


'''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       _snake_case     (  A				,					A )		->					Optional[int]:
							lowerCAmelCase__      =			'''https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'''
							lowerCAmelCase__      =			Image.open(requests.get(__snake_case				,					stream=__snake_case ).raw ).convert('''RGB''' )

							lowerCAmelCase__      =			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) ),
							    ] )
							lowerCAmelCase__      =			transform(__snake_case ).unsqueeze(0 ).to(__snake_case )
							return image


def       _snake_case     (  A )		->					Dict:





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

							if "encoder.pos_embed" in key:
														lowerCAmelCase__      =			re.sub(R'''encoder.pos_embed'''				,					'''embeddings.position_embedding'''				,					__snake_case )
							if "encoder.cls_token" in key:
														lowerCAmelCase__      =			re.sub(R'''encoder.cls_token'''				,					'''embeddings.class_embedding'''				,					__snake_case )

							if "self_attn" in key:
														lowerCAmelCase__      =			re.sub(R'''self_attn.proj'''				,					'''self_attn.projection'''				,					__snake_case )

							return key


@torch.no_grad()
def       _snake_case     (  A				,					A=None )		->					Union[str, Any]:





							if config_path is not None:
														lowerCAmelCase__      =			BlipConfig.from_pretrained(__snake_case )
							else:
														lowerCAmelCase__      =			BlipConfig(projection_dim=512				,					text_config={}				,					vision_config={} )

							lowerCAmelCase__      =			BlipForConditionalGeneration(__snake_case ).eval()

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

							lowerCAmelCase__      =			blip_decoder(pretrained=__snake_case				,					image_size=384				,					vit='''base''' )
							lowerCAmelCase__      =			pt_model.eval()

							lowerCAmelCase__      =			pt_model.state_dict()
							for key in modified_state_dict.copy():
														lowerCAmelCase__      =			modified_state_dict.pop(__snake_case )
														lowerCAmelCase__      =			rename_key(__snake_case )
														lowerCAmelCase__      =			value

							hf_model.load_state_dict(__snake_case )

							lowerCAmelCase__      =			384
							lowerCAmelCase__      =			load_demo_image(image_size=__snake_case				,					device='''cpu''' )
							lowerCAmelCase__      =			BertTokenizer.from_pretrained('''bert-base-uncased''' )
							lowerCAmelCase__      =			tokenizer(['''a picture of'''] ).input_ids

							lowerCAmelCase__      =			hf_model.generate(__snake_case				,					__snake_case )

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

							lowerCAmelCase__      =			hf_model.generate(__snake_case )

							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(__snake_case )

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

							lowerCAmelCase__      =			blip_vqa(pretrained=__snake_case				,					image_size=__snake_case				,					vit='''base''' )
							vqa_model.eval()

							lowerCAmelCase__      =			vqa_model.state_dict()
							for key in modified_state_dict.copy():
														lowerCAmelCase__      =			modified_state_dict.pop(__snake_case )
														lowerCAmelCase__      =			rename_key(__snake_case )
														lowerCAmelCase__      =			value

							lowerCAmelCase__      =			BlipForQuestionAnswering(__snake_case )

							hf_vqa_model.load_state_dict(__snake_case )

							lowerCAmelCase__      =			['''How many dogs are in this image?''']
							lowerCAmelCase__      =			tokenizer(__snake_case				,					return_tensors='''pt''' ).input_ids

							lowerCAmelCase__      =			hf_vqa_model.generate(__snake_case				,					__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''' )

							lowerCAmelCase__      =			'''https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model_base_retrieval_coco.pth'''

							lowerCAmelCase__      =			blip_itm(pretrained=__snake_case				,					image_size=__snake_case				,					vit='''base''' )
							itm_model.eval()

							lowerCAmelCase__      =			itm_model.state_dict()
							for key in modified_state_dict.copy():
														lowerCAmelCase__      =			modified_state_dict.pop(__snake_case )
														lowerCAmelCase__      =			rename_key(__snake_case )
														lowerCAmelCase__      =			value

							lowerCAmelCase__      =			BlipForImageTextRetrieval(__snake_case )

							lowerCAmelCase__      =			['''A picture of a woman with a dog sitting in a beach''']
							lowerCAmelCase__      =			tokenizer(
							    __snake_case				,					return_tensors='''pt'''				,					padding='''max_length'''				,					truncation=__snake_case				,					max_length=35				,					).input_ids

							hf_itm_model.load_state_dict(__snake_case )
							hf_itm_model.eval()

							lowerCAmelCase__      =			hf_itm_model(__snake_case				,					__snake_case				,					use_itm_head=__snake_case )
							lowerCAmelCase__      =			hf_itm_model(__snake_case				,					__snake_case				,					use_itm_head=__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					       =							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					       =							parser.parse_args()

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



'''simple docstring'''






from math import pi, sqrt, tan


def       _snake_case     (  A )		->					float:
							if side_length < 0:
														raise ValueError('''surface_area_cube() only accepts non-negative values''' )
							return 6 * side_length**2


def       _snake_case     (  A				,					A				,					A )		->					float:
							if length < 0 or breadth < 0 or height < 0:
														raise ValueError('''surface_area_cuboid() only accepts non-negative values''' )
							return 2 * ((length * breadth) + (breadth * height) + (length * height))


def       _snake_case     (  A )		->					float:
							if radius < 0:
														raise ValueError('''surface_area_sphere() only accepts non-negative values''' )
							return 4 * pi * radius**2


def       _snake_case     (  A )		->					float:
							if radius < 0:
														raise ValueError('''surface_area_hemisphere() only accepts non-negative values''' )
							return 3 * pi * radius**2


