






"""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 						A_							:







				def __init__(							self							:						Tuple						,    __lowerCamelCase							:						Union[str, Any]						,    __lowerCamelCase							:						Optional[Any]=1_3						,    __lowerCamelCase							:						Any=3_0						,    __lowerCamelCase							:						Dict=2						,    __lowerCamelCase							:						List[str]=3						,    __lowerCamelCase							:						str=True						,    __lowerCamelCase							:						Optional[Any]=True						,    __lowerCamelCase							:						Any=3_2						,    __lowerCamelCase							:						str=2						,    __lowerCamelCase							:						List[Any]=4						,    __lowerCamelCase							:						List[Any]=3_7						,    __lowerCamelCase							:						List[Any]="gelu"						,    __lowerCamelCase							:						Dict=0.1						,    __lowerCamelCase							:						Tuple=0.1						,    __lowerCamelCase							:						Optional[int]=1_0						,    __lowerCamelCase							:						Dict=0.02						,    __lowerCamelCase							:						Tuple=3						,    __lowerCamelCase							:						List[str]=None						,    __lowerCamelCase							:						List[Any]=2						,    )				->					List[str]:
										__magic_name__            =     parent
										__magic_name__            =     batch_size
										__magic_name__            =     image_size
										__magic_name__            =     patch_size
										__magic_name__            =     num_channels
										__magic_name__            =     is_training
										__magic_name__            =     use_labels
										__magic_name__            =     hidden_size
										__magic_name__            =     num_hidden_layers
										__magic_name__            =     num_attention_heads
										__magic_name__            =     intermediate_size
										__magic_name__            =     hidden_act
										__magic_name__            =     hidden_dropout_prob
										__magic_name__            =     attention_probs_dropout_prob
										__magic_name__            =     type_sequence_label_size
										__magic_name__            =     initializer_range
										__magic_name__            =     scope
										__magic_name__            =     encoder_stride

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







				def     _snake_case					(							self							:						List[str]	)				->					Any:
										__magic_name__            =     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

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

										__magic_name__            =     self.get_config()

										return config, pixel_values, labels







				def     _snake_case					(							self							:						Union[str, Any]	)				->					Dict:
										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=__lowerCamelCase						,    initializer_range=self.initializer_range						,    encoder_stride=self.encoder_stride						,    )







				def     _snake_case					(							self							:						Tuple						,    __lowerCamelCase							:						Tuple						,    __lowerCamelCase							:						Dict						,    __lowerCamelCase							:						Union[str, Any]	)				->					Dict:
										__magic_name__            =     TFDeiTModel(config=__lowerCamelCase	)
										__magic_name__            =     model(__lowerCamelCase	)
										self.parent.assertEqual(result.last_hidden_state.shape						,    (self.batch_size, self.seq_length, self.hidden_size)	)







				def     _snake_case					(							self							:						List[str]						,    __lowerCamelCase							:						str						,    __lowerCamelCase							:						int						,    __lowerCamelCase							:						List[Any]	)				->					List[str]:
										__magic_name__            =     TFDeiTForMaskedImageModeling(config=__lowerCamelCase	)
										__magic_name__            =     model(__lowerCamelCase	)
										self.parent.assertEqual(
										    result.reconstruction.shape						,    (self.batch_size, self.num_channels, self.image_size, self.image_size)	)

										# test greyscale images
										__magic_name__            =     1
										__magic_name__            =     TFDeiTForMaskedImageModeling(__lowerCamelCase	)

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







				def     _snake_case					(							self							:						int						,    __lowerCamelCase							:						int						,    __lowerCamelCase							:						Optional[int]						,    __lowerCamelCase							:						Any	)				->					Tuple:
										__magic_name__            =     self.type_sequence_label_size
										__magic_name__            =     TFDeiTForImageClassification(__lowerCamelCase	)
										__magic_name__            =     model(__lowerCamelCase						,    labels=__lowerCamelCase	)
										self.parent.assertEqual(result.logits.shape						,    (self.batch_size, self.type_sequence_label_size)	)

										# test greyscale images
										__magic_name__            =     1
										__magic_name__            =     TFDeiTForImageClassification(__lowerCamelCase	)

