






import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
						import torch

if is_vision_available():
						from PIL import Image

						from transformers import BeitImageProcessor


class        __UpperCamelCase      (     unittest.TestCase   ):







	def __init__(  self      :							Union[str, Any]       ,				_lowerCAmelCase      :							List[str]       ,				_lowerCAmelCase      :							int=7       ,				_lowerCAmelCase      :							Tuple=3       ,				_lowerCAmelCase      :							List[Any]=18       ,				_lowerCAmelCase      :							Optional[Any]=30       ,				_lowerCAmelCase      :							int=400       ,				_lowerCAmelCase      :							Dict=True       ,				_lowerCAmelCase      :							str=None       ,				_lowerCAmelCase      :							Union[str, Any]=True       ,				_lowerCAmelCase      :							Union[str, Any]=None       ,				_lowerCAmelCase      :							str=True       ,				_lowerCAmelCase      :							Union[str, Any]=[0.5, 0.5, 0.5]       ,				_lowerCAmelCase      :							Any=[0.5, 0.5, 0.5]       ,				_lowerCAmelCase      :							List[str]=False       ,				)		-> List[str]:
		"""simple docstring"""







		__lowercase             =  size if size is not None else {"""height""": 20, """width""": 20}
		__lowercase             =  crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
		__lowercase             =  parent
		__lowercase             =  batch_size
		__lowercase             =  num_channels
		__lowercase             =  image_size
		__lowercase             =  min_resolution
		__lowercase             =  max_resolution
		__lowercase             =  do_resize
		__lowercase             =  size
		__lowercase             =  do_center_crop
		__lowercase             =  crop_size
		__lowercase             =  do_normalize
		__lowercase             =  image_mean
		__lowercase             =  image_std
		__lowercase             =  do_reduce_labels







	def  _a   (  self      :							str			)		-> Optional[Any]:
		"""simple docstring"""







		return {
		    "do_resize": self.do_resize,
		    "size": self.size,
		    "do_center_crop": self.do_center_crop,
		    "crop_size": self.crop_size,
		    "do_normalize": self.do_normalize,
		    "image_mean": self.image_mean,
		    "image_std": self.image_std,
		    "do_reduce_labels": self.do_reduce_labels,
		}





def 				snake_case  (     ):




	'''simple docstring'''







	__lowercase             =  load_dataset("""hf-internal-testing/fixtures_ade20k"""				,  split="""test"""					)

	__lowercase             =  Image.open(dataset[0]["""file"""]					)
	__lowercase             =  Image.open(dataset[1]["""file"""]					)

	return image, map





def 				snake_case  (     ):




	'''simple docstring'''







	__lowercase             =  load_dataset("""hf-internal-testing/fixtures_ade20k"""				,  split="""test"""					)

	__lowercase             =  Image.open(ds[0]["""file"""]					)
	__lowercase             =  Image.open(ds[1]["""file"""]					)
	__lowercase             =  Image.open(ds[2]["""file"""]					)
	__lowercase             =  Image.open(ds[3]["""file"""]					)

	return [imagea, imagea], [mapa, mapa]




@require_torch
@require_vision
class        __UpperCamelCase      (     __UpperCAmelCase   ,			unittest.TestCase   ):
	__snake_case	:Union[str, Any]   =       BeitImageProcessor if is_vision_available() else None







	def  _a   (  self      :							int			)		-> Optional[Any]:
		"""simple docstring"""







		__lowercase             =  BeitImageProcessingTester(self			)







	@property
	def  _a   (  self      :							int			)		-> Union[str, Any]:
		"""simple docstring"""







		return self.image_processor_tester.prepare_image_processor_dict()







	def  _a   (  self      :							Optional[Any]			)		-> List[str]:
		"""simple docstring"""







		__lowercase             =  self.image_processing_class(**self.image_processor_dict			)
		self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE       ,				"""do_resize"""			)			)
		self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE       ,				"""size"""			)			)
		self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE       ,				"""do_center_crop"""			)			)
		self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE       ,				"""center_crop"""			)			)
		self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE       ,				"""do_normalize"""			)			)
		self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE       ,				"""image_mean"""			)			)
		self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE       ,				"""image_std"""			)			)







	def  _a   (  self      :							str			)		-> Optional[Any]:
		"""simple docstring"""







