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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


A     :  List[Any]					     =       logging.get_logger(__name__)

A     :  Optional[Any]					     =       {
    "microsoft/swin-tiny-patch4-window7-224": (
        "https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json"
    ),
    # See all Swin models at https://huggingface.co/models?filter=swin
}


class   lowerCamelCase					(SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__					):
  """simple docstring"""







  lowerCamelCase__     =						'''swin'''

  lowerCamelCase__     =						{
      '''num_attention_heads''': '''num_heads''',
      '''num_hidden_layers''': '''num_layers''',
  }







  def __init__(							self :       List[Any]  ,     __magic_name__ :       Dict=224  ,     __magic_name__ :       Tuple=4  ,     __magic_name__ :       str=3  ,     __magic_name__ :       Tuple=96  ,     __magic_name__ :       Optional[int]=[2, 2, 6, 2]  ,     __magic_name__ :       Tuple=[3, 6, 12, 24]  ,     __magic_name__ :       Union[str, Any]=7  ,     __magic_name__ :       Optional[int]=4.0  ,     __magic_name__ :       Optional[Any]=True  ,     __magic_name__ :       Dict=0.0  ,     __magic_name__ :       Optional[Any]=0.0  ,     __magic_name__ :       Any=0.1  ,     __magic_name__ :       Tuple="gelu"  ,     __magic_name__ :       Optional[int]=False  ,     __magic_name__ :       Dict=0.02  ,     __magic_name__ :       Optional[Any]=1e-5  ,     __magic_name__ :       Union[str, Any]=32  ,     __magic_name__ :       Any=None  ,     __magic_name__ :       str=None  ,     **__magic_name__ :       Tuple  ,     )  ->	List[Any]:
        super().__init__(**__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  image_size
        SCREAMING_SNAKE_CASE_          =  patch_size
        SCREAMING_SNAKE_CASE_          =  num_channels
        SCREAMING_SNAKE_CASE_          =  embed_dim
        SCREAMING_SNAKE_CASE_          =  depths
        SCREAMING_SNAKE_CASE_          =  len(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  num_heads
        SCREAMING_SNAKE_CASE_          =  window_size
        SCREAMING_SNAKE_CASE_          =  mlp_ratio
        SCREAMING_SNAKE_CASE_          =  qkv_bias
        SCREAMING_SNAKE_CASE_          =  hidden_dropout_prob
        SCREAMING_SNAKE_CASE_          =  attention_probs_dropout_prob
        SCREAMING_SNAKE_CASE_          =  drop_path_rate
        SCREAMING_SNAKE_CASE_          =  hidden_act
        SCREAMING_SNAKE_CASE_          =  use_absolute_embeddings
        SCREAMING_SNAKE_CASE_          =  layer_norm_eps
        SCREAMING_SNAKE_CASE_          =  initializer_range
        SCREAMING_SNAKE_CASE_          =  encoder_stride
        # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
        # this indicates the channel dimension after the last stage of the model
        SCREAMING_SNAKE_CASE_          =  int(embed_dim * 2 ** (len(__magic_name__   ) - 1)   )
        SCREAMING_SNAKE_CASE_          =  ["stem"] + [F'''stage{idx}''' for idx in range(1  ,     len(__magic_name__   ) + 1   )]
        SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  get_aligned_output_features_output_indices(
            out_features=__magic_name__  ,     out_indices=__magic_name__  ,     stage_names=self.stage_names   )



class   lowerCamelCase					(SCREAMING_SNAKE_CASE__					):
  """simple docstring"""







  lowerCamelCase__     =						version.parse('''1.11'''					)







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







  @property
  def    __A     (							self :       Optional[Any]   )  ->	float:
        return 1e-4



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

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


A     :  str					     =       logging.getLogger(__name__)


@dataclass
class   lowerCamelCase					(SCREAMING_SNAKE_CASE__					):
  """simple docstring"""







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


