
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 				A__ :

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

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

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		int		    =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

			__lowerCAmelCase    :		List[Any]		    =   None
			__lowerCAmelCase    :		Tuple		    =   None
			if self.use_labels:
				__lowerCAmelCase    :		str		    =   ids_tensor([self.batch_size] ,	self.type_sequence_label_size				)
				__lowerCAmelCase    :		List[str]		    =   ids_tensor([self.batch_size, self.image_size, self.image_size] ,	self.num_labels				)

			__lowerCAmelCase    :		List[str]		    =   self.get_config()

			return config, pixel_values, labels, pixel_labels

		def 							__lowerCamelCase							(   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 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		str		    =   BeitModel(config=a_				)
			model.to(a_				)
			model.eval()
			__lowerCAmelCase    :		Dict		    =   model(a_				)
			self.parent.assertEqual(result.last_hidden_state.shape ,	(self.batch_size, self.seq_length, self.hidden_size)				)

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

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		Any		    =   self.type_sequence_label_size
			__lowerCAmelCase    :		Any		    =   BeitForImageClassification(a_				)
			model.to(a_				)
			model.eval()
			__lowerCAmelCase    :		List[Any]		    =   model(a_ ,	labels=a_				)
			self.parent.assertEqual(result.logits.shape ,	(self.batch_size, self.type_sequence_label_size)				)

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

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

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		List[str]		    =   self.num_labels
			__lowerCAmelCase    :		List[Any]		    =   BeitForSemanticSegmentation(a_				)
			model.to(a_				)
			model.eval()
			__lowerCAmelCase    :		List[str]		    =   model(a_				)
			self.parent.assertEqual(
			    result.logits.shape ,	(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2)				)
			__lowerCAmelCase    :		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 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Tuple		    =   self.prepare_config_and_inputs()
			__lowerCAmelCase    :		Any		    =   config_and_inputs
			__lowerCAmelCase    :		Optional[int]		    =   {"""pixel_values""": pixel_values}
			return config, inputs_dict




@require_torch
class 				A__ (			__a ,  __a ,  unittest.TestCase):
		A_		: Dict            =			(
		    (BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
		    if is_torch_available()
		    else ()
		)
		A_		: Optional[int]            =			(
		    {
		        'feature-extraction': BeitModel,
		        'image-classification': BeitForImageClassification,
		        'image-segmentation': BeitForSemanticSegmentation,
		    }
		    if is_torch_available()
		    else {}
		)

		A_		: int            =			False
		A_		: str            =			False
		A_		: List[Any]            =			False

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Dict		    =   BeitModelTester(self				)
			__lowerCAmelCase    :		int		    =   ConfigTester(self ,	config_class=a_ ,	has_text_modality=a_ ,	hidden_size=37				)

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

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

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Any		    =   self.model_tester.prepare_config_and_inputs_for_common()

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

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Optional[Any]		    =   self.model_tester.prepare_config_and_inputs_for_common()

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

				__lowerCAmelCase    :		List[Any]		    =   ["""pixel_values"""]
				self.assertListEqual(arg_names[:1] ,	a_				)

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Optional[int]		    =   self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model(*a_				)

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Optional[Any]		    =   self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_masked_lm(*a_				)

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Dict		    =   self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_image_classification(*a_				)

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		List[Any]		    =   self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_semantic_segmentation(*a_				)

		def 							__lowerCamelCase							(   self				):



			if not self.model_tester.is_training:
				return

			__lowerCAmelCase    :		List[str]		    =   self.model_tester.prepare_config_and_inputs_for_common()
			__lowerCAmelCase    :		Any		    =   True

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

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

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		List[Any]		    =   self.model_tester.prepare_config_and_inputs_for_common()
			if not self.model_tester.is_training:
				return

			__lowerCAmelCase    :		Dict		    =   False
			__lowerCAmelCase    :		Optional[Any]		    =   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

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

		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		List[str]		    =   self.model_tester.prepare_config_and_inputs_for_common()

			__lowerCAmelCase    :		int		    =   _config_zero_init(a_				)
			for model_class in self.all_model_classes:
				__lowerCAmelCase    :		Tuple		    =   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 							__lowerCamelCase							(   self				):



			for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
				__lowerCAmelCase    :		Optional[int]		    =   BeitModel.from_pretrained(a_				)
				self.assertIsNotNone(a_				)



def 					__lowerCAmelCase       ():
	__lowerCAmelCase    :		Union[str, Any]		    =   Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
	return image




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

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

		@slow
		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		str		    =   BeitForMaskedImageModeling.from_pretrained('microsoft/beit-base-patch16-224-pt22k'				).to(a_				)

			__lowerCAmelCase    :		Dict		    =   self.default_image_processor
			__lowerCAmelCase    :		Dict		    =   prepare_img()
			__lowerCAmelCase    :		List[str]		    =   image_processor(images=a_ ,	return_tensors='pt'				).pixel_values.to(a_				)

