"""simple docstring"""







import argparse

import torch

from transformers import MraConfig, MraForMaskedLM
def     __UpperCAmelCase							(       UpperCAmelCase_       :							List[str]    )				->     Any:

	'''simple docstring'''





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

	return orig_key
def     __UpperCAmelCase							(       UpperCAmelCase_       :							str     ,				UpperCAmelCase_       :							List[Any]    )				->     List[str]:

	'''simple docstring'''





	for key in orig_state_dict.copy().keys():
		__snake_case     :				Optional[int] 						=					orig_state_dict.pop(UpperCAmelCase__    )

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

	__snake_case     :				Union[str, Any] 						=					orig_state_dict['cls.predictions.decoder.bias']
	__snake_case     :				Optional[int] 						=					torch.arange(UpperCAmelCase__    ).expand((1, -1)    ) + 2

	return orig_state_dict
def     __UpperCAmelCase							(       UpperCAmelCase_       :							Union[str, Any]     ,				UpperCAmelCase_       :							Union[str, Any]     ,				UpperCAmelCase_       :							Optional[int]    )				->     Optional[Any]:

	'''simple docstring'''





	__snake_case     :				str 						=					torch.load(UpperCAmelCase__     ,				map_location='cpu'    )['model_state_dict']
	__snake_case     :				Union[str, Any] 						=					MraConfig.from_json_file(UpperCAmelCase__    )
	__snake_case     :				Any 						=					MraForMaskedLM(UpperCAmelCase__    )

	__snake_case     :				Optional[int] 						=					convert_checkpoint_helper(config.max_position_embeddings     ,				UpperCAmelCase__    )

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

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


if __name__ == "__main__":
	_a	:	List[Any]=					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	:	List[str]=					parser.parse_args()
	convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)






"""simple docstring"""







from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


UpperCamelCase_							:							Optional[int]				 =		logging.get_logger(__name__)


if is_vision_available():
						import PIL


class       _lowercase		( __lowerCamelCase	):
				lowerCamelCase__							=							['pixel_values']

				def __init__(  self							:     str							,     _lowercase							:     bool = True							,     _lowercase							:     Dict[str, int] = None							,     _lowercase							:     PILImageResampling = PILImageResampling.BILINEAR							,     _lowercase							:     bool = True							,     _lowercase							:     bool = False							,     _lowercase							:     bool = True							,     _lowercase							:     bool = True							,     _lowercase							:     Union[int, float] = 1 / 2_5_5							,     _lowercase							:     bool = True							,     _lowercase							:     Optional[Union[float, List[float]]] = None							,     _lowercase							:     Optional[Union[float, List[float]]] = None							,     **_lowercase							:     List[str]							,     ):


										'''simple docstring'''
										super().__init__(**_lowercase  )

										UpperCAmelCase__       						=		size if size is not None else {"""height""": 2_5_6_0, """width""": 1_9_2_0}
										if isinstance(_lowercase							,     (tuple, list)  ):
																# The previous feature extractor size parameter was in (width, height) format
																UpperCAmelCase__       						=		size[::-1]
										UpperCAmelCase__       						=		get_size_dict(_lowercase  )

										UpperCAmelCase__       						=		do_resize
										UpperCAmelCase__       						=		size
										UpperCAmelCase__       						=		resample
										UpperCAmelCase__       						=		do_thumbnail
										UpperCAmelCase__       						=		do_align_long_axis
										UpperCAmelCase__       						=		do_pad
										UpperCAmelCase__       						=		do_rescale
										UpperCAmelCase__       						=		rescale_factor
										UpperCAmelCase__       						=		do_normalize
										UpperCAmelCase__       						=		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
										UpperCAmelCase__       						=		image_std if image_std is not None else IMAGENET_STANDARD_STD

				def 							A			(  self							:     str							,     _lowercase							:     np.ndarray							,     _lowercase							:     Dict[str, int]							,     _lowercase							:     Optional[Union[str, ChannelDimension]] = None  ):


										'''simple docstring'''
										UpperCAmelCase__						,   UpperCAmelCase__       						=		get_image_size(_lowercase  )
										UpperCAmelCase__						,   UpperCAmelCase__       						=		size["""height"""], size["""width"""]

										if (output_width < output_height and input_width > input_height) or (
										    output_width > output_height and input_width < input_height
										):
																UpperCAmelCase__       						=		np.rotaa(_lowercase							,     3  )

										if data_format is not None:
																UpperCAmelCase__       						=		to_channel_dimension_format(_lowercase							,     _lowercase  )

										return image

				def 							A			(  self							:     Tuple							,     _lowercase							:     np.ndarray							,     _lowercase							:     Dict[str, int]							,     _lowercase							:     bool = False							,     _lowercase							:     Optional[Union[str, ChannelDimension]] = None							,     ):


