
"""simple docstring"""



import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from random import randint
from typing import Optional

import datasets
import evaluate
import numpy as np
from datasets import DatasetDict, load_dataset

import transformers
from transformers import (
    AutoConfig,
    AutoFeatureExtractor,
    AutoModelForAudioClassification,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version


a					:	List[str]							=		logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('''4.31.0''')

require_version('''datasets>=1.14.0''', '''To fix: pip install -r examples/pytorch/audio-classification/requirements.txt''')


def  _UpperCamelCase	(						_A					,      _A					,      _A = 1_6_0_0_0      )    ->  List[Any]:





    """simple docstring"""

    _UpperCAmelCase          =   int(round(sample_rate * max_length      )      )
    if len(_A      ) <= sample_length:
        return wav
    _UpperCAmelCase          =   randint(0					,      len(_A      ) - sample_length - 1      )
    return wav[random_offset : random_offset + sample_length]




@dataclass
class 		a_						:
    a      :				Optional[str]     =				field(default=_UpperCAmelCase     ,						metadata={'help': 'Name of a dataset from the datasets package'}	)
    a      :				Optional[str]     =				field(
        default=_UpperCAmelCase     ,						metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}	)
    a      :				Optional[str]     =				field(
        default=_UpperCAmelCase     ,						metadata={'help': 'A file containing the training audio paths and labels.'}	)
    a      :				Optional[str]     =				field(
        default=_UpperCAmelCase     ,						metadata={'help': 'A file containing the validation audio paths and labels.'}	)
    a      :				str     =				field(
        default='train'     ,						metadata={
            'help': 'The name of the training data set split to use (via the datasets library). Defaults to \'train\''
        }     ,						)
    a      :				str     =				field(
        default='validation'     ,						metadata={
            'help': (
                'The name of the training data set split to use (via the datasets library). Defaults to \'validation\''
            )
        }     ,						)
    a      :				str     =				field(
        default='audio'     ,						metadata={'help': 'The name of the dataset column containing the audio data. Defaults to \'audio\''}     ,						)
    a      :				str     =				field(
        default='label'     ,						metadata={'help': 'The name of the dataset column containing the labels. Defaults to \'label\''}	)
    a      :				Optional[int]     =				field(
        default=_UpperCAmelCase     ,						metadata={
            'help': (
                'For debugging purposes or quicker training, truncate the number of training examples to this '
                'value if set.'
            )
        }     ,						)
    a      :				Optional[int]     =				field(
        default=_UpperCAmelCase     ,						metadata={
            'help': (
                'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
                'value if set.'
            )
        }     ,						)
    a      :				float     =				field(
        default=20     ,						metadata={'help': 'Audio clips will be randomly cut to this length during training if the value is set.'}     ,						)







@dataclass
class 		a_						:
    a      :				str     =				field(
        default='facebook/wav2vec2-base'     ,						metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}     ,						)
    a      :				Optional[str]     =				field(
        default=_UpperCAmelCase     ,						metadata={'help': 'Pretrained config name or path if not the same as model_name'}	)
    a      :				Optional[str]     =				field(
        default=_UpperCAmelCase     ,						metadata={'help': 'Where do you want to store the pretrained models downloaded from the Hub'}	)
    a      :				str     =				field(
        default='main'     ,						metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'}     ,						)
    a      :				Optional[str]     =				field(
        default=_UpperCAmelCase     ,						metadata={'help': 'Name or path of preprocessor config.'}	)
    a      :				bool     =				field(
        default=_UpperCAmelCase     ,						metadata={'help': 'Whether to freeze the feature encoder layers of the model.'}	)
    a      :				bool     =				field(
        default=_UpperCAmelCase     ,						metadata={'help': 'Whether to generate an attention mask in the feature extractor.'}	)
    a      :				bool     =				field(
        default=_UpperCAmelCase     ,						metadata={
            'help': (
                'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
                'with private models).'
            )
        }     ,						)
    a      :				Optional[bool]     =				field(
        default=_UpperCAmelCase     ,						metadata={'help': 'Whether to freeze the feature extractor layers of the model.'}	)
    a      :				bool     =				field(
        default=_UpperCAmelCase     ,						metadata={'help': 'Will enable to load a pretrained model whose head dimensions are different.'}     ,						)







    def      _snake_case					(			self			:       Dict     )		->int:






