'''simple docstring'''

A_        =   "\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"

A_        =   [{"type": "code", "content": INSTALL_CONTENT}]
A_        =   {
    "{processor_class}": "FakeProcessorClass",
    "{model_class}": "FakeModelClass",
    "{object_class}": "FakeObjectClass",
}






'''simple docstring'''

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_        =   {
    "apple/mobilevit-small": "https://huggingface.co/apple/mobilevit-small/resolve/main/config.json",
    "apple/mobilevit-x-small": "https://huggingface.co/apple/mobilevit-x-small/resolve/main/config.json",
    "apple/mobilevit-xx-small": "https://huggingface.co/apple/mobilevit-xx-small/resolve/main/config.json",
    "apple/deeplabv3-mobilevit-small": (
        "https://huggingface.co/apple/deeplabv3-mobilevit-small/resolve/main/config.json"
    ),
    "apple/deeplabv3-mobilevit-x-small": (
        "https://huggingface.co/apple/deeplabv3-mobilevit-x-small/resolve/main/config.json"
    ),
    "apple/deeplabv3-mobilevit-xx-small": (
        "https://huggingface.co/apple/deeplabv3-mobilevit-xx-small/resolve/main/config.json"
    ),
    # See all MobileViT models at https://huggingface.co/models?filter=mobilevit
}




class 		SCREAMING_SNAKE_CASE_				(_UpperCAmelCase			):



			"""simple docstring"""






			__magic_name__   :							Optional[Any]             =					"""mobilevit"""



			def __init__(       self							, snake_case_=3							, snake_case_=256							, snake_case_=2							, snake_case_=[144, 192, 240]							, snake_case_=[16, 32, 64, 96, 128, 160, 640]							, snake_case_=4							, snake_case_=2.0							, snake_case_=4.0							, snake_case_="silu"							, snake_case_=3							, snake_case_=32							, snake_case_=0.1							, snake_case_=0.0							, snake_case_=0.1							, snake_case_=0.02							, snake_case_=1e-5							, snake_case_=True							, snake_case_=256							, snake_case_=[6, 12, 18]							, snake_case_=0.1							, snake_case_=255							, **snake_case_							, ):
									super().__init__(**snake_case_				)

									snake_case   :					Any							=						num_channels
									snake_case   :					Optional[Any]							=						image_size
									snake_case   :					Union[str, Any]							=						patch_size
									snake_case   :					Union[str, Any]							=						hidden_sizes
									snake_case   :					List[Any]							=						neck_hidden_sizes
									snake_case   :					Optional[Any]							=						num_attention_heads
									snake_case   :					Any							=						mlp_ratio
									snake_case   :					List[str]							=						expand_ratio
									snake_case   :					Tuple							=						hidden_act
									snake_case   :					Tuple							=						conv_kernel_size
									snake_case   :					int							=						output_stride
									snake_case   :					Dict							=						hidden_dropout_prob
									snake_case   :					List[str]							=						attention_probs_dropout_prob
									snake_case   :					int							=						classifier_dropout_prob
									snake_case   :					str							=						initializer_range
									snake_case   :					Optional[int]							=						layer_norm_eps
									snake_case   :					Dict							=						qkv_bias

									# decode head attributes for semantic segmentation
									snake_case   :					Optional[int]							=						aspp_out_channels
									snake_case   :					Dict							=						atrous_rates
									snake_case   :					Any							=						aspp_dropout_prob
									snake_case   :					Any							=						semantic_loss_ignore_index







class 		SCREAMING_SNAKE_CASE_				(_UpperCAmelCase			):



			"""simple docstring"""






			__magic_name__   :							Union[str, Any]             =					version.parse("""1.11"""			)



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



			@property
			def      lowerCamelCase   (       self				):
									if self.task == "image-classification":
															return OrderedDict([("logits", {0: "batch"})]				)
									else:
															return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})]				)






			@property
			def      lowerCamelCase   (       self				):
									return 1e-4