		__lowercase             =  self.image_processing_class.from_dict(self.image_processor_dict			)
		self.assertEqual(image_processor.size       ,				{"""height""": 20, """width""": 20}			)
		self.assertEqual(image_processor.crop_size       ,				{"""height""": 18, """width""": 18}			)
		self.assertEqual(image_processor.do_reduce_labels       ,				__SCREAMING_SNAKE_CASE			)

		__lowercase             =  self.image_processing_class.from_dict(
		    self.image_processor_dict       ,				size=42       ,				crop_size=84       ,				reduce_labels=__SCREAMING_SNAKE_CASE			)
		self.assertEqual(image_processor.size       ,				{"""height""": 42, """width""": 42}			)
		self.assertEqual(image_processor.crop_size       ,				{"""height""": 84, """width""": 84}			)
		self.assertEqual(image_processor.do_reduce_labels       ,				__SCREAMING_SNAKE_CASE			)







	def  _a   (  self      :							Dict			)		-> Union[str, Any]:
		"""simple docstring"""







		pass







	def  _a   (  self      :							Tuple			)		-> Dict:
		"""simple docstring"""







		__lowercase             =  self.image_processing_class(**self.image_processor_dict			)
		# create random PIL images
		__lowercase             =  prepare_image_inputs(self.image_processor_tester       ,				equal_resolution=__SCREAMING_SNAKE_CASE			)
		for image in image_inputs:
			self.assertIsInstance(__SCREAMING_SNAKE_CASE       ,				Image.Image			)

		# Test not batched input
		__lowercase             =  image_processing(image_inputs[0]       ,				return_tensors="""pt"""			).pixel_values
		self.assertEqual(
		    encoded_images.shape       ,				(
		        1,
		        self.image_processor_tester.num_channels,
		        self.image_processor_tester.crop_size["""height"""],
		        self.image_processor_tester.crop_size["""width"""],
		    )       ,				)

		# Test batched
		__lowercase             =  image_processing(__SCREAMING_SNAKE_CASE       ,				return_tensors="""pt"""			).pixel_values
		self.assertEqual(
		    encoded_images.shape       ,				(
		        self.image_processor_tester.batch_size,
		        self.image_processor_tester.num_channels,
		        self.image_processor_tester.crop_size["""height"""],
		        self.image_processor_tester.crop_size["""width"""],
		    )       ,				)







	def  _a   (  self      :							int			)		-> str:
		"""simple docstring"""







		__lowercase             =  self.image_processing_class(**self.image_processor_dict			)
		# create random numpy tensors
		__lowercase             =  prepare_image_inputs(self.image_processor_tester       ,				equal_resolution=__SCREAMING_SNAKE_CASE       ,				numpify=__SCREAMING_SNAKE_CASE			)
		for image in image_inputs:
			self.assertIsInstance(__SCREAMING_SNAKE_CASE       ,				np.ndarray			)

		# Test not batched input
		__lowercase             =  image_processing(image_inputs[0]       ,				return_tensors="""pt"""			).pixel_values
		self.assertEqual(
		    encoded_images.shape       ,				(
		        1,
		        self.image_processor_tester.num_channels,
		        self.image_processor_tester.crop_size["""height"""],
		        self.image_processor_tester.crop_size["""width"""],
		    )       ,				)

		# Test batched
		__lowercase             =  image_processing(__SCREAMING_SNAKE_CASE       ,				return_tensors="""pt"""			).pixel_values
		self.assertEqual(
		    encoded_images.shape       ,				(
		        self.image_processor_tester.batch_size,
		        self.image_processor_tester.num_channels,
		        self.image_processor_tester.crop_size["""height"""],
		        self.image_processor_tester.crop_size["""width"""],
		    )       ,				)







	def  _a   (  self      :							List[Any]			)		-> Optional[Any]:
		"""simple docstring"""







		__lowercase             =  self.image_processing_class(**self.image_processor_dict			)
		# create random PyTorch tensors
		__lowercase             =  prepare_image_inputs(self.image_processor_tester       ,				equal_resolution=__SCREAMING_SNAKE_CASE       ,				torchify=__SCREAMING_SNAKE_CASE			)
		for image in image_inputs:
			self.assertIsInstance(__SCREAMING_SNAKE_CASE       ,				torch.Tensor			)

