
"""simple docstring"""




import argparse

import requests
import torch
from PIL import Image

from transformers import (
    CLIPSegConfig,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPSegTextConfig,
    CLIPSegVisionConfig,
    CLIPTokenizer,
    ViTImageProcessor,
)

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_     ):
					SCREAMING_SNAKE_CASE           =      CLIPSegTextConfig()
					SCREAMING_SNAKE_CASE           =      CLIPSegVisionConfig(patch_size=1_6     )

					SCREAMING_SNAKE_CASE           =      True if 'refined' in model_name else False
					SCREAMING_SNAKE_CASE           =      1_6 if 'rd16' in model_name else 6_4

					SCREAMING_SNAKE_CASE           =      CLIPSegConfig.from_text_vision_configs(
					    SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	use_complex_transposed_convolution=SCREAMING_SNAKE_CASE_,	reduce_dim=SCREAMING_SNAKE_CASE_,	)
					return config

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_     ):
					# update prefixes
					if "clip_model" in name:
										SCREAMING_SNAKE_CASE           =      name.replace('clip_model',	'clip'     )
					if "transformer" in name:
										if "visual" in name:
															SCREAMING_SNAKE_CASE           =      name.replace('visual.transformer',	'vision_model'     )
										else:
															SCREAMING_SNAKE_CASE           =      name.replace('transformer',	'text_model'     )
					if "resblocks" in name:
										SCREAMING_SNAKE_CASE           =      name.replace('resblocks',	'encoder.layers'     )
					if "ln_1" in name:
										SCREAMING_SNAKE_CASE           =      name.replace('ln_1',	'layer_norm1'     )
					if "ln_2" in name:
										SCREAMING_SNAKE_CASE           =      name.replace('ln_2',	'layer_norm2'     )
					if "c_fc" in name:
										SCREAMING_SNAKE_CASE           =      name.replace('c_fc',	'fc1'     )
					if "c_proj" in name:
										SCREAMING_SNAKE_CASE           =      name.replace('c_proj',	'fc2'     )
					if "attn" in name and "self" not in name:
										SCREAMING_SNAKE_CASE           =      name.replace('attn',	'self_attn'     )
					# text encoder
					if "token_embedding" in name:
										SCREAMING_SNAKE_CASE           =      name.replace('token_embedding',	'text_model.embeddings.token_embedding'     )
					if "positional_embedding" in name and "visual" not in name:
										SCREAMING_SNAKE_CASE           =      name.replace('positional_embedding',	'text_model.embeddings.position_embedding.weight'     )
					if "ln_final" in name:
										SCREAMING_SNAKE_CASE           =      name.replace('ln_final',	'text_model.final_layer_norm'     )
					# vision encoder
					if "visual.class_embedding" in name:
										SCREAMING_SNAKE_CASE           =      name.replace('visual.class_embedding',	'vision_model.embeddings.class_embedding'     )
					if "visual.conv1" in name:
										SCREAMING_SNAKE_CASE           =      name.replace('visual.conv1',	'vision_model.embeddings.patch_embedding'     )
					if "visual.positional_embedding" in name:
										SCREAMING_SNAKE_CASE           =      name.replace('visual.positional_embedding',	'vision_model.embeddings.position_embedding.weight'     )
					if "visual.ln_pre" in name:
										SCREAMING_SNAKE_CASE           =      name.replace('visual.ln_pre',	'vision_model.pre_layrnorm'     )
					if "visual.ln_post" in name:
										SCREAMING_SNAKE_CASE           =      name.replace('visual.ln_post',	'vision_model.post_layernorm'     )
					# projection layers
					if "visual.proj" in name:
										SCREAMING_SNAKE_CASE           =      name.replace('visual.proj',	'visual_projection.weight'     )
					if "text_projection" in name:
										SCREAMING_SNAKE_CASE           =      name.replace('text_projection',	'text_projection.weight'     )
					# decoder
					if "trans_conv" in name:
										SCREAMING_SNAKE_CASE           =      name.replace('trans_conv',	'transposed_convolution'     )
					if "film_mul" in name or "film_add" in name or "reduce" in name or "transposed_convolution" in name:
										SCREAMING_SNAKE_CASE           =      'decoder.' + name
					if "blocks" in name:
										SCREAMING_SNAKE_CASE           =      name.replace('blocks',	'decoder.layers'     )
					if "linear1" in name:
										SCREAMING_SNAKE_CASE           =      name.replace('linear1',	'mlp.fc1'     )
					if "linear2" in name:
										SCREAMING_SNAKE_CASE           =      name.replace('linear2',	'mlp.fc2'     )
					if "norm1" in name and "layer_" not in name:
										SCREAMING_SNAKE_CASE           =      name.replace('norm1',	'layer_norm1'     )
					if "norm2" in name and "layer_" not in name:
										SCREAMING_SNAKE_CASE           =      name.replace('norm2',	'layer_norm2'     )

