





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

  return flax_key_tuple, flax_tensor




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)    ->       List[Any]:
  if "metadata" in layer:
    lowercase__           =  layer.split('metadata'		)
    lowercase__           =  ''.join(split_layer[0]		)[:-1]
    lowercase__           =  [tuple(('metadata' + split_layer[1]).split('/'		)		)]
  elif "kvstore" in layer:
    lowercase__           =  layer.split('kvstore'		)
    lowercase__           =  ''.join(split_layer[0]		)[:-1]
    lowercase__           =  [tuple(('kvstore' + split_layer[1]).split('/'		)		)]

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

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

  return curr_real_layer_name, split_layer, content




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)    ->       Optional[int]:
  lowercase__           =  rename_keys(_SCREAMING_SNAKE_CASE		)
  lowercase__           =  {}
  for k, v in current_block.items():
    lowercase__           =  v
  lowercase__           =  new_current_block
  torch.save(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE = WEIGHTS_NAME		)    ->       Optional[Any]:
  lowercase__           =  convert_file_size_to_int(_SCREAMING_SNAKE_CASE		)
  lowercase__           =  []
  lowercase__           =  {}
  lowercase__           =  0
  lowercase__           =  0

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

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

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

    # use the renaming pattern from the small conversion scripts
    lowercase__      ,   lowercase__           =  rename_base_flax_keys(tuple(key.split('/'		)		)		,						_SCREAMING_SNAKE_CASE		)
    lowercase__           =  '/'.join(_SCREAMING_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:
      lowercase__           =  os.path.join(
          _SCREAMING_SNAKE_CASE		,						weights_name.replace('.bin'		,						F"""-{len(_SCREAMING_SNAKE_CASE		)+1:05d}-of-???.bin"""		)		)
      rename_and_save_block(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)
      sharded_state_dicts.append(current_block.keys()		)
      del current_block
      lowercase__           =  {}
      lowercase__           =  0

    lowercase__           =  raw_weights.to(getattr(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)		)
    current_block_size += weight_size
    total_size += weight_size

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

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

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

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

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

  return metadata, index


if __name__ == "__main__":
      lowercase_          =		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.""",
      )
      lowercase_          =		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  ()    ->       Dict:
  from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

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

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









from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=UpperCAmelCase				)
class     SCREAMING_SNAKE_CASE       (UpperCAmelCase				):
   _UpperCamelCase   :		str			 =	field(default='language-modeling'      ,     metadata={'include_in_asdict_even_if_is_default': True}				)

   _UpperCamelCase   :		ClassVar[Features]			 =	Features({'text': Value('string'				)}				)
   _UpperCamelCase   :		ClassVar[Features]			 =	Features({}				)
   _UpperCamelCase   :		str			 =	"text"


   @property
   def 	SCREAMING_SNAKE_CASE_							(   self      :    List[str]	)->       Dict[str, str]:





     """simple docstring"""







     return {self.text_column: "text"}


