


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	(							SCREAMING_SNAKE_CASE_      :		int							,			SCREAMING_SNAKE_CASE_      :		Optional[Any]       ):






       """simple docstring"""





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

       UpperCamelCase    :	Optional[Any]   	=		transforms.Compose(
           [
               transforms.Resize((image_size, image_size)							,			interpolation=InterpolationMode.BICUBIC       ),
               transforms.ToTensor(),
               transforms.Normalize((0.48145466, 0.4578275, 0.40821073)							,			(0.26862954, 0.26130258, 0.27577711)       ),
           ]       )
       UpperCamelCase    :	str   	=		transform(SCREAMING_SNAKE_CASE_       ).unsqueeze(0       ).to(SCREAMING_SNAKE_CASE_       )
       return image


def      a	(							SCREAMING_SNAKE_CASE_      :		Tuple       ):






       """simple docstring"""





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

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

       if "self_attn" in key:
              UpperCamelCase    :	str   	=		re.sub(R'''self_attn.proj'''							,			'''self_attn.projection'''							,			SCREAMING_SNAKE_CASE_       )

       return key


@torch.no_grad()
def      a	(							SCREAMING_SNAKE_CASE_      :		Union[str, Any]							,			SCREAMING_SNAKE_CASE_      :		str=None       ):






       """simple docstring"""





       if config_path is not None:
              UpperCamelCase    :	Optional[int]   	=		BlipConfig.from_pretrained(SCREAMING_SNAKE_CASE_       )
       else:
              UpperCamelCase    :	Dict   	=		BlipConfig(projection_dim=5_1_2							,			text_config={}							,			vision_config={}       )

       UpperCamelCase    :	Optional[int]   	=		BlipForConditionalGeneration(SCREAMING_SNAKE_CASE_       ).eval()

       UpperCamelCase    :	Union[str, Any]   	=		'''https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth'''

       UpperCamelCase    :	Optional[int]   	=		blip_decoder(pretrained=SCREAMING_SNAKE_CASE_							,			image_size=3_8_4							,			vit='''base'''       )
       UpperCamelCase    :	Dict   	=		pt_model.eval()

       UpperCamelCase    :	List[str]   	=		pt_model.state_dict()
       for key in modified_state_dict.copy():
              UpperCamelCase    :	Optional[Any]   	=		modified_state_dict.pop(SCREAMING_SNAKE_CASE_       )
              UpperCamelCase    :	Optional[Any]   	=		rename_key(SCREAMING_SNAKE_CASE_       )
              UpperCamelCase    :	Dict   	=		value

       hf_model.load_state_dict(SCREAMING_SNAKE_CASE_       )

       UpperCamelCase    :	int   	=		3_8_4
       UpperCamelCase    :	Union[str, Any]   	=		load_demo_image(image_size=SCREAMING_SNAKE_CASE_							,			device='''cpu'''       )
       UpperCamelCase    :	Tuple   	=		BertTokenizer.from_pretrained('''bert-base-uncased'''       )
       UpperCamelCase    :	Tuple   	=		tokenizer(['''a picture of''']       ).input_ids

       UpperCamelCase    :	Union[str, Any]   	=		hf_model.generate(SCREAMING_SNAKE_CASE_							,			SCREAMING_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]

       UpperCamelCase    :	int   	=		hf_model.generate(SCREAMING_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(SCREAMING_SNAKE_CASE_       )

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

       UpperCamelCase    :	Tuple   	=		blip_vqa(pretrained=SCREAMING_SNAKE_CASE_							,			image_size=SCREAMING_SNAKE_CASE_							,			vit='''base'''       )
       vqa_model.eval()

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

       UpperCamelCase    :	int   	=		BlipForQuestionAnswering(SCREAMING_SNAKE_CASE_       )

       hf_vqa_model.load_state_dict(SCREAMING_SNAKE_CASE_       )