					return name

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					for key in orig_state_dict.copy().keys():
										SCREAMING_SNAKE_CASE           =      orig_state_dict.pop(SCREAMING_SNAKE_CASE_     )

										if key.startswith('clip_model'     ) and "attn.in_proj" in key:
															SCREAMING_SNAKE_CASE           =      key.split('.'     )
															if "visual" in key:
																				SCREAMING_SNAKE_CASE           =      int(key_split[4]     )
																				SCREAMING_SNAKE_CASE           =      config.vision_config.hidden_size
																				SCREAMING_SNAKE_CASE           =      'vision_model'
															else:
																				SCREAMING_SNAKE_CASE           =      int(key_split[3]     )
																				SCREAMING_SNAKE_CASE           =      config.text_config.hidden_size
																				SCREAMING_SNAKE_CASE           =      'text_model'

															if "weight" in key:
																				SCREAMING_SNAKE_CASE           =      val[:dim, :]
																				SCREAMING_SNAKE_CASE           =      val[
																				    dim : dim * 2, :
																				]
																				SCREAMING_SNAKE_CASE           =      val[-dim:, :]
															else:
																				SCREAMING_SNAKE_CASE           =      val[:dim]
																				SCREAMING_SNAKE_CASE           =      val[dim : dim * 2]
																				SCREAMING_SNAKE_CASE           =      val[-dim:]
										elif "self_attn" in key and "out_proj" not in key:
															SCREAMING_SNAKE_CASE           =      key.split('.'     )
															SCREAMING_SNAKE_CASE           =      int(key_split[1]     )
															SCREAMING_SNAKE_CASE           =      config.reduce_dim
															if "weight" in key:
																				SCREAMING_SNAKE_CASE           =      val[:dim, :]
																				SCREAMING_SNAKE_CASE           =      val[dim : dim * 2, :]
																				SCREAMING_SNAKE_CASE           =      val[-dim:, :]
															else:
																				SCREAMING_SNAKE_CASE           =      val[:dim]
																				SCREAMING_SNAKE_CASE           =      val[dim : dim * 2]
																				SCREAMING_SNAKE_CASE           =      val[-dim:]
										else:
															SCREAMING_SNAKE_CASE           =      rename_key(SCREAMING_SNAKE_CASE_     )
															if "visual_projection" in new_name or "text_projection" in new_name:
																				SCREAMING_SNAKE_CASE           =      val.T
															SCREAMING_SNAKE_CASE           =      val

					return orig_state_dict

def     UpperCamelCase_    (						):
					SCREAMING_SNAKE_CASE           =      'http://images.cocodataset.org/val2017/000000039769.jpg'
					SCREAMING_SNAKE_CASE           =      Image.open(requests.get(SCREAMING_SNAKE_CASE_,	stream=SCREAMING_SNAKE_CASE_     ).raw     )
					return image