        '''simple docstring'''






        if not self.freeze_feature_extractor and self.freeze_feature_encoder:
            warnings.warn(
                """The argument `--freeze_feature_extractor` is deprecated and """
                """will be removed in a future version. Use `--freeze_feature_encoder`"""
                """instead. Setting `freeze_feature_encoder==True`."""     ,					__UpperCamelCase     ,					)
        if self.freeze_feature_extractor and not self.freeze_feature_encoder:
            raise ValueError(
                """The argument `--freeze_feature_extractor` is deprecated and """
                """should not be used in combination with `--freeze_feature_encoder`."""
                """Only make use of `--freeze_feature_encoder`."""     )







def  _UpperCamelCase	(						)    ->  List[Any]:





    """simple docstring"""

    _UpperCAmelCase          =   HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)      )
    if len(sys.argv      ) == 2 and sys.argv[1].endswith(""".json"""      ):
        # If we pass only one argument to the script and it's the path to a json file,
        # let's parse it to get our arguments.
        _UpperCAmelCase    ,_UpperCAmelCase    ,_UpperCAmelCase          =   parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]      )      )
    else:
        _UpperCAmelCase    ,_UpperCAmelCase    ,_UpperCAmelCase          =   parser.parse_args_into_dataclasses()

    # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
    # information sent is the one passed as arguments along with your Python/PyTorch versions.
    send_example_telemetry("""run_audio_classification"""					,      _A					,      _A      )

    # Setup logging
    logging.basicConfig(
        format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""					,      datefmt="""%m/%d/%Y %H:%M:%S"""					,      handlers=[logging.StreamHandler(sys.stdout      )]					,      )

    if training_args.should_log:
        # The default of training_args.log_level is passive, so we set log level at info here to have that default.
        transformers.utils.logging.set_verbosity_info()

    _UpperCAmelCase          =   training_args.get_process_log_level()
    logger.setLevel(_A      )
    transformers.utils.logging.set_verbosity(_A      )
    transformers.utils.logging.enable_default_handler()
    transformers.utils.logging.enable_explicit_format()

    # Log on each process the small summary:
    logger.warning(
        F"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu} """
        + F"""distributed training: {bool(training_args.local_rank != -1      )}, 16-bits training: {training_args.fpaa}"""      )
    logger.info(F"""Training/evaluation parameters {training_args}"""      )

    # Set seed before initializing model.
    set_seed(training_args.seed      )

    # Detecting last checkpoint.
    _UpperCAmelCase          =   None
    if os.path.isdir(training_args.output_dir      ) and training_args.do_train and not training_args.overwrite_output_dir:
        _UpperCAmelCase          =   get_last_checkpoint(training_args.output_dir      )
        if last_checkpoint is None and len(os.listdir(training_args.output_dir      )      ) > 0:
            raise ValueError(
                F"""Output directory ({training_args.output_dir}) already exists and is not empty. """
                """Use --overwrite_output_dir to train from scratch."""      )
        elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
            logger.info(
                F"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
                """the `--output_dir` or add `--overwrite_output_dir` to train from scratch."""      )

    # Initialize our dataset and prepare it for the audio classification task.
    _UpperCAmelCase          =   DatasetDict()
    _UpperCAmelCase          =   load_dataset(
        data_args.dataset_name					,      data_args.dataset_config_name					,      split=data_args.train_split_name					,      use_auth_token=True if model_args.use_auth_token else None					,      )
    _UpperCAmelCase          =   load_dataset(
        data_args.dataset_name					,      data_args.dataset_config_name					,      split=data_args.eval_split_name					,      use_auth_token=True if model_args.use_auth_token else None					,      )

    if data_args.audio_column_name not in raw_datasets["train"].column_names:
        raise ValueError(
            F"""--audio_column_name {data_args.audio_column_name} not found in dataset '{data_args.dataset_name}'. """
            """Make sure to set `--audio_column_name` to the correct audio column - one of """
            F"""{", ".join(raw_datasets["train"].column_names      )}."""      )

    if data_args.label_column_name not in raw_datasets["train"].column_names:
        raise ValueError(
            F"""--label_column_name {data_args.label_column_name} not found in dataset '{data_args.dataset_name}'. """
            """Make sure to set `--label_column_name` to the correct text column - one of """
            F"""{", ".join(raw_datasets["train"].column_names      )}."""      )

