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

import sentencepiece as spm

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


__magic_name__								=logging.get_logger(__name__)

__magic_name__								='''▁'''

__magic_name__								={'''vocab_file''': '''sentencepiece.bpe.model'''}

__magic_name__								={
    '''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'''
        ),
    }
}

__magic_name__								={
    '''facebook/mbart-large-en-ro''': 1024,
    '''facebook/mbart-large-cc25''': 1024,
}

# fmt: off
__magic_name__								=['''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     _A   (    __UpperCamelCase							):
  SCREAMING_SNAKE_CASE_	:	Any						=VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE_	:	List[Any]						=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  SCREAMING_SNAKE_CASE_	:	Any						=PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE_	:	List[Any]						=["input_ids", "attention_mask"]

  SCREAMING_SNAKE_CASE_	:	List[int]						=[]
  SCREAMING_SNAKE_CASE_	:	List[int]						=[]






  def __init__(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_="<s>"   ,				SCREAMING_SNAKE_CASE_="</s>"   ,				SCREAMING_SNAKE_CASE_="</s>"   ,				SCREAMING_SNAKE_CASE_="<s>"   ,				SCREAMING_SNAKE_CASE_="<unk>"   ,				SCREAMING_SNAKE_CASE_="<pad>"   ,				SCREAMING_SNAKE_CASE_="<mask>"   ,				SCREAMING_SNAKE_CASE_=None   ,				SCREAMING_SNAKE_CASE_=None   ,				SCREAMING_SNAKE_CASE_=None   ,				SCREAMING_SNAKE_CASE_ = None   ,				SCREAMING_SNAKE_CASE_=None   ,				**SCREAMING_SNAKE_CASE_   ,				)		->      Tuple:





       '''simple docstring'''





       UpperCamelCase__										=      AddedToken(SCREAMING_SNAKE_CASE_   ,				lstrip=SCREAMING_SNAKE_CASE_   ,				rstrip=SCREAMING_SNAKE_CASE_	) if isinstance(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	) else mask_token

       UpperCamelCase__										=      {} if sp_model_kwargs is None else sp_model_kwargs

       super().__init__(
           bos_token=SCREAMING_SNAKE_CASE_   ,				eos_token=SCREAMING_SNAKE_CASE_   ,				unk_token=SCREAMING_SNAKE_CASE_   ,				sep_token=SCREAMING_SNAKE_CASE_   ,				cls_token=SCREAMING_SNAKE_CASE_   ,				pad_token=SCREAMING_SNAKE_CASE_   ,				mask_token=SCREAMING_SNAKE_CASE_   ,				tokenizer_file=SCREAMING_SNAKE_CASE_   ,				src_lang=SCREAMING_SNAKE_CASE_   ,				tgt_lang=SCREAMING_SNAKE_CASE_   ,				additional_special_tokens=SCREAMING_SNAKE_CASE_   ,				sp_model_kwargs=self.sp_model_kwargs   ,				**SCREAMING_SNAKE_CASE_   ,				)

       UpperCamelCase__										=      spm.SentencePieceProcessor(**self.sp_model_kwargs	)
       self.sp_model.Load(str(SCREAMING_SNAKE_CASE_	)	)
       UpperCamelCase__										=      vocab_file

       # Original fairseq vocab and spm vocab must be "aligned":
       # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
       # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
       # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
       # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

       # Mimic fairseq token-to-id alignment for the first 4 token
       UpperCamelCase__										=      {'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

       # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
       UpperCamelCase__										=      1

       UpperCamelCase__										=      len(self.sp_model	)
       UpperCamelCase__										=      {
           code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(SCREAMING_SNAKE_CASE_	)
       }
       UpperCamelCase__										=      {v: k for k, v in self.lang_code_to_id.items()}
       UpperCamelCase__										=      len(self.sp_model	) + len(self.lang_code_to_id	) + self.fairseq_offset

       self.fairseq_tokens_to_ids.update(self.lang_code_to_id	)
       UpperCamelCase__										=      {v: k for k, v in self.fairseq_tokens_to_ids.items()}
       UpperCamelCase__										=      list(self.lang_code_to_id.keys()	)

