





"""simple docstring"""



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 		__lowercase					(       snake_case_  :  str    ,snake_case_  :  Tuple						)       ->Union[str, Any]:



    '''simple docstring'''







    __A   :						List[str]									=					'''https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'''
    __A   :						Optional[int]									=					Image.open(requests.get(snake_case_    ,stream=snake_case_						).raw						).convert('''RGB'''						)

    __A   :						Any									=					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   :						Optional[Any]									=					transform(snake_case_						).unsqueeze(0						).to(snake_case_						)
    return image







def 		__lowercase					(       snake_case_  :  List[Any]						)       ->List[Any]:



    '''simple docstring'''







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

    if "encoder.pos_embed" in key:
        __A   :						Optional[int]									=					re.sub(r'''encoder.pos_embed'''    ,'''embeddings.position_embedding'''    ,snake_case_						)
    if "encoder.cls_token" in key:
        __A   :						Optional[int]									=					re.sub(r'''encoder.cls_token'''    ,'''embeddings.class_embedding'''    ,snake_case_						)

    if "self_attn" in key:
        __A   :						int									=					re.sub(r'''self_attn.proj'''    ,'''self_attn.projection'''    ,snake_case_						)

    return key







@torch.no_grad()
def 		__lowercase					(       snake_case_  :  Optional[Any]    ,snake_case_  :  List[Any]=None						)       ->Union[str, Any]:



    '''simple docstring'''







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

    __A   :						str									=					BlipForConditionalGeneration(snake_case_						).eval()

    __A   :						Tuple									=					'''https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth'''

    __A   :						int									=					blip_decoder(pretrained=snake_case_    ,image_size=384    ,vit='''base'''						)
    __A   :						Tuple									=					pt_model.eval()

    __A   :						Any									=					pt_model.state_dict()
    for key in modified_state_dict.copy():
        __A   :						Optional[int]									=					modified_state_dict.pop(snake_case_						)
        __A   :						List[Any]									=					rename_key(snake_case_						)
        __A   :						int									=					value

    hf_model.load_state_dict(snake_case_						)

    __A   :						List[Any]									=					384
    __A   :						Dict									=					load_demo_image(image_size=snake_case_    ,device='''cpu'''						)
    __A   :						Tuple									=					BertTokenizer.from_pretrained('''bert-base-uncased'''						)
    __A   :						str									=					tokenizer(['''a picture of''']						).input_ids

    __A   :						Optional[int]									=					hf_model.generate(snake_case_    ,snake_case_						)

    assert out[0].tolist() == [30522, 1037, 3861, 1997, 1037, 2450, 3564, 2006, 1996, 3509, 2007, 2014, 3899, 102]

    __A   :						List[Any]									=					hf_model.generate(snake_case_						)

    assert out[0].tolist() == [30522, 1037, 2450, 3564, 2006, 1996, 3509, 2007, 2014, 3899, 102]

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

    # model_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_vqa.pth'
    __A   :						Optional[Any]									=					(
        '''https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth'''
    )

    __A   :						Dict									=					blip_vqa(pretrained=snake_case_    ,image_size=snake_case_    ,vit='''base'''						)
    vqa_model.eval()

    __A   :						int									=					vqa_model.state_dict()
    for key in modified_state_dict.copy():
        __A   :						Optional[Any]									=					modified_state_dict.pop(snake_case_						)
        __A   :						Union[str, Any]									=					rename_key(snake_case_						)
        __A   :						Optional[int]									=					value

    __A   :						Dict									=					BlipForQuestionAnswering(snake_case_						)

    hf_vqa_model.load_state_dict(snake_case_						)

    __A   :						List[Any]									=					['''How many dogs are in this image?''']
    __A   :						Optional[int]									=					tokenizer(snake_case_    ,return_tensors='''pt'''						).input_ids

    __A   :						Optional[Any]									=					hf_vqa_model.generate(snake_case_    ,snake_case_						)
    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   :						Optional[int]									=					'''https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth'''

    __A   :						Any									=					blip_itm(pretrained=snake_case_    ,image_size=snake_case_    ,vit='''base'''						)
    itm_model.eval()

    __A   :						Tuple									=					itm_model.state_dict()
    for key in modified_state_dict.copy():
        __A   :						List[Any]									=					modified_state_dict.pop(snake_case_						)
        __A   :						int									=					rename_key(snake_case_						)
        __A   :						Optional[Any]									=					value

