

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 							UpperCAmelCase_						:
							"""simple docstring"""







							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]			,				)       ->       List[str]:
												_a      :       List[str]   =						parent
												_a      :       Tuple   =						1_0_0
												_a      :       Tuple   =						batch_size
												_a      :       List[str]   =						image_size
												_a      :       List[str]   =						patch_size
												_a      :       Dict   =						num_channels
												_a      :       List[str]   =						is_training
												_a      :       Union[str, Any]   =						use_labels
												_a      :       str   =						hidden_size
												_a      :       int   =						num_hidden_layers
												_a      :       Optional[int]   =						num_attention_heads
												_a      :       List[str]   =						intermediate_size
												_a      :       Any   =						hidden_act
												_a      :       Optional[Any]   =						hidden_dropout_prob
												_a      :       List[Any]   =						attention_probs_dropout_prob
												_a      :       str   =						type_sequence_label_size
												_a      :       Optional[Any]   =						initializer_range
												_a      :       Union[str, Any]   =						scope
												_a      :       Dict   =						out_indices
												_a      :       Dict   =						num_labels

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


							def 							__lowercase							(     self			)       ->       Tuple:
												_a      :       Optional[Any]   =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

												_a      :       Optional[Any]   =						None
												_a      :       str   =						None
												if self.use_labels:
																	_a      :       Optional[int]   =						ids_tensor([self.batch_size]			,				self.type_sequence_label_size			)
																	_a      :       int   =						ids_tensor([self.batch_size, self.image_size, self.image_size]			,				self.num_labels			)

												_a      :       Any   =						self.get_config()

												return config, pixel_values, labels, pixel_labels


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


							def 							__lowercase							(     self			,				_a			,				_a			,				_a			,				_a			)       ->       List[str]:
												_a      :       str   =						self.type_sequence_label_size
												_a      :       Dict   =						BeitForImageClassification(_a			)
												model.to(_a			)
												model.eval()
												_a      :       Union[str, Any]   =						model(_a			,				labels=_a			)
												self.parent.assertEqual(result.logits.shape			,				(self.batch_size, self.type_sequence_label_size)			)

												# test greyscale images
												_a      :       Tuple   =						1
												_a      :       Optional[int]   =						BeitForImageClassification(_a			)
												model.to(_a			)
												model.eval()

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


							def 							__lowercase							(     self			,				_a			,				_a			,				_a			,				_a			)       ->       List[str]:
												_a      :       Optional[int]   =						self.num_labels
												_a      :       List[Any]   =						BeitForSemanticSegmentation(_a			)
												model.to(_a			)
												model.eval()
												_a      :       List[Any]   =						model(_a			)
												self.parent.assertEqual(
												    result.logits.shape			,				(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)			)
												_a      :       List[str]   =						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 							__lowercase							(     self			)       ->       Tuple:
												_a      :       Optional[int]   =						self.prepare_config_and_inputs()
												_a ,   _a ,   _a ,   _a      :       Tuple   =						config_and_inputs
												_a      :       List[Any]   =						{'''pixel_values''': pixel_values}
												return config, inputs_dict



@require_torch
class 							UpperCAmelCase_						(     __lowercase							,			__lowercase							,			unittest.TestCase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		List[str]             =       (
							    (BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
							    if is_torch_available()
							    else ()
							)
							UpperCAmelCase__			:		Tuple             =       (
							    {
							        "feature-extraction": BeitModel,
							        "image-classification": BeitForImageClassification,
							        "image-segmentation": BeitForSemanticSegmentation,
							    }
							    if is_torch_available()
							    else {}
							)

							UpperCAmelCase__			:		int             =       False
							UpperCAmelCase__			:		Dict             =       False
							UpperCAmelCase__			:		Tuple             =       False


							def 							__lowercase							(     self			)       ->       Dict:
												_a      :       Optional[Any]   =						BeitModelTester(self			)
												_a      :       Any   =						ConfigTester(self			,				config_class=_a			,				has_text_modality=_a			,				hidden_size=3_7			)


							def 							__lowercase							(     self			)       ->       int:
												self.config_tester.run_common_tests()


							@unittest.skip(reason='''BEiT does not use inputs_embeds'''			)
							def 							__lowercase							(     self			)       ->       Dict:
												pass


