
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    A (      lowercase					, lowercase				)     ->						str:
		'''simple docstring'''

		UpperCamelCase						=  'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'
		UpperCamelCase						=  Image.open(requests.get(lowercase					, stream=lowercase				).raw				).convert('RGB'				)

		UpperCamelCase						=  transforms.Compose(
		    [
		        transforms.Resize((image_size, image_size)					, interpolation=InterpolationMode.BICUBIC				),
		        transforms.ToTensor(),
		        transforms.Normalize((0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3)					, (0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1)				),
		    ]				)
		UpperCamelCase						=  transform(lowercase				).unsqueeze(0				).to(lowercase				)
		return image


def    A (      lowercase				)     ->						Union[str, Any]:
		'''simple docstring'''

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

		if "encoder.pos_embed" in key:
				UpperCamelCase						=  re.sub(R'encoder.pos_embed'					, 'embeddings.position_embedding'					, lowercase				)
		if "encoder.cls_token" in key:
				UpperCamelCase						=  re.sub(R'encoder.cls_token'					, 'embeddings.class_embedding'					, lowercase				)

		if "self_attn" in key:
				UpperCamelCase						=  re.sub(R'self_attn.proj'					, 'self_attn.projection'					, lowercase				)

		return key


@torch.no_grad()
def    A (      lowercase					, lowercase=None				)     ->						int:
		'''simple docstring'''

		if config_path is not None:
				UpperCamelCase						=  BlipConfig.from_pretrained(lowercase				)
		else:
				UpperCamelCase						=  BlipConfig(projection_dim=512					, text_config={}					, vision_config={}				)

		UpperCamelCase						=  BlipForConditionalGeneration(lowercase				).eval()

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

		UpperCamelCase						=  blip_decoder(pretrained=lowercase					, image_size=384					, vit='base'				)
		UpperCamelCase						=  pt_model.eval()

		UpperCamelCase						=  pt_model.state_dict()
		for key in modified_state_dict.copy():
				UpperCamelCase						=  modified_state_dict.pop(lowercase				)
				UpperCamelCase						=  rename_key(lowercase				)
				UpperCamelCase						=  value

		hf_model.load_state_dict(lowercase				)

		UpperCamelCase						=  384
		UpperCamelCase						=  load_demo_image(image_size=lowercase					, device='cpu'				)
		UpperCamelCase						=  BertTokenizer.from_pretrained('bert-base-uncased'				)
		UpperCamelCase						=  tokenizer(['a picture of']				).input_ids

		UpperCamelCase						=  hf_model.generate(lowercase					, lowercase				)

		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]

		UpperCamelCase						=  hf_model.generate(lowercase				)

		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(lowercase				)

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

		UpperCamelCase						=  blip_vqa(pretrained=lowercase					, image_size=lowercase					, vit='base'				)
		vqa_model.eval()

		UpperCamelCase						=  vqa_model.state_dict()
		for key in modified_state_dict.copy():
				UpperCamelCase						=  modified_state_dict.pop(lowercase				)
				UpperCamelCase						=  rename_key(lowercase				)
				UpperCamelCase						=  value

		UpperCamelCase						=  BlipForQuestionAnswering(lowercase				)

		hf_vqa_model.load_state_dict(lowercase				)

		UpperCamelCase						=  ['How many dogs are in this image?']
		UpperCamelCase						=  tokenizer(lowercase					, return_tensors='pt'				).input_ids

		UpperCamelCase						=  hf_vqa_model.generate(lowercase					, lowercase				)
		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						=  'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth'

		UpperCamelCase						=  blip_itm(pretrained=lowercase					, image_size=lowercase					, vit='base'				)
		itm_model.eval()

		UpperCamelCase						=  itm_model.state_dict()
		for key in modified_state_dict.copy():
				UpperCamelCase						=  modified_state_dict.pop(lowercase				)
				UpperCamelCase						=  rename_key(lowercase				)
				UpperCamelCase						=  value

		UpperCamelCase						=  BlipForImageTextRetrieval(lowercase				)

		UpperCamelCase						=  ['A picture of a woman with a dog sitting in a beach']
		UpperCamelCase						=  tokenizer(
		    lowercase					, return_tensors='pt'					, padding='max_length'					, truncation=lowercase					, max_length=35					, ).input_ids

		hf_itm_model.load_state_dict(lowercase				)
		hf_itm_model.eval()

		UpperCamelCase						=  hf_itm_model(lowercase					, lowercase					, use_itm_head=lowercase				)
		UpperCamelCase						=  hf_itm_model(lowercase					, lowercase					, use_itm_head=lowercase				)

		assert out[0].item() == 0.2_1_1_0_6_8_7_4_9_4_2_7_7_9_5_4
		assert torch.nn.functional.softmax(out_itm[0]					, dim=1				)[:, 1].item() == 0.4_5_6_9_8_8_4_5_3_8_6_5_0_5_1_2_7

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


if __name__ == "__main__":
					_UpperCAmelCase      :				List[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")
					_UpperCAmelCase      :				Optional[int]          = parser.parse_args()

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


import os

_UpperCAmelCase      :				int          = {"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1_000}


def    A (      lowercase				)     ->						int:
		'''simple docstring'''

		UpperCamelCase						=  0

		UpperCamelCase						=  0
		while index < len(lowercase				) - 1:
				UpperCamelCase						=  SYMBOLS[numerals[index]]
				UpperCamelCase						=  SYMBOLS[numerals[index + 1]]
				if current_value < next_value:
						total_value -= current_value
				else:
						total_value += current_value
				index += 1
		total_value += SYMBOLS[numerals[index]]

		return total_value


def    A (      lowercase				)     ->						str:
		'''simple docstring'''

		UpperCamelCase						=  ''

		UpperCamelCase						=  num // 1_000
		numerals += m_count * "M"
		num %= 1_000

		UpperCamelCase						=  num // 100
		if c_count == 9:
				numerals += "CM"
				c_count -= 9
		elif c_count == 4:
				numerals += "CD"
				c_count -= 4
		if c_count >= 5:
				numerals += "D"
				c_count -= 5
		numerals += c_count * "C"
		num %= 100

		UpperCamelCase						=  num // 10
		if x_count == 9:
				numerals += "XC"
				x_count -= 9
		elif x_count == 4:
				numerals += "XL"
				x_count -= 4
		if x_count >= 5:
				numerals += "L"
				x_count -= 5
		numerals += x_count * "X"
		num %= 10

		if num == 9:
				numerals += "IX"
				num -= 9
		elif num == 4:
				numerals += "IV"
				num -= 4
		if num >= 5:
				numerals += "V"
				num -= 5
		numerals += num * "I"

		return numerals


def    A (      lowercase = "/p089_roman.txt"				)     ->						int:
		'''simple docstring'''

		UpperCamelCase						=  0

		with open(os.path.dirname(lowercase				) + roman_numerals_filename				) as filea:
				UpperCamelCase						=  filea.readlines()

		for line in lines:
				UpperCamelCase						=  line.strip()
				UpperCamelCase						=  parse_roman_numerals(lowercase				)
				UpperCamelCase						=  generate_roman_numerals(lowercase				)
				savings += len(lowercase				) - len(lowercase				)

		return savings


if __name__ == "__main__":
					print(F'''{solution() = }''')
