



import inspect
import unittest

from datasets import load_dataset
from packaging import version

from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
							import torch
							from torch import nn

							from transformers import (
							    MODEL_MAPPING,
							    BeitForImageClassification,
							    BeitForMaskedImageModeling,
							    BeitForSemanticSegmentation,
							    BeitModel,
							)
							from transformers.models.beit.modeling_beit import BEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							import PIL
							from PIL import Image

							from transformers import BeitImageProcessor
class 				_snake_case			:




				def __init__(						self					:						Union[str, Any],					__lowercase					:						Optional[Any],					__lowercase					:						Dict=100,					__lowercase					:						str=13,					__lowercase					:						Optional[Any]=30,					__lowercase					:						Optional[Any]=2,					__lowercase					:						str=3,					__lowercase					:						int=True,					__lowercase					:						Any=True,					__lowercase					:						Optional[int]=32,					__lowercase					:						List[str]=4,					__lowercase					:						int=4,					__lowercase					:						Dict=37,					__lowercase					:						List[str]="gelu",					__lowercase					:						List[Any]=0.1,					__lowercase					:						List[str]=0.1,					__lowercase					:						Union[str, Any]=10,					__lowercase					:						Union[str, Any]=0.02,					__lowercase					:						int=3,					__lowercase					:						Tuple=None,					__lowercase					:						List[Any]=[0, 1, 2, 3],					):
							lowercase__					  =				parent
							lowercase__					  =				100
							lowercase__					  =				batch_size
							lowercase__					  =				image_size
							lowercase__					  =				patch_size
							lowercase__					  =				num_channels
							lowercase__					  =				is_training
							lowercase__					  =				use_labels
							lowercase__					  =				hidden_size
							lowercase__					  =				num_hidden_layers
							lowercase__					  =				num_attention_heads
							lowercase__					  =				intermediate_size
							lowercase__					  =				hidden_act
							lowercase__					  =				hidden_dropout_prob
							lowercase__					  =				attention_probs_dropout_prob
							lowercase__					  =				type_sequence_label_size
							lowercase__					  =				initializer_range
							lowercase__					  =				scope
							lowercase__					  =				out_indices
							lowercase__					  =				num_labels

							# in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
							lowercase__					  =				(image_size // patch_size) ** 2
							lowercase__					  =				num_patches + 1




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

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

							lowercase__					  =				self.get_config()

							return config, pixel_values, labels, pixel_labels




				def 							A__      (						self					:						int						):
							return BeitConfig(
							    vocab_size=self.vocab_size,					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,					out_indices=self.out_indices,					)




				def 							A__      (						self					:						List[str],					__lowercase					:						Union[str, Any],					__lowercase					:						Optional[int],					__lowercase					:						Union[str, Any],					__lowercase					:						List[str]						):
							lowercase__					  =				BeitModel(config=__lowercase						)
							model.to(__lowercase						)
							model.eval()
							lowercase__					  =				model(__lowercase						)
							self.parent.assertEqual(result.last_hidden_state.shape,					(self.batch_size, self.seq_length, self.hidden_size)						)




				def 							A__      (						self					:						str,					__lowercase					:						List[str],					__lowercase					:						List[Any],					__lowercase					:						str,					__lowercase					:						str						):
							lowercase__					  =				BeitForMaskedImageModeling(config=__lowercase						)
							model.to(__lowercase						)
							model.eval()
							lowercase__					  =				model(__lowercase						)
							self.parent.assertEqual(result.logits.shape,					(self.batch_size, self.seq_length - 1, self.vocab_size)						)




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

							# test greyscale images
							lowercase__					  =				1
							lowercase__					  =				BeitForImageClassification(__lowercase						)
							model.to(__lowercase						)
							model.eval()

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




				def 							A__      (						self					:						Optional[int],					__lowercase					:						Any,					__lowercase					:						Union[str, Any],					__lowercase					:						Tuple,					__lowercase					:						Optional[Any]						):
							lowercase__					  =				self.num_labels
							lowercase__					  =				BeitForSemanticSegmentation(__lowercase						)
							model.to(__lowercase						)
							model.eval()
							lowercase__					  =				model(__lowercase						)
							self.parent.assertEqual(
							    result.logits.shape,					(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)						)
							lowercase__					  =				model(__lowercase,					labels=__lowercase						)
							self.parent.assertEqual(
							    result.logits.shape,					(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)						)




				def 							A__      (						self					:						int						):
							lowercase__					  =				self.prepare_config_and_inputs()
							lowercase__  ,       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__				:					Any     		=(
				    (BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
				    if is_torch_available()
				    else ()
				)
				UpperCamelCase__				:					Optional[int]     		=(
				    {
				        """feature-extraction""": BeitModel,
				        """image-classification""": BeitForImageClassification,
				        """image-segmentation""": BeitForSemanticSegmentation,
				    }
				    if is_torch_available()
				    else {}
				)

