
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


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

def a__    (       A_,     A_					):






  '''simple docstring'''





  __magic_name__				     =       """\n""".join(A_					)
  Path(A_					).open("""w"""					).writelines(A_					)

def a__    (       A_					):






  '''simple docstring'''





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





class        UpperCAmelCase_			( _A     ):


  '''simple docstring'''







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







    """simple docstring"""






    __magic_name__				     =       AutoTokenizer.from_pretrained(UpperCamelCase__    )
    __magic_name__				     =       make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()    )
    __magic_name__				     =       max(len(tokenizer.encode(UpperCamelCase__    )    ) for a in ARTICLES    )
    __magic_name__				     =       max(len(tokenizer.encode(UpperCamelCase__    )    ) for a in SUMMARIES    )
    __magic_name__				     =       4
    __magic_name__				     =       8
    assert max_len_target > max_src_len  # Will be truncated
    assert max_len_source > max_src_len  # Will be truncated
    __magic_name__     ,    __magic_name__				     =       """ro_RO""", """de_DE"""  # ignored for all but mbart, but never causes error.
    __magic_name__				     =       SeqaSeqDataset(
        UpperCamelCase__			,							data_dir=UpperCamelCase__			,							type_path="""train"""			,							max_source_length=UpperCamelCase__			,							max_target_length=UpperCamelCase__			,							src_lang=UpperCamelCase__			,							tgt_lang=UpperCamelCase__			,							)
    __magic_name__				     =       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
      __magic_name__				     =       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__			: Optional[int]    )			->	Union[str, Any]:







    """simple docstring"""






    __magic_name__				     =       AutoTokenizer.from_pretrained(UpperCamelCase__    )
    __magic_name__				     =       make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()    )
    __magic_name__				     =       max(len(tokenizer.encode(UpperCamelCase__    )    ) for a in ARTICLES    )
    __magic_name__				     =       max(len(tokenizer.encode(UpperCamelCase__    )    ) for a in SUMMARIES    )
    __magic_name__				     =       4
    __magic_name__				     =       LegacySeqaSeqDataset(
        UpperCamelCase__			,							data_dir=UpperCamelCase__			,							type_path="""train"""			,							max_source_length=20			,							max_target_length=UpperCamelCase__			,							)
    __magic_name__				     =       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 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 							_lowercase							( self			: Any    )			->	Dict:







    """simple docstring"""






    __magic_name__				     =       AutoTokenizer.from_pretrained("""facebook/mbart-large-cc25"""    )

    __magic_name__				     =       Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()    )    )
    __magic_name__				     =       tmp_dir.joinpath("""train.source"""    ).open().readlines()
    __magic_name__				     =       Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()    )    )
    pack_data_dir(UpperCamelCase__			,							UpperCamelCase__			,							128			,							UpperCamelCase__    )
    __magic_name__				     =       {x.name for x in tmp_dir.iterdir()}
    __magic_name__				     =       {x.name for x in save_dir.iterdir()}
    __magic_name__				     =       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[Any]    )			->	Tuple:







    """simple docstring"""






    if not FAIRSEQ_AVAILABLE:
      return
    __magic_name__     ,    __magic_name__     ,    __magic_name__				     =       self._get_dataset(max_len=64    )
    __magic_name__				     =       64
    __magic_name__				     =       ds.make_dynamic_sampler(UpperCamelCase__			,							required_batch_size_multiple=UpperCamelCase__    )
    __magic_name__				     =       [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
    __magic_name__				     =       DataLoader(UpperCamelCase__			,							batch_sampler=UpperCamelCase__			,							collate_fn=ds.collate_fn			,							num_workers=2    )
    __magic_name__				     =       []
    __magic_name__				     =       []
    for batch in data_loader:
      __magic_name__				     =       batch["""input_ids"""].shape
      __magic_name__				     =       src_shape[0]
      assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
      __magic_name__				     =       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			: List[Any]    )			->	Any:







    """simple docstring"""






    __magic_name__     ,    __magic_name__     ,    __magic_name__				     =       self._get_dataset(max_len=512    )
    __magic_name__				     =       2
    __magic_name__				     =       ds.make_sortish_sampler(UpperCamelCase__			,							shuffle=UpperCamelCase__    )