		# Test not batched input
		__lowercase             =  image_processing(image_inputs[0]       ,				return_tensors="""pt"""			).pixel_values
		self.assertEqual(
		    encoded_images.shape       ,				(
		        1,
		        self.image_processor_tester.num_channels,
		        self.image_processor_tester.crop_size["""height"""],
		        self.image_processor_tester.crop_size["""width"""],
		    )       ,				)

		# Test batched
		__lowercase             =  image_processing(__SCREAMING_SNAKE_CASE       ,				return_tensors="""pt"""			).pixel_values
		self.assertEqual(
		    encoded_images.shape       ,				(
		        self.image_processor_tester.batch_size,
		        self.image_processor_tester.num_channels,
		        self.image_processor_tester.crop_size["""height"""],
		        self.image_processor_tester.crop_size["""width"""],
		    )       ,				)







	def  _a   (  self      :							Dict			)		-> Optional[int]:
		"""simple docstring"""







		__lowercase             =  self.image_processing_class(**self.image_processor_dict			)
		# create random PyTorch tensors
		__lowercase             =  prepare_image_inputs(self.image_processor_tester       ,				equal_resolution=__SCREAMING_SNAKE_CASE       ,				torchify=__SCREAMING_SNAKE_CASE			)
		__lowercase             =  []
		for image in image_inputs:
			self.assertIsInstance(__SCREAMING_SNAKE_CASE       ,				torch.Tensor			)
			maps.append(torch.zeros(image.shape[-2:]			).long()			)

		# Test not batched input
		__lowercase             =  image_processing(image_inputs[0]       ,				maps[0]       ,				return_tensors="""pt"""			)
		self.assertEqual(
		    encoding["""pixel_values"""].shape       ,				(
		        1,
		        self.image_processor_tester.num_channels,
		        self.image_processor_tester.crop_size["""height"""],
		        self.image_processor_tester.crop_size["""width"""],
		    )       ,				)
		self.assertEqual(
		    encoding["""labels"""].shape       ,				(
		        1,
		        self.image_processor_tester.crop_size["""height"""],
		        self.image_processor_tester.crop_size["""width"""],
		    )       ,				)
		self.assertEqual(encoding["""labels"""].dtype       ,				torch.long			)
		self.assertTrue(encoding["""labels"""].min().item() >= 0			)
		self.assertTrue(encoding["""labels"""].max().item() <= 255			)

		# Test batched
		__lowercase             =  image_processing(__SCREAMING_SNAKE_CASE       ,				__SCREAMING_SNAKE_CASE       ,				return_tensors="""pt"""			)
		self.assertEqual(
		    encoding["""pixel_values"""].shape       ,				(
		        self.image_processor_tester.batch_size,
		        self.image_processor_tester.num_channels,
		        self.image_processor_tester.crop_size["""height"""],
		        self.image_processor_tester.crop_size["""width"""],
		    )       ,				)
		self.assertEqual(
		    encoding["""labels"""].shape       ,				(
		        self.image_processor_tester.batch_size,
		        self.image_processor_tester.crop_size["""height"""],
		        self.image_processor_tester.crop_size["""width"""],
		    )       ,				)
		self.assertEqual(encoding["""labels"""].dtype       ,				torch.long			)
		self.assertTrue(encoding["""labels"""].min().item() >= 0			)
		self.assertTrue(encoding["""labels"""].max().item() <= 255			)

		# Test not batched input (PIL images)
		__lowercase       ,						__lowercase             =  prepare_semantic_single_inputs()

		__lowercase             =  image_processing(__SCREAMING_SNAKE_CASE       ,				__SCREAMING_SNAKE_CASE       ,				return_tensors="""pt"""			)
		self.assertEqual(
		    encoding["""pixel_values"""].shape       ,				(
		        1,
		        self.image_processor_tester.num_channels,
		        self.image_processor_tester.crop_size["""height"""],
		        self.image_processor_tester.crop_size["""width"""],
		    )       ,				)
		self.assertEqual(
		    encoding["""labels"""].shape       ,				(
		        1,
		        self.image_processor_tester.crop_size["""height"""],
		        self.image_processor_tester.crop_size["""width"""],
		    )       ,				)
		self.assertEqual(encoding["""labels"""].dtype       ,				torch.long			)
		self.assertTrue(encoding["""labels"""].min().item() >= 0			)
		self.assertTrue(encoding["""labels"""].max().item() <= 255			)

