






"""simple docstring"""


import argparse

import requests
import torch
from PIL import Image

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




def 			snake_case  (A_      :Dict      ):







  '''simple docstring'''

  a     :			List[Any]				=   CLIPSegTextConfig()
  a     :			Any				=   CLIPSegVisionConfig(patch_size=1_6      )

  a     :			List[str]				=   True if 'refined' in model_name else False
  a     :			Tuple				=   1_6 if 'rd16' in model_name else 6_4

  a     :			str				=   CLIPSegConfig.from_text_vision_configs(
      _UpperCamelCase	,			_UpperCamelCase	,			use_complex_transposed_convolution=_UpperCamelCase	,			reduce_dim=_UpperCamelCase	,			)
  return config




def 			snake_case  (A_      :Union[str, Any]      ):







  '''simple docstring'''

  if "clip_model" in name:
    a     :			Optional[Any]				=   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     :			Tuple				=   name.replace('transformer'	,			'text_model'      )
  if "resblocks" in name:
    a     :			int				=   name.replace('resblocks'	,			'encoder.layers'      )
  if "ln_1" in name:
    a     :			str				=   name.replace('ln_1'	,			'layer_norm1'      )
  if "ln_2" in name:
    a     :			Union[str, 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     :			Optional[int]				=   name.replace('c_proj'	,			'fc2'      )
  if "attn" in name and "self" not in name:
    a     :			Optional[Any]				=   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     :			str				=   name.replace('positional_embedding'	,			'text_model.embeddings.position_embedding.weight'      )
  if "ln_final" in name:
    a     :			Tuple				=   name.replace('ln_final'	,			'text_model.final_layer_norm'      )
  # vision encoder
  if "visual.class_embedding" in name:
    a     :			int				=   name.replace('visual.class_embedding'	,			'vision_model.embeddings.class_embedding'      )
  if "visual.conv1" in name:
    a     :			Union[str, Any]				=   name.replace('visual.conv1'	,			'vision_model.embeddings.patch_embedding'      )
  if "visual.positional_embedding" in name:
    a     :			Union[str, Any]				=   name.replace('visual.positional_embedding'	,			'vision_model.embeddings.position_embedding.weight'      )
  if "visual.ln_pre" in name:
    a     :			int				=   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     :			Union[str, 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     :			Tuple				=   'decoder.' + name
  if "blocks" in name:
    a     :			List[Any]				=   name.replace('blocks'	,			'decoder.layers'      )
  if "linear1" in name:
    a     :			Tuple				=   name.replace('linear1'	,			'mlp.fc1'      )
  if "linear2" in name:
    a     :			Any				=   name.replace('linear2'	,			'mlp.fc2'      )
  if "norm1" in name and "layer_" not in name:
    a     :			List[str]				=   name.replace('norm1'	,			'layer_norm1'      )
  if "norm2" in name and "layer_" not in name:
    a     :			Tuple				=   name.replace('norm2'	,			'layer_norm2'      )

  return name




def 			snake_case  (A_      :int	,			A_      :Dict      ):







  '''simple docstring'''

  for key in orig_state_dict.copy().keys():
    a     :			Tuple				=   orig_state_dict.pop(_UpperCamelCase      )

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

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

  return orig_state_dict




def 			snake_case  ():







  '''simple docstring'''

  a     :			Union[str, Any]				=   'http://images.cocodataset.org/val2017/000000039769.jpg'
  a     :			str				=   Image.open(requests.get(_UpperCamelCase	,			stream=_UpperCamelCase      ).raw      )
  return image




def 			snake_case  (A_      :Tuple	,			A_      :int	,			A_      :List[Any]	,			A_      :str      ):







  '''simple docstring'''

  a     :			Dict				=   get_clipseg_config(_UpperCamelCase      )
  a     :			Optional[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     :			str				=   convert_state_dict(_UpperCamelCase	,			_UpperCamelCase      )
  a,     a     :			Dict				=   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     :			str				=   ViTImageProcessor(size=3_5_2      )
  a     :			Tuple				=   CLIPTokenizer.from_pretrained('openai/clip-vit-base-patch32'      )
  a     :			List[str]				=   CLIPSegProcessor(image_processor=_UpperCamelCase	,			tokenizer=_UpperCamelCase      )

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

  a     :			int				=   processor(text=_UpperCamelCase	,			images=[image] * len(_UpperCamelCase      )	,			padding='max_length'	,			return_tensors='pt'      )

  with torch.no_grad():
    a     :			int				=   model(**_UpperCamelCase      )

  # verify values
  a     :			List[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     :			Union[str, Any]				=   torch.tensor(
        [[-1_0.0_4_0_7, -9.9431, -1_0.2_6_4_6], [-9.9751, -9.7064, -9.9586], [-9.6891, -9.5645, -9.9618]]      )
  elif model_name == "clipseg-rd64":
    a     :			Dict				=   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     :			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]	,			_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				:    Dict     =						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				:    str     =						parser.parse_args()
   convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)





'''simple docstring'''




__snake_case   :      List[Any]						    =  [chr(i) for i in range(32, 126)]
__snake_case   :      Dict						    =  list(range(len(alphabets)))
__snake_case   :      Dict						    =  list(range(len(alphabets)))
__snake_case   :      Dict						    =  list(range(len(alphabets)))
__snake_case   :      List[Any]						    =  list(reversed(range(len(alphabets))))
__snake_case   :      Optional[int]						    =  []
__snake_case   :      List[Any]						    =  0





def      _UpperCAmelCase    (							)	->      Any:
    global gear_one_pos
    global gear_two_pos
    global gear_three_pos
    A_     =   gear_one[0]
    gear_one.append(_UpperCamelCase   )
    del gear_one[0]
    gear_one_pos += 1
    if gear_one_pos % int(len(_UpperCamelCase   )   ) == 0:
        A_     =   gear_two[0]
        gear_two.append(_UpperCamelCase   )
        del gear_two[0]
        gear_two_pos += 1
        if gear_two_pos % int(len(_UpperCamelCase   )   ) == 0:
            A_     =   gear_three[0]
            gear_three.append(_UpperCamelCase   )
            del gear_three[0]
            gear_three_pos += 1





def      _UpperCAmelCase    (							_UpperCamelCase    :	Tuple   )	->      Any:
    A_     =   alphabets.index(_UpperCamelCase   )
    A_     =   gear_one[target]
    A_     =   gear_two[target]
    A_     =   gear_three[target]
    A_     =   reflector[target]
    A_     =   gear_three.index(_UpperCamelCase   )
    A_     =   gear_two.index(_UpperCamelCase   )
    A_     =   gear_one.index(_UpperCamelCase   )
    code.append(alphabets[target]   )
    rotator()


if __name__ == "__main__":
  __snake_case   :      List[str]						    =  list(input('Type your message:\n'))
  while True:
    try:
      __snake_case   :      Any						    =  int(input('Please set token:(must be only digits)\n'))
      break
    except Exception as error:
      print(error)
  for _ in range(token):
    rotator()
  for j in decode:
    engine(j)
  print('\n' + ''.join(code))
  print(
      F"""\nYour Token is {token} please write it down.\nIf you want to decode """
      'this message again you should input same digits as token!'
  )






