

import os
import warnings
from typing import List, Optional

from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig


UpperCamelCase__  :      Dict			=    logging.get_logger(__name__)



class       __snake_case  :

  def __init__(					self						,		_A						,		_A):
      SCREAMING_SNAKE_CASE_									=						question_encoder
      SCREAMING_SNAKE_CASE_									=						generator
      SCREAMING_SNAKE_CASE_									=						self.question_encoder

  def 						lowerCAmelCase__     (					self						,		_A):
      if os.path.isfile(_A):
          raise ValueError(f"""Provided path ({save_directory}) should be a directory, not a file""")
      os.makedirs(_A						,		exist_ok=_A)
      SCREAMING_SNAKE_CASE_									=						os.path.join(_A						,		'question_encoder_tokenizer')
      SCREAMING_SNAKE_CASE_									=						os.path.join(_A						,		'generator_tokenizer')
      self.question_encoder.save_pretrained(_A)
      self.generator.save_pretrained(_A)

  @classmethod
  def 						lowerCAmelCase__     (					cls						,		_A						,		**_A):
      # dynamically import AutoTokenizer
      from ..auto.tokenization_auto import AutoTokenizer

      SCREAMING_SNAKE_CASE_									=						kwargs.pop('config'						,		_A)

      if config is None:
          SCREAMING_SNAKE_CASE_									=						RagConfig.from_pretrained(_A)

      SCREAMING_SNAKE_CASE_									=						AutoTokenizer.from_pretrained(
          _A						,		config=config.question_encoder						,		subfolder='question_encoder_tokenizer')
      SCREAMING_SNAKE_CASE_									=						AutoTokenizer.from_pretrained(
          _A						,		config=config.generator						,		subfolder='generator_tokenizer')
      return cls(question_encoder=_A						,		generator=_A)

  def __call__(					self						,		*_A						,		**_A):
      return self.current_tokenizer(*_A						,		**_A)

  def 						lowerCAmelCase__     (					self						,		*_A						,		**_A):
      return self.generator.batch_decode(*_A						,		**_A)

  def 						lowerCAmelCase__     (					self						,		*_A						,		**_A):
      return self.generator.decode(*_A						,		**_A)

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						self.question_encoder

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						self.generator


  def 						lowerCAmelCase__     (					self						,		_A						,		_A = None						,		_A = None						,		_A = None						,		_A = "longest"						,		_A = None						,		_A = True						,		**_A						,		):
      warnings.warn(
          '`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the '
          'regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` '
          'context manager to prepare your targets. See the documentation of your specific tokenizer for more '
          'details'						,		_A						,		)
      if max_length is None:
          SCREAMING_SNAKE_CASE_									=						self.current_tokenizer.model_max_length
      SCREAMING_SNAKE_CASE_									=						self(
          _A						,		add_special_tokens=_A						,		return_tensors=_A						,		max_length=_A						,		padding=_A						,		truncation=_A						,		**_A						,		)
      if tgt_texts is None:
          return model_inputs
      # Process tgt_texts
      if max_target_length is None:
          SCREAMING_SNAKE_CASE_									=						self.current_tokenizer.model_max_length
      SCREAMING_SNAKE_CASE_									=						self(
          text_target=_A						,		add_special_tokens=_A						,		return_tensors=_A						,		padding=_A						,		max_length=_A						,		truncation=_A						,		**_A						,		)
      SCREAMING_SNAKE_CASE_									=						labels['input_ids']
      return model_inputs





import pytest

import datasets


# Import fixture modules as plugins
UpperCamelCase__  :      Union[str, Any]			=    ["tests.fixtures.files", "tests.fixtures.hub", "tests.fixtures.fsspec"]


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Optional[Any] ,   _SCREAMING_SNAKE_CASE					:		Dict       ):





    """simple docstring"""


    for item in items:
        if any(marker in item.keywords for marker in ['integration', 'unit']       ):
            continue
        item.add_marker(pytest.mark.unit       )


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Optional[int]       ):





    """simple docstring"""


    config.addinivalue_line('markers' ,   'torchaudio_latest: mark test to run with torchaudio>=0.12'       )


@pytest.fixture(autouse=_SCREAMING_SNAKE_CASE       )
def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Any ,   _SCREAMING_SNAKE_CASE					:		str       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						tmp_path_factory.getbasetemp() / 'cache'
    SCREAMING_SNAKE_CASE_									=						test_hf_cache_home / 'datasets'
    SCREAMING_SNAKE_CASE_									=						test_hf_cache_home / 'metrics'
    SCREAMING_SNAKE_CASE_									=						test_hf_cache_home / 'modules'
    monkeypatch.setattr('datasets.config.HF_DATASETS_CACHE' ,   str(_SCREAMING_SNAKE_CASE       )       )
    monkeypatch.setattr('datasets.config.HF_METRICS_CACHE' ,   str(_SCREAMING_SNAKE_CASE       )       )
    monkeypatch.setattr('datasets.config.HF_MODULES_CACHE' ,   str(_SCREAMING_SNAKE_CASE       )       )
    SCREAMING_SNAKE_CASE_									=						test_hf_datasets_cache / 'downloads'
    monkeypatch.setattr('datasets.config.DOWNLOADED_DATASETS_PATH' ,   str(_SCREAMING_SNAKE_CASE       )       )
    SCREAMING_SNAKE_CASE_									=						test_hf_datasets_cache / 'downloads' / 'extracted'
    monkeypatch.setattr('datasets.config.EXTRACTED_DATASETS_PATH' ,   str(_SCREAMING_SNAKE_CASE       )       )


@pytest.fixture(autouse=_SCREAMING_SNAKE_CASE ,   scope='session'       )
def 					_UpperCAmelCase       (  ):





    """simple docstring"""


    datasets.disable_progress_bar()


@pytest.fixture(autouse=_SCREAMING_SNAKE_CASE       )
def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Any       ):





    """simple docstring"""


    monkeypatch.setattr('datasets.config.HF_UPDATE_DOWNLOAD_COUNTS' ,   _SCREAMING_SNAKE_CASE       )





@pytest.fixture
def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Tuple       ):





    """simple docstring"""


    monkeypatch.setattr('sqlalchemy.util.deprecations.SILENCE_UBER_WARNING' ,   _SCREAMING_SNAKE_CASE       )


