
'''simple docstring'''







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""": 1_0_2_4,
    """facebook/mbart-large-cc25""": 1_0_2_4,
}

# 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 UpperCAmelCase__	( lowercase__	):





  """simple docstring"""

  __UpperCAmelCase				:				Tuple   =					VOCAB_FILES_NAMES
  __UpperCAmelCase				:				Dict   =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  __UpperCAmelCase				:				List[Any]   =					PRETRAINED_VOCAB_FILES_MAP
  __UpperCAmelCase				:				Optional[Any]   =					['''input_ids''', '''attention_mask''']
  __UpperCAmelCase				:				Union[str, Any]   =					MBartTokenizer

  __UpperCAmelCase				:				List[int]   =					[]
  __UpperCAmelCase				:				List[int]   =					[]


  def __init__(		self      :     Dict							,_a      :     List[Any]=None							,_a      :     Tuple=None							,_a      :     Optional[int]="<s>"							,_a      :     Dict="</s>"							,_a      :     List[str]="</s>"							,_a      :     Union[str, Any]="<s>"							,_a      :     List[str]="<unk>"							,_a      :     str="<pad>"							,_a      :     Optional[Any]="<mask>"							,_a      :     List[Any]=None							,_a      :     Union[str, Any]=None							,_a      :     Union[str, Any]=None							,**_a      :     Any							,):


     '''simple docstring'''






     _a				:	Union[str, Any]			=   AddedToken(_a							,lstrip=_a							,rstrip=_a       ) if isinstance(_a							,_a       ) else mask_token

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

     _a				:	Tuple			=   vocab_file
     _a				:	Union[str, Any]			=   False if not self.vocab_file else True

     _a				:	Dict			=   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}       )
     _a				:	Dict			=   {
         lang_code: self.convert_tokens_to_ids(_a       ) for lang_code in FAIRSEQ_LANGUAGE_CODES
     }

     _a				:	Dict			=   src_lang if src_lang is not None else 'en_XX'
     _a				:	Optional[Any]			=   self.convert_tokens_to_ids(self._src_lang       )
     _a				:	Any			=   tgt_lang
     self.set_src_lang_special_tokens(self._src_lang       )


  @property
  def   __lowercase		(		self      :     List[Any]       ):


     '''simple docstring'''






     return self._src_lang


  @src_lang.setter
  def   __lowercase		(		self      :     Union[str, Any]							,_a      :     str       ):


     '''simple docstring'''






     _a				:	Dict			=   new_src_lang
     self.set_src_lang_special_tokens(self._src_lang       )


  def   __lowercase		(		self      :     List[str]							,_a      :     List[int]							,_a      :     Optional[List[int]] = 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   __lowercase		(		self      :     Optional[int]							,_a      :     List[int]							,_a      :     Optional[List[int]] = None       ):


     '''simple docstring'''






     _a				:	Union[str, Any]			=   [self.sep_token_id]
     _a				:	Union[str, Any]			=   [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   __lowercase		(		self      :     List[Any]							,_a      :     List[str]							,_a      :     str							,_a      :     Optional[str]							,_a      :     Optional[str]							,**_a      :     List[str]       ):


     '''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'       )
     _a				:	Tuple			=   src_lang
     _a				:	str			=   self(_a							,add_special_tokens=_a							,return_tensors=_a							,**_a       )
     _a				:	Optional[int]			=   self.convert_tokens_to_ids(_a       )
     _a				:	Union[str, Any]			=   tgt_lang_id
     return inputs


  def   __lowercase		(		self      :     Optional[Any]							,_a      :     List[str]							,_a      :     str = "en_XX"							,_a      :     Optional[List[str]] = None							,_a      :     str = "ro_RO"							,**_a      :     Dict							,):


     '''simple docstring'''






     _a				:	Tuple			=   src_lang
     _a				:	List[str]			=   tgt_lang
     return super().prepare_seqaseq_batch(_a							,_a							,**_a       )


  def   __lowercase		(		self      :     str       ):


     '''simple docstring'''






     return self.set_src_lang_special_tokens(self.src_lang       )


  def   __lowercase		(		self      :     Union[str, Any]       ):


     '''simple docstring'''






     return self.set_tgt_lang_special_tokens(self.tgt_lang       )


  def   __lowercase		(		self      :     Optional[Any]							,_a      :     Union[str, Any]       ):


     '''simple docstring'''






     _a				:	Union[str, Any]			=   self.convert_tokens_to_ids(_a       )
     _a				:	Optional[int]			=   []
     _a				:	List[str]			=   [self.eos_token_id, self.cur_lang_code]