    __A   :						int									=					BlipForImageTextRetrieval(snake_case_						)

    __A   :						List[str]									=					['''A picture of a woman with a dog sitting in a beach''']
    __A   :						int									=					tokenizer(
        snake_case_    ,return_tensors='''pt'''    ,padding='''max_length'''    ,truncation=snake_case_    ,max_length=35    ,).input_ids

    hf_itm_model.load_state_dict(snake_case_						)
    hf_itm_model.eval()

    __A   :						List[Any]									=					hf_itm_model(snake_case_    ,snake_case_    ,use_itm_head=snake_case_						)
    __A   :						Optional[int]									=					hf_itm_model(snake_case_    ,snake_case_    ,use_itm_head=snake_case_						)

    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__":
       a_			=      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""")
       a_			=      parser.parse_args()

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











"""simple docstring"""



from collections import UserDict
from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
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_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING

if is_tf_available():
       from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
       from ..tf_utils import stable_softmax

a_			=      logging.get_logger(__name__)






@add_end_docstrings(SCREAMING_SNAKE_CASE__					)
class 		__snake_case (		SCREAMING_SNAKE_CASE__					):





 """simple docstring"""


 def __init__(   self ,						**__lowerCamelCase  ):



     '''simple docstring'''




     super().__init__(**__lowerCamelCase  )

     requires_backends(self ,						'''vision'''  )
     self.check_model_type(
         TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
         if self.framework == '''tf'''
         else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING  )

 def __call__(   self ,						__lowerCamelCase ,						**__lowerCamelCase  ):



     '''simple docstring'''




     return super().__call__(__lowerCamelCase ,						**__lowerCamelCase  )

 def  UpperCamelCase__(   self ,						**__lowerCamelCase  ):



     '''simple docstring'''




     __A   :						Union[str, Any]									=					{}
     if "candidate_labels" in kwargs:
         __A   :						Tuple									=					kwargs['''candidate_labels''']
     if "hypothesis_template" in kwargs:
         __A   :						List[str]									=					kwargs['''hypothesis_template''']

     return preprocess_params, {}, {}

 def  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase=None ,						__lowerCamelCase="This is a photo of {}."  ):



     '''simple docstring'''




     __A   :						Optional[int]									=					load_image(__lowerCamelCase  )
     __A   :						Optional[int]									=					self.image_processor(images=[image] ,						return_tensors=self.framework  )
     __A   :						int									=					candidate_labels
     __A   :						int									=					[hypothesis_template.format(__lowerCamelCase  ) for x in candidate_labels]
     __A   :						Dict									=					self.tokenizer(__lowerCamelCase ,						return_tensors=self.framework ,						padding=__lowerCamelCase  )
     __A   :						int									=					[text_inputs]
     return inputs

 def  UpperCamelCase__(   self ,						__lowerCamelCase  ):



     '''simple docstring'''




     __A   :						Optional[int]									=					model_inputs.pop('''candidate_labels'''  )
     __A   :						str									=					model_inputs.pop('''text_inputs'''  )
     if isinstance(text_inputs[0] ,						__lowerCamelCase  ):
         __A   :						Union[str, Any]									=					text_inputs[0]
     else:
         # Batching case.
         __A   :						str									=					text_inputs[0][0]

     __A   :						List[str]									=					self.model(**__lowerCamelCase ,						**__lowerCamelCase  )

     __A   :						Dict									=					{
         '''candidate_labels''': candidate_labels,
         '''logits''': outputs.logits_per_image,
     }
     return model_outputs







 def  UpperCamelCase__(   self ,						__lowerCamelCase  ):



     '''simple docstring'''




     __A   :						Optional[int]									=					model_outputs.pop('''candidate_labels'''  )
     __A   :						int									=					model_outputs['''logits'''][0]
     if self.framework == "pt":
         __A   :						Union[str, Any]									=					logits.softmax(dim=-1  ).squeeze(-1  )
         __A   :						Dict									=					probs.tolist()
         if not isinstance(__lowerCamelCase ,						__lowerCamelCase  ):
             __A   :						List[Any]									=					[scores]
     elif self.framework == "tf":
         __A   :						List[Any]									=					stable_softmax(__lowerCamelCase ,						axis=-1  )
         __A   :						str									=					probs.numpy().tolist()
     else:
         raise ValueError(F"""Unsupported framework: {self.framework}"""  )

     __A   :						str									=					[
         {'''score''': score, '''label''': candidate_label}
         for score, candidate_label in sorted(zip(__lowerCamelCase ,						__lowerCamelCase  ) ,						key=lambda __lowerCamelCase  : -x[0]  )
     ]
     return result