										'''simple docstring'''
										UpperCAmelCase__						,   UpperCAmelCase__       						=		size["""height"""], size["""width"""]
										UpperCAmelCase__						,   UpperCAmelCase__       						=		get_image_size(_lowercase  )

										UpperCAmelCase__       						=		output_width - input_width
										UpperCAmelCase__       						=		output_height - input_height

										if random_padding:
																UpperCAmelCase__       						=		np.random.randint(low=0							,     high=delta_height + 1  )
																UpperCAmelCase__       						=		np.random.randint(low=0							,     high=delta_width + 1  )
										else:
																UpperCAmelCase__       						=		delta_height // 2
																UpperCAmelCase__       						=		delta_width // 2

										UpperCAmelCase__       						=		delta_height - pad_top
										UpperCAmelCase__       						=		delta_width - pad_left

										UpperCAmelCase__       						=		((pad_top, pad_bottom), (pad_left, pad_right))
										return pad(_lowercase							,     _lowercase							,     data_format=_lowercase  )

				def 							A			(  self							:     Union[str, Any]							,     *_lowercase							:     List[str]							,     **_lowercase							:     int  ):


										'''simple docstring'''
										logger.info("""pad is deprecated and will be removed in version 4.27. Please use pad_image instead."""  )
										return self.pad_image(*_lowercase							,     **_lowercase  )

				def 							A			(  self							:     Optional[int]							,     _lowercase							:     np.ndarray							,     _lowercase							:     Dict[str, int]							,     _lowercase							:     PILImageResampling = PILImageResampling.BICUBIC							,     _lowercase							:     Optional[Union[str, ChannelDimension]] = None							,     **_lowercase							:     Dict							,     ):


										'''simple docstring'''
										UpperCAmelCase__						,   UpperCAmelCase__       						=		get_image_size(_lowercase  )
										UpperCAmelCase__						,   UpperCAmelCase__       						=		size["""height"""], size["""width"""]

										# We always resize to the smallest of either the input or output size.
										UpperCAmelCase__       						=		min(_lowercase							,     _lowercase  )
										UpperCAmelCase__       						=		min(_lowercase							,     _lowercase  )

										if height == input_height and width == input_width:
																return image

										if input_height > input_width:
																UpperCAmelCase__       						=		int(input_width * height / input_height  )
										elif input_width > input_height:
																UpperCAmelCase__       						=		int(input_height * width / input_width  )

										return resize(
										    _lowercase							,     size=(height, width)							,     resample=_lowercase							,     reducing_gap=2.0							,     data_format=_lowercase							,     **_lowercase  )

				def 							A			(  self							:     List[str]							,     _lowercase							:     np.ndarray							,     _lowercase							:     Dict[str, int]							,     _lowercase							:     PILImageResampling = PILImageResampling.BICUBIC							,     _lowercase							:     Optional[Union[str, ChannelDimension]] = None							,     **_lowercase							:     Optional[Any]							,     ):


										'''simple docstring'''
										UpperCAmelCase__       						=		get_size_dict(_lowercase  )
										UpperCAmelCase__       						=		min(size["""height"""]							,     size["""width"""]  )
										UpperCAmelCase__       						=		get_resize_output_image_size(_lowercase							,     size=_lowercase							,     default_to_square=_lowercase  )
										UpperCAmelCase__       						=		resize(_lowercase							,     size=_lowercase							,     resample=_lowercase							,     data_format=_lowercase							,     **_lowercase  )
										return resized_image

				def 							A			(  self							:     int							,     _lowercase							:     np.ndarray							,     _lowercase							:     Union[int, float]							,     _lowercase							:     Optional[Union[str, ChannelDimension]] = None							,     **_lowercase							:     Union[str, Any]							,     ):


										'''simple docstring'''
										return rescale(_lowercase							,     scale=_lowercase							,     data_format=_lowercase							,     **_lowercase  )

				def 							A			(  self							:     Optional[int]							,     _lowercase							:     np.ndarray							,     _lowercase							:     Union[float, List[float]]							,     _lowercase							:     Union[float, List[float]]							,     _lowercase							:     Optional[Union[str, ChannelDimension]] = None							,     **_lowercase							:     Tuple							,     ):


