






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

	snake_case__				:   Tuple     =						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)     ),
	    ]     )
	snake_case__				:   Dict     =						transform(lowerCamelCase_     ).unsqueeze(0     ).to(lowerCamelCase_     )
	return image







def   SCREAMING_SNAKE_CASE       (				snake_case_      :							List[Any]     ):



	if "visual_encoder" in key:
		snake_case__				:   Any     =						re.sub("visual_encoder*"  ,						"vision_model.encoder"  ,						lowerCamelCase_     )
	if "blocks" in key:
		snake_case__				:   Union[str, Any]     =						re.sub(R"blocks"  ,						"layers"  ,						lowerCamelCase_     )
	if "attn" in key:
		snake_case__				:   Dict     =						re.sub(R"attn"  ,						"self_attn"  ,						lowerCamelCase_     )
	if "norm1" in key:
		snake_case__				:   str     =						re.sub(R"norm1"  ,						"layer_norm1"  ,						lowerCamelCase_     )
	if "norm2" in key:
		snake_case__				:   Tuple     =						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__				:   str     =						re.sub(R"encoder.patch_embed.proj"  ,						"embeddings.patch_embedding"  ,						lowerCamelCase_     )

	if "encoder.pos_embed" in key:
		snake_case__				:   Dict     =						re.sub(R"encoder.pos_embed"  ,						"embeddings.position_embedding"  ,						lowerCamelCase_     )
	if "encoder.cls_token" in key:
		snake_case__				:   Optional[Any]     =						re.sub(R"encoder.cls_token"  ,						"embeddings.class_embedding"  ,						lowerCamelCase_     )

	if "self_attn" in key:
		snake_case__				:   Union[str, Any]     =						re.sub(R"self_attn.proj"  ,						"self_attn.projection"  ,						lowerCamelCase_     )

	return key







@torch.no_grad()
def   SCREAMING_SNAKE_CASE       (				snake_case_      :							List[str]  ,						snake_case_      :							Dict=None     ):



	if config_path is not None:
		snake_case__				:   List[Any]     =						BlipConfig.from_pretrained(lowerCamelCase_     )
	else:
		snake_case__				:   Tuple     =						BlipConfig(projection_dim=512  ,						text_config={}  ,						vision_config={}     )

	snake_case__				:   Dict     =						BlipForConditionalGeneration(lowerCamelCase_     ).eval()

	snake_case__				:   List[Any]     =						"""https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth"""

	snake_case__				:   Dict     =						blip_decoder(pretrained=lowerCamelCase_  ,						image_size=384  ,						vit="base"     )
	snake_case__				:   Optional[int]     =						pt_model.eval()

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

	hf_model.load_state_dict(lowerCamelCase_     )

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

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

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

	snake_case__				:   List[Any]     =						hf_model.generate(lowerCamelCase_     )

	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(lowerCamelCase_     )

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

	snake_case__				:   Tuple     =						blip_vqa(pretrained=lowerCamelCase_  ,						image_size=lowerCamelCase_  ,						vit="base"     )
	vqa_model.eval()

	snake_case__				:   Tuple     =						vqa_model.state_dict()
	for key in modified_state_dict.copy():
		snake_case__				:   Dict     =						modified_state_dict.pop(lowerCamelCase_     )
		snake_case__				:   Tuple     =						rename_key(lowerCamelCase_     )
		snake_case__				:   List[Any]     =						value

	snake_case__				:   Any     =						BlipForQuestionAnswering(lowerCamelCase_     )

	hf_vqa_model.load_state_dict(lowerCamelCase_     )

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

	snake_case__				:   str     =						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__				:   str     =						"""https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth"""

	snake_case__				:   List[str]     =						blip_itm(pretrained=lowerCamelCase_  ,						image_size=lowerCamelCase_  ,						vit="base"     )
	itm_model.eval()

	snake_case__				:   Tuple     =						itm_model.state_dict()
	for key in modified_state_dict.copy():
		snake_case__				:   Dict     =						modified_state_dict.pop(lowerCamelCase_     )
		snake_case__				:   Optional[int]     =						rename_key(lowerCamelCase_     )
		snake_case__				:   List[Any]     =						value

	snake_case__				:   str     =						BlipForImageTextRetrieval(lowerCamelCase_     )

	snake_case__				:   str     =						["""A picture of a woman with a dog sitting in a beach"""]
	snake_case__				:   str     =						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__				:   List[Any]     =						hf_itm_model(lowerCamelCase_  ,						lowerCamelCase_  ,						use_itm_head=lowerCamelCase_     )
	snake_case__				:   List[Any]     =						hf_itm_model(lowerCamelCase_  ,						lowerCamelCase_  ,						use_itm_head=lowerCamelCase_     )

	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__":
	__lowerCamelCase      :    Optional[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""")
	__lowerCamelCase      :    List[str]   		=   parser.parse_args()

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









from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
	if not (is_transformers_available() and is_torch_available()):
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	from ...utils.dummy_torch_and_transformers_objects import KandinskyPipeline, KandinskyPriorPipeline
else:
	from .pipeline_kandinsky import KandinskyPipeline
	from .pipeline_kandinsky_imgaimg import KandinskyImgaImgPipeline
	from .pipeline_kandinsky_inpaint import KandinskyInpaintPipeline
	from .pipeline_kandinsky_prior import KandinskyPriorPipeline, KandinskyPriorPipelineOutput
	from .text_encoder import MultilingualCLIP



