





import os
import warnings
from typing import List, Optional

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


_lowerCamelCase   				=			logging.get_logger(__name__)




class   UpperCamelCase_       :


							def __init__(							self    :Any							,				__A    :int							,				__A    :str		)   ->   int:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      question_encoder
									SCREAMING_SNAKE_CASE__			=      generator
									SCREAMING_SNAKE_CASE__			=      self.question_encoder


							def      _snake_case  (							self    :Optional[Any]							,				__A    :Optional[Any]		)   ->   Optional[int]:



									"""simple docstring"""



									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      _snake_case  (							cls    :List[str]							,				__A    :Optional[int]							,				**__A    :str		)   ->   Optional[int]:



									"""simple docstring"""



									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    :Optional[int]							,				*__A    :List[Any]							,				**__A    :str		)   ->   Optional[Any]:



									"""simple docstring"""



									return self.current_tokenizer(*__A							,				**__A		)


							def      _snake_case  (							self    :Optional[int]							,				*__A    :Optional[Any]							,				**__A    :List[Any]		)   ->   Optional[int]:



									"""simple docstring"""



									return self.generator.batch_decode(*__A							,				**__A		)


							def      _snake_case  (							self    :Union[str, Any]							,				*__A    :int							,				**__A    :List[Any]		)   ->   List[Any]:



									"""simple docstring"""



									return self.generator.decode(*__A							,				**__A		)


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



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      self.question_encoder


							def      _snake_case  (							self    :int		)   ->   Dict:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__			=      self.generator


							def      _snake_case  (							self    :str							,				__A    :List[str]							,				__A    :Optional[List[str]] = None							,				__A    :Optional[int] = None							,				__A    :Optional[int] = None							,				__A    :str = "longest"							,				__A    :str = None							,				__A    :bool = True							,				**__A    :int							,				)   ->   BatchEncoding:



									"""simple docstring"""



									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 os
from pathlib import Path
from unittest.mock import patch

import pytest
import zstandard as zstd

from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
    OfflineModeIsEnabled,
    cached_path,
    fsspec_get,
    fsspec_head,
    ftp_get,
    ftp_head,
    get_from_cache,
    http_get,
    http_head,
)


_lowerCamelCase   				=			'\\n    Text data.\n    Second line of data.'

_lowerCamelCase   				=			'file'






@pytest.fixture(scope="""session"""    )
def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							Any    ):
		SCREAMING_SNAKE_CASE__			=      tmp_path_factory.mktemp("""data"""    ) / (FILE_PATH + """.zstd""")
		SCREAMING_SNAKE_CASE__			=      bytes(UpperCamelCase__					,   """utf-8"""    )
		with zstd.open(UpperCamelCase__					,   """wb"""    ) as f:
				f.write(UpperCamelCase__    )
		return path






@pytest.fixture
def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							List[str]    ):
		with open(os.path.join(tmpfs.local_root_dir					,   UpperCamelCase__    )					,   """w"""    ) as f:
				f.write(UpperCamelCase__    )
		return FILE_PATH






@pytest.mark.parametrize("""compression_format"""					,   ["""gzip""", """xz""", """zstd"""]    )
def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							int					,   UpperCamelCase__:							Dict					,   UpperCamelCase__:							int					,   UpperCamelCase__:							str					,   UpperCamelCase__:							Optional[int]					,   UpperCamelCase__:							Optional[Any]    ):
		SCREAMING_SNAKE_CASE__			=      {"""gzip""": gz_file, """xz""": xz_file, """zstd""": zstd_path}
		SCREAMING_SNAKE_CASE__			=      input_paths[compression_format]
		SCREAMING_SNAKE_CASE__			=      tmp_path / """cache"""
		SCREAMING_SNAKE_CASE__			=      DownloadConfig(cache_dir=UpperCamelCase__					,   extract_compressed_file=UpperCamelCase__    )
		SCREAMING_SNAKE_CASE__			=      cached_path(UpperCamelCase__					,   download_config=UpperCamelCase__    )
		with open(UpperCamelCase__    ) as f:
				SCREAMING_SNAKE_CASE__			=      f.read()
		with open(UpperCamelCase__    ) as f:
				SCREAMING_SNAKE_CASE__			=      f.read()
		assert extracted_file_content == expected_file_content