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

       UpperCamelCase__										=      src_lang if src_lang is not None else '''en_XX'''
       UpperCamelCase__										=      self.lang_code_to_id[self._src_lang]
       UpperCamelCase__										=      tgt_lang
       self.set_src_lang_special_tokens(self._src_lang	)






  def __getstate__(self	)		->      Tuple:





       '''simple docstring'''





       UpperCamelCase__										=      self.__dict__.copy()
       UpperCamelCase__										=      None
       UpperCamelCase__										=      self.sp_model.serialized_model_proto()
       return state






  def __setstate__(self   ,				SCREAMING_SNAKE_CASE_	)		->      Optional[Any]:





       '''simple docstring'''





       UpperCamelCase__										=      d

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

       UpperCamelCase__										=      spm.SentencePieceProcessor(**self.sp_model_kwargs	)
       self.sp_model.LoadFromSerializedProto(self.sp_model_proto	)






  @property
  def 		_a					(self	)		->      Tuple:





       '''simple docstring'''





       return len(self.sp_model	) + len(self.lang_code_to_id	) + self.fairseq_offset + 1  # Plus 1 for the mask token






  @property
  def 		_a					(self	)		->      str:





       '''simple docstring'''





       return self._src_lang






  @src_lang.setter
  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_	)		->      None:





       '''simple docstring'''





       UpperCamelCase__										=      new_src_lang
       self.set_src_lang_special_tokens(self._src_lang	)






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_ = None   ,				SCREAMING_SNAKE_CASE_ = False	)		->      List[int]:





       '''simple docstring'''





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

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






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_ = 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   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_ = 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   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				**SCREAMING_SNAKE_CASE_	)		->      int:





       '''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(SCREAMING_SNAKE_CASE_   ,				add_special_tokens=SCREAMING_SNAKE_CASE_   ,				return_tensors=SCREAMING_SNAKE_CASE_   ,				**SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      self.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      tgt_lang_id
       return inputs






  def 		_a					(self	)		->      Optional[int]:





       '''simple docstring'''





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






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_	)		->      List[str]:





       '''simple docstring'''





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






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_	)		->      Dict:





       '''simple docstring'''





       if token in self.fairseq_tokens_to_ids:
            return self.fairseq_tokens_to_ids[token]
       UpperCamelCase__										=      self.sp_model.PieceToId(SCREAMING_SNAKE_CASE_	)

       # Need to return unknown token if the SP model returned 0
       return spm_id + self.fairseq_offset if spm_id else self.unk_token_id






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_	)		->      List[str]:





       '''simple docstring'''





       if index in self.fairseq_ids_to_tokens:
            return self.fairseq_ids_to_tokens[index]
       return self.sp_model.IdToPiece(index - self.fairseq_offset	)






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_	)		->      str:





       '''simple docstring'''





       UpperCamelCase__										=      ''''''.join(SCREAMING_SNAKE_CASE_	).replace(SCREAMING_SNAKE_CASE_   ,				''' '''	).strip()
       return out_string






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_ = None	)		->      Tuple[str]:





       '''simple docstring'''





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

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

       return (out_vocab_file,)






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_ = "en_XX"   ,				SCREAMING_SNAKE_CASE_ = None   ,				SCREAMING_SNAKE_CASE_ = "ro_RO"   ,				**SCREAMING_SNAKE_CASE_   ,				)		->      BatchEncoding:





       '''simple docstring'''





       UpperCamelCase__										=      src_lang
       UpperCamelCase__										=      tgt_lang
       return super().prepare_seqaseq_batch(SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				**SCREAMING_SNAKE_CASE_	)






  def 		_a					(self	)		->      Optional[int]:





       '''simple docstring'''





       return self.set_src_lang_special_tokens(self.src_lang	)






  def 		_a					(self	)		->      Optional[Any]:





       '''simple docstring'''





       return self.set_tgt_lang_special_tokens(self.tgt_lang	)






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_	)		->      None:





       '''simple docstring'''





       UpperCamelCase__										=      self.lang_code_to_id[src_lang]
       UpperCamelCase__										=      []
       UpperCamelCase__										=      [self.eos_token_id, self.cur_lang_code]






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_	)		->      None:





       '''simple docstring'''





