




'''simple docstring'''


import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

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


lowerCAmelCase_      :  int				     =  logging.get_logger(__name__)


lowerCAmelCase_      :  Dict				     =  {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt'}

# See all BART models at https://huggingface.co/models?filter=bart
lowerCAmelCase_      :  int				     =  {
    'vocab_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/vocab.json',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/vocab.json',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json',
    },
    'merges_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/merges.txt',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/merges.txt',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt',
    },
}

lowerCAmelCase_      :  Optional[int]				     =  {
    'facebook/bart-base': 1024,
    'facebook/bart-large': 1024,
    'facebook/bart-large-mnli': 1024,
    'facebook/bart-large-cnn': 1024,
    'facebook/bart-large-xsum': 1024,
    'yjernite/bart_eli5': 1024,
}



@lru_cache()
def  _SCREAMING_SNAKE_CASE     (      ):




     """simple docstring"""


     a_     :							int     						=  (
         list(range(ord("""!"""				) ,    ord("""~"""				) + 1				)				) + list(range(ord("""¡"""				) ,    ord("""¬"""				) + 1				)				) + list(range(ord("""®"""				) ,    ord("""ÿ"""				) + 1				)				)
     )
     a_     :							Any     						=  bs[:]
     a_     :							Optional[Any]     						=  0
     for b in range(2**8				):
          if b not in bs:
               bs.append(UpperCamelCase__				)
               cs.append(2**8 + n				)
               n += 1
     a_     :							Union[str, Any]     						=  [chr(UpperCamelCase__				) for n in cs]
     return dict(zip(UpperCamelCase__ ,    UpperCamelCase__				)				)



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					Dict				):




     """simple docstring"""


     a_     :							Any     						=  set()
     a_     :							Optional[int]     						=  word[0]
     for char in word[1:]:
          pairs.add((prev_char, char)				)
          a_     :							List[Any]     						=  char
     return pairs







class      SCREAMING_SNAKE_CASE    (						snake_case_				):
      __magic_name__  :       Union[str, Any]   							=					VOCAB_FILES_NAMES
      __magic_name__  :       List[Any]   							=					PRETRAINED_VOCAB_FILES_MAP
      __magic_name__  :       List[Any]   							=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __magic_name__  :       List[str]   							=					['''input_ids''', '''attention_mask''']






      def __init__( self   :  Optional[Any]						,						lowercase__   :  str						,						lowercase__   :  Any						,						lowercase__   :  List[Any]="replace"						,						lowercase__   :  List[str]="<s>"						,						lowercase__   :  Any="</s>"						,						lowercase__   :  str="</s>"						,						lowercase__   :  Optional[Any]="<s>"						,						lowercase__   :  Tuple="<unk>"						,						lowercase__   :  Dict="<pad>"						,						lowercase__   :  str="<mask>"						,						lowercase__   :  Optional[int]=False						,						**lowercase__   :  Union[str, Any]						,						):


           '''simple docstring'''

           a_     :							Optional[Any]     						=  AddedToken(lowercase__						,						lstrip=lowercase__						,						rstrip=lowercase__       ) if isinstance(lowercase__						,						lowercase__       ) else bos_token
           a_     :							Tuple     						=  AddedToken(lowercase__						,						lstrip=lowercase__						,						rstrip=lowercase__       ) if isinstance(lowercase__						,						lowercase__       ) else eos_token
           a_     :							List[str]     						=  AddedToken(lowercase__						,						lstrip=lowercase__						,						rstrip=lowercase__       ) if isinstance(lowercase__						,						lowercase__       ) else sep_token
           a_     :							Union[str, Any]     						=  AddedToken(lowercase__						,						lstrip=lowercase__						,						rstrip=lowercase__       ) if isinstance(lowercase__						,						lowercase__       ) else cls_token
           a_     :							Any     						=  AddedToken(lowercase__						,						lstrip=lowercase__						,						rstrip=lowercase__       ) if isinstance(lowercase__						,						lowercase__       ) else unk_token
           a_     :							List[Any]     						=  AddedToken(lowercase__						,						lstrip=lowercase__						,						rstrip=lowercase__       ) if isinstance(lowercase__						,						lowercase__       ) else pad_token