    # Setting `return_attention_mask=True` is the way to get a correctly masked mean-pooling over
    # transformer outputs in the classifier, but it doesn't always lead to better accuracy
    _UpperCAmelCase          =   AutoFeatureExtractor.from_pretrained(
        model_args.feature_extractor_name or model_args.model_name_or_path					,      return_attention_mask=model_args.attention_mask					,      cache_dir=model_args.cache_dir					,      revision=model_args.model_revision					,      use_auth_token=True if model_args.use_auth_token else None					,      )

    # `datasets` takes care of automatically loading and resampling the audio,
    # so we just need to set the correct target sampling rate.
    _UpperCAmelCase          =   raw_datasets.cast_column(
        data_args.audio_column_name					,      datasets.features.Audio(sampling_rate=feature_extractor.sampling_rate      )      )

    _UpperCAmelCase          =   feature_extractor.model_input_names[0]

    def train_transforms(_A      ):
        _UpperCAmelCase          =   []
        for audio in batch[data_args.audio_column_name]:
            _UpperCAmelCase          =   random_subsample(
                audio["""array"""]					,      max_length=data_args.max_length_seconds					,      sample_rate=feature_extractor.sampling_rate      )
            subsampled_wavs.append(_A      )
        _UpperCAmelCase          =   feature_extractor(_A					,      sampling_rate=feature_extractor.sampling_rate      )
        _UpperCAmelCase          =   {model_input_name: inputs.get(_A      )}
        _UpperCAmelCase          =   list(batch[data_args.label_column_name]      )

        return output_batch

    def val_transforms(_A      ):
        _UpperCAmelCase          =   [audio["""array"""] for audio in batch[data_args.audio_column_name]]
        _UpperCAmelCase          =   feature_extractor(_A					,      sampling_rate=feature_extractor.sampling_rate      )
        _UpperCAmelCase          =   {model_input_name: inputs.get(_A      )}
        _UpperCAmelCase          =   list(batch[data_args.label_column_name]      )

        return output_batch

    # Prepare label mappings.
    # We'll include these in the model's config to get human readable labels in the Inference API.
    _UpperCAmelCase          =   raw_datasets["""train"""].features[data_args.label_column_name].names
    _UpperCAmelCase    ,_UpperCAmelCase          =   {}, {}
    for i, label in enumerate(_A      ):
        _UpperCAmelCase          =   str(_A      )
        _UpperCAmelCase          =   label

    # Load the accuracy metric from the datasets package
    _UpperCAmelCase          =   evaluate.load("""accuracy"""      )

    # Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with
    # `predictions` and `label_ids` fields) and has to return a dictionary string to float.
    def compute_metrics(_A      ):
        _UpperCAmelCase          =   np.argmax(eval_pred.predictions					,      axis=1      )
        return metric.compute(predictions=_A					,      references=eval_pred.label_ids      )

    _UpperCAmelCase          =   AutoConfig.from_pretrained(
        model_args.config_name or model_args.model_name_or_path					,      num_labels=len(_A      )					,      labelaid=_A					,      idalabel=_A					,      finetuning_task="""audio-classification"""					,      cache_dir=model_args.cache_dir					,      revision=model_args.model_revision					,      use_auth_token=True if model_args.use_auth_token else None					,      )
    _UpperCAmelCase          =   AutoModelForAudioClassification.from_pretrained(
        model_args.model_name_or_path					,      from_tf=bool(""".ckpt""" in model_args.model_name_or_path      )					,      config=_A					,      cache_dir=model_args.cache_dir					,      revision=model_args.model_revision					,      use_auth_token=True if model_args.use_auth_token else None					,      ignore_mismatched_sizes=model_args.ignore_mismatched_sizes					,      )

    # freeze the convolutional waveform encoder
    if model_args.freeze_feature_encoder:
        model.freeze_feature_encoder()

    if training_args.do_train:
        if data_args.max_train_samples is not None:
            _UpperCAmelCase          =   (
                raw_datasets["""train"""].shuffle(seed=training_args.seed      ).select(range(data_args.max_train_samples      )      )
            )
        # Set the training transforms
        raw_datasets["train"].set_transform(_A					,      output_all_columns=_A      )

    if training_args.do_eval:
        if data_args.max_eval_samples is not None:
            _UpperCAmelCase          =   (
                raw_datasets["""eval"""].shuffle(seed=training_args.seed      ).select(range(data_args.max_eval_samples      )      )
            )
        # Set the validation transforms
        raw_datasets["eval"].set_transform(_A					,      output_all_columns=_A      )

