

import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


_lowerCamelCase    :       Optional[Any]        =							logging.getLogger(__name__)





@dataclass
@add_start_docstrings(TrainingArguments.__doc__						)
class  __UpperCAmelCase					(	a_						):
			UpperCamelCase       					=					field(default=a_      ,  metadata={"""help""": """Whether to use SortishSampler or not."""}						)
			UpperCamelCase       					=					field(
			    default=a_      ,  metadata={"""help""": """Whether to use generate to calculate generative metrics (ROUGE, BLEU)."""}						)
			UpperCamelCase       					=					field(
			    default=a_      ,  metadata={
			        """help""": (
			            """The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default """
			            """to the `max_length` value of the model configuration."""
			        )
			    }      ,  )
			UpperCamelCase       					=					field(
			    default=a_      ,  metadata={
			        """help""": (
			            """The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default """
			            """to the `num_beams` value of the model configuration."""
			        )
			    }      ,  )
			UpperCamelCase       					=					field(
			    default=a_      ,  metadata={
			        """help""": """Model id, file path or url pointing to a GenerationConfig json file, to use during prediction."""
			    }      ,  )



			def 	__magic_name__   (							self							:					Optional[Any]						):
					UpperCAmelCase	:							Optional[int]						=			super().to_dict()
					for k, v in d.items():
							if isinstance(lowercase_,		lowercase_						):
									UpperCAmelCase	:							Tuple						=			v.to_dict()
					return d








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

_lowerCamelCase    :       Dict        =							logging.get_logger(__name__)





@add_end_docstrings(lowerCamelCase__						)
class  __UpperCAmelCase					(	lowerCamelCase__						):



   def __init__(							self							:					Optional[Any],		*__A							:					Tuple,		**__A							:					Tuple						):
     super().__init__(*__A,		**__A						)
     self.check_model_type(__A						)



   def 	__magic_name__   (							self							:					Union[str, Any],		__A							:					int=None,		__A							:					Tuple=None,		__A							:					Any=None,		**__A							:					Optional[int]						):
     UpperCAmelCase			,						UpperCAmelCase	:							List[Any]						=			{}, {}
     if padding is not None:
       UpperCAmelCase	:							Optional[int]						=			padding
     if truncation is not None:
       UpperCAmelCase	:							Optional[int]						=			truncation
     if top_k is not None:
       UpperCAmelCase	:							Tuple						=			top_k
     return preprocess_params, {}, postprocess_params



   def __call__(							self							:					Union[str, Any],		__A							:					Union["Image.Image", str],		__A							:					str = None,		**__A							:					Optional[int]						):
     if isinstance(__A,		(Image.Image, str)						) and isinstance(__A,		__A						):
       UpperCAmelCase	:							int						=			{'''image''': image, '''question''': question}
     else:
       UpperCAmelCase	:							str						=			image
     UpperCAmelCase	:							Union[str, Any]						=			super().__call__(__A,		**__A						)
     return results



   def 	__magic_name__   (							self							:					List[str],		__A							:					Union[str, Any],		__A							:					Tuple=False,		__A							:					List[Any]=False						):
     UpperCAmelCase	:							int						=			load_image(inputs['''image''']						)
     UpperCAmelCase	:							List[str]						=			self.tokenizer(
         inputs['''question'''],		return_tensors=self.framework,		padding=__A,		truncation=__A						)
     UpperCAmelCase	:							Union[str, Any]						=			self.image_processor(images=__A,		return_tensors=self.framework						)
     model_inputs.update(__A						)
     return model_inputs



   def 	__magic_name__   (							self							:					Optional[Any],		__A							:					List[Any]						):
     UpperCAmelCase	:							Optional[int]						=			self.model(**__A						)
     return model_outputs





   def 	__magic_name__   (							self							:					Any,		__A							:					List[str],		__A							:					Union[str, Any]=5						):
     if top_k > self.model.config.num_labels:
       UpperCAmelCase	:							Any						=			self.model.config.num_labels

     if self.framework == "pt":
       UpperCAmelCase	:							Any						=			model_outputs.logits.sigmoid()[0]
       UpperCAmelCase			,						UpperCAmelCase	:							Union[str, Any]						=			probs.topk(__A						)
     else:
       raise ValueError(F'''Unsupported framework: {self.framework}'''						)

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