def       _snake_case     (  A				,					A )		->					float:
							if radius < 0 or height < 0:
														raise ValueError('''surface_area_cone() only accepts non-negative values''' )
							return pi * radius * (radius + (height**2 + radius**2) ** 0.5)


def       _snake_case     (  A				,					A				,					A )		->					float:
							if radius_a < 0 or radius_a < 0 or height < 0:
														raise ValueError(
														    '''surface_area_conical_frustum() only accepts non-negative values''' )
							lowerCAmelCase__      =			(height**2 + (radius_a - radius_a) ** 2) ** 0.5
							return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)


def       _snake_case     (  A				,					A )		->					float:
							if radius < 0 or height < 0:
														raise ValueError('''surface_area_cylinder() only accepts non-negative values''' )
							return 2 * pi * radius * (height + radius)


def       _snake_case     (  A				,					A )		->					float:
							if torus_radius < 0 or tube_radius < 0:
														raise ValueError('''surface_area_torus() only accepts non-negative values''' )
							if torus_radius < tube_radius:
														raise ValueError(
														    '''surface_area_torus() does not support spindle or self intersecting tori''' )
							return 4 * pow(A				,					2 ) * torus_radius * tube_radius


def       _snake_case     (  A				,					A )		->					float:
							if length < 0 or width < 0:
														raise ValueError('''area_rectangle() only accepts non-negative values''' )
							return length * width


def       _snake_case     (  A )		->					float:
							if side_length < 0:
														raise ValueError('''area_square() only accepts non-negative values''' )
							return side_length**2


def       _snake_case     (  A				,					A )		->					float:
							if base < 0 or height < 0:
														raise ValueError('''area_triangle() only accepts non-negative values''' )
							return (base * height) / 2


def       _snake_case     (  A				,					A				,					A )		->					float:
							if sidea < 0 or sidea < 0 or sidea < 0:
														raise ValueError('''area_triangle_three_sides() only accepts non-negative values''' )
							elif sidea + sidea < sidea or sidea + sidea < sidea or sidea + sidea < sidea:
														raise ValueError('''Given three sides do not form a triangle''' )
							lowerCAmelCase__      =			(sidea + sidea + sidea) / 2
							lowerCAmelCase__      =			sqrt(
							    semi_perimeter
							    * (semi_perimeter - sidea)
							    * (semi_perimeter - sidea)
							    * (semi_perimeter - sidea) )
							return area


def       _snake_case     (  A				,					A )		->					float:
							if base < 0 or height < 0:
														raise ValueError('''area_parallelogram() only accepts non-negative values''' )
							return base * height


def       _snake_case     (  A				,					A				,					A )		->					float:
							if basea < 0 or basea < 0 or height < 0:
														raise ValueError('''area_trapezium() only accepts non-negative values''' )
							return 1 / 2 * (basea + basea) * height


def       _snake_case     (  A )		->					float:
							if radius < 0:
														raise ValueError('''area_circle() only accepts non-negative values''' )
							return pi * radius**2


def       _snake_case     (  A				,					A )		->					float:
							if radius_x < 0 or radius_y < 0:
														raise ValueError('''area_ellipse() only accepts non-negative values''' )
							return pi * radius_x * radius_y


def       _snake_case     (  A				,					A )		->					float:
							if diagonal_a < 0 or diagonal_a < 0:
														raise ValueError('''area_rhombus() only accepts non-negative values''' )
							return 1 / 2 * diagonal_a * diagonal_a


def       _snake_case     (  A				,					A )		->					float:
							if not isinstance(A				,					A ) or sides < 3:
														raise ValueError(
														    '''area_reg_polygon() only accepts integers greater than or \
equal to three as number of sides''' )
							elif length < 0:
														raise ValueError(
														    '''area_reg_polygon() only accepts non-negative values as \
length of a side''' )
							return (sides * length**2) / (4 * tan(pi / sides ))
							return (sides * length**2) / (4 * tan(pi / sides ))


if __name__ == "__main__":
		import doctest

		doctest.testmod(verbose=True)  # verbose so we can see methods missing tests

		print('''[DEMO] Areas of various geometric shapes: \n''')
		print(f"""Rectangle: {area_rectangle(10, 20) = }""")
		print(f"""Square: {area_square(10) = }""")
		print(f"""Triangle: {area_triangle(10, 10) = }""")
		print(f"""Triangle: {area_triangle_three_sides(5, 12, 13) = }""")
		print(f"""Parallelogram: {area_parallelogram(10, 20) = }""")
		print(f"""Rhombus: {area_rhombus(10, 20) = }""")
		print(f"""Trapezium: {area_trapezium(10, 20, 30) = }""")
		print(f"""Circle: {area_circle(20) = }""")
		print(f"""Ellipse: {area_ellipse(10, 20) = }""")
		print('''\nSurface Areas of various geometric shapes: \n''')
		print(f"""Cube: {surface_area_cube(20) = }""")
		print(f"""Cuboid: {surface_area_cuboid(10, 20, 30) = }""")
		print(f"""Sphere: {surface_area_sphere(20) = }""")
		print(f"""Hemisphere: {surface_area_hemisphere(20) = }""")
		print(f"""Cone: {surface_area_cone(10, 20) = }""")
		print(f"""Conical Frustum: {surface_area_conical_frustum(10, 20, 30) = }""")
		print(f"""Cylinder: {surface_area_cylinder(10, 20) = }""")
		print(f"""Torus: {surface_area_torus(20, 10) = }""")
		print(f"""Equilateral Triangle: {area_reg_polygon(3, 10) = }""")
		print(f"""Square: {area_reg_polygon(4, 10) = }""")
		print(f"""Reqular Pentagon: {area_reg_polygon(5, 10) = }""")