




"""simple docstring"""







import os
from typing import List, Optional, Union

from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging


a      =							logging.get_logger(__name__)

a      =							{'vocab_file': 'vocab.txt'}

a      =							{
    'vocab_file': {
        'facebook/esm2_t6_8M_UR50D': 'https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/vocab.txt',
        'facebook/esm2_t12_35M_UR50D': 'https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/vocab.txt',
    },
}

a      =							{
    'facebook/esm2_t6_8M_UR50D': 1_0_2_4,
    'facebook/esm2_t12_35M_UR50D': 1_0_2_4,
}





def 							lowercase					(snake_case__   : int )     ->  int:


				'''simple docstring'''






				with open(snake_case__      ,       """r""" ) as f:
								lowerCAmelCase  =				f.read().splitlines()
								return [l.strip() for l in lines]






class       SCREAMING_SNAKE_CASE__					(					_a					):
						_a    						=						VOCAB_FILES_NAMES
						_a    						=						PRETRAINED_VOCAB_FILES_MAP
						_a    						=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_a    						=						['input_ids', 'attention_mask']


						def __init__(							self				:			int						,						lowerCAmelCase				:			Tuple						,						lowerCAmelCase				:			Optional[Any]="<unk>"						,						lowerCAmelCase				:			Union[str, Any]="<cls>"						,						lowerCAmelCase				:			List[str]="<pad>"						,						lowerCAmelCase				:			Any="<mask>"						,						lowerCAmelCase				:			Tuple="<eos>"						,						**lowerCAmelCase				:			Optional[Any]						,						):
										super().__init__(**lowerCAmelCase					)
										lowerCAmelCase  =				load_vocab_file(lowerCAmelCase					)
										lowerCAmelCase  =				dict(enumerate(self.all_tokens					)					)
										lowerCAmelCase  =				{tok: ind for ind, tok in enumerate(self.all_tokens					)}
										lowerCAmelCase  =				unk_token
										lowerCAmelCase  =				cls_token
										lowerCAmelCase  =				pad_token
										lowerCAmelCase  =				mask_token
										lowerCAmelCase  =				eos_token
										lowerCAmelCase  =				self.all_tokens
										self._create_trie(self.unique_no_split_tokens					)


						def   __lowercase (							self				:			Optional[int]						,						lowerCAmelCase				:			int					):
										return self._id_to_token.get(lowerCAmelCase						,						self.unk_token					)


						def   __lowercase (							self				:			Optional[Any]						,						lowerCAmelCase				:			str					):
										return self._token_to_id.get(lowerCAmelCase						,						self._token_to_id.get(self.unk_token					)					)


						def   __lowercase (							self				:			Union[str, Any]						,						lowerCAmelCase				:			Optional[Any]						,						**lowerCAmelCase				:			int					):
										return text.split()


						def   __lowercase (							self				:			Tuple						,						lowerCAmelCase				:			Optional[int]=False					):
										return len(self._id_to_token					)


						def   __lowercase (							self				:			Optional[Any]					):
										return {token: i for i, token in enumerate(self.all_tokens					)}


						def   __lowercase (							self				:			int						,						lowerCAmelCase				:			str					):
										return self._token_to_id.get(lowerCAmelCase						,						self._token_to_id.get(self.unk_token					)					)


						def   __lowercase (							self				:			Optional[Any]						,						lowerCAmelCase				:			int					):
										return self._id_to_token.get(lowerCAmelCase						,						self.unk_token					)


						def   __lowercase (							self				:			Tuple						,						lowerCAmelCase				:			List[int]						,						lowerCAmelCase				:			Optional[List[int]] = None					):
										lowerCAmelCase  =				[self.cls_token_id]
										lowerCAmelCase  =				[self.eos_token_id]  # No sep token in ESM vocabulary
										if token_ids_a is None:
														if self.eos_token_id is None:
																		return cls + token_ids_a
														else:
																		return cls + token_ids_a + sep
										elif self.eos_token_id is None:
														raise ValueError("""Cannot tokenize multiple sequences when EOS token is not set!"""					)
										return cls + token_ids_a + sep + token_ids_a + sep  # Multiple inputs always have an EOS token


