'''simple docstring'''





from . import __version__

# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
    CLOUDFRONT_DISTRIB_PREFIX,
    CONFIG_NAME,
    DISABLE_TELEMETRY,
    DUMMY_INPUTS,
    DUMMY_MASK,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    FEATURE_EXTRACTOR_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    MODEL_CARD_NAME,
    MULTIPLE_CHOICE_DUMMY_INPUTS,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    SENTENCEPIECE_UNDERLINE,
    SPIECE_UNDERLINE,
    TF2_WEIGHTS_NAME,
    TF_WEIGHTS_NAME,
    TORCH_FX_REQUIRED_VERSION,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
    ContextManagers,
    DummyObject,
    EntryNotFoundError,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    TensorType,
    _LazyModule,
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    cached_property,
    copy_func,
    default_cache_path,
    define_sagemaker_information,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    get_torch_version,
    has_file,
    http_user_agent,
    is_apex_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_datasets_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_librosa_available,
    is_offline_mode,
    is_onnx_available,
    is_pandas_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_tensor,
    is_tensorflow_probability_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    replace_return_docstrings,
    requires_backends,
    to_numpy,
    to_py_obj,
    torch_only_method,
)




'''simple docstring'''





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


_lowerCAmelCase  :str  				=    logging.get_logger(__name__)

_lowerCAmelCase  :Optional[int]  				=    {
    """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 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    snake_case__							:   Optional[int]							=						"segformer"






    def __init__(       self    ,							lowercase__=3    ,							lowercase__=4    ,							lowercase__=[2, 2, 2, 2]    ,							lowercase__=[8, 4, 2, 1]    ,							lowercase__=[32, 64, 160, 256]    ,							lowercase__=[7, 3, 3, 3]    ,							lowercase__=[4, 2, 2, 2]    ,							lowercase__=[1, 2, 5, 8]    ,							lowercase__=[4, 4, 4, 4]    ,							lowercase__="gelu"    ,							lowercase__=0.0    ,							lowercase__=0.0    ,							lowercase__=0.1    ,							lowercase__=0.0_2    ,							lowercase__=0.1    ,							lowercase__=1E-6    ,							lowercase__=256    ,							lowercase__=255    ,							**lowercase__    ,							)							-> Union[str, Any]:
         super().__init__(**lowercase__ )

         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.'    ,							lowercase__    ,							)

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






class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    snake_case__							:   Tuple							=						version.parse("1.11" )






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






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






    @property
    def       _UpperCamelCase    (       self )							-> int:
         return 12



