






import os
from shutil import copyfile
from typing import List, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_mbart import MBartTokenizer
else:
		lowerCamelCase__	=							None


lowerCamelCase__	=							logging.get_logger(__name__)


lowerCamelCase__	=							{"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

lowerCamelCase__	=							{
    "vocab_file": {
        "facebook/mbart-large-en-ro": (
            "https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model"
        ),
        "facebook/mbart-large-cc25": (
            "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model"
        ),
    },
    "tokenizer_file": {
        "facebook/mbart-large-en-ro": "https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json",
        "facebook/mbart-large-cc25": "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json",
    },
}

lowerCamelCase__	=							{
    "facebook/mbart-large-en-ro": 1024,
    "facebook/mbart-large-cc25": 1024,
}

# fmt: off
lowerCamelCase__	=							["ar_AR", "cs_CZ", "de_DE", "en_XX", "es_XX", "et_EE", "fi_FI", "fr_XX", "gu_IN", "hi_IN", "it_IT", "ja_XX", "kk_KZ", "ko_KR", "lt_LT", "lv_LV", "my_MM", "ne_NP", "nl_XX", "ro_RO", "ru_RU", "si_LK", "tr_TR", "vi_VN", "zh_CN"]







class        lowerCAmelCase__      (   UpperCAmelCase__	):
						UpperCamelCase_							: List[str]	=		VOCAB_FILES_NAMES
						UpperCamelCase_							: Tuple	=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						UpperCamelCase_							: str	=		PRETRAINED_VOCAB_FILES_MAP
						UpperCamelCase_							: Any	=		["input_ids", "attention_mask"]
						UpperCamelCase_							: int	=		MBartTokenizer

						UpperCamelCase_							: List[int]	=		[]
						UpperCamelCase_							: List[int]	=		[]

						def __init__(			self       ,							a=None       ,							a=None       ,							a="<s>"       ,							a="</s>"       ,							a="</s>"       ,							a="<s>"       ,							a="<unk>"       ,							a="<pad>"       ,							a="<mask>"       ,							a=None       ,							a=None       ,							a=None       ,							**a       ,							)					->      Optional[Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	AddedToken(lowerCamelCase__       ,							lstrip=lowerCamelCase__       ,							rstrip=lowerCamelCase__				) if isinstance(lowerCamelCase__       ,							lowerCamelCase__				) else mask_token

								super().__init__(
								    vocab_file=lowerCamelCase__       ,							tokenizer_file=lowerCamelCase__       ,							bos_token=lowerCamelCase__       ,							eos_token=lowerCamelCase__       ,							sep_token=lowerCamelCase__       ,							cls_token=lowerCamelCase__       ,							unk_token=lowerCamelCase__       ,							pad_token=lowerCamelCase__       ,							mask_token=lowerCamelCase__       ,							src_lang=lowerCamelCase__       ,							tgt_lang=lowerCamelCase__       ,							additional_special_tokens=lowerCamelCase__       ,							**lowerCamelCase__       ,							)

								_UpperCamelCase						    =	vocab_file
								_UpperCamelCase						    =	False if not self.vocab_file else True

								_UpperCamelCase						    =	FAIRSEQ_LANGUAGE_CODES.copy()

								if additional_special_tokens is not None:
										# Only add those special tokens if they are not already there.
										_additional_special_tokens.extend(
										    [t for t in additional_special_tokens if t not in _additional_special_tokens]				)

								self.add_special_tokens({"""additional_special_tokens""": _additional_special_tokens}				)
								_UpperCamelCase						    =	{
								    lang_code: self.convert_tokens_to_ids(lowerCamelCase__				) for lang_code in FAIRSEQ_LANGUAGE_CODES
								}

								_UpperCamelCase						    =	src_lang if src_lang is not None else "en_XX"
								_UpperCamelCase						    =	self.convert_tokens_to_ids(self._src_lang				)
								_UpperCamelCase						    =	tgt_lang
								self.set_src_lang_special_tokens(self._src_lang				)

						@property
						def  A_				(			self				)					->      str:


								'''simple docstring'''

								return self._src_lang

						@src_lang.setter
						def  A_				(			self       ,							a				)					->      None:


								'''simple docstring'''

								_UpperCamelCase						    =	new_src_lang
								self.set_src_lang_special_tokens(self._src_lang				)

						def  A_				(			self       ,							a       ,							a = None				)					->      List[int]:


								'''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  A_				(			self       ,							a       ,							a = None				)					->      List[int]:


								'''simple docstring'''

								_UpperCamelCase						    =	[self.sep_token_id]
								_UpperCamelCase						    =	[self.cls_token_id]

								if token_ids_a is None:
										return len(cls + token_ids_a + sep				) * [0]
								return len(cls + token_ids_a + sep + sep + token_ids_a + sep				) * [0]

						def  A_				(			self       ,							a       ,							a       ,							a       ,							a       ,							**a				)					->      Dict:


