"""simple docstring"""


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(lowerCAmelCase__   )
class  UpperCamelCase					(				lowerCAmelCase__   ):







       def __init__( self,							*lowerCAmelCase__,							**lowerCAmelCase__)      ->      Tuple:
            super().__init__(*lowerCAmelCase__,							**lowerCAmelCase__)
            self.check_model_type(lowerCAmelCase__)







       def 		a_						( self,							lowerCAmelCase__=None,							lowerCAmelCase__=None,							lowerCAmelCase__=None,							**lowerCAmelCase__)      ->      Any:
            snake_case_       ,	snake_case_					       =					{}, {}
            if padding is not None:
                 snake_case_					       =					padding
            if truncation is not None:
                 snake_case_					       =					truncation
            if top_k is not None:
                 snake_case_					       =					top_k
            return preprocess_params, {}, postprocess_params







       def __call__( self,							lowerCAmelCase__,							lowerCAmelCase__ = None,							**lowerCAmelCase__)      ->      Optional[Any]:
            if isinstance(lowerCAmelCase__,							(Image.Image, str)) and isinstance(lowerCAmelCase__,							lowerCAmelCase__):
                 snake_case_					       =					{'image': image, 'question': question}
            else:
                 snake_case_					       =					image
            snake_case_					       =					super().__call__(lowerCAmelCase__,							**lowerCAmelCase__)
            return results







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







       def 		a_						( self,							lowerCAmelCase__)      ->      Optional[int]:
            snake_case_					       =					self.model(**lowerCAmelCase__)
            return model_outputs







       def 		a_						( self,							lowerCAmelCase__,							lowerCAmelCase__=5)      ->      Dict:
            if top_k > self.model.config.num_labels:
                 snake_case_					       =					self.model.config.num_labels

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

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


"""simple docstring"""






import argparse

import torch

from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging


logging.set_verbosity_info()



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase )  ->  List[Any]:
   # Initialise PyTorch model
   lowercase__:   Optional[Any]					=      FunnelConfig.from_json_file(__UpperCAmelCase )
   print(F"""Building PyTorch model from configuration: {config}""" )
   lowercase__:   List[Any]					=      FunnelBaseModel(__UpperCAmelCase ) if base_model else FunnelModel(__UpperCAmelCase )

   # Load weights from tf checkpoint
   load_tf_weights_in_funnel(__UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase )

   # Save pytorch-model
   print(F"""Save PyTorch model to {pytorch_dump_path}""" )
   torch.save(model.state_dict()       ,    __UpperCAmelCase )


if __name__ == "__main__":
       __A			   = argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
       )
       parser.add_argument(
           "--config_file",
           default=None,
           type=str,
           required=True,
           help="The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.",
       )
       parser.add_argument(
           "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
       )
       parser.add_argument(
           "--base_model", action="store_true", help="Whether you want just the base model (no decoder) or not."
       )
       __A			   = parser.parse_args()
       convert_tf_checkpoint_to_pytorch(
           args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model
       )
