
'''simple docstring'''





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    					=   logging.get_logger(__name__)

class 						a   :




				def __init__(							self	:				Tuple,    SCREAMING_SNAKE_CASE_	:				int,    SCREAMING_SNAKE_CASE_	:				List[Any]      ):
										snake_case       :     Dict =    question_encoder
										snake_case       :     Optional[Any] =    generator
										snake_case       :     List[Any] =    self.question_encoder




				def 	__snake_case							(							self	:				Union[str, Any],    SCREAMING_SNAKE_CASE_	:				Optional[int]      ):
										if os.path.isfile(SCREAMING_SNAKE_CASE_      ):
																raise ValueError(F"""Provided path ({save_directory}) should be a directory, not a file"""      )
										os.makedirs(SCREAMING_SNAKE_CASE_,    exist_ok=SCREAMING_SNAKE_CASE_      )
										snake_case       :     List[str] =    os.path.join(SCREAMING_SNAKE_CASE_,    '''question_encoder_tokenizer'''      )
										snake_case       :     Union[str, Any] =    os.path.join(SCREAMING_SNAKE_CASE_,    '''generator_tokenizer'''      )
										self.question_encoder.save_pretrained(SCREAMING_SNAKE_CASE_      )
										self.generator.save_pretrained(SCREAMING_SNAKE_CASE_      )




				@classmethod
				def 	__snake_case							(							cls	:				List[str],    SCREAMING_SNAKE_CASE_	:				Tuple,    **SCREAMING_SNAKE_CASE_	:				str      ):
										# dynamically import AutoTokenizer
										from ..auto.tokenization_auto import AutoTokenizer

										snake_case       :     List[str] =    kwargs.pop('''config''',    SCREAMING_SNAKE_CASE_      )

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

										snake_case       :     List[Any] =    AutoTokenizer.from_pretrained(
										    SCREAMING_SNAKE_CASE_,    config=config.question_encoder,    subfolder='''question_encoder_tokenizer'''      )
										snake_case       :     Optional[int] =    AutoTokenizer.from_pretrained(
										    SCREAMING_SNAKE_CASE_,    config=config.generator,    subfolder='''generator_tokenizer'''      )
										return cls(question_encoder=SCREAMING_SNAKE_CASE_,    generator=SCREAMING_SNAKE_CASE_      )




				def __call__(							self	:				Any,    *SCREAMING_SNAKE_CASE_	:				Tuple,    **SCREAMING_SNAKE_CASE_	:				Tuple      ):
										return self.current_tokenizer(*SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_      )




				def 	__snake_case							(							self	:				str,    *SCREAMING_SNAKE_CASE_	:				Tuple,    **SCREAMING_SNAKE_CASE_	:				int      ):
										return self.generator.batch_decode(*SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_      )




				def 	__snake_case							(							self	:				str,    *SCREAMING_SNAKE_CASE_	:				List[str],    **SCREAMING_SNAKE_CASE_	:				Tuple      ):
										return self.generator.decode(*SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_      )




				def 	__snake_case							(							self	:				List[str]      ):
										snake_case       :     Tuple =    self.question_encoder




				def 	__snake_case							(							self	:				List[str]      ):
										snake_case       :     List[Any] =    self.generator




				def 	__snake_case							(							self	:				str,    SCREAMING_SNAKE_CASE_	:				List[str],    SCREAMING_SNAKE_CASE_	:				Optional[List[str]] = None,    SCREAMING_SNAKE_CASE_	:				Optional[int] = None,    SCREAMING_SNAKE_CASE_	:				Optional[int] = None,    SCREAMING_SNAKE_CASE_	:				str = "longest",    SCREAMING_SNAKE_CASE_	:				str = None,    SCREAMING_SNAKE_CASE_	:				bool = True,    **SCREAMING_SNAKE_CASE_	:				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''',    SCREAMING_SNAKE_CASE_,    )
										if max_length is None:
																snake_case       :     List[str] =    self.current_tokenizer.model_max_length
										snake_case       :     Optional[int] =    self(
										    SCREAMING_SNAKE_CASE_,    add_special_tokens=SCREAMING_SNAKE_CASE_,    return_tensors=SCREAMING_SNAKE_CASE_,    max_length=SCREAMING_SNAKE_CASE_,    padding=SCREAMING_SNAKE_CASE_,    truncation=SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_,    )
										if tgt_texts is None:
																return model_inputs
										# Process tgt_texts
										if max_target_length is None:
																snake_case       :     int =    self.current_tokenizer.model_max_length
										snake_case       :     Optional[int] =    self(
										    text_target=SCREAMING_SNAKE_CASE_,    add_special_tokens=SCREAMING_SNAKE_CASE_,    return_tensors=SCREAMING_SNAKE_CASE_,    padding=SCREAMING_SNAKE_CASE_,    max_length=SCREAMING_SNAKE_CASE_,    truncation=SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_,    )
										snake_case       :     Optional[int] =    labels['''input_ids''']
										return model_inputs





'''simple docstring'''





import os
import sys

UpperCAmelCase    					=   os.path.join(os.path.dirname(__file__), "src")
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


UpperCAmelCase    					=   [
    "torch",
    "numpy",
    "tokenizers",
    "filelock",
    "requests",
    "tqdm",
    "regex",
    "sentencepiece",
    "sacremoses",
    "importlib_metadata",
    "huggingface_hub",
]

@add_start_docstrings(AutoConfig.__doc__       )
def      A (       *A_							:					Optional[Any]    ,							**A_							:					List[str]       ):
						return AutoConfig.from_pretrained(*A_    ,							**A_       )

@add_start_docstrings(AutoTokenizer.__doc__       )
def      A (       *A_							:					Dict    ,							**A_							:					str       ):
						return AutoTokenizer.from_pretrained(*A_    ,							**A_       )

@add_start_docstrings(AutoModel.__doc__       )
def      A (       *A_							:					Union[str, Any]    ,							**A_							:					Optional[Any]       ):
						return AutoModel.from_pretrained(*A_    ,							**A_       )

@add_start_docstrings(AutoModelForCausalLM.__doc__       )
def      A (       *A_							:					str    ,							**A_							:					Optional[Any]       ):
						return AutoModelForCausalLM.from_pretrained(*A_    ,							**A_       )

@add_start_docstrings(AutoModelForMaskedLM.__doc__       )
def      A (       *A_							:					List[str]    ,							**A_							:					Optional[Any]       ):
						return AutoModelForMaskedLM.from_pretrained(*A_    ,							**A_       )

@add_start_docstrings(AutoModelForSequenceClassification.__doc__       )
def      A (       *A_							:					Tuple    ,							**A_							:					List[str]       ):
						return AutoModelForSequenceClassification.from_pretrained(*A_    ,							**A_       )





@add_start_docstrings(AutoModelForQuestionAnswering.__doc__       )
def      A (       *A_							:					Tuple    ,							**A_							:					Any       ):
						return AutoModelForQuestionAnswering.from_pretrained(*A_    ,							**A_       )

