





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 (							__a       :Tuple   ,					__a       :Optional[Any]   )		->    List[str]:






		"""simple docstring"""

		A__  				=       """https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg"""
		A__  				=       Image.open(requests.get(__a   ,					stream=__a   ).raw   ).convert("""RGB"""   )

		A__  				=       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)   ),
		    ]   )
		A__  				=       transform(__a   ).unsqueeze(0   ).to(__a   )
		return image







def        __lowerCamelCase (							__a       :Optional[Any]   )		->    Optional[Any]:






		"""simple docstring"""

		if "visual_encoder" in key:
				A__  				=       re.sub("""visual_encoder*"""   ,					"""vision_model.encoder"""   ,					__a   )
		if "blocks" in key:
				A__  				=       re.sub(R"""blocks"""   ,					"""layers"""   ,					__a   )
		if "attn" in key:
				A__  				=       re.sub(R"""attn"""   ,					"""self_attn"""   ,					__a   )
		if "norm1" in key:
				A__  				=       re.sub(R"""norm1"""   ,					"""layer_norm1"""   ,					__a   )
		if "norm2" in key:
				A__  				=       re.sub(R"""norm2"""   ,					"""layer_norm2"""   ,					__a   )
		if "encoder.norm" in key:
				A__  				=       re.sub(R"""encoder.norm"""   ,					"""post_layernorm"""   ,					__a   )
		if "encoder.patch_embed.proj" in key:
				A__  				=       re.sub(R"""encoder.patch_embed.proj"""   ,					"""embeddings.patch_embedding"""   ,					__a   )

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

		if "self_attn" in key:
				A__  				=       re.sub(R"""self_attn.proj"""   ,					"""self_attn.projection"""   ,					__a   )

		return key







@torch.no_grad()
def        __lowerCamelCase (							__a       :Optional[int]   ,					__a       :Dict=None   )		->    Union[str, Any]:






		"""simple docstring"""

		if config_path is not None:
				A__  				=       BlipConfig.from_pretrained(__a   )
		else:
				A__  				=       BlipConfig(projection_dim=5_1_2   ,					text_config={}   ,					vision_config={}   )

		A__  				=       BlipForConditionalGeneration(__a   ).eval()

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

		A__  				=       blip_decoder(pretrained=__a   ,					image_size=3_8_4   ,					vit="""base"""   )
		A__  				=       pt_model.eval()

		A__  				=       pt_model.state_dict()
		for key in modified_state_dict.copy():
				A__  				=       modified_state_dict.pop(__a   )
				A__  				=       rename_key(__a   )
				A__  				=       value

		hf_model.load_state_dict(__a   )

		A__  				=       3_8_4
		A__  				=       load_demo_image(image_size=__a   ,					device="""cpu"""   )
		A__  				=       BertTokenizer.from_pretrained("""bert-base-uncased"""   )
		A__  				=       tokenizer(["""a picture of"""]   ).input_ids

		A__  				=       hf_model.generate(__a   ,					__a   )

		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__  				=       hf_model.generate(__a   )

		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(__a   )

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

		A__  				=       blip_vqa(pretrained=__a   ,					image_size=__a   ,					vit="""base"""   )
		vqa_model.eval()

		A__  				=       vqa_model.state_dict()
		for key in modified_state_dict.copy():
				A__  				=       modified_state_dict.pop(__a   )
				A__  				=       rename_key(__a   )
				A__  				=       value

		A__  				=       BlipForQuestionAnswering(__a   )

		hf_vqa_model.load_state_dict(__a   )

		A__  				=       ["""How many dogs are in this image?"""]
		A__  				=       tokenizer(__a   ,					return_tensors="""pt"""   ).input_ids

		A__  				=       hf_vqa_model.generate(__a   ,					__a   )
		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__  				=       """https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth"""

		A__  				=       blip_itm(pretrained=__a   ,					image_size=__a   ,					vit="""base"""   )
		itm_model.eval()

