

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

      return flax_key_tuple, flax_tensor




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

      else:
            SCREAMING_SNAKE_CASE__ :   Tuple   		=						layer.split("""/"""				)
            SCREAMING_SNAKE_CASE__ :   Dict   		=						"""/""".join(split_layer[:-1]				)
            SCREAMING_SNAKE_CASE__ :   List[str]   		=						(split_layer[-1],)

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

      return curr_real_layer_name, split_layer, content




def 			_lowercase       (     __lowerCAmelCase	,       __lowerCAmelCase				)							->					List[Any]:
      SCREAMING_SNAKE_CASE__ :   List[Any]   		=						rename_keys(snake_case__				)
      SCREAMING_SNAKE_CASE__ :   Optional[int]   		=						{}
      for k, v in current_block.items():
            SCREAMING_SNAKE_CASE__ :   int   		=						v
      SCREAMING_SNAKE_CASE__ :   Any   		=						new_current_block
      torch.save(snake_case__	,       snake_case__				)




def 			_lowercase       (     __lowerCAmelCase	,       __lowerCAmelCase	,       __lowerCAmelCase	,       __lowerCAmelCase	,       __lowerCAmelCase = WEIGHTS_NAME				)							->					Optional[Any]:
      SCREAMING_SNAKE_CASE__ :   Tuple   		=						convert_file_size_to_int(snake_case__				)
      SCREAMING_SNAKE_CASE__ :   Tuple   		=						[]
      SCREAMING_SNAKE_CASE__ :   Dict   		=						{}
      SCREAMING_SNAKE_CASE__ :   Tuple   		=						0
      SCREAMING_SNAKE_CASE__ :   Union[str, Any]   		=						0

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

      SCREAMING_SNAKE_CASE__ :   Any   		=						{}
      for layer in checkpoint_info.keys():
            SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__ :   Optional[Any]   		=						get_key_and_tensorstore_dict(
                snake_case__	,       snake_case__	,       snake_case__				)
            if curr_real_layer_name in all_layers:
                  SCREAMING_SNAKE_CASE__ :   Optional[Any]   		=						content
            else:
                  SCREAMING_SNAKE_CASE__ :   Dict   		=						{split_layer[-1]: content}

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

            # use the renaming pattern from the small conversion scripts
            SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__ :   Dict   		=						rename_base_flax_keys(tuple(key.split("""/"""				)				)	,       snake_case__				)
            SCREAMING_SNAKE_CASE__ :   Union[str, Any]   		=						"""/""".join(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:
                  SCREAMING_SNAKE_CASE__ :   Dict   		=						os.path.join(
                      snake_case__	,       weights_name.replace(""".bin"""	,       F'''-{len(snake_case__				)+1:05d}-of-???.bin'''				)				)
                  rename_and_save_block(snake_case__	,       snake_case__				)
                  sharded_state_dicts.append(current_block.keys()				)
                  del current_block
                  SCREAMING_SNAKE_CASE__ :   Optional[Any]   		=						{}
                  SCREAMING_SNAKE_CASE__ :   str   		=						0

            SCREAMING_SNAKE_CASE__ :   List[Any]   		=						raw_weights.to(getattr(snake_case__	,       snake_case__				)				)
            current_block_size += weight_size
            total_size += weight_size

      # Add the last block
      SCREAMING_SNAKE_CASE__ :   Any   		=						os.path.join(snake_case__	,       weights_name.replace(""".bin"""	,       F'''-{len(snake_case__				)+1:05d}-of-???.bin'''				)				)
      rename_and_save_block(snake_case__	,       snake_case__				)
      sharded_state_dicts.append(current_block.keys()				)

      # If we only have one shard, we return it
      if len(snake_case__				) == 1:
            return {weights_name: sharded_state_dicts[0]}, None

      # Otherwise, let's build the index
      SCREAMING_SNAKE_CASE__ :   Optional[Any]   		=						{}
      SCREAMING_SNAKE_CASE__ :   Any   		=						{}
      for idx, shard in enumerate(snake_case__				):
            SCREAMING_SNAKE_CASE__ :   str   		=						weights_name.replace(
                """.bin"""	,       F'''-{idx+1:05d}-of-{len(snake_case__				):05d}.bin'''				)  # len(sharded_state_dicts):05d}
            SCREAMING_SNAKE_CASE__ :   Tuple   		=						os.path.join(snake_case__	,       weights_name.replace(""".bin"""	,       F'''-{idx+1:05d}-of-???.bin'''				)				)
            os.rename(snake_case__	,       os.path.join(snake_case__	,       snake_case__				)				)
            SCREAMING_SNAKE_CASE__ :   Dict   		=						shard
            for key in shard:
                  SCREAMING_SNAKE_CASE__ :   List[Any]   		=						shard_file

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

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

      return metadata, index


if __name__ == "__main__":
  a       :Tuple     =			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.",
  )
  a       :List[Any]     =			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       (     )							->					Dict:
      from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

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

      SCREAMING_SNAKE_CASE__ :   List[str]   		=						tokenizer(snake_case__	,       return_tensors="""pt"""				).input_ids
      SCREAMING_SNAKE_CASE__ :   Optional[int]   		=						model.generate(snake_case__	,       decoder_start_token_id=0				)
      print(tokenizer.decode(out[0]				)				)






from __future__ import annotations




def 							a		(				snake_case__:   list[list[int]]   ):


				'''simple docstring'''

				# preprocessing the first row
				for i in range(1		,  len(matrix[0]   )   ):
								matrix[0][i] += matrix[0][i - 1]

				# preprocessing the first column
				for i in range(1		,  len(snake_case__   )   ):
								matrix[i][0] += matrix[i - 1][0]

				# updating the path cost for current position
				for i in range(1		,  len(snake_case__   )   ):
								for j in range(1		,  len(matrix[0]   )   ):
												matrix[i][j] += min(matrix[i - 1][j]		,  matrix[i][j - 1]   )

				return matrix[-1][-1]


if __name__ == "__main__":
						import doctest

						doctest.testmod()




