



"""simple docstring"""
import argparse

import torch

from transformers import MraConfig, MraForMaskedLM
def        A_  (      _lowercase     ):







						'''simple docstring'''


						if "model" in orig_key:
												snake_case_			:Tuple						=      orig_key.replace("""model.""", """"""     )
						if "norm1" in orig_key:
												snake_case_			:Any						=      orig_key.replace("""norm1""", """attention.output.LayerNorm"""     )
						if "norm2" in orig_key:
												snake_case_			:Optional[Any]						=      orig_key.replace("""norm2""", """output.LayerNorm"""     )
						if "norm" in orig_key:
												snake_case_			:Dict						=      orig_key.replace("""norm""", """LayerNorm"""     )
						if "transformer" in orig_key:
												snake_case_			:Any						=      orig_key.split("""."""     )[0].split("""_"""     )[-1]
												snake_case_			:Any						=      orig_key.replace(f"""transformer_{layer_num}""", f"""encoder.layer.{layer_num}"""     )
						if "mha.attn" in orig_key:
												snake_case_			:Union[str, Any]						=      orig_key.replace("""mha.attn""", """attention.self"""     )
						if "mha" in orig_key:
												snake_case_			:List[str]						=      orig_key.replace("""mha""", """attention"""     )
						if "W_q" in orig_key:
												snake_case_			:List[str]						=      orig_key.replace("""W_q""", """self.query"""     )
						if "W_k" in orig_key:
												snake_case_			:List[str]						=      orig_key.replace("""W_k""", """self.key"""     )
						if "W_v" in orig_key:
												snake_case_			:Tuple						=      orig_key.replace("""W_v""", """self.value"""     )
						if "ff.0" in orig_key:
												snake_case_			:int						=      orig_key.replace("""ff.0""", """intermediate.dense"""     )
						if "ff.2" in orig_key:
												snake_case_			:Any						=      orig_key.replace("""ff.2""", """output.dense"""     )
						if "ff" in orig_key:
												snake_case_			:Union[str, Any]						=      orig_key.replace("""ff""", """output.dense"""     )
						if "mlm_class" in orig_key:
												snake_case_			:Optional[int]						=      orig_key.replace("""mlm.mlm_class""", """cls.predictions.decoder"""     )
						if "mlm" in orig_key:
												snake_case_			:Optional[Any]						=      orig_key.replace("""mlm""", """cls.predictions.transform"""     )
						if "backbone.backbone.encoders" in orig_key:
												snake_case_			:str						=      orig_key.replace("""backbone.backbone.encoders""", """encoder.layer"""     )
						if "cls" not in orig_key:
												snake_case_			:Tuple						=      """mra.""" + orig_key

						return orig_key
def        A_  (      _lowercase, _lowercase     ):







						'''simple docstring'''


						for key in orig_state_dict.copy().keys():
												snake_case_			:str						=      orig_state_dict.pop(_lowercase     )

												if ("pooler" in key) or ("sen_class" in key):
																		continue
												else:
																		snake_case_			:Dict						=      val

						snake_case_			:Dict						=      orig_state_dict["""cls.predictions.decoder.bias"""]
						snake_case_			:Any						=      torch.arange(_lowercase     ).expand((1, -1)     ) + 2

						return orig_state_dict
def        A_  (      _lowercase, _lowercase, _lowercase     ):







						'''simple docstring'''


						snake_case_			:List[Any]						=      torch.load(_lowercase, map_location="""cpu"""     )["""model_state_dict"""]
						snake_case_			:int						=      MraConfig.from_json_file(_lowercase     )
						snake_case_			:List[Any]						=      MraForMaskedLM(_lowercase     )

						snake_case_			:Dict						=      convert_checkpoint_helper(config.max_position_embeddings, _lowercase     )

						print(model.load_state_dict(_lowercase     )     )
						model.eval()
						model.save_pretrained(_lowercase     )

						print(f"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""     )


if __name__ == "__main__":
						__a     =    argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--pytorch_model_path", default=None, type=str, required=True, help="Path to Mra pytorch checkpoint."
						)
						parser.add_argument(
						    "--config_file",
						    default=None,
						    type=str,
						    required=True,
						    help="The json file for Mra model config.",
						)
						parser.add_argument(
						    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
						)
						__a     =    parser.parse_args()
						convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)





"""simple docstring"""
import unittest

import numpy as np

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 ViTImageProcessor
class     lowerCamelCase  (      unittest.TestCase    ):






