




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__							:
				"""simple docstring"""



				def __init__(   self       :  Dict      ,   __lowerCamelCase       :  int      ,   __lowerCamelCase       :  Optional[int]=1_00      ,   __lowerCamelCase       :  int=13      ,   __lowerCamelCase       :  str=30      ,   __lowerCamelCase       :  Optional[Any]=2      ,   __lowerCamelCase       :  Dict=3      ,   __lowerCamelCase       :  List[Any]=True      ,   __lowerCamelCase       :  Optional[int]=True      ,   __lowerCamelCase       :  Optional[Any]=32      ,   __lowerCamelCase       :  Tuple=4      ,   __lowerCamelCase       :  Any=4      ,   __lowerCamelCase       :  List[str]=37      ,   __lowerCamelCase       :  int="gelu"      ,   __lowerCamelCase       :  int=0.1      ,   __lowerCamelCase       :  List[Any]=0.1      ,   __lowerCamelCase       :  Dict=10      ,   __lowerCamelCase       :  List[Any]=0.02      ,   __lowerCamelCase       :  Dict=3      ,   __lowerCamelCase       :  Optional[int]=None      ,   __lowerCamelCase       :  int=[0, 1, 2, 3]      ,   )      ->  List[str]:
							a					=    parent
							a					=    1_00
							a					=    batch_size
							a					=    image_size
							a					=    patch_size
							a					=    num_channels
							a					=    is_training
							a					=    use_labels
							a					=    hidden_size
							a					=    num_hidden_layers
							a					=    num_attention_heads
							a					=    intermediate_size
							a					=    hidden_act
							a					=    hidden_dropout_prob
							a					=    attention_probs_dropout_prob
							a					=    type_sequence_label_size
							a					=    initializer_range
							a					=    scope
							a					=    out_indices
							a					=    num_labels

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





				def 							__UpperCAmelCase							(   self       :  List[str]							)      ->  int:
							a					=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

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

							a					=    self.get_config()

							return config, pixel_values, labels, pixel_labels





				def 							__UpperCAmelCase							(   self       :  Dict							)      ->  Dict:
							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=__lowerCamelCase      ,   initializer_range=self.initializer_range      ,   out_indices=self.out_indices      ,   )





				def 							__UpperCAmelCase							(   self       :  Dict      ,   __lowerCamelCase       :  Tuple      ,   __lowerCamelCase       :  List[str]      ,   __lowerCamelCase       :  Tuple      ,   __lowerCamelCase       :  Any							)      ->  List[Any]:
							a					=    BeitModel(config=__lowerCamelCase							)
							model.to(__lowerCamelCase							)
							model.eval()
							a					=    model(__lowerCamelCase							)
							self.parent.assertEqual(result.last_hidden_state.shape      ,   (self.batch_size, self.seq_length, self.hidden_size)							)





				def 							__UpperCAmelCase							(   self       :  str      ,   __lowerCamelCase       :  List[Any]      ,   __lowerCamelCase       :  str      ,   __lowerCamelCase       :  Any      ,   __lowerCamelCase       :  Dict							)      ->  Union[str, Any]:
							a					=    BeitForMaskedImageModeling(config=__lowerCamelCase							)
							model.to(__lowerCamelCase							)
							model.eval()
							a					=    model(__lowerCamelCase							)
							self.parent.assertEqual(result.logits.shape      ,   (self.batch_size, self.seq_length - 1, self.vocab_size)							)





				def 							__UpperCAmelCase							(   self       :  List[Any]      ,   __lowerCamelCase       :  Tuple      ,   __lowerCamelCase       :  Optional[int]      ,   __lowerCamelCase       :  Any      ,   __lowerCamelCase       :  int							)      ->  List[Any]:
							a					=    self.type_sequence_label_size
							a					=    BeitForImageClassification(__lowerCamelCase							)
							model.to(__lowerCamelCase							)
							model.eval()
							a					=    model(__lowerCamelCase      ,   labels=__lowerCamelCase							)
							self.parent.assertEqual(result.logits.shape      ,   (self.batch_size, self.type_sequence_label_size)							)

							# test greyscale images
							a					=    1
							a					=    BeitForImageClassification(__lowerCamelCase							)
							model.to(__lowerCamelCase							)
							model.eval()

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





				def 							__UpperCAmelCase							(   self       :  Dict      ,   __lowerCamelCase       :  List[Any]      ,   __lowerCamelCase       :  Union[str, Any]      ,   __lowerCamelCase       :  Optional[Any]      ,   __lowerCamelCase       :  Tuple							)      ->  List[Any]:
							a					=    self.num_labels
							a					=    BeitForSemanticSegmentation(__lowerCamelCase							)
							model.to(__lowerCamelCase							)
							model.eval()
							a					=    model(__lowerCamelCase							)
							self.parent.assertEqual(
							    result.logits.shape      ,   (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)							)
							a					=    model(__lowerCamelCase      ,   labels=__lowerCamelCase							)
							self.parent.assertEqual(
							    result.logits.shape      ,   (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)							)





				def 							__UpperCAmelCase							(   self       :  Optional[int]							)      ->  Optional[Any]:
							a					=    self.prepare_config_and_inputs()
							a					,	a					,	a					,	a					=    config_and_inputs
							a					=    {"pixel_values": pixel_values}
							return config, inputs_dict




