


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

							a__    =  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__    =  transform(__lowerCAmelCase     ).unsqueeze(0     ).to(__lowerCAmelCase     )
							return image






def 							__lowercase     (				__lowerCAmelCase							:       Optional[int]     ):
							if "visual_encoder" in key:
														a__    =  re.sub('visual_encoder*'      ,					'vision_model.encoder'      ,					__lowerCAmelCase     )
							if "blocks" in key:
														a__    =  re.sub(R'blocks'      ,					'layers'      ,					__lowerCAmelCase     )
							if "attn" in key:
														a__    =  re.sub(R'attn'      ,					'self_attn'      ,					__lowerCAmelCase     )
							if "norm1" in key:
														a__    =  re.sub(R'norm1'      ,					'layer_norm1'      ,					__lowerCAmelCase     )
							if "norm2" in key:
														a__    =  re.sub(R'norm2'      ,					'layer_norm2'      ,					__lowerCAmelCase     )
							if "encoder.norm" in key:
														a__    =  re.sub(R'encoder.norm'      ,					'post_layernorm'      ,					__lowerCAmelCase     )
							if "encoder.patch_embed.proj" in key:
														a__    =  re.sub(R'encoder.patch_embed.proj'      ,					'embeddings.patch_embedding'      ,					__lowerCAmelCase     )

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

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

							return key






@torch.no_grad()
def 							__lowercase     (				__lowerCAmelCase							:       Union[str, Any]      ,					__lowerCAmelCase							:       List[str]=None     ):
							if config_path is not None:
														a__    =  BlipConfig.from_pretrained(__lowerCAmelCase     )
							else:
														a__    =  BlipConfig(projection_dim=5_1_2      ,					text_config={}      ,					vision_config={}     )

							a__    =  BlipForConditionalGeneration(__lowerCAmelCase     ).eval()

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

							a__    =  blip_decoder(pretrained=__lowerCAmelCase      ,					image_size=3_8_4      ,					vit='base'     )
							a__    =  pt_model.eval()

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

							hf_model.load_state_dict(__lowerCAmelCase     )

							a__    =  3_8_4
							a__    =  load_demo_image(image_size=__lowerCAmelCase      ,					device='cpu'     )
							a__    =  BertTokenizer.from_pretrained('bert-base-uncased'     )
							a__    =  tokenizer(['a picture of']     ).input_ids

							a__    =  hf_model.generate(__lowerCAmelCase      ,					__lowerCAmelCase     )

							assert out[0].tolist() == [3_0_5_2_2, 1_0_3_7, 3_8_6_1, 1_9_9_7, 1_0_3_7, 2_4_5_0, 3_5_6_4, 2_0_0_6, 1_9_9_6, 3_5_0_9, 2_0_0_7, 2_0_1_4, 3_8_9_9, 1_0_2]

							a__    =  hf_model.generate(__lowerCAmelCase     )

							assert out[0].tolist() == [3_0_5_2_2, 1_0_3_7, 2_4_5_0, 3_5_6_4, 2_0_0_6, 1_9_9_6, 3_5_0_9, 2_0_0_7, 2_0_1_4, 3_8_9_9, 1_0_2]

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

							a__    =  blip_vqa(pretrained=__lowerCAmelCase      ,					image_size=__lowerCAmelCase      ,					vit='base'     )
							vqa_model.eval()

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

							a__    =  BlipForQuestionAnswering(__lowerCAmelCase     )

							hf_vqa_model.load_state_dict(__lowerCAmelCase     )

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

							a__    =  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'     )

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

							a__    =  blip_itm(pretrained=__lowerCAmelCase      ,					image_size=__lowerCAmelCase      ,					vit='base'     )
							itm_model.eval()

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

							a__    =  BlipForImageTextRetrieval(__lowerCAmelCase     )

							a__    =  ['A picture of a woman with a dog sitting in a beach']
							a__    =  tokenizer(
							    __lowerCAmelCase      ,					return_tensors='pt'      ,					padding='max_length'      ,					truncation=__lowerCAmelCase      ,					max_length=3_5      ,					).input_ids

							hf_itm_model.load_state_dict(__lowerCAmelCase     )
							hf_itm_model.eval()

							a__    =  hf_itm_model(__lowerCAmelCase      ,					__lowerCAmelCase      ,					use_itm_head=__lowerCAmelCase     )
							a__    =  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__":
						snake_case							: 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''')
						snake_case							: int      =   parser.parse_args()

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








from decimal import Decimal, getcontext
from math import ceil, factorial






def 							__lowercase     (				__lowerCAmelCase							:       int     ):

							if not isinstance(__lowerCAmelCase      ,					__lowerCAmelCase     ):
														raise TypeError('Undefined for non-integers'     )
							elif precision < 1:
														raise ValueError('Undefined for non-natural numbers'     )

							a__    =  precision
							a__    =  ceil(precision / 1_4     )
							a__    =  4_2_6_8_8_0 * Decimal(1_0_0_0_5     ).sqrt()
							a__    =  1
							a__    =  1_3_5_9_1_4_0_9
							a__    =  Decimal(__lowerCAmelCase     )
							for k in range(1      ,					__lowerCAmelCase     ):
														a__    =  factorial(6 * k     ) // (factorial(3 * k     ) * factorial(__lowerCAmelCase     ) ** 3)
														linear_term += 5_4_5_1_4_0_1_3_4
														exponential_term *= -2_6_2_5_3_7_4_1_2_6_4_0_7_6_8_0_0_0
														partial_sum += Decimal(multinomial_term * linear_term     ) / exponential_term
							return str(constant_term / partial_sum     )[:-1]


if __name__ == "__main__":
						snake_case							: Tuple      =   50
						print(f"""The first {n} digits of pi is: {pi(n)}""")




