


from typing import Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
		from PIL import Image

		from ..image_utils import load_image

if is_torch_available():
		from ..models.auto.modeling_auto import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING

__UpperCAmelCase					       =							logging.get_logger(__name__)




@add_end_docstrings(__a						)
class 	a__       (							__a						):





							'''simple docstring'''






							def __init__(	self      ,					*lowerCamelCase_      ,					**lowerCamelCase_     ) ->							Optional[Any]:
														super().__init__(*lowerCAmelCase_      ,					**lowerCAmelCase_     )
														self.check_model_type(lowerCAmelCase_     )







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_=None      ,					lowerCamelCase_=None      ,					lowerCamelCase_=None      ,					**lowerCamelCase_     ) ->							List[str]:
														lowerCAmelCase__     ,      lowerCAmelCase__      =			{}, {}
														if padding is not None:
																					lowerCAmelCase__      =			padding
														if truncation is not None:
																					lowerCAmelCase__      =			truncation
														if top_k is not None:
																					lowerCAmelCase__      =			top_k
														return preprocess_params, {}, postprocess_params







							def __call__(	self      ,					lowerCamelCase_      ,					lowerCamelCase_ = None      ,					**lowerCamelCase_     ) ->							Dict:







														if isinstance(lowerCAmelCase_      ,					(Image.Image, str)     ) and isinstance(lowerCAmelCase_      ,					lowerCAmelCase_     ):
																					lowerCAmelCase__      =			{'''image''': image, '''question''': question}
														else:
																					lowerCAmelCase__      =			image
														lowerCAmelCase__      =			super().__call__(lowerCAmelCase_      ,					**lowerCAmelCase_     )
														return results







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_      ,					lowerCamelCase_=False      ,					lowerCamelCase_=False     ) ->							Optional[int]:
														lowerCAmelCase__      =			load_image(inputs['''image''']     )
														lowerCAmelCase__      =			self.tokenizer(
														    inputs['''question''']      ,					return_tensors=self.framework      ,					padding=lowerCAmelCase_      ,					truncation=lowerCAmelCase_     )
														lowerCAmelCase__      =			self.image_processor(images=lowerCAmelCase_      ,					return_tensors=self.framework     )
														model_inputs.update(lowerCAmelCase_     )
														return model_inputs







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_     ) ->							Any:
														lowerCAmelCase__      =			self.model(**lowerCAmelCase_     )
														return model_outputs







							def 						__SCREAMING_SNAKE_CASE							(	self      ,					lowerCamelCase_      ,					lowerCamelCase_=5     ) ->							Any:







														if top_k > self.model.config.num_labels:
																					lowerCAmelCase__      =			self.model.config.num_labels

														if self.framework == "pt":
																					lowerCAmelCase__      =			model_outputs.logits.sigmoid()[0]
																					lowerCAmelCase__     ,      lowerCAmelCase__      =			probs.topk(lowerCAmelCase_     )
														else:
																					raise ValueError(F"""Unsupported framework: {self.framework}"""     )

														lowerCAmelCase__      =			scores.tolist()
														lowerCAmelCase__      =			ids.tolist()
														return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(lowerCAmelCase_      ,					lowerCAmelCase_     )]



'''simple docstring'''






import jax.numpy as jnp

from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig


__UpperCAmelCase					       =							logging.get_logger(__name__)

__UpperCAmelCase					       =							'''T5Config'''


def       _snake_case     (  A				,					A				,					A )		->					jnp.ndarray:
							lowerCAmelCase__      =			jnp.zeros_like(A )
							lowerCAmelCase__      =			shifted_input_ids.at[:, 1:].set(input_ids[:, :-1] )
							lowerCAmelCase__      =			shifted_input_ids.at[:, 0].set(A )

							lowerCAmelCase__      =			jnp.where(shifted_input_ids == -100				,					A				,					A )
							return shifted_input_ids




class 	a__       (							a__						):





							'''simple docstring'''






							lowercase__    :     int					=   "mt5"
							lowercase__    :     Dict					=   MTaConfig




class 	a__       (							a__						):





							'''simple docstring'''






							lowercase__    :     int					=   "mt5"
							lowercase__    :     Any					=   MTaConfig




class 	a__       (							a__						):





							'''simple docstring'''






							lowercase__    :     Union[str, Any]					=   "mt5"
							lowercase__    :     Tuple					=   MTaConfig