





import argparse

import torch

from transformers import MraConfig, MraForMaskedLM


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

		return orig_key


def        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) ->  List[Any]:
		for key in orig_state_dict.copy().keys():
				lowerCamelCase :			Tuple    		=orig_state_dict.pop(SCREAMING_SNAKE_CASE_		)

				if ("pooler" in key) or ("sen_class" in key):
						continue
				else:
						lowerCamelCase :			Optional[Any]    		=val

		lowerCamelCase :			List[str]    		=orig_state_dict['''cls.predictions.decoder.bias''']
		lowerCamelCase :			int    		=torch.arange(SCREAMING_SNAKE_CASE_		).expand((1, -1)		) + 2

		return orig_state_dict


def        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) ->  str:
		lowerCamelCase :			List[Any]    		=torch.load(SCREAMING_SNAKE_CASE_				,							map_location='''cpu'''		)['''model_state_dict''']
		lowerCamelCase :			Tuple    		=MraConfig.from_json_file(SCREAMING_SNAKE_CASE_		)
		lowerCamelCase :			Dict    		=MraForMaskedLM(SCREAMING_SNAKE_CASE_		)

		lowerCamelCase :			Dict    		=convert_checkpoint_helper(config.max_position_embeddings				,							SCREAMING_SNAKE_CASE_		)

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

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


if __name__ == "__main__":
				snake_case_				=      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.'''
				)
				snake_case_				=      parser.parse_args()
				convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)








import sys

from transformers import AutoTokenizer


snake_case_				=      sys.argv[1]
snake_case_				=      sys.argv[2]
snake_case_				=      int(sys.argv[3])

snake_case_				=      0

snake_case_				=      AutoTokenizer.from_pretrained(model_name_or_path)
max_len -= tokenizer.num_special_tokens_to_add()

with open(dataset, '''rt''') as f_p:
				for line in f_p:
								snake_case_				=      line.rstrip()

								if not line:
												print(line)
												snake_case_				=      0
												continue

								snake_case_				=      line.split()[0]

								snake_case_				=      len(tokenizer.tokenize(token))

								# Token contains strange control characters like \x96 or \x95
								# Just filter out the complete line
								if current_subwords_len == 0:
												continue

								if (subword_len_counter + current_subwords_len) > max_len:
												print('''''')
												print(line)
												snake_case_				=      current_subwords_len
												continue

								subword_len_counter += current_subwords_len

								print(line)

