



'''simple docstring'''





import inspect
import unittest
import warnings
from math import ceil, floor

from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import (
							    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
							    MODEL_MAPPING,
							    LevitForImageClassification,
							    LevitForImageClassificationWithTeacher,
							    LevitModel,
							)
							from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image

							from transformers import LevitImageProcessor
class 				_snake_case			(					lowercase__):




				def 							A__      (						self					:						str						):
							lowercase__					  =				self.config_class(**self.inputs_dict						)
							self.parent.assertTrue(hasattr(__lowercase,					"hidden_sizes"						)						)
							self.parent.assertTrue(hasattr(__lowercase,					"num_attention_heads"						)						)
class 				_snake_case			:




				def __init__(						self					:						Any,					__lowercase					:						List[str],					__lowercase					:						Dict=13,					__lowercase					:						str=64,					__lowercase					:						str=3,					__lowercase					:						Any=3,					__lowercase					:						Tuple=2,					__lowercase					:						List[str]=1,					__lowercase					:						int=16,					__lowercase					:						Optional[int]=[128, 256, 384],					__lowercase					:						List[Any]=[4, 6, 8],					__lowercase					:						str=[2, 3, 4],					__lowercase					:						List[Any]=[16, 16, 16],					__lowercase					:						int=0,					__lowercase					:						str=[2, 2, 2],					__lowercase					:						Tuple=[2, 2, 2],					__lowercase					:						Dict=0.02,					__lowercase					:						Optional[Any]=True,					__lowercase					:						Any=True,					__lowercase					:						List[str]=2,					):
							lowercase__					  =				parent
							lowercase__					  =				batch_size
							lowercase__					  =				image_size
							lowercase__					  =				num_channels
							lowercase__					  =				kernel_size
							lowercase__					  =				stride
							lowercase__					  =				padding
							lowercase__					  =				hidden_sizes
							lowercase__					  =				num_attention_heads
							lowercase__					  =				depths
							lowercase__					  =				key_dim
							lowercase__					  =				drop_path_rate
							lowercase__					  =				patch_size
							lowercase__					  =				attention_ratio
							lowercase__					  =				mlp_ratio
							lowercase__					  =				initializer_range
							lowercase__					  =				[
							    ["Subsample", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
							    ["Subsample", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
							]
							lowercase__					  =				is_training
							lowercase__					  =				use_labels
							lowercase__					  =				num_labels
							lowercase__					  =				initializer_range




				def 							A__      (						self					:						Dict						):
							lowercase__					  =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

							lowercase__					  =				None
							if self.use_labels:
										lowercase__					  =				ids_tensor([self.batch_size],					self.num_labels						)

							lowercase__					  =				self.get_config()
							return config, pixel_values, labels




				def 							A__      (						self					:						Tuple						):
							return LevitConfig(
							    image_size=self.image_size,					num_channels=self.num_channels,					kernel_size=self.kernel_size,					stride=self.stride,					padding=self.padding,					patch_size=self.patch_size,					hidden_sizes=self.hidden_sizes,					num_attention_heads=self.num_attention_heads,					depths=self.depths,					key_dim=self.key_dim,					drop_path_rate=self.drop_path_rate,					mlp_ratio=self.mlp_ratio,					attention_ratio=self.attention_ratio,					initializer_range=self.initializer_range,					down_ops=self.down_ops,					)




				def 							A__      (						self					:						Dict,					__lowercase					:						Optional[int],					__lowercase					:						Any,					__lowercase					:						str						):
							lowercase__					  =				LevitModel(config=__lowercase						)
							model.to(__lowercase						)
							model.eval()
							lowercase__					  =				model(__lowercase						)
							lowercase__					  =				(self.image_size, self.image_size)
							lowercase__  ,       lowercase__					  =				image_size[0], image_size[1]
							for _ in range(4						):
										lowercase__					  =				floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1						)
										lowercase__					  =				floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1						)
							self.parent.assertEqual(
							    result.last_hidden_state.shape,					(self.batch_size, ceil(height / 4						) * ceil(width / 4						), self.hidden_sizes[-1]),					)




				def 							A__      (						self					:						List[Any],					__lowercase					:						int,					__lowercase					:						List[str],					__lowercase					:						List[str]						):
							lowercase__					  =				self.num_labels
							lowercase__					  =				LevitForImageClassification(__lowercase						)
							model.to(__lowercase						)
							model.eval()
							lowercase__					  =				model(__lowercase,					labels=__lowercase						)
							self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.num_labels)						)




				def 							A__      (						self					:						str						):
							lowercase__					  =				self.prepare_config_and_inputs()
							lowercase__  ,       lowercase__  ,       lowercase__					  =				config_and_inputs
							lowercase__					  =				{"pixel_values": pixel_values}
							return config, inputs_dict
@require_torch
class 				_snake_case			(					lowercase__    ,	lowercase__    ,	unittest.TestCase):
				UpperCamelCase__				:					str     		=(
				    (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
				    if is_torch_available()
				    else ()
				)
				UpperCamelCase__				:					Dict     		=(
				    {
				        """feature-extraction""": LevitModel,
				        """image-classification""": (LevitForImageClassification, LevitForImageClassificationWithTeacher),
				    }
				    if is_torch_available()
				    else {}
				)