								'''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"""				)
								_UpperCamelCase						    =	src_lang
								_UpperCamelCase						    =	self(lowerCamelCase__       ,							add_special_tokens=lowerCamelCase__       ,							return_tensors=lowerCamelCase__       ,							**lowerCamelCase__				)
								_UpperCamelCase						    =	self.convert_tokens_to_ids(lowerCamelCase__				)
								_UpperCamelCase						    =	tgt_lang_id
								return inputs

						def  A_				(			self       ,							a       ,							a = "en_XX"       ,							a = None       ,							a = "ro_RO"       ,							**a       ,							)					->      BatchEncoding:


								'''simple docstring'''

								_UpperCamelCase						    =	src_lang
								_UpperCamelCase						    =	tgt_lang
								return super().prepare_seqaseq_batch(lowerCamelCase__       ,							lowerCamelCase__       ,							**lowerCamelCase__				)

						def  A_				(			self				)					->      List[str]:


								'''simple docstring'''

								return self.set_src_lang_special_tokens(self.src_lang				)

						def  A_				(			self				)					->      str:


								'''simple docstring'''

								return self.set_tgt_lang_special_tokens(self.tgt_lang				)

						def  A_				(			self       ,							a				)					->      None:


								'''simple docstring'''

								_UpperCamelCase						    =	self.convert_tokens_to_ids(lowerCamelCase__				)
								_UpperCamelCase						    =	[]
								_UpperCamelCase						    =	[self.eos_token_id, self.cur_lang_code]

								_UpperCamelCase						    =	self.convert_ids_to_tokens(self.prefix_tokens				)
								_UpperCamelCase						    =	self.convert_ids_to_tokens(self.suffix_tokens				)

								_UpperCamelCase						    =	processors.TemplateProcessing(
								    single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str       ,							pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str       ,							special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str       ,							self.prefix_tokens + self.suffix_tokens				)				)       ,							)

						def  A_				(			self       ,							a				)					->      None:


								'''simple docstring'''

								_UpperCamelCase						    =	self.convert_tokens_to_ids(lowerCamelCase__				)
								_UpperCamelCase						    =	[]
								_UpperCamelCase						    =	[self.eos_token_id, self.cur_lang_code]

								_UpperCamelCase						    =	self.convert_ids_to_tokens(self.prefix_tokens				)
								_UpperCamelCase						    =	self.convert_ids_to_tokens(self.suffix_tokens				)

								_UpperCamelCase						    =	processors.TemplateProcessing(
								    single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str       ,							pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str       ,							special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str       ,							self.prefix_tokens + self.suffix_tokens				)				)       ,							)







						def  A_				(			self       ,							a       ,							a = None				)					->      Tuple[str]:


								'''simple docstring'''

								if not self.can_save_slow_tokenizer:
										raise ValueError(
										    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
										    """tokenizer."""				)

								if not os.path.isdir(lowerCamelCase__				):
										logger.error(F'Vocabulary path ({save_directory}) should be a directory.'				)
										return
								_UpperCamelCase						    =	os.path.join(
								    lowerCamelCase__       ,							(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]				)

								if os.path.abspath(self.vocab_file				) != os.path.abspath(lowerCamelCase__				):
										copyfile(self.vocab_file       ,							lowerCamelCase__				)

								return (out_vocab_file,)





import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM


def 							__A(lowerCAmelCase )		->				Any:

		"""simple docstring"""






		_UpperCamelCase						    =	[
		    """decoder.version""",
		    """decoder.output_projection.weight""",
		    """_float_tensor""",
		    """decoder.embed_positions._float_tensor""",
		]
		for k in ignore_keys:
				state_dict.pop(lowerCAmelCase						,  lowerCAmelCase )


def 							__A(lowerCAmelCase )		->				Optional[int]:

		"""simple docstring"""






		_UpperCamelCase    ,	_UpperCamelCase						    =	emb.weight.shape
		_UpperCamelCase						    =	nn.Linear(lowerCAmelCase						,  lowerCAmelCase						,  bias=lowerCAmelCase )
		_UpperCamelCase						    =	emb.weight.data
		return lin_layer


def 							__A(lowerCAmelCase )		->				Tuple:

		"""simple docstring"""






		_UpperCamelCase						    =	torch.load(lowerCAmelCase						,  map_location="""cpu""" )
		_UpperCamelCase						    =	Namespace(**checkpoint["""cfg"""]["""model"""] )
		_UpperCamelCase						    =	checkpoint["""model"""]
		remove_ignore_keys_(lowerCAmelCase )
		_UpperCamelCase						    =	state_dict["""decoder.embed_tokens.weight"""].shape[0]

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

		_UpperCamelCase						    =	XGLMConfig(
		    vocab_size=lowerCAmelCase						,  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						,  )

		_UpperCamelCase						    =	XGLMForCausalLM(lowerCAmelCase )
		_UpperCamelCase						    =	model.load_state_dict(lowerCAmelCase						,  strict=lowerCAmelCase )
		print(lowerCAmelCase )
		_UpperCamelCase						    =	make_linear_from_emb(model.model.embed_tokens )

		return model


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



