



"""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  lowerCamelCase_						(UpperCamelCase__		:		Any     ,       UpperCamelCase__		:		Dict							):
    _UpperCAmelCase							:  int											=    '''https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'''
    _UpperCAmelCase							:  List[Any]											=    Image.open(requests.get(UpperCamelCase__     ,       stream=UpperCamelCase__							).raw							).convert('''RGB'''							)

    _UpperCAmelCase							:  List[Any]											=    transforms.Compose(
        [
            transforms.Resize((image_size, image_size)     ,       interpolation=InterpolationMode.BICUBIC							),
            transforms.ToTensor(),
            transforms.Normalize((0.4814_5466, 0.457_8275, 0.4082_1073)     ,       (0.2686_2954, 0.2613_0258, 0.2757_7711)							),
        ]							)
    _UpperCAmelCase							:  int											=    transform(UpperCamelCase__							).unsqueeze(0							).to(UpperCamelCase__							)
    return image





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

    if "encoder.pos_embed" in key:
        _UpperCAmelCase							:  Union[str, Any]											=    re.sub(r'''encoder.pos_embed'''     ,       '''embeddings.position_embedding'''     ,       UpperCamelCase__							)
    if "encoder.cls_token" in key:
        _UpperCAmelCase							:  Tuple											=    re.sub(r'''encoder.cls_token'''     ,       '''embeddings.class_embedding'''     ,       UpperCamelCase__							)

    if "self_attn" in key:
        _UpperCAmelCase							:  Union[str, Any]											=    re.sub(r'''self_attn.proj'''     ,       '''self_attn.projection'''     ,       UpperCamelCase__							)

    return key





@torch.no_grad()
def  lowerCamelCase_						(UpperCamelCase__		:		int     ,       UpperCamelCase__		:		str=None							):
    if config_path is not None:
        _UpperCAmelCase							:  Tuple											=    BlipConfig.from_pretrained(UpperCamelCase__							)
    else:
        _UpperCAmelCase							:  Any											=    BlipConfig(projection_dim=512     ,       text_config={}     ,       vision_config={}							)

    _UpperCAmelCase							:  Union[str, Any]											=    BlipForConditionalGeneration(UpperCamelCase__							).eval()

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

    _UpperCAmelCase							:  Dict											=    blip_decoder(pretrained=UpperCamelCase__     ,       image_size=384     ,       vit='''base'''							)
    _UpperCAmelCase							:  Any											=    pt_model.eval()

    _UpperCAmelCase							:  Optional[Any]											=    pt_model.state_dict()
    for key in modified_state_dict.copy():
        _UpperCAmelCase							:  Any											=    modified_state_dict.pop(UpperCamelCase__							)
        _UpperCAmelCase							:  Dict											=    rename_key(UpperCamelCase__							)
        _UpperCAmelCase							:  Union[str, Any]											=    value

    hf_model.load_state_dict(UpperCamelCase__							)

    _UpperCAmelCase							:  int											=    384
    _UpperCAmelCase							:  List[Any]											=    load_demo_image(image_size=UpperCamelCase__     ,       device='''cpu'''							)
    _UpperCAmelCase							:  Any											=    BertTokenizer.from_pretrained('''bert-base-uncased'''							)
    _UpperCAmelCase							:  int											=    tokenizer(['''a picture of''']							).input_ids

    _UpperCAmelCase							:  List[str]											=    hf_model.generate(UpperCamelCase__     ,       UpperCamelCase__							)

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

    _UpperCAmelCase							:  List[Any]											=    hf_model.generate(UpperCamelCase__							)

    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(UpperCamelCase__							)

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

    _UpperCAmelCase							:  Tuple											=    blip_vqa(pretrained=UpperCamelCase__     ,       image_size=UpperCamelCase__     ,       vit='''base'''							)
    vqa_model.eval()

    _UpperCAmelCase							:  List[str]											=    vqa_model.state_dict()
    for key in modified_state_dict.copy():
        _UpperCAmelCase							:  List[str]											=    modified_state_dict.pop(UpperCamelCase__							)
        _UpperCAmelCase							:  str											=    rename_key(UpperCamelCase__							)
        _UpperCAmelCase							:  Tuple											=    value

    _UpperCAmelCase							:  Dict											=    BlipForQuestionAnswering(UpperCamelCase__							)

    hf_vqa_model.load_state_dict(UpperCamelCase__							)

    _UpperCAmelCase							:  Optional[int]											=    ['''How many dogs are in this image?''']
    _UpperCAmelCase							:  Tuple											=    tokenizer(UpperCamelCase__     ,       return_tensors='''pt'''							).input_ids

    _UpperCAmelCase							:  Any											=    hf_vqa_model.generate(UpperCamelCase__     ,       UpperCamelCase__							)
    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'''							)

    _UpperCAmelCase							:  Optional[Any]											=    '''https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth'''

    _UpperCAmelCase							:  str											=    blip_itm(pretrained=UpperCamelCase__     ,       image_size=UpperCamelCase__     ,       vit='''base'''							)
    itm_model.eval()

    _UpperCAmelCase							:  Tuple											=    itm_model.state_dict()
    for key in modified_state_dict.copy():
        _UpperCAmelCase							:  List[str]											=    modified_state_dict.pop(UpperCamelCase__							)
        _UpperCAmelCase							:  List[Any]											=    rename_key(UpperCamelCase__							)
        _UpperCAmelCase							:  List[Any]											=    value

