






'''simple docstring'''





from ...configuration_utils import PretrainedConfig
from ...utils import logging


a_      = logging.get_logger(__name__)

a_      = {
    'uw-madison/yoso-4096': 'https://huggingface.co/uw-madison/yoso-4096/resolve/main/config.json',
    # See all YOSO models at https://huggingface.co/models?filter=yoso
}





class 		SCREAMING_SNAKE_CASE__   (     lowercase_    ):
		_UpperCAmelCase         ='''yoso'''



		def __init__(    self:    Optional[Any]				,   a:    List[str]=5_02_65				,   a:    List[str]=7_68				,   a:    List[Any]=12				,   a:    List[Any]=12				,   a:    Union[str, Any]=30_72				,   a:    Union[str, Any]="gelu"				,   a:    List[Any]=0.1				,   a:    List[Any]=0.1				,   a:    str=40_96				,   a:    Optional[Any]=1				,   a:    str=0.02				,   a:    Dict=1e-12				,   a:    int="absolute"				,   a:    int=True				,   a:    Dict=9				,   a:    Optional[Any]=64				,   a:    Union[str, Any]=None				,   a:    int=True				,   a:    str=True				,   a:    Any=1				,   a:    Tuple=0				,   a:    Tuple=2				,   **a:    Union[str, Any]				,   )  ->Optional[Any]:






								'''simple docstring'''


								super().__init__(pad_token_id=a				,   bos_token_id=a				,   eos_token_id=a				,   **a)

								a_				     =   vocab_size
								a_				     =   max_position_embeddings
								a_				     =   hidden_size
								a_				     =   num_hidden_layers
								a_				     =   num_attention_heads
								a_				     =   intermediate_size
								a_				     =   hidden_act
								a_				     =   hidden_dropout_prob
								a_				     =   attention_probs_dropout_prob
								a_				     =   initializer_range
								a_				     =   type_vocab_size
								a_				     =   layer_norm_eps
								a_				     =   position_embedding_type
								a_				     =   use_expectation
								a_				     =   hash_code_len
								a_				     =   num_hash
								a_				     =   conv_window
								a_				     =   use_fast_hash
								a_				     =   lsh_backward











'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


a_      = {
    'configuration_segformer': ['SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SegformerConfig', 'SegformerOnnxConfig']
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a_      = ['SegformerFeatureExtractor']
							a_      = ['SegformerImageProcessor']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a_      = [
							    'SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'SegformerDecodeHead',
							    'SegformerForImageClassification',
							    'SegformerForSemanticSegmentation',
							    'SegformerLayer',
							    'SegformerModel',
							    'SegformerPreTrainedModel',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a_      = [
							    'TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'TFSegformerDecodeHead',
							    'TFSegformerForImageClassification',
							    'TFSegformerForSemanticSegmentation',
							    'TFSegformerModel',
							    'TFSegformerPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_segformer import SEGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, SegformerConfig, SegformerOnnxConfig

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_segformer import SegformerFeatureExtractor
														from .image_processing_segformer import SegformerImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_segformer import (
														    SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    SegformerDecodeHead,
														    SegformerForImageClassification,
														    SegformerForSemanticSegmentation,
														    SegformerLayer,
														    SegformerModel,
														    SegformerPreTrainedModel,
														)
							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_segformer import (
														    TF_SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFSegformerDecodeHead,
														    TFSegformerForImageClassification,
														    TFSegformerForSemanticSegmentation,
														    TFSegformerModel,
														    TFSegformerPreTrainedModel,
														)

else:
							import sys

							a_      = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