										'''simple docstring'''
										return normalize(_lowercase							,     mean=_lowercase							,     std=_lowercase							,     data_format=_lowercase							,     **_lowercase  )

				def 							A			(  self							:     List[str]							,     _lowercase							:     ImageInput							,     _lowercase							:     bool = None							,     _lowercase							:     Dict[str, int] = None							,     _lowercase							:     PILImageResampling = None							,     _lowercase							:     bool = None							,     _lowercase							:     bool = None							,     _lowercase							:     bool = None							,     _lowercase							:     bool = False							,     _lowercase							:     bool = None							,     _lowercase							:     float = None							,     _lowercase							:     bool = None							,     _lowercase							:     Optional[Union[float, List[float]]] = None							,     _lowercase							:     Optional[Union[float, List[float]]] = None							,     _lowercase							:     Optional[Union[str, TensorType]] = None							,     _lowercase							:     Optional[ChannelDimension] = ChannelDimension.FIRST							,     **_lowercase							:     Dict							,     ):


										'''simple docstring'''
										UpperCAmelCase__       						=		do_resize if do_resize is not None else self.do_resize
										UpperCAmelCase__       						=		size if size is not None else self.size
										if isinstance(_lowercase							,     (tuple, list)  ):
																# Previous feature extractor had size in (width, height) format
																UpperCAmelCase__       						=		size[::-1]
										UpperCAmelCase__       						=		get_size_dict(_lowercase  )
										UpperCAmelCase__       						=		resample if resample is not None else self.resample
										UpperCAmelCase__       						=		do_thumbnail if do_thumbnail is not None else self.do_thumbnail
										UpperCAmelCase__       						=		do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
										UpperCAmelCase__       						=		do_pad if do_pad is not None else self.do_pad
										UpperCAmelCase__       						=		do_rescale if do_rescale is not None else self.do_rescale
										UpperCAmelCase__       						=		rescale_factor if rescale_factor is not None else self.rescale_factor
										UpperCAmelCase__       						=		do_normalize if do_normalize is not None else self.do_normalize
										UpperCAmelCase__       						=		image_mean if image_mean is not None else self.image_mean
										UpperCAmelCase__       						=		image_std if image_std is not None else self.image_std

										UpperCAmelCase__       						=		make_list_of_images(_lowercase  )

										if not valid_images(_lowercase  ):
																raise ValueError(
																    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
																    """torch.Tensor, tf.Tensor or jax.ndarray."""  )

										if do_resize and size is None:
																raise ValueError("""Size must be specified if do_resize is True."""  )

										if do_rescale and rescale_factor is None:
																raise ValueError("""Rescale factor must be specified if do_rescale is True."""  )

										if do_pad and size is None:
																raise ValueError("""Size must be specified if do_pad is True."""  )

										if do_normalize and (image_mean is None or image_std is None):
																raise ValueError("""Image mean and std must be specified if do_normalize is True."""  )

										# All transformations expect numpy arrays.
										UpperCAmelCase__       						=		[to_numpy_array(_lowercase  ) for image in images]

										if do_align_long_axis:
																UpperCAmelCase__       						=		[self.align_long_axis(_lowercase							,     size=_lowercase  ) for image in images]

										if do_resize:
																UpperCAmelCase__       						=		[self.resize(image=_lowercase							,     size=_lowercase							,     resample=_lowercase  ) for image in images]

										if do_thumbnail:
																UpperCAmelCase__       						=		[self.thumbnail(image=_lowercase							,     size=_lowercase  ) for image in images]

										if do_pad:
																UpperCAmelCase__       						=		[self.pad_image(image=_lowercase							,     size=_lowercase							,     random_padding=_lowercase  ) for image in images]

										if do_rescale:
																UpperCAmelCase__       						=		[self.rescale(image=_lowercase							,     scale=_lowercase  ) for image in images]

										if do_normalize:
																UpperCAmelCase__       						=		[self.normalize(image=_lowercase							,     mean=_lowercase							,     std=_lowercase  ) for image in images]

										UpperCAmelCase__       						=		[to_channel_dimension_format(_lowercase							,     _lowercase  ) for image in images]

										UpperCAmelCase__       						=		{"""pixel_values""": images}
										return BatchFeature(data=_lowercase							,     tensor_type=_lowercase  )

