




'''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 					UpperCamelCase_(     snake_case							:	List[str]							,    snake_case							:	Optional[Any]					):

       '''simple docstring'''






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

       return flax_key_tuple, flax_tensor


def 					UpperCamelCase_(     snake_case							:	int							,    snake_case							:	Tuple							,    snake_case							:	int					):

       '''simple docstring'''






       if "metadata" in layer:
              snake_case_      =				layer.split("metadata"					)
              snake_case_      =				"".join(split_layer[0]					)[:-1]
              snake_case_      =				[tuple(("metadata" + split_layer[1]).split("/"					)					)]
       elif "kvstore" in layer:
              snake_case_      =				layer.split("kvstore"					)
              snake_case_      =				"".join(split_layer[0]					)[:-1]
              snake_case_      =				[tuple(("kvstore" + split_layer[1]).split("/"					)					)]

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

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

       return curr_real_layer_name, split_layer, content


def 					UpperCamelCase_(     snake_case							:	Tuple							,    snake_case							:	List[Any]					):

       '''simple docstring'''






       snake_case_      =				rename_keys(snake_case					)
       snake_case_      =				{}
       for k, v in current_block.items():
              snake_case_      =				v
       snake_case_      =				new_current_block
       torch.save(snake_case							,    snake_case					)


def 					UpperCamelCase_(     snake_case							:	Optional[Any]							,    snake_case							:	Any							,    snake_case							:	Optional[int]							,    snake_case							:	List[str]							,    snake_case							:	str = WEIGHTS_NAME					):

       '''simple docstring'''






       snake_case_      =				convert_file_size_to_int(snake_case					)
       snake_case_      =				[]
       snake_case_      =				{}
       snake_case_      =				0
       snake_case_      =				0

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

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

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

              # use the renaming pattern from the small conversion scripts
              snake_case_ ,				snake_case_      =				rename_base_flax_keys(tuple(key.split("/"					)					)							,    snake_case					)
              snake_case_      =				"/".join(snake_case					)

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

              snake_case_      =				raw_weights.to(getattr(snake_case							,    snake_case					)					)
              current_block_size += weight_size
              total_size += weight_size

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

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

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

    # Add the metadata
       snake_case_      =				{"total_size": total_size}
       snake_case_      =				{"metadata": metadata, "weight_map": weight_map}

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

       return metadata, index


if __name__ == "__main__":
  _SCREAMING_SNAKE_CASE							:       Union[str, 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.",
  )
  _SCREAMING_SNAKE_CASE							:       Optional[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 					UpperCamelCase_(     ):

       '''simple docstring'''






       from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

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

       snake_case_      =				tokenizer(snake_case							,    return_tensors="pt"					).input_ids
       snake_case_      =				model.generate(snake_case							,    decoder_start_token_id=0					)
       print(tokenizer.decode(out[0]					)					)










'''simple docstring'''





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

import sentencepiece as spm

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


_SCREAMING_SNAKE_CASE							:       Tuple						=			logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE							:       List[Any]						=			{"vocab_file": "sentencepiece.bpe.model"}

_SCREAMING_SNAKE_CASE							:       Tuple						=			{
    "vocab_file": {
        "moussaKam/mbarthez": "https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model",
        "moussaKam/barthez": "https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model",
        "moussaKam/barthez-orangesum-title": (
            "https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"
        ),
    },
}

_SCREAMING_SNAKE_CASE							:       List[Any]						=			{
    "moussaKam/mbarthez": 1024,
    "moussaKam/barthez": 1024,
    "moussaKam/barthez-orangesum-title": 1024,
}

_SCREAMING_SNAKE_CASE							:       Any						=			"▁"




class 			_snake_case		(     lowercase_							):
			lowerCAmelCase_					:				List[Any]							=   VOCAB_FILES_NAMES
			lowerCAmelCase_					:				Optional[Any]							=   PRETRAINED_VOCAB_FILES_MAP
			lowerCAmelCase_					:				int							=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCAmelCase_					:				Tuple							=   ["input_ids", "attention_mask"]




