



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


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




def 	__UpperCamelCase	(  _lowerCAmelCase      ,     _lowerCAmelCase		)       ->  List[str]:



    """simple docstring"""






    A :       Any          =   """\n""".join(_lowerCAmelCase		)
    Path(_lowerCAmelCase		).open("""w"""		).writelines(_lowerCAmelCase		)




def 	__UpperCamelCase	(  _lowerCAmelCase		)       ->  List[str]:



    """simple docstring"""






    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 SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    @parameterized.expand(
        [
            MBART_TINY,
            MARIAN_TINY,
            T5_TINY,
            BART_TINY,
            PEGASUS_XSUM,
        ],		)
    @slow
    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        A :       Optional[int]          =   AutoTokenizer.from_pretrained(lowerCamelCase__		)
        A :       Optional[int]          =   make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)
        A :       str          =   max(len(tokenizer.encode(lowerCamelCase__		)		) for a in ARTICLES		)
        A :       Dict          =   max(len(tokenizer.encode(lowerCamelCase__		)		) for a in SUMMARIES		)
        A :       Any          =   4
        A :       Tuple          =   8
        assert max_len_target > max_src_len  # Will be truncated
        assert max_len_source > max_src_len  # Will be truncated
        A     ,							A :       List[str]          =   """ro_RO""", """de_DE"""  # ignored for all but mbart, but never causes error.
        A :       List[str]          =   SeqaSeqDataset(
            lowerCamelCase__,		data_dir=lowerCamelCase__,		type_path="""train""",		max_source_length=lowerCamelCase__,		max_target_length=lowerCamelCase__,		src_lang=lowerCamelCase__,		tgt_lang=lowerCamelCase__,		)
        A :       Optional[int]          =   DataLoader(lowerCamelCase__,		batch_size=2,		collate_fn=train_dataset.collate_fn		)
        for batch in dataloader:
            assert isinstance(lowerCamelCase__,		lowerCamelCase__		)
            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
            A :       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 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        A :       List[Any]          =   AutoTokenizer.from_pretrained(lowerCamelCase__		)
        A :       List[Any]          =   make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)
        A :       Any          =   max(len(tokenizer.encode(lowerCamelCase__		)		) for a in ARTICLES		)
        A :       Optional[int]          =   max(len(tokenizer.encode(lowerCamelCase__		)		) for a in SUMMARIES		)
        A :       Dict          =   4
        A :       Any          =   LegacySeqaSeqDataset(
            lowerCamelCase__,		data_dir=lowerCamelCase__,		type_path="""train""",		max_source_length=20,		max_target_length=lowerCamelCase__,		)
        A :       Optional[int]          =   DataLoader(lowerCamelCase__,		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 			_lowerCAmelCase					(       self		):
        A :       List[str]          =   AutoTokenizer.from_pretrained("""facebook/mbart-large-cc25"""		)

        A :       str          =   Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)		)
        A :       Dict          =   tmp_dir.joinpath("""train.source"""		).open().readlines()
        A :       Optional[Any]          =   Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)		)
        pack_data_dir(lowerCamelCase__,		lowerCamelCase__,		128,		lowerCamelCase__		)
        A :       Dict          =   {x.name for x in tmp_dir.iterdir()}
        A :       Dict          =   {x.name for x in save_dir.iterdir()}
        A :       int          =   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(lowerCamelCase__		) < len(lowerCamelCase__		)
        assert len(lowerCamelCase__		) == 1
        assert len(packed_examples[0]		) == sum(len(lowerCamelCase__		) for x in orig_examples		)
        assert orig_paths == new_paths






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






    def 			_lowerCAmelCase					(       self		):
        A     ,							A     ,							A :       Any          =   self._get_dataset(max_len=512		)
        A :       Any          =   2
        A :       List[Any]          =   ds.make_sortish_sampler(lowerCamelCase__,		shuffle=lowerCamelCase__		)

        A :       Dict          =   DataLoader(lowerCamelCase__,		batch_size=lowerCamelCase__,		collate_fn=ds.collate_fn,		num_workers=2		)
        A :       Optional[Any]          =   DataLoader(lowerCamelCase__,		batch_size=lowerCamelCase__,		collate_fn=ds.collate_fn,		num_workers=2,		sampler=lowerCamelCase__		)

