



'''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    __lowerCamelCase						(      _lowercase			,       _lowercase	)   ->    Union[str, Any]:
							UpperCAmelCase  :	Optional[Any]							  =	"""https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg"""
							UpperCAmelCase  :	Optional[Any]							  =	Image.open(requests.get(_lowercase			,       stream=_lowercase	).raw	).convert("""RGB"""	)

							UpperCAmelCase  :	Optional[Any]							  =	transforms.Compose(
							    [
							        transforms.Resize((image_size, image_size)			,       interpolation=InterpolationMode.BICUBIC	),
							        transforms.ToTensor(),
							        transforms.Normalize((0.4814_5466, 0.457_8275, 0.4082_1073)			,       (0.2686_2954, 0.2613_0258, 0.2757_7711)	),
							    ]	)
							UpperCAmelCase  :	str							  =	transform(_lowercase	).unsqueeze(0	).to(_lowercase	)
							return image



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

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

							if "self_attn" in key:
														UpperCAmelCase  :	Any							  =	re.sub(R"""self_attn.proj"""			,       """self_attn.projection"""			,       _lowercase	)

							return key



@torch.no_grad()
def    __lowerCamelCase						(      _lowercase			,       _lowercase=None	)   ->    str:
							if config_path is not None:
														UpperCAmelCase  :	Optional[int]							  =	BlipConfig.from_pretrained(_lowercase	)
							else:
														UpperCAmelCase  :	Union[str, Any]							  =	BlipConfig(projection_dim=5_1_2			,       text_config={}			,       vision_config={}	)

							UpperCAmelCase  :	Tuple							  =	BlipForConditionalGeneration(_lowercase	).eval()

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

							UpperCAmelCase  :	Tuple							  =	blip_decoder(pretrained=_lowercase			,       image_size=3_8_4			,       vit="""base"""	)
							UpperCAmelCase  :	Any							  =	pt_model.eval()

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

							hf_model.load_state_dict(_lowercase	)

							UpperCAmelCase  :	List[str]							  =	3_8_4
							UpperCAmelCase  :	Optional[Any]							  =	load_demo_image(image_size=_lowercase			,       device="""cpu"""	)
							UpperCAmelCase  :	Optional[int]							  =	BertTokenizer.from_pretrained("""bert-base-uncased"""	)
							UpperCAmelCase  :	Optional[Any]							  =	tokenizer(["""a picture of"""]	).input_ids

							UpperCAmelCase  :	Dict							  =	hf_model.generate(_lowercase			,       _lowercase	)

							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  :	Tuple							  =	hf_model.generate(_lowercase	)

							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(_lowercase	)

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

							UpperCAmelCase  :	Optional[int]							  =	blip_vqa(pretrained=_lowercase			,       image_size=_lowercase			,       vit="""base"""	)
							vqa_model.eval()

							UpperCAmelCase  :	Optional[int]							  =	vqa_model.state_dict()
							for key in modified_state_dict.copy():
														UpperCAmelCase  :	Optional[int]							  =	modified_state_dict.pop(_lowercase	)
														UpperCAmelCase  :	str							  =	rename_key(_lowercase	)
														UpperCAmelCase  :	List[str]							  =	value

							UpperCAmelCase  :	Tuple							  =	BlipForQuestionAnswering(_lowercase	)

							hf_vqa_model.load_state_dict(_lowercase	)

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

							UpperCAmelCase  :	Any							  =	hf_vqa_model.generate(_lowercase			,       _lowercase	)
							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  :	Tuple							  =	"""https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth"""

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

							UpperCAmelCase  :	Tuple							  =	itm_model.state_dict()
							for key in modified_state_dict.copy():
														UpperCAmelCase  :	str							  =	modified_state_dict.pop(_lowercase	)
														UpperCAmelCase  :	List[str]							  =	rename_key(_lowercase	)
														UpperCAmelCase  :	List[str]							  =	value

							UpperCAmelCase  :	Optional[int]							  =	BlipForImageTextRetrieval(_lowercase	)

							UpperCAmelCase  :	int							  =	["""A picture of a woman with a dog sitting in a beach"""]
							UpperCAmelCase  :	Optional[int]							  =	tokenizer(
							    _lowercase			,       return_tensors="""pt"""			,       padding="""max_length"""			,       truncation=_lowercase			,       max_length=3_5			,       ).input_ids

							hf_itm_model.load_state_dict(_lowercase	)
							hf_itm_model.eval()

							UpperCAmelCase  :	List[Any]							  =	hf_itm_model(_lowercase			,       _lowercase			,       use_itm_head=_lowercase	)
							UpperCAmelCase  :	Optional[Any]							  =	hf_itm_model(_lowercase			,       _lowercase			,       use_itm_head=_lowercase	)

							assert out[0].item() == 0.2110_6874_9427_7954
							assert torch.nn.functional.softmax(out_itm[0]			,       dim=1	)[:, 1].item() == 0.4_5698_8453_8650_5127

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


if __name__ == "__main__":
							a     :    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""")
							a     :    str      						=					parser.parse_args()

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






'''simple docstring'''




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


a     :    str      						=					logging.get_logger(__name__)

a     :    Any      						=					{
    """google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/config.json""",
    """google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/config.json"""
    # See all FNet models at https://huggingface.co/models?filter=fnet
}

class     UpperCamelCase_				(							__magic_name__    ):
	lowercase    =				'fnet'





	def __init__(						self	,	A=32000	,	A=768	,	A=12	,	A=3072	,	A="gelu_new"	,	A=0.1	,	A=512	,	A=4	,	A=0.0_2	,	A=1e-12	,	A=False	,	A=512	,	A=3	,	A=1	,	A=2	,	**A	,	)    ->    int:
								super().__init__(pad_token_id=A	,	bos_token_id=A	,	eos_token_id=A	,	**A					)

								UpperCAmelCase  :	Optional[Any]							  =	vocab_size
								UpperCAmelCase  :	Dict							  =	max_position_embeddings
								UpperCAmelCase  :	List[str]							  =	hidden_size
								UpperCAmelCase  :	int							  =	num_hidden_layers
								UpperCAmelCase  :	Any							  =	intermediate_size
								UpperCAmelCase  :	List[str]							  =	hidden_act
								UpperCAmelCase  :	Tuple							  =	hidden_dropout_prob
								UpperCAmelCase  :	List[str]							  =	initializer_range
								UpperCAmelCase  :	List[Any]							  =	type_vocab_size
								UpperCAmelCase  :	int							  =	layer_norm_eps
								UpperCAmelCase  :	Optional[Any]							  =	use_tpu_fourier_optimizations
								UpperCAmelCase  :	List[Any]							  =	tpu_short_seq_length

