






import argparse

import requests
import torch
from PIL import Image

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






def   lowerCAmelCase__			(					SCREAMING_SNAKE_CASE__		):
 __a    :	Optional[int]									=						CLIPSegTextConfig()
 __a    :	Any									=						CLIPSegVisionConfig(patch_size=16		)

 __a    :	List[str]									=						True if 'refined' in model_name else False
 __a    :	int									=						16 if 'rd16' in model_name else 64

 __a    :	Optional[int]									=						CLIPSegConfig.from_text_vision_configs(
     __snake_case       ,		__snake_case       ,		use_complex_transposed_convolution=__snake_case       ,		reduce_dim=__snake_case       ,		)
 return config






def   lowerCAmelCase__			(					SCREAMING_SNAKE_CASE__		):
 # update prefixes
 if "clip_model" in name:
  __a    :	int									=						name.replace('clip_model'       ,		'clip'		)
 if "transformer" in name:
  if "visual" in name:
   __a    :	Union[str, Any]									=						name.replace('visual.transformer'       ,		'vision_model'		)
  else:
   __a    :	Union[str, Any]									=						name.replace('transformer'       ,		'text_model'		)
 if "resblocks" in name:
  __a    :	List[Any]									=						name.replace('resblocks'       ,		'encoder.layers'		)
 if "ln_1" in name:
  __a    :	List[Any]									=						name.replace('ln_1'       ,		'layer_norm1'		)
 if "ln_2" in name:
  __a    :	Optional[Any]									=						name.replace('ln_2'       ,		'layer_norm2'		)
 if "c_fc" in name:
  __a    :	Tuple									=						name.replace('c_fc'       ,		'fc1'		)
 if "c_proj" in name:
  __a    :	Tuple									=						name.replace('c_proj'       ,		'fc2'		)
 if "attn" in name and "self" not in name:
  __a    :	int									=						name.replace('attn'       ,		'self_attn'		)
 # text encoder
 if "token_embedding" in name:
  __a    :	Optional[int]									=						name.replace('token_embedding'       ,		'text_model.embeddings.token_embedding'		)
 if "positional_embedding" in name and "visual" not in name:
  __a    :	Optional[int]									=						name.replace('positional_embedding'       ,		'text_model.embeddings.position_embedding.weight'		)
 if "ln_final" in name:
  __a    :	Optional[Any]									=						name.replace('ln_final'       ,		'text_model.final_layer_norm'		)
 # vision encoder
 if "visual.class_embedding" in name:
  __a    :	List[Any]									=						name.replace('visual.class_embedding'       ,		'vision_model.embeddings.class_embedding'		)
 if "visual.conv1" in name:
  __a    :	Tuple									=						name.replace('visual.conv1'       ,		'vision_model.embeddings.patch_embedding'		)
 if "visual.positional_embedding" in name:
  __a    :	Optional[Any]									=						name.replace('visual.positional_embedding'       ,		'vision_model.embeddings.position_embedding.weight'		)
 if "visual.ln_pre" in name:
  __a    :	Any									=						name.replace('visual.ln_pre'       ,		'vision_model.pre_layrnorm'		)
 if "visual.ln_post" in name:
  __a    :	int									=						name.replace('visual.ln_post'       ,		'vision_model.post_layernorm'		)
 # projection layers
 if "visual.proj" in name:
  __a    :	str									=						name.replace('visual.proj'       ,		'visual_projection.weight'		)
 if "text_projection" in name:
  __a    :	str									=						name.replace('text_projection'       ,		'text_projection.weight'		)
 # decoder
 if "trans_conv" in name:
  __a    :	Any									=						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:
  __a    :	List[Any]									=						'decoder.' + name
 if "blocks" in name:
  __a    :	List[Any]									=						name.replace('blocks'       ,		'decoder.layers'		)
 if "linear1" in name:
  __a    :	Dict									=						name.replace('linear1'       ,		'mlp.fc1'		)
 if "linear2" in name:
  __a    :	Dict									=						name.replace('linear2'       ,		'mlp.fc2'		)
 if "norm1" in name and "layer_" not in name:
  __a    :	Any									=						name.replace('norm1'       ,		'layer_norm1'		)
 if "norm2" in name and "layer_" not in name:
  __a    :	List[Any]									=						name.replace('norm2'       ,		'layer_norm2'		)

 return name






def   lowerCAmelCase__			(					SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__		):
 for key in orig_state_dict.copy().keys():
  __a    :	List[Any]									=						orig_state_dict.pop(__snake_case		)