							@require_torch_multi_gpu
							@unittest.skip(reason='''BEiT has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'''			)
							def 							__lowercase							(     self			)       ->       Union[str, Any]:
												pass


							def 							__lowercase							(     self			)       ->       List[str]:
												_a ,   _a      :       Optional[int]   =						self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																	_a      :       Dict   =						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 							__lowercase							(     self			)       ->       List[str]:
												_a ,   _a      :       str   =						self.model_tester.prepare_config_and_inputs_for_common()

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

																	_a      :       List[Any]   =						['''pixel_values''']
																	self.assertListEqual(arg_names[:1]			,				_a			)


							def 							__lowercase							(     self			)       ->       Dict:
												_a      :       List[Any]   =						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*_a			)


							def 							__lowercase							(     self			)       ->       Dict:
												_a      :       Dict   =						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_masked_lm(*_a			)


							def 							__lowercase							(     self			)       ->       Dict:
												_a      :       List[str]   =						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_classification(*_a			)


							def 							__lowercase							(     self			)       ->       Optional[Any]:
												_a      :       List[str]   =						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_semantic_segmentation(*_a			)


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

												_a ,   _a      :       int   =						self.model_tester.prepare_config_and_inputs_for_common()
												_a      :       List[str]   =						True

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

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


							def 							__lowercase							(     self			)       ->       Tuple:
												_a ,   _a      :       Optional[Any]   =						self.model_tester.prepare_config_and_inputs_for_common()
												if not self.model_tester.is_training:
																	return

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


							def 							__lowercase							(     self			)       ->       Union[str, Any]:
												_a ,   _a      :       List[str]   =						self.model_tester.prepare_config_and_inputs_for_common()

												_a      :       Dict   =						_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 							__lowercase							(     self			)       ->       Any:
												for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	_a      :       str   =						BeitModel.from_pretrained(_a			)
																	self.assertIsNotNone(_a			)



def   __UpperCAmelCase					(      )     ->	str:

					"""simple docstring"""

					_a      :       Optional[int]   =						Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
					return image







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







							@cached_property
							def 							__lowercase							(     self			)       ->       Optional[int]:
												return BeitImageProcessor.from_pretrained('''microsoft/beit-base-patch16-224'''			) if is_vision_available() else None


							@slow
							def 							__lowercase							(     self			)       ->       Optional[int]:
												_a      :       int   =						BeitForMaskedImageModeling.from_pretrained('''microsoft/beit-base-patch16-224-pt22k'''			).to(_a			)

												_a      :       Any   =						self.default_image_processor
												_a      :       Tuple   =						prepare_img()
												_a      :       List[str]   =						image_processor(images=_a			,				return_tensors='''pt'''			).pixel_values.to(_a			)

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

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

												# verify the logits
												_a      :       List[str]   =						torch.Size((1, 1_9_6, 8_1_9_2)			)
												self.assertEqual(logits.shape			,				_a			)

												_a      :       Optional[Any]   =						torch.tensor(
												    [[-3.2437, 0.5072, -13.9174], [-3.2456, 0.4948, -13.9401], [-3.2033, 0.5121, -13.8550]]			).to(_a			)

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


							@slow
							def 							__lowercase							(     self			)       ->       Optional[Any]:
												_a      :       Dict   =						BeitForImageClassification.from_pretrained('''microsoft/beit-base-patch16-224'''			).to(_a			)

												_a      :       Tuple   =						self.default_image_processor
												_a      :       Optional[Any]   =						prepare_img()
												_a      :       Tuple   =						image_processor(images=_a			,				return_tensors='''pt'''			).to(_a			)

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

												# verify the logits
												_a      :       Dict   =						torch.Size((1, 1_0_0_0)			)
												self.assertEqual(logits.shape			,				_a			)

												_a      :       Optional[Any]   =						torch.tensor([-1.2385, -1.0987, -1.0108]			).to(_a			)

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

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


							@slow
							def 							__lowercase							(     self			)       ->       Optional[Any]:
												_a      :       int   =						BeitForImageClassification.from_pretrained('''microsoft/beit-large-patch16-224-pt22k-ft22k'''			).to(
												    _a			)

