



"""simple docstring"""
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			,     a_			,     a_=1_0_0			,     a_=1_3			,     a_=3_0			,     a_=2			,     a_=3			,     a_=True			,     a_=True			,     a_=3_2			,     a_=4			,     a_=4			,     a_=3_7			,     a_="gelu"			,     a_=0.1			,     a_=0.1			,     a_=1_0			,     a_=0.02			,     a_=3			,     a_=None			,     a_=[0, 1, 2, 3]			,     ):
									a_     :      Dict 						=     parent
									a_     :      Optional[int] 						=     1_0_0
									a_     :      Dict 						=     batch_size
									a_     :      str 						=     image_size
									a_     :      Optional[int] 						=     patch_size
									a_     :      Optional[int] 						=     num_channels
									a_     :      int 						=     is_training
									a_     :      Dict 						=     use_labels
									a_     :      Dict 						=     hidden_size
									a_     :      Optional[int] 						=     num_hidden_layers
									a_     :      Any 						=     num_attention_heads
									a_     :      List[Any] 						=     intermediate_size
									a_     :      Union[str, Any] 						=     hidden_act
									a_     :      str 						=     hidden_dropout_prob
									a_     :      Tuple 						=     attention_probs_dropout_prob
									a_     :      Optional[int] 						=     type_sequence_label_size
									a_     :      Optional[int] 						=     initializer_range
									a_     :      List[str] 						=     scope
									a_     :      Dict 						=     out_indices
									a_     :      Union[str, Any] 						=     num_labels

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






							def snake_case_  (   self      ):
									a_     :      List[Any] 						=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

									a_     :      Dict 						=     None
									a_     :      Optional[int] 						=     None
									if self.use_labels:
											a_     :      Tuple 						=     ids_tensor([self.batch_size]			,     self.type_sequence_label_size      )
											a_     :      List[Any] 						=     ids_tensor([self.batch_size, self.image_size, self.image_size]			,     self.num_labels      )

									a_     :      Optional[int] 						=     self.get_config()

									return config, pixel_values, labels, pixel_labels






							def snake_case_  (   self      ):
									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=a_			,     initializer_range=self.initializer_range			,     out_indices=self.out_indices			,     )






							def snake_case_  (   self			,     a_			,     a_			,     a_			,     a_      ):
									a_     :      Any 						=     BeitModel(config=a_      )
									model.to(a_      )
									model.eval()
									a_     :      List[Any] 						=     model(a_      )
									self.parent.assertEqual(result.last_hidden_state.shape			,     (self.batch_size, self.seq_length, self.hidden_size)      )






							def snake_case_  (   self			,     a_			,     a_			,     a_			,     a_      ):
									a_     :      int 						=     BeitForMaskedImageModeling(config=a_      )
									model.to(a_      )
									model.eval()
									a_     :      str 						=     model(a_      )
									self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.seq_length - 1, self.vocab_size)      )






							def snake_case_  (   self			,     a_			,     a_			,     a_			,     a_      ):
									a_     :      Union[str, Any] 						=     self.type_sequence_label_size
									a_     :      Dict 						=     BeitForImageClassification(a_      )
									model.to(a_      )
									model.eval()
									a_     :      Dict 						=     model(a_			,     labels=a_      )
									self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.type_sequence_label_size)      )

									# test greyscale images
									a_     :      List[Any] 						=     1
									a_     :      List[str] 						=     BeitForImageClassification(a_      )
									model.to(a_      )
									model.eval()

									a_     :      List[Any] 						=     floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
									a_     :      Optional[int] 						=     model(a_			,     labels=a_      )
									self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.type_sequence_label_size)      )






							def snake_case_  (   self			,     a_			,     a_			,     a_			,     a_      ):
									a_     :      List[Any] 						=     self.num_labels
									a_     :      str 						=     BeitForSemanticSegmentation(a_      )
									model.to(a_      )
									model.eval()
									a_     :      Union[str, Any] 						=     model(a_      )
									self.parent.assertEqual(
									    result.logits.shape			,     (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)      )
									a_     :      Union[str, Any] 						=     model(a_			,     labels=a_      )
									self.parent.assertEqual(
									    result.logits.shape			,     (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)      )






							def snake_case_  (   self      ):
									a_     :      List[Any] 						=     self.prepare_config_and_inputs()
									a_						,				a_						,				a_						,				a_     :      Dict 						=     config_and_inputs
									a_     :      List[Any] 						=     {"pixel_values": pixel_values}
									return config, inputs_dict




