
"""simple docstring"""


from typing import List

from ...configuration_utils import PretrainedConfig
from ...utils import logging


A_    							=					logging.get_logger(__name__)

A_    							=					{
    """snap-research/efficientformer-l1-300""": (
        """https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json"""
    ),
}




class  __lowerCamelCase  ( lowerCAmelCase			):
				a__:      str          =    'efficientformer'




				def __init__(				self							,						UpperCAmelCase = [3, 2, 6, 4]							,						UpperCAmelCase = [48, 96, 224, 448]							,						UpperCAmelCase = [True, True, True, True]							,						UpperCAmelCase = 448							,						UpperCAmelCase = 32							,						UpperCAmelCase = 4							,						UpperCAmelCase = 7							,						UpperCAmelCase = 5							,						UpperCAmelCase = 8							,						UpperCAmelCase = 4							,						UpperCAmelCase = 0.0							,						UpperCAmelCase = 16							,						UpperCAmelCase = 3							,						UpperCAmelCase = 3							,						UpperCAmelCase = 3							,						UpperCAmelCase = 2							,						UpperCAmelCase = 1							,						UpperCAmelCase = 0.0							,						UpperCAmelCase = 1							,						UpperCAmelCase = True							,						UpperCAmelCase = True							,						UpperCAmelCase = 1e-5							,						UpperCAmelCase = "gelu"							,						UpperCAmelCase = 0.0_2							,						UpperCAmelCase = 1e-1_2							,						UpperCAmelCase = 224							,						UpperCAmelCase = 1e-0_5							,						**UpperCAmelCase							,						):
							super().__init__(**UpperCAmelCase   )

							lowerCamelCase_					=  hidden_act
							lowerCamelCase_					=  hidden_dropout_prob
							lowerCamelCase_					=  hidden_sizes
							lowerCamelCase_					=  num_hidden_layers
							lowerCamelCase_					=  num_attention_heads
							lowerCamelCase_					=  initializer_range
							lowerCamelCase_					=  layer_norm_eps
							lowerCamelCase_					=  patch_size
							lowerCamelCase_					=  num_channels
							lowerCamelCase_					=  depths
							lowerCamelCase_					=  mlp_expansion_ratio
							lowerCamelCase_					=  downsamples
							lowerCamelCase_					=  dim
							lowerCamelCase_					=  key_dim
							lowerCamelCase_					=  attention_ratio
							lowerCamelCase_					=  resolution
							lowerCamelCase_					=  pool_size
							lowerCamelCase_					=  downsample_patch_size
							lowerCamelCase_					=  downsample_stride
							lowerCamelCase_					=  downsample_pad
							lowerCamelCase_					=  drop_path_rate
							lowerCamelCase_					=  num_metaad_blocks
							lowerCamelCase_					=  distillation
							lowerCamelCase_					=  use_layer_scale
							lowerCamelCase_					=  layer_scale_init_value
							lowerCamelCase_					=  image_size
							lowerCamelCase_					=  batch_norm_eps





"""simple docstring"""


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


A_    							=					logging.getLogger(__name__)




@dataclass
@add_start_docstrings(TrainingArguments.__doc__			)
class  __lowerCamelCase  ( lowerCAmelCase			):
				a__:      bool          =    field(default=lowerCAmelCase						,	metadata={'help': 'Whether to use SortishSampler or not.'}			)
				a__:      bool          =    field(
				    default=lowerCAmelCase						,	metadata={'help': 'Whether to use generate to calculate generative metrics (ROUGE, BLEU).'}			)
				a__:      Optional[int]          =    field(
				    default=lowerCAmelCase						,	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.'
				        )
				    }						,	)
				a__:      Optional[int]          =    field(
				    default=lowerCAmelCase						,	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.'
				        )
				    }						,	)
				a__:      Optional[Union[str, Path, GenerationConfig]]          =    field(
				    default=lowerCAmelCase						,	metadata={
				        'help': 'Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.'
				    }						,	)




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  super().to_dict()
							for k, v in d.items():
										if isinstance(UpperCAmelCase							,						UpperCAmelCase   ):
													lowerCamelCase_					=  v.to_dict()
							return d