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					SCREAMING_SNAKE_CASE           =      get_clipseg_config(SCREAMING_SNAKE_CASE_     )
					SCREAMING_SNAKE_CASE           =      CLIPSegForImageSegmentation(SCREAMING_SNAKE_CASE_     )
					model.eval()

					SCREAMING_SNAKE_CASE           =      torch.load(SCREAMING_SNAKE_CASE_,	map_location='cpu'     )

					# remove some keys
					for key in state_dict.copy().keys():
										if key.startswith('model'     ):
															state_dict.pop(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     )

    # rename some keys
					SCREAMING_SNAKE_CASE           =      convert_state_dict(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     )
					SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE           =      model.load_state_dict(SCREAMING_SNAKE_CASE_,	strict=SCREAMING_SNAKE_CASE_     )

					if missing_keys != ["clip.text_model.embeddings.position_ids", "clip.vision_model.embeddings.position_ids"]:
										raise ValueError('Missing keys that are not expected: {}'.format(SCREAMING_SNAKE_CASE_     )     )
					if unexpected_keys != ["decoder.reduce.weight", "decoder.reduce.bias"]:
										raise ValueError(f'''Unexpected keys: {unexpected_keys}'''     )

					SCREAMING_SNAKE_CASE           =      ViTImageProcessor(size=3_5_2     )
					SCREAMING_SNAKE_CASE           =      CLIPTokenizer.from_pretrained('openai/clip-vit-base-patch32'     )
					SCREAMING_SNAKE_CASE           =      CLIPSegProcessor(image_processor=SCREAMING_SNAKE_CASE_,	tokenizer=SCREAMING_SNAKE_CASE_     )

					SCREAMING_SNAKE_CASE           =      prepare_img()
					SCREAMING_SNAKE_CASE           =      ['a glass', 'something to fill', 'wood', 'a jar']

					SCREAMING_SNAKE_CASE           =      processor(text=SCREAMING_SNAKE_CASE_,	images=[image] * len(SCREAMING_SNAKE_CASE_     ),	padding='max_length',	return_tensors='pt'     )

					with torch.no_grad():
										SCREAMING_SNAKE_CASE           =      model(**SCREAMING_SNAKE_CASE_     )

					# verify values
					SCREAMING_SNAKE_CASE           =      torch.tensor([0.1_110, -0.1_882, 0.1_645]     )
					SCREAMING_SNAKE_CASE           =      torch.tensor([0.2_692, -0.7_197, -0.1_328]     )
					if model_name == "clipseg-rd64-refined":
										SCREAMING_SNAKE_CASE           =      torch.tensor(
										    [[-10.0_407, -9.9_431, -10.2_646], [-9.9_751, -9.7_064, -9.9_586], [-9.6_891, -9.5_645, -9.9_618]]     )
					elif model_name == "clipseg-rd64":
										SCREAMING_SNAKE_CASE           =      torch.tensor(
										    [[-7.2_877, -7.2_711, -7.2_463], [-7.2_652, -7.2_780, -7.2_520], [-7.2_239, -7.2_204, -7.2_001]]     )
					elif model_name == "clipseg-rd16":
										SCREAMING_SNAKE_CASE           =      torch.tensor(
										    [[-6.3_955, -6.4_055, -6.4_151], [-6.3_911, -6.4_033, -6.4_100], [-6.3_474, -6.3_702, -6.3_762]]     )
					else:
										raise ValueError(f'''Model name {model_name} not supported.'''     )

					assert torch.allclose(outputs.logits[0, :3, :3],	SCREAMING_SNAKE_CASE_,	atol=1E-3     )
					assert torch.allclose(outputs.conditional_embeddings[0, :3],	SCREAMING_SNAKE_CASE_,	atol=1E-3     )
					assert torch.allclose(outputs.pooled_output[0, :3],	SCREAMING_SNAKE_CASE_,	atol=1E-3     )
					print('Looks ok!'     )