    # Initialize our trainer
    _UpperCAmelCase          =   Trainer(
        model=_A					,      args=_A					,      train_dataset=raw_datasets["""train"""] if training_args.do_train else None					,      eval_dataset=raw_datasets["""eval"""] if training_args.do_eval else None					,      compute_metrics=_A					,      tokenizer=_A					,      )

    # Training
    if training_args.do_train:
        _UpperCAmelCase          =   None
        if training_args.resume_from_checkpoint is not None:
            _UpperCAmelCase          =   training_args.resume_from_checkpoint
        elif last_checkpoint is not None:
            _UpperCAmelCase          =   last_checkpoint
        _UpperCAmelCase          =   trainer.train(resume_from_checkpoint=_A      )
        trainer.save_model()
        trainer.log_metrics("""train"""					,      train_result.metrics      )
        trainer.save_metrics("""train"""					,      train_result.metrics      )
        trainer.save_state()

    # Evaluation
    if training_args.do_eval:
        _UpperCAmelCase          =   trainer.evaluate()
        trainer.log_metrics("""eval"""					,      _A      )
        trainer.save_metrics("""eval"""					,      _A      )

    # Write model card and (optionally) push to hub
    _UpperCAmelCase          =   {
        """finetuned_from""": model_args.model_name_or_path,
        """tasks""": """audio-classification""",
        """dataset""": data_args.dataset_name,
        """tags""": ["""audio-classification"""],
    }
    if training_args.push_to_hub:
        trainer.push_to_hub(**_A      )
    else:
        trainer.create_model_card(**_A      )


if __name__ == "__main__":
 main()

"""simple docstring"""



import enum
import warnings

from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
 import tensorflow as tf

 from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

if is_torch_available():
 from ..models.auto.modeling_auto import MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

a					:	List[str]							=		logging.get_logger(__name__)




class 		a_						( enum.Enum	):
    a      :				Optional[Any]     =				0
    a      :				Dict     =				1







@add_end_docstrings(_UpperCAmelCase	)
class 		a_						( _UpperCAmelCase	):
    a      :				List[Any]     =				'generated'







    def __init__(			self			:       int     ,					*__UpperCamelCase			:       Optional[int]     ,					**__UpperCamelCase			:       str     )		->Any:






        '''simple docstring'''






        super().__init__(*__UpperCamelCase     ,					**__UpperCamelCase     )

        self.check_model_type(
            TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
            if self.framework == """tf"""
            else MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING     )







    def      _snake_case					(			self			:       Optional[int]     ,					__UpperCamelCase			:       List[Any]=None     ,					__UpperCamelCase			:       Union[str, Any]=None     ,					__UpperCamelCase			:       Any=None     ,					__UpperCamelCase			:       int=None     ,					__UpperCamelCase			:       Tuple=None     ,					__UpperCamelCase			:       Dict=None     ,					**__UpperCamelCase			:       Any     ,					)		->Tuple:






        '''simple docstring'''






        _UpperCAmelCase          =   {}
        if truncation is not None:
            _UpperCAmelCase          =   truncation

        _UpperCAmelCase          =   generate_kwargs

        _UpperCAmelCase          =   {}
        if return_tensors is not None and return_type is None:
            _UpperCAmelCase          =   ReturnType.TENSORS if return_tensors else ReturnType.TEXT
        if return_type is not None:
            _UpperCAmelCase          =   return_type

        if clean_up_tokenization_spaces is not None:
            _UpperCAmelCase          =   clean_up_tokenization_spaces

        if stop_sequence is not None:
            _UpperCAmelCase          =   self.tokenizer.encode(__UpperCamelCase     ,					add_special_tokens=__UpperCamelCase     )
            if len(__UpperCamelCase     ) > 1:
                warnings.warn(
                    """Stopping on a multiple token sequence is not yet supported on transformers. The first token of"""
                    """ the stop sequence will be used as the stop sequence string in the interim."""     )
            _UpperCAmelCase          =   stop_sequence_ids[0]

        return preprocess_params, forward_params, postprocess_params







    def      _snake_case					(			self			:       int     ,					__UpperCamelCase			:       int     ,					__UpperCamelCase			:       int     ,					__UpperCamelCase			:       int     )		->Any:






        '''simple docstring'''






        return True







    def      _snake_case					(			self			:       Optional[Any]     ,					*__UpperCamelCase			:       Any     ,					__UpperCamelCase			:       Dict     )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase          =   self.model.config.prefix if self.model.config.prefix is not None else """"""
        if isinstance(args[0]     ,					__UpperCamelCase     ):
            if self.tokenizer.pad_token_id is None:
                raise ValueError("""Please make sure that the tokenizer has a pad_token_id when using a batch input"""     )
            _UpperCAmelCase          =   ([prefix + arg for arg in args[0]],)
            _UpperCAmelCase          =   True

        elif isinstance(args[0]     ,					__UpperCamelCase     ):
            _UpperCAmelCase          =   (prefix + args[0],)
            _UpperCAmelCase          =   False
        else:
            raise ValueError(
                f""" `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`"""     )
        _UpperCAmelCase          =   self.tokenizer(*__UpperCamelCase     ,					padding=__UpperCamelCase     ,					truncation=__UpperCamelCase     ,					return_tensors=self.framework     )
        # This is produced by tokenizers but is an invalid generate kwargs
        if "token_type_ids" in inputs:
            del inputs["token_type_ids"]
        return inputs







    def __call__(			self			:       Dict     ,					*__UpperCamelCase			:       str     ,					**__UpperCamelCase			:       Dict     )		->Optional[Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   super().__call__(*__UpperCamelCase     ,					**__UpperCamelCase     )
        if (
            isinstance(args[0]     ,					__UpperCamelCase     )
            and all(isinstance(__UpperCamelCase     ,					__UpperCamelCase     ) for el in args[0]     )
            and all(len(__UpperCamelCase     ) == 1 for res in result     )
        ):
            return [res[0] for res in result]
        return result







    def      _snake_case					(			self			:       List[str]     ,					__UpperCamelCase			:       Any     ,					__UpperCamelCase			:       str=TruncationStrategy.DO_NOT_TRUNCATE     ,					**__UpperCamelCase			:       Optional[int]     )		->Optional[int]:






        '''simple docstring'''






        _UpperCAmelCase          =   self._parse_and_tokenize(__UpperCamelCase     ,					truncation=__UpperCamelCase     ,					**__UpperCamelCase     )
        return inputs







    def      _snake_case					(			self			:       str     ,					__UpperCamelCase			:       Dict     ,					**__UpperCamelCase			:       Dict     )		->Tuple:






        '''simple docstring'''






        if self.framework == "pt":
            _UpperCAmelCase    ,_UpperCAmelCase          =   model_inputs["""input_ids"""].shape
        elif self.framework == "tf":
            _UpperCAmelCase    ,_UpperCAmelCase          =   tf.shape(model_inputs["""input_ids"""]     ).numpy()

        _UpperCAmelCase          =   generate_kwargs.get("""min_length"""     ,					self.model.config.min_length     )
        _UpperCAmelCase          =   generate_kwargs.get("""max_length"""     ,					self.model.config.max_length     )
        self.check_inputs(__UpperCamelCase     ,					generate_kwargs["""min_length"""]     ,					generate_kwargs["""max_length"""]     )
        _UpperCAmelCase          =   self.model.generate(**__UpperCamelCase     ,					**__UpperCamelCase     )
        _UpperCAmelCase          =   output_ids.shape[0]
        if self.framework == "pt":
            _UpperCAmelCase          =   output_ids.reshape(__UpperCamelCase     ,					out_b // in_b     ,					*output_ids.shape[1:]     )
        elif self.framework == "tf":
            _UpperCAmelCase          =   tf.reshape(__UpperCamelCase     ,					(in_b, out_b // in_b, *output_ids.shape[1:])     )
        return {"output_ids": output_ids}







    def      _snake_case					(			self			:       Tuple     ,					__UpperCamelCase			:       str     ,					__UpperCamelCase			:       Union[str, Any]=ReturnType.TEXT     ,					__UpperCamelCase			:       int=False     )		->Any:






        '''simple docstring'''






