




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 						__lowerCAmelCase      (      unittest.TestCase					):
							def __init__(		self		:Dict						, __magic_name__		:Optional[int]						, __magic_name__		:Any=7						, __magic_name__		:Union[str, Any]=3						, __magic_name__		:str=18						, __magic_name__		:List[Any]=30						, __magic_name__		:List[str]=400						, __magic_name__		:Dict=True						, __magic_name__		:Tuple=None						, __magic_name__		:Dict=True						, __magic_name__		:List[str]=None						, __magic_name__		:Any=True						, __magic_name__		:Optional[int]=[0.5, 0.5, 0.5]						, __magic_name__		:Optional[Any]=[0.5, 0.5, 0.5]						, __magic_name__		:int=False						, ):







								'''simple docstring'''







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





							def  lowerCamelCase__				(		self		:List[str]							):







								'''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 						__A						(      ) ->			List[str]:
	a									=				load_dataset("""hf-internal-testing/fixtures_ade20k"""				,     split="""test"""       )

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

	return image, map





def 						__A						(      ) ->			int:
	a									=				load_dataset("""hf-internal-testing/fixtures_ade20k"""				,     split="""test"""       )

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

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



@require_torch
@require_vision
class 						__lowerCAmelCase      (      __magic_name__ ,			unittest.TestCase					):
							UpperCamelCase__   	=    BeitImageProcessor if is_vision_available() else None
							def  lowerCamelCase__				(		self		:List[str]							):







								'''simple docstring'''







								a									=				BeitImageProcessingTester(self							)
							@property
							def  lowerCamelCase__				(		self		:List[str]							):







								'''simple docstring'''







								return self.image_processor_tester.prepare_image_processor_dict()
							def  lowerCamelCase__				(		self		:Dict							):







								'''simple docstring'''







								a									=				self.image_processing_class(**self.image_processor_dict							)
								self.assertTrue(hasattr(__magic_name__						, """do_resize"""							)							)
								self.assertTrue(hasattr(__magic_name__						, """size"""							)							)
								self.assertTrue(hasattr(__magic_name__						, """do_center_crop"""							)							)
								self.assertTrue(hasattr(__magic_name__						, """center_crop"""							)							)
								self.assertTrue(hasattr(__magic_name__						, """do_normalize"""							)							)
								self.assertTrue(hasattr(__magic_name__						, """image_mean"""							)							)
								self.assertTrue(hasattr(__magic_name__						, """image_std"""							)							)
							def  lowerCamelCase__				(		self		:List[str]							):







								'''simple docstring'''







								a									=				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						, __magic_name__							)

								a									=				self.image_processing_class.from_dict(
								    self.image_processor_dict						, size=42						, crop_size=84						, reduce_labels=__magic_name__							)
								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						, __magic_name__							)
							def  lowerCamelCase__				(		self		:str							):







								'''simple docstring'''







								pass
							def  lowerCamelCase__				(		self		:Optional[Any]							):







								'''simple docstring'''







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

								# Test not batched input
								a									=				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
								a									=				image_processing(__magic_name__						, 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  lowerCamelCase__				(		self		:Dict							):







								'''simple docstring'''







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

								# Test not batched input
								a									=				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
								a									=				image_processing(__magic_name__						, 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  lowerCamelCase__				(		self		:Tuple							):







								'''simple docstring'''







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

								# Test not batched input
								a									=				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
								a									=				image_processing(__magic_name__						, 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  lowerCamelCase__				(		self		:Any							):







								'''simple docstring'''







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

								# Test not batched input
								a									=				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
								a									=				image_processing(__magic_name__						, __magic_name__						, 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)
								a							,					a									=				prepare_semantic_single_inputs()

								a									=				image_processing(__magic_name__						, __magic_name__						, 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)
								a							,					a									=				prepare_semantic_batch_inputs()

								a									=				image_processing(__magic_name__						, __magic_name__						, 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  lowerCamelCase__				(		self		:Optional[int]							):







								'''simple docstring'''







								a									=				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
								a							,					a									=				prepare_semantic_single_inputs()
								a									=				image_processing(__magic_name__						, __magic_name__						, return_tensors="""pt"""							)
								self.assertTrue(encoding["""labels"""].min().item() >= 0							)
								self.assertTrue(encoding["""labels"""].max().item() <= 150							)

								a									=				True
								a									=				image_processing(__magic_name__						, __magic_name__						, 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   :			Optional[Any]          = logging.get_logger(__name__)





def 						__A						(      __lowerCamelCase				,     __lowerCamelCase       ) ->			Dict:
	a									=				RobertaPreLayerNormConfig.from_pretrained(
	    __lowerCamelCase				,     architectures=["""RobertaPreLayerNormForMaskedLM"""]       )

	# convert state_dict
	a									=				torch.load(hf_hub_download(repo_id=__lowerCamelCase				,     filename="""pytorch_model.bin"""       )       )
	a									=				{}
	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."""       ):
			a									=				"""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

		a									=				tensor_value

	a									=				RobertaPreLayerNormForMaskedLM.from_pretrained(
	    pretrained_model_name_or_path=__lowerCamelCase				,     config=__lowerCamelCase				,     state_dict=__lowerCamelCase       )
	model.save_pretrained(__lowerCamelCase       )

	# convert tokenizer
	a									=				AutoTokenizer.from_pretrained(__lowerCamelCase       )
	tokenizer.save_pretrained(__lowerCamelCase       )


if __name__ == "__main__":
		__UpperCamelCase   :			Union[str, 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   :			Any          = parser.parse_args()
		convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)

