






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   UpperCamelCase  (       snake_case__     :   Any	,snake_case__     :   List[Any]  ):




     '''simple docstring'''






     __snake_case			:List[str]          =						'''https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'''
     __snake_case			:List[str]          =						Image.open(requests.get(lowerCamelCase_	,stream=lowerCamelCase_  ).raw  ).convert("""RGB"""  )

     __snake_case			:List[str]          =						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)  ),
         ]  )
     __snake_case			:List[Any]          =						transform(lowerCamelCase_  ).unsqueeze(0  ).to(lowerCamelCase_  )
     return image






def   UpperCamelCase  (       snake_case__     :   str  ):




     '''simple docstring'''






     if "visual_encoder" in key:
          __snake_case			:int          =						re.sub("""visual_encoder*"""	,"""vision_model.encoder"""	,lowerCamelCase_  )
     if "blocks" in key:
          __snake_case			:Optional[Any]          =						re.sub(R"""blocks"""	,"""layers"""	,lowerCamelCase_  )
     if "attn" in key:
          __snake_case			:Tuple          =						re.sub(R"""attn"""	,"""self_attn"""	,lowerCamelCase_  )
     if "norm1" in key:
          __snake_case			:Optional[Any]          =						re.sub(R"""norm1"""	,"""layer_norm1"""	,lowerCamelCase_  )
     if "norm2" in key:
          __snake_case			:Optional[Any]          =						re.sub(R"""norm2"""	,"""layer_norm2"""	,lowerCamelCase_  )
     if "encoder.norm" in key:
          __snake_case			:Dict          =						re.sub(R"""encoder.norm"""	,"""post_layernorm"""	,lowerCamelCase_  )
     if "encoder.patch_embed.proj" in key:
          __snake_case			:int          =						re.sub(R"""encoder.patch_embed.proj"""	,"""embeddings.patch_embedding"""	,lowerCamelCase_  )

     if "encoder.pos_embed" in key:
          __snake_case			:Optional[int]          =						re.sub(R"""encoder.pos_embed"""	,"""embeddings.position_embedding"""	,lowerCamelCase_  )
     if "encoder.cls_token" in key:
          __snake_case			:List[str]          =						re.sub(R"""encoder.cls_token"""	,"""embeddings.class_embedding"""	,lowerCamelCase_  )

     if "self_attn" in key:
          __snake_case			:int          =						re.sub(R"""self_attn.proj"""	,"""self_attn.projection"""	,lowerCamelCase_  )

     return key






@torch.no_grad()
def   UpperCamelCase  (       snake_case__     :   Optional[Any]	,snake_case__     :   Dict=None  ):




     '''simple docstring'''






     if config_path is not None:
          __snake_case			:Union[str, Any]          =						BlipConfig.from_pretrained(lowerCamelCase_  )
     else:
          __snake_case			:Dict          =						BlipConfig(projection_dim=512	,text_config={}	,vision_config={}  )

     __snake_case			:Optional[Any]          =						BlipForConditionalGeneration(lowerCamelCase_  ).eval()

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

     __snake_case			:int          =						blip_decoder(pretrained=lowerCamelCase_	,image_size=384	,vit="""base"""  )
     __snake_case			:List[str]          =						pt_model.eval()

     __snake_case			:str          =						pt_model.state_dict()
     for key in modified_state_dict.copy():
          __snake_case			:int          =						modified_state_dict.pop(lowerCamelCase_  )
          __snake_case			:List[Any]          =						rename_key(lowerCamelCase_  )
          __snake_case			:Tuple          =						value

     hf_model.load_state_dict(lowerCamelCase_  )

     __snake_case			:Any          =						384
     __snake_case			:List[Any]          =						load_demo_image(image_size=lowerCamelCase_	,device="""cpu"""  )
     __snake_case			:Tuple          =						BertTokenizer.from_pretrained("""bert-base-uncased"""  )
     __snake_case			:Union[str, Any]          =						tokenizer(["""a picture of"""]  ).input_ids

     __snake_case			:int          =						hf_model.generate(lowerCamelCase_	,lowerCamelCase_  )

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

     __snake_case			:Optional[int]          =						hf_model.generate(lowerCamelCase_  )

     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(lowerCamelCase_  )

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

     __snake_case			:str          =						blip_vqa(pretrained=lowerCamelCase_	,image_size=lowerCamelCase_	,vit="""base"""  )
     vqa_model.eval()

     __snake_case			:Any          =						vqa_model.state_dict()
     for key in modified_state_dict.copy():
          __snake_case			:List[str]          =						modified_state_dict.pop(lowerCamelCase_  )
          __snake_case			:Any          =						rename_key(lowerCamelCase_  )
          __snake_case			:Union[str, Any]          =						value

     __snake_case			:Any          =						BlipForQuestionAnswering(lowerCamelCase_  )

     hf_vqa_model.load_state_dict(lowerCamelCase_  )

