
"""simple docstring"""
def 					a__ ( a :					int = 1_000		):

  """simple docstring"""







  _snake_case  :    Optional[int]            = 0
  for i in range(a		):
    if i % 3 == 0 or i % 5 == 0:
      result += i
  return result


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









"""simple docstring"""


import argparse

import requests
import torch
from PIL import Image

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




def 					a			(  UpperCamelCase_				:					Optional[Any]		)	->		str:
 __A						:							Union[str, Any]      =						CLIPSegTextConfig()
 __A						:							Dict      =						CLIPSegVisionConfig(patch_size=1_6		)

 __A						:							Dict      =						True if 'refined' in model_name else False
 __A						:							Optional[int]      =						1_6 if 'rd16' in model_name else 6_4

 __A						:							Tuple      =						CLIPSegConfig.from_text_vision_configs(
     UpperCamelCase_  ,						UpperCamelCase_  ,						use_complex_transposed_convolution=UpperCamelCase_  ,						reduce_dim=UpperCamelCase_  ,						)
 return config




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

 return name




def 					a			(  UpperCamelCase_				:					List[str]  ,						UpperCamelCase_				:					Dict		)	->		Any:
 for key in orig_state_dict.copy().keys():
  __A						:							str      =						orig_state_dict.pop(UpperCamelCase_		)

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

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

 return orig_state_dict




def 					a			(  )	->		Dict:
 __A						:							List[Any]      =						'http://images.cocodataset.org/val2017/000000039769.jpg'
 __A						:							Any      =						Image.open(requests.get(UpperCamelCase_  ,						stream=UpperCamelCase_		).raw		)
 return image




def 					a			(  UpperCamelCase_				:					List[str]  ,						UpperCamelCase_				:					List[str]  ,						UpperCamelCase_				:					Dict  ,						UpperCamelCase_				:					int		)	->		Optional[int]:
 __A						:							str      =						get_clipseg_config(UpperCamelCase_		)
 __A						:							int      =						CLIPSegForImageSegmentation(UpperCamelCase_		)
 model.eval()

 __A						:							Optional[int]      =						torch.load(UpperCamelCase_  ,						map_location='cpu'		)

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

    # rename some keys
 __A						:							int      =						convert_state_dict(UpperCamelCase_  ,						UpperCamelCase_		)
 __A     ,       __A						:							Tuple      =						model.load_state_dict(UpperCamelCase_  ,						strict=UpperCamelCase_		)

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

 __A						:							Optional[Any]      =						ViTImageProcessor(size=3_5_2		)
 __A						:							List[Any]      =						CLIPTokenizer.from_pretrained('openai/clip-vit-base-patch32'		)
 __A						:							Optional[int]      =						CLIPSegProcessor(image_processor=UpperCamelCase_  ,						tokenizer=UpperCamelCase_		)

 __A						:							List[str]      =						prepare_img()
 __A						:							Optional[Any]      =						['a glass', 'something to fill', 'wood', 'a jar']

 __A						:							Optional[Any]      =						processor(text=UpperCamelCase_  ,						images=[image] * len(UpperCamelCase_		)  ,						padding='max_length'  ,						return_tensors='pt'		)

 with torch.no_grad():
  __A						:							int      =						model(**UpperCamelCase_		)

 # verify values
 __A						:							Union[str, Any]      =						torch.tensor([0.1110, -0.1882, 0.1645]		)
 __A						:							List[Any]      =						torch.tensor([0.2692, -0.7197, -0.1328]		)
 if model_name == "clipseg-rd64-refined":
  __A						:							int      =						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						:							Optional[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						:							Optional[Any]      =						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]  ,						UpperCamelCase_  ,						atol=1E-3		)
 assert torch.allclose(outputs.conditional_embeddings[0, :3]  ,						UpperCamelCase_  ,						atol=1E-3		)
 assert torch.allclose(outputs.pooled_output[0, :3]  ,						UpperCamelCase_  ,						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(UpperCamelCase_		)
  processor.save_pretrained(UpperCamelCase_		)

 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__":
   _UpperCamelCase :    str      =			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."""
   )

   _UpperCamelCase :    Tuple      =			parser.parse_args()
   convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)





