





'''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 						UpperCamelCase    (							_lowerCamelCase : Any						,	_lowerCamelCase : Tuple	):
    if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
        # expert layer
        A__    =      flax_key_tuple[:-1] + ("weight",)
        A__    =      torch.permute(_lowerCamelCase						,	(0, 2, 1)	)
    elif flax_key_tuple[-1] == "kernel" and ".".join(_lowerCamelCase	):
        # 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    (							_lowerCamelCase : Tuple						,	_lowerCamelCase : Tuple						,	_lowerCamelCase : Optional[int]	):
    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    (							_lowerCamelCase : Union[str, Any]						,	_lowerCamelCase : Dict	):
    A__    =      rename_keys(_lowerCamelCase	)
    A__    =      {}
    for k, v in current_block.items():
        A__    =      v
    A__    =      new_current_block
    torch.save(_lowerCamelCase						,	_lowerCamelCase	)





def 						UpperCamelCase    (							_lowerCamelCase : Union[str, Any]						,	_lowerCamelCase : Tuple						,	_lowerCamelCase : Any						,	_lowerCamelCase : Any						,	_lowerCamelCase : str = WEIGHTS_NAME	):
    A__    =      convert_file_size_to_int(_lowerCamelCase	)
    A__    =      []
    A__    =      {}
    A__    =      0
    A__    =      0

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

    A__    =      {}
    for layer in checkpoint_info.keys():
        A__,     A__,     A__    =      get_key_and_tensorstore_dict(
            _lowerCamelCase						,	_lowerCamelCase						,	_lowerCamelCase	)
        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(_lowerCamelCase	)
        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("/"	)	)						,	_lowerCamelCase	)
        A__    =      "/".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:
            A__    =      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
            A__    =      {}
            A__    =      0

        A__    =      raw_weights.to(getattr(_lowerCamelCase						,	_lowerCamelCase	)	)
        current_block_size += weight_size
        total_size += weight_size

    # Add the last block
    A__    =      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
    A__    =      {}
    A__    =      {}
    for idx, shard in enumerate(_lowerCamelCase	):
        A__    =      weights_name.replace(
            ".bin"						,	F"-{idx+1:05d}-of-{len(_lowerCamelCase	):05d}.bin"	)  # len(sharded_state_dicts):05d}
        A__    =      os.path.join(_lowerCamelCase						,	weights_name.replace(".bin"						,	F"-{idx+1:05d}-of-???.bin"	)	)
        os.rename(_lowerCamelCase						,	os.path.join(_lowerCamelCase						,	_lowerCamelCase	)	)
        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(_lowerCamelCase						,	_lowerCamelCase	)						,	"w"						,	encoding="utf-8"	) as f:
        A__    =      json.dumps(_lowerCamelCase						,	indent=2						,	sort_keys=_lowerCamelCase	) + "\n"
        f.write(_lowerCamelCase	)

    return metadata, index


if __name__ == "__main__":
  __lowerCAmelCase							:     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.",
  )
  __lowerCAmelCase							:     List[str]		     =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    (							):
    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(_lowerCamelCase						,	return_tensors="pt"	).input_ids
    A__    =      model.generate(_lowerCamelCase						,	decoder_start_token_id=0	)
    print(tokenizer.decode(out[0]	)	)










'''simple docstring'''

def 						UpperCamelCase    (							_lowerCamelCase : int	):

    if not isinstance(_lowerCamelCase						,	_lowerCamelCase	):
        A__    =      F"Input value of [number={number}] must be an integer"
        raise TypeError(_lowerCamelCase	)

    if number < 1:
        A__    =      F"Input value of [number={number}] must be > 0"
        raise ValueError(_lowerCamelCase	)

    A__    =      1

    for i in range(1						,	_lowerCamelCase	):
        current_number *= 4 * i - 2
        current_number //= i + 1

    return current_number


if __name__ == "__main__":
  import doctest

  doctest.testmod()



