


"""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__			(				SCREAMING_SNAKE_CASE    :    List[Any]				,      SCREAMING_SNAKE_CASE    :    Optional[Any]				):


  '''simple docstring'''







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

  return flax_key_tuple, flax_tensor
def 					a__			(				SCREAMING_SNAKE_CASE    :    Optional[int]				,      SCREAMING_SNAKE_CASE    :    List[Any]				,      SCREAMING_SNAKE_CASE    :    Optional[int]				):


  '''simple docstring'''







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

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

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

  return curr_real_layer_name, split_layer, content
def 					a__			(				SCREAMING_SNAKE_CASE    :    Union[str, Any]				,      SCREAMING_SNAKE_CASE    :    Optional[int]				):


  '''simple docstring'''







  lowerCAmelCase     :     str								=							rename_keys(SCREAMING_SNAKE_CASE				)
  lowerCAmelCase     :     Any								=							{}
  for k, v in current_block.items():
    lowerCAmelCase     :     List[str]								=							v
  lowerCAmelCase     :     str								=							new_current_block
  torch.save(SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				)
def 					a__			(				SCREAMING_SNAKE_CASE    :    Any				,      SCREAMING_SNAKE_CASE    :    Tuple				,      SCREAMING_SNAKE_CASE    :    Any				,      SCREAMING_SNAKE_CASE    :    List[str]				,      SCREAMING_SNAKE_CASE    :    str = WEIGHTS_NAME				):


  '''simple docstring'''







  lowerCAmelCase     :     str								=							convert_file_size_to_int(SCREAMING_SNAKE_CASE				)
  lowerCAmelCase     :     Optional[Any]								=							[]
  lowerCAmelCase     :     int								=							{}
  lowerCAmelCase     :     List[str]								=							0
  lowerCAmelCase     :     str								=							0

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

  lowerCAmelCase     :     Union[str, Any]								=							{}
  for layer in checkpoint_info.keys():
    lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase     :     int								=							get_key_and_tensorstore_dict(
        SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				)
    if curr_real_layer_name in all_layers:
      lowerCAmelCase     :     Any								=							content
    else:
      lowerCAmelCase     :     Tuple								=							{split_layer[-1]: content}

  for key in all_layers.keys():
    # open tensorstore file
    lowerCAmelCase     :     Optional[Any]								=							ts.open(unflatten_dict(all_layers[key]				)				).result().read().result()
    lowerCAmelCase     :     Tuple								=							torch.tensor(SCREAMING_SNAKE_CASE				)
    lowerCAmelCase     :     Union[str, Any]								=							raw_weights.numel() * dtype_byte_size(raw_weights.dtype				)

    # use the renaming pattern from the small conversion scripts
    lowerCAmelCase						,  lowerCAmelCase     :     Union[str, Any]								=							rename_base_flax_keys(tuple(key.split("/"				)				)				,      SCREAMING_SNAKE_CASE				)
    lowerCAmelCase     :     int								=							"/".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:
      lowerCAmelCase     :     Optional[Any]								=							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
      lowerCAmelCase     :     Optional[Any]								=							{}
      lowerCAmelCase     :     Any								=							0

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

  # Add the last block
  lowerCAmelCase     :     Tuple								=							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
  lowerCAmelCase     :     Dict								=							{}
  lowerCAmelCase     :     List[str]								=							{}
  for idx, shard in enumerate(SCREAMING_SNAKE_CASE				):
    lowerCAmelCase     :     Union[str, Any]								=							weights_name.replace(
        ".bin"				,      f"""-{idx+1:05d}-of-{len(SCREAMING_SNAKE_CASE				):05d}.bin"""				)  # len(sharded_state_dicts):05d}
    lowerCAmelCase     :     Any								=							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				)				)
    lowerCAmelCase     :     int								=							shard
    for key in shard:
      lowerCAmelCase     :     Optional[int]								=							shard_file

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

  with open(os.path.join(SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				)				,      "w"				,      encoding="utf-8"				) as f:
    lowerCAmelCase     :     Union[str, Any]								=							json.dumps(SCREAMING_SNAKE_CASE				,      indent=2				,      sort_keys=SCREAMING_SNAKE_CASE				) + "\n"
    f.write(SCREAMING_SNAKE_CASE				)

  return metadata, index


if __name__ == "__main__":
     lowerCAmelCase__      				=   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__      				=   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     :     Tuple								=							SwitchTransformersConfig.from_pretrained("google/switch-base-8"				)
  config.save_pretrained("/home/arthur_huggingface_co/transformers/switch_converted"				)
  lowerCAmelCase     :     Dict								=							SwitchTransformersForConditionalGeneration.from_pretrained(
      "/home/arthur_huggingface_co/transformers/switch_converted"				,      device_map="auto"				)

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

  lowerCAmelCase     :     List[str]								=							tokenizer(SCREAMING_SNAKE_CASE				,      return_tensors="pt"				).input_ids
  lowerCAmelCase     :     Union[str, Any]								=							model.generate(SCREAMING_SNAKE_CASE				,      decoder_start_token_id=0				)
  print(tokenizer.decode(out[0]				)				)









"""simple docstring"""







from __future__ import annotations

from collections import namedtuple
from dataclasses import dataclass


@dataclass
class       SCREAMING_SNAKE_CASE__				:
       """simple docstring"""


       a  :				int
       a  :				TreeNode | None   =None
       a  :				TreeNode | None   =None

lowerCAmelCase__      				=   namedtuple('''CoinsDistribResult''', '''moves excess''')
def 					a__			(				SCREAMING_SNAKE_CASE    :    TreeNode | None				):


  '''simple docstring'''







  if root is None:
    return 0

  # Validation
  def count_nodes(SCREAMING_SNAKE_CASE    :    TreeNode | None				) -> int:
    if node is None:
      return 0

    return count_nodes(node.left				) + count_nodes(node.right				) + 1

  def count_coins(SCREAMING_SNAKE_CASE    :    TreeNode | None				) -> int:
    if node is None:
      return 0

    return count_coins(node.left				) + count_coins(node.right				) + node.data

  if count_nodes(SCREAMING_SNAKE_CASE				) != count_coins(SCREAMING_SNAKE_CASE				):
    raise ValueError("The nodes number should be same as the number of coins"				)

  # Main calculation
  def get_distrib(SCREAMING_SNAKE_CASE    :    TreeNode | None				) -> CoinsDistribResult:

    if node is None:
      return CoinsDistribResult(0				,      1				)

    lowerCAmelCase						,  lowerCAmelCase     :     List[str]								=							get_distrib(node.left				)
    lowerCAmelCase						,  lowerCAmelCase     :     List[str]								=							get_distrib(node.right				)

    lowerCAmelCase     :     Union[str, Any]								=							1 - left_distrib_excess
    lowerCAmelCase     :     List[Any]								=							1 - right_distrib_excess

    lowerCAmelCase     :     int								=							(
        left_distrib_moves
        + right_distrib_moves
        + abs(SCREAMING_SNAKE_CASE				)
        + abs(SCREAMING_SNAKE_CASE				)
    )
    lowerCAmelCase     :     int								=							node.data - coins_to_left - coins_to_right

    return CoinsDistribResult(SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				)

  return get_distrib(SCREAMING_SNAKE_CASE				)[0]


if __name__ == "__main__":
     import doctest

     doctest.testmod()





