

import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


SCREAMING_SNAKE_CASE_        =      logging.getLogger(__name__)

@dataclass
@add_start_docstrings(TrainingArguments.__doc__				)
class 				a  (	UpperCAmelCase				):
						_lowercase    						=			field(default=UpperCAmelCase							, metadata={"help": "Whether to use SortishSampler or not."}				)
						_lowercase    						=			field(
						    default=UpperCAmelCase							, metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}				)
						_lowercase    						=			field(
						    default=UpperCAmelCase							, metadata={
						        "help": (
						            "The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default "
						            "to the `max_length` value of the model configuration."
						        )
						    }							, )
						_lowercase    						=			field(
						    default=UpperCAmelCase							, metadata={
						        "help": (
						            "The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default "
						            "to the `num_beams` value of the model configuration."
						        )
						    }							, )
						_lowercase    						=			field(
						    default=UpperCAmelCase							, metadata={
						        "help": "Model id, file path or url pointing to a GenerationConfig json file, to use during prediction."
						    }							, )


						def        _UpperCAmelCase				(       self	):







										'''simple docstring'''


										_UpperCAmelCase       :		int 		=    super().to_dict()
										for k, v in d.items():
														if isinstance(A_						,					A_	):
																		_UpperCAmelCase       :		Tuple 		=    v.to_dict()
										return d





import warnings

from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor


SCREAMING_SNAKE_CASE_        =      logging.get_logger(__name__)

class 				a  (	UpperCAmelCase				):


						def __init__(       self						,					*A_						,					**A_	):







										'''simple docstring'''


										warnings.warn(
										    "The class PerceiverFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
										    " Please use PerceiverImageProcessor instead."						,					A_						,					)
										super().__init__(*A_						,					**A_	)