       UpperCamelCase__										=      self.lang_code_to_id[lang]
       UpperCamelCase__										=      []
       UpperCamelCase__										=      [self.eos_token_id, self.cur_lang_code]




import argparse
import json
import os

import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile

from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switch_transformers_original_flax_checkpoint_to_pytorch import (
    rename_keys,
)
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
from transformers.utils.hub import convert_file_size_to_int







def  __UpperCamelCase    (						A ,							A							):
     if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
          # expert layer
          UpperCamelCase__										=      flax_key_tuple[:-1] + ('''weight''',)
          UpperCamelCase__										=      torch.permute(A ,							(0, 2, 1)							)
     elif flax_key_tuple[-1] == "kernel" and ".".join(A							):
          # linear layer
          UpperCamelCase__										=      flax_key_tuple[:-1] + ('''weight''',)
          UpperCamelCase__										=      flax_tensor.T
     elif flax_key_tuple[-1] in ["scale", "embedding"]:
          UpperCamelCase__										=      flax_key_tuple[:-1] + ('''weight''',)

     return flax_key_tuple, flax_tensor







def  __UpperCamelCase    (						A ,							A ,							A							):
     if "metadata" in layer:
          UpperCamelCase__										=      layer.split('''metadata'''							)
          UpperCamelCase__										=      ''''''.join(split_layer[0]							)[:-1]
          UpperCamelCase__										=      [tuple(('''metadata''' + split_layer[1]).split('''/'''							)							)]
     elif "kvstore" in layer:
          UpperCamelCase__										=      layer.split('''kvstore'''							)
          UpperCamelCase__										=      ''''''.join(split_layer[0]							)[:-1]
          UpperCamelCase__										=      [tuple(('''kvstore''' + split_layer[1]).split('''/'''							)							)]

     else:
          UpperCamelCase__										=      layer.split('''/'''							)
          UpperCamelCase__										=      '''/'''.join(split_layer[:-1]							)
          UpperCamelCase__										=      (split_layer[-1],)

     if "kvstore/path" in layer:
          UpperCamelCase__										=      f"{switch_checkpoint_path}/{checkpoint_info[layer]}"
     elif "kvstore/driver" in layer:
          UpperCamelCase__										=      '''file'''
     else:
          UpperCamelCase__										=      checkpoint_info[layer]

     return curr_real_layer_name, split_layer, content







def  __UpperCamelCase    (						A ,							A							):
     UpperCamelCase__										=      rename_keys(A							)
     UpperCamelCase__										=      {}
     for k, v in current_block.items():
          UpperCamelCase__										=      v
     UpperCamelCase__										=      new_current_block
     torch.save(A ,							A							)







def  __UpperCamelCase    (						A ,							A ,							A ,							A ,							A = WEIGHTS_NAME							):
     UpperCamelCase__										=      convert_file_size_to_int(A							)
     UpperCamelCase__										=      []
     UpperCamelCase__										=      {}
     UpperCamelCase__										=      0
     UpperCamelCase__										=      0

     os.makedirs(A ,							exist_ok=A							)
     with gfile.GFile(switch_checkpoint_path + '''/checkpoint''' ,							'''rb'''							) as fp:
          UpperCamelCase__										=      serialization.msgpack_restore(fp.read()							)['''optimizer''']['''target''']
          UpperCamelCase__										=      flatten_dict(A ,							sep='''/'''							)

     UpperCamelCase__										=      {}
     for layer in checkpoint_info.keys():
          UpperCamelCase__						,       UpperCamelCase__						,       UpperCamelCase__										=      get_key_and_tensorstore_dict(
              A ,							A ,							A							)
          if curr_real_layer_name in all_layers:
               UpperCamelCase__										=      content
          else:
               UpperCamelCase__										=      {split_layer[-1]: content}

     for key in all_layers.keys():
          # open tensorstore file
          UpperCamelCase__										=      ts.open(unflatten_dict(all_layers[key]							)							).result().read().result()
          UpperCamelCase__										=      torch.tensor(A							)
          UpperCamelCase__										=      raw_weights.numel() * dtype_byte_size(raw_weights.dtype							)

