'''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       (      _UpperCamelCase  :  Optional[int]							,    _UpperCamelCase  :  Optional[Any]					):


      '''simple docstring'''


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

      return flax_key_tuple, flax_tensor



def  __lowerCamelCase       (      _UpperCamelCase  :  List[Any]							,    _UpperCamelCase  :  int							,    _UpperCamelCase  :  List[Any]					):


      '''simple docstring'''


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

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

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

      return curr_real_layer_name, split_layer, content



def  __lowerCamelCase       (      _UpperCamelCase  :  Union[str, Any]							,    _UpperCamelCase  :  Optional[int]					):


      '''simple docstring'''


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



def  __lowerCamelCase       (      _UpperCamelCase  :  List[str]							,    _UpperCamelCase  :  List[str]							,    _UpperCamelCase  :  str							,    _UpperCamelCase  :  Any							,    _UpperCamelCase  :  str = WEIGHTS_NAME					):


      '''simple docstring'''


      UpperCAmelCase_            =  convert_file_size_to_int(_UpperCamelCase					)
      UpperCAmelCase_            =  []
      UpperCAmelCase_            =  {}
      UpperCAmelCase_            =  0
      UpperCAmelCase_            =  0

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

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

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

            # use the renaming pattern from the small conversion scripts
            UpperCAmelCase_      ,		UpperCAmelCase_            =  rename_base_flax_keys(tuple(key.split('''/'''					)					)							,    _UpperCamelCase					)
            UpperCAmelCase_            =  '''/'''.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:
                  UpperCAmelCase_            =  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
                  UpperCAmelCase_            =  {}
                  UpperCAmelCase_            =  0

            UpperCAmelCase_            =  raw_weights.to(getattr(_UpperCamelCase							,    _UpperCamelCase					)					)
            current_block_size += weight_size
            total_size += weight_size

      # Add the last block
      UpperCAmelCase_            =  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
      UpperCAmelCase_            =  {}
      UpperCAmelCase_            =  {}
      for idx, shard in enumerate(_UpperCamelCase					):
            UpperCAmelCase_            =  weights_name.replace(
                '''.bin'''							,    F"""-{idx+1:05d}-of-{len(_UpperCamelCase					):05d}.bin"""					)  # len(sharded_state_dicts):05d}
            UpperCAmelCase_            =  os.path.join(_UpperCamelCase							,    weights_name.replace('''.bin'''							,    F"""-{idx+1:05d}-of-???.bin"""					)					)
            os.rename(_UpperCamelCase							,    os.path.join(_UpperCamelCase							,    _UpperCamelCase					)					)
            UpperCAmelCase_            =  shard
            for key in shard:
                  UpperCAmelCase_            =  shard_file

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

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

      return metadata, index


if __name__ == "__main__":
      lowercase__  :      Optional[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.",
      )
      lowercase__  :      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  __lowerCamelCase       (      ):


      '''simple docstring'''


      from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

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

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






'''simple docstring'''
import re



def  __lowerCamelCase       (      _UpperCamelCase  :  str					):


      '''simple docstring'''


      return [char.split() for char in re.split(R'''[^ a-z A-Z 0-9 \s]'''							,    str_					)]



def  __lowerCamelCase       (      _UpperCamelCase  :  str					):


      '''simple docstring'''


      UpperCAmelCase_            =  split_input(str_					)
      return "".join(
          [''''''.join([char.capitalize() for char in sub_str]					) for sub_str in string_split]					)



def  __lowerCamelCase       (      _UpperCamelCase  :  str							,    _UpperCamelCase  :  bool							,    _UpperCamelCase  :  str					):


      '''simple docstring'''


      try:
            UpperCAmelCase_            =  split_input(_UpperCamelCase					)
            if upper:
                  UpperCAmelCase_            =  ''''''.join(
                      [
                          separator.join([char.upper() for char in sub_str]					)
                          for sub_str in string_split
                      ]					)
            else:
                  UpperCAmelCase_            =  ''''''.join(
                      [
                          separator.join([char.lower() for char in sub_str]					)
                          for sub_str in string_split
                      ]					)
            return res_str
      except IndexError:
            return "not valid string"



def  __lowerCamelCase       (      _UpperCamelCase  :  str					):


      '''simple docstring'''


      return to_simple_case(_UpperCamelCase					)



def  __lowerCamelCase       (      _UpperCamelCase  :  str					):


      '''simple docstring'''


      try:
            UpperCAmelCase_            =  to_simple_case(_UpperCamelCase					)
            return res_str[0].lower() + res_str[1:]
      except IndexError:
            return "not valid string"



def  __lowerCamelCase       (      _UpperCamelCase  :  str							,    _UpperCamelCase  :  bool					):


      '''simple docstring'''


      return to_complex_case(_UpperCamelCase							,    _UpperCamelCase							,    '''_'''					)



def  __lowerCamelCase       (      _UpperCamelCase  :  str							,    _UpperCamelCase  :  bool					):


      '''simple docstring'''


      return to_complex_case(_UpperCamelCase							,    _UpperCamelCase							,    '''-'''					)


if __name__ == "__main__":
      __import__("doctest").testmod()





