






from __future__ import annotations

from pathlib import Path






def        lowerCamelCase__   (					__lowerCamelCase    :  tuple[int, int]						,	__lowerCamelCase    :  tuple[int, int]	):
  return pointa[0] * pointa[1] - pointa[1] * pointa[0]






def        lowerCamelCase__   (					__lowerCamelCase    :  int						,	__lowerCamelCase    :  int						,	__lowerCamelCase    :  int						,	__lowerCamelCase    :  int						,	__lowerCamelCase    :  int						,	__lowerCamelCase    :  int	):
  __UpperCAmelCase							:			tuple[int, int]     =						(xa, ya)
  __UpperCAmelCase							:			tuple[int, int]     =						(xa - xa, ya - ya)
  __UpperCAmelCase							:			tuple[int, int]     =						(xa - xa, ya - ya)
  __UpperCAmelCase							:			float     =						-vector_product(__lowerCamelCase						,	__lowerCamelCase	) / vector_product(
      __lowerCamelCase						,	__lowerCamelCase	)
  __UpperCAmelCase							:			float     =						+vector_product(__lowerCamelCase						,	__lowerCamelCase	) / vector_product(
      __lowerCamelCase						,	__lowerCamelCase	)

  return a > 0 and b > 0 and a + b < 1






def        lowerCamelCase__   (					__lowerCamelCase    :  str = "p102_triangles.txt"	):
  __UpperCAmelCase							:			str     =						Path(__lowerCamelCase	).parent.joinpath(__lowerCamelCase	).read_text(encoding="""utf-8"""	)

  __UpperCAmelCase							:			list[list[int]]     =						[]
  for line in data.strip().split("""\n"""	):
    triangles.append([int(__lowerCamelCase	) for number in line.split(""","""	)]	)

  __UpperCAmelCase							:			int     =						0
  __UpperCAmelCase							:			list[int]

  for triangle in triangles:
    ret += contains_origin(*__lowerCamelCase	)

  return ret


if __name__ == "__main__":
       print(f"""{solution() = }""")










import argparse

import requests
import torch
from PIL import Image

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






def        lowerCamelCase__   (					__lowerCamelCase    :  int	):
  __UpperCAmelCase							:			Any     =						CLIPSegTextConfig()
  __UpperCAmelCase							:			Optional[int]     =						CLIPSegVisionConfig(patch_size=16	)

  __UpperCAmelCase							:			List[str]     =						True if """refined""" in model_name else False
  __UpperCAmelCase							:			List[Any]     =						16 if """rd16""" in model_name else 64

  __UpperCAmelCase							:			Tuple     =						CLIPSegConfig.from_text_vision_configs(
      __lowerCamelCase						,	__lowerCamelCase						,	use_complex_transposed_convolution=__lowerCamelCase						,	reduce_dim=__lowerCamelCase						,	)
  return config






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

  return name






def        lowerCamelCase__   (					__lowerCamelCase    :  Union[str, Any]						,	__lowerCamelCase    :  int	):
  for key in orig_state_dict.copy().keys():
    __UpperCAmelCase							:			Optional[Any]     =						orig_state_dict.pop(__lowerCamelCase	)

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

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

  return orig_state_dict






def        lowerCamelCase__   (					):
  __UpperCAmelCase							:			Optional[Any]     =						"""http://images.cocodataset.org/val2017/000000039769.jpg"""
  __UpperCAmelCase							:			List[Any]     =						Image.open(requests.get(__lowerCamelCase						,	stream=__lowerCamelCase	).raw	)
  return image






def        lowerCamelCase__   (					__lowerCamelCase    :  Optional[int]						,	__lowerCamelCase    :  Optional[Any]						,	__lowerCamelCase    :  str						,	__lowerCamelCase    :  List[str]	):
  __UpperCAmelCase							:			Optional[Any]     =						get_clipseg_config(__lowerCamelCase	)
  __UpperCAmelCase							:			Tuple     =						CLIPSegForImageSegmentation(__lowerCamelCase	)
  model.eval()

  __UpperCAmelCase							:			Optional[int]     =						torch.load(__lowerCamelCase						,	map_location="""cpu"""	)

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

    # rename some keys
  __UpperCAmelCase							:			Optional[Any]     =						convert_state_dict(__lowerCamelCase						,	__lowerCamelCase	)
  __UpperCAmelCase   ,				__UpperCAmelCase							:			Optional[Any]     =						model.load_state_dict(__lowerCamelCase						,	strict=__lowerCamelCase	)

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

  __UpperCAmelCase							:			Optional[Any]     =						ViTImageProcessor(size=352	)
  __UpperCAmelCase							:			Tuple     =						CLIPTokenizer.from_pretrained("""openai/clip-vit-base-patch32"""	)
  __UpperCAmelCase							:			List[Any]     =						CLIPSegProcessor(image_processor=__lowerCamelCase						,	tokenizer=__lowerCamelCase	)

  __UpperCAmelCase							:			int     =						prepare_img()
  __UpperCAmelCase							:			Optional[Any]     =						["""a glass""", """something to fill""", """wood""", """a jar"""]

  __UpperCAmelCase							:			Union[str, Any]     =						processor(text=__lowerCamelCase						,	images=[image] * len(__lowerCamelCase	)						,	padding="""max_length"""						,	return_tensors="""pt"""	)

  with torch.no_grad():
    __UpperCAmelCase							:			int     =						model(**__lowerCamelCase	)

  # verify values
  __UpperCAmelCase							:			Dict     =						torch.tensor([0.1_1_1_0, -0.1_8_8_2, 0.1_6_4_5]	)
  __UpperCAmelCase							:			Union[str, Any]     =						torch.tensor([0.2_6_9_2, -0.7_1_9_7, -0.1_3_2_8]	)
  if model_name == "clipseg-rd64-refined":
    __UpperCAmelCase							:			str     =						torch.tensor(
        [[-1_0.0_4_0_7, -9.9_4_3_1, -1_0.2_6_4_6], [-9.9_7_5_1, -9.7_0_6_4, -9.9_5_8_6], [-9.6_8_9_1, -9.5_6_4_5, -9.9_6_1_8]]	)
  elif model_name == "clipseg-rd64":
    __UpperCAmelCase							:			List[Any]     =						torch.tensor(
        [[-7.2_8_7_7, -7.2_7_1_1, -7.2_4_6_3], [-7.2_6_5_2, -7.2_7_8_0, -7.2_5_2_0], [-7.2_2_3_9, -7.2_2_0_4, -7.2_0_0_1]]	)
  elif model_name == "clipseg-rd16":
    __UpperCAmelCase							:			str     =						torch.tensor(
        [[-6.3_9_5_5, -6.4_0_5_5, -6.4_1_5_1], [-6.3_9_1_1, -6.4_0_3_3, -6.4_1_0_0], [-6.3_4_7_4, -6.3_7_0_2, -6.3_7_6_2]]	)
  else:
    raise ValueError(f"""Model name {model_name} not supported."""	)

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

  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__":
       a	: Optional[Any]      =				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."
       )

       a	: int      =				parser.parse_args()
       convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)