          # use the renaming pattern from the small conversion scripts
          UpperCamelCase__						,       UpperCamelCase__										=      rename_base_flax_keys(tuple(key.split('''/'''							)							) ,							A							)
          UpperCamelCase__										=      '''/'''.join(A							)

          # If this weight is going to tip up over the maximal size, we split.
          if current_block_size + weight_size > max_shard_size:
               UpperCamelCase__										=      os.path.join(
                   A ,							weights_name.replace('''.bin''' ,							f"-{len(A							)+1:05d}-of-???.bin"							)							)
               rename_and_save_block(A ,							A							)
               sharded_state_dicts.append(current_block.keys()							)
               del current_block
               UpperCamelCase__										=      {}
               UpperCamelCase__										=      0

          UpperCamelCase__										=      raw_weights.to(getattr(A ,							A							)							)
          current_block_size += weight_size
          total_size += weight_size

     # Add the last block
     UpperCamelCase__										=      os.path.join(A ,							weights_name.replace('''.bin''' ,							f"-{len(A							)+1:05d}-of-???.bin"							)							)
     rename_and_save_block(A ,							A							)
     sharded_state_dicts.append(current_block.keys()							)

     # If we only have one shard, we return it
     if len(A							) == 1:
          return {weights_name: sharded_state_dicts[0]}, None

     # Otherwise, let's build the index
     UpperCamelCase__										=      {}
     UpperCamelCase__										=      {}
     for idx, shard in enumerate(A							):
          UpperCamelCase__										=      weights_name.replace(
              '''.bin''' ,							f"-{idx+1:05d}-of-{len(A							):05d}.bin"							)  # len(sharded_state_dicts):05d}
          UpperCamelCase__										=      os.path.join(A ,							weights_name.replace('''.bin''' ,							f"-{idx+1:05d}-of-???.bin"							)							)
          os.rename(A ,							os.path.join(A ,							A							)							)
          UpperCamelCase__										=      shard
          for key in shard:
               UpperCamelCase__										=      shard_file

    # Add the metadata
     UpperCamelCase__										=      {'''total_size''': total_size}
     UpperCamelCase__										=      {'''metadata''': metadata, '''weight_map''': weight_map}

     with open(os.path.join(A ,							A							) ,							'''w''' ,							encoding='''utf-8'''							) as f:
          UpperCamelCase__										=      json.dumps(A ,							indent=2 ,							sort_keys=A							) + '''\n'''
          f.write(A							)

     return metadata, index


if __name__ == "__main__":
     __magic_name__								=argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--switch_t5x_checkpoint_path''',
         default='''/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128/checkpoint_634600''',
         type=str,
         required=False,
         help='''Path to a directory containing a folder per layer. Follows the original Google format.''',
     )
     parser.add_argument('''--max_shard_size''', default='''10GB''', required=False, help='''Max shard size''')
     parser.add_argument('''--dtype''', default='''bfloat16''', type=str, required=False, help='''dtype of the saved model''')
     parser.add_argument(
         '''--pytorch_dump_folder_path''',
         default='''/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128-converted''',
         type=str,
         required=False,
         help='''Path to the output pytorch model.''',
     )
     __magic_name__								=parser.parse_args()
     shard_on_the_fly(
         args.switch_tax_checkpoint_path,
         args.pytorch_dump_folder_path,
         args.max_shard_size,
         args.dtype,
     )







def  __UpperCamelCase    (						):
     from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

     UpperCamelCase__										=      SwitchTransformersConfig.from_pretrained('''google/switch-base-8'''							)
     config.save_pretrained('''/home/arthur_huggingface_co/transformers/switch_converted'''							)
     UpperCamelCase__										=      SwitchTransformersForConditionalGeneration.from_pretrained(
         '''/home/arthur_huggingface_co/transformers/switch_converted''' ,							device_map='''auto'''							)

     UpperCamelCase__										=      TaTokenizer.from_pretrained('''t5-small'''							)
     UpperCamelCase__										=      '''A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.'''

     UpperCamelCase__										=      tokenizer(A ,							return_tensors='''pt'''							).input_ids
     UpperCamelCase__										=      model.generate(A ,							decoder_start_token_id=0							)
     print(tokenizer.decode(out[0]							)							)



