




import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging


_A     =			logging.get_logger(__name__)

_A     =			'''▁'''

_A     =			{
    '''vocab_file''': '''vocab.json''',
    '''spm_file''': '''sentencepiece.bpe.model''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

_A     =			{
    '''vocab_file''': {
        '''facebook/m2m100_418M''': '''https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json''',
        '''facebook/m2m100_1.2B''': '''https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json''',
    },
    '''spm_file''': {
        '''facebook/m2m100_418M''': '''https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model''',
        '''facebook/m2m100_1.2B''': '''https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model''',
    },
    '''tokenizer_config_file''': {
        '''facebook/m2m100_418M''': '''https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json''',
        '''facebook/m2m100_1.2B''': '''https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json''',
    },
}

_A     =			{
    '''facebook/m2m100_418M''': 1_024,
}

# fmt: off
_A     =			{
    '''m2m100''': ['''af''', '''am''', '''ar''', '''ast''', '''az''', '''ba''', '''be''', '''bg''', '''bn''', '''br''', '''bs''', '''ca''', '''ceb''', '''cs''', '''cy''', '''da''', '''de''', '''el''', '''en''', '''es''', '''et''', '''fa''', '''ff''', '''fi''', '''fr''', '''fy''', '''ga''', '''gd''', '''gl''', '''gu''', '''ha''', '''he''', '''hi''', '''hr''', '''ht''', '''hu''', '''hy''', '''id''', '''ig''', '''ilo''', '''is''', '''it''', '''ja''', '''jv''', '''ka''', '''kk''', '''km''', '''kn''', '''ko''', '''lb''', '''lg''', '''ln''', '''lo''', '''lt''', '''lv''', '''mg''', '''mk''', '''ml''', '''mn''', '''mr''', '''ms''', '''my''', '''ne''', '''nl''', '''no''', '''ns''', '''oc''', '''or''', '''pa''', '''pl''', '''ps''', '''pt''', '''ro''', '''ru''', '''sd''', '''si''', '''sk''', '''sl''', '''so''', '''sq''', '''sr''', '''ss''', '''su''', '''sv''', '''sw''', '''ta''', '''th''', '''tl''', '''tn''', '''tr''', '''uk''', '''ur''', '''uz''', '''vi''', '''wo''', '''xh''', '''yi''', '''yo''', '''zh''', '''zu'''],
    '''wmt21''': ['''en''', '''ha''', '''is''', '''ja''', '''cs''', '''ru''', '''zh''', '''de''']
}


class  A		(  __UpperCAmelCase     ):
		__snake_case									=   VOCAB_FILES_NAMES
		__snake_case									=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__snake_case									=   PRETRAINED_VOCAB_FILES_MAP
		__snake_case									=   ['input_ids', 'attention_mask']

		__snake_case									=   []
		__snake_case									=   []



		def __init__(     self,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__=None,	UpperCamelCase__=None,	UpperCamelCase__="<s>",	UpperCamelCase__="</s>",	UpperCamelCase__="</s>",	UpperCamelCase__="<pad>",	UpperCamelCase__="<unk>",	UpperCamelCase__="m2m100",	UpperCamelCase__ = None,	UpperCamelCase__=8,	**UpperCamelCase__,	):







								"""simple docstring"""



								lowerCAmelCase_      			=  {} if sp_model_kwargs is None else sp_model_kwargs

								lowerCAmelCase_      			=  language_codes
								lowerCAmelCase_      			=  FAIRSEQ_LANGUAGE_CODES[language_codes]
								lowerCAmelCase_      			=  {lang_code: f"__{lang_code}__" for lang_code in fairseq_language_code}

								lowerCAmelCase_      			=  kwargs.get('''additional_special_tokens''',	[]   )
								kwargs["additional_special_tokens"] += [
								    self.get_lang_token(UpperCamelCase__   )
								    for lang_code in fairseq_language_code
								    if self.get_lang_token(UpperCamelCase__   ) not in kwargs["additional_special_tokens"]
								]

								super().__init__(
								    src_lang=UpperCamelCase__,	tgt_lang=UpperCamelCase__,	bos_token=UpperCamelCase__,	eos_token=UpperCamelCase__,	sep_token=UpperCamelCase__,	unk_token=UpperCamelCase__,	pad_token=UpperCamelCase__,	language_codes=UpperCamelCase__,	sp_model_kwargs=self.sp_model_kwargs,	num_madeup_words=UpperCamelCase__,	**UpperCamelCase__,	)