@pytest.mark.parametrize("""default_extracted"""					,   [True, False]    )
@pytest.mark.parametrize("""default_cache_dir"""					,   [True, False]    )
def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							Tuple					,   UpperCamelCase__:							List[str]					,   UpperCamelCase__:							Optional[int]					,   UpperCamelCase__:							Any					,   UpperCamelCase__:							Union[str, Any]    ):
		SCREAMING_SNAKE_CASE__			=      """custom_cache"""
		SCREAMING_SNAKE_CASE__			=      """custom_extracted_dir"""
		SCREAMING_SNAKE_CASE__			=      tmp_path / """custom_extracted_path"""
		if default_extracted:
				SCREAMING_SNAKE_CASE__			=      ("""downloads""" if default_cache_dir else custom_cache_dir, """extracted""")
		else:
				monkeypatch.setattr("""datasets.config.EXTRACTED_DATASETS_DIR"""					,   UpperCamelCase__    )
				monkeypatch.setattr("""datasets.config.EXTRACTED_DATASETS_PATH"""					,   str(UpperCamelCase__    )    )
				SCREAMING_SNAKE_CASE__			=      custom_extracted_path.parts[-2:] if default_cache_dir else (custom_cache_dir, custom_extracted_dir)

		SCREAMING_SNAKE_CASE__			=      xz_file
		SCREAMING_SNAKE_CASE__			=      (
		    DownloadConfig(extract_compressed_file=UpperCamelCase__    )
		    if default_cache_dir
		    else DownloadConfig(cache_dir=tmp_path / custom_cache_dir					,   extract_compressed_file=UpperCamelCase__    )
		)
		SCREAMING_SNAKE_CASE__			=      cached_path(UpperCamelCase__					,   download_config=UpperCamelCase__    )
		assert Path(UpperCamelCase__    ).parent.parts[-2:] == expected






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							Optional[int]    ):
		# absolute path
		SCREAMING_SNAKE_CASE__			=      str(Path(UpperCamelCase__    ).resolve()    )
		assert cached_path(UpperCamelCase__    ) == text_file
		# relative path
		SCREAMING_SNAKE_CASE__			=      str(Path(UpperCamelCase__    ).resolve().relative_to(Path(os.getcwd()    )    )    )
		assert cached_path(UpperCamelCase__    ) == text_file






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							List[str]    ):
		# absolute path
		SCREAMING_SNAKE_CASE__			=      str(tmp_path.resolve() / """__missing_file__.txt"""    )
		with pytest.raises(UpperCamelCase__    ):
				cached_path(UpperCamelCase__    )
		# relative path
		SCREAMING_SNAKE_CASE__			=      """./__missing_file__.txt"""
		with pytest.raises(UpperCamelCase__    ):
				cached_path(UpperCamelCase__    )






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							List[str]    ):
		SCREAMING_SNAKE_CASE__			=      get_from_cache(f'''tmp://{tmpfs_file}'''    )
		with open(UpperCamelCase__    ) as f:
				SCREAMING_SNAKE_CASE__			=      f.read()
		assert output_file_content == FILE_CONTENT






@patch("""datasets.config.HF_DATASETS_OFFLINE"""					,   UpperCamelCase__    )
def 				SCREAMING_SNAKE_CASE__							(       ):
		with pytest.raises(UpperCamelCase__    ):
				cached_path("""https://huggingface.co"""    )






@patch("""datasets.config.HF_DATASETS_OFFLINE"""					,   UpperCamelCase__    )
def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							Optional[Any]    ):
		SCREAMING_SNAKE_CASE__			=      tmp_path_factory.mktemp("""data"""    ) / """file.html"""
		with pytest.raises(UpperCamelCase__    ):
				http_get("""https://huggingface.co"""					,   temp_file=UpperCamelCase__    )
		with pytest.raises(UpperCamelCase__    ):
				http_head("""https://huggingface.co"""    )






@patch("""datasets.config.HF_DATASETS_OFFLINE"""					,   UpperCamelCase__    )
def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							List[Any]    ):
		SCREAMING_SNAKE_CASE__			=      tmp_path_factory.mktemp("""data"""    ) / """file.html"""
		with pytest.raises(UpperCamelCase__    ):
				ftp_get("""ftp://huggingface.co"""					,   temp_file=UpperCamelCase__    )
		with pytest.raises(UpperCamelCase__    ):
				ftp_head("""ftp://huggingface.co"""    )






@patch("""datasets.config.HF_DATASETS_OFFLINE"""					,   UpperCamelCase__    )
def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							int    ):
		SCREAMING_SNAKE_CASE__			=      tmp_path_factory.mktemp("""data"""    ) / """file.html"""
		with pytest.raises(UpperCamelCase__    ):
				fsspec_get("""s3://huggingface.co"""					,   temp_file=UpperCamelCase__    )
		with pytest.raises(UpperCamelCase__    ):
				fsspec_head("""s3://huggingface.co"""    )