"""simple docstring"""




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				:		Dict	   =       [
    '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				:		Optional[Any]	   =       [
    '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 							SCREAMING_SNAKE_CASE					(      )	->				int:
  _lowerCAmelCase						:	str        =					calculate_rouge(_lowerCamelCase      ,_lowerCamelCase      ,bootstrap_aggregation=_lowerCamelCase      ,rouge_keys=["""rouge2""", """rougeL"""]     )
  assert isinstance(_lowerCamelCase      ,_lowerCamelCase     )
  _lowerCAmelCase						:	Optional[Any]        =					calculate_rouge(_lowerCamelCase      ,_lowerCamelCase      ,bootstrap_aggregation=_lowerCamelCase      ,rouge_keys=["""rouge2"""]     )
  assert (
      pd.DataFrame(no_aggregation["""rouge2"""]     ).fmeasure.mean()
      == pd.DataFrame(no_aggregation_just_ra["""rouge2"""]     ).fmeasure.mean()
  )





def 							SCREAMING_SNAKE_CASE					(      )	->				Any:
  _lowerCAmelCase						:	Dict        =					"""rougeLsum"""
  _lowerCAmelCase						:	int        =					calculate_rouge(_lowerCamelCase      ,_lowerCamelCase      ,newline_sep=_lowerCamelCase      ,rouge_keys=[k]     )[k]
  _lowerCAmelCase						:	str        =					calculate_rouge(_lowerCamelCase      ,_lowerCamelCase      ,newline_sep=_lowerCamelCase      ,rouge_keys=[k]     )[k]
  assert score > score_no_sep





def 							SCREAMING_SNAKE_CASE					(      )	->				int:
  _lowerCAmelCase						:	str        =					["""rouge1""", """rouge2""", """rougeL"""]
  _lowerCAmelCase						:	List[Any]        =					calculate_rouge(_lowerCamelCase      ,_lowerCamelCase      ,newline_sep=_lowerCamelCase      ,rouge_keys=_lowerCamelCase     )
  _lowerCAmelCase						:	List[Any]        =					calculate_rouge(_lowerCamelCase      ,_lowerCamelCase      ,newline_sep=_lowerCamelCase      ,rouge_keys=_lowerCamelCase     )
  assert score_sep == score_no_sep





def 							SCREAMING_SNAKE_CASE					(      )	->				Any:
  _lowerCAmelCase						:	Dict        =					[
      """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 .""",
  ]
  _lowerCAmelCase						:	Optional[int]        =					[
      """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(_lowerCamelCase      ,_lowerCamelCase      ,newline_sep=_lowerCamelCase     ) == calculate_rouge(_lowerCamelCase      ,_lowerCamelCase      ,newline_sep=_lowerCamelCase     )





def 							SCREAMING_SNAKE_CASE					(      )	->				Union[str, Any]:
  _lowerCAmelCase						:	Optional[Any]        =					[
      """\" \"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\" """
  ]
  _lowerCAmelCase						:	int        =					[
      """ 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 ."""
  ]

  _lowerCAmelCase						:	Optional[Any]        =					calculate_rouge(_lowerCamelCase      ,_lowerCamelCase      ,rouge_keys=["""rougeLsum"""]      ,newline_sep=_lowerCamelCase     )["""rougeLsum"""]
  _lowerCAmelCase						:	str        =					calculate_rouge(_lowerCamelCase      ,_lowerCamelCase      ,rouge_keys=["""rougeLsum"""]     )["""rougeLsum"""]
  assert new_score > prev_score






def 							SCREAMING_SNAKE_CASE					(      )	->				List[str]:
  _lowerCAmelCase						:	List[str]        =					Path("""examples/seq2seq/test_data/wmt_en_ro"""     )
  _lowerCAmelCase						:	List[str]        =					calculate_rouge_path(data_dir.joinpath("""test.source"""     )      ,data_dir.joinpath("""test.target"""     )     )
  assert isinstance(_lowerCamelCase      ,_lowerCamelCase     )
  _lowerCAmelCase						:	Dict        =					calculate_rouge_path(
      data_dir.joinpath("""test.source"""     )      ,data_dir.joinpath("""test.target"""     )      ,bootstrap_aggregation=_lowerCamelCase     )
  assert isinstance(_lowerCamelCase      ,_lowerCamelCase     )





"""simple docstring"""




import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	List[str]      ,_lowerCamelCase						:	Any      ,_lowerCamelCase						:	Optional[Any]     )	->				str:
  _lowerCAmelCase						:	str        =					AutoConfig.from_pretrained(_lowerCamelCase     )
  _lowerCAmelCase						:	int        =					FlaxAutoModelForSeqaSeqLM.from_config(config=_lowerCamelCase     )
  _lowerCAmelCase						:	Any        =					checkpoints.load_tax_checkpoint(_lowerCamelCase     )

