




'''simple docstring'''

import argparse

import torch

from transformers import MraConfig, MraForMaskedLM




def    snake_case__     (  a	)			->       int:





			'''simple docstring'''






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

			return orig_key




def    snake_case__     (  a       ,      a	)			->       Optional[int]:





			'''simple docstring'''






			for key in orig_state_dict.copy().keys():
						snake_case__          =						orig_state_dict.pop(a	)

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

			snake_case__          =						orig_state_dict["""cls.predictions.decoder.bias"""]
			snake_case__          =						torch.arange(a	).expand((1, -1)	) + 2

			return orig_state_dict




def    snake_case__     (  a       ,      a       ,      a	)			->       Dict:





			'''simple docstring'''






			snake_case__          =						torch.load(a       ,      map_location="""cpu"""	)["""model_state_dict"""]
			snake_case__          =						MraConfig.from_json_file(a	)
			snake_case__          =						MraForMaskedLM(a	)

			snake_case__          =						convert_checkpoint_helper(config.max_position_embeddings       ,      a	)

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

			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 warnings

from ...utils import logging
from .image_processing_levit import LevitImageProcessor


a__         =     logging.get_logger(__name__)





class __magic_name__(						__lowerCAmelCase      ):






						def __init__(   self						:						Optional[Any]					,   *__UpperCamelCase						:						Union[str, Any]					,   **__UpperCamelCase						:						Dict       ):



									'''simple docstring'''






									warnings.warn(
									    """The class LevitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
									    """ use LevitImageProcessor instead."""					,   __UpperCamelCase					,   )
									super().__init__(*__UpperCamelCase					,   **__UpperCamelCase       )