     _a				:	List[str]			=   self.convert_ids_to_tokens(self.prefix_tokens       )
     _a				:	List[Any]			=   self.convert_ids_to_tokens(self.suffix_tokens       )

     _a				:	Dict			=   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   __lowercase		(		self      :     Optional[Any]							,_a      :     str       ):


     '''simple docstring'''






     _a				:	Union[str, Any]			=   self.convert_tokens_to_ids(_a       )
     _a				:	List[str]			=   []
     _a				:	Tuple			=   [self.eos_token_id, self.cur_lang_code]

     _a				:	str			=   self.convert_ids_to_tokens(self.prefix_tokens       )
     _a				:	Tuple			=   self.convert_ids_to_tokens(self.suffix_tokens       )

     _a				:	Dict			=   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   __lowercase		(		self      :     Dict							,_a      :     str							,_a      :     Optional[str] = None       ):


     '''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(_a       ):
        logger.error(F"""Vocabulary path ({save_directory}) should be a directory."""       )
        return
     _a				:	List[Any]			=   os.path.join(
         _a							,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']       )

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

     return (out_vocab_file,)





'''simple docstring'''







import argparse
import pickle

import numpy as np
import torch
from torch import nn

from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging


logging.set_verbosity_info()





def 					UpperCAmelCase_		(__a      :    Optional[Any] ,	__a      :    str ,	__a      :    Optional[Any]=None ):






   """simple docstring"""

   assert torch_layer.weight.shape == weight.shape, f"""{torch_layer} layer.weight does not match"""
   _a				:	str			=   nn.Parameter(__a )
   if bias is not None:
      assert torch_layer.bias.shape == bias.shape, f"""{torch_layer} layer.bias does not match"""
      _a				:	Any			=   nn.Parameter(__a )





def 					UpperCAmelCase_		(__a      :    int ,	__a      :    Optional[Any] ,	__a      :    int ):






   """simple docstring"""

   _a				:	Tuple			=   np.asarray(weights[0] )
   _a				:	Union[str, Any]			=   np.asarray(weights[1] )
   _a				:	Dict			=   np.asarray(weights[2] )

   set_param(
       torch_layer.self_attention.query_key ,	torch.tensor(__a ).transpose(1 ,	2 ).contiguous().view(-1 ,	__a ) ,	)
   set_param(
       torch_layer.self_attention.value ,	torch.tensor(__a ).transpose(1 ,	2 ).contiguous().view(-1 ,	__a ) ,	)
   set_param(
       torch_layer.output.dense ,	torch.tensor(__a ).view(-1 ,	__a ).contiguous().transpose(0 ,	1 ) ,	)





def 					UpperCAmelCase_		(__a      :    Optional[Any] ,	__a      :    Optional[int] ,	__a      :    List[str] ):






   """simple docstring"""

   _a				:	Dict			=   np.asarray(weights[0] )
   _a				:	Union[str, Any]			=   np.asarray(weights[1] )
   _a				:	str			=   np.asarray(weights[2] )
   _a				:	int			=   np.asarray(weights[3] )

   set_param(
       torch_layer.self_attention.query ,	torch.tensor(__a ).transpose(1 ,	2 ).contiguous().view(-1 ,	__a ) ,	)
   set_param(
       torch_layer.self_attention.key ,	torch.tensor(__a ).transpose(1 ,	2 ).contiguous().view(-1 ,	__a ) ,	)
   set_param(
       torch_layer.self_attention.value ,	torch.tensor(__a ).transpose(1 ,	2 ).contiguous().view(-1 ,	__a ) ,	)
   set_param(
       torch_layer.output.dense ,	torch.tensor(__a ).view(-1 ,	__a ).contiguous().transpose(0 ,	1 ) ,	)





def 					UpperCAmelCase_		(__a      :    Any ,	__a      :    Any ,	__a      :    Optional[Any] ):






   """simple docstring"""

   _a				:	List[str]			=   weights[0][0][0]
   _a				:	List[Any]			=   np.asarray(layer_norm_a[0] )
   _a				:	List[str]			=   np.asarray(layer_norm_a[1] )
   set_param(
       torch_block.attention.layer_norm ,	torch.tensor(__a ) ,	torch.tensor(__a ) ,	)

   # lsh weights + output
   _a				:	List[str]			=   weights[0][1]
   if len(__a ) < 4:
      set_layer_weights_in_torch_lsh(__a ,	torch_block.attention ,	__a )
   else:
      set_layer_weights_in_torch_local(__a ,	torch_block.attention ,	__a )

