





import os
from typing import List, Optional, Union

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


lowerCAmelCase							:       List[str]					 =	logging.get_logger(__name__)

lowerCAmelCase							:       int					 =	{"""vocab_file""": """vocab.txt"""}

lowerCAmelCase							:       Tuple					 =	{
    """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""",
    },
}

lowerCAmelCase							:       Any					 =	{
    """facebook/esm2_t6_8M_UR50D""": 1024,
    """facebook/esm2_t12_35M_UR50D""": 1024,
}






def 		lowerCAmelCase  (	UpperCamelCase__   :   Union[str, Any]					)      ->    Union[str, Any]:

						"""simple docstring"""





						with open(UpperCamelCase__       ,			'''r'''					) as f:
												__SCREAMING_SNAKE_CASE:				List[Any]           =   f.read().splitlines()
												return [l.strip() for l in lines]





class 				a			(      __lowercase			):
				SCREAMING_SNAKE_CASE__     :   Dict		      =     VOCAB_FILES_NAMES
				SCREAMING_SNAKE_CASE__     :   Tuple		      =     PRETRAINED_VOCAB_FILES_MAP
				SCREAMING_SNAKE_CASE__     :   Tuple		      =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				SCREAMING_SNAKE_CASE__     :   Optional[Any]		      =     ['''input_ids''', '''attention_mask''']






				def __init__(  self			, _lowerCAmelCase			, _lowerCAmelCase="<unk>"			, _lowerCAmelCase="<cls>"			, _lowerCAmelCase="<pad>"			, _lowerCAmelCase="<mask>"			, _lowerCAmelCase="<eos>"			, **_lowerCAmelCase			, ):

										"""simple docstring"""




										super().__init__(**_lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				str           =   load_vocab_file(_lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				Tuple           =   dict(enumerate(self.all_tokens					)					)
										__SCREAMING_SNAKE_CASE:				str           =   {tok: ind for ind, tok in enumerate(self.all_tokens					)}
										__SCREAMING_SNAKE_CASE:				Any           =   unk_token
										__SCREAMING_SNAKE_CASE:				int           =   cls_token
										__SCREAMING_SNAKE_CASE:				Dict           =   pad_token
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   mask_token
										__SCREAMING_SNAKE_CASE:				str           =   eos_token
										__SCREAMING_SNAKE_CASE:				Dict           =   self.all_tokens
										self._create_trie(self.unique_no_split_tokens					)






				def 			snake_case_    (  self			, _lowerCAmelCase					):

										"""simple docstring"""




										return self._id_to_token.get(_lowerCAmelCase			, self.unk_token					)






				def 			snake_case_    (  self			, _lowerCAmelCase					):

										"""simple docstring"""




										return self._token_to_id.get(_lowerCAmelCase			, self._token_to_id.get(self.unk_token					)					)






				def 			snake_case_    (  self			, _lowerCAmelCase			, **_lowerCAmelCase					):

										"""simple docstring"""




										return text.split()






				def 			snake_case_    (  self			, _lowerCAmelCase=False					):

										"""simple docstring"""




										return len(self._id_to_token					)






				def 			snake_case_    (  self					):

										"""simple docstring"""




										return {token: i for i, token in enumerate(self.all_tokens					)}






				def 			snake_case_    (  self			, _lowerCAmelCase					):

										"""simple docstring"""




										return self._token_to_id.get(_lowerCAmelCase			, self._token_to_id.get(self.unk_token					)					)






				def 			snake_case_    (  self			, _lowerCAmelCase					):

										"""simple docstring"""




										return self._id_to_token.get(_lowerCAmelCase			, self.unk_token					)






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase = None					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   [self.cls_token_id]
										__SCREAMING_SNAKE_CASE:				List[str]           =   [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 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase = None			, _lowerCAmelCase = False					):

										"""simple docstring"""




										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]
										__SCREAMING_SNAKE_CASE:				Tuple           =   [1] + ([0] * len(_lowerCAmelCase					)) + [1]
										if token_ids_a is not None:
																mask += [0] * len(_lowerCAmelCase					) + [1]
										return mask






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   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 			snake_case_    (  self					):

										"""simple docstring"""




										return self.get_vocab_size(with_added_tokens=_lowerCAmelCase					)






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase = False					):

										"""simple docstring"""




										return super()._add_tokens(_lowerCAmelCase			, special_tokens=_lowerCAmelCase					)










import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM






def 		lowerCAmelCase  (	UpperCamelCase__   :   Union[str, Any]					)      ->    int:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				List[Any]           =   [
						    '''decoder.version''',
						    '''decoder.output_projection.weight''',
						    '''_float_tensor''',
						    '''decoder.embed_positions._float_tensor''',
						]
						for k in ignore_keys:
												state_dict.pop(UpperCamelCase__       ,			UpperCamelCase__					)






def 		lowerCAmelCase  (	UpperCamelCase__   :   str					)      ->    int:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE:				str           =   emb.weight.shape
						__SCREAMING_SNAKE_CASE:				List[str]           =   nn.Linear(UpperCamelCase__       ,			UpperCamelCase__       ,			bias=UpperCamelCase__					)
						__SCREAMING_SNAKE_CASE:				Tuple           =   emb.weight.data
						return lin_layer






def 		lowerCAmelCase  (	UpperCamelCase__   :   Optional[int]					)      ->    Optional[Any]:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				int           =   torch.load(UpperCamelCase__       ,			map_location='''cpu'''					)
						__SCREAMING_SNAKE_CASE:				Optional[int]           =   Namespace(**checkpoint['''cfg''']['''model''']					)
						__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   checkpoint['''model''']
						remove_ignore_keys_(UpperCamelCase__					)
						__SCREAMING_SNAKE_CASE:				int           =   state_dict['''decoder.embed_tokens.weight'''].shape[0]

						__SCREAMING_SNAKE_CASE:				List[Any]           =   {key.replace('''decoder'''       ,			'''model'''					): val for key, val in state_dict.items()}

						__SCREAMING_SNAKE_CASE:				Dict           =   XGLMConfig(
						    vocab_size=UpperCamelCase__       ,			max_position_embeddings=args.max_target_positions       ,			num_layers=args.decoder_layers       ,			attention_heads=args.decoder_attention_heads       ,			ffn_dim=args.decoder_ffn_embed_dim       ,			d_model=args.decoder_embed_dim       ,			layerdrop=args.decoder_layerdrop       ,			dropout=args.dropout       ,			attention_dropout=args.attention_dropout       ,			activation_dropout=args.activation_dropout       ,			activation_function='''gelu'''       ,			scale_embedding=not args.no_scale_embedding       ,			tie_word_embeddings=args.share_decoder_input_output_embed       ,			)

						__SCREAMING_SNAKE_CASE:				int           =   XGLMForCausalLM(UpperCamelCase__					)
						__SCREAMING_SNAKE_CASE:				Optional[Any]           =   model.load_state_dict(UpperCamelCase__       ,			strict=UpperCamelCase__					)
						print(UpperCamelCase__					)
						__SCREAMING_SNAKE_CASE:				List[str]           =   make_linear_from_emb(model.model.embed_tokens					)

						return model


if __name__ == "__main__":
							lowerCAmelCase							:       Union[str, Any]					 =	argparse.ArgumentParser()
							# Required parameters
							parser.add_argument("""fairseq_path""", type=str, help="""path to a model.pt on local filesystem.""")
							parser.add_argument("""pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
							lowerCAmelCase							:       List[str]					 =	parser.parse_args()
							lowerCAmelCase							:       Optional[int]					 =	convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
							model.save_pretrained(args.pytorch_dump_folder_path)



