
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool


SCREAMING_SNAKE_CASE : Optional[Any]          =    """Here is a text containing a lot of information: '''{text}'''.

Can you answer this question about the text: '{question}'"""





class 					snake_case__  ( lowerCAmelCase_       ):
	UpperCAmelCase   :							Optional[int]         = """google/flan-t5-base"""
	UpperCAmelCase   :							Optional[Any]         = (
	    """This is a tool that answers questions related to a text. It takes two arguments named `text`, which is the """
	    """text where to find the answer, and `question`, which is the question, and returns the answer to the question."""
	)
	UpperCAmelCase   :							Union[str, Any]         = """text_qa"""
	UpperCAmelCase   :							int         = AutoTokenizer
	UpperCAmelCase   :							Optional[Any]         = AutoModelForSeqaSeqLM

	UpperCAmelCase   :							int         = ["""text""", """text"""]
	UpperCAmelCase   :							Optional[int]         = ["""text"""]





	def     A				(  self				,    UpperCamelCase_				,    UpperCamelCase_							)		->			List[Any]:
								"""simple docstring"""






								a_       :   Union[str, Any]			=	QA_PROMPT.format(text=__SCREAMING_SNAKE_CASE				,    question=__SCREAMING_SNAKE_CASE							)
								return self.pre_processor(__SCREAMING_SNAKE_CASE				,    return_tensors="""pt"""							)





	def     A				(  self				,    UpperCamelCase_							)		->			Dict:
								"""simple docstring"""






								a_       :   List[str]			=	self.model.generate(**__SCREAMING_SNAKE_CASE							)

								a_		,				a_       :   Dict			=	inputs["""input_ids"""].shape
								a_       :   Union[str, Any]			=	output_ids.shape[0]

								return output_ids.reshape(__SCREAMING_SNAKE_CASE				,    out_b // in_b				,    *output_ids.shape[1:]							)[0][0]





	def     A				(  self				,    UpperCamelCase_							)		->			Tuple:
								"""simple docstring"""






								return self.pre_processor.decode(__SCREAMING_SNAKE_CASE				,    skip_special_tokens=__SCREAMING_SNAKE_CASE				,    clean_up_tokenization_spaces=__SCREAMING_SNAKE_CASE							)






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


SCREAMING_SNAKE_CASE : Dict          =    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE : str          =    {
    "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 					snake_case__  ( __A       ):
	UpperCAmelCase   :							List[str]         = """mobilevit"""





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






								super().__init__(**UpperCamelCase_							)

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

								# decode head attributes for semantic segmentation
								a_       :   Optional[Any]			=	aspp_out_channels
								a_       :   Any			=	atrous_rates
								a_       :   Union[str, Any]			=	aspp_dropout_prob
								a_       :   Any			=	semantic_loss_ignore_index





class 					snake_case__  ( __A       ):
	UpperCAmelCase   :							Optional[int]         = version.parse("""1.11"""       )





	@property
	def     A				(  self							)		->			Mapping[str, Mapping[int, str]]:
								"""simple docstring"""






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





	@property
	def     A				(  self							)		->			Mapping[str, Mapping[int, str]]:
								"""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							)		->			float:
								"""simple docstring"""






								return 1e-4