@require_torch
class 			snake_case_						(						a_     ,a_     ,unittest.TestCase				):
							__lowerCAmelCase    					=					(
							    (BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
							    if is_torch_available()
							    else ()
							)
							__lowerCAmelCase    					=					(
							    {
							        "feature-extraction": BeitModel,
							        "image-classification": BeitForImageClassification,
							        "image-segmentation": BeitForSemanticSegmentation,
							    }
							    if is_torch_available()
							    else {}
							)

							__lowerCAmelCase    					=					False
							__lowerCAmelCase    					=					False
							__lowerCAmelCase    					=					False






							def snake_case_  (   self      ):
									a_     :      str 						=     BeitModelTester(self      )
									a_     :      Dict 						=     ConfigTester(self			,     config_class=a_			,     has_text_modality=a_			,     hidden_size=3_7      )






							def snake_case_  (   self      ):
									self.config_tester.run_common_tests()






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






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






							def snake_case_  (   self      ):
									a_						,				a_     :      Any 						=     self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											a_     :      List[Any] 						=     model_class(a_      )
											self.assertIsInstance(model.get_input_embeddings()			,     (nn.Module)      )
											a_     :      int 						=     model.get_output_embeddings()
											self.assertTrue(x is None or isinstance(a_			,     nn.Linear      )      )






							def snake_case_  (   self      ):
									a_						,				a_     :      int 						=     self.model_tester.prepare_config_and_inputs_for_common()

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

											a_     :      str 						=     ["pixel_values"]
											self.assertListEqual(arg_names[:1]			,     a_      )






							def snake_case_  (   self      ):
									a_     :      Dict 						=     self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*a_      )






							def snake_case_  (   self      ):
									a_     :      str 						=     self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_lm(*a_      )






							def snake_case_  (   self      ):
									a_     :      Any 						=     self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*a_      )






							def snake_case_  (   self      ):
									a_     :      Dict 						=     self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_semantic_segmentation(*a_      )






							def snake_case_  (   self      ):
									if not self.model_tester.is_training:
											return

									a_						,				a_     :      Tuple 						=     self.model_tester.prepare_config_and_inputs_for_common()
									a_     :      Any 						=     True

									for model_class in self.all_model_classes:
											# we don't test BeitForMaskedImageModeling
											if model_class in [*get_values(a_      ), BeitForMaskedImageModeling]:
													continue

											a_     :      Union[str, Any] 						=     model_class(a_      )
											model.to(a_      )
											model.train()
											a_     :      List[str] 						=     self._prepare_for_class(a_			,     a_			,     return_labels=a_      )
											a_     :      Tuple 						=     model(**a_      ).loss
											loss.backward()






							def snake_case_  (   self      ):
									a_						,				a_     :      Dict 						=     self.model_tester.prepare_config_and_inputs_for_common()
									if not self.model_tester.is_training:
											return

									a_     :      List[Any] 						=     False
									a_     :      int 						=     True

									for model_class in self.all_model_classes:
											# we don't test BeitForMaskedImageModeling
											if (
											    model_class in [*get_values(a_      ), BeitForMaskedImageModeling]
											    or not model_class.supports_gradient_checkpointing
											):
													continue

											a_     :      Optional[int] 						=     model_class(a_      )
											model.gradient_checkpointing_enable()
											model.to(a_      )
											model.train()
											a_     :      str 						=     self._prepare_for_class(a_			,     a_			,     return_labels=a_      )
											a_     :      Optional[Any] 						=     model(**a_      ).loss
											loss.backward()






							def snake_case_  (   self      ):
									a_						,				a_     :      List[str] 						=     self.model_tester.prepare_config_and_inputs_for_common()

									a_     :      Optional[int] 						=     _config_zero_init(a_      )
									for model_class in self.all_model_classes:
											a_     :      Dict 						=     model_class(config=a_      )
											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 snake_case_  (   self      ):
									for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											a_     :      Any 						=     BeitModel.from_pretrained(a_      )
											self.assertIsNotNone(a_      )



def        lowerCAmelCase_						(							)			->      Tuple:
		a_     :      Tuple 						=     Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"	)
		return image




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






							@cached_property
							def snake_case_  (   self      ):
									return BeitImageProcessor.from_pretrained("microsoft/beit-base-patch16-224"      ) if is_vision_available() else None






