


"""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    , _UpperCamelCase      ):

			'''simple docstring'''




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

			__lowerCAmelCase				    =			transforms.Compose(
			    [
			        transforms.Resize((image_size, image_size)    , interpolation=InterpolationMode.BICUBIC      ),
			        transforms.ToTensor(),
			        transforms.Normalize((0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73)    , (0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11)      ),
			    ]      )
			__lowerCAmelCase				    =			transform(_UpperCamelCase      ).unsqueeze(0      ).to(_UpperCamelCase      )
			return image


def _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




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

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

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

			return key


@torch.no_grad()
def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase=None      ):

			'''simple docstring'''




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

			__lowerCAmelCase				    =			BlipForConditionalGeneration(_UpperCamelCase      ).eval()

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

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

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

			hf_model.load_state_dict(_UpperCamelCase      )

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

			__lowerCAmelCase				    =			hf_model.generate(_UpperCamelCase    , _UpperCamelCase      )

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

			__lowerCAmelCase				    =			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'
			__lowerCAmelCase				    =			(
			    "https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth"
			)

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

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

			__lowerCAmelCase				    =			BlipForQuestionAnswering(_UpperCamelCase      )

			hf_vqa_model.load_state_dict(_UpperCamelCase      )

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

			__lowerCAmelCase				    =			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"      )

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

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

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

			__lowerCAmelCase				    =			BlipForImageTextRetrieval(_UpperCamelCase      )

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

			__lowerCAmelCase				    =			hf_itm_model(_UpperCamelCase    , _UpperCamelCase    , use_itm_head=_UpperCamelCase      )
			__lowerCAmelCase				    =			hf_itm_model(_UpperCamelCase    , _UpperCamelCase    , use_itm_head=_UpperCamelCase      )

			assert out[0].item() == 0.21_10_68_74_94_27_79_54
			assert torch.nn.functional.softmax(out_itm[0]    , dim=1      )[:, 1].item() == 0.4_56_98_84_53_86_50_51_27

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


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

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







"""simple docstring"""





from collections.abc import Sequence




def   _A  (  UpperCamelCase_     :       Sequence[float],			UpperCamelCase_     :       float)	->						float:
   '''simple docstring'''


   return sum(c * (x**i) for i, c in enumerate(UpperCamelCase_))




def   _A  (  UpperCamelCase_     :       Sequence[float],			UpperCamelCase_     :       float)	->						float:
   '''simple docstring'''


   __lowercase								=						0.0
   for coeff in reversed(UpperCamelCase_):
      __lowercase								=						result * x + coeff
   return result


if __name__ == "__main__":
   _a       =							(0.0, 0.0, 5.0, 9.3, 7.0)
   _a       =							10.0
   print(evaluate_poly(poly, x))
   print(horner(poly, x))