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

										'''simple docstring'''




										snake_case_      =				AddedToken(a__    ,							lstrip=a__    ,							rstrip=a__							) if isinstance(a__    ,							a__							) else mask_token

										snake_case_      =				{} if sp_model_kwargs is None else sp_model_kwargs

										super().__init__(
										    bos_token=a__    ,							eos_token=a__    ,							unk_token=a__    ,							sep_token=a__    ,							cls_token=a__    ,							pad_token=a__    ,							mask_token=a__    ,							sp_model_kwargs=self.sp_model_kwargs    ,							**a__    ,							)

										snake_case_      =				vocab_file
										snake_case_      =				spm.SentencePieceProcessor(**self.sp_model_kwargs							)
										self.sp_model.Load(str(a__							)							)

										snake_case_      =				{"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

										snake_case_      =				len(self.sp_model							) - 1
										snake_case_      =				{v: k for k, v in self.fairseq_tokens_to_ids.items()}




			def        lowerCAmelCase__    ( self    ,							a__    ,							a__ = None							)    ->    List[int]:

										'''simple docstring'''




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




			def        lowerCAmelCase__    ( self    ,							a__    ,							a__ = None    ,							a__ = False							)    ->    List[int]:

										'''simple docstring'''




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

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




			def        lowerCAmelCase__    ( self    ,							a__    ,							a__ = None							)    ->    List[int]:

										'''simple docstring'''




										snake_case_      =				[self.sep_token_id]
										snake_case_      =				[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]




			@property
			def        lowerCAmelCase__    ( self							)    ->    List[Any]:

										'''simple docstring'''




										return len(self.sp_model							)




			def        lowerCAmelCase__    ( self							)    ->    int:

										'''simple docstring'''




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




			def        lowerCAmelCase__    ( self    ,							a__							)    ->    List[str]:

										'''simple docstring'''




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




			def        lowerCAmelCase__    ( self    ,							a__							)    ->    int:

										'''simple docstring'''




										if token in self.fairseq_tokens_to_ids:
																	return self.fairseq_tokens_to_ids[token]
										snake_case_      =				self.sp_model.PieceToId(a__							)

										return spm_id if spm_id else self.unk_token_id




			def        lowerCAmelCase__    ( self    ,							a__							)    ->    Tuple:

										'''simple docstring'''




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




			def        lowerCAmelCase__    ( self    ,							a__							)    ->    Optional[Any]:

										'''simple docstring'''




										snake_case_      =				[]
										snake_case_      =				""
										snake_case_      =				False
										for token in tokens:
																	# make sure that special tokens are not decoded using sentencepiece model
																	if token in self.all_special_tokens:
																								if not prev_is_special:
																															out_string += " "
																								out_string += self.sp_model.decode(a__							) + token
																								snake_case_      =				True
																								snake_case_      =				[]
																	else:
																								current_sub_tokens.append(a__							)
																								snake_case_      =				False
										out_string += self.sp_model.decode(a__							)
										return out_string.strip()




			def __getstate__( self							)    ->    Dict:

										'''simple docstring'''




										snake_case_      =				self.__dict__.copy()
										snake_case_      =				None
										return state




			def __setstate__( self    ,							a__							)    ->    str:

										'''simple docstring'''




										snake_case_      =				d

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

										snake_case_      =				spm.SentencePieceProcessor(**self.sp_model_kwargs							)
										self.sp_model.Load(self.vocab_file							)




			def        lowerCAmelCase__    ( self    ,							a__    ,							a__ = None							)    ->    Tuple[str]:

										'''simple docstring'''




										if not os.path.isdir(a__							):
																	logger.error(F'Vocabulary path ({save_directory}) should be a directory'							)
																	return
										snake_case_      =				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__							) and os.path.isfile(self.vocab_file							):
																	copyfile(self.vocab_file    ,							a__							)
										elif not os.path.isfile(self.vocab_file							):
																	with open(a__    ,							"wb"							) as fi:
																								snake_case_      =				self.sp_model.serialized_model_proto()
																								fi.write(a__							)

										return (out_vocab_file,)




