






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      _lowerCamelCase	(						__A    :							int		,		__A    :							str )      ->					Dict:
    if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
        # expert layer
        _UpperCAmelCase				:      Dict			  =				flax_key_tuple[:-1] + ('''weight''',)
        _UpperCAmelCase				:      List[str]			  =				torch.permute(__A		,		(0, 2, 1) )
    elif flax_key_tuple[-1] == "kernel" and ".".join(__A ):
        # linear layer
        _UpperCAmelCase				:      int			  =				flax_key_tuple[:-1] + ('''weight''',)
        _UpperCAmelCase				:      Optional[int]			  =				flax_tensor.T
    elif flax_key_tuple[-1] in ["scale", "embedding"]:
        _UpperCAmelCase				:      Any			  =				flax_key_tuple[:-1] + ('''weight''',)

    return flax_key_tuple, flax_tensor

def      _lowerCamelCase	(						__A    :							List[Any]		,		__A    :							str		,		__A    :							List[Any] )      ->					Optional[Any]:
    if "metadata" in layer:
        _UpperCAmelCase				:      int			  =				layer.split('''metadata''' )
        _UpperCAmelCase				:      List[str]			  =				''''''.join(split_layer[0] )[:-1]
        _UpperCAmelCase				:      Dict			  =				[tuple(('''metadata''' + split_layer[1]).split('''/''' ) )]
    elif "kvstore" in layer:
        _UpperCAmelCase				:      Any			  =				layer.split('''kvstore''' )
        _UpperCAmelCase				:      str			  =				''''''.join(split_layer[0] )[:-1]
        _UpperCAmelCase				:      List[Any]			  =				[tuple(('''kvstore''' + split_layer[1]).split('''/''' ) )]

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

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

    return curr_real_layer_name, split_layer, content

def      _lowerCamelCase	(						__A    :							Union[str, Any]		,		__A    :							Union[str, Any] )      ->					Dict:
    _UpperCAmelCase				:      List[Any]			  =				rename_keys(__A )
    _UpperCAmelCase				:      List[str]			  =				{}
    for k, v in current_block.items():
        _UpperCAmelCase				:      List[str]			  =				v
    _UpperCAmelCase				:      int			  =				new_current_block
    torch.save(__A		,		__A )

def      _lowerCamelCase	(						__A    :							Optional[int]		,		__A    :							Tuple		,		__A    :							Any		,		__A    :							Dict		,		__A    :							str = WEIGHTS_NAME )      ->					List[str]:
    _UpperCAmelCase				:      int			  =				convert_file_size_to_int(__A )
    _UpperCAmelCase				:      List[str]			  =				[]
    _UpperCAmelCase				:      Any			  =				{}
    _UpperCAmelCase				:      Tuple			  =				0
    _UpperCAmelCase				:      List[Any]			  =				0

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

    _UpperCAmelCase				:      Tuple			  =				{}
    for layer in checkpoint_info.keys():
        _UpperCAmelCase  , _UpperCAmelCase  , _UpperCAmelCase				:      List[str]			  =				get_key_and_tensorstore_dict(
            __A		,		__A		,		__A )
        if curr_real_layer_name in all_layers:
            _UpperCAmelCase				:      str			  =				content
        else:
            _UpperCAmelCase				:      Any			  =				{split_layer[-1]: content}

    for key in all_layers.keys():
        # open tensorstore file
        _UpperCAmelCase				:      Union[str, Any]			  =				ts.open(unflatten_dict(all_layers[key] ) ).result().read().result()
        _UpperCAmelCase				:      Optional[Any]			  =				torch.tensor(__A )
        _UpperCAmelCase				:      Optional[int]			  =				raw_weights.numel() * dtype_byte_size(raw_weights.dtype )

        # use the renaming pattern from the small conversion scripts
        _UpperCAmelCase  , _UpperCAmelCase				:      Any			  =				rename_base_flax_keys(tuple(key.split('''/''' ) )		,		__A )
        _UpperCAmelCase				:      Any			  =				'''/'''.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				:      Any			  =				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				:      List[str]			  =				{}
            _UpperCAmelCase				:      List[str]			  =				0