    _UpperCAmelCase							:  Union[str, Any]											=    BlipForImageTextRetrieval(UpperCamelCase__							)

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

    hf_itm_model.load_state_dict(UpperCamelCase__							)
    hf_itm_model.eval()

    _UpperCAmelCase							:  Tuple											=    hf_itm_model(UpperCamelCase__     ,       UpperCamelCase__     ,       use_itm_head=UpperCamelCase__							)
    _UpperCAmelCase							:  Tuple											=    hf_itm_model(UpperCamelCase__     ,       UpperCamelCase__     ,       use_itm_head=UpperCamelCase__							)

    assert out[0].item() == 0.2110_6874_9427_7954
    assert torch.nn.functional.softmax(out_itm[0]     ,       dim=1							)[:, 1].item() == 0.4_5698_8453_8650_5127

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


if __name__ == "__main__":
  _lowerCAmelCase    :Optional[int]     =					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')
  _lowerCAmelCase    :int     =					parser.parse_args()

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







"""simple docstring"""
import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


_lowerCAmelCase    :Optional[Any]     =					False
class 							_UpperCAmelCase							( unittest.TestCase      ):





     '''simple docstring'''



     pass



@nightly
@require_torch_gpu
class 							_UpperCAmelCase							( unittest.TestCase      ):





     '''simple docstring'''



     def  __lowerCAmelCase	(					self		)							->					List[Any]:
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()





     def  __lowerCAmelCase	(					self		)							->					Dict:
         _UpperCAmelCase							:  Tuple											=    VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''       ,	torch_dtype=torch.floataa		)
         pipe.to(A		)
         pipe.set_progress_bar_config(disable=A		)

         _UpperCAmelCase							:  List[str]											=    load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''		)

         _UpperCAmelCase							:  Optional[Any]											=    torch.manual_seed(0		)
         _UpperCAmelCase							:  List[Any]											=    pipe.dual_guided(
             prompt='''first prompt'''       ,	image=A       ,	text_to_image_strength=0.75       ,	generator=A       ,	guidance_scale=7.5       ,	num_inference_steps=2       ,	output_type='''numpy'''       ,	).images

         with tempfile.TemporaryDirectory() as tmpdirname:
             pipe.save_pretrained(A		)
             _UpperCAmelCase							:  int											=    VersatileDiffusionPipeline.from_pretrained(A       ,	torch_dtype=torch.floataa		)
         pipe.to(A		)
         pipe.set_progress_bar_config(disable=A		)

         _UpperCAmelCase							:  int											=    generator.manual_seed(0		)
         _UpperCAmelCase							:  Union[str, Any]											=    pipe.dual_guided(
             prompt='''first prompt'''       ,	image=A       ,	text_to_image_strength=0.75       ,	generator=A       ,	guidance_scale=7.5       ,	num_inference_steps=2       ,	output_type='''numpy'''       ,	).images

         assert np.abs(image - new_image		).sum() < 1E-5, "Models don't have the same forward pass"





     def  __lowerCAmelCase	(					self		)							->					List[str]:
         _UpperCAmelCase							:  List[Any]											=    VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''       ,	torch_dtype=torch.floataa		)
         pipe.to(A		)
         pipe.set_progress_bar_config(disable=A		)

         _UpperCAmelCase							:  int											=    '''cyberpunk 2077'''
         _UpperCAmelCase							:  Optional[int]											=    load_image(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''		)
         _UpperCAmelCase							:  str											=    torch.manual_seed(0		)
         _UpperCAmelCase							:  Optional[Any]											=    pipe.dual_guided(
             prompt=A       ,	image=A       ,	text_to_image_strength=0.75       ,	generator=A       ,	guidance_scale=7.5       ,	num_inference_steps=5_0       ,	output_type='''numpy'''       ,	).images

         _UpperCAmelCase							:  Union[str, Any]											=    image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         _UpperCAmelCase							:  List[Any]											=    np.array([0.1_448, 0.1_619, 0.1_741, 0.1_086, 0.1_147, 0.1_128, 0.1_199, 0.1_165, 0.1_001]		)

         assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-1

         _UpperCAmelCase							:  Dict											=    '''A painting of a squirrel eating a burger '''
         _UpperCAmelCase							:  Tuple											=    torch.manual_seed(0		)
         _UpperCAmelCase							:  Optional[Any]											=    pipe.text_to_image(
             prompt=A       ,	generator=A       ,	guidance_scale=7.5       ,	num_inference_steps=5_0       ,	output_type='''numpy'''		).images

         _UpperCAmelCase							:  Tuple											=    image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         _UpperCAmelCase							:  int											=    np.array([0.3_367, 0.3_169, 0.2_656, 0.3_870, 0.4_790, 0.3_796, 0.4_009, 0.4_878, 0.4_778]		)

         assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-1

         _UpperCAmelCase							:  int											=    pipe.image_variation(A       ,	generator=A       ,	output_type='''numpy'''		).images

         _UpperCAmelCase							:  Optional[int]											=    image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

         assert image.shape == (1, 5_1_2, 5_1_2, 3)
         _UpperCAmelCase							:  List[str]											=    np.array([0.3_076, 0.3_123, 0.3_284, 0.3_782, 0.3_770, 0.3_894, 0.4_297, 0.4_331, 0.4_456]		)

         assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-1


