

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




def   _A  (  UpperCamelCase_					:	Path,      UpperCamelCase_					:	list)     ->						Optional[Any]:
   '''simple docstring'''


   __lowercase								=						"\n".join(UpperCamelCase_)
   Path(UpperCamelCase_).open("w").writelines(UpperCamelCase_)




def   _A  (  UpperCamelCase_					:	Union[str, Any])     ->						int:
   '''simple docstring'''


   for split in ["train", "val", "test"]:
      _dump_articles(os.path.join(UpperCamelCase_,      F"""{split}.source"""),      UpperCamelCase_)
      _dump_articles(os.path.join(UpperCamelCase_,      F"""{split}.target"""),      UpperCamelCase_)
   return tmp_dir




class  _lowerCAmelCase  (     lowercase       ):







 """simple docstring"""







 @parameterized.expand(
     [
         MBART_TINY,
         MARIAN_TINY,
         T5_TINY,
         BART_TINY,
         PEGASUS_XSUM,
     ], )
 @slow
 def  _lowercase			(      self							:      List[Any], UpperCAmelCase__							:      Optional[Any]     ):
    __lowercase								=						AutoTokenizer.from_pretrained(UpperCAmelCase__     )
    __lowercase								=						make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     )
    __lowercase								=						max(len(tokenizer.encode(UpperCAmelCase__     )     ) for a in ARTICLES     )
    __lowercase								=						max(len(tokenizer.encode(UpperCAmelCase__     )     ) for a in SUMMARIES     )
    __lowercase								=						4
    __lowercase								=						8
    assert max_len_target > max_src_len  # Will be truncated
    assert max_len_source > max_src_len  # Will be truncated
    __lowercase		,__lowercase								=						"ro_RO", "de_DE"  # ignored for all but mbart, but never causes error.
    __lowercase								=						SeqaSeqDataset(
        UpperCAmelCase__, data_dir=UpperCAmelCase__, type_path="train", max_source_length=UpperCAmelCase__, max_target_length=UpperCAmelCase__, src_lang=UpperCAmelCase__, tgt_lang=UpperCAmelCase__, )
    __lowercase								=						DataLoader(UpperCAmelCase__, batch_size=2, collate_fn=train_dataset.collate_fn     )
    for batch in dataloader:
       assert isinstance(UpperCAmelCase__, UpperCAmelCase__     )
       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
       __lowercase								=						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  _lowercase			(      self							:      Dict, UpperCAmelCase__							:      int     ):
    __lowercase								=						AutoTokenizer.from_pretrained(UpperCAmelCase__     )
    __lowercase								=						make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     )
    __lowercase								=						max(len(tokenizer.encode(UpperCAmelCase__     )     ) for a in ARTICLES     )
    __lowercase								=						max(len(tokenizer.encode(UpperCAmelCase__     )     ) for a in SUMMARIES     )
    __lowercase								=						4
    __lowercase								=						LegacySeqaSeqDataset(
        UpperCAmelCase__, data_dir=UpperCAmelCase__, type_path="train", max_source_length=2_0, max_target_length=UpperCAmelCase__, )
    __lowercase								=						DataLoader(UpperCAmelCase__, 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  _lowercase			(      self							:      str     ):
    __lowercase								=						AutoTokenizer.from_pretrained("facebook/mbart-large-cc25"     )

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


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


 def  _lowercase			(      self							:      Any     ):
    __lowercase		,__lowercase		,__lowercase								=						self._get_dataset(max_len=5_1_2     )
    __lowercase								=						2
    __lowercase								=						ds.make_sortish_sampler(UpperCAmelCase__, shuffle=UpperCAmelCase__     )

    __lowercase								=						DataLoader(UpperCAmelCase__, batch_size=UpperCAmelCase__, collate_fn=ds.collate_fn, num_workers=2     )
    __lowercase								=						DataLoader(UpperCAmelCase__, batch_size=UpperCAmelCase__, collate_fn=ds.collate_fn, num_workers=2, sampler=UpperCAmelCase__     )