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

   if "weight" in key:
    __a    :	Union[str, Any]									=						val[:dim, :]
    __a    :	Tuple									=						val[
        dim : dim * 2, :
    ]
    __a    :	Optional[int]									=						val[-dim:, :]
   else:
    __a    :	List[str]									=						val[:dim]
    __a    :	Optional[int]									=						val[dim : dim * 2]
    __a    :	Dict									=						val[-dim:]
  elif "self_attn" in key and "out_proj" not in key:
   __a    :	Tuple									=						key.split('.'		)
   __a    :	int									=						int(key_split[1]		)
   __a    :	Optional[int]									=						config.reduce_dim
   if "weight" in key:
    __a    :	Tuple									=						val[:dim, :]
    __a    :	str									=						val[dim : dim * 2, :]
    __a    :	Dict									=						val[-dim:, :]
   else:
    __a    :	List[str]									=						val[:dim]
    __a    :	Optional[int]									=						val[dim : dim * 2]
    __a    :	Tuple									=						val[-dim:]
  else:
   __a    :	List[Any]									=						rename_key(__snake_case		)
   if "visual_projection" in new_name or "text_projection" in new_name:
    __a    :	int									=						val.T
   __a    :	Optional[Any]									=						val

 return orig_state_dict






def   lowerCAmelCase__			(					):
 __a    :	List[Any]									=						'http://images.cocodataset.org/val2017/000000039769.jpg'
 __a    :	List[str]									=						Image.open(requests.get(__snake_case       ,		stream=__snake_case		).raw		)
 return image






def   lowerCAmelCase__			(					SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__		):
 __a    :	List[str]									=						get_clipseg_config(__snake_case		)
 __a    :	List[str]									=						CLIPSegForImageSegmentation(__snake_case		)
 model.eval()

 __a    :	Union[str, Any]									=						torch.load(__snake_case       ,		map_location='cpu'		)

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

  # rename some keys
 __a    :	str									=						convert_state_dict(__snake_case       ,		__snake_case		)
 __a			,    __a    :	Union[str, Any]									=						model.load_state_dict(__snake_case       ,		strict=__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(__snake_case		)		)
 if unexpected_keys != ["decoder.reduce.weight", "decoder.reduce.bias"]:
  raise ValueError(f'''Unexpected keys: {unexpected_keys}'''		)

 __a    :	Tuple									=						ViTImageProcessor(size=352		)
 __a    :	List[Any]									=						CLIPTokenizer.from_pretrained('openai/clip-vit-base-patch32'		)
 __a    :	Tuple									=						CLIPSegProcessor(image_processor=__snake_case       ,		tokenizer=__snake_case		)

 __a    :	Dict									=						prepare_img()
 __a    :	Any									=						['a glass', 'something to fill', 'wood', 'a jar']

 __a    :	List[str]									=						processor(text=__snake_case       ,		images=[image] * len(__snake_case		)       ,		padding='max_length'       ,		return_tensors='pt'		)

 with torch.no_grad():
  __a    :	Any									=						model(**__snake_case		)

 # verify values
 __a    :	Any									=						torch.tensor([0.1110, -0.1882, 0.1645]		)
 __a    :	Union[str, Any]									=						torch.tensor([0.2692, -0.7197, -0.1328]		)
 if model_name == "clipseg-rd64-refined":
  __a    :	Optional[Any]									=						torch.tensor(
      [[-10.0407, -9.9431, -10.2646], [-9.9751, -9.7064, -9.9586], [-9.6891, -9.5645, -9.9618]]		)
 elif model_name == "clipseg-rd64":
  __a    :	Any									=						torch.tensor(
      [[-7.2877, -7.2711, -7.2463], [-7.2652, -7.2780, -7.2520], [-7.2239, -7.2204, -7.2001]]		)
 elif model_name == "clipseg-rd16":
  __a    :	List[str]									=						torch.tensor(
      [[-6.3955, -6.4055, -6.4151], [-6.3911, -6.4033, -6.4100], [-6.3474, -6.3702, -6.3762]]		)
 else:
  raise ValueError(f'''Model name {model_name} not supported.'''		)

 assert torch.allclose(outputs.logits[0, :3, :3]       ,		__snake_case       ,		atol=1E-3		)
 assert torch.allclose(outputs.conditional_embeddings[0, :3]       ,		__snake_case       ,		atol=1E-3		)
 assert torch.allclose(outputs.pooled_output[0, :3]       ,		__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(__snake_case		)
  processor.save_pretrained(__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__":
  SCREAMING_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."
  )