           # Mask token behave like a normal word, i.e. include the space before it
           a_     :							Tuple     						=  AddedToken(lowercase__						,						lstrip=lowercase__						,						rstrip=lowercase__       ) if isinstance(lowercase__						,						lowercase__       ) else mask_token

           super().__init__(
               errors=lowercase__						,						bos_token=lowercase__						,						eos_token=lowercase__						,						unk_token=lowercase__						,						sep_token=lowercase__						,						cls_token=lowercase__						,						pad_token=lowercase__						,						mask_token=lowercase__						,						add_prefix_space=lowercase__						,						**lowercase__						,						)

           with open(lowercase__						,						encoding="""utf-8"""       ) as vocab_handle:
                a_     :							str     						=  json.load(lowercase__       )
           a_     :							str     						=  {v: k for k, v in self.encoder.items()}
           a_     :							str     						=  errors  # how to handle errors in decoding
           a_     :							Dict     						=  bytes_to_unicode()
           a_     :							Optional[int]     						=  {v: k for k, v in self.byte_encoder.items()}
           with open(lowercase__						,						encoding="""utf-8"""       ) as merges_handle:
                a_     :							Optional[Any]     						=  merges_handle.read().split("""\n"""       )[1:-1]
           a_     :							Union[str, Any]     						=  [tuple(merge.split()       ) for merge in bpe_merges]
           a_     :							Optional[Any]     						=  dict(zip(lowercase__						,						range(len(lowercase__       )       )       )       )
           a_     :							Optional[Any]     						=  {}
           a_     :							List[Any]     						=  add_prefix_space

           # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
           a_     :							Union[str, Any]     						=  re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""       )






      @property
      def 							lowercase_						( self   :  Union[str, Any]       ):


           '''simple docstring'''

           return len(self.encoder       )






      def 							lowercase_						( self   :  Optional[Any]       ):


           '''simple docstring'''

           return dict(self.encoder						,						**self.added_tokens_encoder       )






      def 							lowercase_						( self   :  Optional[Any]						,						lowercase__   :  List[Any]       ):


           '''simple docstring'''

           if token in self.cache:
                return self.cache[token]
           a_     :							List[str]     						=  tuple(lowercase__       )
           a_     :							Optional[int]     						=  get_pairs(lowercase__       )

           if not pairs:
                return token

           while True:
                a_     :							Any     						=  min(lowercase__						,						key=lambda lowercase__       : self.bpe_ranks.get(lowercase__						,						float("""inf"""       )       )       )
                if bigram not in self.bpe_ranks:
                     break
                a_						,     a_     :							Tuple     						=  bigram
                a_     :							Any     						=  []
                a_     :							Dict     						=  0
                while i < len(lowercase__       ):
                     try:
                          a_     :							str     						=  word.index(lowercase__						,						lowercase__       )
                     except ValueError:
                          new_word.extend(word[i:]       )
                          break
                     else:
                          new_word.extend(word[i:j]       )
                          a_     :							List[str]     						=  j

                     if word[i] == first and i < len(lowercase__       ) - 1 and word[i + 1] == second:
                          new_word.append(first + second       )
                          i += 2
                     else:
                          new_word.append(word[i]       )
                          i += 1
                a_     :							List[Any]     						=  tuple(lowercase__       )
                a_     :							Optional[Any]     						=  new_word
                if len(lowercase__       ) == 1:
                     break
                else:
                     a_     :							str     						=  get_pairs(lowercase__       )
           a_     :							Any     						=  """ """.join(lowercase__       )
           a_     :							List[str]     						=  word
           return word






      def 							lowercase_						( self   :  Dict						,						lowercase__   :  List[str]       ):


           '''simple docstring'''

           a_     :							Tuple     						=  []
           for token in re.findall(self.pat						,						lowercase__       ):
                a_     :							int     						=  """""".join(
                    self.byte_encoder[b] for b in token.encode("""utf-8"""       )       )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
                bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowercase__       ).split(""" """       )       )
           return bpe_tokens






      def 							lowercase_						( self   :  str						,						lowercase__   :  Any       ):


           '''simple docstring'''

           return self.encoder.get(lowercase__						,						self.encoder.get(self.unk_token       )       )






      def 							lowercase_						( self   :  int						,						lowercase__   :  Optional[int]       ):


           '''simple docstring'''

           return self.decoder.get(lowercase__       )






      def 							lowercase_						( self   :  Optional[Any]						,						lowercase__   :  Any       ):


           '''simple docstring'''

           a_     :							Optional[int]     						=  """""".join(lowercase__       )
           a_     :							Dict     						=  bytearray([self.byte_decoder[c] for c in text]       ).decode("""utf-8"""						,						errors=self.errors       )
           return text






      def 							lowercase_						( self   :  Any						,						lowercase__   :  str						,						lowercase__   :  Optional[str] = None       ):


