"""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 os
from pathlib import Path

import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader

from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeqaSeqDataset, SeqaSeqDataset


_a				:		Optional[int]	   =       'bert-base-cased'
_a				:		Optional[Any]	   =       'google/pegasus-xsum'
_a				:		Union[str, Any]	   =       [' Sam ate lunch today.', 'Sams lunch ingredients.']
_a				:		int	   =       ['A very interesting story about what I ate for lunch.', 'Avocado, celery, turkey, coffee']
_a				:		Union[str, Any]	   =       'patrickvonplaten/t5-tiny-random'
_a				:		Tuple	   =       'sshleifer/bart-tiny-random'
_a				:		str	   =       'sshleifer/tiny-mbart'
_a				:		Optional[int]	   =       'sshleifer/tiny-marian-en-de'





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	Path      ,_lowerCamelCase						:	list     )	->				str:
  _lowerCAmelCase						:	List[Any]        =					"""\n""".join(_lowerCamelCase     )
  Path(_lowerCamelCase     ).open("""w"""     ).writelines(_lowerCamelCase     )





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	Optional[int]     )	->				Union[str, Any]:
  for split in ["train", "val", "test"]:
    _dump_articles(os.path.join(_lowerCamelCase      ,f"{split}.source"     )      ,_lowerCamelCase     )
    _dump_articles(os.path.join(_lowerCamelCase      ,f"{split}.target"     )      ,_lowerCamelCase     )
  return tmp_dir






class 						__A							(			SCREAMING_SNAKE_CASE_		):






     @parameterized.expand(
         [
             MBART_TINY,
             MARIAN_TINY,
             T5_TINY,
             BART_TINY,
             PEGASUS_XSUM,
         ]    ,					)
     @slow
     def    __A  (		self    ,					a__  ):
       _lowerCAmelCase						:	str        =					AutoTokenizer.from_pretrained(a__  )
       _lowerCAmelCase						:	Any        =					make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()  )
       _lowerCAmelCase						:	Union[str, Any]        =					max(len(tokenizer.encode(a__  )  ) for a in ARTICLES  )
       _lowerCAmelCase						:	Optional[int]        =					max(len(tokenizer.encode(a__  )  ) for a in SUMMARIES  )
       _lowerCAmelCase						:	str        =					4
       _lowerCAmelCase						:	Optional[int]        =					8
       assert max_len_target > max_src_len  # Will be truncated
       assert max_len_source > max_src_len  # Will be truncated
       _lowerCAmelCase      ,     _lowerCAmelCase						:	Optional[int]        =					"""ro_RO""", """de_DE"""  # ignored for all but mbart, but never causes error.
       _lowerCAmelCase						:	Optional[int]        =					SeqaSeqDataset(
           a__    ,					data_dir=a__    ,					type_path="""train"""    ,					max_source_length=a__    ,					max_target_length=a__    ,					src_lang=a__    ,					tgt_lang=a__    ,					)
       _lowerCAmelCase						:	int        =					DataLoader(a__    ,					batch_size=2    ,					collate_fn=train_dataset.collate_fn  )
       for batch in dataloader:
         assert isinstance(a__    ,					a__  )
         assert batch["attention_mask"].shape == batch["input_ids"].shape
         # show that articles were trimmed.
         assert batch["input_ids"].shape[1] == max_src_len
         # show that targets are the same len
         assert batch["labels"].shape[1] == max_tgt_len
         if tok_name != MBART_TINY:
           continue
         # check language codes in correct place
         _lowerCAmelCase						:	Any        =					shift_tokens_right(batch["""labels"""]    ,					tokenizer.pad_token_id  )
         assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
         assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
         assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
         assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]