@require_torch
class 					snake_case__							(_UpperCamelCase					, _UpperCamelCase					, unittest.TestCase     ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_ :							Optional[Any]											=      (
				    (BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
				    if is_torch_available()
				    else ()
				)
				SCREAMING_SNAKE_CASE_ :							int											=      (
				    {
				        """feature-extraction""": BeitModel,
				        """image-classification""": BeitForImageClassification,
				        """image-segmentation""": BeitForSemanticSegmentation,
				    }
				    if is_torch_available()
				    else {}
				)

				SCREAMING_SNAKE_CASE_ :							Optional[Any]											=      False
				SCREAMING_SNAKE_CASE_ :							Optional[Any]											=      False
				SCREAMING_SNAKE_CASE_ :							Optional[int]											=      False





				def 							__UpperCAmelCase							(   self       :  List[Any]							)      ->  List[Any]:
							a					=    BeitModelTester(self							)
							a					=    ConfigTester(self      ,   config_class=__lowerCamelCase      ,   has_text_modality=__lowerCamelCase      ,   hidden_size=37							)





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





				@unittest.skip(reason="BEiT does not use inputs_embeds"							)
				def 							__UpperCAmelCase							(   self       :  Optional[int]							)      ->  Tuple:
							pass





				@require_torch_multi_gpu
				@unittest.skip(reason="BEiT has some layers using `add_module` which doesn't work well with `nn.DataParallel`"							)
				def 							__UpperCAmelCase							(   self       :  Optional[int]							)      ->  Any:
							pass





				def 							__UpperCAmelCase							(   self       :  Tuple							)      ->  Dict:
							a					,	a					=    self.model_tester.prepare_config_and_inputs_for_common()

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





				def 							__UpperCAmelCase							(   self       :  int							)      ->  Dict:
							a					,	a					=    self.model_tester.prepare_config_and_inputs_for_common()

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

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





				def 							__UpperCAmelCase							(   self       :  Union[str, Any]							)      ->  List[Any]:
							a					=    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  Optional[int]							)      ->  Union[str, Any]:
							a					=    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_lm(*__lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  List[Any]							)      ->  List[Any]:
							a					=    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  Tuple							)      ->  List[str]:
							a					=    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_semantic_segmentation(*__lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  List[str]							)      ->  List[str]:
							if not self.model_tester.is_training:
										return

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

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

										a					=    model_class(__lowerCamelCase							)
										model.to(__lowerCamelCase							)
										model.train()
										a					=    self._prepare_for_class(__lowerCamelCase      ,   __lowerCamelCase      ,   return_labels=__lowerCamelCase							)
										a					=    model(**__lowerCamelCase							).loss
										loss.backward()





				def 							__UpperCAmelCase							(   self       :  Union[str, Any]							)      ->  Any:
							a					,	a					=    self.model_tester.prepare_config_and_inputs_for_common()
							if not self.model_tester.is_training:
										return

							a					=    False
							a					=    True

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

										a					=    model_class(__lowerCamelCase							)
										model.gradient_checkpointing_enable()
										model.to(__lowerCamelCase							)
										model.train()
										a					=    self._prepare_for_class(__lowerCamelCase      ,   __lowerCamelCase      ,   return_labels=__lowerCamelCase							)
										a					=    model(**__lowerCamelCase							).loss
										loss.backward()





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

							a					=    _config_zero_init(__lowerCamelCase							)
							for model_class in self.all_model_classes:
										a					=    model_class(config=__lowerCamelCase							)
										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 							__UpperCAmelCase							(   self       :  Optional[Any]							)      ->  Union[str, Any]:
							for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										a					=    BeitModel.from_pretrained(__lowerCamelCase							)
										self.assertIsNotNone(__lowerCamelCase							)




def     __magic_name__       (				):






			'''simple docstring'''







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






@require_torch
@require_vision
class 					snake_case__							(unittest.TestCase     ):
				"""simple docstring"""



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





				@slow
				def 							__UpperCAmelCase							(   self       :  Any							)      ->  Dict:
							a					=    BeitForMaskedImageModeling.from_pretrained("microsoft/beit-base-patch16-224-pt22k"							).to(__lowerCamelCase							)

							a					=    self.default_image_processor
							a					=    prepare_img()
							a					=    image_processor(images=__lowerCamelCase      ,   return_tensors="pt"							).pixel_values.to(__lowerCamelCase							)

							# prepare bool_masked_pos
							a					=    torch.ones((1, 1_96)      ,   dtype=torch.bool							).to(__lowerCamelCase							)