						def   __lowercase (							self				:			Union[str, Any]						,						lowerCAmelCase				:			List						,						lowerCAmelCase				:			Optional[List] = None						,						lowerCAmelCase				:			bool = False					):
										if already_has_special_tokens:
														if token_ids_a is not None:
																		raise ValueError(
																		    """You should not supply a second sequence if the provided sequence of """
																		    """ids is already formatted with special tokens for the model."""					)

														return [1 if token in self.all_special_ids else 0 for token in token_ids_a]
										lowerCAmelCase  =				[1] + ([0] * len(lowerCAmelCase					)) + [1]
										if token_ids_a is not None:
														mask += [0] * len(lowerCAmelCase					) + [1]
										return mask


						def   __lowercase (							self				:			str						,						lowerCAmelCase				:			List[Any]						,						lowerCAmelCase				:			Union[str, Any]					):
										lowerCAmelCase  =				os.path.join(lowerCAmelCase						,						(filename_prefix + """-""" if filename_prefix else """""") + """vocab.txt"""					)
										with open(lowerCAmelCase						,						"""w"""					) as f:
														f.write("""\n""".join(self.all_tokens					)					)
										return (vocab_file,)


						@property
						def   __lowercase (							self				:			List[str]					):
										return self.get_vocab_size(with_added_tokens=lowerCAmelCase					)




						def   __lowercase (							self				:			Any						,						lowerCAmelCase				:			Union[List[str], List[AddedToken]]						,						lowerCAmelCase				:			bool = False					):
										return super()._add_tokens(lowerCAmelCase						,						special_tokens=lowerCAmelCase					)











"""simple docstring"""







import argparse
import json

import numpy
import torch

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()





def 							lowercase					(snake_case__   : int      ,       snake_case__   : Union[str, Any] )     ->  List[Any]:


				'''simple docstring'''






				lowerCAmelCase  =				torch.load(snake_case__      ,       map_location="""cpu""" )

				lowerCAmelCase  =				chkpt["""model"""]

				# We have the base model one level deeper than the original XLM repository
				lowerCAmelCase  =				{}
				for k, v in state_dict.items():
								if "pred_layer" in k:
												lowerCAmelCase  =				v
								else:
												lowerCAmelCase  =				v

				lowerCAmelCase  =				chkpt["""params"""]
				lowerCAmelCase  =				{n: v for n, v in config.items() if not isinstance(snake_case__      ,       (torch.FloatTensor, numpy.ndarray) )}

				lowerCAmelCase  =				chkpt["""dico_word2id"""]
				lowerCAmelCase  =				{s + """</w>""" if s.find("""@@""" ) == -1 and i > 13 else s.replace("""@@"""      ,       """""" ): i for s, i in vocab.items()}

				# Save pytorch-model
				lowerCAmelCase  =				pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
				lowerCAmelCase  =				pytorch_dump_folder_path + """/""" + CONFIG_NAME
				lowerCAmelCase  =				pytorch_dump_folder_path + """/""" + VOCAB_FILES_NAMES["""vocab_file"""]

				print(f'''Save PyTorch model to {pytorch_weights_dump_path}''' )
				torch.save(snake_case__      ,       snake_case__ )

				print(f'''Save configuration file to {pytorch_config_dump_path}''' )
				with open(snake_case__      ,       """w"""      ,       encoding="""utf-8""" ) as f:
								f.write(json.dumps(snake_case__      ,       indent=2 ) + """\n""" )

				print(f'''Save vocab file to {pytorch_config_dump_path}''' )
				with open(snake_case__      ,       """w"""      ,       encoding="""utf-8""" ) as f:
								f.write(json.dumps(snake_case__      ,       indent=2 ) + """\n""" )


if __name__ == "__main__":
		a      =							argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--xlm_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
		)
		parser.add_argument(
		    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
		)
		a      =							parser.parse_args()
		convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)