    __lowercase								=						tokenizer.pad_token_id

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

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


 def  _lowercase			(      self							:      List[Any], UpperCAmelCase__							:      int=1_0_0_0, UpperCAmelCase__							:      str=1_2_8     ):
    if os.getenv("USE_REAL_DATA", UpperCAmelCase__     ):
       __lowercase								=						"examples/seq2seq/wmt_en_ro"
       __lowercase								=						max_len * 2 * 6_4
       if not Path(UpperCAmelCase__     ).joinpath("train.len"     ).exists():
          save_len_file(UpperCAmelCase__, UpperCAmelCase__     )
    else:
       __lowercase								=						"examples/seq2seq/test_data/wmt_en_ro"
       __lowercase								=						max_len * 4
       save_len_file(UpperCAmelCase__, UpperCAmelCase__     )

    __lowercase								=						AutoTokenizer.from_pretrained(UpperCAmelCase__     )
    __lowercase								=						SeqaSeqDataset(
        UpperCAmelCase__, data_dir=UpperCAmelCase__, type_path="train", max_source_length=UpperCAmelCase__, max_target_length=UpperCAmelCase__, n_obs=UpperCAmelCase__, )
    return ds, max_tokens, tokenizer


 def  _lowercase			(      self							:      Union[str, Any]     ):
    __lowercase		,__lowercase		,__lowercase								=						self._get_dataset()
    __lowercase								=						set(DistributedSortishSampler(UpperCAmelCase__, 2_5_6, num_replicas=2, rank=0, add_extra_examples=UpperCAmelCase__     )     )
    __lowercase								=						set(DistributedSortishSampler(UpperCAmelCase__, 2_5_6, num_replicas=2, rank=1, add_extra_examples=UpperCAmelCase__     )     )
    assert idsa.intersection(UpperCAmelCase__     ) == set()



 @parameterized.expand(
     [
         MBART_TINY,
         MARIAN_TINY,
         T5_TINY,
         BART_TINY,
         PEGASUS_XSUM,
     ], )
 def  _lowercase			(      self							:      int, UpperCAmelCase__							:      Optional[Any]     ):
    __lowercase								=						AutoTokenizer.from_pretrained(UpperCAmelCase__, use_fast=UpperCAmelCase__     )
    if tok_name == MBART_TINY:
       __lowercase								=						SeqaSeqDataset(
           UpperCAmelCase__, 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", )
       __lowercase								=						train_dataset.dataset_kwargs
       assert "src_lang" in kwargs and "tgt_lang" in kwargs
    else:
       __lowercase								=						SeqaSeqDataset(
           UpperCAmelCase__, 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, )
       __lowercase								=						train_dataset.dataset_kwargs
       assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
       assert len(UpperCAmelCase__     ) == 1 if tok_name == BART_TINY else len(UpperCAmelCase__     ) == 0







"""simple docstring"""





import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch

import numpy as np
import pytest
from absl.testing import parameterized

import datasets
from datasets import load_metric

from .utils import for_all_test_methods, local, slow


# mark all tests as integration
_a							=							pytest.mark.integration


_a							=							{'comet'}
_a							=							importlib.util.find_spec('fairseq') is not None

_a							=							{'code_eval'}
_a							=							os.name == 'nt'

_a							=							{'bertscore', 'frugalscore', 'perplexity'}
_a							=							importlib.util.find_spec('transformers') is not None




def   _A  (  UpperCamelCase_					:	Dict)     ->						Any:
   '''simple docstring'''


   @wraps(UpperCamelCase_)
   def wrapper(self					:	Dict,      UpperCamelCase_					:	Dict):
      if not _has_fairseq and metric_name in REQUIRE_FAIRSEQ:
         self.skipTest("\"test requires Fairseq\"")
      else:
         test_case(self,      UpperCamelCase_)

   return wrapper




def   _A  (  UpperCamelCase_					:	Dict)     ->						int:
   '''simple docstring'''


