






'''simple docstring'''

import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel






class     a_						(      pl.LightningModule    ):

  def __init__(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				List[str]:



        """simple docstring"""






        super().__init__()
        UpperCamelCase      = model
        UpperCamelCase      = 2
        UpperCamelCase      = nn.Linear(self.model.config.hidden_size      ,		self.num_labels				)


  def   A__						(				self				)  ->				str:



        """simple docstring"""






        pass







def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase   )->			Any:
      # load longformer model from model identifier
      UpperCamelCase      = LongformerModel.from_pretrained(__UpperCamelCase   )
      UpperCamelCase      = LightningModel(__UpperCamelCase   )

      UpperCamelCase      = torch.load(__UpperCamelCase		,						map_location=torch.device("""cpu"""   )   )
      lightning_model.load_state_dict(ckpt["""state_dict"""]   )

      # init longformer question answering model
      UpperCamelCase      = LongformerForQuestionAnswering.from_pretrained(__UpperCamelCase   )

      # transfer weights
      longformer_for_qa.longformer.load_state_dict(lightning_model.model.state_dict()   )
      longformer_for_qa.qa_outputs.load_state_dict(lightning_model.qa_outputs.state_dict()   )
      longformer_for_qa.eval()

      # save model
      longformer_for_qa.save_pretrained(__UpperCamelCase   )

      print(F"Conversion successful. Model saved under {pytorch_dump_folder_path}"   )


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE__     =       argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--longformer_model',
         default=None,
         type=str,
         required=True,
         help='model identifier of longformer. Should be either `longformer-base-4096` or `longformer-large-4096`.',
     )
     parser.add_argument(
         '--longformer_question_answering_ckpt_path',
         default=None,
         type=str,
         required=True,
         help='Path the official PyTorch Lightning Checkpoint.',
     )
     parser.add_argument(
         '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
     )
     SCREAMING_SNAKE_CASE__     =       parser.parse_args()
     convert_longformer_qa_checkpoint_to_pytorch(
         args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
     )








'''simple docstring'''

# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ..models.auto import AutoModelForVisionaSeq
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
     from PIL import Image






class     a_						(      lowerCamelCase    ):
  lowercase							=				"""Salesforce/blip-image-captioning-base"""
  lowercase							=				(
      """This is a tool that generates a description of an image. It takes an input named `image` which should be the """
      """image to caption, and returns a text that contains the description in English."""
  )
  lowercase							=				"""image_captioner"""
  lowercase							=				AutoModelForVisionaSeq

  lowercase							=				["""image"""]
  lowercase							=				["""text"""]

  def __init__(				self      ,		*_SCREAMING_SNAKE_CASE      ,		**_SCREAMING_SNAKE_CASE				)  ->				Any:



        """simple docstring"""






        requires_backends(self      ,		["""vision"""]				)
        super().__init__(*_SCREAMING_SNAKE_CASE      ,		**_SCREAMING_SNAKE_CASE				)

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				str:



        """simple docstring"""






        return self.pre_processor(images=_SCREAMING_SNAKE_CASE      ,		return_tensors="""pt"""				)

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				str:



        """simple docstring"""






        return self.model.generate(**_SCREAMING_SNAKE_CASE				)


  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE				)  ->				Union[str, Any]:



        """simple docstring"""






        return self.pre_processor.batch_decode(_SCREAMING_SNAKE_CASE      ,		skip_special_tokens=_SCREAMING_SNAKE_CASE				)[0].strip()
