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     :   str     ,      __UpperCamelCase     :   int				):
      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(lowerCamelCase_     ,      (0, 2, 1)				)
      elif flax_key_tuple[-1] == "kernel" and ".".join(lowerCamelCase_				):
            # 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     :   Optional[Any]     ,      __UpperCamelCase     :   List[Any]     ,      __UpperCamelCase     :   Union[str, Any]				):
      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     :   Dict     ,      __UpperCamelCase     :   Optional[Any]				):
      SCREAMING_SNAKE_CASE_          =  rename_keys(lowerCamelCase_				)
      SCREAMING_SNAKE_CASE_          =  {}
      for k, v in current_block.items():
            SCREAMING_SNAKE_CASE_          =  v
      SCREAMING_SNAKE_CASE_          =  new_current_block
      torch.save(lowerCamelCase_     ,      lowerCamelCase_				)


def      a__ (		__UpperCamelCase     :   int     ,      __UpperCamelCase     :   str     ,      __UpperCamelCase     :   Tuple     ,      __UpperCamelCase     :   List[Any]     ,      __UpperCamelCase     :   str = WEIGHTS_NAME				):
      SCREAMING_SNAKE_CASE_          =  convert_file_size_to_int(lowerCamelCase_				)
      SCREAMING_SNAKE_CASE_          =  []
      SCREAMING_SNAKE_CASE_          =  {}
      SCREAMING_SNAKE_CASE_          =  0
      SCREAMING_SNAKE_CASE_          =  0

      os.makedirs(lowerCamelCase_     ,      exist_ok=lowerCamelCase_				)
      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(lowerCamelCase_     ,      sep="/"				)

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

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

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

      return metadata, index


if __name__ == "__main__":
      A     :  List[Any]					     =       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     :  int					     =       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(lowerCamelCase_     ,      return_tensors="pt"				).input_ids
      SCREAMING_SNAKE_CASE_          =  model.generate(lowerCamelCase_     ,      decoder_start_token_id=0				)
      print(tokenizer.decode(out[0]				)				)



from collections import defaultdict
from pathlib import Path

import pandas as pd
from rouge_cli import calculate_rouge_path

from utils import calculate_rouge


A     :  Any					     =       [
    "Prosecutor: \"No videos were used in the crash investigation\" German papers say they saw a cell phone video of the"
    " final seconds on board Flight 9525. The Germanwings co-pilot says he had a \"previous episode of severe"
    " depression\" German airline confirms it knew of Andreas Lubitz's depression years before he took control.",
    "The Palestinian Authority officially becomes the 123rd member of the International Criminal Court. The formal"
    " accession was marked with a ceremony at The Hague, in the Netherlands. The Palestinians signed the ICC's"
    " founding Rome Statute in January. Israel and the United States opposed the Palestinians' efforts to join the"
    " body.",
    "Amnesty International releases its annual report on the death penalty. The report catalogs the use of"
    " state-sanctioned killing as a punitive measure across the globe. At least 607 people were executed around the"
    " world in 2014, compared to 778 in 2013. The U.S. remains one of the worst offenders for imposing capital"
    " punishment.",
]

A     :  Dict					     =       [
    "Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports ."
    " Journalists at Bild and Paris Match are \"very confident\" the video clip is real, an editor says . Andreas Lubitz"
    " had informed his Lufthansa training school of an episode of severe depression, airline says .",
    "Membership gives the ICC jurisdiction over alleged crimes committed in Palestinian territories since last June ."
    " Israel and the United States opposed the move, which could open the door to war crimes investigations against"
    " Israelis .",
    "Amnesty's annual death penalty report catalogs encouraging signs, but setbacks in numbers of those sentenced to"
    " death . Organization claims that governments around the world are using the threat of terrorism to advance"
    " executions . The number of executions worldwide has gone down by almost 22% compared with 2013, but death"
    " sentences up by 28% .",
]


