
import functools
import logging
import os
import sys
import threading
from logging import (
    CRITICAL,  # NOQA
    DEBUG,  # NOQA
    ERROR,  # NOQA
    FATAL,  # NOQA
    INFO,  # NOQA
    NOTSET,  # NOQA
    WARN,  # NOQA
    WARNING,  # NOQA
)
from typing import Optional

import huggingface_hub.utils as hf_hub_utils
from tqdm import auto as tqdm_lib


UpperCAmelCase_       :   str   =       threading.Lock()
UpperCAmelCase_       :   Optional[logging.Handler]   =       None

UpperCAmelCase_       :   List[Any]   =       {
    'debug': logging.DEBUG,
    'info': logging.INFO,
    'warning': logging.WARNING,
    'error': logging.ERROR,
    'critical': logging.CRITICAL,
}

UpperCAmelCase_       :   str   =       logging.WARNING

UpperCAmelCase_       :   Dict   =       True







def 		A_       (	):



      """simple docstring"""




      _lowerCamelCase				:   List[str]								=   os.getenv("TRANSFORMERS_VERBOSITY"				,	_lowerCAmelCase							)
      if env_level_str:
            if env_level_str in log_levels:
                  return log_levels[env_level_str]
            else:
                  logging.getLogger().warning(
                      F'Unknown option TRANSFORMERS_VERBOSITY={env_level_str}, '
                      F'has to be one of: { ", ".join(log_levels.keys()							) }'							)
      return _default_log_level







def 		A_       (	):



      """simple docstring"""




      return __name__.split("."							)[0]







def 		A_       (	):



      """simple docstring"""




      return logging.getLogger(_get_library_name()							)







def 		A_       (	):



      """simple docstring"""




      global _default_handler

      with _lock:
            if _default_handler:
                  # This library has already configured the library root logger.
                  return
            _lowerCamelCase				:   Optional[int]								=   logging.StreamHandler()  # Set sys.stderr as stream.
            _lowerCamelCase				:   Optional[Any]								=   sys.stderr.flush

            # Apply our default configuration to the library root logger.
            _lowerCamelCase				:   List[str]								=   _get_library_root_logger()
            library_root_logger.addHandler(_default_handler							)
            library_root_logger.setLevel(_get_default_logging_level()							)
            _lowerCamelCase				:   Tuple								=   False







def 		A_       (	):



      """simple docstring"""




      global _default_handler

      with _lock:
            if not _default_handler:
                  return

            _lowerCamelCase				:   str								=   _get_library_root_logger()
            library_root_logger.removeHandler(_default_handler							)
            library_root_logger.setLevel(logging.NOTSET							)
            _lowerCamelCase				:   int								=   None







def 		A_       (	):



      """simple docstring"""




      return log_levels







def 		A_       (	_lowerCAmelCase   :    Optional[str] = None							):



      """simple docstring"""




      if name is None:
            _lowerCamelCase				:   Optional[Any]								=   _get_library_name()

      _configure_library_root_logger()
      return logging.getLogger(_lowerCAmelCase							)







def 		A_       (	):



      """simple docstring"""




      _configure_library_root_logger()
      return _get_library_root_logger().getEffectiveLevel()







def 		A_       (	_lowerCAmelCase   :    int							):



      """simple docstring"""




      _configure_library_root_logger()
      _get_library_root_logger().setLevel(_lowerCAmelCase							)







def 		A_       (	):



      """simple docstring"""




      return set_verbosity(_lowerCAmelCase							)







def 		A_       (	):



      """simple docstring"""




      return set_verbosity(_lowerCAmelCase							)







def 		A_       (	):



      """simple docstring"""




      return set_verbosity(_lowerCAmelCase							)







def 		A_       (	):



      """simple docstring"""




      return set_verbosity(_lowerCAmelCase							)







def 		A_       (	):



      """simple docstring"""




      _configure_library_root_logger()

      assert _default_handler is not None
      _get_library_root_logger().removeHandler(_default_handler							)







def 		A_       (	):



      """simple docstring"""




      _configure_library_root_logger()

      assert _default_handler is not None
      _get_library_root_logger().addHandler(_default_handler							)







def 		A_       (	_lowerCAmelCase   :    logging.Handler							):



      """simple docstring"""




      _configure_library_root_logger()

      assert handler is not None
      _get_library_root_logger().addHandler(_lowerCAmelCase							)







def 		A_       (	_lowerCAmelCase   :    logging.Handler							):



      """simple docstring"""




      _configure_library_root_logger()

      assert handler is not None and handler not in _get_library_root_logger().handlers
      _get_library_root_logger().removeHandler(_lowerCAmelCase							)







def 		A_       (	):