        _UpperCAmelCase				:      Union[str, Any]			  =				raw_weights.to(getattr(__A		,		__A ) )
        current_block_size += weight_size
        total_size += weight_size

    # Add the last block
    _UpperCAmelCase				:      str			  =				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				:      Optional[int]			  =				{}
    _UpperCAmelCase				:      Union[str, Any]			  =				{}
    for idx, shard in enumerate(__A ):
        _UpperCAmelCase				:      Dict			  =				weights_name.replace(
            '''.bin'''		,		f'''-{idx+1:05d}-of-{len(__A ):05d}.bin''' )  # len(sharded_state_dicts):05d}
        _UpperCAmelCase				:      str			  =				os.path.join(__A		,		weights_name.replace('''.bin'''		,		f'''-{idx+1:05d}-of-???.bin''' ) )
        os.rename(__A		,		os.path.join(__A		,		__A ) )
        _UpperCAmelCase				:      Optional[int]			  =				shard
        for key in shard:
            _UpperCAmelCase				:      List[Any]			  =				shard_file

    # Add the metadata
    _UpperCAmelCase				:      List[Any]			  =				{'''total_size''': total_size}
    _UpperCAmelCase				:      List[Any]			  =				{'''metadata''': metadata, '''weight_map''': weight_map}

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

    return metadata, index


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE 							=						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.',
  )
  SCREAMING_SNAKE_CASE 							=						parser.parse_args()
  shard_on_the_fly(
      args.switch_tax_checkpoint_path,
      args.pytorch_dump_folder_path,
      args.max_shard_size,
      args.dtype,
  )


def      _lowerCamelCase	(						)      ->					List[str]:
    from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

    _UpperCAmelCase				:      Optional[Any]			  =				TaTokenizer.from_pretrained('''t5-small''' )
    _UpperCAmelCase				:      Dict			  =				'''A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.'''

    _UpperCAmelCase				:      Tuple			  =				tokenizer(__A		,		return_tensors='''pt''' ).input_ids
    _UpperCAmelCase				:      Tuple			  =				model.generate(__A		,		decoder_start_token_id=0 )
    print(tokenizer.decode(out[0] ) )












import json
from typing import TYPE_CHECKING, 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_blenderbot import BlenderbotTokenizer


if TYPE_CHECKING:
  from transformers.pipelines.conversational import Conversation

SCREAMING_SNAKE_CASE 							=						logging.get_logger(__name__)


SCREAMING_SNAKE_CASE 							=						{
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
    'tokenizer_config_file': 'tokenizer_config.json',
}

SCREAMING_SNAKE_CASE 							=						{
    'vocab_file': {'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json'},
    'merges_file': {'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt'},
    'tokenizer_config_file': {
        'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json'
    },
}

SCREAMING_SNAKE_CASE 							=						{'facebook/blenderbot-3B': 128}
class 							A_    (						__lowercase			):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:			Tuple =					VOCAB_FILES_NAMES
 _SCREAMING_SNAKE_CASE							:			Optional[Any] =					PRETRAINED_VOCAB_FILES_MAP
 _SCREAMING_SNAKE_CASE							:			Optional[int] =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _SCREAMING_SNAKE_CASE							:			List[Any] =					["input_ids", "attention_mask"]
 _SCREAMING_SNAKE_CASE							:			Dict =					BlenderbotTokenizer


 def __init__(	self							,  _A=None							,  _A=None							,  _A=None							,  _A="replace"							,  _A="<s>"							,  _A="</s>"							,  _A="</s>"							,  _A="<s>"							,  _A="<unk>"							,  _A="<pad>"							,  _A="<mask>"							,  _A=False							,  _A=True							,  **_A							,  ) ->		str:

     """simple docstring"""



