




'''simple docstring'''


import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer


lowerCAmelCase_      :  Optional[Any]				     =  logging.get_logger(__name__)

lowerCAmelCase_      :  int				     =  {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

lowerCAmelCase_      :  Union[str, Any]				     =  {
    'vocab_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/vocab.json',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/vocab.json',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/vocab.json',
        'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json',
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/merges.txt',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/merges.txt',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/merges.txt',
        'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt',
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt'
        ),
    },
    'tokenizer_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/tokenizer.json',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/tokenizer.json',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json',
        'roberta-base-openai-detector': (
            'https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json'
        ),
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json'
        ),
    },
}

lowerCAmelCase_      :  Union[str, Any]				     =  {
    'roberta-base': 512,
    'roberta-large': 512,
    'roberta-large-mnli': 512,
    'distilroberta-base': 512,
    'roberta-base-openai-detector': 512,
    'roberta-large-openai-detector': 512,
}







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






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


           '''simple docstring'''

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

           a_     :							Any     						=  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()       )
           if pre_tok_state.get("""add_prefix_space"""						,						lowercase__       ) != add_prefix_space:
                a_     :							List[Any]     						=  getattr(lowercase__						,						pre_tok_state.pop("""type"""       )       )
                a_     :							Optional[Any]     						=  add_prefix_space
                a_     :							List[str]     						=  pre_tok_class(**lowercase__       )

           a_     :							List[Any]     						=  add_prefix_space

           a_     :							Any     						=  """post_processor"""
           a_     :							Dict     						=  getattr(self.backend_tokenizer						,						lowercase__						,						lowercase__       )
           if tokenizer_component_instance:
                a_     :							Optional[Any]     						=  json.loads(tokenizer_component_instance.__getstate__()       )

                # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
                if "sep" in state:
                     a_     :							Dict     						=  tuple(state["""sep"""]       )
                if "cls" in state:
                     a_     :							List[str]     						=  tuple(state["""cls"""]       )

                a_     :							str     						=  False

                if state.get("""add_prefix_space"""						,						lowercase__       ) != add_prefix_space:
                     a_     :							Any     						=  add_prefix_space
                     a_     :							List[str]     						=  True

                if state.get("""trim_offsets"""						,						lowercase__       ) != trim_offsets:
                     a_     :							Optional[int]     						=  trim_offsets
                     a_     :							Tuple     						=  True

                if changes_to_apply:
                     a_     :							str     						=  getattr(lowercase__						,						state.pop("""type"""       )       )
                     a_     :							Union[str, Any]     						=  component_class(**lowercase__       )
                     setattr(self.backend_tokenizer						,						lowercase__						,						lowercase__       )






      @property
      def 							lowercase_						( self   :  List[str]       ):


           '''simple docstring'''

           if self._mask_token is None:
                if self.verbose:
                     logger.error("""Using mask_token, but it is not set yet."""       )
                return None
           return str(self._mask_token       )






      @mask_token.setter
      def 							lowercase_						( self   :  Union[str, Any]						,						lowercase__   :  Tuple       ):


           '''simple docstring'''

           a_     :							List[Any]     						=  AddedToken(lowercase__						,						lstrip=lowercase__						,						rstrip=lowercase__       ) if isinstance(lowercase__						,						lowercase__       ) else value
           a_     :							Union[str, Any]     						=  value






      def 							lowercase_						( self   :  Union[str, Any]						,						*lowercase__   :  Union[str, Any]						,						**lowercase__   :  Union[str, Any]       ):


           '''simple docstring'''

           a_     :							Any     						=  kwargs.get("""is_split_into_words"""						,						lowercase__       )
           assert self.add_prefix_space or not is_split_into_words, (
               F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
               "to use it with pretokenized inputs."
           )

           return super()._batch_encode_plus(*lowercase__						,						**lowercase__       )






      def 							lowercase_						( self   :  Dict						,						*lowercase__   :  int						,						**lowercase__   :  Dict       ):


           '''simple docstring'''

           a_     :							Any     						=  kwargs.get("""is_split_into_words"""						,						lowercase__       )

           assert self.add_prefix_space or not is_split_into_words, (
               F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
               "to use it with pretokenized inputs."
           )

           return super()._encode_plus(*lowercase__						,						**lowercase__       )






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


           '''simple docstring'''

           a_     :							str     						=  self._tokenizer.model.save(lowercase__						,						name=lowercase__       )
           return tuple(lowercase__       )






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


           '''simple docstring'''

           a_     :							int     						=  [self.bos_token_id] + token_ids_a + [self.eos_token_id]
           if token_ids_a is None:
                return output

           return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]






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


           '''simple docstring'''

           a_     :							Optional[Any]     						=  [self.sep_token_id]
           a_     :							Optional[int]     						=  [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]






'''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]				)				)