      """simple docstring"""




      _configure_library_root_logger()
      _lowerCamelCase				:   Union[str, Any]								=   False







def 		A_       (	):



      """simple docstring"""




      _configure_library_root_logger()
      _lowerCamelCase				:   List[Any]								=   True







def 		A_       (	):



      """simple docstring"""




      _lowerCamelCase				:   Optional[Any]								=   _get_library_root_logger().handlers

      for handler in handlers:
            _lowerCamelCase				:   Dict								=   logging.Formatter("[%(levelname)s|%(filename)s:%(lineno)s] %(asctime)s >> %(message)s"							)
            handler.setFormatter(_lowerCAmelCase							)







def 		A_       (	):



      """simple docstring"""




      _lowerCamelCase				:   Optional[Any]								=   _get_library_root_logger().handlers

      for handler in handlers:
            handler.setFormatter(_lowerCAmelCase							)







def 		A_       (	self   :    List[str]				,	*_lowerCAmelCase   :    int				,	**_lowerCAmelCase   :    str							):



      """simple docstring"""




      _lowerCamelCase				:   Union[str, Any]								=   os.getenv("TRANSFORMERS_NO_ADVISORY_WARNINGS"				,	_lowerCAmelCase							)
      if no_advisory_warnings:
            return
      self.warning(*_lowerCAmelCase				,	**_lowerCAmelCase							)


UpperCAmelCase_       :   Any   =       warning_advice







@functools.lru_cache(_lowerCAmelCase							)
def 		A_       (	self   :    Dict				,	*_lowerCAmelCase   :    Any				,	**_lowerCAmelCase   :    str							):



      """simple docstring"""




      self.warning(*_lowerCAmelCase				,	**_lowerCAmelCase							)


UpperCAmelCase_       :   int   =       warning_once

class  UpperCAmelCase__						:



    def __init__(	self      :       Optional[Any],*__A      :       List[str],**__A      :       Union[str, Any]     ):  # pylint: disable=unused-argument
          _lowerCamelCase				:   Optional[Any]								=   args[0] if args else None



    def __iter__(	self      :       str     ):
          return iter(self._iterator     )



    def __getattr__(	self      :       List[Any],__A      :       Union[str, Any]     ):

          def empty_fn(*__A      :       Optional[Any],**__A      :       Tuple     ):  # pylint: disable=unused-argument
                return

          return empty_fn



    def __enter__(	self      :       Any     ):
          return self



    def __exit__(	self      :       Any,__A      :       List[Any],__A      :       Optional[int],__A      :       int     ):
          return


class  UpperCAmelCase__						:



    def __call__(	self      :       Optional[Any],*__A      :       Optional[Any],**__A      :       Union[str, Any]     ):
          if _tqdm_active:
                return tqdm_lib.tqdm(*__A,**__A     )
          else:
                return EmptyTqdm(*__A,**__A     )



    def   lowerCamelCase_  (	self      :       str,*__A      :       Tuple,**__A      :       Optional[int]     ):
          _lowerCamelCase				:   Any								=   None
          if _tqdm_active:
                return tqdm_lib.tqdm.set_lock(*__A,**__A     )



    def   lowerCamelCase_  (	self      :       Optional[Any]     ):
          if _tqdm_active:
                return tqdm_lib.tqdm.get_lock()


UpperCAmelCase_       :   str   =       _tqdm_cls()







def 		A_       (	):



      """simple docstring"""




      global _tqdm_active
      return bool(_tqdm_active							)







def 		A_       (	):



      """simple docstring"""




      global _tqdm_active
      _lowerCamelCase				:   Union[str, Any]								=   True
      hf_hub_utils.enable_progress_bars()







def 		A_       (	):



      """simple docstring"""




      global _tqdm_active
      _lowerCamelCase				:   List[Any]								=   False
      hf_hub_utils.disable_progress_bars()

'''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 		A_       (	_lowerCAmelCase   :    Any				,	_lowerCAmelCase   :    Tuple							):



      """simple docstring"""




      if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
            # expert layer
            _lowerCamelCase				:   List[str]								=   flax_key_tuple[:-1] + ("weight",)
            _lowerCamelCase				:   Union[str, Any]								=   torch.permute(_lowerCAmelCase				,	(0, 2, 1)							)
      elif flax_key_tuple[-1] == "kernel" and ".".join(_lowerCAmelCase							):
            # linear layer
            _lowerCamelCase				:   Union[str, Any]								=   flax_key_tuple[:-1] + ("weight",)
            _lowerCamelCase				:   int								=   flax_tensor.T
      elif flax_key_tuple[-1] in ["scale", "embedding"]:
            _lowerCamelCase				:   Tuple								=   flax_key_tuple[:-1] + ("weight",)

      return flax_key_tuple, flax_tensor







def 		A_       (	_lowerCAmelCase   :    Optional[int]				,	_lowerCAmelCase   :    int				,	_lowerCAmelCase   :    Any							):