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

     _UpperCAmelCase				:      Any			  =				json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
     if pre_tok_state.get('''add_prefix_space'''							,  _A) != add_prefix_space:
         _UpperCAmelCase				:      Optional[int]			  =				getattr(_A							,  pre_tok_state.pop('''type'''))
         _UpperCAmelCase				:      Optional[int]			  =				add_prefix_space
         _UpperCAmelCase				:      Dict			  =				pre_tok_class(**_A)

     _UpperCAmelCase				:      Dict			  =				add_prefix_space

     _UpperCAmelCase				:      Tuple			  =				'''post_processor'''
     _UpperCAmelCase				:      str			  =				getattr(self.backend_tokenizer							,  _A							,  _A)
     if tokenizer_component_instance:
         _UpperCAmelCase				:      str			  =				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:
             _UpperCAmelCase				:      str			  =				tuple(state['''sep'''])
         if "cls" in state:
             _UpperCAmelCase				:      List[str]			  =				tuple(state['''cls'''])

         _UpperCAmelCase				:      List[str]			  =				False

         if state.get('''add_prefix_space'''							,  _A) != add_prefix_space:
             _UpperCAmelCase				:      int			  =				add_prefix_space
             _UpperCAmelCase				:      Optional[int]			  =				True

         if state.get('''trim_offsets'''							,  _A) != trim_offsets:
             _UpperCAmelCase				:      List[Any]			  =				trim_offsets
             _UpperCAmelCase				:      Any			  =				True

         if changes_to_apply:
             _UpperCAmelCase				:      Optional[Any]			  =				getattr(_A							,  state.pop('''type'''))
             _UpperCAmelCase				:      Union[str, Any]			  =				component_class(**_A)
             setattr(self.backend_tokenizer							,  _A							,  _A)


 @property
 # Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
 def  snake_case__				(	self) ->		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  snake_case__				(	self							,  _A) ->		str:

     """simple docstring"""



     _UpperCAmelCase				:      Optional[int]			  =				AddedToken(_A							,  lstrip=_A							,  rstrip=_A) if isinstance(_A							,  _A) else value
     _UpperCAmelCase				:      Union[str, Any]			  =				value


 def  snake_case__				(	self							,  *_A							,  **_A) ->		BatchEncoding:

     """simple docstring"""



     _UpperCAmelCase				:      List[Any]			  =				kwargs.get('''is_split_into_words'''							,  _A)
     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(*_A							,  **_A)


 def  snake_case__				(	self							,  *_A							,  **_A) ->		BatchEncoding:

     """simple docstring"""



     _UpperCAmelCase				:      Tuple			  =				kwargs.get('''is_split_into_words'''							,  _A)

     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(*_A							,  **_A)


 def  snake_case__				(	self							,  _A							,  _A = None) ->		Tuple[str]:

     """simple docstring"""



     _UpperCAmelCase				:      str			  =				self._tokenizer.model.save(_A							,  name=_A)
     return tuple(_A)


 def  snake_case__				(	self							,  _A							,  _A = None) ->		List[int]:

     """simple docstring"""



     _UpperCAmelCase				:      Optional[int]			  =				[self.sep_token_id]
     _UpperCAmelCase				:      str			  =				[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  snake_case__				(	self							,  _A							,  _A = None) ->		Optional[Any]:

     """simple docstring"""



     return token_ids_a + [self.eos_token_id]


 def  snake_case__				(	self							,  _A) ->		List[int]:

     """simple docstring"""



     _UpperCAmelCase				:      Dict			  =				[]
     for is_user, text in conversation.iter_texts():
         if is_user:
             # We need to space prefix as it's being done within blenderbot
             inputs.append(''' ''' + text)
         else:
             # Generated responses should contain them already.
             inputs.append(_A)

     _UpperCAmelCase				:      int			  =				'''  '''.join(_A)
     _UpperCAmelCase				:      Optional[int]			  =				self.encode(_A)
     if len(_A) > self.model_max_length:
         _UpperCAmelCase				:      str			  =				input_ids[-self.model_max_length :]
         logger.warning(f'''Trimmed input from conversation as it was longer than {self.model_max_length} tokens.''')
     return input_ids




