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

 __a					:    List[Any]													=    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)    ),
     ]    )
 __a					:    Any													=    transform(snake_case__    ).unsqueeze(0    ).to(snake_case__    )
 return image




def   __UpperCamelCase	(							lowerCAmelCase__				:							Optional[Any]    ):
 if "visual_encoder" in key:
  __a					:    Optional[Any]													=    re.sub('''visual_encoder*'''    ,  '''vision_model.encoder'''    ,  snake_case__    )
 if "blocks" in key:
  __a					:    Optional[int]													=    re.sub(R'''blocks'''    ,  '''layers'''    ,  snake_case__    )
 if "attn" in key:
  __a					:    str													=    re.sub(R'''attn'''    ,  '''self_attn'''    ,  snake_case__    )
 if "norm1" in key:
  __a					:    Union[str, Any]													=    re.sub(R'''norm1'''    ,  '''layer_norm1'''    ,  snake_case__    )
 if "norm2" in key:
  __a					:    List[Any]													=    re.sub(R'''norm2'''    ,  '''layer_norm2'''    ,  snake_case__    )
 if "encoder.norm" in key:
  __a					:    List[Any]													=    re.sub(R'''encoder.norm'''    ,  '''post_layernorm'''    ,  snake_case__    )
 if "encoder.patch_embed.proj" in key:
  __a					:    List[Any]													=    re.sub(R'''encoder.patch_embed.proj'''    ,  '''embeddings.patch_embedding'''    ,  snake_case__    )

 if "encoder.pos_embed" in key:
  __a					:    str													=    re.sub(R'''encoder.pos_embed'''    ,  '''embeddings.position_embedding'''    ,  snake_case__    )
 if "encoder.cls_token" in key:
  __a					:    Any													=    re.sub(R'''encoder.cls_token'''    ,  '''embeddings.class_embedding'''    ,  snake_case__    )

 if "self_attn" in key:
  __a					:    Optional[int]													=    re.sub(R'''self_attn.proj'''    ,  '''self_attn.projection'''    ,  snake_case__    )

 return key




@torch.no_grad()
def   __UpperCamelCase	(							lowerCAmelCase__				:							Any    ,  lowerCAmelCase__				:							str=None    ):
 if config_path is not None:
  __a					:    Union[str, Any]													=    BlipConfig.from_pretrained(snake_case__    )
 else:
  __a					:    Optional[Any]													=    BlipConfig(projection_dim=5_1_2    ,  text_config={}    ,  vision_config={}    )

 __a					:    Optional[Any]													=    BlipForConditionalGeneration(snake_case__    ).eval()

 __a					:    Optional[int]													=    '''https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth'''

 __a					:    Optional[Any]													=    blip_decoder(pretrained=snake_case__    ,  image_size=3_8_4    ,  vit='''base'''    )
 __a					:    str													=    pt_model.eval()

 __a					:    Optional[int]													=    pt_model.state_dict()
 for key in modified_state_dict.copy():
  __a					:    Tuple													=    modified_state_dict.pop(snake_case__    )
  __a					:    Dict													=    rename_key(snake_case__    )
  __a					:    Union[str, Any]													=    value

 hf_model.load_state_dict(snake_case__    )

 __a					:    List[str]													=    3_8_4
 __a					:    Any													=    load_demo_image(image_size=snake_case__    ,  device='''cpu'''    )
 __a					:    str													=    BertTokenizer.from_pretrained('''bert-base-uncased'''    )
 __a					:    List[str]													=    tokenizer(['''a picture of''']    ).input_ids

 __a					:    Optional[Any]													=    hf_model.generate(snake_case__    ,  snake_case__    )

 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					:    List[str]													=    hf_model.generate(snake_case__    )

 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(snake_case__    )

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

 __a					:    Any													=    blip_vqa(pretrained=snake_case__    ,  image_size=snake_case__    ,  vit='''base'''    )
 vqa_model.eval()

 __a					:    Optional[int]													=    vqa_model.state_dict()
 for key in modified_state_dict.copy():
  __a					:    Any													=    modified_state_dict.pop(snake_case__    )
  __a					:    Any													=    rename_key(snake_case__    )
  __a					:    Union[str, Any]													=    value

 __a					:    int													=    BlipForQuestionAnswering(snake_case__    )

 hf_vqa_model.load_state_dict(snake_case__    )

 __a					:    int													=    ['''How many dogs are in this image?''']
 __a					:    Dict													=    tokenizer(snake_case__    ,  return_tensors='''pt'''    ).input_ids

 __a					:    List[Any]													=    hf_vqa_model.generate(snake_case__    ,  snake_case__    )
 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					:    int													=    '''https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth'''

 __a					:    Optional[Any]													=    blip_itm(pretrained=snake_case__    ,  image_size=snake_case__    ,  vit='''base'''    )
 itm_model.eval()

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

 __a					:    Dict													=    BlipForImageTextRetrieval(snake_case__    )

 __a					:    Dict													=    ['''A picture of a woman with a dog sitting in a beach''']
 __a					:    Tuple													=    tokenizer(
     snake_case__    ,  return_tensors='''pt'''    ,  padding='''max_length'''    ,  truncation=snake_case__    ,  max_length=3_5    ,  ).input_ids

 hf_itm_model.load_state_dict(snake_case__    )
 hf_itm_model.eval()