			# prepare bool_masked_pos
			__lowerCAmelCase    :		Optional[int]		    =   torch.ones((1, 1_96) ,	dtype=torch.bool				).to(a_				)

			# forward pass
			with torch.no_grad():
				__lowerCAmelCase    :		int		    =   model(pixel_values=a_ ,	bool_masked_pos=a_				)
			__lowerCAmelCase    :		Dict		    =   outputs.logits

			# verify the logits
			__lowerCAmelCase    :		Optional[int]		    =   torch.Size((1, 1_96, 81_92)				)
			self.assertEqual(logits.shape ,	a_				)

			__lowerCAmelCase    :		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 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		Dict		    =   BeitForImageClassification.from_pretrained('microsoft/beit-base-patch16-224'				).to(a_				)

			__lowerCAmelCase    :		List[Any]		    =   self.default_image_processor
			__lowerCAmelCase    :		Any		    =   prepare_img()
			__lowerCAmelCase    :		Any		    =   image_processor(images=a_ ,	return_tensors='pt'				).to(a_				)

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

			# verify the logits
			__lowerCAmelCase    :		Tuple		    =   torch.Size((1, 10_00)				)
			self.assertEqual(logits.shape ,	a_				)

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

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

			__lowerCAmelCase    :		str		    =   2_81
			self.assertEqual(logits.argmax(-1				).item() ,	a_				)

		@slow
		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		int		    =   BeitForImageClassification.from_pretrained('microsoft/beit-large-patch16-224-pt22k-ft22k'				).to(
			    a_				)

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

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

			# verify the logits
			__lowerCAmelCase    :		Tuple		    =   torch.Size((1, 2_18_41)				)
			self.assertEqual(logits.shape ,	a_				)

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

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

			__lowerCAmelCase    :		List[str]		    =   23_96
			self.assertEqual(logits.argmax(-1				).item() ,	a_				)

		@slow
		def 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		List[str]		    =   BeitForSemanticSegmentation.from_pretrained('microsoft/beit-base-finetuned-ade-640-640'				)
			__lowerCAmelCase    :		int		    =   model.to(a_				)

			__lowerCAmelCase    :		List[str]		    =   BeitImageProcessor(do_resize=a_ ,	size=6_40 ,	do_center_crop=a_				)

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

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

			# verify the logits
			__lowerCAmelCase    :		List[str]		    =   torch.Size((1, 1_50, 1_60, 1_60)				)
			self.assertEqual(logits.shape ,	a_				)

			__lowerCAmelCase    :		Optional[int]		    =   version.parse(PIL.__version__				) < version.parse('9.0.0'				)

			if is_pillow_less_than_a:
				__lowerCAmelCase    :		Any		    =   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:
				__lowerCAmelCase    :		Optional[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 							__lowerCamelCase							(   self				):
			__lowerCAmelCase    :		int		    =   BeitForSemanticSegmentation.from_pretrained('microsoft/beit-base-finetuned-ade-640-640'				)
			__lowerCAmelCase    :		List[Any]		    =   model.to(a_				)

			__lowerCAmelCase    :		Tuple		    =   BeitImageProcessor(do_resize=a_ ,	size=6_40 ,	do_center_crop=a_				)

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

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

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

			__lowerCAmelCase    :		Union[str, Any]		    =   image_processor.post_process_semantic_segmentation(outputs=a_ ,	target_sizes=[(5_00, 3_00)]				)
			__lowerCAmelCase    :		Optional[int]		    =   torch.Size((5_00, 3_00)				)
			self.assertEqual(segmentation[0].shape ,	a_				)

			__lowerCAmelCase    :		List[str]		    =   image_processor.post_process_semantic_segmentation(outputs=a_				)
			__lowerCAmelCase    :		List[str]		    =   torch.Size((1_60, 1_60)				)
			self.assertEqual(segmentation[0].shape ,	a_				)

"""simple docstring"""







def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase = False	):

	if not isinstance(_UpperCamelCase ,						_UpperCamelCase	):
		__lowerCAmelCase    :		str		    =   F"Expected string as input, found {type(_UpperCamelCase	)}"
		raise ValueError(_UpperCamelCase	)
	if not isinstance(_UpperCamelCase ,						_UpperCamelCase	):
		__lowerCAmelCase    :		Union[str, Any]		    =   F"Expected boolean as use_pascal parameter, found {type(_UpperCamelCase	)}"
		raise ValueError(_UpperCamelCase	)

	__lowerCAmelCase    :		Tuple		    =   input_str.split('_'	)

	__lowerCAmelCase    :		int		    =   0 if use_pascal else 1

	__lowerCAmelCase    :		Any		    =   words[start_index:]

	__lowerCAmelCase    :		Any		    =   [word[0].upper() + word[1:] for word in words_to_capitalize]

	__lowerCAmelCase    :		Any		    =   '' if use_pascal else words[0]

	return "".join([initial_word, *capitalized_words]	)


if __name__ == "__main__":
							from doctest import testmod

							testmod()