         break  # No need to test every batch






     @parameterized.expand([BART_TINY, BERT_BASE_CASED]  )
     def    __A  (		self    ,					a__  ):
       _lowerCAmelCase						:	Any        =					AutoTokenizer.from_pretrained(a__  )
       _lowerCAmelCase						:	Union[str, Any]        =					make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()  )
       _lowerCAmelCase						:	Optional[int]        =					max(len(tokenizer.encode(a__  )  ) for a in ARTICLES  )
       _lowerCAmelCase						:	Any        =					max(len(tokenizer.encode(a__  )  ) for a in SUMMARIES  )
       _lowerCAmelCase						:	int        =					4
       _lowerCAmelCase						:	List[str]        =					LegacySeqaSeqDataset(
           a__    ,					data_dir=a__    ,					type_path="""train"""    ,					max_source_length=20    ,					max_target_length=a__    ,					)
       _lowerCAmelCase						:	List[Any]        =					DataLoader(a__    ,					batch_size=2    ,					collate_fn=train_dataset.collate_fn  )
       for batch in dataloader:
         assert batch["attention_mask"].shape == batch["input_ids"].shape
         # show that articles were trimmed.
         assert batch["input_ids"].shape[1] == max_len_source
         assert 20 >= batch["input_ids"].shape[1]  # trimmed significantly
         # show that targets were truncated
         assert batch["labels"].shape[1] == trunc_target  # Truncated
         assert max_len_target > trunc_target  # Truncated
         break  # No need to test every batch






     def    __A  (		self  ):
       _lowerCAmelCase						:	Any        =					AutoTokenizer.from_pretrained("""facebook/mbart-large-cc25"""  )

       _lowerCAmelCase						:	List[Any]        =					Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()  )  )
       _lowerCAmelCase						:	List[Any]        =					tmp_dir.joinpath("""train.source"""  ).open().readlines()
       _lowerCAmelCase						:	str        =					Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()  )  )
       pack_data_dir(a__    ,					a__    ,					128    ,					a__  )
       _lowerCAmelCase						:	List[Any]        =					{x.name for x in tmp_dir.iterdir()}
       _lowerCAmelCase						:	Tuple        =					{x.name for x in save_dir.iterdir()}
       _lowerCAmelCase						:	Union[str, Any]        =					save_dir.joinpath("""train.source"""  ).open().readlines()
       # orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
       # desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
       assert len(a__  ) < len(a__  )
       assert len(a__  ) == 1
       assert len(packed_examples[0]  ) == sum(len(a__  ) for x in orig_examples  )
       assert orig_paths == new_paths






     @pytest.mark.skipif(not FAIRSEQ_AVAILABLE    ,					reason="""This test requires fairseq"""  )
     def    __A  (		self  ):
       if not FAIRSEQ_AVAILABLE:
         return
       _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase						:	Tuple        =					self._get_dataset(max_len=64  )
       _lowerCAmelCase						:	Optional[int]        =					64
       _lowerCAmelCase						:	str        =					ds.make_dynamic_sampler(a__    ,					required_batch_size_multiple=a__  )
       _lowerCAmelCase						:	int        =					[len(a__  ) for x in batch_sampler]
       assert len(set(a__  )  ) > 1  # it's not dynamic batch size if every batch is the same length
       assert sum(a__  ) == len(a__  )  # no dropped or added examples
       _lowerCAmelCase						:	List[str]        =					DataLoader(a__    ,					batch_sampler=a__    ,					collate_fn=ds.collate_fn    ,					num_workers=2  )
       _lowerCAmelCase						:	List[Any]        =					[]
       _lowerCAmelCase						:	Optional[int]        =					[]
       for batch in data_loader:
         _lowerCAmelCase						:	int        =					batch["""input_ids"""].shape
         _lowerCAmelCase						:	Union[str, Any]        =					src_shape[0]
         assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
         _lowerCAmelCase						:	List[str]        =					np.product(batch["""input_ids"""].shape  )
         num_src_per_batch.append(a__  )
         if num_src_tokens > (max_tokens * 1.1):
           failures.append(a__  )
       assert num_src_per_batch[0] == max(a__  )
       if failures:
         raise AssertionError(F"too many tokens in {len(a__  )} batches"  )






     def    __A  (		self  ):
       _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase						:	Dict        =					self._get_dataset(max_len=512  )
       _lowerCAmelCase						:	int        =					2
       _lowerCAmelCase						:	List[str]        =					ds.make_sortish_sampler(a__    ,					shuffle=a__  )