								lowerCAmelCase_      			=  vocab_file
								lowerCAmelCase_      			=  load_json(UpperCamelCase__   )
								lowerCAmelCase_      			=  {v: k for k, v in self.encoder.items()}
								lowerCAmelCase_      			=  spm_file
								lowerCAmelCase_      			=  load_spm(UpperCamelCase__,	self.sp_model_kwargs   )

								lowerCAmelCase_      			=  len(self.encoder   )

								lowerCAmelCase_      			=  {
								    self.get_lang_token(UpperCamelCase__   ): self.encoder_size + i for i, lang_code in enumerate(UpperCamelCase__   )
								}
								lowerCAmelCase_      			=  {lang_code: self.encoder_size + i for i, lang_code in enumerate(UpperCamelCase__   )}
								lowerCAmelCase_      			=  {v: k for k, v in self.lang_token_to_id.items()}

								lowerCAmelCase_      			=  src_lang if src_lang is not None else '''en'''
								lowerCAmelCase_      			=  tgt_lang
								lowerCAmelCase_      			=  self.get_lang_id(self._src_lang   )
								self.set_src_lang_special_tokens(self._src_lang   )

								lowerCAmelCase_      			=  num_madeup_words



		@property
		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								return len(self.encoder   ) + len(self.lang_token_to_id   )



		@property
		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								return self._src_lang



		@src_lang.setter
		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  new_src_lang
								self.set_src_lang_special_tokens(self._src_lang   )



		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__   ):







								"""simple docstring"""



								return self.sp_model.encode(UpperCamelCase__,	out_type=UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__   ):







								"""simple docstring"""



								if token in self.lang_token_to_id:
														return self.lang_token_to_id[token]
								return self.encoder.get(UpperCamelCase__,	self.encoder[self.unk_token]   )



		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__   ):







								"""simple docstring"""



								if index in self.id_to_lang_token:
														return self.id_to_lang_token[index]
								return self.decoder.get(UpperCamelCase__,	self.unk_token   )



		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  []
								lowerCAmelCase_      			=  ''''''
								for token in tokens:
														# make sure that special tokens are not decoded using sentencepiece model
														if token in self.all_special_tokens:
																				out_string += self.sp_model.decode(UpperCamelCase__   ) + token
																				lowerCAmelCase_      			=  []
														else:
																				current_sub_tokens.append(UpperCamelCase__   )
								out_string += self.sp_model.decode(UpperCamelCase__   )
								return out_string.strip()



		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__,	UpperCamelCase__ = None,	UpperCamelCase__ = False   ):







								"""simple docstring"""



								if already_has_special_tokens:
														return super().get_special_tokens_mask(
														    token_ids_a=UpperCamelCase__,	token_ids_a=UpperCamelCase__,	already_has_special_tokens=UpperCamelCase__   )

								lowerCAmelCase_      			=  [1] * len(self.prefix_tokens   )
								lowerCAmelCase_      			=  [1] * len(self.suffix_tokens   )
								if token_ids_a is None:
														return prefix_ones + ([0] * len(UpperCamelCase__   )) + suffix_ones
								return prefix_ones + ([0] * len(UpperCamelCase__   )) + ([0] * len(UpperCamelCase__   )) + suffix_ones



		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__,	UpperCamelCase__ = None   ):







								"""simple docstring"""



								if token_ids_a is None:
														return self.prefix_tokens + token_ids_a + self.suffix_tokens
								# We don't expect to process pairs, but leave the pair logic for API consistency
								return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  {self.convert_ids_to_tokens(UpperCamelCase__   ): i for i in range(self.vocab_size   )}
								vocab.update(self.added_tokens_encoder   )
								return vocab



		def __getstate__(     self   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.__dict__.copy()
								lowerCAmelCase_      			=  None
								return state



		def __setstate__(     self,	UpperCamelCase__   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  d

								# for backward compatibility
								if not hasattr(self,	'''sp_model_kwargs'''   ):
														lowerCAmelCase_      			=  {}

								lowerCAmelCase_      			=  load_spm(self.spm_file,	self.sp_model_kwargs   )



		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__,	UpperCamelCase__ = None   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  Path(UpperCamelCase__   )
								if not save_dir.is_dir():
														raise OSError(f"{save_directory} should be a directory"   )
								lowerCAmelCase_      			=  save_dir / (
								    (filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''vocab_file''']
								)
								lowerCAmelCase_      			=  save_dir / (
								    (filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''spm_file''']
								)

								save_json(self.encoder,	UpperCamelCase__   )

								if os.path.abspath(self.spm_file   ) != os.path.abspath(UpperCamelCase__   ) and os.path.isfile(self.spm_file   ):
														copyfile(self.spm_file,	UpperCamelCase__   )
								elif not os.path.isfile(self.spm_file   ):
														with open(UpperCamelCase__,	'''wb'''   ) as fi:
																				lowerCAmelCase_      			=  self.sp_model.serialized_model_proto()
																				fi.write(UpperCamelCase__   )

								return (str(UpperCamelCase__   ), str(UpperCamelCase__   ))