   @wraps(UpperCamelCase_)
   def wrapper(self					:	int,      UpperCamelCase_					:	str):
      if not _has_transformers and metric_name in REQUIRE_TRANSFORMERS:
         self.skipTest("\"test requires transformers\"")
      else:
         test_case(self,      UpperCamelCase_)

   return wrapper




def   _A  (  UpperCamelCase_					:	Tuple)     ->						str:
   '''simple docstring'''


   @wraps(UpperCamelCase_)
   def wrapper(self					:	Optional[int],      UpperCamelCase_					:	Optional[Any]):
      if _on_windows and metric_name in UNSUPPORTED_ON_WINDOWS:
         self.skipTest("\"test not supported on Windows\"")
      else:
         test_case(self,      UpperCamelCase_)

   return wrapper




def   _A  (  )     ->						str:
   '''simple docstring'''


   __lowercase								=						[metric_dir.split(os.sep)[-2] for metric_dir in glob.glob("./metrics/*/")]
   return [{"testcase_name": x, "metric_name": x} for x in metrics if x != "gleu"]  # gleu is unfinished




@parameterized.named_parameters(get_local_metric_names()       )
@for_all_test_methods(
    lowercase					,lowercase					,lowercase       )
@local
class  _lowerCAmelCase  (     parameterized.TestCase       ):







 """simple docstring"""







 __UpperCAmelCase     : Optional[int]       		=     {}
 __UpperCAmelCase     : Tuple       		=     None


 @pytest.mark.filterwarnings("ignore:metric_module_factory is deprecated:FutureWarning"     )
 @pytest.mark.filterwarnings("ignore:load_metric is deprecated:FutureWarning"     )
 def  _lowercase			(      self							:      Dict, UpperCAmelCase__							:      int     ):
    __lowercase								=						"[...]"
    __lowercase								=						importlib.import_module(
        datasets.load.metric_module_factory(os.path.join("metrics", UpperCAmelCase__     )     ).module_path     )
    __lowercase								=						datasets.load.import_main_class(metric_module.__name__, dataset=UpperCAmelCase__     )
    # check parameters
    __lowercase								=						inspect.signature(metric._compute     ).parameters
    self.assertTrue(all(p.kind != p.VAR_KEYWORD for p in parameters.values()     )     )  # no **kwargs
    # run doctest
    with self.patch_intensive_calls(UpperCAmelCase__, metric_module.__name__     ):
       with self.use_local_metrics():
          try:
             __lowercase								=						doctest.testmod(UpperCAmelCase__, verbose=UpperCAmelCase__, raise_on_error=UpperCAmelCase__     )
          except doctest.UnexpectedException as e:
             raise e.exc_info[1]  # raise the exception that doctest caught
    self.assertEqual(results.failed, 0     )
    self.assertGreater(results.attempted, 1     )


 @slow
 def  _lowercase			(      self							:      List[Any], UpperCAmelCase__							:      Optional[Any]     ):
    __lowercase								=						"[...]"
    __lowercase								=						importlib.import_module(
        datasets.load.metric_module_factory(os.path.join("metrics", UpperCAmelCase__     )     ).module_path     )
    # run doctest
    with self.use_local_metrics():
       __lowercase								=						doctest.testmod(UpperCAmelCase__, verbose=UpperCAmelCase__, raise_on_error=UpperCAmelCase__     )
    self.assertEqual(results.failed, 0     )
    self.assertGreater(results.attempted, 1     )


 @contextmanager
 def  _lowercase			(      self							:      List[Any], UpperCAmelCase__							:      Any, UpperCAmelCase__							:      Tuple     ):
    if metric_name in self.INTENSIVE_CALLS_PATCHER:
       with self.INTENSIVE_CALLS_PATCHER[metric_name](UpperCAmelCase__     ):
          yield
    else:
       yield