       _lowerCAmelCase						:	Dict        =					DataLoader(a__    ,					batch_size=a__    ,					collate_fn=ds.collate_fn    ,					num_workers=2  )
       _lowerCAmelCase						:	int        =					DataLoader(a__    ,					batch_size=a__    ,					collate_fn=ds.collate_fn    ,					num_workers=2    ,					sampler=a__  )

       _lowerCAmelCase						:	int        =					tokenizer.pad_token_id

       def count_pad_tokens(a__    ,					a__="input_ids"  ):
         return [batch[k].eq(a__  ).sum().item() for batch in data_loader]

       assert sum(count_pad_tokens(a__    ,					k="""labels"""  )  ) < sum(count_pad_tokens(a__    ,					k="""labels"""  )  )
       assert sum(count_pad_tokens(a__  )  ) < sum(count_pad_tokens(a__  )  )
       assert len(a__  ) == len(a__  )






     def    __A  (		self    ,					a__=1000    ,					a__=128  ):
       if os.getenv("""USE_REAL_DATA"""    ,					a__  ):
         _lowerCAmelCase						:	List[str]        =					"""examples/seq2seq/wmt_en_ro"""
         _lowerCAmelCase						:	str        =					max_len * 2 * 64
         if not Path(a__  ).joinpath("""train.len"""  ).exists():
           save_len_file(a__    ,					a__  )
       else:
         _lowerCAmelCase						:	List[str]        =					"""examples/seq2seq/test_data/wmt_en_ro"""
         _lowerCAmelCase						:	Dict        =					max_len * 4
         save_len_file(a__    ,					a__  )

       _lowerCAmelCase						:	Optional[Any]        =					AutoTokenizer.from_pretrained(a__  )
       _lowerCAmelCase						:	Dict        =					SeqaSeqDataset(
           a__    ,					data_dir=a__    ,					type_path="""train"""    ,					max_source_length=a__    ,					max_target_length=a__    ,					n_obs=a__    ,					)
       return ds, max_tokens, tokenizer






     def    __A  (		self  ):
       _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase						:	Union[str, Any]        =					self._get_dataset()
       _lowerCAmelCase						:	Any        =					set(DistributedSortishSampler(a__    ,					256    ,					num_replicas=2    ,					rank=0    ,					add_extra_examples=a__  )  )
       _lowerCAmelCase						:	Optional[int]        =					set(DistributedSortishSampler(a__    ,					256    ,					num_replicas=2    ,					rank=1    ,					add_extra_examples=a__  )  )
       assert idsa.intersection(a__  ) == set()






     @parameterized.expand(
         [
             MBART_TINY,
             MARIAN_TINY,
             T5_TINY,
             BART_TINY,
             PEGASUS_XSUM,
         ]    ,					)
     def    __A  (		self    ,					a__  ):
       _lowerCAmelCase						:	int        =					AutoTokenizer.from_pretrained(a__    ,					use_fast=a__  )
       if tok_name == MBART_TINY:
         _lowerCAmelCase						:	Dict        =					SeqaSeqDataset(
             a__    ,					data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()  )    ,					type_path="""train"""    ,					max_source_length=4    ,					max_target_length=8    ,					src_lang="""EN"""    ,					tgt_lang="""FR"""    ,					)
         _lowerCAmelCase						:	Optional[Any]        =					train_dataset.dataset_kwargs
         assert "src_lang" in kwargs and "tgt_lang" in kwargs
       else:
         _lowerCAmelCase						:	List[Any]        =					SeqaSeqDataset(
             a__    ,					data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()  )    ,					type_path="""train"""    ,					max_source_length=4    ,					max_target_length=8    ,					)
         _lowerCAmelCase						:	Tuple        =					train_dataset.dataset_kwargs
         assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
         assert len(a__  ) == 1 if tok_name == BART_TINY else len(a__  ) == 0