				UpperCamelCase__				:					List[str]     		=False
				UpperCamelCase__				:					Tuple     		=False
				UpperCamelCase__				:					List[str]     		=False




				def 							A__      (						self					:						int						):
							lowercase__					  =				BeitModelTester(self						)
							lowercase__					  =				ConfigTester(self,					config_class=__lowercase,					has_text_modality=__lowercase,					hidden_size=37						)




				def 							A__      (						self					:						Tuple						):
							self.config_tester.run_common_tests()




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




				@require_torch_multi_gpu
				@unittest.skip(reason="BEiT has some layers using `add_module` which doesn't work well with `nn.DataParallel`"						)
				def 							A__      (						self					:						Dict						):
							pass




				def 							A__      (						self					:						int						):
							lowercase__  ,       lowercase__					  =				self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
										lowercase__					  =				model_class(__lowercase						)
										self.assertIsInstance(model.get_input_embeddings(),					(nn.Module)						)
										lowercase__					  =				model.get_output_embeddings()
										self.assertTrue(x is None or isinstance(__lowercase,					nn.Linear						)						)




				def 							A__      (						self					:						List[str]						):
							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					:						Dict						):
							lowercase__					  =				self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__lowercase						)




				def 							A__      (						self					:						List[Any]						):
							lowercase__					  =				self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_lm(*__lowercase						)




				def 							A__      (						self					:						Tuple						):
							lowercase__					  =				self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*__lowercase						)




				def 							A__      (						self					:						Optional[Any]						):
							lowercase__					  =				self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_semantic_segmentation(*__lowercase						)




				def 							A__      (						self					:						List[str]						):
							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:
										# we don't test BeitForMaskedImageModeling
										if model_class in [*get_values(__lowercase						), BeitForMaskedImageModeling]:
													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					:						List[str]						):
							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:
										# we don't test BeitForMaskedImageModeling
										if (
										    model_class in [*get_values(__lowercase						), BeitForMaskedImageModeling]
										    or not model_class.supports_gradient_checkpointing
										):
													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					:						int						):
							lowercase__  ,       lowercase__					  =				self.model_tester.prepare_config_and_inputs_for_common()

							lowercase__					  =				_config_zero_init(__lowercase						)
							for model_class in self.all_model_classes:
										lowercase__					  =				model_class(config=__lowercase						)
										for name, param in model.named_parameters():
													# we skip lambda parameters as these require special initial values
													# determined by config.layer_scale_init_value
													if "lambda" in name:
																continue
													if param.requires_grad:
																self.assertIn(
																    ((param.data.mean() * 1e9).round() / 1e9).item(),					[0.0, 1.0],					msg=F'''Parameter {name} of model {model_class} seems not properly initialized''',					)




				@slow
				def 							A__      (						self					:						Optional[Any]						):
							for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										lowercase__					  =				BeitModel.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					:						Optional[int]						):
							return BeitImageProcessor.from_pretrained("microsoft/beit-base-patch16-224"						) if is_vision_available() else None




				@slow
				def 							A__      (						self					:						Any						):
							lowercase__					  =				BeitForMaskedImageModeling.from_pretrained("microsoft/beit-base-patch16-224-pt22k"						).to(__lowercase						)

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

							# prepare bool_masked_pos
							lowercase__					  =				torch.ones((1, 196),					dtype=torch.bool						).to(__lowercase						)

							# forward pass
							with torch.no_grad():
										lowercase__					  =				model(pixel_values=__lowercase,					bool_masked_pos=__lowercase						)
							lowercase__					  =				outputs.logits

							# verify the logits
							lowercase__					  =				torch.Size((1, 196, 8192)						)
							self.assertEqual(logits.shape,					__lowercase						)

							lowercase__					  =				torch.tensor(
							    [[-3.2437, 0.5072, -13.9174], [-3.2456, 0.4948, -13.9401], [-3.2033, 0.5121, -13.8550]]						).to(__lowercase						)

							self.assertTrue(torch.allclose(logits[bool_masked_pos][:3, :3],					__lowercase,					atol=1e-2						)						)




