
import argparse

import requests
import torch
from PIL import Image

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


def     a__			(    snake_case    ):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						CLIPSegTextConfig()
  __SCREAMING_SNAKE_CASE     :					Optional[int]			=						CLIPSegVisionConfig(patch_size=16    )

  __SCREAMING_SNAKE_CASE     :					Dict			=						True if '''refined''' in model_name else False
  __SCREAMING_SNAKE_CASE     :					Union[str, Any]			=						16 if '''rd16''' in model_name else 64

  __SCREAMING_SNAKE_CASE     :					List[str]			=						CLIPSegConfig.from_text_vision_configs(
      snake_case   ,	snake_case   ,	use_complex_transposed_convolution=snake_case   ,	reduce_dim=snake_case   ,	)
  return config


def     a__			(    snake_case    ):





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

  return name


def     a__			(    snake_case   ,	snake_case    ):





  """simple docstring"""
  for key in orig_state_dict.copy().keys():
    __SCREAMING_SNAKE_CASE     :					List[str]			=						orig_state_dict.pop(snake_case    )

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

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

  return orig_state_dict


def     a__			(    ):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE     :					Optional[int]			=						'''http://images.cocodataset.org/val2017/000000039769.jpg'''
  __SCREAMING_SNAKE_CASE     :					Any			=						Image.open(requests.get(snake_case   ,	stream=snake_case    ).raw    )
  return image


def     a__			(    snake_case   ,	snake_case   ,	snake_case   ,	snake_case    ):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE     :					List[Any]			=						get_clipseg_config(snake_case    )
  __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						CLIPSegForImageSegmentation(snake_case    )
  model.eval()

  __SCREAMING_SNAKE_CASE     :					int			=						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
  __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						convert_state_dict(snake_case   ,	snake_case    )
  __SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE     :					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}'''    )

  __SCREAMING_SNAKE_CASE     :					Optional[Any]			=						ViTImageProcessor(size=352    )
  __SCREAMING_SNAKE_CASE     :					str			=						CLIPTokenizer.from_pretrained('''openai/clip-vit-base-patch32'''    )
  __SCREAMING_SNAKE_CASE     :					Tuple			=						CLIPSegProcessor(image_processor=snake_case   ,	tokenizer=snake_case    )

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

  __SCREAMING_SNAKE_CASE     :					List[Any]			=						processor(text=snake_case   ,	images=[image] * len(snake_case    )   ,	padding='''max_length'''   ,	return_tensors='''pt'''    )

  with torch.no_grad():
    __SCREAMING_SNAKE_CASE     :					List[str]			=						model(**snake_case    )

  # verify values
  __SCREAMING_SNAKE_CASE     :					List[Any]			=						torch.tensor([0.1110, -0.1882, 0.1645]    )
  __SCREAMING_SNAKE_CASE     :					Tuple			=						torch.tensor([0.2692, -0.7197, -0.1328]    )
  if model_name == "clipseg-rd64-refined":
    __SCREAMING_SNAKE_CASE     :					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":
    __SCREAMING_SNAKE_CASE     :					str			=						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":
    __SCREAMING_SNAKE_CASE     :					Tuple			=						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__":
   lowercase_											=	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."""
   )

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


from collections import defaultdict


def     a__			(    snake_case    ):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE     :					defaultdict[str, int]			=						defaultdict(snake_case    )
  # Creating a dictionary containing count of each word
  for word in sentence.split():
    occurrence[word] += 1
  return occurrence


if __name__ == "__main__":
   for word, count in word_occurrence("""INPUT STRING""").items():
      print(f'''{word}: {count}''')
