



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

  return flax_key_tuple, flax_tensor
def    lowerCamelCase__ (		_A				,    _A				,    _A       ):
  if "metadata" in layer:
    a		:      List[str]       =  layer.split('metadata'       )
    a		:      str       =  ''.join(split_layer[0]       )[:-1]
    a		:      Optional[Any]       =  [tuple(('metadata' + split_layer[1]).split('/'       )       )]
  elif "kvstore" in layer:
    a		:      Tuple       =  layer.split('kvstore'       )
    a		:      List[Any]       =  ''.join(split_layer[0]       )[:-1]
    a		:      Tuple       =  [tuple(('kvstore' + split_layer[1]).split('/'       )       )]

  else:
    a		:      Dict       =  layer.split('/'       )
    a		:      int       =  '/'.join(split_layer[:-1]       )
    a		:      Union[str, Any]       =  (split_layer[-1],)

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

  return curr_real_layer_name, split_layer, content
def    lowerCamelCase__ (		_A				,    _A       ):
  a		:      int       =  rename_keys(_A       )
  a		:      Union[str, Any]       =  {}
  for k, v in current_block.items():
    a		:      Tuple       =  v
  a		:      List[str]       =  new_current_block
  torch.save(_A				,    _A       )
def    lowerCamelCase__ (		_A				,    _A				,    _A				,    _A				,    _A = WEIGHTS_NAME       ):
  a		:      Optional[Any]       =  convert_file_size_to_int(_A       )
  a		:      Any       =  []
  a		:      Tuple       =  {}
  a		:      Union[str, Any]       =  0
  a		:      Union[str, Any]       =  0

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

  a		:      Tuple       =  {}
  for layer in checkpoint_info.keys():
    a     ,			a     ,			a		:      str       =  get_key_and_tensorstore_dict(
        _A				,    _A				,    _A       )
    if curr_real_layer_name in all_layers:
      a		:      Dict       =  content
    else:
      a		:      Dict       =  {split_layer[-1]: content}

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

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

    a		:      Optional[int]       =  raw_weights.to(getattr(_A				,    _A       )       )
    current_block_size += weight_size
    total_size += weight_size

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

    # Add the metadata
  a		:      Dict       =  {'total_size': total_size}
  a		:      str       =  {'metadata': metadata, 'weight_map': weight_map}

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

  return metadata, index


if __name__ == "__main__":
     lowerCAmelCase:      int		 =		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:      str		 =		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__ (		):
  from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

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

  a		:      Union[str, Any]       =  tokenizer(_A				,    return_tensors='pt'       ).input_ids
  a		:      Union[str, Any]       =  model.generate(_A				,    decoder_start_token_id=0       )
  print(tokenizer.decode(out[0]       )       )




'''simple docstring'''






def    lowerCamelCase__ (		_A = 6008_5147_5143       ):

  try:
    a		:      Optional[int]       =  int(_A       )
  except (TypeError, ValueError):
    raise TypeError('Parameter n must be int or castable to int.'       )
  if n <= 0:
    raise ValueError('Parameter n must be greater than or equal to one.'       )
  a		:      Any       =  1
  a		:      Union[str, Any]       =  2
  while i * i <= n:
    while n % i == 0:
      a		:      str       =  i
      n //= i
    i += 1
  if n > 1:
    a		:      Any       =  n
  return int(_A       )


if __name__ == "__main__":
     print(F"{solution() = }")