    __magic_name__				     =       DataLoader(UpperCamelCase__			,							batch_size=UpperCamelCase__			,							collate_fn=ds.collate_fn			,							num_workers=2    )
    __magic_name__				     =       DataLoader(UpperCamelCase__			,							batch_size=UpperCamelCase__			,							collate_fn=ds.collate_fn			,							num_workers=2			,							sampler=UpperCamelCase__    )

    __magic_name__				     =       tokenizer.pad_token_id

    def count_pad_tokens(UpperCamelCase__			: str			,							UpperCamelCase__			: 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[str]			,							UpperCamelCase__			: Union[str, Any]=1000			,							UpperCamelCase__			: int=128    )			->	List[str]:







    """simple docstring"""






    if os.getenv("""USE_REAL_DATA"""			,							UpperCamelCase__    ):
      __magic_name__				     =       """examples/seq2seq/wmt_en_ro"""
      __magic_name__				     =       max_len * 2 * 64
      if not Path(UpperCamelCase__    ).joinpath("""train.len"""    ).exists():
        save_len_file(UpperCamelCase__			,							UpperCamelCase__    )
    else:
      __magic_name__				     =       """examples/seq2seq/test_data/wmt_en_ro"""
      __magic_name__				     =       max_len * 4
      save_len_file(UpperCamelCase__			,							UpperCamelCase__    )

    __magic_name__				     =       AutoTokenizer.from_pretrained(UpperCamelCase__    )
    __magic_name__				     =       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			: Dict    )			->	List[Any]:







    """simple docstring"""






    __magic_name__     ,    __magic_name__     ,    __magic_name__				     =       self._get_dataset()
    __magic_name__				     =       set(DistributedSortishSampler(UpperCamelCase__			,							256			,							num_replicas=2			,							rank=0			,							add_extra_examples=UpperCamelCase__    )    )
    __magic_name__				     =       set(DistributedSortishSampler(UpperCamelCase__			,							256			,							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			: Optional[int]			,							UpperCamelCase__			: str    )			->	Any:







    """simple docstring"""






    __magic_name__				     =       AutoTokenizer.from_pretrained(UpperCamelCase__			,							use_fast=UpperCamelCase__    )
    if tok_name == MBART_TINY:
      __magic_name__				     =       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"""			,							)
      __magic_name__				     =       train_dataset.dataset_kwargs
      assert "src_lang" in kwargs and "tgt_lang" in kwargs
    else:
      __magic_name__				     =       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			,							)
      __magic_name__				     =       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







import pytest

from datasets import inspect_metric, list_metrics, load_metric







@pytest.fixture
def 	_a      (						SCREAMING_SNAKE_CASE_							:				Optional[Any]     ):
					monkeypatch.setattr("datasets.utils.deprecation_utils._emitted_deprecation_warnings"      ,      set()     )







@pytest.fixture
def 	_a      (						SCREAMING_SNAKE_CASE_							:				List[Any]     ):
					class 			a__    :






											def __init__(   self		,			_A			):




																"""simple docstring"""



																__lowerCAmelCase				   =       metric_id






					class 			a__    :
											_a							:    Optional[int]					=    [MetricMock(snake_case__      ) for metric_id in ["""accuracy""", """mse""", """precision""", """codeparrot/apps_metric"""]]






											def 				__SCREAMING_SNAKE_CASE(   self			):




																"""simple docstring"""



																return self._metrics






					monkeypatch.setattr("datasets.inspect.huggingface_hub"      ,      HfhMock()     )







@pytest.mark.parametrize(
    "func, args"      ,      [(load_metric, ("metrics/mse",)), (list_metrics, ()), (inspect_metric, ("metrics/mse", "tmp_path"))]     )
def 	_a      (						SCREAMING_SNAKE_CASE_							:				Tuple      ,      SCREAMING_SNAKE_CASE_							:				List[str]      ,      SCREAMING_SNAKE_CASE_							:				int      ,      SCREAMING_SNAKE_CASE_							:				Optional[int]      ,      SCREAMING_SNAKE_CASE_							:				Optional[int]     ):
					if "tmp_path" in args:
										__lowerCAmelCase				   =       tuple(arg if arg != "tmp_path" else tmp_path for arg in args     )
					with pytest.warns(SCREAMING_SNAKE_CASE_      ,      match="https://huggingface.co/docs/evaluate"     ):
										func(*SCREAMING_SNAKE_CASE_     )