  _lowerCAmelCase						:	Tuple        =					"""wi_0""" in tax_model["""target"""]["""encoder"""]["""layers_0"""]["""mlp"""]

  if config.model_type == "t5":
    _lowerCAmelCase						:	Tuple        =					"""SelfAttention"""
  if config.model_type == "longt5" and config.encoder_attention_type == "local":
    _lowerCAmelCase						:	Optional[Any]        =					"""LocalSelfAttention"""
  elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
    _lowerCAmelCase						:	Union[str, Any]        =					"""TransientGlobalSelfAttention"""
  else:
    raise ValueError(
        """Given config is expected to have `model_type='t5'`, or `model_type='longt5` with `encoder_attention_type`"""
        """ attribute with a value from ['local', 'transient-global]."""     )

  # Encoder
  for layer_index in range(config.num_layers     ):
    _lowerCAmelCase						:	Tuple        =					f"layers_{str(_lowerCamelCase     )}"

    # Self-Attention
    _lowerCAmelCase						:	List[str]        =					tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""key"""]["""kernel"""]
    _lowerCAmelCase						:	str        =					tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""out"""]["""kernel"""]
    _lowerCAmelCase						:	str        =					tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""query"""]["""kernel"""]
    _lowerCAmelCase						:	Any        =					tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""value"""]["""kernel"""]

    # Global input layer norm
    if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
      _lowerCAmelCase						:	Dict        =					tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""T5LayerNorm_0"""]["""scale"""]

    # Layer Normalization
    _lowerCAmelCase						:	Any        =					tax_model["""target"""]["""encoder"""][layer_name]["""pre_attention_layer_norm"""]["""scale"""]

    if split_mlp_wi:
      _lowerCAmelCase						:	List[str]        =					tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
      _lowerCAmelCase						:	int        =					tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
    else:
      _lowerCAmelCase						:	List[str]        =					tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]

    _lowerCAmelCase						:	Tuple        =					tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

    # Layer Normalization
    _lowerCAmelCase						:	Tuple        =					tax_model["""target"""]["""encoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]

    # Assigning
    _lowerCAmelCase						:	Any        =					flax_model.params["""encoder"""]["""block"""][str(_lowerCamelCase     )]["""layer"""]
    _lowerCAmelCase						:	Any        =					tax_attention_key
    _lowerCAmelCase						:	str        =					tax_attention_out
    _lowerCAmelCase						:	Union[str, Any]        =					tax_attention_query
    _lowerCAmelCase						:	Optional[Any]        =					tax_attention_value

    _lowerCAmelCase						:	List[str]        =					tax_attention_layer_norm

    # Global input layer norm
    if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
      _lowerCAmelCase						:	Any        =					tax_global_layer_norm

    if split_mlp_wi:
      _lowerCAmelCase						:	Dict        =					tax_mlp_wi_a
      _lowerCAmelCase						:	List[Any]        =					tax_mlp_wi_a
    else:
      _lowerCAmelCase						:	List[str]        =					tax_mlp_wi

    _lowerCAmelCase						:	str        =					tax_mlp_wo
    _lowerCAmelCase						:	Optional[Any]        =					tax_mlp_layer_norm

    _lowerCAmelCase						:	Any        =					flax_model_encoder_layer_block

  # Only for layer 0:
  _lowerCAmelCase						:	Union[str, Any]        =					tax_model["""target"""]["""encoder"""]["""relpos_bias"""]["""rel_embedding"""].T
  _lowerCAmelCase						:	Optional[Any]        =					tax_encoder_rel_embedding

  # Side/global relative position_bias + layer norm
  if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
    _lowerCAmelCase						:	List[str]        =					tax_model["""target"""]["""encoder"""]["""side_relpos_bias"""]["""rel_embedding"""].T
    _lowerCAmelCase						:	Optional[int]        =					tax_encoder_global_rel_embedding

  # Assigning
  _lowerCAmelCase						:	Any        =					tax_model["""target"""]["""encoder"""]["""encoder_norm"""]["""scale"""]
  _lowerCAmelCase						:	Any        =					tax_encoder_norm

  # Decoder
  for layer_index in range(config.num_layers     ):
    _lowerCAmelCase						:	Optional[int]        =					f"layers_{str(_lowerCamelCase     )}"

    # Self-Attention
    _lowerCAmelCase						:	List[str]        =					tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""key"""]["""kernel"""]
    _lowerCAmelCase						:	int        =					tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""out"""]["""kernel"""]
    _lowerCAmelCase						:	List[Any]        =					tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""query"""]["""kernel"""]
    _lowerCAmelCase						:	str        =					tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""value"""]["""kernel"""]