					if pytorch_dump_folder_path is not None:
										print(f'''Saving model and processor to {pytorch_dump_folder_path}'''     )
										model.save_pretrained(SCREAMING_SNAKE_CASE_     )
										processor.save_pretrained(SCREAMING_SNAKE_CASE_     )

					if push_to_hub:
										print(f'''Pushing model and processor for {model_name} to the hub'''     )
										model.push_to_hub(f'''CIDAS/{model_name}'''     )
										processor.push_to_hub(f'''CIDAS/{model_name}'''     )


if __name__ == "__main__":
	snake_case					  =						argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--model_name',
	    default='clipseg-rd64',
	    type=str,
	    choices=['clipseg-rd16', 'clipseg-rd64', 'clipseg-rd64-refined'],
	    help=(
	        'Name of the model. Supported models are: clipseg-rd64, clipseg-rd16 and clipseg-rd64-refined (rd meaning'
	        ' reduce dimension)'
	    ),
	)
	parser.add_argument(
	    '--checkpoint_path',
	    default='/Users/nielsrogge/Documents/CLIPSeg/clip_plus_rd64-uni.pth',
	    type=str,
	    help=(
	        'Path to the original checkpoint. Note that the script assumes that the checkpoint includes both CLIP and'
	        ' the decoder weights.'
	    ),
	)
	parser.add_argument(
	    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
	)
	parser.add_argument(
	    '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
	)

	snake_case					  =						parser.parse_args()
	convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)


"""simple docstring"""




from __future__ import annotations

from math import pi, pow

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_     ):
					if side_length < 0:
										raise ValueError('vol_cube() only accepts non-negative values'     )
					return pow(SCREAMING_SNAKE_CASE_,	3     )

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					if height < 0 or radius < 0:
										raise ValueError('vol_spherical_cap() only accepts non-negative values'     )
					# Volume is 1/3 pi * height squared * (3 * radius - height)
					return 1 / 3 * pi * pow(SCREAMING_SNAKE_CASE_,	2     ) * (3 * radius - height)

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					if radius_a < 0 or radius_a < 0 or centers_distance < 0:
										raise ValueError('vol_spheres_intersect() only accepts non-negative values'     )
					if centers_distance == 0:
										return vol_sphere(min(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     )     )

					SCREAMING_SNAKE_CASE           =      (
					    (radius_a - radius_a + centers_distance)
					    * (radius_a + radius_a - centers_distance)
					    / (2 * centers_distance)
					)
					SCREAMING_SNAKE_CASE           =      (
					    (radius_a - radius_a + centers_distance)
					    * (radius_a + radius_a - centers_distance)
					    / (2 * centers_distance)
					)

					return vol_spherical_cap(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ) + vol_spherical_cap(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     )

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):

					if radius_a <= 0 or radius_a <= 0 or centers_distance < 0:
										raise ValueError(
										    'vol_spheres_union() only accepts non-negative values, non-zero radius'     )

					if centers_distance == 0:
										return vol_sphere(max(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     )     )

					return (
					    vol_sphere(SCREAMING_SNAKE_CASE_     )
					    + vol_sphere(SCREAMING_SNAKE_CASE_     )
					    - vol_spheres_intersect(SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     )
					)

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					if width < 0 or height < 0 or length < 0:
										raise ValueError('vol_cuboid() only accepts non-negative values'     )
					return float(width * height * length     )

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					if height < 0 or area_of_base < 0:
										raise ValueError('vol_cone() only accepts non-negative values'     )
					return area_of_base * height / 3.0

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					if height < 0 or radius < 0:
										raise ValueError('vol_right_circ_cone() only accepts non-negative values'     )
					return pi * pow(SCREAMING_SNAKE_CASE_,	2     ) * height / 3.0

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					if height < 0 or area_of_base < 0:
										raise ValueError('vol_prism() only accepts non-negative values'     )
					return float(area_of_base * height     )

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					if height < 0 or area_of_base < 0:
										raise ValueError('vol_pyramid() only accepts non-negative values'     )
					return area_of_base * height / 3.0

