

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_     (							UpperCAmelCase_							:     List[Any]							,       UpperCAmelCase_							:     List[Any]	)						->				Optional[Any]:




	'''simple docstring'''




	_UpperCamelCase   :		Any			=						'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'
	_UpperCamelCase   :		Tuple			=						Image.open(requests.get(UpperCAmelCase_							,       stream=UpperCAmelCase_	).raw	).convert('RGB'	)

	_UpperCamelCase   :		Optional[Any]			=						transforms.Compose(
	    [
	        transforms.Resize((image_size, image_size)							,       interpolation=InterpolationMode.BICUBIC	),
	        transforms.ToTensor(),
	        transforms.Normalize((0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3)							,       (0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1)	),
	    ]	)
	_UpperCamelCase   :		int			=						transform(UpperCAmelCase_	).unsqueeze(0	).to(UpperCAmelCase_	)
	return image






def 					lowerCamelCase_     (							UpperCAmelCase_							:     Optional[Any]	)						->				int:




	'''simple docstring'''




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

	if "encoder.pos_embed" in key:
		_UpperCamelCase   :		Dict			=						re.sub(R'encoder.pos_embed'							,       'embeddings.position_embedding'							,       UpperCAmelCase_	)
	if "encoder.cls_token" in key:
		_UpperCamelCase   :		List[Any]			=						re.sub(R'encoder.cls_token'							,       'embeddings.class_embedding'							,       UpperCAmelCase_	)

	if "self_attn" in key:
		_UpperCamelCase   :		Tuple			=						re.sub(R'self_attn.proj'							,       'self_attn.projection'							,       UpperCAmelCase_	)

	return key






@torch.no_grad()
def 					lowerCamelCase_     (							UpperCAmelCase_							:     Optional[int]							,       UpperCAmelCase_							:     List[str]=None	)						->				List[str]:




	'''simple docstring'''




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

	_UpperCamelCase   :		Dict			=						BlipForConditionalGeneration(UpperCAmelCase_	).eval()

	_UpperCamelCase   :		Dict			=						'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_capfilt_large.pth'

	_UpperCamelCase   :		List[Any]			=						blip_decoder(pretrained=UpperCAmelCase_							,       image_size=3_8_4							,       vit='base'	)
	_UpperCamelCase   :		Optional[int]			=						pt_model.eval()

	_UpperCamelCase   :		List[Any]			=						pt_model.state_dict()
	for key in modified_state_dict.copy():
		_UpperCamelCase   :		Optional[Any]			=						modified_state_dict.pop(UpperCAmelCase_	)
		_UpperCamelCase   :		Dict			=						rename_key(UpperCAmelCase_	)
		_UpperCamelCase   :		Union[str, Any]			=						value

	hf_model.load_state_dict(UpperCAmelCase_	)

	_UpperCamelCase   :		List[str]			=						3_8_4
	_UpperCamelCase   :		Dict			=						load_demo_image(image_size=UpperCAmelCase_							,       device='cpu'	)
	_UpperCamelCase   :		Tuple			=						BertTokenizer.from_pretrained('bert-base-uncased'	)
	_UpperCamelCase   :		Any			=						tokenizer(['a picture of']	).input_ids

	_UpperCamelCase   :		Tuple			=						hf_model.generate(UpperCAmelCase_							,       UpperCAmelCase_	)

	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   :		Optional[int]			=						hf_model.generate(UpperCAmelCase_	)

	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(UpperCAmelCase_	)

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

	_UpperCamelCase   :		List[str]			=						blip_vqa(pretrained=UpperCAmelCase_							,       image_size=UpperCAmelCase_							,       vit='base'	)
	vqa_model.eval()

	_UpperCamelCase   :		Tuple			=						vqa_model.state_dict()
	for key in modified_state_dict.copy():
		_UpperCamelCase   :		Dict			=						modified_state_dict.pop(UpperCAmelCase_	)
		_UpperCamelCase   :		str			=						rename_key(UpperCAmelCase_	)
		_UpperCamelCase   :		Optional[Any]			=						value

	_UpperCamelCase   :		Optional[int]			=						BlipForQuestionAnswering(UpperCAmelCase_	)

	hf_vqa_model.load_state_dict(UpperCAmelCase_	)

	_UpperCamelCase   :		Optional[int]			=						['How many dogs are in this image?']
	_UpperCamelCase   :		List[Any]			=						tokenizer(UpperCAmelCase_							,       return_tensors='pt'	).input_ids