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

									a_     :      Tuple 						=     self.default_image_processor
									a_     :      List[Any] 						=     prepare_img()
									a_     :      int 						=     image_processor(images=a_			,     return_tensors="pt"      ).pixel_values.to(a_      )

									# prepare bool_masked_pos
									a_     :      List[str] 						=     torch.ones((1, 1_9_6)			,     dtype=torch.bool      ).to(a_      )

									# forward pass
									with torch.no_grad():
											a_     :      Optional[Any] 						=     model(pixel_values=a_			,     bool_masked_pos=a_      )
									a_     :      List[str] 						=     outputs.logits

									# verify the logits
									a_     :      Optional[int] 						=     torch.Size((1, 1_9_6, 8_1_9_2)      )
									self.assertEqual(logits.shape			,     a_      )

									a_     :      str 						=     torch.tensor(
									    [[-3.2_437, 0.5_072, -13.9_174], [-3.2_456, 0.4_948, -13.9_401], [-3.2_033, 0.5_121, -13.8_550]]      ).to(a_      )

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






							@slow
							def snake_case_  (   self      ):
									a_     :      List[str] 						=     BeitForImageClassification.from_pretrained("microsoft/beit-base-patch16-224"      ).to(a_      )

									a_     :      Union[str, Any] 						=     self.default_image_processor
									a_     :      Tuple 						=     prepare_img()
									a_     :      Optional[Any] 						=     image_processor(images=a_			,     return_tensors="pt"      ).to(a_      )

									# forward pass
									with torch.no_grad():
											a_     :      List[Any] 						=     model(**a_      )
									a_     :      Optional[int] 						=     outputs.logits

									# verify the logits
									a_     :      List[str] 						=     torch.Size((1, 1_0_0_0)      )
									self.assertEqual(logits.shape			,     a_      )

									a_     :      Dict 						=     torch.tensor([-1.2_385, -1.0_987, -1.0_108]      ).to(a_      )

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

									a_     :      List[Any] 						=     2_8_1
									self.assertEqual(logits.argmax(-1      ).item()			,     a_      )






							@slow
							def snake_case_  (   self      ):
									a_     :      Tuple 						=     BeitForImageClassification.from_pretrained("microsoft/beit-large-patch16-224-pt22k-ft22k"      ).to(
									    a_      )

									a_     :      Tuple 						=     self.default_image_processor
									a_     :      Optional[Any] 						=     prepare_img()
									a_     :      int 						=     image_processor(images=a_			,     return_tensors="pt"      ).to(a_      )

									# forward pass
									with torch.no_grad():
											a_     :      Union[str, Any] 						=     model(**a_      )
									a_     :      List[str] 						=     outputs.logits

									# verify the logits
									a_     :      str 						=     torch.Size((1, 2_1_8_4_1)      )
									self.assertEqual(logits.shape			,     a_      )

									a_     :      Tuple 						=     torch.tensor([1.6_881, -0.2_787, 0.5_901]      ).to(a_      )

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

									a_     :      List[str] 						=     2_3_9_6
									self.assertEqual(logits.argmax(-1      ).item()			,     a_      )






							@slow
							def snake_case_  (   self      ):
									a_     :      Union[str, Any] 						=     BeitForSemanticSegmentation.from_pretrained("microsoft/beit-base-finetuned-ade-640-640"      )
									a_     :      int 						=     model.to(a_      )

									a_     :      Union[str, Any] 						=     BeitImageProcessor(do_resize=a_			,     size=6_4_0			,     do_center_crop=a_      )

									a_     :      Dict 						=     load_dataset("hf-internal-testing/fixtures_ade20k"			,     split="test"      )
									a_     :      Tuple 						=     Image.open(ds[0]["file"]      )
									a_     :      List[str] 						=     image_processor(images=a_			,     return_tensors="pt"      ).to(a_      )

									# forward pass
									with torch.no_grad():
											a_     :      Union[str, Any] 						=     model(**a_      )
									a_     :      int 						=     outputs.logits

