






'''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 							snake_case					(     a_							: Optional[Any]    ,   a_							: str	)       ->				Optional[int]:





      """simple docstring"""

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

      return flax_key_tuple, flax_tensor



def 							snake_case					(     a_							: Optional[Any]    ,   a_							: Optional[int]    ,   a_							: Optional[Any]	)       ->				Optional[Any]:





      """simple docstring"""

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

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

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

      return curr_real_layer_name, split_layer, content



def 							snake_case					(     a_							: List[Any]    ,   a_							: Union[str, Any]	)       ->				int:





      """simple docstring"""

      UpperCamelCase_				:   str					    =							rename_keys(a_	)
      UpperCamelCase_				:   Dict					    =							{}
      for k, v in current_block.items():
            UpperCamelCase_				:   str					    =							v
      UpperCamelCase_				:   Union[str, Any]					    =							new_current_block
      torch.save(a_    ,   a_	)



def 							snake_case					(     a_							: Optional[Any]    ,   a_							: Optional[int]    ,   a_							: str    ,   a_							: Union[str, Any]    ,   a_							: str = WEIGHTS_NAME	)       ->				Tuple:





      """simple docstring"""

      UpperCamelCase_				:   Dict					    =							convert_file_size_to_int(a_	)
      UpperCamelCase_				:   List[str]					    =							[]
      UpperCamelCase_				:   str					    =							{}
      UpperCamelCase_				:   Union[str, Any]					    =							0
      UpperCamelCase_				:   str					    =							0

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

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

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

            # use the renaming pattern from the small conversion scripts
            UpperCamelCase_    ,			UpperCamelCase_				:   List[Any]					    =							rename_base_flax_keys(tuple(key.split("""/"""	)	)    ,   a_	)
            UpperCamelCase_				:   str					    =							"""/""".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:
                  UpperCamelCase_				:   Optional[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
                  UpperCamelCase_				:   Tuple					    =							{}
                  UpperCamelCase_				:   Dict					    =							0

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

      # Add the last block
      UpperCamelCase_				:   List[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()	)

      # 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
      UpperCamelCase_				:   Any					    =							{}
      UpperCamelCase_				:   int					    =							{}
      for idx, shard in enumerate(a_	):
            UpperCamelCase_				:   Optional[Any]					    =							weights_name.replace(
                """.bin"""    ,   f"-{idx+1:05d}-of-{len(a_	):05d}.bin"	)  # len(sharded_state_dicts):05d}
            UpperCamelCase_				:   Union[str, Any]					    =							os.path.join(a_    ,   weights_name.replace(""".bin"""    ,   f"-{idx+1:05d}-of-???.bin"	)	)
            os.rename(a_    ,   os.path.join(a_    ,   a_	)	)
            UpperCamelCase_				:   Optional[Any]					    =							shard
            for key in shard:
                  UpperCamelCase_				:   List[str]					    =							shard_file

    # Add the metadata
      UpperCamelCase_				:   int					    =							{"""total_size""": total_size}
      UpperCamelCase_				:   Tuple					    =							{"""metadata""": metadata, """weight_map""": weight_map}

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

      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 							snake_case					(     )       ->				List[str]:





      """simple docstring"""

      from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

      UpperCamelCase_				:   Optional[Any]					    =							SwitchTransformersConfig.from_pretrained("""google/switch-base-8"""	)
      config.save_pretrained("""/home/arthur_huggingface_co/transformers/switch_converted"""	)
      UpperCamelCase_				:   List[str]					    =							SwitchTransformersForConditionalGeneration.from_pretrained(
          """/home/arthur_huggingface_co/transformers/switch_converted"""    ,   device_map="""auto"""	)

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

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














'''simple docstring'''

from itertools import count



def 							snake_case					(     a_							: int = 50	)       ->				int:





      """simple docstring"""

      UpperCamelCase_				:   int					    =							[1] * min_block_length

      for n in count(a_	):
            fill_count_functions.append(1	)

            for block_length in range(a_    ,   n + 1	):
                  for block_start in range(n - block_length	):
                        fill_count_functions[n] += fill_count_functions[
                            n - block_start - block_length - 1
                        ]

                  fill_count_functions[n] += 1

            if fill_count_functions[n] > 1_000_000:
                  break

      return n


if __name__ == "__main__":
      print(f"{solution() = }")






