



import argparse
import re

import requests
import torch

# git clone https://github.com/salesforce/BLIP.git
from models.blip import blip_decoder
from models.blip_itm import blip_itm
from models.blip_vqa import blip_vqa
from PIL import Image
from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode

from transformers import (
    BertTokenizer,
    BlipConfig,
    BlipForConditionalGeneration,
    BlipForImageTextRetrieval,
    BlipForQuestionAnswering,
)




def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase		)	->  Any:



    """simple docstring"""






    A			:	Optional[Any]										=       """https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg"""
    A			:	Optional[int]										=       Image.open(requests.get(lowerCAmelCase__				,					stream=lowerCAmelCase__		).raw		).convert("""RGB"""		)

    A			:	Tuple										=       transforms.Compose(
        [
            transforms.Resize((image_size, image_size)				,					interpolation=InterpolationMode.BICUBIC		),
            transforms.ToTensor(),
            transforms.Normalize((0.48_145_466, 0.4_578_275, 0.40_821_073)				,					(0.26_862_954, 0.26_130_258, 0.27_577_711)		),
        ]		)
    A			:	Any										=       transform(lowerCAmelCase__		).unsqueeze(0		).to(lowerCAmelCase__		)
    return image




def      __UpperCamelCase     (  _lowerCAmelCase		)	->  int:



    """simple docstring"""






    if "visual_encoder" in key:
        A			:	Optional[int]										=       re.sub("""visual_encoder*"""				,					"""vision_model.encoder"""				,					lowerCAmelCase__		)
    if "blocks" in key:
        A			:	Optional[int]										=       re.sub(R"""blocks"""				,					"""layers"""				,					lowerCAmelCase__		)
    if "attn" in key:
        A			:	Dict										=       re.sub(R"""attn"""				,					"""self_attn"""				,					lowerCAmelCase__		)
    if "norm1" in key:
        A			:	Tuple										=       re.sub(R"""norm1"""				,					"""layer_norm1"""				,					lowerCAmelCase__		)
    if "norm2" in key:
        A			:	List[str]										=       re.sub(R"""norm2"""				,					"""layer_norm2"""				,					lowerCAmelCase__		)
    if "encoder.norm" in key:
        A			:	Tuple										=       re.sub(R"""encoder.norm"""				,					"""post_layernorm"""				,					lowerCAmelCase__		)
    if "encoder.patch_embed.proj" in key:
        A			:	Optional[int]										=       re.sub(R"""encoder.patch_embed.proj"""				,					"""embeddings.patch_embedding"""				,					lowerCAmelCase__		)

    if "encoder.pos_embed" in key:
        A			:	Optional[Any]										=       re.sub(R"""encoder.pos_embed"""				,					"""embeddings.position_embedding"""				,					lowerCAmelCase__		)
    if "encoder.cls_token" in key:
        A			:	Tuple										=       re.sub(R"""encoder.cls_token"""				,					"""embeddings.class_embedding"""				,					lowerCAmelCase__		)

    if "self_attn" in key:
        A			:	List[Any]										=       re.sub(R"""self_attn.proj"""				,					"""self_attn.projection"""				,					lowerCAmelCase__		)

    return key




@torch.no_grad()
def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase=None		)	->  Optional[Any]:



    """simple docstring"""






    if config_path is not None:
        A			:	Union[str, Any]										=       BlipConfig.from_pretrained(lowerCAmelCase__		)
    else:
        A			:	List[Any]										=       BlipConfig(projection_dim=512				,					text_config={}				,					vision_config={}		)

    A			:	Any										=       BlipForConditionalGeneration(lowerCAmelCase__		).eval()

    A			:	Optional[Any]										=       """https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth"""

    A			:	str										=       blip_decoder(pretrained=lowerCAmelCase__				,					image_size=384				,					vit="""base"""		)
    A			:	int										=       pt_model.eval()

    A			:	Dict										=       pt_model.state_dict()
    for key in modified_state_dict.copy():
        A			:	List[str]										=       modified_state_dict.pop(lowerCAmelCase__		)
        A			:	List[str]										=       rename_key(lowerCAmelCase__		)
        A			:	int										=       value

    hf_model.load_state_dict(lowerCAmelCase__		)

    A			:	str										=       384
    A			:	Optional[int]										=       load_demo_image(image_size=lowerCAmelCase__				,					device="""cpu"""		)
    A			:	Dict										=       BertTokenizer.from_pretrained("""bert-base-uncased"""		)
    A			:	Tuple										=       tokenizer(["""a picture of"""]		).input_ids

    A			:	List[Any]										=       hf_model.generate(lowerCAmelCase__				,					lowerCAmelCase__		)

    assert out[0].tolist() == [3_0522, 1037, 3861, 1997, 1037, 2450, 3564, 2006, 1996, 3509, 2007, 2014, 3899, 102]