     __snake_case			:List[str]          =						['''How many dogs are in this image?''']
     __snake_case			:List[str]          =						tokenizer(lowerCamelCase_	,return_tensors="""pt"""  ).input_ids

     __snake_case			:List[Any]          =						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			:Any          =						'''https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth'''

     __snake_case			:Union[str, Any]          =						blip_itm(pretrained=lowerCamelCase_	,image_size=lowerCamelCase_	,vit="""base"""  )
     itm_model.eval()

     __snake_case			:Union[str, Any]          =						itm_model.state_dict()
     for key in modified_state_dict.copy():
          __snake_case			:Any          =						modified_state_dict.pop(lowerCamelCase_  )
          __snake_case			:List[Any]          =						rename_key(lowerCamelCase_  )
          __snake_case			:List[str]          =						value

     __snake_case			:List[Any]          =						BlipForImageTextRetrieval(lowerCamelCase_  )

     __snake_case			:Any          =						['''A picture of a woman with a dog sitting in a beach''']
     __snake_case			:int          =						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			:Dict          =						hf_itm_model(lowerCamelCase_	,lowerCamelCase_	,use_itm_head=lowerCamelCase_  )
     __snake_case			:Optional[int]          =						hf_itm_model(lowerCamelCase_	,lowerCamelCase_	,use_itm_head=lowerCamelCase_  )

     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__":
     lowerCamelCase__							=     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__							=     parser.parse_args()

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










from __future__ import annotations

lowerCamelCase__							=     list[list[int]]

# assigning initial values to the grid
lowerCamelCase__							=     [
    [3, 0, 6, 5, 0, 8, 4, 0, 0],
    [5, 2, 0, 0, 0, 0, 0, 0, 0],
    [0, 8, 7, 0, 0, 0, 0, 3, 1],
    [0, 0, 3, 0, 1, 0, 0, 8, 0],
    [9, 0, 0, 8, 6, 3, 0, 0, 5],
    [0, 5, 0, 0, 9, 0, 6, 0, 0],
    [1, 3, 0, 0, 0, 0, 2, 5, 0],
    [0, 0, 0, 0, 0, 0, 0, 7, 4],
    [0, 0, 5, 2, 0, 6, 3, 0, 0],
]

# a grid with no solution
lowerCamelCase__							=     [
    [5, 0, 6, 5, 0, 8, 4, 0, 3],
    [5, 2, 0, 0, 0, 0, 0, 0, 2],
    [1, 8, 7, 0, 0, 0, 0, 3, 1],
    [0, 0, 3, 0, 1, 0, 0, 8, 0],
    [9, 0, 0, 8, 6, 3, 0, 0, 5],
    [0, 5, 0, 0, 9, 0, 6, 0, 0],
    [1, 3, 0, 0, 0, 0, 2, 5, 0],
    [0, 0, 0, 0, 0, 0, 0, 7, 4],
    [0, 0, 5, 2, 0, 6, 3, 0, 0],
]






def   UpperCamelCase  (       snake_case__     :   Matrix	,snake_case__     :   int	,snake_case__     :   int	,snake_case__     :   int  ):




     '''simple docstring'''






     for i in range(9  ):
          if grid[row][i] == n or grid[i][column] == n:
               return False

     for i in range(3  ):
          for j in range(3  ):
               if grid[(row - row % 3) + i][(column - column % 3) + j] == n:
                    return False

     return True






def   UpperCamelCase  (       snake_case__     :   Matrix  ):




     '''simple docstring'''






     for i in range(9  ):
          for j in range(9  ):
               if grid[i][j] == 0:
                    return i, j
     return None






def   UpperCamelCase  (       snake_case__     :   Matrix  ):




     '''simple docstring'''






     if location := find_empty_location(snake_case__  ):
          __snake_case       , __snake_case			:Optional[int]          =						location
     else:
          # If the location is ``None``, then the grid is solved.
          return grid

     for digit in range(1	,10  ):
          if is_safe(snake_case__	,snake_case__	,snake_case__	,snake_case__  ):
               __snake_case			:Union[str, Any]          =						digit

               if sudoku(snake_case__  ) is not None:
                    return grid

               __snake_case			:Tuple          =						0

     return None






def   UpperCamelCase  (       snake_case__     :   Matrix  ):




     '''simple docstring'''






     for row in grid:
          for cell in row:
               print(snake_case__	,end=""" """  )
          print()


if __name__ == "__main__":
     # make a copy of grid so that you can compare with the unmodified grid
     for example_grid in (initial_grid, no_solution):
          print("""\nExample grid:\n""" + """=""" * 20)
          print_solution(example_grid)
          print("""\nExample grid solution:""")
          lowerCamelCase__							=     sudoku(example_grid)
          if solution is not None:
               print_solution(solution)
          else:
               print("""Cannot find a solution.""")