       UpperCamelCase    :	Union[str, Any]   	=		['''How many dogs are in this image?''']
       UpperCamelCase    :	Dict   	=		tokenizer(SCREAMING_SNAKE_CASE_							,			return_tensors='''pt'''       ).input_ids

       UpperCamelCase    :	Any   	=		hf_vqa_model.generate(SCREAMING_SNAKE_CASE_							,			SCREAMING_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'''       )

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

       UpperCamelCase    :	Optional[int]   	=		blip_itm(pretrained=SCREAMING_SNAKE_CASE_							,			image_size=SCREAMING_SNAKE_CASE_							,			vit='''base'''       )
       itm_model.eval()

       UpperCamelCase    :	Dict   	=		itm_model.state_dict()
       for key in modified_state_dict.copy():
              UpperCamelCase    :	List[Any]   	=		modified_state_dict.pop(SCREAMING_SNAKE_CASE_       )
              UpperCamelCase    :	Dict   	=		rename_key(SCREAMING_SNAKE_CASE_       )
              UpperCamelCase    :	str   	=		value

       UpperCamelCase    :	List[str]   	=		BlipForImageTextRetrieval(SCREAMING_SNAKE_CASE_       )

       UpperCamelCase    :	Optional[Any]   	=		['''A picture of a woman with a dog sitting in a beach''']
       UpperCamelCase    :	List[Any]   	=		tokenizer(
           SCREAMING_SNAKE_CASE_							,			return_tensors='''pt'''							,			padding='''max_length'''							,			truncation=SCREAMING_SNAKE_CASE_							,			max_length=3_5							,			).input_ids

       hf_itm_model.load_state_dict(SCREAMING_SNAKE_CASE_       )
       hf_itm_model.eval()

       UpperCamelCase    :	Optional[int]   	=		hf_itm_model(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_							,			use_itm_head=SCREAMING_SNAKE_CASE_       )
       UpperCamelCase    :	Union[str, Any]   	=		hf_itm_model(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_							,			use_itm_head=SCREAMING_SNAKE_CASE_       )

       assert out[0].item() == 0.2110687494277954
       assert torch.nn.functional.softmax(out_itm[0]							,			dim=1       )[:, 1].item() == 0.45698845386505127

       if pytorch_dump_folder_path is not None:
              hf_itm_model.save_pretrained(pytorch_dump_folder_path + '''_itm'''       )


if __name__ == "__main__":
  __UpperCAmelCase : Union[str, Any] 			=				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")
  __UpperCAmelCase : List[Any] 			=				parser.parse_args()

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





import os
from shutil import copyfile
from typing import List, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
  from .tokenization_mbart import MBartTokenizer
else:
  __UpperCAmelCase : Optional[int] 			=				None


__UpperCAmelCase : int 			=				logging.get_logger(__name__)


__UpperCAmelCase : List[Any] 			=				{"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

__UpperCAmelCase : str 			=				{
    "vocab_file": {
        "facebook/mbart-large-en-ro": (
            "https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model"
        ),
        "facebook/mbart-large-cc25": (
            "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model"
        ),
    },
    "tokenizer_file": {
        "facebook/mbart-large-en-ro": "https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json",
        "facebook/mbart-large-cc25": "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json",
    },
}

__UpperCAmelCase : Union[str, Any] 			=				{
    "facebook/mbart-large-en-ro": 1024,
    "facebook/mbart-large-cc25": 1024,
}

# fmt: off
__UpperCAmelCase : Any 			=				["ar_AR", "cs_CZ", "de_DE", "en_XX", "es_XX", "et_EE", "fi_FI", "fr_XX", "gu_IN", "hi_IN", "it_IT", "ja_XX", "kk_KZ", "ko_KR", "lt_LT", "lv_LV", "my_MM", "ne_NP", "nl_XX", "ro_RO", "ru_RU", "si_LK", "tr_TR", "vi_VN", "zh_CN"]



class    UpperCAmelCase_	(							_a):
       '''simple docstring'''
       __UpperCamelCase      :     List[str]							=					VOCAB_FILES_NAMES
       __UpperCamelCase      :     str							=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       __UpperCamelCase      :     Tuple							=					PRETRAINED_VOCAB_FILES_MAP
       __UpperCamelCase      :     Union[str, Any]							=					["input_ids", "attention_mask"]
       __UpperCamelCase      :     Any							=					MBartTokenizer