		A__  				=       itm_model.state_dict()
		for key in modified_state_dict.copy():
				A__  				=       modified_state_dict.pop(__a   )
				A__  				=       rename_key(__a   )
				A__  				=       value

		A__  				=       BlipForImageTextRetrieval(__a   )

		A__  				=       ["""A picture of a woman with a dog sitting in a beach"""]
		A__  				=       tokenizer(
		    __a   ,					return_tensors="""pt"""   ,					padding="""max_length"""   ,					truncation=__a   ,					max_length=3_5   ,					).input_ids

		hf_itm_model.load_state_dict(__a   )
		hf_itm_model.eval()

		A__  				=       hf_itm_model(__a   ,					__a   ,					use_itm_head=__a   )
		A__  				=       hf_itm_model(__a   ,					__a   ,					use_itm_head=__a   )

		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__":
					A   : List[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''')
					A   : Union[str, Any]			=				parser.parse_args()

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










import unittest

import numpy as np







def        __lowerCamelCase (							__a       :np.ndarray   ,					__a       :np.ndarray   ,					__a       :np.ndarray   ,					__a       :np.ndarray | None = None   ,					)		->    np.ndarray:






		"""simple docstring"""

		A__  				=       np.shape(__a   )
		A__  				=       np.shape(__a   )
		A__  				=       np.shape(__a   )

		if shape_a[0] != shape_b[0]:
				A__  				=       (
				    """Expected the same number of rows for A and B. """
				    F'Instead found A of size {shape_a} and B of size {shape_b}'
				)
				raise ValueError(__a   )

		if shape_b[1] != shape_c[1]:
				A__  				=       (
				    """Expected the same number of columns for B and C. """
				    F'Instead found B of size {shape_b} and C of size {shape_c}'
				)
				raise ValueError(__a   )

		A__  				=       pseudo_inv
		if a_inv is None:
				try:
						A__  				=       np.linalg.inv(__a   )
				except np.linalg.LinAlgError:
						raise ValueError(
						    """Input matrix A is not invertible. Cannot compute Schur complement."""   )

		return mat_c - mat_b.T @ a_inv @ mat_b


class   A      (unittest.TestCase   ):
			'''simple docstring'''




			def        a_	(      self       :   Union[str, Any]       )     ->     None:







					"""simple docstring"""






					A__  				=       np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]       )
					A__  				=       np.array([[0, 3], [3, 0], [2, 3]]       )
					A__  				=       np.array([[2, 1], [6, 3]]       )

					A__  				=       schur_complement(__lowerCAmelCase							,     __lowerCAmelCase							,     __lowerCAmelCase       )

					A__  				=       np.block([[a, b], [b.T, c]]       )

					A__  				=       np.linalg.det(__lowerCAmelCase       )
					A__  				=       np.linalg.det(__lowerCAmelCase       )
					A__  				=       np.linalg.det(__lowerCAmelCase       )

					self.assertAlmostEqual(__lowerCAmelCase							,     det_a * det_s       )






			def        a_	(      self       :   str       )     ->     None:







					"""simple docstring"""






					A__  				=       np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]       )
					A__  				=       np.array([[0, 3], [3, 0], [2, 3]]       )
					A__  				=       np.array([[2, 1], [6, 3]]       )

					with self.assertRaises(__lowerCAmelCase       ):
							schur_complement(__lowerCAmelCase							,     __lowerCAmelCase							,     __lowerCAmelCase       )







			def        a_	(      self       :   List[str]       )     ->     None:







					"""simple docstring"""






					A__  				=       np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]]       )
					A__  				=       np.array([[0, 3], [3, 0], [2, 3]]       )
					A__  				=       np.array([[2, 1, 3], [6, 3, 5]]       )

					with self.assertRaises(__lowerCAmelCase       ):
							schur_complement(__lowerCAmelCase							,     __lowerCAmelCase							,     __lowerCAmelCase       )


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					unittest.main()