           '''simple docstring'''

           if not os.path.isdir(lowercase__       ):
                logger.error(F"Vocabulary path ({save_directory}) should be a directory"       )
                return
           a_     :							int     						=  os.path.join(
               lowercase__						,						(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]       )
           a_     :							List[str]     						=  os.path.join(
               lowercase__						,						(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]       )

           with open(lowercase__						,						"""w"""						,						encoding="""utf-8"""       ) as f:
                f.write(json.dumps(self.encoder						,						indent=2						,						sort_keys=lowercase__						,						ensure_ascii=lowercase__       ) + """\n"""       )

           a_     :							List[Any]     						=  0
           with open(lowercase__						,						"""w"""						,						encoding="""utf-8"""       ) as writer:
                writer.write("""#version: 0.2\n"""       )
                for bpe_tokens, token_index in sorted(self.bpe_ranks.items()						,						key=lambda lowercase__       : kv[1]       ):
                     if index != token_index:
                          logger.warning(
                              F"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
                              """ Please check that the tokenizer is not corrupted!"""       )
                          a_     :							Dict     						=  token_index
                     writer.write(""" """.join(lowercase__       ) + """\n"""       )
                     index += 1

           return vocab_file, merge_file






      def 							lowercase_						( self   :  Optional[int]						,						lowercase__   :  List[int]						,						lowercase__   :  Optional[List[int]] = None       ):


           '''simple docstring'''

           if token_ids_a is None:
                return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
           a_     :							Dict     						=  [self.cls_token_id]
           a_     :							str     						=  [self.sep_token_id]
           return cls + token_ids_a + sep + sep + token_ids_a + sep






      def 							lowercase_						( self   :  Any						,						lowercase__   :  List[int]						,						lowercase__   :  Optional[List[int]] = None						,						lowercase__   :  bool = False       ):


           '''simple docstring'''

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

           if token_ids_a is None:
                return [1] + ([0] * len(lowercase__       )) + [1]
           return [1] + ([0] * len(lowercase__       )) + [1, 1] + ([0] * len(lowercase__       )) + [1]






      def 							lowercase_						( self   :  Optional[int]						,						lowercase__   :  List[int]						,						lowercase__   :  Optional[List[int]] = None       ):


           '''simple docstring'''

           a_     :							Optional[int]     						=  [self.sep_token_id]
           a_     :							Dict     						=  [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   :  Union[str, Any]						,						lowercase__   :  int						,						lowercase__   :  Any=False						,						**lowercase__   :  int       ):


           '''simple docstring'''

           a_     :							Tuple     						=  kwargs.pop("""add_prefix_space"""						,						self.add_prefix_space       )
           if (is_split_into_words or add_prefix_space) and (len(lowercase__       ) > 0 and not text[0].isspace()):
                a_     :							List[str]     						=  """ """ + text
           return (text, kwargs)






'''simple docstring'''


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  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					List[str] ,    UpperCamelCase__    :					List[str]				):




     """simple docstring"""


     if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
          # expert layer
          a_     :							Optional[int]     						=  flax_key_tuple[:-1] + ("""weight""",)
          a_     :							Optional[int]     						=  torch.permute(UpperCamelCase__ ,    (0, 2, 1)				)
     elif flax_key_tuple[-1] == "kernel" and ".".join(UpperCamelCase__				):
          # linear layer
          a_     :							Any     						=  flax_key_tuple[:-1] + ("""weight""",)
          a_     :							Optional[int]     						=  flax_tensor.T
     elif flax_key_tuple[-1] in ["scale", "embedding"]:
          a_     :							Any     						=  flax_key_tuple[:-1] + ("""weight""",)

     return flax_key_tuple, flax_tensor



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					Union[str, Any] ,    UpperCamelCase__    :					str ,    UpperCamelCase__    :					Any				):




     """simple docstring"""


     if "metadata" in layer:
          a_     :							Optional[Any]     						=  layer.split("""metadata"""				)
          a_     :							List[str]     						=  """""".join(split_layer[0]				)[:-1]
          a_     :							List[str]     						=  [tuple(("""metadata""" + split_layer[1]).split("""/"""				)				)]
     elif "kvstore" in layer:
          a_     :							Union[str, Any]     						=  layer.split("""kvstore"""				)
          a_     :							Tuple     						=  """""".join(split_layer[0]				)[:-1]
          a_     :							Union[str, Any]     						=  [tuple(("""kvstore""" + split_layer[1]).split("""/"""				)				)]

     else:
          a_     :							List[str]     						=  layer.split("""/"""				)
          a_     :							Union[str, Any]     						=  """/""".join(split_layer[:-1]				)
          a_     :							Union[str, Any]     						=  (split_layer[-1],)

     if "kvstore/path" in layer:
          a_     :							Any     						=  F"{switch_checkpoint_path}/{checkpoint_info[layer]}"
     elif "kvstore/driver" in layer:
          a_     :							Optional[int]     						=  """file"""
     else:
          a_     :							str     						=  checkpoint_info[layer]

     return curr_real_layer_name, split_layer, content



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					Union[str, Any] ,    UpperCamelCase__    :					Any				):