       __UpperCamelCase      :     List[int]							=					[]
       __UpperCamelCase      :     List[int]							=					[]






       def __init__(     self ,					__SCREAMING_SNAKE_CASE=None ,					__SCREAMING_SNAKE_CASE=None ,					__SCREAMING_SNAKE_CASE="<s>" ,					__SCREAMING_SNAKE_CASE="</s>" ,					__SCREAMING_SNAKE_CASE="</s>" ,					__SCREAMING_SNAKE_CASE="<s>" ,					__SCREAMING_SNAKE_CASE="<unk>" ,					__SCREAMING_SNAKE_CASE="<pad>" ,					__SCREAMING_SNAKE_CASE="<mask>" ,					__SCREAMING_SNAKE_CASE=None ,					__SCREAMING_SNAKE_CASE=None ,					__SCREAMING_SNAKE_CASE=None ,					**__SCREAMING_SNAKE_CASE ,					):




              """simple docstring"""




              UpperCamelCase    :	Union[str, Any]   	=		AddedToken(__SCREAMING_SNAKE_CASE ,					lstrip=__SCREAMING_SNAKE_CASE ,					rstrip=__SCREAMING_SNAKE_CASE						) if isinstance(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						) else mask_token

              super().__init__(
                  vocab_file=__SCREAMING_SNAKE_CASE ,					tokenizer_file=__SCREAMING_SNAKE_CASE ,					bos_token=__SCREAMING_SNAKE_CASE ,					eos_token=__SCREAMING_SNAKE_CASE ,					sep_token=__SCREAMING_SNAKE_CASE ,					cls_token=__SCREAMING_SNAKE_CASE ,					unk_token=__SCREAMING_SNAKE_CASE ,					pad_token=__SCREAMING_SNAKE_CASE ,					mask_token=__SCREAMING_SNAKE_CASE ,					src_lang=__SCREAMING_SNAKE_CASE ,					tgt_lang=__SCREAMING_SNAKE_CASE ,					additional_special_tokens=__SCREAMING_SNAKE_CASE ,					**__SCREAMING_SNAKE_CASE ,					)

              UpperCamelCase    :	Dict   	=		vocab_file
              UpperCamelCase    :	List[str]   	=		False if not self.vocab_file else True

              UpperCamelCase    :	List[Any]   	=		FAIRSEQ_LANGUAGE_CODES.copy()

              if additional_special_tokens is not None:
                     # Only add those special tokens if they are not already there.
                     _additional_special_tokens.extend(
                         [t for t in additional_special_tokens if t not in _additional_special_tokens]						)

              self.add_special_tokens({'''additional_special_tokens''': _additional_special_tokens}						)
              UpperCamelCase    :	List[Any]   	=		{
                  lang_code: self.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE						) for lang_code in FAIRSEQ_LANGUAGE_CODES
              }

              UpperCamelCase    :	Dict   	=		src_lang if src_lang is not None else '''en_XX'''
              UpperCamelCase    :	List[Any]   	=		self.convert_tokens_to_ids(self._src_lang						)
              UpperCamelCase    :	str   	=		tgt_lang
              self.set_src_lang_special_tokens(self._src_lang						)






       @property
       def  _lowercase   (     self						):




              """simple docstring"""




              return self._src_lang






       @src_lang.setter
       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	Union[str, Any]   	=		new_src_lang
              self.set_src_lang_special_tokens(self._src_lang						)






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE = None						):




