


'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__snake_case    						=logging.get_logger(__name__)

__snake_case    						={
    """facebook/levit-128S""": """https://huggingface.co/facebook/levit-128S/resolve/main/config.json""",
    # See all LeViT models at https://huggingface.co/models?filter=levit
}

class 		UpperCAmelCase_      (  __lowercase		):
     lowerCamelCase				:	Tuple		=     '''levit'''





     def __init__( self       :      Tuple    ,      UpperCAmelCase__       :      Optional[Any]=2_2_4    ,      UpperCAmelCase__       :      int=3    ,      UpperCAmelCase__       :      List[Any]=3    ,      UpperCAmelCase__       :      int=2    ,      UpperCAmelCase__       :      Any=1    ,      UpperCAmelCase__       :      Dict=1_6    ,      UpperCAmelCase__       :      int=[1_2_8, 2_5_6, 3_8_4]    ,      UpperCAmelCase__       :      str=[4, 8, 1_2]    ,      UpperCAmelCase__       :      Optional[int]=[4, 4, 4]    ,      UpperCAmelCase__       :      Optional[Any]=[1_6, 1_6, 1_6]    ,      UpperCAmelCase__       :      Any=0    ,      UpperCAmelCase__       :      Dict=[2, 2, 2]    ,      UpperCAmelCase__       :      List[str]=[2, 2, 2]    ,      UpperCAmelCase__       :      Dict=0.02    ,      **UpperCAmelCase__       :      Optional[int]    ,      )				->  Optional[Any]:
        super().__init__(**UpperCAmelCase__    )
        lowerCAmelCase     =			image_size
        lowerCAmelCase     =			num_channels
        lowerCAmelCase     =			kernel_size
        lowerCAmelCase     =			stride
        lowerCAmelCase     =			padding
        lowerCAmelCase     =			hidden_sizes
        lowerCAmelCase     =			num_attention_heads
        lowerCAmelCase     =			depths
        lowerCAmelCase     =			key_dim
        lowerCAmelCase     =			drop_path_rate
        lowerCAmelCase     =			patch_size
        lowerCAmelCase     =			attention_ratio
        lowerCAmelCase     =			mlp_ratio
        lowerCAmelCase     =			initializer_range
        lowerCAmelCase     =			[
            ['Subsample', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
            ['Subsample', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
        ]




class 		UpperCAmelCase_      (  __lowercase		):
     lowerCamelCase				:	int		=     version.parse('''1.11'''		)





     @property
     def 							__UpperCAmelCase      ( self       :      List[str]    )				->  Mapping[str, Mapping[int, str]]:
        return OrderedDict(
            [
                ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
            ]    )





     @property
     def 							__UpperCAmelCase      ( self       :      Dict    )				->  float:
        return 1E-4









'''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


__snake_case    						=logging.getLogger(__name__)

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





     def 							__UpperCAmelCase      ( self       :      Dict    )				->  List[str]:
        lowerCAmelCase     =			super().to_dict()
        for k, v in d.items():
           if isinstance(UpperCAmelCase__    ,      UpperCAmelCase__    ):
              lowerCAmelCase     =			v.to_dict()
        return d





