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__ (		__UpperCamelCase     ,      __UpperCamelCase				):
      if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
            # expert layer
            SCREAMING_SNAKE_CASE_          =  flax_key_tuple[:-1] + ("weight",)
            SCREAMING_SNAKE_CASE_          =  torch.permute(__UpperCamelCase     ,      (0, 2, 1)				)
      elif flax_key_tuple[-1] == "kernel" and ".".join(__UpperCamelCase				):
            # linear layer
            SCREAMING_SNAKE_CASE_          =  flax_key_tuple[:-1] + ("weight",)
            SCREAMING_SNAKE_CASE_          =  flax_tensor.T
      elif flax_key_tuple[-1] in ["scale", "embedding"]:
            SCREAMING_SNAKE_CASE_          =  flax_key_tuple[:-1] + ("weight",)

      return flax_key_tuple, flax_tensor


def      a__ (		__UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase				):
      if "metadata" in layer:
            SCREAMING_SNAKE_CASE_          =  layer.split("metadata"				)
            SCREAMING_SNAKE_CASE_          =  "".join(split_layer[0]				)[:-1]
            SCREAMING_SNAKE_CASE_          =  [tuple(("metadata" + split_layer[1]).split("/"				)				)]
      elif "kvstore" in layer:
            SCREAMING_SNAKE_CASE_          =  layer.split("kvstore"				)
            SCREAMING_SNAKE_CASE_          =  "".join(split_layer[0]				)[:-1]
            SCREAMING_SNAKE_CASE_          =  [tuple(("kvstore" + split_layer[1]).split("/"				)				)]

      else:
            SCREAMING_SNAKE_CASE_          =  layer.split("/"				)
            SCREAMING_SNAKE_CASE_          =  "/".join(split_layer[:-1]				)
            SCREAMING_SNAKE_CASE_          =  (split_layer[-1],)

      if "kvstore/path" in layer:
            SCREAMING_SNAKE_CASE_          =  F'''{switch_checkpoint_path}/{checkpoint_info[layer]}'''
      elif "kvstore/driver" in layer:
            SCREAMING_SNAKE_CASE_          =  "file"
      else:
            SCREAMING_SNAKE_CASE_          =  checkpoint_info[layer]

      return curr_real_layer_name, split_layer, content


def      a__ (		__UpperCamelCase     ,      __UpperCamelCase				):
      SCREAMING_SNAKE_CASE_          =  rename_keys(__UpperCamelCase				)
      SCREAMING_SNAKE_CASE_          =  {}
      for k, v in current_block.items():
            SCREAMING_SNAKE_CASE_          =  v
      SCREAMING_SNAKE_CASE_          =  new_current_block
      torch.save(__UpperCamelCase     ,      __UpperCamelCase				)


def      a__ (		__UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase = WEIGHTS_NAME				):
      SCREAMING_SNAKE_CASE_          =  convert_file_size_to_int(__UpperCamelCase				)
      SCREAMING_SNAKE_CASE_          =  []
      SCREAMING_SNAKE_CASE_          =  {}
      SCREAMING_SNAKE_CASE_          =  0
      SCREAMING_SNAKE_CASE_          =  0

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

      SCREAMING_SNAKE_CASE_          =  {}
      for layer in checkpoint_info.keys():
            SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  get_key_and_tensorstore_dict(
                __UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase				)
            if curr_real_layer_name in all_layers:
                  SCREAMING_SNAKE_CASE_          =  content
            else:
                  SCREAMING_SNAKE_CASE_          =  {split_layer[-1]: content}

      for key in all_layers.keys():
            # open tensorstore file
            SCREAMING_SNAKE_CASE_          =  ts.open(unflatten_dict(all_layers[key]				)				).result().read().result()
            SCREAMING_SNAKE_CASE_          =  torch.tensor(__UpperCamelCase				)
            SCREAMING_SNAKE_CASE_          =  raw_weights.numel() * dtype_byte_size(raw_weights.dtype				)

            # use the renaming pattern from the small conversion scripts
            SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  rename_base_flax_keys(tuple(key.split("/"				)				)     ,      __UpperCamelCase				)
            SCREAMING_SNAKE_CASE_          =  "/".join(__UpperCamelCase				)

            # 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_          =  os.path.join(
                      __UpperCamelCase     ,      weights_name.replace(".bin"     ,      F'''-{len(__UpperCamelCase				)+1:05d}-of-???.bin'''				)				)
                  rename_and_save_block(__UpperCamelCase     ,      __UpperCamelCase				)
                  sharded_state_dicts.append(current_block.keys()				)
                  del current_block
                  SCREAMING_SNAKE_CASE_          =  {}
                  SCREAMING_SNAKE_CASE_          =  0

            SCREAMING_SNAKE_CASE_          =  raw_weights.to(getattr(__UpperCamelCase     ,      __UpperCamelCase				)				)
            current_block_size += weight_size
            total_size += weight_size

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

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

      # Otherwise, let's build the index
      SCREAMING_SNAKE_CASE_          =  {}
      SCREAMING_SNAKE_CASE_          =  {}
      for idx, shard in enumerate(__UpperCamelCase				):
            SCREAMING_SNAKE_CASE_          =  weights_name.replace(
                ".bin"     ,      F'''-{idx+1:05d}-of-{len(__UpperCamelCase				):05d}.bin'''				)  # len(sharded_state_dicts):05d}
            SCREAMING_SNAKE_CASE_          =  os.path.join(__UpperCamelCase     ,      weights_name.replace(".bin"     ,      F'''-{idx+1:05d}-of-???.bin'''				)				)
            os.rename(__UpperCamelCase     ,      os.path.join(__UpperCamelCase     ,      __UpperCamelCase				)				)
            SCREAMING_SNAKE_CASE_          =  shard
            for key in shard:
                  SCREAMING_SNAKE_CASE_          =  shard_file

    # Add the metadata
      SCREAMING_SNAKE_CASE_          =  {"total_size": total_size}
      SCREAMING_SNAKE_CASE_          =  {"metadata": metadata, "weight_map": weight_map}

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

      return metadata, index


if __name__ == "__main__":
      A     :  Dict					     =       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     :  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      a__ (		):
      from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

      SCREAMING_SNAKE_CASE_          =  TaTokenizer.from_pretrained("t5-small"				)
      SCREAMING_SNAKE_CASE_          =  "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_          =  tokenizer(__UpperCamelCase     ,      return_tensors="pt"				).input_ids
      SCREAMING_SNAKE_CASE_          =  model.generate(__UpperCamelCase     ,      decoder_start_token_id=0				)
      print(tokenizer.decode(out[0]				)				)



from __future__ import annotations


def      a__ (		__UpperCamelCase				):

      # 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(__UpperCamelCase				)				):
            matrix[i][0] += matrix[i - 1][0]

      # updating the path cost for current position
      for i in range(1     ,      len(__UpperCamelCase				)				):
            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()


