



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

					snake_case_    :int										=    transforms.Compose(
					    [
					        transforms.Resize((image_size, image_size),  interpolation=InterpolationMode.BICUBIC				),
					        transforms.ToTensor(),
					        transforms.Normalize((0.48_145_466, 0.4_578_275, 0.40_821_073),  (0.26_862_954, 0.26_130_258, 0.27_577_711)				),
					    ]				)
					snake_case_    :List[str]										=    transform(SCREAMING_SNAKE_CASE_				).unsqueeze(0				).to(SCREAMING_SNAKE_CASE_				)
					return image
def   A   (      _A				):




					"""simple docstring"""







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

					if "encoder.pos_embed" in key:
										snake_case_    :Optional[Any]										=    re.sub(R"encoder.pos_embed",  "embeddings.position_embedding",  SCREAMING_SNAKE_CASE_				)
					if "encoder.cls_token" in key:
										snake_case_    :Union[str, Any]										=    re.sub(R"encoder.cls_token",  "embeddings.class_embedding",  SCREAMING_SNAKE_CASE_				)

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

					return key
@torch.no_grad()
def   A   (      _A,  _A=None				):




					"""simple docstring"""







					if config_path is not None:
										snake_case_    :Optional[Any]										=    BlipConfig.from_pretrained(SCREAMING_SNAKE_CASE_				)
					else:
										snake_case_    :Any										=    BlipConfig(projection_dim=512,  text_config={},  vision_config={}				)

					snake_case_    :List[Any]										=    BlipForConditionalGeneration(SCREAMING_SNAKE_CASE_				).eval()

					snake_case_    :int										=    "https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth"

					snake_case_    :Union[str, Any]										=    blip_decoder(pretrained=SCREAMING_SNAKE_CASE_,  image_size=384,  vit="base"				)
					snake_case_    :List[Any]										=    pt_model.eval()

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

					hf_model.load_state_dict(SCREAMING_SNAKE_CASE_				)

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

					snake_case_    :Dict										=    hf_model.generate(SCREAMING_SNAKE_CASE_,  SCREAMING_SNAKE_CASE_				)

					assert out[0].tolist() == [30_522, 1_037, 3_861, 1_997, 1_037, 2_450, 3_564, 2_006, 1_996, 3_509, 2_007, 2_014, 3_899, 102]

					snake_case_    :Optional[Any]										=    hf_model.generate(SCREAMING_SNAKE_CASE_				)

					assert out[0].tolist() == [30_522, 1_037, 2_450, 3_564, 2_006, 1_996, 3_509, 2_007, 2_014, 3_899, 102]

					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'
					snake_case_    :Optional[Any]										=    (
					    "https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_vqa_capfilt_large.pth"
					)

					snake_case_    :Optional[Any]										=    blip_vqa(pretrained=SCREAMING_SNAKE_CASE_,  image_size=SCREAMING_SNAKE_CASE_,  vit="base"				)
					vqa_model.eval()

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

					snake_case_    :List[str]										=    BlipForQuestionAnswering(SCREAMING_SNAKE_CASE_				)

					hf_vqa_model.load_state_dict(SCREAMING_SNAKE_CASE_				)

					snake_case_    :Dict										=    ["How many dogs are in this image?"]
					snake_case_    :Dict										=    tokenizer(SCREAMING_SNAKE_CASE_,  return_tensors="pt"				).input_ids

					snake_case_    :List[str]										=    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"				)

					snake_case_    :Optional[Any]										=    "https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth"

					snake_case_    :List[Any]										=    blip_itm(pretrained=SCREAMING_SNAKE_CASE_,  image_size=SCREAMING_SNAKE_CASE_,  vit="base"				)
					itm_model.eval()

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

					snake_case_    :Any										=    BlipForImageTextRetrieval(SCREAMING_SNAKE_CASE_				)

					snake_case_    :Optional[Any]										=    ["A picture of a woman with a dog sitting in a beach"]
					snake_case_    :str										=    tokenizer(
					    SCREAMING_SNAKE_CASE_,  return_tensors="pt",  padding="max_length",  truncation=SCREAMING_SNAKE_CASE_,  max_length=35,  ).input_ids

					hf_itm_model.load_state_dict(SCREAMING_SNAKE_CASE_				)
					hf_itm_model.eval()

					snake_case_    :List[str]										=    hf_itm_model(SCREAMING_SNAKE_CASE_,  SCREAMING_SNAKE_CASE_,  use_itm_head=SCREAMING_SNAKE_CASE_				)
					snake_case_    :List[str]										=    hf_itm_model(SCREAMING_SNAKE_CASE_,  SCREAMING_SNAKE_CASE_,  use_itm_head=SCREAMING_SNAKE_CASE_				)

					assert out[0].item() == 0.2_110_687_494_277_954
					assert torch.nn.functional.softmax(out_itm[0],  dim=1				)[:, 1].item() == 0.45_698_845_386_505_127

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


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

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








from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_           =	logging.get_logger(__name__)

UpperCAmelCase_           =	{
    "microsoft/biogpt": "https://huggingface.co/microsoft/biogpt/resolve/main/config.json",
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}




class 			__UpperCamelCase    (							A__    ):
     __A	:  Any							  =   """biogpt"""





     def __init__(     self				,							_UpperCamelCase=42384				,							_UpperCamelCase=1024				,							_UpperCamelCase=24				,							_UpperCamelCase=16				,							_UpperCamelCase=4096				,							_UpperCamelCase="gelu"				,							_UpperCamelCase=0.1				,							_UpperCamelCase=0.1				,							_UpperCamelCase=1024				,							_UpperCamelCase=0.02				,							_UpperCamelCase=1e-12				,							_UpperCamelCase=True				,							_UpperCamelCase=True				,							_UpperCamelCase=0.0				,							_UpperCamelCase=0.0				,							_UpperCamelCase=1				,							_UpperCamelCase=0				,							_UpperCamelCase=2				,							**_UpperCamelCase				,							):
        _UpperCAmelCase           =   vocab_size
        _UpperCAmelCase           =   max_position_embeddings
        _UpperCAmelCase           =   hidden_size
        _UpperCAmelCase           =   num_hidden_layers
        _UpperCAmelCase           =   num_attention_heads
        _UpperCAmelCase           =   intermediate_size
        _UpperCAmelCase           =   hidden_act
        _UpperCAmelCase           =   hidden_dropout_prob
        _UpperCAmelCase           =   attention_probs_dropout_prob
        _UpperCAmelCase           =   initializer_range
        _UpperCAmelCase           =   layer_norm_eps
        _UpperCAmelCase           =   scale_embedding
        _UpperCAmelCase           =   use_cache
        _UpperCAmelCase           =   layerdrop
        _UpperCAmelCase           =   activation_dropout
        super().__init__(pad_token_id=_UpperCamelCase				,							bos_token_id=_UpperCamelCase				,							eos_token_id=_UpperCamelCase				,							**_UpperCamelCase					)