Tuple		)       ->						List[Any]:





						'''simple docstring'''







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

						_A					  =			self.default_image_processor
						_A					  =			prepare_img()
						_A					  =			image_processor(images=a__    ,   return_tensors="tf"		)

						# forward pass
						_A					  =			model(**a__		)

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

						_A					  =			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]    ,   a__    ,   atol=1E-4		)		)



"""simple docstring"""


import random






def      a__ (				__lowercase     ,			__lowercase     ,			__lowercase   )	->	Optional[Any]:
				_A					  =			a[left_index]
				_A					  =			left_index + 1
				for j in range(left_index + 1     ,			__lowercase   ):
								if a[j] < pivot:
												_A  ,		_A					  =			a[i], a[j]
												i += 1
				_A  ,		_A					  =			a[i - 1], a[left_index]
				return i - 1






def      a__ (				__lowercase     ,			__lowercase     ,			__lowercase   )	->	int:
				if left < right:
								_A					  =			random.randint(__lowercase     ,			right - 1   )
								_A  ,		_A					  =			(
								    a[left],
								    a[pivot],
								)  # switches the pivot with the left most bound
								_A					  =			partition(__lowercase     ,			__lowercase     ,			__lowercase   )
								quick_sort_random(
								    __lowercase     ,			__lowercase     ,			__lowercase   )  # recursive quicksort to the left of the pivot point
								quick_sort_random(
								    __lowercase     ,			pivot_index + 1     ,			__lowercase   )  # recursive quicksort to the right of the pivot point






def      a__ (				)	->	Dict:
				_A					  =			input("Enter numbers separated by a comma:\n"   ).strip()
				_A					  =			[int(__lowercase   ) for item in user_input.split(","   )]

				quick_sort_random(__lowercase     ,			0     ,			len(__lowercase   )   )

				print(__lowercase   )


if __name__ == "__main__":
		main()