






"""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


_UpperCamelCase    :   Optional[Any]   			=						logging.get_logger(__name__)

_UpperCamelCase    :   Optional[Any]   			=						{
    'sail/poolformer_s12': 'https://huggingface.co/sail/poolformer_s12/resolve/main/config.json',
    # See all PoolFormer models at https://huggingface.co/models?filter=poolformer
}






class 						snake_case  (  UpperCAmelCase	):
    __magic_name__   					=					'''poolformer'''





    def __init__(							self     : Any    ,		A     : Tuple=3    ,		A     : Optional[int]=1_6    ,		A     : Optional[int]=1_6    ,		A     : Tuple=3    ,		A     : Tuple=4.0    ,		A     : str=[2, 2, 6, 2]    ,		A     : Optional[int]=[6_4, 1_2_8, 3_2_0, 5_1_2]    ,		A     : Union[str, Any]=[7, 3, 3, 3]    ,		A     : Optional[int]=[4, 2, 2, 2]    ,		A     : int=[2, 1, 1, 1]    ,		A     : Optional[Any]=4    ,		A     : List[str]=0.0    ,		A     : List[str]="gelu"    ,		A     : Tuple=True    ,		A     : Dict=1E-5    ,		A     : Tuple=0.02    ,		**A     : Any    ,		):


      '''simple docstring'''




      a					:      Dict    =       num_channels
      a					:      Union[str, Any]    =       patch_size
      a					:      List[str]    =       stride
      a					:      Optional[int]    =       padding
      a					:      Tuple    =       pool_size
      a					:      Optional[Any]    =       hidden_sizes
      a					:      Tuple    =       mlp_ratio
      a					:      Tuple    =       depths
      a					:      List[str]    =       patch_sizes
      a					:      Optional[Any]    =       strides
      a					:      Optional[Any]    =       num_encoder_blocks
      a					:      Any    =       drop_path_rate
      a					:      int    =       hidden_act
      a					:      int    =       use_layer_scale
      a					:      Any    =       layer_scale_init_value
      a					:      str    =       initializer_range
      super().__init__(**__UpperCAmelCase      )






class 						snake_case  (  UpperCAmelCase	):
    __magic_name__   					=					version.parse('''1.11'''	)





    @property
    def     lowerCamelCase__				(							self     : str      ):


      '''simple docstring'''




      return OrderedDict(
          [
              ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
          ]      )





    @property
    def     lowerCamelCase__				(							self     : List[str]      ):


      '''simple docstring'''




      return 2E-3












"""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


_UpperCamelCase    :   Dict   			=						logging.getLogger(__name__)






@dataclass
@add_start_docstrings(TrainingArguments.__doc__	)
class 						snake_case  (  UpperCAmelCase	):
    __magic_name__   					=					field(default=UpperCAmelCase       ,			metadata={'''help''': '''Whether to use SortishSampler or not.'''}	)
    __magic_name__   					=					field(
        default=UpperCAmelCase       ,			metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''}	)
    __magic_name__   					=					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.'''
            )
        }       ,			)
    __magic_name__   					=					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.'''
            )
        }       ,			)
    __magic_name__   					=					field(
        default=UpperCAmelCase       ,			metadata={
            '''help''': '''Model id, file path or url pointing to a GenerationConfig json file, to use during prediction.'''
        }       ,			)





    def     lowerCamelCase__				(							self     : Optional[int]      ):


      '''simple docstring'''




      a					:      List[Any]    =       super().to_dict()
      for k, v in d.items():
        if isinstance(A    ,		A      ):
          a					:      str    =       v.to_dict()
      return d




