
'''simple docstring'''
from ..utils import DummyObject, requires_backends






class 	snake_case__       (      metaclass=_SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		lowerCamelCase     							=						["""torch""", """scipy"""]



		def __init__(      self				:			int  ,	*UpperCamelCase__				:			Dict  ,	**UpperCamelCase__				:			int      )						->			Optional[int]:






				"""simple docstring"""







				requires_backends(self  ,	['''torch''', '''scipy''']      )



		@classmethod
		def       lowerCAmelCase				(      cls				:			Tuple  ,	*UpperCamelCase__				:			Dict  ,	**UpperCamelCase__				:			List[Any]      )						->			str:






				"""simple docstring"""







				requires_backends(cls  ,	['''torch''', '''scipy''']      )



		@classmethod
		def       lowerCAmelCase				(      cls				:			Dict  ,	*UpperCamelCase__				:			str  ,	**UpperCamelCase__				:			List[Any]      )						->			List[str]:






				"""simple docstring"""







				requires_backends(cls  ,	['''torch''', '''scipy''']      )









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_    (    _UpperCAmelCase							:str  ,						_UpperCAmelCase							:Optional[Any]				)	->				Optional[int]:
     '''simple docstring'''


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

     return flax_key_tuple, flax_tensor

def      UpperCAmelCase_    (    _UpperCAmelCase							:Optional[int]  ,						_UpperCAmelCase							:str  ,						_UpperCAmelCase							:Any				)	->				Dict:
     '''simple docstring'''


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

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

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

     return curr_real_layer_name, split_layer, content

def      UpperCAmelCase_    (    _UpperCAmelCase							:int  ,						_UpperCAmelCase							:Union[str, Any]				)	->				Any:
     '''simple docstring'''


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

def      UpperCAmelCase_    (    _UpperCAmelCase							:Union[str, Any]  ,						_UpperCAmelCase							:str  ,						_UpperCAmelCase							:List[Any]  ,						_UpperCAmelCase							:str  ,						_UpperCAmelCase							:str = WEIGHTS_NAME				)	->				Optional[int]:
     '''simple docstring'''


     A_          =       convert_file_size_to_int(_UpperCAmelCase				)
     A_          =       []
     A_          =       {}
     A_          =       0
     A_          =       0

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

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

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

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

          A_          =       raw_weights.to(getattr(_UpperCAmelCase  ,						_UpperCAmelCase				)				)
          current_block_size += weight_size
          total_size += weight_size

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

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

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

     return metadata, index


if __name__ == "__main__":
      a__      :     Dict         =		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.',
      )
      a__      :     Tuple         =		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_    (    )	->				Union[str, Any]:
     '''simple docstring'''


     from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

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

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