        _UpperCAmelCase          =   []
        for output_ids in model_outputs["output_ids"][0]:
            if return_type == ReturnType.TENSORS:
                _UpperCAmelCase          =   {f"""{self.return_name}_token_ids""": output_ids}
            elif return_type == ReturnType.TEXT:
                _UpperCAmelCase          =   {
                    f"""{self.return_name}_text""": self.tokenizer.decode(
                        __UpperCamelCase     ,					skip_special_tokens=__UpperCamelCase     ,					clean_up_tokenization_spaces=__UpperCamelCase     ,					)
                }
            records.append(__UpperCamelCase     )
        return records







@add_end_docstrings(_UpperCAmelCase	)
class 		a_						( _UpperCAmelCase	):
    a      :				List[Any]     =				'summary'







    def __call__(			self			:       Optional[Any]     ,					*__UpperCamelCase			:       Optional[Any]     ,					**__UpperCamelCase			:       Optional[int]     )		->Any:






        '''simple docstring'''






        return super().__call__(*__UpperCamelCase     ,					**__UpperCamelCase     )







    def      _snake_case					(			self			:       str     ,					__UpperCamelCase			:       int     ,					__UpperCamelCase			:       int     ,					__UpperCamelCase			:       int     )		->bool:






        '''simple docstring'''






        if max_length < min_length:
            logger.warning(f"""Your min_length={min_length} must be inferior than your max_length={max_length}."""     )

        if input_length < max_length:
            logger.warning(
                f"""Your max_length is set to {max_length}, but your input_length is only {input_length}. Since this is """
                """a summarization task, where outputs shorter than the input are typically wanted, you might """
                f"""consider decreasing max_length manually, e.g. summarizer('...', max_length={input_length//2})"""     )







@add_end_docstrings(_UpperCAmelCase	)
class 		a_						( _UpperCAmelCase	):
    a      :				Optional[int]     =				'translation'







    def      _snake_case					(			self			:       Union[str, Any]     ,					__UpperCamelCase			:       int     ,					__UpperCamelCase			:       int     ,					__UpperCamelCase			:       int     )		->Any:






        '''simple docstring'''






        if input_length > 0.9 * max_length:
            logger.warning(
                f"""Your input_length: {input_length} is bigger than 0.9 * max_length: {max_length}. You might consider """
                """increasing your max_length manually, e.g. translator('...', max_length=400)"""     )
        return True







    def      _snake_case					(			self			:       Tuple     ,					*__UpperCamelCase			:       List[str]     ,					__UpperCamelCase			:       Tuple=TruncationStrategy.DO_NOT_TRUNCATE     ,					__UpperCamelCase			:       Tuple=None     ,					__UpperCamelCase			:       Union[str, Any]=None     )		->Tuple:






        '''simple docstring'''






        if getattr(self.tokenizer     ,					"""_build_translation_inputs"""     ,					__UpperCamelCase     ):
            return self.tokenizer._build_translation_inputs(
                *__UpperCamelCase     ,					return_tensors=self.framework     ,					truncation=__UpperCamelCase     ,					src_lang=__UpperCamelCase     ,					tgt_lang=__UpperCamelCase     )
        else:
            return super()._parse_and_tokenize(*__UpperCamelCase     ,					truncation=__UpperCamelCase     )







    def      _snake_case					(			self			:       List[str]     ,					__UpperCamelCase			:       int=None     ,					__UpperCamelCase			:       int=None     ,					**__UpperCamelCase			:       Any     )		->int:






        '''simple docstring'''






        _UpperCAmelCase    ,_UpperCAmelCase    ,_UpperCAmelCase          =   super()._sanitize_parameters(**__UpperCamelCase     )
        if src_lang is not None:
            _UpperCAmelCase          =   src_lang
        if tgt_lang is not None:
            _UpperCAmelCase          =   tgt_lang
        if src_lang is None and tgt_lang is None:
            # Backward compatibility, direct arguments use is preferred.
            _UpperCAmelCase          =   kwargs.get("""task"""     ,					self.task     )
            _UpperCAmelCase          =   task.split("""_"""     )
            if task and len(__UpperCamelCase     ) == 4:
                # translation, XX, to YY
                _UpperCAmelCase          =   items[1]
                _UpperCAmelCase          =   items[3]
        return preprocess_params, forward_params, postprocess_params







    def __call__(			self			:       List[str]     ,					*__UpperCamelCase			:       str     ,					**__UpperCamelCase			:       Optional[Any]     )		->int:






        '''simple docstring'''






        return super().__call__(*__UpperCamelCase     ,					**__UpperCamelCase     )