      """simple docstring"""




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

      else:
            _lowerCamelCase				:   Tuple								=   layer.split("/"							)
            _lowerCamelCase				:   int								=   "/".join(split_layer[:-1]							)
            _lowerCamelCase				:   Optional[Any]								=   (split_layer[-1],)

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

      return curr_real_layer_name, split_layer, content







def 		A_       (	_lowerCAmelCase   :    Tuple				,	_lowerCAmelCase   :    Optional[Any]							):



      """simple docstring"""




      _lowerCamelCase				:   str								=   rename_keys(_lowerCAmelCase							)
      _lowerCamelCase				:   Dict								=   {}
      for k, v in current_block.items():
            _lowerCamelCase				:   Union[str, Any]								=   v
      _lowerCamelCase				:   str								=   new_current_block
      torch.save(_lowerCAmelCase				,	_lowerCAmelCase							)







def 		A_       (	_lowerCAmelCase   :    Dict				,	_lowerCAmelCase   :    Tuple				,	_lowerCAmelCase   :    Optional[Any]				,	_lowerCAmelCase   :    Tuple				,	_lowerCAmelCase   :    str = WEIGHTS_NAME							):



      """simple docstring"""




      _lowerCamelCase				:   Dict								=   convert_file_size_to_int(_lowerCAmelCase							)
      _lowerCamelCase				:   Union[str, Any]								=   []
      _lowerCamelCase				:   Dict								=   {}
      _lowerCamelCase				:   Optional[int]								=   0
      _lowerCamelCase				:   List[str]								=   0

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

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

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

            # use the renaming pattern from the small conversion scripts
            _lowerCamelCase						,		_lowerCamelCase				:   Optional[Any]								=   rename_base_flax_keys(tuple(key.split("/"							)							)				,	_lowerCAmelCase							)
            _lowerCamelCase				:   Optional[Any]								=   "/".join(_lowerCAmelCase							)

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

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

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

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

      # Otherwise, let's build the index
      _lowerCamelCase				:   Union[str, Any]								=   {}
      _lowerCamelCase				:   str								=   {}
      for idx, shard in enumerate(_lowerCAmelCase							):
            _lowerCamelCase				:   Dict								=   weights_name.replace(
                ".bin"				,	F'-{idx+1:05d}-of-{len(_lowerCAmelCase							):05d}.bin'							)  # len(sharded_state_dicts):05d}
            _lowerCamelCase				:   Union[str, Any]								=   os.path.join(_lowerCAmelCase				,	weights_name.replace(".bin"				,	F'-{idx+1:05d}-of-???.bin'							)							)
            os.rename(_lowerCAmelCase				,	os.path.join(_lowerCAmelCase				,	_lowerCAmelCase							)							)
            _lowerCamelCase				:   Tuple								=   shard
            for key in shard:
                  _lowerCamelCase				:   str								=   shard_file

    # Add the metadata
      _lowerCamelCase				:   Optional[Any]								=   {"total_size": total_size}
      _lowerCamelCase				:   Dict								=   {"metadata": metadata, "weight_map": weight_map}

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

      return metadata, index


if __name__ == "__main__":
 UpperCAmelCase_       :   List[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.',
 )
 UpperCAmelCase_       :   Dict   =       parser.parse_args()
 shard_on_the_fly(
     args.switch_tax_checkpoint_path,
     args.pytorch_dump_folder_path,
     args.max_shard_size,
     args.dtype,
 )







def 		A_       (	):



      """simple docstring"""




      from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

      _lowerCamelCase				:   Optional[int]								=   SwitchTransformersConfig.from_pretrained("google/switch-base-8"							)
      config.save_pretrained("/home/arthur_huggingface_co/transformers/switch_converted"							)
      _lowerCamelCase				:   int								=   SwitchTransformersForConditionalGeneration.from_pretrained(
          "/home/arthur_huggingface_co/transformers/switch_converted"				,	device_map="auto"							)

      _lowerCamelCase				:   List[str]								=   TaTokenizer.from_pretrained("t5-small"							)
      _lowerCamelCase				:   Tuple								=   "A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>."

      _lowerCamelCase				:   Optional[int]								=   tokenizer(_lowerCAmelCase				,	return_tensors="pt"							).input_ids
      _lowerCamelCase				:   List[Any]								=   model.generate(_lowerCAmelCase				,	decoder_start_token_id=0							)
      print(tokenizer.decode(out[0]							)							)