  SCREAMING_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

import random

from matplotlib import animation
from matplotlib import pyplot as plt

# Frame rate of the animation
SCREAMING_SNAKE_CASE_				      =							2_0

# Time between time steps in seconds
SCREAMING_SNAKE_CASE_				      =							INTERVAL / 1_0_0_0




class 							lowerCAmelCase					:







   """simple docstring"""







   def __init__( self   ,   _A   ,   _A   ,   _A   ,   _A   ,   _A = 1.0   ,   _A = 1.0   ,   _A = "blue"   ,   )    ->   None:
    __a    :	str									=						position_x
    __a    :	Optional[Any]									=						position_y
    __a    :	List[Any]									=						velocity_x
    __a    :	Tuple									=						velocity_y
    __a    :	Dict									=						mass
    __a    :	int									=						size
    __a    :	Tuple									=						color



   @property
   def    __magic_name__    ( self			)    ->   tuple[float, float]:
    return self.position_x, self.position_y



   @property
   def    __magic_name__    ( self			)    ->   tuple[float, float]:
    return self.velocity_x, self.velocity_y



   def    __magic_name__    ( self   ,   _A   ,   _A   ,   _A			)    ->   None:
    self.velocity_x += force_x * delta_time
    self.velocity_y += force_y * delta_time



   def    __magic_name__    ( self   ,   _A			)    ->   None:
    self.position_x += self.velocity_x * delta_time
    self.position_y += self.velocity_y * delta_time




class 							lowerCAmelCase					:







   """simple docstring"""







   def __init__( self   ,   _A   ,   _A = 1.0   ,   _A = 1.0   ,   _A = 0.0   ,   )    ->   None:
    __a    :	List[str]									=						bodies
    __a    :	List[str]									=						gravitation_constant
    __a    :	int									=						time_factor
    __a    :	List[str]									=						softening_factor



   def __len__( self			)    ->   int:
    return len(self.bodies			)



   def    __magic_name__    ( self   ,   _A			)    ->   None:
    for bodya in self.bodies:
     __a    :	Any									=						0.0
     __a    :	str									=						0.0
     for bodya in self.bodies:
      if bodya != bodya:
       __a    :	Optional[Any]									=						bodya.position_x - bodya.position_x
       __a    :	Dict									=						bodya.position_y - bodya.position_y

       # Calculation of the distance using Pythagoras's theorem
       # Extra factor due to the softening technique
       __a    :	Optional[Any]									=						(dif_x**2 + dif_y**2 + self.softening_factor) ** (
           1 / 2
       )

       # Newton's law of universal gravitation.
       force_x += (
           self.gravitation_constant * bodya.mass * dif_x / distance**3
       )
       force_y += (
           self.gravitation_constant * bodya.mass * dif_y / distance**3
       )

            # Update the body's velocity once all the force components have been added
     bodya.update_velocity(_A   ,   _A   ,   delta_time * self.time_factor			)

    # Update the positions only after all the velocities have been updated
    for body in self.bodies:
     body.update_position(delta_time * self.time_factor			)






def   lowerCAmelCase__			(					SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__		):
 body_system.update_system(SCREAMING_SNAKE_CASE__		)

 # Update the positions of the patches
 for patch, body in zip(SCREAMING_SNAKE_CASE__       ,		body_system.bodies		):
  __a    :	Dict									=						(body.position_x, body.position_y)






def   lowerCAmelCase__			(					SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__ = -1       ,		SCREAMING_SNAKE_CASE__ = 1       ,		SCREAMING_SNAKE_CASE__ = -1       ,		SCREAMING_SNAKE_CASE__ = 1       ,		):
 __a    :	int									=						plt.figure()
 fig.canvas.set_window_title(SCREAMING_SNAKE_CASE__		)
 __a    :	int									=						plt.axes(
     xlim=(x_start, x_end)       ,		ylim=(y_start, y_end)		)  # Set section to be plotted
 plt.gca().set_aspect('equal'		)  # Fix aspect ratio

