

'''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 	_A  (					A__  ,       A__   ):






 """simple docstring"""



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

 __lowercase													=				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)   ),
     ]   )
 __lowercase													=				transform(A__   ).unsqueeze(0   ).to(A__   )
 return image


def 	_A  (					A__   ):






 """simple docstring"""



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

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

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

 return key


@torch.no_grad()
def 	_A  (					A__  ,       A__=None   ):






 """simple docstring"""



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

 __lowercase													=				BlipForConditionalGeneration(A__   ).eval()

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

 __lowercase													=				blip_decoder(pretrained=A__  ,       image_size=384  ,       vit='''base'''   )
 __lowercase													=				pt_model.eval()

 __lowercase													=				pt_model.state_dict()
 for key in modified_state_dict.copy():
  __lowercase													=				modified_state_dict.pop(A__   )
  __lowercase													=				rename_key(A__   )
  __lowercase													=				value

 hf_model.load_state_dict(A__   )

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

 __lowercase													=				hf_model.generate(A__  ,       A__   )

 assert out[0].tolist() == [30522, 1037, 3861, 1997, 1037, 2450, 3564, 2006, 1996, 3509, 2007, 2014, 3899, 102]

 __lowercase													=				hf_model.generate(A__   )

 assert out[0].tolist() == [30522, 1037, 2450, 3564, 2006, 1996, 3509, 2007, 2014, 3899, 102]

 if pytorch_dump_folder_path is not None:
  hf_model.save_pretrained(A__   )

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

 __lowercase													=				blip_vqa(pretrained=A__  ,       image_size=A__  ,       vit='''base'''   )
 vqa_model.eval()

 __lowercase													=				vqa_model.state_dict()
 for key in modified_state_dict.copy():
  __lowercase													=				modified_state_dict.pop(A__   )
  __lowercase													=				rename_key(A__   )
  __lowercase													=				value

 __lowercase													=				BlipForQuestionAnswering(A__   )

 hf_vqa_model.load_state_dict(A__   )

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

 __lowercase													=				hf_vqa_model.generate(A__  ,       A__   )
 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'''   )

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

 __lowercase													=				blip_itm(pretrained=A__  ,       image_size=A__  ,       vit='''base'''   )
 itm_model.eval()

 __lowercase													=				itm_model.state_dict()
 for key in modified_state_dict.copy():
  __lowercase													=				modified_state_dict.pop(A__   )
  __lowercase													=				rename_key(A__   )
  __lowercase													=				value

 __lowercase													=				BlipForImageTextRetrieval(A__   )

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

 hf_itm_model.load_state_dict(A__   )
 hf_itm_model.eval()

 __lowercase													=				hf_itm_model(A__  ,       A__  ,       use_itm_head=A__   )
 __lowercase													=				hf_itm_model(A__  ,       A__  ,       use_itm_head=A__   )

 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)




'''simple docstring'''






import glob
import os
import random
from string import ascii_lowercase, digits

import cva
import numpy as np

# Parrameters
lowerCAmelCase__      		=			(720, 1280)  # Height, Width
lowerCAmelCase__      		=			(0.4, 0.6)  # if height or width lower than this scale, drop it.
lowerCAmelCase__      		=			1 / 100
lowerCAmelCase__      		=			''''''
lowerCAmelCase__      		=			''''''
lowerCAmelCase__      		=			''''''
lowerCAmelCase__      		=			250


def 	_A  (					):






 """simple docstring"""



 __lowercase						,			__lowercase													=				get_dataset(A__  ,       A__   )
 for index in range(A__   ):
  __lowercase													=				random.sample(range(len(A__   )   )  ,       4   )
  __lowercase						,			__lowercase						,			__lowercase													=				update_image_and_anno(
      A__  ,       A__  ,       A__  ,       A__  ,       A__  ,       filter_scale=A__  ,       )

  # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
  __lowercase													=				random_chars(32   )
  __lowercase													=				path.split(os.sep   )[-1].rsplit('''.'''  ,       1   )[0]
  __lowercase													=				F"{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}"
  cva.imwrite(F"{file_root}.jpg"  ,       A__  ,       [cva.IMWRITE_JPEG_QUALITY, 85]   )
  print(F"Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}"   )
  __lowercase													=				[]
  for anno in new_annos:
   __lowercase													=				anno[3] - anno[1]
   __lowercase													=				anno[4] - anno[2]
   __lowercase													=				anno[1] + width / 2
   __lowercase													=				anno[2] + height / 2
   __lowercase													=				F"{anno[0]} {x_center} {y_center} {width} {height}"
   annos_list.append(A__   )
  with open(F"{file_root}.txt"  ,       '''w'''   ) as outfile:
   outfile.write('''\n'''.join(line for line in annos_list   )   )


def 	_A  (					A__  ,       A__   ):