              """simple docstring"""




              if token_ids_a is None:
                     return self.prefix_tokens + token_ids_a + self.suffix_tokens
              # We don't expect to process pairs, but leave the pair logic for API consistency
              return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE = None						):




              """simple docstring"""




              UpperCamelCase    :	str   	=		[self.sep_token_id]
              UpperCamelCase    :	Union[str, Any]   	=		[self.cls_token_id]

              if token_ids_a is None:
                     return len(cls + token_ids_a + sep						) * [0]
              return len(cls + token_ids_a + sep + sep + token_ids_a + sep						) * [0]






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					**__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              if src_lang is None or tgt_lang is None:
                     raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model'''						)
              UpperCamelCase    :	List[str]   	=		src_lang
              UpperCamelCase    :	Dict   	=		self(__SCREAMING_SNAKE_CASE ,					add_special_tokens=__SCREAMING_SNAKE_CASE ,					return_tensors=__SCREAMING_SNAKE_CASE ,					**__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	List[Any]   	=		self.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	Any   	=		tgt_lang_id
              return inputs






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE = "en_XX" ,					__SCREAMING_SNAKE_CASE = None ,					__SCREAMING_SNAKE_CASE = "ro_RO" ,					**__SCREAMING_SNAKE_CASE ,					):




              """simple docstring"""




              UpperCamelCase    :	Optional[Any]   	=		src_lang
              UpperCamelCase    :	Optional[int]   	=		tgt_lang
              return super().prepare_seqaseq_batch(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					**__SCREAMING_SNAKE_CASE						)






       def  _lowercase   (     self						):




              """simple docstring"""




              return self.set_src_lang_special_tokens(self.src_lang						)






       def  _lowercase   (     self						):




              """simple docstring"""




              return self.set_tgt_lang_special_tokens(self.tgt_lang						)






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	Optional[int]   	=		self.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	Any   	=		[]
              UpperCamelCase    :	Dict   	=		[self.eos_token_id, self.cur_lang_code]

              UpperCamelCase    :	Union[str, Any]   	=		self.convert_ids_to_tokens(self.prefix_tokens						)
              UpperCamelCase    :	int   	=		self.convert_ids_to_tokens(self.suffix_tokens						)

              UpperCamelCase    :	Tuple   	=		processors.TemplateProcessing(
                  single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str ,					pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str ,					special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str ,					self.prefix_tokens + self.suffix_tokens						)						) ,					)






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	Optional[int]   	=		self.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	int   	=		[]
              UpperCamelCase    :	Optional[Any]   	=		[self.eos_token_id, self.cur_lang_code]

              UpperCamelCase    :	Optional[Any]   	=		self.convert_ids_to_tokens(self.prefix_tokens						)
              UpperCamelCase    :	List[str]   	=		self.convert_ids_to_tokens(self.suffix_tokens						)

              UpperCamelCase    :	Optional[int]   	=		processors.TemplateProcessing(
                  single=prefix_tokens_str + ['''$A'''] + suffix_tokens_str ,					pair=prefix_tokens_str + ['''$A''', '''$B'''] + suffix_tokens_str ,					special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str ,					self.prefix_tokens + self.suffix_tokens						)						) ,					)






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE = None						):




              """simple docstring"""




              if not self.can_save_slow_tokenizer:
                     raise ValueError(
                         '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
                         '''tokenizer.'''						)

              if not os.path.isdir(__SCREAMING_SNAKE_CASE						):
                     logger.error(f"""Vocabulary path ({save_directory}) should be a directory."""						)
                     return
              UpperCamelCase    :	Optional[int]   	=		os.path.join(
                  __SCREAMING_SNAKE_CASE ,					(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']						)

              if os.path.abspath(self.vocab_file						) != os.path.abspath(__SCREAMING_SNAKE_CASE						):
                     copyfile(self.vocab_file ,					__SCREAMING_SNAKE_CASE						)

              return (out_vocab_file,)