    # Layer Normalization
    _lowerCAmelCase						:	Optional[Any]        =					tax_model["""target"""]["""decoder"""][layer_name]["""pre_self_attention_layer_norm"""][
        """scale"""
    ]

    # Encoder-Decoder-Attention
    _lowerCAmelCase						:	List[Any]        =					tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]
    _lowerCAmelCase						:	List[str]        =					tax_enc_dec_attention_module["""key"""]["""kernel"""]
    _lowerCAmelCase						:	List[Any]        =					tax_enc_dec_attention_module["""out"""]["""kernel"""]
    _lowerCAmelCase						:	List[str]        =					tax_enc_dec_attention_module["""query"""]["""kernel"""]
    _lowerCAmelCase						:	Dict        =					tax_enc_dec_attention_module["""value"""]["""kernel"""]

    # Layer Normalization
    _lowerCAmelCase						:	Any        =					tax_model["""target"""]["""decoder"""][layer_name]["""pre_cross_attention_layer_norm"""]["""scale"""]

    # MLP
    if split_mlp_wi:
      _lowerCAmelCase						:	Any        =					tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
      _lowerCAmelCase						:	List[str]        =					tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
    else:
      _lowerCAmelCase						:	Any        =					tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]

    _lowerCAmelCase						:	Optional[int]        =					tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

    # Layer Normalization
    _lowerCAmelCase						:	Optional[int]        =					tax_model["""target"""]["""decoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]

    # Assigning
    _lowerCAmelCase						:	str        =					flax_model.params["""decoder"""]["""block"""][str(_lowerCamelCase     )]["""layer"""]
    _lowerCAmelCase						:	int        =					tax_attention_key
    _lowerCAmelCase						:	List[str]        =					tax_attention_out
    _lowerCAmelCase						:	Optional[Any]        =					tax_attention_query
    _lowerCAmelCase						:	Dict        =					tax_attention_value

    _lowerCAmelCase						:	str        =					tax_pre_attention_layer_norm

    _lowerCAmelCase						:	List[Any]        =					tax_enc_dec_attention_key
    _lowerCAmelCase						:	List[Any]        =					tax_enc_dec_attention_out
    _lowerCAmelCase						:	Tuple        =					tax_enc_dec_attention_query
    _lowerCAmelCase						:	Any        =					tax_enc_dec_attention_value

    _lowerCAmelCase						:	Dict        =					tax_cross_layer_norm

    if split_mlp_wi:
      _lowerCAmelCase						:	Dict        =					tax_mlp_wi_a
      _lowerCAmelCase						:	int        =					tax_mlp_wi_a
    else:
      _lowerCAmelCase						:	Optional[int]        =					tax_mlp_wi

    _lowerCAmelCase						:	Dict        =					tax_mlp_wo

    _lowerCAmelCase						:	List[Any]        =					txa_mlp_layer_norm

    _lowerCAmelCase						:	Optional[Any]        =					flax_model_decoder_layer_block

  # Decoder Normalization
  _lowerCAmelCase						:	Any        =					tax_model["""target"""]["""decoder"""]["""decoder_norm"""]["""scale"""]
  _lowerCAmelCase						:	List[str]        =					txa_decoder_norm

  # Only for layer 0:
  _lowerCAmelCase						:	Optional[int]        =					tax_model["""target"""]["""decoder"""]["""relpos_bias"""]["""rel_embedding"""].T
  _lowerCAmelCase						:	Union[str, Any]        =					tax_decoder_rel_embedding

  # Token Embeddings
  _lowerCAmelCase						:	Optional[int]        =					tax_model["""target"""]["""token_embedder"""]["""embedding"""]
  _lowerCAmelCase						:	Optional[int]        =					txa_token_embeddings

  # LM Head (only in v1.1 and LongT5 checkpoints)
  if "logits_dense" in tax_model["target"]["decoder"]:
    _lowerCAmelCase						:	Tuple        =					tax_model["""target"""]["""decoder"""]["""logits_dense"""]["""kernel"""]

  flax_model.save_pretrained(_lowerCamelCase     )
  print("""T5X Model was sucessfully converted!"""     )


if __name__ == "__main__":
     _a				:		Any	   =       argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--t5x_checkpoint_path', default=None, type=str, required=True, help='Path the T5X checkpoint.'
     )
     parser.add_argument('--config_name', default=None, type=str, required=True, help='Config name of LongT5/T5 model.')
     parser.add_argument(
         '--flax_dump_folder_path', default=None, type=str, required=True, help='Path to the output FLAX model.'
     )
     _a				:		List[str]	   =       parser.parse_args()
     convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)