		# Test batched input (PIL images)
		__lowercase       ,						__lowercase             =  prepare_semantic_batch_inputs()

		__lowercase             =  image_processing(__SCREAMING_SNAKE_CASE       ,				__SCREAMING_SNAKE_CASE       ,				return_tensors="""pt"""			)
		self.assertEqual(
		    encoding["""pixel_values"""].shape       ,				(
		        2,
		        self.image_processor_tester.num_channels,
		        self.image_processor_tester.crop_size["""height"""],
		        self.image_processor_tester.crop_size["""width"""],
		    )       ,				)
		self.assertEqual(
		    encoding["""labels"""].shape       ,				(
		        2,
		        self.image_processor_tester.crop_size["""height"""],
		        self.image_processor_tester.crop_size["""width"""],
		    )       ,				)
		self.assertEqual(encoding["""labels"""].dtype       ,				torch.long			)
		self.assertTrue(encoding["""labels"""].min().item() >= 0			)
		self.assertTrue(encoding["""labels"""].max().item() <= 255			)







	def  _a   (  self      :							Dict			)		-> Any:
		"""simple docstring"""







		__lowercase             =  self.image_processing_class(**self.image_processor_dict			)

		# ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
		__lowercase       ,						__lowercase             =  prepare_semantic_single_inputs()
		__lowercase             =  image_processing(__SCREAMING_SNAKE_CASE       ,				__SCREAMING_SNAKE_CASE       ,				return_tensors="""pt"""			)
		self.assertTrue(encoding["""labels"""].min().item() >= 0			)
		self.assertTrue(encoding["""labels"""].max().item() <= 150			)

		__lowercase             =  True
		__lowercase             =  image_processing(__SCREAMING_SNAKE_CASE       ,				__SCREAMING_SNAKE_CASE       ,				return_tensors="""pt"""			)
		self.assertTrue(encoding["""labels"""].min().item() >= 0			)
		self.assertTrue(encoding["""labels"""].max().item() <= 255			)









import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCamelCase      :      str     						=						logging.get_logger(__name__)





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







	__lowercase             =  RobertaPreLayerNormConfig.from_pretrained(
	    lowerCamelCase				,  architectures=["""RobertaPreLayerNormForMaskedLM"""]					)

	# convert state_dict
	__lowercase             =  torch.load(hf_hub_download(repo_id=lowerCamelCase				,  filename="""pytorch_model.bin"""					)					)
	__lowercase             =  {}
	for tensor_key, tensor_value in original_state_dict.items():
		# The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
		if tensor_key.startswith("""roberta."""					):
			__lowercase             =  """roberta_prelayernorm.""" + tensor_key[len("""roberta."""					) :]

		# The original implementation contains weights which are not used, remove them from the state_dict
		if tensor_key.endswith(""".self.LayerNorm.weight"""					) or tensor_key.endswith(""".self.LayerNorm.bias"""					):
			continue

		__lowercase             =  tensor_value

	__lowercase             =  RobertaPreLayerNormForMaskedLM.from_pretrained(
	    pretrained_model_name_or_path=lowerCamelCase				,  config=lowerCamelCase				,  state_dict=lowerCamelCase					)
	model.save_pretrained(lowerCamelCase					)

	# convert tokenizer
	__lowercase             =  AutoTokenizer.from_pretrained(lowerCamelCase					)
	tokenizer.save_pretrained(lowerCamelCase					)


if __name__ == "__main__":
						__UpperCamelCase      :      List[Any]     						=						argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    """--checkpoint-repo""",
						    default=None,
						    type=str,
						    required=True,
						    help="""Path the official PyTorch dump, e.g. 'andreasmadsen/efficient_mlm_m0.40'.""",
						)
						parser.add_argument(
						    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
						)
						__UpperCamelCase      :      Dict     						=						parser.parse_args()
						convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)

