





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__       (    SCREAMING_SNAKE_CASE_   :      Union[str, Any]				,	SCREAMING_SNAKE_CASE_   :      List[Any]						)  ->  str:






   """simple docstring"""







   if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
      # expert layer
      _UpperCAmelCase           =   flax_key_tuple[:-1] + ('''weight''',)
      _UpperCAmelCase           =   torch.permute(SCREAMING_SNAKE_CASE_				,	(0, 2, 1)						)
   elif flax_key_tuple[-1] == "kernel" and ".".join(SCREAMING_SNAKE_CASE_						):
      # 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      A__       (    SCREAMING_SNAKE_CASE_   :      Optional[int]				,	SCREAMING_SNAKE_CASE_   :      Dict				,	SCREAMING_SNAKE_CASE_   :      List[Any]						)  ->  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      A__       (    SCREAMING_SNAKE_CASE_   :      Union[str, Any]				,	SCREAMING_SNAKE_CASE_   :      Optional[int]						)  ->  Any:






   """simple docstring"""







   _UpperCAmelCase           =   rename_keys(SCREAMING_SNAKE_CASE_						)
   _UpperCAmelCase           =   {}
   for k, v in current_block.items():
      _UpperCAmelCase           =   v
   _UpperCAmelCase           =   new_current_block
   torch.save(SCREAMING_SNAKE_CASE_				,	SCREAMING_SNAKE_CASE_						)
def      A__       (    SCREAMING_SNAKE_CASE_   :      str				,	SCREAMING_SNAKE_CASE_   :      List[str]				,	SCREAMING_SNAKE_CASE_   :      List[Any]				,	SCREAMING_SNAKE_CASE_   :      Optional[int]				,	SCREAMING_SNAKE_CASE_   :      str = WEIGHTS_NAME						)  ->  Tuple:






   """simple docstring"""







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

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

   _UpperCAmelCase           =   {}
   for layer in checkpoint_info.keys():
      _UpperCAmelCase       ,      _UpperCAmelCase       ,      _UpperCAmelCase           =   get_key_and_tensorstore_dict(
          SCREAMING_SNAKE_CASE_				,	SCREAMING_SNAKE_CASE_				,	SCREAMING_SNAKE_CASE_						)
      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(SCREAMING_SNAKE_CASE_						)
      _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('''/'''						)						)				,	SCREAMING_SNAKE_CASE_						)
      _UpperCAmelCase           =   '''/'''.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:
         _UpperCAmelCase           =   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
         _UpperCAmelCase           =   {}
         _UpperCAmelCase           =   0

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

   # Add the last block
   _UpperCAmelCase           =   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
   _UpperCAmelCase           =   {}
   _UpperCAmelCase           =   {}
   for idx, shard in enumerate(SCREAMING_SNAKE_CASE_						):
      _UpperCAmelCase           =   weights_name.replace(
          '''.bin'''				,	F'''-{idx+1:05d}-of-{len(SCREAMING_SNAKE_CASE_						):05d}.bin'''						)  # len(sharded_state_dicts):05d}
      _UpperCAmelCase           =   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_						)						)
      _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(SCREAMING_SNAKE_CASE_				,	SCREAMING_SNAKE_CASE_						)				,	'''w'''				,	encoding='''utf-8'''						) as f:
      _UpperCAmelCase           =   json.dumps(SCREAMING_SNAKE_CASE_				,	indent=2				,	sort_keys=SCREAMING_SNAKE_CASE_						) + '''\n'''
      f.write(SCREAMING_SNAKE_CASE_						)

   return metadata, index


if __name__ == "__main__":
   UpperCAmelCase_           =	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_           =	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__       (    )  ->  Dict:






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






import numpy as np
def      A__       (    SCREAMING_SNAKE_CASE_   :      np.ndarray				,	SCREAMING_SNAKE_CASE_   :      float						)  ->  np.ndarray:






   """simple docstring"""







   return np.where(vector > 0				,	SCREAMING_SNAKE_CASE_				,	(alpha * (np.exp(SCREAMING_SNAKE_CASE_						) - 1))						)


if __name__ == "__main__":
   import doctest

   doctest.testmod()