				UpperCamelCase__				:					Optional[Any]     		=False
				UpperCamelCase__				:					str     		=False
				UpperCamelCase__				:					Any     		=False
				UpperCamelCase__				:					str     		=False
				UpperCamelCase__				:					List[str]     		=False




				def 							A__      (						self					:						Union[str, Any]						):
							lowercase__					  =				LevitModelTester(self						)
							lowercase__					  =				ConfigTester(self,					config_class=__lowercase,					has_text_modality=__lowercase,					hidden_size=37						)




				def 							A__      (						self					:						Optional[int]						):
							self.create_and_test_config_common_properties()
							self.config_tester.create_and_test_config_to_json_string()
							self.config_tester.create_and_test_config_to_json_file()
							self.config_tester.create_and_test_config_from_and_save_pretrained()
							self.config_tester.create_and_test_config_with_num_labels()
							self.config_tester.check_config_can_be_init_without_params()
							self.config_tester.check_config_arguments_init()




				def 							A__      (						self					:						int						):
							return




				@unittest.skip(reason="Levit does not use inputs_embeds"						)
				def 							A__      (						self					:						Tuple						):
							pass




				@unittest.skip(reason="Levit does not support input and output embeddings"						)
				def 							A__      (						self					:						Any						):
							pass




				@unittest.skip(reason="Levit does not output attentions"						)
				def 							A__      (						self					:						Tuple						):
							pass




				def 							A__      (						self					:						Optional[Any]						):
							lowercase__  ,       lowercase__					  =				self.model_tester.prepare_config_and_inputs_for_common()

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

										lowercase__					  =				["pixel_values"]
										self.assertListEqual(arg_names[:1],					__lowercase						)




				def 							A__      (						self					:						int						):
							def check_hidden_states_output(__lowercase					:						str,					__lowercase					:						Tuple,					__lowercase					:						List[Any]						):
										lowercase__					  =				model_class(__lowercase						)
										model.to(__lowercase						)
										model.eval()

										with torch.no_grad():
													lowercase__					  =				model(**self._prepare_for_class(__lowercase,					__lowercase						)						)

										lowercase__					  =				outputs.hidden_states

										lowercase__					  =				len(self.model_tester.depths						) + 1
										self.assertEqual(len(__lowercase						),					__lowercase						)

										lowercase__					  =				(self.model_tester.image_size, self.model_tester.image_size)
										lowercase__  ,       lowercase__					  =				image_size[0], image_size[1]
										for _ in range(4						):
													lowercase__					  =				floor(
													    (
													        (height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
													        / self.model_tester.stride
													    )
													    + 1						)
													lowercase__					  =				floor(
													    (
													        (width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
													        / self.model_tester.stride
													    )
													    + 1						)
										# verify the first hidden states (first block)
										self.assertListEqual(
										    list(hidden_states[0].shape[-2:]						),					[
										        height * width,
										        self.model_tester.hidden_sizes[0],
										    ],					)

							lowercase__  ,       lowercase__					  =				self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
										lowercase__					  =				True
										check_hidden_states_output(__lowercase,					__lowercase,					__lowercase						)

										# check that output_hidden_states also work using config
										del inputs_dict["output_hidden_states"]
										lowercase__					  =				True

										check_hidden_states_output(__lowercase,					__lowercase,					__lowercase						)




				@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."						)
				def 							A__      (						self					:						Optional[int]						):
							pass




				def 							A__      (						self					:						int,					__lowercase					:						List[str],					__lowercase					:						List[str],					__lowercase					:						Dict=False						):
							lowercase__					  =				super()._prepare_for_class(__lowercase,					__lowercase,					return_labels=__lowercase						)

							if return_labels:
										if model_class.__name__ == "LevitForImageClassificationWithTeacher":
													del inputs_dict["labels"]

							return inputs_dict




				def 							A__      (						self					:						str						):
							lowercase__					  =				self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__lowercase						)




				def 							A__      (						self					:						Union[str, Any]						):
							lowercase__					  =				self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*__lowercase						)




				def 							A__      (						self					:						Dict						):
							if not self.model_tester.is_training:
										return

							lowercase__  ,       lowercase__					  =				self.model_tester.prepare_config_and_inputs_for_common()
							lowercase__					  =				True