     """simple docstring"""


     a_     :							int     						=  rename_keys(UpperCamelCase__				)
     a_     :							Any     						=  {}
     for k, v in current_block.items():
          a_     :							Union[str, Any]     						=  v
     a_     :							List[str]     						=  new_current_block
     torch.save(UpperCamelCase__ ,    UpperCamelCase__				)



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					List[Any] ,    UpperCamelCase__    :					Any ,    UpperCamelCase__    :					Optional[int] ,    UpperCamelCase__    :					List[str] ,    UpperCamelCase__    :					str = WEIGHTS_NAME				):




     """simple docstring"""


     a_     :							str     						=  convert_file_size_to_int(UpperCamelCase__				)
     a_     :							str     						=  []
     a_     :							Optional[int]     						=  {}
     a_     :							Optional[int]     						=  0
     a_     :							Any     						=  0

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

     a_     :							Dict     						=  {}
     for layer in checkpoint_info.keys():
          a_						,     a_						,     a_     :							List[Any]     						=  get_key_and_tensorstore_dict(
              UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__				)
          if curr_real_layer_name in all_layers:
               a_     :							int     						=  content
          else:
               a_     :							Tuple     						=  {split_layer[-1]: content}

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

          # use the renaming pattern from the small conversion scripts
          a_						,     a_     :							Tuple     						=  rename_base_flax_keys(tuple(key.split("""/"""				)				) ,    UpperCamelCase__				)
          a_     :							Dict     						=  """/""".join(UpperCamelCase__				)

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

          a_     :							List[str]     						=  raw_weights.to(getattr(UpperCamelCase__ ,    UpperCamelCase__				)				)
          current_block_size += weight_size
          total_size += weight_size

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

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

     # Otherwise, let's build the index
     a_     :							Tuple     						=  {}
     a_     :							List[Any]     						=  {}
     for idx, shard in enumerate(UpperCamelCase__				):
          a_     :							str     						=  weights_name.replace(
              """.bin""" ,    F"-{idx+1:05d}-of-{len(UpperCamelCase__				):05d}.bin"				)  # len(sharded_state_dicts):05d}
          a_     :							Union[str, Any]     						=  os.path.join(UpperCamelCase__ ,    weights_name.replace(""".bin""" ,    F"-{idx+1:05d}-of-???.bin"				)				)
          os.rename(UpperCamelCase__ ,    os.path.join(UpperCamelCase__ ,    UpperCamelCase__				)				)
          a_     :							str     						=  shard
          for key in shard:
               a_     :							Union[str, Any]     						=  shard_file

    # Add the metadata
     a_     :							List[str]     						=  {"""total_size""": total_size}
     a_     :							str     						=  {"""metadata""": metadata, """weight_map""": weight_map}

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

     return metadata, index


if __name__ == "__main__":
 lowerCAmelCase_      :  List[Any]				     =  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.',
 )
 lowerCAmelCase_      :  List[Any]				     =  parser.parse_args()
 shard_on_the_fly(
     args.switch_tax_checkpoint_path,
     args.pytorch_dump_folder_path,
     args.max_shard_size,
     args.dtype,
 )



def  _SCREAMING_SNAKE_CASE     (      ):




     """simple docstring"""


     from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

     a_     :							Union[str, Any]     						=  SwitchTransformersConfig.from_pretrained("""google/switch-base-8"""				)
     config.save_pretrained("""/home/arthur_huggingface_co/transformers/switch_converted"""				)
     a_     :							List[Any]     						=  SwitchTransformersForConditionalGeneration.from_pretrained(
         """/home/arthur_huggingface_co/transformers/switch_converted""" ,    device_map="""auto"""				)

     a_     :							Union[str, Any]     						=  TaTokenizer.from_pretrained("""t5-small"""				)
     a_     :							Optional[Any]     						=  """A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>."""

     a_     :							List[Any]     						=  tokenizer(UpperCamelCase__ ,    return_tensors="""pt"""				).input_ids
     a_     :							str     						=  model.generate(UpperCamelCase__ ,    decoder_start_token_id=0				)
     print(tokenizer.decode(out[0]				)				)
