


"""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 					lowercase      (    A_  ,       A_	)->				Dict:
  '''simple docstring'''







  if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
    # expert layer
    a						:		List[str]											=      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						:		str											=      flax_key_tuple[:-1] + ("weight",)
    a						:		Union[str, Any]											=      flax_tensor.T
  elif flax_key_tuple[-1] in ["scale", "embedding"]:
    a						:		Union[str, Any]											=      flax_key_tuple[:-1] + ("weight",)

  return flax_key_tuple, flax_tensor


def 					lowercase      (    A_  ,       A_  ,       A_	)->				List[Any]:
  '''simple docstring'''







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

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

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

  return curr_real_layer_name, split_layer, content


def 					lowercase      (    A_  ,       A_	)->				Optional[int]:
  '''simple docstring'''







  a						:		Dict											=      rename_keys(A_	)
  a						:		str											=      {}
  for k, v in current_block.items():
    a						:		int											=      v
  a						:		Any											=      new_current_block
  torch.save(A_  ,       A_	)


def 					lowercase      (    A_  ,       A_  ,       A_  ,       A_  ,       A_ = WEIGHTS_NAME	)->				Optional[Any]:
  '''simple docstring'''







  a						:		Tuple											=      convert_file_size_to_int(A_	)
  a						:		Union[str, Any]											=      []
  a						:		Optional[int]											=      {}
  a						:		List[str]											=      0
  a						:		Dict											=      0

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

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

  for key in all_layers.keys():
    # open tensorstore file
    a						:		Optional[int]											=      ts.open(unflatten_dict(all_layers[key]	)	).result().read().result()
    a						:		Any											=      torch.tensor(A_	)
    a						:		Union[str, Any]											=      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						:		Optional[int]											=      "/".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						:		Union[str, Any]											=      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						:		Union[str, Any]											=      {}
      a						:		Any											=      0

    a						:		Tuple											=      raw_weights.to(getattr(A_  ,       A_	)	)
    current_block_size += weight_size
    total_size += weight_size

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

    # Add the metadata
  a						:		List[Any]											=      {"total_size": total_size}
  a						:		List[str]											=      {"metadata": metadata, "weight_map": weight_map}

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

  return metadata, index


if __name__ == "__main__":
 __lowercase    =				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    =				parser.parse_args()
 shard_on_the_fly(
     args.switch_tax_checkpoint_path,
     args.pytorch_dump_folder_path,
     args.max_shard_size,
     args.dtype,
 )







def 					lowercase      (    )->				Optional[Any]:
  '''simple docstring'''







  from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

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

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










"""simple docstring"""







from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowercase    =				logging.get_logger(__name__)

__lowercase    =				{
    """EleutherAI/gpt-neox-20b""": """https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json""",
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}






class 						_A  (  _a  ):







    """simple docstring"""




    UpperCAmelCase	:							Union[str, Any]     =	"""gpt_neox"""






    def __init__( self    :   List[str]    ,			__UpperCAmelCase    :   Tuple=50432    ,			__UpperCAmelCase    :   str=6144    ,			__UpperCAmelCase    :   Any=44    ,			__UpperCAmelCase    :   Union[str, Any]=64    ,			__UpperCAmelCase    :   Dict=24576    ,			__UpperCAmelCase    :   Any="gelu"    ,			__UpperCAmelCase    :   List[Any]=0.25    ,			__UpperCAmelCase    :   Optional[Any]=10000    ,			__UpperCAmelCase    :   Tuple=0.0    ,			__UpperCAmelCase    :   Union[str, Any]=0.0    ,			__UpperCAmelCase    :   Optional[int]=0.1    ,			__UpperCAmelCase    :   List[Any]=2048    ,			__UpperCAmelCase    :   Optional[Any]=0.02    ,			__UpperCAmelCase    :   Optional[Any]=1e-5    ,			__UpperCAmelCase    :   Dict=True    ,			__UpperCAmelCase    :   Union[str, Any]=0    ,			__UpperCAmelCase    :   Union[str, Any]=2    ,			__UpperCAmelCase    :   Any=False    ,			__UpperCAmelCase    :   List[Any]=True    ,			__UpperCAmelCase    :   int=None    ,			**__UpperCAmelCase    :   Tuple    ,			):
      super().__init__(bos_token_id=__UpperCAmelCase    ,			eos_token_id=__UpperCAmelCase    ,			**__UpperCAmelCase)
      a						:		List[Any]											=      vocab_size
      a						:		Optional[int]											=      max_position_embeddings
      a						:		List[Any]											=      hidden_size
      a						:		Union[str, Any]											=      num_hidden_layers
      a						:		int											=      num_attention_heads
      a						:		Union[str, Any]											=      intermediate_size
      a						:		Optional[Any]											=      hidden_act
      a						:		Dict											=      rotary_pct
      a						:		Any											=      rotary_emb_base
      a						:		Dict											=      attention_dropout
      a						:		List[str]											=      hidden_dropout
      a						:		List[str]											=      classifier_dropout
      a						:		Any											=      initializer_range
      a						:		Union[str, Any]											=      layer_norm_eps
      a						:		int											=      use_cache
      a						:		int											=      tie_word_embeddings
      a						:		str											=      use_parallel_residual
      a						:		Union[str, Any]											=      rope_scaling
      self._rope_scaling_validation()

      if self.hidden_size % self.num_attention_heads != 0:
        raise ValueError(
            "The hidden size is not divisble by the number of attention heads! Make sure to update them!")






    def      __snake_case							( self    :   Any):
      if self.rope_scaling is None:
        return

      if not isinstance(self.rope_scaling    ,			__UpperCAmelCase) or len(self.rope_scaling) != 2:
        raise ValueError(
            "`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, "
            f'''got {self.rope_scaling}''')
      a						:		str											=      self.rope_scaling.get("type"    ,			__UpperCAmelCase)
      a						:		List[str]											=      self.rope_scaling.get("factor"    ,			__UpperCAmelCase)
      if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
        raise ValueError(
            f'''`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}''')
      if rope_scaling_factor is None or not isinstance(__UpperCAmelCase    ,			__UpperCAmelCase) or rope_scaling_factor <= 1.0:
        raise ValueError(f'''`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}''')






