
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     lowercase :



		def __init__(			self    ,				A_    ,				A_=13    ,				A_=30    ,				A_=2    ,				A_=3    ,				A_=True    ,				A_=True    ,				A_=32    ,				A_=2    ,				A_=4    ,				A_=37    ,				A_="gelu"    ,				A_=0.1    ,				A_=0.1    ,				A_=10    ,				A_=0.02    ,				A_=3    ,				A_=None    ,				A_=2    ,				)  -> 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	)  -> int:
				"""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	)  -> List[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=A_    ,				initializer_range=self.initializer_range    ,				encoder_stride=self.encoder_stride    ,				)



		def __UpperCamelCase			(			self    ,				A_    ,				A_    ,				A_	)  -> Dict:
				"""simple docstring"""
				UpperCamelCase          =					TFDeiTModel(config=A_	)
				UpperCamelCase          =					model(A_	)
				self.parent.assertEqual(result.last_hidden_state.shape    ,				(self.batch_size, self.seq_length, self.hidden_size)	)



		def __UpperCamelCase			(			self    ,				A_    ,				A_    ,				A_	)  -> List[Any]:
				"""simple docstring"""
				UpperCamelCase          =					TFDeiTForMaskedImageModeling(config=A_	)
				UpperCamelCase          =					model(A_	)
				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(A_	)

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



		def __UpperCamelCase			(			self    ,				A_    ,				A_    ,				A_	)  -> Any:
				"""simple docstring"""
				UpperCamelCase          =					self.type_sequence_label_size
				UpperCamelCase          =					TFDeiTForImageClassification(A_	)
				UpperCamelCase          =					model(A_    ,				labels=A_	)
				self.parent.assertEqual(result.logits.shape    ,				(self.batch_size, self.type_sequence_label_size)	)

				# test greyscale images
				UpperCamelCase          =					1
				UpperCamelCase          =					TFDeiTForImageClassification(A_	)

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



		def __UpperCamelCase			(			self	)  -> Union[str, 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     lowercase (		_SCREAMING_SNAKE_CASE  ,       _SCREAMING_SNAKE_CASE  ,       unittest.TestCase       ):
		__lowercase	:	Any     					=    (
		    (
		        TFDeiTModel,
		        TFDeiTForImageClassification,
		        TFDeiTForImageClassificationWithTeacher,
		        TFDeiTForMaskedImageModeling,
		    )
		    if is_tf_available()
		    else ()
		)
		__lowercase	:	Dict     					=    (
		    {
		        "feature-extraction": TFDeiTModel,
		        "image-classification": (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
		    }
		    if is_tf_available()
		    else {}
		)

		__lowercase	:	str     					=    False
		__lowercase	:	List[Any]     					=    False
		__lowercase	:	Optional[int]     					=    False
		__lowercase	:	Optional[Any]     					=    False



		def __UpperCamelCase			(			self	)  -> List[str]:
				"""simple docstring"""
				UpperCamelCase          =					TFDeiTModelTester(self	)
				UpperCamelCase          =					ConfigTester(self    ,				config_class=A_    ,				has_text_modality=A_    ,				hidden_size=37	)



		def __UpperCamelCase			(			self	)  -> Tuple:
				"""simple docstring"""
				self.config_tester.run_common_tests()



		@unittest.skip(reason='DeiT does not use inputs_embeds'	)
		def __UpperCamelCase			(			self	)  -> Any:
				"""simple docstring"""
				pass



		def __UpperCamelCase			(			self	)  -> 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(A_	)
						self.assertIsInstance(model.get_input_embeddings()    ,				(tf.keras.layers.Layer)	)
						UpperCamelCase          =					model.get_output_embeddings()
						self.assertTrue(x is None or isinstance(A_    ,				tf.keras.layers.Dense	)	)



		def __UpperCamelCase			(			self	)  -> Dict:
				"""simple docstring"""
				UpperCamelCase     ,  UpperCamelCase          =					self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
						UpperCamelCase          =					model_class(A_	)
						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]    ,				A_	)



		def __UpperCamelCase			(			self	)  -> List[str]:
				"""simple docstring"""
				UpperCamelCase          =					self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*A_	)



		def __UpperCamelCase			(			self	)  -> Tuple:
				"""simple docstring"""
				UpperCamelCase          =					self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_masked_image_modeling(*A_	)



		def __UpperCamelCase			(			self	)  -> List[str]:
				"""simple docstring"""
				UpperCamelCase          =					self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*A_	)



		def __UpperCamelCase			(			self    ,				A_    ,				A_    ,				A_=False	)  -> str:
				"""simple docstring"""
				UpperCamelCase          =					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 __UpperCamelCase			(			self	)  -> Tuple:
				"""simple docstring"""
				for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						UpperCamelCase          =					TFDeiTModel.from_pretrained(A_	)
						self.assertIsNotNone(A_	)






def 				A	( )       ->  Union[str, Any]:
		'''simple docstring'''

		UpperCamelCase          =					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
		return image


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



		@cached_property
		def __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				return (
				    DeiTImageProcessor.from_pretrained('facebook/deit-base-distilled-patch16-224'	)
				    if is_vision_available()
				    else None
				)



		@slow
		def __UpperCamelCase			(			self	)  -> Optional[int]:
				"""simple docstring"""
				UpperCamelCase          =					TFDeiTForImageClassificationWithTeacher.from_pretrained('facebook/deit-base-distilled-patch16-224'	)

				UpperCamelCase          =					self.default_image_processor
				UpperCamelCase          =					prepare_img()
				UpperCamelCase          =					image_processor(images=A_    ,				return_tensors='tf'	)

				# forward pass
				UpperCamelCase          =					model(**A_	)

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

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

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


from __future__ import annotations


def 				A	( lowercase   ,	lowercase   ,	lowercase   ,	lowercase			)       ->  list:
		'''simple docstring'''

		UpperCamelCase          =					[]
		UpperCamelCase     ,  UpperCamelCase          =					input_list[low:mid], input_list[mid : high + 1]
		while left and right:
				result.append((left if left[0] <= right[0] else right).pop(0			)			)
		UpperCamelCase          =					result + left + right
		return input_list


def 				A	( lowercase			)       ->  list:
		'''simple docstring'''

		if len(lowercase			) <= 1:
				return input_list
		UpperCamelCase          =					list(lowercase			)

		# iteration for two-way merging
		UpperCamelCase          =					2
		while p <= len(lowercase			):
				# getting low, high and middle value for merge-sort of single list
				for i in range(0   ,	len(lowercase			)   ,	lowercase			):
						UpperCamelCase          =					i
						UpperCamelCase          =					i + p - 1
						UpperCamelCase          =					(low + high + 1) // 2
						UpperCamelCase          =					merge(lowercase   ,	lowercase   ,	lowercase   ,	lowercase			)
				# final merge of last two parts
				if p * 2 >= len(lowercase			):
						UpperCamelCase          =					i
						UpperCamelCase          =					merge(lowercase   ,	0   ,	lowercase   ,	len(lowercase			) - 1			)
						break
				p *= 2

		return input_list


if __name__ == "__main__":
					_UpperCAmelCase :			Any              =	input("Enter numbers separated by a comma:\n").strip()
					if user_input == "":
										_UpperCAmelCase :			Optional[Any]              =	[]
					else:
										_UpperCAmelCase :			Any              =	[int(item.strip()) for item in user_input.split(",")]
					print(iter_merge_sort(unsorted))