 __a					:    List[str]													=    hf_itm_model(snake_case__    ,  snake_case__    ,  use_itm_head=snake_case__    )
 __a					:    int													=    hf_itm_model(snake_case__    ,  snake_case__    ,  use_itm_head=snake_case__    )

 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__":
      lowercase__         =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')
      lowercase__         =parser.parse_args()

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


"""simple docstring"""


import copy
import re







class 					lowerCAmelCase_					:



						'''simple docstring'''


						_lowerCamelCase:     str   =     '''hp'''
						_lowerCamelCase:     List[Any]   =     {}
						_lowerCamelCase:     List[Any]   =     None


						@classmethod
						def     _SCREAMING_SNAKE_CASE   (			cls  :   List[Any]	,A_  :   List[str]	,A_  :   Optional[Any]   )  -> Tuple:
							A     = prefix
							A     = defaults
							cls.build_naming_info()


						@staticmethod
						def     _SCREAMING_SNAKE_CASE   (			A_  :   Any	,A_  :   List[Any]   )  -> int:
							if len(A_   ) == 0:
								return ""
							A     = None
							if any(char.isdigit() for char in word   ):
								raise Exception(F'Parameters should not contain numbers: \'{word}\' contains a number'   )
							if word in info["short_word"]:
								return info["short_word"][word]
							for prefix_len in range(1	,len(A_   ) + 1   ):
								A     = word[:prefix_len]
								if prefix in info["reverse_short_word"]:
									continue
								else:
									A     = prefix
									break

							if short_word is None:
								# Paranoid fallback
								def int_to_alphabetic(A_  :   Optional[Any]   ):
									A     = ''
									while integer != 0:
										A     = chr(ord('A'   ) + integer % 10   ) + s
										integer //= 10
									return s

								A     = 0
								while True:
									A     = word + '#' + int_to_alphabetic(A_   )
									if sword in info["reverse_short_word"]:
										continue
									else:
										A     = sword
										break

							A     = short_word
							A     = word
							return short_word


						@staticmethod
						def     _SCREAMING_SNAKE_CASE   (			A_  :   List[Any]	,A_  :   Union[str, Any]   )  -> Union[str, Any]:
							A     = param_name.split('_'   )

							A     = [TrialShortNamer.shortname_for_word(A_	,A_   ) for word in words]

							# We try to create a separatorless short name, but if there is a collision we have to fallback
							# to a separated short name
							A     = ['', '_']

							for separator in separators:
								A     = separator.join(A_   )
								if shortname not in info["reverse_short_param"]:
									A     = shortname
									A     = param_name
									return shortname

							return param_name


						@staticmethod
						def     _SCREAMING_SNAKE_CASE   (			A_  :   List[Any]	,A_  :   Any   )  -> Tuple:
							A     = TrialShortNamer.shortname_for_key(A_	,A_   )
							A     = short_name
							A     = param_name


						@classmethod
						def     _SCREAMING_SNAKE_CASE   (			cls  :   Dict   )  -> List[Any]:
							if cls.NAMING_INFO is not None:
								return

							A     = {
							    'short_word': {},
							    'reverse_short_word': {},
							    'short_param': {},
							    'reverse_short_param': {},
							}

							A     = list(cls.DEFAULTS.keys()   )

							for k in field_keys:
								cls.add_new_param_name(A_	,A_   )

							A     = info


						@classmethod
						def     _SCREAMING_SNAKE_CASE   (			cls  :   List[Any]	,A_  :   Union[str, Any]   )  -> Union[str, Any]:
							cls.build_naming_info()
							assert cls.PREFIX is not None
							A     = [copy.copy(cls.PREFIX   )]

							for k, v in params.items():
								if k not in cls.DEFAULTS:
									raise Exception(F'You should provide a default value for the param name {k} with value {v}'   )
								if v == cls.DEFAULTS[k]:
									# The default value is not added to the name
									continue

								A     = cls.NAMING_INFO['short_param'][k]

								if isinstance(A_	,A_   ):
									A     = 1 if v else 0

								A     = '' if isinstance(A_	,(int, float)   ) else '-'
								A     = F'{key}{sep}{v}'
								name.append(A_   )

							return "_".join(A_   )




						@classmethod
						def     _SCREAMING_SNAKE_CASE   (			cls  :   List[str]	,A_  :   Any   )  -> int:
							A     = repr[len(cls.PREFIX   ) + 1 :]
							if repr == "":
								A     = []
							else:
								A     = repr.split('_'   )

							A     = {}

							for value in values:
								if "-" in value:
									A ,   A     = value.split('-'   )
								else:
									A     = re.sub('[0-9.]'	,''	,A_   )
									A     = float(re.sub('[^0-9.]'	,''	,A_   )   )

								A     = cls.NAMING_INFO['reverse_short_param'][p_k]

								A     = p_v

							for k in cls.DEFAULTS:
								if k not in parameters:
									A     = cls.DEFAULTS[k]

							return parameters