 """simple docstring"""



 __lowercase													=				[]
 __lowercase													=				[]
 for label_file in glob.glob(os.path.join(A__  ,       '''*.txt'''   )   ):
  __lowercase													=				label_file.split(os.sep   )[-1].rsplit('''.'''  ,       1   )[0]
  with open(A__   ) as in_file:
   __lowercase													=				in_file.readlines()
  __lowercase													=				os.path.join(A__  ,       F"{label_name}.jpg"   )

  __lowercase													=				[]
  for obj_list in obj_lists:
   __lowercase													=				obj_list.rstrip('''\n'''   ).split(''' '''   )
   __lowercase													=				float(obj[1]   ) - float(obj[3]   ) / 2
   __lowercase													=				float(obj[2]   ) - float(obj[4]   ) / 2
   __lowercase													=				float(obj[1]   ) + float(obj[3]   ) / 2
   __lowercase													=				float(obj[2]   ) + float(obj[4]   ) / 2

   boxes.append([int(obj[0]   ), xmin, ymin, xmax, ymax]   )
  if not boxes:
   continue
  img_paths.append(A__   )
  labels.append(A__   )
 return img_paths, labels


def 	_A  (					A__  ,       A__  ,       A__  ,       A__  ,       A__  ,       A__ = 0.0  ,       ):






 """simple docstring"""



 __lowercase													=				np.zeros([output_size[0], output_size[1], 3]  ,       dtype=np.uinta   )
 __lowercase													=				scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
 __lowercase													=				scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
 __lowercase													=				int(scale_x * output_size[1]   )
 __lowercase													=				int(scale_y * output_size[0]   )

 __lowercase													=				[]
 __lowercase													=				[]
 for i, index in enumerate(A__   ):
  __lowercase													=				all_img_list[index]
  path_list.append(A__   )
  __lowercase													=				all_annos[index]
  __lowercase													=				cva.imread(A__   )
  if i == 0:  # top-left
   __lowercase													=				cva.resize(A__  ,       (divid_point_x, divid_point_y)   )
   __lowercase													=				img
   for bbox in img_annos:
    __lowercase													=				bbox[1] * scale_x
    __lowercase													=				bbox[2] * scale_y
    __lowercase													=				bbox[3] * scale_x
    __lowercase													=				bbox[4] * scale_y
    new_anno.append([bbox[0], xmin, ymin, xmax, ymax]   )
  elif i == 1:  # top-right
   __lowercase													=				cva.resize(A__  ,       (output_size[1] - divid_point_x, divid_point_y)   )
   __lowercase													=				img
   for bbox in img_annos:
    __lowercase													=				scale_x + bbox[1] * (1 - scale_x)
    __lowercase													=				bbox[2] * scale_y
    __lowercase													=				scale_x + bbox[3] * (1 - scale_x)
    __lowercase													=				bbox[4] * scale_y
    new_anno.append([bbox[0], xmin, ymin, xmax, ymax]   )
  elif i == 2:  # bottom-left
   __lowercase													=				cva.resize(A__  ,       (divid_point_x, output_size[0] - divid_point_y)   )
   __lowercase													=				img
   for bbox in img_annos:
    __lowercase													=				bbox[1] * scale_x
    __lowercase													=				scale_y + bbox[2] * (1 - scale_y)
    __lowercase													=				bbox[3] * scale_x
    __lowercase													=				scale_y + bbox[4] * (1 - scale_y)
    new_anno.append([bbox[0], xmin, ymin, xmax, ymax]   )
  else:  # bottom-right
   __lowercase													=				cva.resize(
       A__  ,       (output_size[1] - divid_point_x, output_size[0] - divid_point_y)   )
   __lowercase													=				img
   for bbox in img_annos:
    __lowercase													=				scale_x + bbox[1] * (1 - scale_x)
    __lowercase													=				scale_y + bbox[2] * (1 - scale_y)
    __lowercase													=				scale_x + bbox[3] * (1 - scale_x)
    __lowercase													=				scale_y + bbox[4] * (1 - scale_y)
    new_anno.append([bbox[0], xmin, ymin, xmax, ymax]   )

    # Remove bounding box small than scale of filter
 if filter_scale > 0:
  __lowercase													=				[
      anno
      for anno in new_anno
      if filter_scale < (anno[3] - anno[1]) and filter_scale < (anno[4] - anno[2])
  ]

 return output_img, new_anno, path_list[0]


def 	_A  (					A__   ):






 """simple docstring"""



 assert number_char > 1, "The number of character should greater than 1"
 __lowercase													=				ascii_lowercase + digits
 return "".join(random.choice(A__   ) for _ in range(A__   )   )


if __name__ == "__main__":
 main()
 print('''DONE ✅''')