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







				def     _snake_case					(							self							:						Dict	)				->					Optional[Any]:
										__magic_name__            =     self.prepare_config_and_inputs()
										__magic_name__      ,    __magic_name__      ,    __magic_name__            =     config_and_inputs
										__magic_name__            =     {"pixel_values": pixel_values}
										return config, inputs_dict





@require_tf
class 						A_							(      snake_case_					,							snake_case_					,							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     _snake_case					(							self							:						Optional[int]	)				->					Tuple:
										__magic_name__            =     TFDeiTModelTester(self	)
										__magic_name__            =     ConfigTester(self						,    config_class=__lowerCamelCase						,    has_text_modality=__lowerCamelCase						,    hidden_size=3_7	)







				def     _snake_case					(							self							:						Union[str, Any]	)				->					int:
										self.config_tester.run_common_tests()







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







				def     _snake_case					(							self							:						Union[str, Any]	)				->					Dict:
										__magic_name__      ,    __magic_name__            =     self.model_tester.prepare_config_and_inputs_for_common()

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







				def     _snake_case					(							self							:						List[str]	)				->					List[Any]:
										__magic_name__      ,    __magic_name__            =     self.model_tester.prepare_config_and_inputs_for_common()

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

																__magic_name__            =     ["pixel_values"]
																self.assertListEqual(arg_names[:1]						,    __lowerCamelCase	)







				def     _snake_case					(							self							:						Union[str, Any]	)				->					Optional[Any]:
										__magic_name__            =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*__lowerCamelCase	)







				def     _snake_case					(							self							:						Dict	)				->					str:
										__magic_name__            =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_masked_image_modeling(*__lowerCamelCase	)







				def     _snake_case					(							self							:						str	)				->					List[Any]:
										__magic_name__            =     self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase	)







				def     _snake_case					(							self							:						Optional[Any]						,    __lowerCamelCase							:						int						,    __lowerCamelCase							:						str						,    __lowerCamelCase							:						Dict=False	)				->					int:
										__magic_name__            =     super()._prepare_for_class(__lowerCamelCase						,    __lowerCamelCase						,    return_labels=__lowerCamelCase	)

										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     _snake_case					(							self							:						Union[str, Any]	)				->					List[Any]:
										for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																__magic_name__            =     TFDeiTModel.from_pretrained(__lowerCamelCase	)
																self.assertIsNotNone(__lowerCamelCase	)






def _lowerCAmelCase	(  ):

						'''simple docstring'''






						__magic_name__            =     Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"   )
						return image


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







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







				@slow
				def     _snake_case					(							self							:						Optional[int]	)				->					Tuple:
										__magic_name__            =     TFDeiTForImageClassificationWithTeacher.from_pretrained("facebook/deit-base-distilled-patch16-224"	)

										__magic_name__            =     self.default_image_processor
										__magic_name__            =     prepare_img()
										__magic_name__            =     image_processor(images=__lowerCamelCase						,    return_tensors="tf"	)

										# forward pass
										__magic_name__            =     model(**__lowerCamelCase	)

										# verify the logits
										__magic_name__            =     tf.TensorShape((1, 1_0_0_0)	)
										self.assertEqual(outputs.logits.shape						,    __lowerCamelCase	)

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

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











"""simple docstring"""

def _lowerCAmelCase	(  __lowerCamelCase:list   ):

						'''simple docstring'''






						__magic_name__            =     len(__lowerCamelCase   )
						for i in range(1 ,						__lowerCamelCase   ):
												__magic_name__            =     collection[i]
												__magic_name__            =     0
												__magic_name__            =     i - 1

												while low <= high:
																		__magic_name__            =     (low + high) // 2
																		if val < collection[mid]:
																								__magic_name__            =     mid - 1
																		else:
																								__magic_name__            =     mid + 1
												for j in range(__lowerCamelCase ,						__lowerCamelCase ,						-1   ):
																		__magic_name__            =     collection[j - 1]
												__magic_name__            =     val
						return collection


if __name__ == "__main__":
			lowercase    			=       input('''Enter numbers separated by a comma:\n''').strip()
			lowercase    			=       [int(item) for item in user_input.split(''',''')]
			print(binary_insertion_sort(unsorted))