 @contextmanager
 def  _lowercase			(      self							:      List[Any]     ):
    def load_local_metric(UpperCAmelCase__							:      Any, *UpperCAmelCase__							:      List[Any], **UpperCAmelCase__							:      Any     ):
       return load_metric(os.path.join("metrics", UpperCAmelCase__     ), *UpperCAmelCase__, **UpperCAmelCase__     )

    with patch("datasets.load_metric"     ) as mock_load_metric:
       __lowercase								=						load_local_metric
       yield



 @classmethod
 def  _lowercase			(      cls							:      Optional[Any], UpperCAmelCase__							:      List[Any]     ):
    def wrapper(UpperCAmelCase__							:      Tuple     ):
       __lowercase								=						contextmanager(UpperCAmelCase__     )
       __lowercase								=						patcher
       return patcher

    return wrapper







@LocalMetricTest.register_intensive_calls_patcher("bleurt")
def   _A  (  UpperCamelCase_					:	Any)     ->						Optional[Any]:
   '''simple docstring'''


   import tensorflow.compat.va as tf
   from bleurt.score import Predictor

   tf.flags.DEFINE_string("sv",      "",      "")  # handle pytest cli flags




   class  _lowerCAmelCase  (     lowercase       ):







    """simple docstring"""







    def  _lowercase			(      self							:      Tuple, UpperCAmelCase__							:      Tuple     ):
       assert len(input_dict["input_ids"]     ) == 2
       return np.array([1.03, 1.04]     )

    # mock predict_fn which is supposed to do a forward pass with a bleurt model
   with patch("bleurt.score._create_predictor") as mock_create_predictor:
      __lowercase								=						MockedPredictor()
      yield




@LocalMetricTest.register_intensive_calls_patcher("bertscore")
def   _A  (  UpperCamelCase_					:	Tuple)     ->						int:
   '''simple docstring'''


   import torch

   def bert_cos_score_idf(UpperCamelCase_					:	Tuple,      UpperCamelCase_					:	str,      *UpperCamelCase_					:	Optional[Any],      **UpperCamelCase_					:	Dict):
      return torch.tensor([[1.0, 1.0, 1.0]] * len(UpperCamelCase_))

   # mock get_model which is supposed to do download a bert model
   # mock bert_cos_score_idf which is supposed to do a forward pass with a bert model
   with patch("bert_score.scorer.get_model"), patch(
       "bert_score.scorer.bert_cos_score_idf") as mock_bert_cos_score_idf:
      __lowercase								=						bert_cos_score_idf
      yield




@LocalMetricTest.register_intensive_calls_patcher("comet")
def   _A  (  UpperCamelCase_					:	Tuple)     ->						List[Any]:
   '''simple docstring'''


   def load_from_checkpoint(UpperCamelCase_					:	Tuple):




      class  _lowerCAmelCase  :







       """simple docstring"""







       def  _lowercase			(      self							:      str, UpperCAmelCase__							:      int, *UpperCAmelCase__							:      Dict, **UpperCAmelCase__							:      Dict     ):
          assert len(UpperCAmelCase__     ) == 2
          __lowercase								=						[0.19, 0.92]
          return scores, sum(UpperCAmelCase__     ) / len(UpperCAmelCase__     )







      return Model()

   # mock load_from_checkpoint which is supposed to do download a bert model
   # mock load_from_checkpoint which is supposed to do download a bert model
   with patch("comet.download_model") as mock_download_model:
      __lowercase								=						None
      with patch("comet.load_from_checkpoint") as mock_load_from_checkpoint:
         __lowercase								=						load_from_checkpoint
         yield




def   _A  (  )     ->						Tuple:
   '''simple docstring'''


   __lowercase								=						load_metric(os.path.join("metrics",      "seqeval"))
   __lowercase								=						"ERROR"
   __lowercase								=						F"""Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {wrong_scheme}"""
   with pytest.raises(UpperCamelCase_,      match=re.escape(UpperCamelCase_)):
      metric.compute(predictions=[],      references=[],      scheme=UpperCamelCase_)




