






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


_UpperCAmelCase			:		Dict   				=    logging.get_logger(__name__)

_UpperCAmelCase			:		str   				=    {
    "alibaba-damo/mgp-str-base": "https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json",
}
class  lowerCAmelCase_       (				snake_case__					):
							UpperCamelCase_		:Union[str, Any]		      =    'mgp-str'




							def __init__(   self     :   Any ,							SCREAMING_SNAKE_CASE_     :   int=[32, 128] ,							SCREAMING_SNAKE_CASE_     :   List[str]=4 ,							SCREAMING_SNAKE_CASE_     :   Tuple=3 ,							SCREAMING_SNAKE_CASE_     :   Any=27 ,							SCREAMING_SNAKE_CASE_     :   Dict=38 ,							SCREAMING_SNAKE_CASE_     :   int=50_257 ,							SCREAMING_SNAKE_CASE_     :   Optional[Any]=30_522 ,							SCREAMING_SNAKE_CASE_     :   Dict=768 ,							SCREAMING_SNAKE_CASE_     :   Any=12 ,							SCREAMING_SNAKE_CASE_     :   List[str]=12 ,							SCREAMING_SNAKE_CASE_     :   Dict=4.0 ,							SCREAMING_SNAKE_CASE_     :   Union[str, Any]=True ,							SCREAMING_SNAKE_CASE_     :   Dict=False ,							SCREAMING_SNAKE_CASE_     :   Optional[int]=1e-5 ,							SCREAMING_SNAKE_CASE_     :   int=0.0 ,							SCREAMING_SNAKE_CASE_     :   str=0.0 ,							SCREAMING_SNAKE_CASE_     :   Dict=0.0 ,							SCREAMING_SNAKE_CASE_     :   int=False ,							SCREAMING_SNAKE_CASE_     :   Dict=0.02 ,							**SCREAMING_SNAKE_CASE_     :   Dict ,							):
											super().__init__(**SCREAMING_SNAKE_CASE_	)

											lowerCAmelCase__      			=						image_size
											lowerCAmelCase__      			=						patch_size
											lowerCAmelCase__      			=						num_channels
											lowerCAmelCase__      			=						max_token_length
											lowerCAmelCase__      			=						num_character_labels
											lowerCAmelCase__      			=						num_bpe_labels
											lowerCAmelCase__      			=						num_wordpiece_labels
											lowerCAmelCase__      			=						hidden_size
											lowerCAmelCase__      			=						num_hidden_layers
											lowerCAmelCase__      			=						num_attention_heads
											lowerCAmelCase__      			=						mlp_ratio
											lowerCAmelCase__      			=						distilled
											lowerCAmelCase__      			=						layer_norm_eps
											lowerCAmelCase__      			=						drop_rate
											lowerCAmelCase__      			=						qkv_bias
											lowerCAmelCase__      			=						attn_drop_rate
											lowerCAmelCase__      			=						drop_path_rate
											lowerCAmelCase__      			=						output_aa_attentions
											lowerCAmelCase__      			=						initializer_range













from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_UpperCAmelCase			:		List[Any]   				=    {
    "configuration_mobilenet_v2": [
        "MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "MobileNetV2Config",
        "MobileNetV2OnnxConfig",
    ],
}

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_UpperCAmelCase			:		str   				=    ["MobileNetV2FeatureExtractor"]
			_UpperCAmelCase			:		Union[str, Any]   				=    ["MobileNetV2ImageProcessor"]


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_UpperCAmelCase			:		Any   				=    [
			    "MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "MobileNetV2ForImageClassification",
			    "MobileNetV2ForSemanticSegmentation",
			    "MobileNetV2Model",
			    "MobileNetV2PreTrainedModel",
			    "load_tf_weights_in_mobilenet_v2",
			]


if TYPE_CHECKING:
			from .configuration_mobilenet_va import (
			    MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    MobileNetVaConfig,
			    MobileNetVaOnnxConfig,
			)

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
						from .image_processing_mobilenet_va import MobileNetVaImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_mobilenet_va import (
						    MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    MobileNetVaForImageClassification,
						    MobileNetVaForSemanticSegmentation,
						    MobileNetVaModel,
						    MobileNetVaPreTrainedModel,
						    load_tf_weights_in_mobilenet_va,
						)


else:
			import sys

			_UpperCAmelCase			:		Dict   				=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