					'''simple docstring'''





					def __init__(	self:							int						,					snake_case:							List[str]						,					snake_case:							List[str]=7						,					snake_case:							Union[str, Any]=3						,					snake_case:							List[str]=18						,					snake_case:							Any=30						,					snake_case:							Any=400						,					snake_case:							Any=True						,					snake_case:							List[str]=None						,					snake_case:							Union[str, Any]=True						,					snake_case:							int=[0.5, 0.5, 0.5]						,					snake_case:							Tuple=[0.5, 0.5, 0.5]						,					)       ->     int:
											snake_case_			:Dict						=      size if size is not None else {"""height""": 18, """width""": 18}
											snake_case_			:Optional[Any]						=      parent
											snake_case_			:Tuple						=      batch_size
											snake_case_			:Tuple						=      num_channels
											snake_case_			:Optional[int]						=      image_size
											snake_case_			:Tuple						=      min_resolution
											snake_case_			:str						=      max_resolution
											snake_case_			:Any						=      do_resize
											snake_case_			:Union[str, Any]						=      size
											snake_case_			:Tuple						=      do_normalize
											snake_case_			:int						=      image_mean
											snake_case_			:List[Any]						=      image_std






					def        lowerCAmelCase_    (	self:							List[Any]						)       ->     List[Any]:
											return {
											    "image_mean": self.image_mean,
											    "image_std": self.image_std,
											    "do_normalize": self.do_normalize,
											    "do_resize": self.do_resize,
											    "size": self.size,
											}

@require_torch
@require_vision
class     lowerCamelCase  (      _lowerCAmelCase   ,						unittest.TestCase    ):






					'''simple docstring'''





					_A		:      Tuple						=					ViTImageProcessor if is_vision_available() else None






					def        lowerCAmelCase_    (	self:							str						)       ->     Optional[Any]:
											snake_case_			:Dict						=      ViTImageProcessingTester(self						)






					@property
					def        lowerCAmelCase_    (	self:							Tuple						)       ->     Optional[Any]:
											return self.image_processor_tester.prepare_image_processor_dict()






					def        lowerCAmelCase_    (	self:							str						)       ->     List[str]:
											snake_case_			:List[str]						=      self.image_processing_class(**self.image_processor_dict						)
											self.assertTrue(hasattr(snake_case						,					"""image_mean"""						)						)
											self.assertTrue(hasattr(snake_case						,					"""image_std"""						)						)
											self.assertTrue(hasattr(snake_case						,					"""do_normalize"""						)						)
											self.assertTrue(hasattr(snake_case						,					"""do_resize"""						)						)
											self.assertTrue(hasattr(snake_case						,					"""size"""						)						)






					def        lowerCAmelCase_    (	self:							Dict						)       ->     Union[str, Any]:
											snake_case_			:int						=      self.image_processing_class.from_dict(self.image_processor_dict						)
											self.assertEqual(image_processor.size						,					{"""height""": 18, """width""": 18}						)

											snake_case_			:int						=      self.image_processing_class.from_dict(self.image_processor_dict						,					size=42						)
											self.assertEqual(image_processor.size						,					{"""height""": 42, """width""": 42}						)






					def        lowerCAmelCase_    (	self:							Tuple						)       ->     int:
											pass






					def        lowerCAmelCase_    (	self:							Dict						)       ->     Union[str, Any]:
											# Initialize image_processing
											snake_case_			:Any						=      self.image_processing_class(**self.image_processor_dict						)
											# create random PIL images
											snake_case_			:str						=      prepare_image_inputs(self.image_processor_tester						,					equal_resolution=snake_case						)
											for image in image_inputs:
																	self.assertIsInstance(snake_case						,					Image.Image						)

											# Test not batched input
											snake_case_			:List[str]						=      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.size["""height"""],
											        self.image_processor_tester.size["""width"""],
											    )						,					)

											# Test batched
											snake_case_			:Dict						=      image_processing(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.size["""height"""],
											        self.image_processor_tester.size["""width"""],
											    )						,					)






					def        lowerCAmelCase_    (	self:							Optional[Any]						)       ->     Tuple:
											# Initialize image_processing
											snake_case_			:int						=      self.image_processing_class(**self.image_processor_dict						)
											# create random numpy tensors
											snake_case_			:str						=      prepare_image_inputs(self.image_processor_tester						,					equal_resolution=snake_case						,					numpify=snake_case						)
											for image in image_inputs:
																	self.assertIsInstance(snake_case						,					np.ndarray						)

											# Test not batched input
											snake_case_			:Any						=      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.size["""height"""],
											        self.image_processor_tester.size["""width"""],
											    )						,					)

											# Test batched
											snake_case_			:Any						=      image_processing(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.size["""height"""],
											        self.image_processor_tester.size["""width"""],
											    )						,					)






					def        lowerCAmelCase_    (	self:							List[Any]						)       ->     Optional[int]:
											# Initialize image_processing
											snake_case_			:Union[str, Any]						=      self.image_processing_class(**self.image_processor_dict						)
											# create random PyTorch tensors
											snake_case_			:List[Any]						=      prepare_image_inputs(self.image_processor_tester						,					equal_resolution=snake_case						,					torchify=snake_case						)
											for image in image_inputs:
																	self.assertIsInstance(snake_case						,					torch.Tensor						)

											# Test not batched input
											snake_case_			:Optional[Any]						=      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.size["""height"""],
											        self.image_processor_tester.size["""width"""],
											    )						,					)

											# Test batched
											snake_case_			:List[Any]						=      image_processing(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.size["""height"""],
											        self.image_processor_tester.size["""width"""],
											    )						,					)