		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__,	UpperCamelCase__ = "en",	UpperCamelCase__ = None,	UpperCamelCase__ = "ro",	**UpperCamelCase__,	):







								"""simple docstring"""



								lowerCAmelCase_      			=  src_lang
								lowerCAmelCase_      			=  tgt_lang
								self.set_src_lang_special_tokens(self.src_lang   )
								return super().prepare_seqaseq_batch(UpperCamelCase__,	UpperCamelCase__,	**UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__,	**UpperCamelCase__   ):







								"""simple docstring"""



								if src_lang is None or tgt_lang is None:
														raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model'''   )
								lowerCAmelCase_      			=  src_lang
								lowerCAmelCase_      			=  self(UpperCamelCase__,	add_special_tokens=UpperCamelCase__,	**UpperCamelCase__   )
								lowerCAmelCase_      			=  self.get_lang_id(UpperCamelCase__   )
								lowerCAmelCase_      			=  tgt_lang_id
								return inputs



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								self.set_src_lang_special_tokens(self.src_lang   )



		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								self.set_tgt_lang_special_tokens(self.tgt_lang   )



		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.get_lang_token(UpperCamelCase__   )
								lowerCAmelCase_      			=  self.lang_token_to_id[lang_token]
								lowerCAmelCase_      			=  [self.cur_lang_id]
								lowerCAmelCase_      			=  [self.eos_token_id]



		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.get_lang_token(UpperCamelCase__   )
								lowerCAmelCase_      			=  self.lang_token_to_id[lang_token]
								lowerCAmelCase_      			=  [self.cur_lang_id]
								lowerCAmelCase_      			=  [self.eos_token_id]



		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__   ):







								"""simple docstring"""



								return self.lang_code_to_token[lang]







		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  self.get_lang_token(UpperCamelCase__   )
								return self.lang_token_to_id[lang_token]







def __UpperCamelCase			(       _A ,  _A  ):
						lowerCAmelCase_      			=  sentencepiece.SentencePieceProcessor(**_A  )
						spm.Load(str(_A  )  )
						return spm





def __UpperCamelCase			(       _A  ):
						with open(_A ,  '''r'''  ) as f:
												return json.load(_A  )






def __UpperCamelCase			(       _A ,  _A  ):
						with open(_A ,  '''w'''  ) as f:
												json.dump(_A ,  _A ,  indent=2  )










import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM





def __UpperCamelCase			(       _A  ):
						lowerCAmelCase_      			=  [
						    '''decoder.version''',
						    '''decoder.output_projection.weight''',
						    '''_float_tensor''',
						    '''decoder.embed_positions._float_tensor''',
						]
						for k in ignore_keys:
												state_dict.pop(_A ,  _A  )





def __UpperCamelCase			(       _A  ):
						lowerCAmelCase_			,					lowerCAmelCase_      			=  emb.weight.shape
						lowerCAmelCase_      			=  nn.Linear(_A ,  _A ,  bias=_A  )
						lowerCAmelCase_      			=  emb.weight.data
						return lin_layer





def __UpperCamelCase			(       _A  ):
						lowerCAmelCase_      			=  torch.load(_A ,  map_location='''cpu'''  )
						lowerCAmelCase_      			=  Namespace(**checkpoint['''cfg''']['''model''']  )
						lowerCAmelCase_      			=  checkpoint['''model''']
						remove_ignore_keys_(_A  )
						lowerCAmelCase_      			=  state_dict['''decoder.embed_tokens.weight'''].shape[0]

						lowerCAmelCase_      			=  {key.replace('''decoder''' ,  '''model'''  ): val for key, val in state_dict.items()}

						lowerCAmelCase_      			=  XGLMConfig(
						    vocab_size=_A ,  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 ,  )

						lowerCAmelCase_      			=  XGLMForCausalLM(_A  )
						lowerCAmelCase_      			=  model.load_state_dict(_A ,  strict=_A  )
						print(_A  )
						lowerCAmelCase_      			=  make_linear_from_emb(model.model.embed_tokens  )

						return model


if __name__ == "__main__":
						_A     =			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.''')
						_A     =			parser.parse_args()
						_A     =			convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
						model.save_pretrained(args.pytorch_dump_folder_path)




