
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 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase							):
    snake_case__						=				"https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg"
    snake_case__						=				Image.open(requests.get(__lowerCAmelCase    ,		stream=__lowerCAmelCase							).raw							).convert("RGB"							)

    snake_case__						=				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)							),
        ]							)
    snake_case__						=				transform(__lowerCAmelCase							).unsqueeze(0							).to(__lowerCAmelCase							)
    return image





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase							):
    if "visual_encoder" in key:
        snake_case__						=				re.sub("visual_encoder*"    ,		"vision_model.encoder"    ,		__lowerCAmelCase							)
    if "blocks" in key:
        snake_case__						=				re.sub(R"blocks"    ,		"layers"    ,		__lowerCAmelCase							)
    if "attn" in key:
        snake_case__						=				re.sub(R"attn"    ,		"self_attn"    ,		__lowerCAmelCase							)
    if "norm1" in key:
        snake_case__						=				re.sub(R"norm1"    ,		"layer_norm1"    ,		__lowerCAmelCase							)
    if "norm2" in key:
        snake_case__						=				re.sub(R"norm2"    ,		"layer_norm2"    ,		__lowerCAmelCase							)
    if "encoder.norm" in key:
        snake_case__						=				re.sub(R"encoder.norm"    ,		"post_layernorm"    ,		__lowerCAmelCase							)
    if "encoder.patch_embed.proj" in key:
        snake_case__						=				re.sub(R"encoder.patch_embed.proj"    ,		"embeddings.patch_embedding"    ,		__lowerCAmelCase							)

    if "encoder.pos_embed" in key:
        snake_case__						=				re.sub(R"encoder.pos_embed"    ,		"embeddings.position_embedding"    ,		__lowerCAmelCase							)
    if "encoder.cls_token" in key:
        snake_case__						=				re.sub(R"encoder.cls_token"    ,		"embeddings.class_embedding"    ,		__lowerCAmelCase							)

    if "self_attn" in key:
        snake_case__						=				re.sub(R"self_attn.proj"    ,		"self_attn.projection"    ,		__lowerCAmelCase							)

    return key





@torch.no_grad()
def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase=None							):
    if config_path is not None:
        snake_case__						=				BlipConfig.from_pretrained(__lowerCAmelCase							)
    else:
        snake_case__						=				BlipConfig(projection_dim=512    ,		text_config={}    ,		vision_config={}							)

    snake_case__						=				BlipForConditionalGeneration(__lowerCAmelCase							).eval()

    snake_case__						=				"https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth"

    snake_case__						=				blip_decoder(pretrained=__lowerCAmelCase    ,		image_size=384    ,		vit="base"							)
    snake_case__						=				pt_model.eval()

    snake_case__						=				pt_model.state_dict()
    for key in modified_state_dict.copy():
        snake_case__						=				modified_state_dict.pop(__lowerCAmelCase							)
        snake_case__						=				rename_key(__lowerCAmelCase							)
        snake_case__						=				value

    hf_model.load_state_dict(__lowerCAmelCase							)

    snake_case__						=				384
    snake_case__						=				load_demo_image(image_size=__lowerCAmelCase    ,		device="cpu"							)
    snake_case__						=				BertTokenizer.from_pretrained("bert-base-uncased"							)
    snake_case__						=				tokenizer(["a picture of"]							).input_ids

    snake_case__						=				hf_model.generate(__lowerCAmelCase    ,		__lowerCAmelCase							)

    assert out[0].tolist() == [30_522, 1_037, 3_861, 1_997, 1_037, 2_450, 3_564, 2_006, 1_996, 3_509, 2_007, 2_014, 3_899, 102]

    snake_case__						=				hf_model.generate(__lowerCAmelCase							)

    assert out[0].tolist() == [30_522, 1_037, 2_450, 3_564, 2_006, 1_996, 3_509, 2_007, 2_014, 3_899, 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'
    snake_case__						=				(
        "https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth"
    )

    snake_case__						=				blip_vqa(pretrained=__lowerCAmelCase    ,		image_size=__lowerCAmelCase    ,		vit="base"							)
    vqa_model.eval()

    snake_case__						=				vqa_model.state_dict()
    for key in modified_state_dict.copy():
        snake_case__						=				modified_state_dict.pop(__lowerCAmelCase							)
        snake_case__						=				rename_key(__lowerCAmelCase							)
        snake_case__						=				value

    snake_case__						=				BlipForQuestionAnswering(__lowerCAmelCase							)

    hf_vqa_model.load_state_dict(__lowerCAmelCase							)

    snake_case__						=				["How many dogs are in this image?"]
    snake_case__						=				tokenizer(__lowerCAmelCase    ,		return_tensors="pt"							).input_ids

    snake_case__						=				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"							)

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

    snake_case__						=				blip_itm(pretrained=__lowerCAmelCase    ,		image_size=__lowerCAmelCase    ,		vit="base"							)
    itm_model.eval()

    snake_case__						=				itm_model.state_dict()
    for key in modified_state_dict.copy():
        snake_case__						=				modified_state_dict.pop(__lowerCAmelCase							)
        snake_case__						=				rename_key(__lowerCAmelCase							)
        snake_case__						=				value

    snake_case__						=				BlipForImageTextRetrieval(__lowerCAmelCase							)

    snake_case__						=				["A picture of a woman with a dog sitting in a beach"]
    snake_case__						=				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()

    snake_case__						=				hf_itm_model(__lowerCAmelCase    ,		__lowerCAmelCase    ,		use_itm_head=__lowerCAmelCase							)
    snake_case__						=				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__":
     __magic_name__            =  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''')
     __magic_name__            =  parser.parse_args()

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





from __future__ import annotations

from fractions import Fraction





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase							):
    return (
        num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
    )





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase							):
    snake_case__						=				[]
    snake_case__						=				11
    snake_case__						=				int("1" + "0" * digit_len							)
    for num in range(__lowerCAmelCase    ,		__lowerCAmelCase							):
        while den <= 99:
            if (num != den) and (num % 10 == den // 10) and (den % 10 != 0):
                if is_digit_cancelling(__lowerCAmelCase    ,		__lowerCAmelCase							):
                    solutions.append(F"""{num}/{den}"""							)
            den += 1
        num += 1
        snake_case__						=				10
    return solutions





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase = 2							):
    snake_case__						=				1.0
    for fraction in fraction_list(__lowerCAmelCase							):
        snake_case__						=				Fraction(__lowerCAmelCase							)
        result *= frac.denominator / frac.numerator
    return int(__lowerCAmelCase							)


if __name__ == "__main__":
     print(solution())