 # Each body is drawn as a patch by the plt-function
 __a    :	str									=						[
     plt.Circle((body.position_x, body.position_y)       ,		body.size       ,		fc=body.color		)
     for body in body_system.bodies
 ]

 for patch in patches:
  ax.add_patch(SCREAMING_SNAKE_CASE__		)

 # Function called at each step of the animation
 def update(SCREAMING_SNAKE_CASE__		) -> list[plt.Circle]:
  update_step(SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__		)
  return patches

 __a    :	Dict									=						animation.FuncAnimation(  # noqa: F841
     SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__       ,		interval=SCREAMING_SNAKE_CASE__       ,		blit=SCREAMING_SNAKE_CASE__		)

 plt.show()






def   lowerCAmelCase__			(					):
 __a    :	Dict									=						0.970_0436
 __a    :	List[Any]									=						-0.2430_8753
 __a    :	Union[str, Any]									=						0.4_6620_3685
 __a    :	Any									=						0.4323_6573

 __a    :	Optional[int]									=						[
     Body(SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__       ,		size=0.2       ,		color='red'		),
     Body(-position_x       ,		-position_y       ,		SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__       ,		size=0.2       ,		color='green'		),
     Body(0       ,		0       ,		-2 * velocity_x       ,		-2 * velocity_y       ,		size=0.2       ,		color='blue'		),
 ]
 return BodySystem(SCREAMING_SNAKE_CASE__       ,		time_factor=3		)






def   lowerCAmelCase__			(					):
 __a    :	Optional[int]									=						7.34_76E22
 __a    :	List[Any]									=						5.9_72E24
 __a    :	List[Any]									=						1022
 __a    :	str									=						384399000
 __a    :	Optional[int]									=						6.6_74E-11

 # Calculation of the respective velocities so that total impulse is zero,
 # i.e. the two bodies together don't move
 __a    :	str									=						earth_mass * velocity_dif / (earth_mass + moon_mass)
 __a    :	Optional[int]									=						moon_velocity - velocity_dif

 __a    :	Union[str, Any]									=						Body(-earth_moon_distance       ,		0       ,		0       ,		SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__       ,		10000000       ,		'grey'		)
 __a    :	Union[str, Any]									=						Body(0       ,		0       ,		0       ,		SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__       ,		50000000       ,		'blue'		)
 return BodySystem([earth, moon]       ,		SCREAMING_SNAKE_CASE__       ,		time_factor=1000000		)






def   lowerCAmelCase__			(					):
 __a    :	Tuple									=						[]
 for _ in range(10		):
  __a    :	Dict									=						random.uniform(-0.5       ,		0.5		)
  __a    :	Optional[int]									=						random.uniform(-0.5       ,		0.5		)

  # Bodies are created pairwise with opposite velocities so that the
  # total impulse remains zero
  bodies.append(
      Body(
          random.uniform(-0.5       ,		0.5		)       ,		random.uniform(-0.5       ,		0.5		)       ,		SCREAMING_SNAKE_CASE__       ,		SCREAMING_SNAKE_CASE__       ,		size=0.05       ,		)		)
  bodies.append(
      Body(
          random.uniform(-0.5       ,		0.5		)       ,		random.uniform(-0.5       ,		0.5		)       ,		-velocity_x       ,		-velocity_y       ,		size=0.05       ,		)		)
 return BodySystem(SCREAMING_SNAKE_CASE__       ,		0.01       ,		10       ,		0.1		)


if __name__ == "__main__":
  plot("Figure-8 solution to the 3-body-problem", example_a(), -2, 2, -2, 2)
  plot(
      "Moon's orbit around the earth",
      example_a(),
      -4_3_0_0_0_0_0_0_0,
      4_3_0_0_0_0_0_0_0,
      -4_3_0_0_0_0_0_0_0,
      4_3_0_0_0_0_0_0_0,
  )
  plot("Random system with many bodies", example_a(), -1.5, 1.5, -1.5, 1.5)