   # intermediate weighs
   _a				:	Optional[Any]			=   weights[2][0][1][2]

   # Chunked Feed Forward
   if len(__a ) == 4:
      _a				:	Union[str, Any]			=   intermediate_weights[2]

   # layernorm 2
   _a				:	Any			=   np.asarray(intermediate_weights[0][0] )
   _a				:	List[Any]			=   np.asarray(intermediate_weights[0][1] )
   set_param(
       torch_block.feed_forward.layer_norm ,	torch.tensor(__a ) ,	torch.tensor(__a ) ,	)

   # intermediate dense
   _a				:	Any			=   np.asarray(intermediate_weights[1][0] )
   _a				:	Any			=   np.asarray(intermediate_weights[1][1] )
   set_param(
       torch_block.feed_forward.dense.dense ,	torch.tensor(__a ).transpose(0 ,	1 ).contiguous() ,	torch.tensor(__a ) ,	)

   # intermediate out
   _a				:	Optional[int]			=   np.asarray(intermediate_weights[4][0] )
   _a				:	int			=   np.asarray(intermediate_weights[4][1] )
   set_param(
       torch_block.feed_forward.output.dense ,	torch.tensor(__a ).transpose(0 ,	1 ).contiguous() ,	torch.tensor(__a ) ,	)





def 					UpperCAmelCase_		(__a      :    Dict ,	__a      :    Dict ,	__a      :    List[Any] ):






   """simple docstring"""

   _a				:	Optional[int]			=   torch_model.reformer

   # word embeds
   _a				:	Tuple			=   np.asarray(weights[1] )
   set_param(
       torch_model_reformer.embeddings.word_embeddings ,	torch.tensor(__a ) ,	)

   if isinstance(weights[3] ,	__a ):
      _a				:	Any			=   torch_model_reformer.embeddings.position_embeddings
      for emb_idx in range(len(position_embeddings.weights ) ):
         _a				:	List[Any]			=   np.asarray(weights[3][emb_idx][0] )
         assert (
             position_embeddings.weights[emb_idx].shape == emb_weights.shape
         ), f"""{position_embeddings[emb_idx]} emb does not match"""
         _a				:	Any			=   nn.Parameter(torch.tensor(__a ) )

   _a				:	List[str]			=   weights[5]
   assert len(torch_model_reformer.encoder.layers ) * 4 == len(
       __a ), "HF and trax model do not have the same number of layers"
   for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers ):
      _a				:	Tuple			=   trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
      set_block_weights_in_torch(__a ,	__a ,	__a )

   # output layer norm
   _a				:	Optional[Any]			=   np.asarray(weights[7][0] )
   _a				:	int			=   np.asarray(weights[7][1] )
   set_param(
       torch_model_reformer.encoder.layer_norm ,	torch.tensor(__a ) ,	torch.tensor(__a ) ,	)

   # output embeddings
   _a				:	List[str]			=   np.asarray(weights[9][0] )
   _a				:	int			=   np.asarray(weights[9][1] )
   set_param(
       torch_model.lm_head.decoder ,	torch.tensor(__a ).transpose(0 ,	1 ).contiguous() ,	torch.tensor(__a ) ,	)





def 					UpperCAmelCase_		(__a      :    Tuple ,	__a      :    Optional[Any] ,	__a      :    Dict ):






   """simple docstring"""

   _a				:	List[Any]			=   ReformerConfig.from_json_file(__a )
   print(f"""Building PyTorch model from configuration: {config}""" )
   _a				:	int			=   ReformerModelWithLMHead(__a )

   with open(__a ,	'rb' ) as f:
      _a				:	Optional[Any]			=   pickle.load(__a )['weights']

   set_model_weights_in_torch(__a ,	__a ,	config.hidden_size )

   # Save pytorch-model
   print(f"""Save PyTorch model to {pytorch_dump_path}""" )
   torch.save(model.state_dict() ,	__a )


if __name__ == "__main__":
  __lowerCAmelCase         =    argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--trax_model_pkl_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
  )
  parser.add_argument(
      """--config_file""",
      default=None,
      type=str,
      required=True,
      help=(
          """The config json file corresponding to the pre-trained Reformer model. \n"""
          """This specifies the model architecture."""
      ),
  )
  parser.add_argument(
      """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
  )
  __lowerCAmelCase         =    parser.parse_args()
  convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)