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

							# verify the logits
							a					=    torch.Size((1, 1_96, 81_92)							)
							self.assertEqual(logits.shape      ,   __lowerCamelCase							)

							a					=    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(__lowerCamelCase							)

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





				@slow
				def 							__UpperCAmelCase							(   self       :  int							)      ->  List[Any]:
							a					=    BeitForImageClassification.from_pretrained("microsoft/beit-base-patch16-224"							).to(__lowerCamelCase							)

							a					=    self.default_image_processor
							a					=    prepare_img()
							a					=    image_processor(images=__lowerCamelCase      ,   return_tensors="pt"							).to(__lowerCamelCase							)

							# forward pass
							with torch.no_grad():
										a					=    model(**__lowerCamelCase							)
							a					=    outputs.logits

							# verify the logits
							a					=    torch.Size((1, 10_00)							)
							self.assertEqual(logits.shape      ,   __lowerCamelCase							)

							a					=    torch.tensor([-1.2_385, -1.0_987, -1.0_108]							).to(__lowerCamelCase							)

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

							a					=    2_81
							self.assertEqual(logits.argmax(-1							).item()      ,   __lowerCamelCase							)





				@slow
				def 							__UpperCAmelCase							(   self       :  str							)      ->  Optional[int]:
							a					=    BeitForImageClassification.from_pretrained("microsoft/beit-large-patch16-224-pt22k-ft22k"							).to(
							    __lowerCamelCase							)

							a					=    self.default_image_processor
							a					=    prepare_img()
							a					=    image_processor(images=__lowerCamelCase      ,   return_tensors="pt"							).to(__lowerCamelCase							)

							# forward pass
							with torch.no_grad():
										a					=    model(**__lowerCamelCase							)
							a					=    outputs.logits

							# verify the logits
							a					=    torch.Size((1, 2_18_41)							)
							self.assertEqual(logits.shape      ,   __lowerCamelCase							)

							a					=    torch.tensor([1.6_881, -0.2_787, 0.5_901]							).to(__lowerCamelCase							)

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

							a					=    23_96
							self.assertEqual(logits.argmax(-1							).item()      ,   __lowerCamelCase							)





				@slow
				def 							__UpperCAmelCase							(   self       :  Any							)      ->  Optional[Any]:
							a					=    BeitForSemanticSegmentation.from_pretrained("microsoft/beit-base-finetuned-ade-640-640"							)
							a					=    model.to(__lowerCamelCase							)

							a					=    BeitImageProcessor(do_resize=__lowerCamelCase      ,   size=6_40      ,   do_center_crop=__lowerCamelCase							)

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

							# forward pass
							with torch.no_grad():
										a					=    model(**__lowerCamelCase							)
							a					=    outputs.logits

							# verify the logits
							a					=    torch.Size((1, 1_50, 1_60, 1_60)							)
							self.assertEqual(logits.shape      ,   __lowerCamelCase							)

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

							if is_pillow_less_than_a:
										a					=    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=__lowerCamelCase      ,   )
							else:
										a					=    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=__lowerCamelCase      ,   )

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





				@slow
				def 							__UpperCAmelCase							(   self       :  Optional[Any]							)      ->  List[str]:
							a					=    BeitForSemanticSegmentation.from_pretrained("microsoft/beit-base-finetuned-ade-640-640"							)
							a					=    model.to(__lowerCamelCase							)

							a					=    BeitImageProcessor(do_resize=__lowerCamelCase      ,   size=6_40      ,   do_center_crop=__lowerCamelCase							)

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

							# forward pass
							with torch.no_grad():
										a					=    model(**__lowerCamelCase							)

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

							a					=    image_processor.post_process_semantic_segmentation(outputs=__lowerCamelCase      ,   target_sizes=[(5_00, 3_00)]							)
							a					=    torch.Size((5_00, 3_00)							)
							self.assertEqual(segmentation[0].shape      ,   __lowerCamelCase							)

							a					=    image_processor.post_process_semantic_segmentation(outputs=__lowerCamelCase							)
							a					=    torch.Size((1_60, 1_60)							)
							self.assertEqual(segmentation[0].shape      ,   __lowerCamelCase							)












def     __magic_name__       (				A	:      list     ):






			'''simple docstring'''







			for i in range(len(A     ) - 1,						0,						-1     ):
						a					=    False

						for j in range(A,						0,						-1     ):
									if unsorted[j] < unsorted[j - 1]:
												a					,	a					=    unsorted[j - 1], unsorted[j]
												a					=    True

						for j in range(A     ):
									if unsorted[j] > unsorted[j + 1]:
												a					,	a					=    unsorted[j + 1], unsorted[j]
												a					=    True

						if not swapped:
									break
			return unsorted


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		__lowerCAmelCase	:       Tuple     =	input('Enter numbers separated by a comma:\n').strip()
		__lowerCAmelCase	:       List[Any]     =	[int(item) for item in user_input.split(',')]
		print(F'''{cocktail_shaker_sort(unsorted) = }''')