        A :       List[Any]          =   tokenizer.pad_token_id

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

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






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

        A :       Tuple          =   AutoTokenizer.from_pretrained(lowerCamelCase__		)
        A :       List[Any]          =   SeqaSeqDataset(
            lowerCamelCase__,		data_dir=lowerCamelCase__,		type_path="""train""",		max_source_length=lowerCamelCase__,		max_target_length=lowerCamelCase__,		n_obs=lowerCamelCase__,		)
        return ds, max_tokens, tokenizer






    def 			_lowerCAmelCase					(       self		):
        A     ,							A     ,							A :       List[str]          =   self._get_dataset()
        A :       Any          =   set(DistributedSortishSampler(lowerCamelCase__,		256,		num_replicas=2,		rank=0,		add_extra_examples=lowerCamelCase__		)		)
        A :       Union[str, Any]          =   set(DistributedSortishSampler(lowerCamelCase__,		256,		num_replicas=2,		rank=1,		add_extra_examples=lowerCamelCase__		)		)
        assert idsa.intersection(lowerCamelCase__		) == set()






    @parameterized.expand(
        [
            MBART_TINY,
            MARIAN_TINY,
            T5_TINY,
            BART_TINY,
            PEGASUS_XSUM,
        ],		)
    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        A :       Optional[int]          =   AutoTokenizer.from_pretrained(lowerCamelCase__,		use_fast=lowerCamelCase__		)
        if tok_name == MBART_TINY:
            A :       Optional[Any]          =   SeqaSeqDataset(
                lowerCamelCase__,		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""",		)
            A :       Union[str, Any]          =   train_dataset.dataset_kwargs
            assert "src_lang" in kwargs and "tgt_lang" in kwargs
        else:
            A :       Dict          =   SeqaSeqDataset(
                lowerCamelCase__,		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,		)
            A :       List[str]          =   train_dataset.dataset_kwargs
            assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
            assert len(lowerCamelCase__		) == 1 if tok_name == BART_TINY else len(lowerCamelCase__		) == 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


SCREAMING_SNAKE_CASE_: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.""",
]

SCREAMING_SNAKE_CASE_:Optional[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 .""",
    """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 	__UpperCamelCase	(  )       ->  Optional[Any]:



    """simple docstring"""






    A :       Tuple          =   calculate_rouge(_lowerCAmelCase      ,     _lowerCAmelCase      ,     bootstrap_aggregation=_lowerCAmelCase      ,     rouge_keys=["""rouge2""", """rougeL"""]		)
    assert isinstance(_lowerCAmelCase      ,     _lowerCAmelCase		)
    A :       Tuple          =   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 	__UpperCamelCase	(  )       ->  List[Any]:



    """simple docstring"""






    A :       Dict          =   """rougeLsum"""
    A :       str          =   calculate_rouge(_lowerCAmelCase      ,     _lowerCAmelCase      ,     newline_sep=_lowerCAmelCase      ,     rouge_keys=[k]		)[k]
    A :       Tuple          =   calculate_rouge(_lowerCAmelCase      ,     _lowerCAmelCase      ,     newline_sep=_lowerCAmelCase      ,     rouge_keys=[k]		)[k]
    assert score > score_no_sep




def 	__UpperCamelCase	(  )       ->  Tuple:



    """simple docstring"""






    A :       Union[str, Any]          =   ["""rouge1""", """rouge2""", """rougeL"""]
    A :       Dict          =   calculate_rouge(_lowerCAmelCase      ,     _lowerCAmelCase      ,     newline_sep=_lowerCAmelCase      ,     rouge_keys=_lowerCAmelCase		)
    A :       List[str]          =   calculate_rouge(_lowerCAmelCase      ,     _lowerCAmelCase      ,     newline_sep=_lowerCAmelCase      ,     rouge_keys=_lowerCAmelCase		)
    assert score_sep == score_no_sep




def 	__UpperCamelCase	(  )       ->  Optional[Any]:



    """simple docstring"""






    A :       Optional[Any]          =   [
        """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 .""",
    ]
    A :       Optional[Any]          =   [
        """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 	__UpperCamelCase	(  )       ->  List[Any]:



    """simple docstring"""






    A :       Tuple          =   [
        """\" \"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\" """
    ]
    A :       Union[str, 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 ."""
    ]

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




def 	__UpperCamelCase	(  )       ->  Any:



    """simple docstring"""






    A :       Tuple          =   Path("""examples/seq2seq/test_data/wmt_en_ro"""		)
    A :       Optional[Any]          =   calculate_rouge_path(data_dir.joinpath("""test.source"""		)      ,     data_dir.joinpath("""test.target"""		)		)
    assert isinstance(_lowerCAmelCase      ,     _lowerCAmelCase		)
    A :       List[Any]          =   calculate_rouge_path(
        data_dir.joinpath("""test.source"""		)      ,     data_dir.joinpath("""test.target"""		)      ,     bootstrap_aggregation=_lowerCAmelCase		)
    assert isinstance(_lowerCAmelCase      ,     _lowerCAmelCase		)


