





import warnings
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


__A							   =							logging.get_logger(__name__)

__A							   =							{
    "nvidia/segformer-b0-finetuned-ade-512-512": (
        "https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json"
    ),
    # See all SegFormer models at https://huggingface.co/models?filter=segformer
}



class   _SCREAMING_SNAKE_CASE		(      __SCREAMING_SNAKE_CASE       ):







 '''simple docstring'''
 lowercase_						=     "segformer"

 def __init__(self							:  List[Any]			,					UpperCAmelCase_							:  Optional[Any]=3			,					UpperCAmelCase_							:  int=4			,					UpperCAmelCase_							:  List[str]=[2, 2, 2, 2]			,					UpperCAmelCase_							:  Optional[Any]=[8, 4, 2, 1]			,					UpperCAmelCase_							:  Dict=[32, 64, 160, 256]			,					UpperCAmelCase_							:  List[Any]=[7, 3, 3, 3]			,					UpperCAmelCase_							:  int=[4, 2, 2, 2]			,					UpperCAmelCase_							:  Dict=[1, 2, 5, 8]			,					UpperCAmelCase_							:  Tuple=[4, 4, 4, 4]			,					UpperCAmelCase_							:  int="gelu"			,					UpperCAmelCase_							:  Union[str, Any]=0.0			,					UpperCAmelCase_							:  Any=0.0			,					UpperCAmelCase_							:  Any=0.1			,					UpperCAmelCase_							:  List[str]=0.02			,					UpperCAmelCase_							:  Optional[int]=0.1			,					UpperCAmelCase_							:  Optional[Any]=1E-6			,					UpperCAmelCase_							:  Union[str, Any]=256			,					UpperCAmelCase_							:  Tuple=255			,					**UpperCAmelCase_							:  Dict			,					)			->Optional[Any]:



   '''simple docstring'''



   super().__init__(**UpperCAmelCase_)

   if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
     warnings.warn(
         "Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be"
         " removed, as the behaviour will default to that of reshape_last_stage = True."			,					UpperCAmelCase_			,					)

   lowerCamelCase__: Union[str, Any]         =num_channels
   lowerCamelCase__: Optional[Any]         =num_encoder_blocks
   lowerCamelCase__: List[Any]         =depths
   lowerCamelCase__: str         =sr_ratios
   lowerCamelCase__: Dict         =hidden_sizes
   lowerCamelCase__: Dict         =patch_sizes
   lowerCamelCase__: str         =strides
   lowerCamelCase__: Dict         =mlp_ratios
   lowerCamelCase__: Dict         =num_attention_heads
   lowerCamelCase__: List[Any]         =hidden_act
   lowerCamelCase__: int         =hidden_dropout_prob
   lowerCamelCase__: Optional[Any]         =attention_probs_dropout_prob
   lowerCamelCase__: Union[str, Any]         =classifier_dropout_prob
   lowerCamelCase__: List[Any]         =initializer_range
   lowerCamelCase__: Tuple         =drop_path_rate
   lowerCamelCase__: Optional[int]         =layer_norm_eps
   lowerCamelCase__: int         =decoder_hidden_size
   lowerCamelCase__: Tuple         =kwargs.get("reshape_last_stage"			,					UpperCAmelCase_)
   lowerCamelCase__: List[str]         =semantic_loss_ignore_index



class   _SCREAMING_SNAKE_CASE		(      __SCREAMING_SNAKE_CASE       ):







 '''simple docstring'''
 lowercase_						=     version.parse("1.11"       )

 @property
 def 				SCREAMING_SNAKE_CASE_   (self							:  Optional[Any])			->Mapping[str, Mapping[int, str]]:



   '''simple docstring'''



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

 @property
 def 				SCREAMING_SNAKE_CASE_   (self							:  Optional[Any])			->float:



   '''simple docstring'''



   return 1E-4







 @property
 def 				SCREAMING_SNAKE_CASE_   (self							:  Any)			->int:



   '''simple docstring'''



   return 12





'''simple docstring'''
import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


__snake_case				 =	logging.getLogger(__name__)
@dataclass
class   lowercase      (				A__   ):

		"""simple docstring"""

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