

'''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						:     List[str]							, _UpperCamelCase						:     Any				)      ->			str:
     """simple docstring"""




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

     _SCREAMING_SNAKE_CASE							    =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)				),
         ]				)
     _SCREAMING_SNAKE_CASE							    =transform(_UpperCamelCase				).unsqueeze(0				).to(_UpperCamelCase				)
     return image


def        _lowerCAmelCase					(						_UpperCamelCase						:     Dict				)      ->			str:
     """simple docstring"""




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

     if "encoder.pos_embed" in key:
          _SCREAMING_SNAKE_CASE							    =re.sub(r'encoder.pos_embed'							, 'embeddings.position_embedding'							, _UpperCamelCase				)
     if "encoder.cls_token" in key:
          _SCREAMING_SNAKE_CASE							    =re.sub(r'encoder.cls_token'							, 'embeddings.class_embedding'							, _UpperCamelCase				)

     if "self_attn" in key:
          _SCREAMING_SNAKE_CASE							    =re.sub(r'self_attn.proj'							, 'self_attn.projection'							, _UpperCamelCase				)

     return key


@torch.no_grad()
def        _lowerCAmelCase					(						_UpperCamelCase						:     Union[str, Any]							, _UpperCamelCase						:     int=None				)      ->			Tuple:
     """simple docstring"""




     if config_path is not None:
          _SCREAMING_SNAKE_CASE							    =BlipConfig.from_pretrained(_UpperCamelCase				)
     else:
          _SCREAMING_SNAKE_CASE							    =BlipConfig(projection_dim=5_12							, text_config={}							, vision_config={}				)

     _SCREAMING_SNAKE_CASE							    =BlipForConditionalGeneration(_UpperCamelCase				).eval()

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

     _SCREAMING_SNAKE_CASE							    =blip_decoder(pretrained=_UpperCamelCase							, image_size=3_84							, vit='base'				)
     _SCREAMING_SNAKE_CASE							    =pt_model.eval()

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

     hf_model.load_state_dict(_UpperCamelCase				)

     _SCREAMING_SNAKE_CASE							    =3_84
     _SCREAMING_SNAKE_CASE							    =load_demo_image(image_size=_UpperCamelCase							, device='cpu'				)
     _SCREAMING_SNAKE_CASE							    =BertTokenizer.from_pretrained('bert-base-uncased'				)
     _SCREAMING_SNAKE_CASE							    =tokenizer(['a picture of']				).input_ids

     _SCREAMING_SNAKE_CASE							    =hf_model.generate(_UpperCamelCase							, _UpperCamelCase				)

     assert out[0].tolist() == [3_05_22, 10_37, 38_61, 19_97, 10_37, 24_50, 35_64, 20_06, 19_96, 35_09, 20_07, 20_14, 38_99, 1_02]

     _SCREAMING_SNAKE_CASE							    =hf_model.generate(_UpperCamelCase				)

     assert out[0].tolist() == [3_05_22, 10_37, 24_50, 35_64, 20_06, 19_96, 35_09, 20_07, 20_14, 38_99, 1_02]

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

     _SCREAMING_SNAKE_CASE							    =blip_vqa(pretrained=_UpperCamelCase							, image_size=_UpperCamelCase							, vit='base'				)
     vqa_model.eval()

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

     _SCREAMING_SNAKE_CASE							    =BlipForQuestionAnswering(_UpperCamelCase				)

     hf_vqa_model.load_state_dict(_UpperCamelCase				)

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

     _SCREAMING_SNAKE_CASE							    =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'				)

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

     _SCREAMING_SNAKE_CASE							    =blip_itm(pretrained=_UpperCamelCase							, image_size=_UpperCamelCase							, vit='base'				)
     itm_model.eval()

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

     _SCREAMING_SNAKE_CASE							    =BlipForImageTextRetrieval(_UpperCamelCase				)

     _SCREAMING_SNAKE_CASE							    =['A picture of a woman with a dog sitting in a beach']
     _SCREAMING_SNAKE_CASE							    =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()

     _SCREAMING_SNAKE_CASE							    =hf_itm_model(_UpperCamelCase							, _UpperCamelCase							, use_itm_head=_UpperCamelCase				)
     _SCREAMING_SNAKE_CASE							    =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__":
     lowerCamelCase :      Dict					 =     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)


def 						UpperCAmelCase			(	a_						)		->	Optional[int]:







				"""simple docstring"""



				__A   			= [0] * len(a_						)
				__A   			= []
				__A   			= [1] * len(a_						)

				for values in graph.values():
								for i in values:
												indegree[i] += 1

				for i in range(len(a_						)						):
								if indegree[i] == 0:
												queue.append(a_						)

				while queue:
								__A   			= queue.pop(0						)
								for x in graph[vertex]:
												indegree[x] -= 1

												if long_dist[vertex] + 1 > long_dist[x]:
																__A   			= long_dist[vertex] + 1

												if indegree[x] == 0:
																queue.append(a_						)

				print(max(a_						)						)


# Adjacency list of Graph
SCREAMING_SNAKE_CASE							:List[Any]							 =				{0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}
longest_distance(graph)