				@slow
				def 							A__      (						self					:						Optional[int]						):
							lowercase__					  =				BeitForImageClassification.from_pretrained("microsoft/beit-base-patch16-224"						).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						)
							lowercase__					  =				outputs.logits

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

							lowercase__					  =				torch.tensor([-1.2385, -1.0987, -1.0108]						).to(__lowercase						)

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

							lowercase__					  =				281
							self.assertEqual(logits.argmax(-1						).item(),					__lowercase						)




				@slow
				def 							A__      (						self					:						Optional[Any]						):
							lowercase__					  =				BeitForImageClassification.from_pretrained("microsoft/beit-large-patch16-224-pt22k-ft22k"						).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						)
							lowercase__					  =				outputs.logits

							# verify the logits
							lowercase__					  =				torch.Size((1, 2_1841)						)
							self.assertEqual(logits.shape,					__lowercase						)

							lowercase__					  =				torch.tensor([1.6881, -0.2787, 0.5901]						).to(__lowercase						)

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

							lowercase__					  =				2396
							self.assertEqual(logits.argmax(-1						).item(),					__lowercase						)




				@slow
				def 							A__      (						self					:						List[str]						):
							lowercase__					  =				BeitForSemanticSegmentation.from_pretrained("microsoft/beit-base-finetuned-ade-640-640"						)
							lowercase__					  =				model.to(__lowercase						)

							lowercase__					  =				BeitImageProcessor(do_resize=__lowercase,					size=640,					do_center_crop=__lowercase						)

							lowercase__					  =				load_dataset("hf-internal-testing/fixtures_ade20k",					split="test"						)
							lowercase__					  =				Image.open(ds[0]["file"]						)
							lowercase__					  =				image_processor(images=__lowercase,					return_tensors="pt"						).to(__lowercase						)

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

							# verify the logits
							lowercase__					  =				torch.Size((1, 150, 160, 160)						)
							self.assertEqual(logits.shape,					__lowercase						)

							lowercase__					  =				version.parse(PIL.__version__						) < version.parse("9.0.0"						)

							if is_pillow_less_than_a:
										lowercase__					  =				torch.tensor(
										    [
										        [[-4.9225, -2.3954, -3.0522], [-2.8822, -1.0046, -1.7561], [-2.9549, -1.3228, -2.1347]],
										        [[-5.8168, -3.4129, -4.0778], [-3.8651, -2.2214, -3.0277], [-3.8356, -2.4643, -3.3535]],
										        [[-0.0078, 3.9952, 4.0754], [2.9856, 4.6944, 5.0035], [3.2413, 4.7813, 4.9969]],
										    ],					device=__lowercase,					)
							else:
										lowercase__					  =				torch.tensor(
										    [
										        [[-4.8960, -2.3688, -3.0355], [-2.8478, -0.9836, -1.7418], [-2.9449, -1.3332, -2.1456]],
										        [[-5.8081, -3.4124, -4.1006], [-3.8561, -2.2081, -3.0323], [-3.8365, -2.4601, -3.3669]],
										        [[-0.0309, 3.9868, 4.0540], [2.9640, 4.6877, 4.9976], [3.2081, 4.7690, 4.9942]],
										    ],					device=__lowercase,					)

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




				@slow
				def 							A__      (						self					:						Optional[Any]						):
							lowercase__					  =				BeitForSemanticSegmentation.from_pretrained("microsoft/beit-base-finetuned-ade-640-640"						)
							lowercase__					  =				model.to(__lowercase						)

							lowercase__					  =				BeitImageProcessor(do_resize=__lowercase,					size=640,					do_center_crop=__lowercase						)

							lowercase__					  =				load_dataset("hf-internal-testing/fixtures_ade20k",					split="test"						)
							lowercase__					  =				Image.open(ds[0]["file"]						)
							lowercase__					  =				image_processor(images=__lowercase,					return_tensors="pt"						).to(__lowercase						)

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

							lowercase__					  =				outputs.logits.detach().cpu()

							lowercase__					  =				image_processor.post_process_semantic_segmentation(outputs=__lowercase,					target_sizes=[(500, 300)]						)
							lowercase__					  =				torch.Size((500, 300)						)
							self.assertEqual(segmentation[0].shape,					__lowercase						)

							lowercase__					  =				image_processor.post_process_semantic_segmentation(outputs=__lowercase						)
							lowercase__					  =				torch.Size((160, 160)						)
							self.assertEqual(segmentation[0].shape,					__lowercase						)









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))




