






# 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

import torch

from ..models.auto import AutoModelForVisualQuestionAnswering, AutoProcessor
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
     from PIL import Image




class 			snake_case__   (							lowercase_):

       '''simple docstring'''
       lowerCamelCase							:				Tuple 				= "dandelin/vilt-b32-finetuned-vqa"
       lowerCamelCase							:				List[str] 				= (
           "This is a tool that answers a question about an image. It takes an input named `image` which should be the "
           "image containing the information, as well as a `question` which should be the question in English. It "
           "returns a text that is the answer to the question."
       )
       lowerCamelCase							:				Optional[Any] 				= "image_qa"
       lowerCamelCase							:				str 				= AutoProcessor
       lowerCamelCase							:				Union[str, Any] 				= AutoModelForVisualQuestionAnswering

       lowerCamelCase							:				Any 				= ["image", "text"]
       lowerCamelCase							:				Dict 				= ["text"]



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






            '''simple docstring'''





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



       def 					__lowercase						(							self			,						a__			,						a__    ) ->      int:






            '''simple docstring'''





            return self.pre_processor(a__			,						a__			,						return_tensors="""pt"""    )



       def 					__lowercase						(							self			,						a__    ) ->      Union[str, Any]:






            '''simple docstring'''





            with torch.no_grad():
                 return self.model(**a__    ).logits



       def 					__lowercase						(							self			,						a__    ) ->      Tuple:






            '''simple docstring'''





            __snake_case			:str          =						outputs.argmax(-1    ).item()
            return self.model.config.idalabel[idx]










import argparse
import os
from io import BytesIO
from pathlib import Path

import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm






def   UpperCamelCase  (       snake_case__     :   str	,snake_case__     :   Dict	,snake_case__     :   List[str]  ):




     '''simple docstring'''






     __snake_case			:Tuple          =						1.5
     __snake_case			:Any          =						int(factor * num_class_images  )
     __snake_case			:List[str]          =						ClipClient(
         url="""https://knn.laion.ai/knn-service"""	,indice_name="""laion_400m"""	,num_images=snake_case__	,aesthetic_weight=0.1  )

     os.makedirs(f'''{class_data_dir}/images'''	,exist_ok=snake_case__  )
     if len(list(Path(f'''{class_data_dir}/images'''  ).iterdir()  )  ) >= num_class_images:
          return

     while True:
          __snake_case			:Optional[Any]          =						client.query(text=snake_case__  )
          if len(snake_case__  ) >= factor * num_class_images or num_images > 1e4:
               break
          else:
               __snake_case			:Tuple          =						int(factor * num_images  )
               __snake_case			:Any          =						ClipClient(
                   url="""https://knn.laion.ai/knn-service"""	,indice_name="""laion_400m"""	,num_images=snake_case__	,aesthetic_weight=0.1	,)

     __snake_case			:Dict          =						0
     __snake_case			:Tuple          =						0
     __snake_case			:Dict          =						tqdm(desc="""downloading real regularization images"""	,total=snake_case__  )

     with open(f'''{class_data_dir}/caption.txt'''	,"""w"""  ) as fa, open(f'''{class_data_dir}/urls.txt'''	,"""w"""  ) as fa, open(
         f'''{class_data_dir}/images.txt'''	,"""w"""  ) as fa:
          while total < num_class_images:
               __snake_case			:List[Any]          =						class_images[count]
               count += 1
               try:
                    __snake_case			:str          =						requests.get(images["""url"""]  )
                    if img.status_code == 200:
                         __snake_case			:Any          =						Image.open(BytesIO(img.content  )  )
                         with open(f'''{class_data_dir}/images/{total}.jpg'''	,"""wb"""  ) as f:
                              f.write(img.content  )
                         fa.write(images["""caption"""] + """\n"""  )
                         fa.write(images["""url"""] + """\n"""  )
                         fa.write(f'''{class_data_dir}/images/{total}.jpg''' + """\n"""  )
                         total += 1
                         pbar.update(1  )
                    else:
                         continue
               except Exception:
                    continue
     return






def   UpperCamelCase  (       ):




     '''simple docstring'''






     __snake_case			:List[str]          =						argparse.ArgumentParser(""""""	,add_help=snake_case__  )
     parser.add_argument("""--class_prompt"""	,help="""text prompt to retrieve images"""	,required=snake_case__	,type=snake_case__  )
     parser.add_argument("""--class_data_dir"""	,help="""path to save images"""	,required=snake_case__	,type=snake_case__  )
     parser.add_argument("""--num_class_images"""	,help="""number of images to download"""	,default=200	,type=snake_case__  )
     return parser.parse_args()


if __name__ == "__main__":
     lowerCamelCase__							=     parse_args()
     retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)


