import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


__lowercase							 =					logging.getLogger(__name__)


@dataclass
class 	lowerCamelCase_     (   UpperCAmelCase_ ):






    '''simple docstring'''






    a__	:						Optional[float]						=      field(
        default=0.0						,	metadata={"""help""": """The label smoothing epsilon to apply (if not zero)."""} )
    a__	:						bool						=      field(default=UpperCAmelCase_						,	metadata={"""help""": """Whether to SortishSamler or not."""} )
    a__	:						bool						=      field(
        default=UpperCAmelCase_						,	metadata={"""help""": """Whether to use generate to calculate generative metrics (ROUGE, BLEU)."""} )
    a__	:						bool						=      field(default=UpperCAmelCase_						,	metadata={"""help""": """whether to use adafactor"""} )
    a__	:						Optional[float]						=      field(
        default=UpperCAmelCase_						,	metadata={"""help""": """Encoder layer dropout probability. Goes into model.config."""} )
    a__	:						Optional[float]						=      field(
        default=UpperCAmelCase_						,	metadata={"""help""": """Decoder layer dropout probability. Goes into model.config."""} )
    a__	:						Optional[float]						=      field(default=UpperCAmelCase_						,	metadata={"""help""": """Dropout probability. Goes into model.config."""} )
    a__	:						Optional[float]						=      field(
        default=UpperCAmelCase_						,	metadata={"""help""": """Attention dropout probability. Goes into model.config."""} )
    a__	:						Optional[str]						=      field(
        default="""linear"""						,	metadata={"""help""": F'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys() )}'''}						,	)







"""simple docstring"""



UpperCAmelCase__          = {
    'Pillow': 'Pillow',
    'accelerate': 'accelerate>=0.11.0',
    'compel': 'compel==0.1.8',
    'black': 'black~=23.1',
    'datasets': 'datasets',
    'filelock': 'filelock',
    'flax': 'flax>=0.4.1',
    'hf-doc-builder': 'hf-doc-builder>=0.3.0',
    'huggingface-hub': 'huggingface-hub>=0.13.2',
    'requests-mock': 'requests-mock==1.10.0',
    'importlib_metadata': 'importlib_metadata',
    'invisible-watermark': 'invisible-watermark',
    'isort': 'isort>=5.5.4',
    'jax': 'jax>=0.2.8,!=0.3.2',
    'jaxlib': 'jaxlib>=0.1.65',
    'Jinja2': 'Jinja2',
    'k-diffusion': 'k-diffusion>=0.0.12',
    'torchsde': 'torchsde',
    'note_seq': 'note_seq',
    'librosa': 'librosa',
    'numpy': 'numpy',
    'omegaconf': 'omegaconf',
    'parameterized': 'parameterized',
    'protobuf': 'protobuf>=3.20.3,<4',
    'pytest': 'pytest',
    'pytest-timeout': 'pytest-timeout',
    'pytest-xdist': 'pytest-xdist',
    'ruff': 'ruff>=0.0.241',
    'safetensors': 'safetensors',
    'sentencepiece': 'sentencepiece>=0.1.91,!=0.1.92',
    'scipy': 'scipy',
    'onnx': 'onnx',
    'regex': 'regex!=2019.12.17',
    'requests': 'requests',
    'tensorboard': 'tensorboard',
    'torch': 'torch>=1.4',
    'torchvision': 'torchvision',
    'transformers': 'transformers>=4.25.1',
    'urllib3': 'urllib3<=2.0.0',
}



