

'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a       :      Dict  	=			{
    "configuration_bart": ["BART_PRETRAINED_CONFIG_ARCHIVE_MAP", "BartConfig", "BartOnnxConfig"],
    "tokenization_bart": ["BartTokenizer"],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				a       :      Tuple  	=			["BartTokenizerFast"]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				a       :      Dict  	=			[
				    "BART_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "BartForCausalLM",
				    "BartForConditionalGeneration",
				    "BartForQuestionAnswering",
				    "BartForSequenceClassification",
				    "BartModel",
				    "BartPretrainedModel",
				    "PretrainedBartModel",
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				a       :      Optional[Any]  	=			[
				    "TFBartForConditionalGeneration",
				    "TFBartForSequenceClassification",
				    "TFBartModel",
				    "TFBartPretrainedModel",
				]

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				a       :      str  	=			[
				    "FlaxBartDecoderPreTrainedModel",
				    "FlaxBartForCausalLM",
				    "FlaxBartForConditionalGeneration",
				    "FlaxBartForQuestionAnswering",
				    "FlaxBartForSequenceClassification",
				    "FlaxBartModel",
				    "FlaxBartPreTrainedModel",
				]

if TYPE_CHECKING:
				from .configuration_bart import BART_PRETRAINED_CONFIG_ARCHIVE_MAP, BartConfig, BartOnnxConfig
				from .tokenization_bart import BartTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_bart_fast import BartTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_bart import (
								    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
								    BartForCausalLM,
								    BartForConditionalGeneration,
								    BartForQuestionAnswering,
								    BartForSequenceClassification,
								    BartModel,
								    BartPretrainedModel,
								    PretrainedBartModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_bart import (
								    TFBartForConditionalGeneration,
								    TFBartForSequenceClassification,
								    TFBartModel,
								    TFBartPretrainedModel,
								)

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_bart import (
								    FlaxBartDecoderPreTrainedModel,
								    FlaxBartForCausalLM,
								    FlaxBartForConditionalGeneration,
								    FlaxBartForQuestionAnswering,
								    FlaxBartForSequenceClassification,
								    FlaxBartModel,
								    FlaxBartPreTrainedModel,
								)

else:
				import sys

				a       :      List[str]  	=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



'''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       :      List[Any]  	=			logging.get_logger(__name__)

a       :      Dict  	=			{
    "apple/mobilevitv2-1.0": "https://huggingface.co/apple/mobilevitv2-1.0/resolve/main/config.json",
}







class   UpperCamelCase__   (       lowercase__       ):


		"""simple docstring"""

		SCREAMING_SNAKE_CASE__  :     List[Any]       =							"mobilevitv2"







		def __init__(			self   ,      snake_case=3   ,      snake_case=2_5_6   ,      snake_case=2   ,      snake_case=2.0   ,      snake_case="swish"   ,      snake_case=3   ,      snake_case=3_2   ,      snake_case=0.1   ,      snake_case=0.02   ,      snake_case=1e-5   ,      snake_case=5_1_2   ,      snake_case=[6, 1_2, 1_8]   ,      snake_case=0.1   ,      snake_case=2_5_5   ,      snake_case=[2, 4, 3]   ,      snake_case=[1_2_8, 1_9_2, 2_5_6]   ,      snake_case=1.0   ,      snake_case=2   ,      snake_case=0.0   ,      snake_case=0.0   ,      **snake_case   ,      ):


							'''simple docstring'''

							super().__init__(**snake_case      )

							UpperCAmelCase		:							Tuple        =       num_channels
							UpperCAmelCase		:							Optional[int]        =       image_size
							UpperCAmelCase		:							Optional[int]        =       patch_size
							UpperCAmelCase		:							Optional[int]        =       expand_ratio
							UpperCAmelCase		:							List[Any]        =       hidden_act
							UpperCAmelCase		:							Optional[Any]        =       conv_kernel_size
							UpperCAmelCase		:							str        =       output_stride
							UpperCAmelCase		:							Optional[Any]        =       initializer_range
							UpperCAmelCase		:							Optional[Any]        =       layer_norm_eps
							UpperCAmelCase		:							List[str]        =       n_attn_blocks
							UpperCAmelCase		:							Any        =       base_attn_unit_dims
							UpperCAmelCase		:							str        =       width_multiplier
							UpperCAmelCase		:							Optional[int]        =       ffn_multiplier
							UpperCAmelCase		:							List[Any]        =       ffn_dropout
							UpperCAmelCase		:							List[str]        =       attn_dropout
							UpperCAmelCase		:							Optional[Any]        =       classifier_dropout_prob

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







class   UpperCamelCase__   (       lowercase__       ):


		"""simple docstring"""

		SCREAMING_SNAKE_CASE__  :     Optional[int]       =							version.parse("1.11"       )







		@property
		def 				A_	(			self      ):


							'''simple docstring'''

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







		@property
		def 				A_	(			self      ):


							'''simple docstring'''

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







		@property
		def 				A_	(			self      ):


							'''simple docstring'''

							return 1e-4