	_UpperCamelCase   :		Any			=						hf_vqa_model.generate(UpperCAmelCase_							,       UpperCAmelCase_	)
	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   :		Any			=						blip_itm(pretrained=UpperCAmelCase_							,       image_size=UpperCAmelCase_							,       vit='base'	)
	itm_model.eval()

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

	_UpperCamelCase   :		Tuple			=						BlipForImageTextRetrieval(UpperCAmelCase_	)

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

	hf_itm_model.load_state_dict(UpperCAmelCase_	)
	hf_itm_model.eval()

	_UpperCamelCase   :		Union[str, Any]			=						hf_itm_model(UpperCAmelCase_							,       UpperCAmelCase_							,       use_itm_head=UpperCAmelCase_	)
	_UpperCamelCase   :		Optional[int]			=						hf_itm_model(UpperCAmelCase_							,       UpperCAmelCase_							,       use_itm_head=UpperCAmelCase_	)

	assert out[0].item() == 0.2_1_1_0_6_8_7_4_9_4_2_7_7_9_5_4
	assert torch.nn.functional.softmax(out_itm[0]							,       dim=1	)[:, 1].item() == 0.4_5_6_9_8_8_4_5_3_8_6_5_0_5_1_2_7

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


if __name__ == "__main__":
							lowerCAmelCase__								=		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__								=		parser.parse_args()

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







from __future__ import annotations

import matplotlib.pyplot as plt  # type: ignore
import numpy

# initial triangle of Koch snowflake
lowerCAmelCase__								=		numpy.array([0, 0])
lowerCAmelCase__								=		numpy.array([0.5, 0.8_66_02_54])
lowerCAmelCase__								=		numpy.array([1, 0])
lowerCAmelCase__								=		[VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]






def 					lowerCamelCase_     (							UpperCAmelCase_							:     list[numpy.ndarray]							,       UpperCAmelCase_							:     int	)						->				list[numpy.ndarray]:




	'''simple docstring'''




	_UpperCamelCase   :		Tuple			=						initial_vectors
	for _ in range(UpperCAmelCase_	):
		_UpperCamelCase   :		str			=						iteration_step(UpperCAmelCase_	)
	return vectors






def 					lowerCamelCase_     (							UpperCAmelCase_							:     list[numpy.ndarray]	)						->				list[numpy.ndarray]:




	'''simple docstring'''




	_UpperCamelCase   :		int			=						[]
	for i, start_vector in enumerate(vectors[:-1]	):
		_UpperCamelCase   :		Union[str, Any]			=						vectors[i + 1]
		new_vectors.append(UpperCAmelCase_	)
		_UpperCamelCase   :		Tuple			=						end_vector - start_vector
		new_vectors.append(start_vector + difference_vector / 3	)
		new_vectors.append(
		    start_vector + difference_vector / 3 + rotate(difference_vector / 3							,       6_0	)	)
		new_vectors.append(start_vector + difference_vector * 2 / 3	)
	new_vectors.append(vectors[-1]	)
	return new_vectors






def 					lowerCamelCase_     (							UpperCAmelCase_							:     numpy.ndarray							,       UpperCAmelCase_							:     float	)						->				numpy.ndarray:




	'''simple docstring'''




	_UpperCamelCase   :		str			=						numpy.radians(UpperCAmelCase_	)
	_UpperCamelCase	,		_UpperCamelCase   :		Optional[Any]			=						numpy.cos(UpperCAmelCase_	), numpy.sin(UpperCAmelCase_	)
	_UpperCamelCase   :		Any			=						numpy.array(((c, -s), (s, c))	)
	return numpy.dot(UpperCAmelCase_							,       UpperCAmelCase_	)






def 					lowerCamelCase_     (							UpperCAmelCase_							:     list[numpy.ndarray]	)						->				None:




	'''simple docstring'''




	_UpperCamelCase   :		str			=						plt.gca()
	axes.set_aspect('equal'	)

	# matplotlib.pyplot.plot takes a list of all x-coordinates and a list of all
	# y-coordinates as inputs, which are constructed from the vector-list using
	# zip()
	_UpperCamelCase	,		_UpperCamelCase   :		Dict			=						zip(*UpperCAmelCase_	)
	plt.plot(UpperCAmelCase_							,       UpperCAmelCase_	)
	plt.show()


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							lowerCAmelCase__								=		iterate(INITIAL_VECTORS, 5)
							plot(processed_vectors)