    A			:	str										=       hf_model.generate(lowerCAmelCase__		)

    assert out[0].tolist() == [3_0522, 1037, 2450, 3564, 2006, 1996, 3509, 2007, 2014, 3899, 102]

    if pytorch_dump_folder_path is not None:
        hf_model.save_pretrained(lowerCAmelCase__		)

    # model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_vqa.pth'
    A			:	str										=       (
        """https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth"""
    )

    A			:	str										=       blip_vqa(pretrained=lowerCAmelCase__				,					image_size=lowerCAmelCase__				,					vit="""base"""		)
    vqa_model.eval()

    A			:	Optional[int]										=       vqa_model.state_dict()
    for key in modified_state_dict.copy():
        A			:	Tuple										=       modified_state_dict.pop(lowerCAmelCase__		)
        A			:	int										=       rename_key(lowerCAmelCase__		)
        A			:	Optional[Any]										=       value

    A			:	Dict										=       BlipForQuestionAnswering(lowerCAmelCase__		)

    hf_vqa_model.load_state_dict(lowerCAmelCase__		)

    A			:	int										=       ["""How many dogs are in this image?"""]
    A			:	Optional[Any]										=       tokenizer(lowerCAmelCase__				,					return_tensors="""pt"""		).input_ids

    A			:	Optional[int]										=       hf_vqa_model.generate(lowerCAmelCase__				,					lowerCAmelCase__		)
    print(tokenizer.decode(answer[0]		)		)

    assert tokenizer.decode(answer[0]		) == "[UNK] 1 [SEP]"
    if pytorch_dump_folder_path is not None:
        hf_vqa_model.save_pretrained(pytorch_dump_folder_path + """_vqa"""		)

    A			:	Any										=       """https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth"""

    A			:	Tuple										=       blip_itm(pretrained=lowerCAmelCase__				,					image_size=lowerCAmelCase__				,					vit="""base"""		)
    itm_model.eval()

    A			:	Any										=       itm_model.state_dict()
    for key in modified_state_dict.copy():
        A			:	List[str]										=       modified_state_dict.pop(lowerCAmelCase__		)
        A			:	int										=       rename_key(lowerCAmelCase__		)
        A			:	Optional[int]										=       value

    A			:	Any										=       BlipForImageTextRetrieval(lowerCAmelCase__		)

    A			:	List[str]										=       ["""A picture of a woman with a dog sitting in a beach"""]
    A			:	Optional[Any]										=       tokenizer(
        lowerCAmelCase__				,					return_tensors="""pt"""				,					padding="""max_length"""				,					truncation=lowerCAmelCase__				,					max_length=35				,					).input_ids

    hf_itm_model.load_state_dict(lowerCAmelCase__		)
    hf_itm_model.eval()

    A			:	Dict										=       hf_itm_model(lowerCAmelCase__				,					lowerCAmelCase__				,					use_itm_head=lowerCAmelCase__		)
    A			:	Union[str, Any]										=       hf_itm_model(lowerCAmelCase__				,					lowerCAmelCase__				,					use_itm_head=lowerCAmelCase__		)

    assert out[0].item() == 0.2_110_687_494_277_954
    assert torch.nn.functional.softmax(out_itm[0]				,					dim=1		)[:, 1].item() == 0.45_698_845_386_505_127

    if pytorch_dump_folder_path is not None:
        hf_itm_model.save_pretrained(pytorch_dump_folder_path + """_itm"""		)


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE_:str					=						argparse.ArgumentParser()
     parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
     parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
     SCREAMING_SNAKE_CASE_:List[Any]					=						parser.parse_args()

     convert_blip_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)






import requests

SCREAMING_SNAKE_CASE_:List[str]					=						""""""  # <-- Put your OpenWeatherMap appid here!
SCREAMING_SNAKE_CASE_:Dict					=						"""https://api.openweathermap.org/data/2.5/"""




def      __UpperCamelCase     (  _lowerCAmelCase = "Chicago"				,					_lowerCAmelCase = APPID		)	->  dict:



    """simple docstring"""






    return requests.get(URL_BASE + """weather"""				,					params=locals()		).json()




def      __UpperCamelCase     (  _lowerCAmelCase = "Kolkata, India"				,					_lowerCAmelCase = APPID		)	->  dict:



    """simple docstring"""






    return requests.get(URL_BASE + """forecast"""				,					params=locals()		).json()




def      __UpperCamelCase     (  _lowerCAmelCase = 55.68				,					_lowerCAmelCase = 12.57				,					_lowerCAmelCase = APPID		)	->  dict:



    """simple docstring"""






    return requests.get(URL_BASE + """onecall"""				,					params=locals()		).json()


if __name__ == "__main__":
     from pprint import pprint

     while True:
          SCREAMING_SNAKE_CASE_:int					=						input("""Enter a location:""").strip()
          if location:
               pprint(current_weather(location))
          else:
               break


