

import os
import warnings
from typing import List, Optional

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


__A			:					List[Any]						=			logging.get_logger(__name__)





class 			_SCREAMING_SNAKE_CASE :







   '''simple docstring'''




   def __init__(       self					:			int     , __lowerCamelCase					:			Optional[Any]     , __lowerCamelCase					:			Dict			):
       SCREAMING_SNAKE_CASE										=   question_encoder
       SCREAMING_SNAKE_CASE										=   generator
       SCREAMING_SNAKE_CASE										=   self.question_encoder





   def      _snake_case		(       self					:			Union[str, Any]     , __lowerCamelCase					:			List[str]			):
       if os.path.isfile(__lowerCamelCase			):
           raise ValueError(f"Provided path ({save_directory}) should be a directory, not a file"			)
       os.makedirs(__lowerCamelCase     , exist_ok=__lowerCamelCase			)
       SCREAMING_SNAKE_CASE										=   os.path.join(__lowerCamelCase     , "question_encoder_tokenizer"			)
       SCREAMING_SNAKE_CASE										=   os.path.join(__lowerCamelCase     , "generator_tokenizer"			)
       self.question_encoder.save_pretrained(__lowerCamelCase			)
       self.generator.save_pretrained(__lowerCamelCase			)





   @classmethod
   def      _snake_case		(       cls					:			Tuple     , __lowerCamelCase					:			str     , **__lowerCamelCase					:			Optional[Any]			):
       # dynamically import AutoTokenizer
       from ..auto.tokenization_auto import AutoTokenizer

       SCREAMING_SNAKE_CASE										=   kwargs.pop("config"     , __lowerCamelCase			)

       if config is None:
           SCREAMING_SNAKE_CASE										=   RagConfig.from_pretrained(__lowerCamelCase			)

       SCREAMING_SNAKE_CASE										=   AutoTokenizer.from_pretrained(
           __lowerCamelCase     , config=config.question_encoder     , subfolder="question_encoder_tokenizer"			)
       SCREAMING_SNAKE_CASE										=   AutoTokenizer.from_pretrained(
           __lowerCamelCase     , config=config.generator     , subfolder="generator_tokenizer"			)
       return cls(question_encoder=__lowerCamelCase     , generator=__lowerCamelCase			)





   def __call__(       self					:			int     , *__lowerCamelCase					:			Optional[Any]     , **__lowerCamelCase					:			Any			):
       return self.current_tokenizer(*__lowerCamelCase     , **__lowerCamelCase			)





   def      _snake_case		(       self					:			Union[str, Any]     , *__lowerCamelCase					:			Dict     , **__lowerCamelCase					:			Union[str, Any]			):
       return self.generator.batch_decode(*__lowerCamelCase     , **__lowerCamelCase			)





   def      _snake_case		(       self					:			List[str]     , *__lowerCamelCase					:			Any     , **__lowerCamelCase					:			Optional[int]			):
       return self.generator.decode(*__lowerCamelCase     , **__lowerCamelCase			)





   def      _snake_case		(       self					:			Optional[Any]			):
       SCREAMING_SNAKE_CASE										=   self.question_encoder





   def      _snake_case		(       self					:			List[Any]			):
       SCREAMING_SNAKE_CASE										=   self.generator





   def      _snake_case		(       self					:			str     , __lowerCamelCase					:			List[str]     , __lowerCamelCase					:			Optional[List[str]] = None     , __lowerCamelCase					:			Optional[int] = None     , __lowerCamelCase					:			Optional[int] = None     , __lowerCamelCase					:			str = "longest"     , __lowerCamelCase					:			str = None     , __lowerCamelCase					:			bool = True     , **__lowerCamelCase					:			List[str]     , ):
       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"     , __lowerCamelCase     , )
       if max_length is None:
           SCREAMING_SNAKE_CASE										=   self.current_tokenizer.model_max_length
       SCREAMING_SNAKE_CASE										=   self(
           __lowerCamelCase     , add_special_tokens=__lowerCamelCase     , return_tensors=__lowerCamelCase     , max_length=__lowerCamelCase     , padding=__lowerCamelCase     , truncation=__lowerCamelCase     , **__lowerCamelCase     , )
       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=__lowerCamelCase     , add_special_tokens=__lowerCamelCase     , return_tensors=__lowerCamelCase     , padding=__lowerCamelCase     , max_length=__lowerCamelCase     , truncation=__lowerCamelCase     , **__lowerCamelCase     , )
       SCREAMING_SNAKE_CASE										=   labels["input_ids"]
       return model_inputs


import pytest


__A			:					Optional[Any]						=			'__dummy_dataset1__'

__A			:					Optional[int]						=			'\nimport json\nimport os\n\nimport datasets\n\n\nREPO_URL = "https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/"\nURLS = {"train": REPO_URL + "wikiann-bn-train.jsonl", "validation": REPO_URL + "wikiann-bn-validation.jsonl"}\n\n\nclass __DummyDataset1__(datasets.GeneratorBasedBuilder):\n\n    def _info(self):\n        features = datasets.Features(\n            {\n                "tokens": datasets.Sequence(datasets.Value("string")),\n                "ner_tags": datasets.Sequence(\n                    datasets.features.ClassLabel(\n                        names=[\n                            "O",\n                            "B-PER",\n                            "I-PER",\n                            "B-ORG",\n                            "I-ORG",\n                            "B-LOC",\n                            "I-LOC",\n                        ]\n                    )\n                ),\n                "langs": datasets.Sequence(datasets.Value("string")),\n                "spans": datasets.Sequence(datasets.Value("string")),\n            }\n        )\n        return datasets.DatasetInfo(features=features)\n\n    def _split_generators(self, dl_manager):\n        dl_path = dl_manager.download(URLS)\n        return [\n            datasets.SplitGenerator(datasets.Split.TRAIN, gen_kwargs={"filepath": dl_path["train"]}),\n            datasets.SplitGenerator(datasets.Split.VALIDATION, gen_kwargs={"filepath": dl_path["validation"]}),\n        ]\n\n    def _generate_examples(self, filepath):\n        with open(filepath, "r", encoding="utf-8") as f:\n            for i, line in enumerate(f):\n                yield i, json.loads(line)\n'





@pytest.fixture
def 	__a     (      ):
    return DATASET_LOADING_SCRIPT_NAME





@pytest.fixture
def 	__a     (      ):
    return DATASET_LOADING_SCRIPT_CODE





@pytest.fixture
def 	__a     (      A__			:  Optional[Any]			,  A__			:  List[str]			,  A__			:  Optional[int]       ):
    SCREAMING_SNAKE_CASE										=   dataset_loading_script_name
    SCREAMING_SNAKE_CASE										=   tmp_path / "datasets" / script_name
    script_dir.mkdir(parents=A__       )
    SCREAMING_SNAKE_CASE										=   script_dir / F"{script_name}.py"
    with open(A__			,  "w"       ) as f:
        f.write(A__       )
    return str(A__       )