							for model_class in self.all_model_classes:
										# LevitForImageClassificationWithTeacher supports inference-only
										if (
										    model_class in get_values(__lowercase						)
										    or model_class.__name__ == "LevitForImageClassificationWithTeacher"
										):
													continue
										lowercase__					  =				model_class(__lowercase						)
										model.to(__lowercase						)
										model.train()
										lowercase__					  =				self._prepare_for_class(__lowercase,					__lowercase,					return_labels=__lowercase						)
										lowercase__					  =				model(**__lowercase						).loss
										loss.backward()




				def 							A__      (						self					:						Optional[int]						):
							lowercase__  ,       lowercase__					  =				self.model_tester.prepare_config_and_inputs_for_common()
							if not self.model_tester.is_training:
										return

							lowercase__					  =				False
							lowercase__					  =				True

							for model_class in self.all_model_classes:
										if model_class in get_values(__lowercase						) or not model_class.supports_gradient_checkpointing:
													continue
										# LevitForImageClassificationWithTeacher supports inference-only
										if model_class.__name__ == "LevitForImageClassificationWithTeacher":
													continue
										lowercase__					  =				model_class(__lowercase						)
										model.gradient_checkpointing_enable()
										model.to(__lowercase						)
										model.train()
										lowercase__					  =				self._prepare_for_class(__lowercase,					__lowercase,					return_labels=__lowercase						)
										lowercase__					  =				model(**__lowercase						).loss
										loss.backward()




				def 							A__      (						self					:						Optional[Any]						):
							lowercase__  ,       lowercase__					  =				self.model_tester.prepare_config_and_inputs_for_common()

							lowercase__					  =				[
							    {"title": "multi_label_classification", "num_labels": 2, "dtype": torch.float},
							    {"title": "single_label_classification", "num_labels": 1, "dtype": torch.long},
							    {"title": "regression", "num_labels": 1, "dtype": torch.float},
							]

							for model_class in self.all_model_classes:
										if (
										    model_class
										    not in [
										        *get_values(__lowercase						),
										    ]
										    or model_class.__name__ == "LevitForImageClassificationWithTeacher"
										):
													continue

										for problem_type in problem_types:
													with self.subTest(msg=F'''Testing {model_class} with {problem_type["title"]}'''						):
																lowercase__					  =				problem_type["title"]
																lowercase__					  =				problem_type["num_labels"]

																lowercase__					  =				model_class(__lowercase						)
																model.to(__lowercase						)
																model.train()

																lowercase__					  =				self._prepare_for_class(__lowercase,					__lowercase,					return_labels=__lowercase						)

																if problem_type["num_labels"] > 1:
																			lowercase__					  =				inputs["labels"].unsqueeze(1						).repeat(1,					problem_type["num_labels"]						)

																lowercase__					  =				inputs["labels"].to(problem_type["dtype"]						)

																# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
																# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
																# they have the same size." which is a symptom something in wrong for the regression problem.
																# See https://github.com/huggingface/transformers/issues/11780
																with warnings.catch_warnings(record=__lowercase						) as warning_list:
																			lowercase__					  =				model(**__lowercase						).loss
																for w in warning_list:
																			if "Using a target size that is different to the input size" in str(w.message						):
																						raise ValueError(
																						    F'''Something is going wrong in the regression problem: intercepted {w.message}'''						)

																loss.backward()




				@slow
				def 							A__      (						self					:						Any						):
							for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										lowercase__					  =				LevitModel.from_pretrained(__lowercase						)
										self.assertIsNotNone(__lowercase						)



def __lowerCAmelCase (     ):
			lowercase__					  =				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"  )
			return image




@require_torch
@require_vision
class 				_snake_case			(					unittest.TestCase):




				@cached_property
				def 							A__      (						self					:						int						):
							return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)




				@slow
				def 							A__      (						self					:						Optional[Any]						):
							lowercase__					  =				LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]						).to(
							    __lowercase						)

							lowercase__					  =				self.default_image_processor
							lowercase__					  =				prepare_img()
							lowercase__					  =				image_processor(images=__lowercase,					return_tensors="pt"						).to(__lowercase						)

							# forward pass
							with torch.no_grad():
										lowercase__					  =				model(**__lowercase						)

							# verify the logits
							lowercase__					  =				torch.Size((1, 1000)						)
							self.assertEqual(outputs.logits.shape,					__lowercase						)

							lowercase__					  =				torch.tensor([1.0448, -0.3745, -1.8317]						).to(__lowercase						)

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









def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_  ):
			lowercase__					  =				len(SCREAMING_SNAKE_CASE_  )
			for i in range(SCREAMING_SNAKE_CASE_  ):
						for j in range(i + 1						,  SCREAMING_SNAKE_CASE_  ):
									if numbers[j] < numbers[i]:
												lowercase__  ,       lowercase__					  =				numbers[j], numbers[i]
			return numbers


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