def      a__ (		):
      SCREAMING_SNAKE_CASE_          =  calculate_rouge(__UpperCamelCase     ,      __UpperCamelCase     ,      bootstrap_aggregation=__UpperCamelCase     ,      rouge_keys=["rouge2", "rougeL"]				)
      assert isinstance(__UpperCamelCase     ,      __UpperCamelCase				)
      SCREAMING_SNAKE_CASE_          =  calculate_rouge(__UpperCamelCase     ,      __UpperCamelCase     ,      bootstrap_aggregation=__UpperCamelCase     ,      rouge_keys=["rouge2"]				)
      assert (
          pd.DataFrame(no_aggregation["rouge2"]				).fmeasure.mean()
          == pd.DataFrame(no_aggregation_just_ra["rouge2"]				).fmeasure.mean()
      )


def      a__ (		):
      SCREAMING_SNAKE_CASE_          =  "rougeLsum"
      SCREAMING_SNAKE_CASE_          =  calculate_rouge(__UpperCamelCase     ,      __UpperCamelCase     ,      newline_sep=__UpperCamelCase     ,      rouge_keys=[k]				)[k]
      SCREAMING_SNAKE_CASE_          =  calculate_rouge(__UpperCamelCase     ,      __UpperCamelCase     ,      newline_sep=__UpperCamelCase     ,      rouge_keys=[k]				)[k]
      assert score > score_no_sep


def      a__ (		):
      SCREAMING_SNAKE_CASE_          =  ["rouge1", "rouge2", "rougeL"]
      SCREAMING_SNAKE_CASE_          =  calculate_rouge(__UpperCamelCase     ,      __UpperCamelCase     ,      newline_sep=__UpperCamelCase     ,      rouge_keys=__UpperCamelCase				)
      SCREAMING_SNAKE_CASE_          =  calculate_rouge(__UpperCamelCase     ,      __UpperCamelCase     ,      newline_sep=__UpperCamelCase     ,      rouge_keys=__UpperCamelCase				)
      assert score_sep == score_no_sep


def      a__ (		):
      SCREAMING_SNAKE_CASE_          =  [
          "Her older sister, Margot Frank, died in 1945, a month earlier than previously thought.",
          "Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports .",
      ]
      SCREAMING_SNAKE_CASE_          =  [
          "Margot Frank, died in 1945, a month earlier than previously thought.",
          "Prosecutor: \"No videos were used in the crash investigation\" German papers say they saw a cell phone video of"
          " the final seconds on board Flight 9525.",
      ]
      assert calculate_rouge(__UpperCamelCase     ,      __UpperCamelCase     ,      newline_sep=__UpperCamelCase				) == calculate_rouge(__UpperCamelCase     ,      __UpperCamelCase     ,      newline_sep=__UpperCamelCase				)


def      a__ (		):
      SCREAMING_SNAKE_CASE_          =  [
          "\" \"a person who has such a video needs to immediately give it to the investigators,\" prosecutor says .<n> \"it is a very disturbing scene,\" editor-in-chief of bild online tells \"erin burnett: outfront\" "
      ]
      SCREAMING_SNAKE_CASE_          =  [
          " Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports . Journalists at Bild and Paris Match are \"very confident\" the video clip is real, an editor says . Andreas Lubitz had informed his Lufthansa training school of an episode of severe depression, airline says ."
      ]

      SCREAMING_SNAKE_CASE_          =  calculate_rouge(__UpperCamelCase     ,      __UpperCamelCase     ,      rouge_keys=["rougeLsum"]     ,      newline_sep=__UpperCamelCase				)["rougeLsum"]
      SCREAMING_SNAKE_CASE_          =  calculate_rouge(__UpperCamelCase     ,      __UpperCamelCase     ,      rouge_keys=["rougeLsum"]				)["rougeLsum"]
      assert new_score > prev_score





def      a__ (		):
      SCREAMING_SNAKE_CASE_          =  Path("examples/seq2seq/test_data/wmt_en_ro"				)
      SCREAMING_SNAKE_CASE_          =  calculate_rouge_path(data_dir.joinpath("test.source"				)     ,      data_dir.joinpath("test.target"				)				)
      assert isinstance(__UpperCamelCase     ,      __UpperCamelCase				)
      SCREAMING_SNAKE_CASE_          =  calculate_rouge_path(
          data_dir.joinpath("test.source"				)     ,      data_dir.joinpath("test.target"				)     ,      bootstrap_aggregation=__UpperCamelCase				)
      assert isinstance(__UpperCamelCase     ,      __UpperCamelCase				)