									# verify the logits
									a_     :      Union[str, Any] 						=     torch.Size((1, 1_5_0, 1_6_0, 1_6_0)      )
									self.assertEqual(logits.shape			,     a_      )

									a_     :      Tuple 						=     version.parse(PIL.__version__      ) < version.parse("9.0.0"      )

									if is_pillow_less_than_a:
											a_     :      Dict 						=     torch.tensor(
											    [
											        [[-4.9_225, -2.3_954, -3.0_522], [-2.8_822, -1.0_046, -1.7_561], [-2.9_549, -1.3_228, -2.1_347]],
											        [[-5.8_168, -3.4_129, -4.0_778], [-3.8_651, -2.2_214, -3.0_277], [-3.8_356, -2.4_643, -3.3_535]],
											        [[-0.0_078, 3.9_952, 4.0_754], [2.9_856, 4.6_944, 5.0_035], [3.2_413, 4.7_813, 4.9_969]],
											    ]			,     device=a_			,     )
									else:
											a_     :      int 						=     torch.tensor(
											    [
											        [[-4.8_960, -2.3_688, -3.0_355], [-2.8_478, -0.9_836, -1.7_418], [-2.9_449, -1.3_332, -2.1_456]],
											        [[-5.8_081, -3.4_124, -4.1_006], [-3.8_561, -2.2_081, -3.0_323], [-3.8_365, -2.4_601, -3.3_669]],
											        [[-0.0_309, 3.9_868, 4.0_540], [2.9_640, 4.6_877, 4.9_976], [3.2_081, 4.7_690, 4.9_942]],
											    ]			,     device=a_			,     )

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






							@slow
							def snake_case_  (   self      ):
									a_     :      Tuple 						=     BeitForSemanticSegmentation.from_pretrained("microsoft/beit-base-finetuned-ade-640-640"      )
									a_     :      Union[str, Any] 						=     model.to(a_      )

									a_     :      int 						=     BeitImageProcessor(do_resize=a_			,     size=6_4_0			,     do_center_crop=a_      )

									a_     :      Optional[Any] 						=     load_dataset("hf-internal-testing/fixtures_ade20k"			,     split="test"      )
									a_     :      Union[str, Any] 						=     Image.open(ds[0]["file"]      )
									a_     :      Dict 						=     image_processor(images=a_			,     return_tensors="pt"      ).to(a_      )

									# forward pass
									with torch.no_grad():
											a_     :      Optional[Any] 						=     model(**a_      )

									a_     :      List[str] 						=     outputs.logits.detach().cpu()

									a_     :      Any 						=     image_processor.post_process_semantic_segmentation(outputs=a_			,     target_sizes=[(5_0_0, 3_0_0)]      )
									a_     :      Optional[Any] 						=     torch.Size((5_0_0, 3_0_0)      )
									self.assertEqual(segmentation[0].shape			,     a_      )

									a_     :      str 						=     image_processor.post_process_semantic_segmentation(outputs=a_      )
									a_     :      Any 						=     torch.Size((1_6_0, 1_6_0)      )
									self.assertEqual(segmentation[0].shape			,     a_      )




"""simple docstring"""
SCREAMING_SNAKE_CASE_				= 0  # The first color of the flag.
SCREAMING_SNAKE_CASE_				= 1  # The second color of the flag.
SCREAMING_SNAKE_CASE_				= 2  # The third color of the flag.
SCREAMING_SNAKE_CASE_				= (red, white, blue)



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__	)			->      list:
		if not sequence:
				return []
		if len(SCREAMING_SNAKE_CASE__	) == 1:
				return list(SCREAMING_SNAKE_CASE__	)
		a_     :      Dict 						=     0
		a_     :      List[Any] 						=     len(SCREAMING_SNAKE_CASE__	) - 1
		a_     :      str 						=     0
		while mid <= high:
				if sequence[mid] == colors[0]:
						a_						,				a_     :      int 						=     sequence[mid], sequence[low]
						low += 1
						mid += 1
				elif sequence[mid] == colors[1]:
						mid += 1
				elif sequence[mid] == colors[2]:
						a_						,				a_     :      List[Any] 						=     sequence[high], sequence[mid]
						high -= 1
				else:
						a_     :      Dict 						=     F"""The elements inside the sequence must contains only {colors} values"""
						raise ValueError(SCREAMING_SNAKE_CASE__	)
		return sequence


if __name__ == "__main__":
					import doctest

					doctest.testmod()

					SCREAMING_SNAKE_CASE_				= input("""Enter numbers separated by commas:\n""").strip()
					SCREAMING_SNAKE_CASE_				= [int(item.strip()) for item in user_input.split(""",""")]
					print(F"""{dutch_national_flag_sort(unsorted)}""")