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_     ):
					if radius < 0:
										raise ValueError('vol_sphere() only accepts non-negative values'     )
					# Volume is 4/3 * pi * radius cubed
					return 4 / 3 * pi * pow(SCREAMING_SNAKE_CASE_,	3     )

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_     ):
					if radius < 0:
										raise ValueError('vol_hemisphere() only accepts non-negative values'     )
					# Volume is radius cubed * pi * 2/3
					return pow(SCREAMING_SNAKE_CASE_,	3     ) * pi * 2 / 3

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					if height < 0 or radius < 0:
										raise ValueError('vol_circular_cylinder() only accepts non-negative values'     )
					# Volume is radius squared * height * pi
					return pow(SCREAMING_SNAKE_CASE_,	2     ) * height * pi

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					# Volume - (outer_radius squared - inner_radius squared) * pi * height
					if inner_radius < 0 or outer_radius < 0 or height < 0:
										raise ValueError(
										    'vol_hollow_circular_cylinder() only accepts non-negative values'     )
					if outer_radius <= inner_radius:
										raise ValueError('outer_radius must be greater than inner_radius'     )
					return pi * (pow(SCREAMING_SNAKE_CASE_,	2     ) - pow(SCREAMING_SNAKE_CASE_,	2     )) * height

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					# Volume is 1/3 * pi * height *
					#           (radius_1 squared + radius_2 squared + radius_1 * radius_2)
					if radius_a < 0 or radius_a < 0 or height < 0:
										raise ValueError('vol_conical_frustum() only accepts non-negative values'     )
					return (
					    1
					    / 3
					    * pi
					    * height
					    * (pow(SCREAMING_SNAKE_CASE_,	2     ) + pow(SCREAMING_SNAKE_CASE_,	2     ) + radius_a * radius_a)
					)

def     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					if torus_radius < 0 or tube_radius < 0:
										raise ValueError('vol_torus() only accepts non-negative values'     )
					return 2 * pow(SCREAMING_SNAKE_CASE_,	2     ) * torus_radius * pow(SCREAMING_SNAKE_CASE_,	2     )

def     UpperCamelCase_    (						):
					print('Volumes:'     )
					print(f'''Cube: {vol_cube(2     ) = }'''     )  # = 8
					print(f'''Cuboid: {vol_cuboid(2,	2,	2     ) = }'''     )  # = 8
					print(f'''Cone: {vol_cone(2,	2     ) = }'''     )  # ~= 1.33
					print(f'''Right Circular Cone: {vol_right_circ_cone(2,	2     ) = }'''     )  # ~= 8.38
					print(f'''Prism: {vol_prism(2,	2     ) = }'''     )  # = 4
					print(f'''Pyramid: {vol_pyramid(2,	2     ) = }'''     )  # ~= 1.33
					print(f'''Sphere: {vol_sphere(2     ) = }'''     )  # ~= 33.5
					print(f'''Hemisphere: {vol_hemisphere(2     ) = }'''     )  # ~= 16.75
					print(f'''Circular Cylinder: {vol_circular_cylinder(2,	2     ) = }'''     )  # ~= 25.1
					print(f'''Torus: {vol_torus(2,	2     ) = }'''     )  # ~= 157.9
					print(f'''Conical Frustum: {vol_conical_frustum(2,	2,	4     ) = }'''     )  # ~= 58.6
					print(f'''Spherical cap: {vol_spherical_cap(1,	2     ) = }'''     )  # ~= 5.24
					print(f'''Spheres intersetion: {vol_spheres_intersect(2,	2,	1     ) = }'''     )  # ~= 21.21
					print(f'''Spheres union: {vol_spheres_union(2,	2,	1     ) = }'''     )  # ~= 45.81
					print(
					    f'''Hollow Circular Cylinder: {vol_hollow_circular_cylinder(1,	2,	3     ) = }'''     )  # ~= 28.3


if __name__ == "__main__":
	main()
