

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


lowercase_			=  "bert-base-cased"
lowercase_			=  "google/pegasus-xsum"
lowercase_			=  [" Sam ate lunch today.", "Sams lunch ingredients."]
lowercase_			=  ["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
lowercase_			=  "patrickvonplaten/t5-tiny-random"
lowercase_			=  "sshleifer/bart-tiny-random"
lowercase_			=  "sshleifer/tiny-mbart"
lowercase_			=  "sshleifer/tiny-marian-en-de"
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Path   ,      SCREAMING_SNAKE_CASE__      :     list				)							->				List[Any]:





    '''simple docstring'''






    A__   				=       '''\n'''.join(lowerCAmelCase__				)
    Path(lowerCAmelCase__				).open('w'				).writelines(lowerCAmelCase__				)
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     int				)							->				Optional[Any]:





    '''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 			A						(							__lowercase			):







    """simple docstring"""





    @parameterized.expand(
        [
            MBART_TINY,
            MARIAN_TINY,
            T5_TINY,
            BART_TINY,
            PEGASUS_XSUM,
        ],)
    @slow
    def 						snake_case__					(				self			:	int,lowercase_			:	int		)->   Dict:





        '''simple docstring'''







        A__   				=       AutoTokenizer.from_pretrained(snake_case_		)
        A__   				=       make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)
        A__   				=       max(len(tokenizer.encode(snake_case_		)		) for a in ARTICLES		)
        A__   				=       max(len(tokenizer.encode(snake_case_		)		) for a in SUMMARIES		)
        A__   				=       4
        A__   				=       8
        assert max_len_target > max_src_len  # Will be truncated
        assert max_len_source > max_src_len  # Will be truncated
        A__   				=       '''ro_RO''', '''de_DE'''  # ignored for all but mbart, but never causes error.
        A__   				=       SeqaSeqDataset(
            snake_case_,data_dir=snake_case_,type_path='train',max_source_length=snake_case_,max_target_length=snake_case_,src_lang=snake_case_,tgt_lang=snake_case_,)
        A__   				=       DataLoader(snake_case_,batch_size=2,collate_fn=train_dataset.collate_fn		)
        for batch in dataloader:
            assert isinstance(snake_case_,snake_case_		)
            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__   				=       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 						snake_case__					(				self			:	Optional[Any],lowercase_			:	str		)->   Union[str, Any]:





        '''simple docstring'''







        A__   				=       AutoTokenizer.from_pretrained(snake_case_		)
        A__   				=       make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)
        A__   				=       max(len(tokenizer.encode(snake_case_		)		) for a in ARTICLES		)
        A__   				=       max(len(tokenizer.encode(snake_case_		)		) for a in SUMMARIES		)
        A__   				=       4
        A__   				=       LegacySeqaSeqDataset(
            snake_case_,data_dir=snake_case_,type_path='train',max_source_length=2_0,max_target_length=snake_case_,)
        A__   				=       DataLoader(snake_case_,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 2_0 >= 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 						snake_case__					(				self			:	List[str]		)->   Union[str, Any]:





        '''simple docstring'''







        A__   				=       AutoTokenizer.from_pretrained('facebook/mbart-large-cc25'		)

        A__   				=       Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)		)
        A__   				=       tmp_dir.joinpath('train.source'		).open().readlines()
        A__   				=       Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)		)
        pack_data_dir(snake_case_,snake_case_,1_2_8,snake_case_		)
        A__   				=       {x.name for x in tmp_dir.iterdir()}
        A__   				=       {x.name for x in save_dir.iterdir()}
        A__   				=       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(snake_case_		) < len(snake_case_		)
        assert len(snake_case_		) == 1
        assert len(packed_examples[0]		) == sum(len(snake_case_		) for x in orig_examples		)
        assert orig_paths == new_paths


    @pytest.mark.skipif(not FAIRSEQ_AVAILABLE,reason='This test requires fairseq'		)
    def 						snake_case__					(				self			:	Any		)->   Dict:





        '''simple docstring'''







        if not FAIRSEQ_AVAILABLE:
            return
        A__   				=       self._get_dataset(max_len=6_4		)
        A__   				=       6_4
        A__   				=       ds.make_dynamic_sampler(snake_case_,required_batch_size_multiple=snake_case_		)
        A__   				=       [len(snake_case_		) for x in batch_sampler]
        assert len(set(snake_case_		)		) > 1  # it's not dynamic batch size if every batch is the same length
        assert sum(snake_case_		) == len(snake_case_		)  # no dropped or added examples
        A__   				=       DataLoader(snake_case_,batch_sampler=snake_case_,collate_fn=ds.collate_fn,num_workers=2		)
        A__   				=       []
        A__   				=       []
        for batch in data_loader:
            A__   				=       batch['''input_ids'''].shape
            A__   				=       src_shape[0]
            assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
            A__   				=       np.product(batch['input_ids'].shape		)
            num_src_per_batch.append(snake_case_		)
            if num_src_tokens > (max_tokens * 1.1):
                failures.append(snake_case_		)
        assert num_src_per_batch[0] == max(snake_case_		)
        if failures:
            raise AssertionError(F'too many tokens in {len(snake_case_		)} batches'		)


    def 						snake_case__					(				self			:	int		)->   Optional[int]:





        '''simple docstring'''







        A__   				=       self._get_dataset(max_len=5_1_2		)
        A__   				=       2
        A__   				=       ds.make_sortish_sampler(snake_case_,shuffle=snake_case_		)

        A__   				=       DataLoader(snake_case_,batch_size=snake_case_,collate_fn=ds.collate_fn,num_workers=2		)
        A__   				=       DataLoader(snake_case_,batch_size=snake_case_,collate_fn=ds.collate_fn,num_workers=2,sampler=snake_case_		)

        A__   				=       tokenizer.pad_token_id

        def count_pad_tokens(lowercase_			:	Union[str, Any],lowercase_			:	List[str]="input_ids"		):
            return [batch[k].eq(snake_case_		).sum().item() for batch in data_loader]

        assert sum(count_pad_tokens(snake_case_,k='labels'		)		) < sum(count_pad_tokens(snake_case_,k='labels'		)		)
        assert sum(count_pad_tokens(snake_case_		)		) < sum(count_pad_tokens(snake_case_		)		)
        assert len(snake_case_		) == len(snake_case_		)


    def 						snake_case__					(				self			:	int,lowercase_			:	int=1_0_0_0,lowercase_			:	Optional[Any]=1_2_8		)->   Optional[int]:





        '''simple docstring'''







        if os.getenv('USE_REAL_DATA',snake_case_		):
            A__   				=       '''examples/seq2seq/wmt_en_ro'''
            A__   				=       max_len * 2 * 6_4
            if not Path(snake_case_		).joinpath('train.len'		).exists():
                save_len_file(snake_case_,snake_case_		)
        else:
            A__   				=       '''examples/seq2seq/test_data/wmt_en_ro'''
            A__   				=       max_len * 4
            save_len_file(snake_case_,snake_case_		)

        A__   				=       AutoTokenizer.from_pretrained(snake_case_		)
        A__   				=       SeqaSeqDataset(
            snake_case_,data_dir=snake_case_,type_path='train',max_source_length=snake_case_,max_target_length=snake_case_,n_obs=snake_case_,)
        return ds, max_tokens, tokenizer


    def 						snake_case__					(				self			:	List[str]		)->   int:





        '''simple docstring'''







        A__   				=       self._get_dataset()
        A__   				=       set(DistributedSortishSampler(snake_case_,2_5_6,num_replicas=2,rank=0,add_extra_examples=snake_case_		)		)
        A__   				=       set(DistributedSortishSampler(snake_case_,2_5_6,num_replicas=2,rank=1,add_extra_examples=snake_case_		)		)
        assert idsa.intersection(snake_case_		) == set()


    @parameterized.expand(
        [
            MBART_TINY,
            MARIAN_TINY,
            T5_TINY,
            BART_TINY,
            PEGASUS_XSUM,
        ],)
    def 						snake_case__					(				self			:	str,lowercase_			:	Union[str, Any]		)->   Optional[int]:





        '''simple docstring'''







        A__   				=       AutoTokenizer.from_pretrained(snake_case_,use_fast=snake_case_		)
        if tok_name == MBART_TINY:
            A__   				=       SeqaSeqDataset(
                snake_case_,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__   				=       train_dataset.dataset_kwargs
            assert "src_lang" in kwargs and "tgt_lang" in kwargs
        else:
            A__   				=       SeqaSeqDataset(
                snake_case_,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__   				=       train_dataset.dataset_kwargs
            assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
            assert len(snake_case_		) == 1 if tok_name == BART_TINY else len(snake_case_		) == 0




from jiwer import compute_measures

import datasets


lowercase_			=  "\\n@inproceedings{inproceedings,\n    author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n    year = {2004},\n    month = {01},\n    pages = {},\n    title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}\n}\n"

lowercase_			=  "\\nWord error rate (WER) is a common metric of the performance of an automatic speech recognition system.\n\nThe general difficulty of measuring performance lies in the fact that the recognized word sequence can have a different length from the reference word sequence (supposedly the correct one). The WER is derived from the Levenshtein distance, working at the word level instead of the phoneme level. The WER is a valuable tool for comparing different systems as well as for evaluating improvements within one system. This kind of measurement, however, provides no details on the nature of translation errors and further work is therefore required to identify the main source(s) of error and to focus any research effort.\n\nThis problem is solved by first aligning the recognized word sequence with the reference (spoken) word sequence using dynamic string alignment. Examination of this issue is seen through a theory called the power law that states the correlation between perplexity and word error rate.\n\nWord error rate can then be computed as:\n\nWER = (S + D + I) / N = (S + D + I) / (S + D + C)\n\nwhere\n\nS is the number of substitutions,\nD is the number of deletions,\nI is the number of insertions,\nC is the number of correct words,\nN is the number of words in the reference (N=S+D+C).\n\nThis value indicates the average number of errors per reference word. The lower the value, the better the\nperformance of the ASR system with a WER of 0 being a perfect score.\n"

lowercase_			=  "\nCompute WER score of transcribed segments against references.\n\nArgs:\n    references: List of references for each speech input.\n    predictions: List of transcriptions to score.\n    concatenate_texts (bool, default=False): Whether to concatenate all input texts or compute WER iteratively.\n\nReturns:\n    (float): the word error rate\n\nExamples:\n\n    >>> predictions = [\"this is the prediction\", \"there is an other sample\"]\n    >>> references = [\"this is the reference\", \"there is another one\"]\n    >>> wer = datasets.load_metric(\"wer\")\n    >>> wer_score = wer.compute(predictions=predictions, references=references)\n    >>> print(wer_score)\n    0.5\n"


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION   ,							_KWARGS_DESCRIPTION			)
class 			A						(							datasets.Metric			):







    """simple docstring"""





    def 						snake_case__					(				self			:	Any		)->   str:





        '''simple docstring'''







        return datasets.MetricInfo(
            description=_DESCRIPTION,citation=_CITATION,inputs_description=_KWARGS_DESCRIPTION,features=datasets.Features(
                {
                    'predictions': datasets.Value('string',id='sequence'		),
                    'references': datasets.Value('string',id='sequence'		),
                }		),codebase_urls=['https://github.com/jitsi/jiwer/'],reference_urls=[
                'https://en.wikipedia.org/wiki/Word_error_rate',
            ],)


    def 						snake_case__					(				self			:	int,lowercase_			:	Any=None,lowercase_			:	List[str]=None,lowercase_			:	Dict=False		)->   Optional[int]:





        '''simple docstring'''







        if concatenate_texts:
            return compute_measures(lowercase_,lowercase_		)["wer"]
        else:
            A__   				=       0
            A__   				=       0
            for prediction, reference in zip(lowercase_,lowercase_		):
                A__   				=       compute_measures(lowercase_,lowercase_		)
                incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
                total += measures["substitutions"] + measures["deletions"] + measures["hits"]
            return incorrect / total