												_a      :       Dict   =						self.default_image_processor
												_a      :       Optional[Any]   =						prepare_img()
												_a      :       Optional[Any]   =						image_processor(images=_a			,				return_tensors='''pt'''			).to(_a			)

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

												# verify the logits
												_a      :       Optional[Any]   =						torch.Size((1, 2_1_8_4_1)			)
												self.assertEqual(logits.shape			,				_a			)

												_a      :       Optional[int]   =						torch.tensor([1.6881, -0.2787, 0.5901]			).to(_a			)

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

												_a      :       Dict   =						2_3_9_6
												self.assertEqual(logits.argmax(-1			).item()			,				_a			)


							@slow
							def 							__lowercase							(     self			)       ->       str:
												_a      :       List[str]   =						BeitForSemanticSegmentation.from_pretrained('''microsoft/beit-base-finetuned-ade-640-640'''			)
												_a      :       Union[str, Any]   =						model.to(_a			)

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

												_a      :       Optional[int]   =						load_dataset('''hf-internal-testing/fixtures_ade20k'''			,				split='''test'''			)
												_a      :       List[Any]   =						Image.open(ds[0]['''file''']			)
												_a      :       Tuple   =						image_processor(images=_a			,				return_tensors='''pt'''			).to(_a			)

												# forward pass
												with torch.no_grad():
																	_a      :       Dict   =						model(**_a			)
												_a      :       str   =						outputs.logits

												# verify the logits
												_a      :       int   =						torch.Size((1, 1_5_0, 1_6_0, 1_6_0)			)
												self.assertEqual(logits.shape			,				_a			)

												_a      :       str   =						version.parse(PIL.__version__			) < version.parse('''9.0.0'''			)

												if is_pillow_less_than_a:
																	_a      :       List[str]   =						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=_a			,				)
												else:
																	_a      :       Any   =						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=_a			,				)

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




							@slow
							def 							__lowercase							(     self			)       ->       Optional[Any]:
												_a      :       str   =						BeitForSemanticSegmentation.from_pretrained('''microsoft/beit-base-finetuned-ade-640-640'''			)
												_a      :       Dict   =						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      :       List[str]   =						Image.open(ds[0]['''file''']			)
												_a      :       Optional[int]   =						image_processor(images=_a			,				return_tensors='''pt'''			).to(_a			)

												# forward pass
												with torch.no_grad():
																	_a      :       str   =						model(**_a			)

												_a      :       Union[str, Any]   =						outputs.logits.detach().cpu()

												_a      :       str   =						image_processor.post_process_semantic_segmentation(outputs=_a			,				target_sizes=[(5_0_0, 3_0_0)]			)
												_a      :       Dict   =						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      :       Tuple   =						torch.Size((1_6_0, 1_6_0)			)
												self.assertEqual(segmentation[0].shape			,				_a			)






from __future__ import annotations

from dataclasses import dataclass

@dataclass
class 							UpperCAmelCase_						:
							"""simple docstring"""







							UpperCAmelCase__			:		float
							UpperCAmelCase__			:		TreeNode | None             =       None
							UpperCAmelCase__			:		TreeNode | None             =       None



def   __UpperCAmelCase					(      __a :     TreeNode | None      )     ->	bool:

					"""simple docstring"""

					def is_valid_tree(__a :     TreeNode | None      ) -> bool:
										if node is None:
															return True

										if not isinstance(__a						,__a      ):
															return False

										try:
															float(node.data      )
										except (TypeError, ValueError):
															return False

										return is_valid_tree(node.left      ) and is_valid_tree(node.right      )

					if not is_valid_tree(__a      ):
										raise ValueError(
										    '''Each node should be type of TreeNode and data should be float.'''      )

					def is_binary_search_tree_recursive_check(
					    __a :     TreeNode | None						,__a :     float						,__a :     float      ) -> bool:

										if node is None:
															return True

										return (
										    left_bound < node.data < right_bound
										    and is_binary_search_tree_recursive_check(node.left						,__a						,node.data      )
										    and is_binary_search_tree_recursive_check(
										        node.right						,node.data						,__a      )
										)

					return is_binary_search_tree_recursive_check(__a						,-float('''inf'''      )						,float('''inf'''      )      )


if __name__ == "__main__":
			import doctest

			doctest.testmod()



