



import argparse

import requests
import torch
from PIL import Image

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




def     UpperCAmelCase__ (							__snake_case							)   -> Any:
  _A           =    CLIPSegTextConfig()
  _A           =    CLIPSegVisionConfig(patch_size=16							)

  _A           =    True if "refined" in model_name else False
  _A           =    16 if "rd16" in model_name else 64

  _A           =    CLIPSegConfig.from_text_vision_configs(
      lowercase__						,				lowercase__						,				use_complex_transposed_convolution=lowercase__						,				reduce_dim=lowercase__						,				)
  return config




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

  return name




def     UpperCAmelCase__ (							__snake_case						,				__snake_case							)   -> List[Any]:
  for key in orig_state_dict.copy().keys():
    _A           =    orig_state_dict.pop(lowercase__							)

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

      if "weight" in key:
        _A           =    val[:dim, :]
        _A           =    val[
            dim : dim * 2, :
        ]
        _A           =    val[-dim:, :]
      else:
        _A           =    val[:dim]
        _A           =    val[dim : dim * 2]
        _A           =    val[-dim:]
    elif "self_attn" in key and "out_proj" not in key:
      _A           =    key.split('''.'''							)
      _A           =    int(key_split[1]							)
      _A           =    config.reduce_dim
      if "weight" in key:
        _A           =    val[:dim, :]
        _A           =    val[dim : dim * 2, :]
        _A           =    val[-dim:, :]
      else:
        _A           =    val[:dim]
        _A           =    val[dim : dim * 2]
        _A           =    val[-dim:]
    else:
      _A           =    rename_key(lowercase__							)
      if "visual_projection" in new_name or "text_projection" in new_name:
        _A           =    val.T
      _A           =    val

  return orig_state_dict




def     UpperCAmelCase__ (							)   -> Dict:
  _A           =    "http://images.cocodataset.org/val2017/000000039769.jpg"
  _A           =    Image.open(requests.get(lowercase__						,				stream=lowercase__							).raw							)
  return image




def     UpperCAmelCase__ (							__snake_case						,				__snake_case						,				__snake_case						,				__snake_case							)   -> Union[str, Any]:
  _A           =    get_clipseg_config(lowercase__							)
  _A           =    CLIPSegForImageSegmentation(lowercase__							)
  model.eval()

  _A           =    torch.load(lowercase__						,				map_location='''cpu'''							)

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

    # rename some keys
  _A           =    convert_state_dict(lowercase__						,				lowercase__							)
  _A           =    model.load_state_dict(lowercase__						,				strict=lowercase__							)

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

  _A           =    ViTImageProcessor(size=352							)
  _A           =    CLIPTokenizer.from_pretrained('''openai/clip-vit-base-patch32'''							)
  _A           =    CLIPSegProcessor(image_processor=lowercase__						,				tokenizer=lowercase__							)

  _A           =    prepare_img()
  _A           =    ["a glass", "something to fill", "wood", "a jar"]

  _A           =    processor(text=lowercase__						,				images=[image] * len(lowercase__							)						,				padding='''max_length'''						,				return_tensors='''pt'''							)

  with torch.no_grad():
    _A           =    model(**lowercase__							)

  # verify values
  _A           =    torch.tensor([0.11_10, -0.18_82, 0.16_45]							)
  _A           =    torch.tensor([0.26_92, -0.71_97, -0.13_28]							)
  if model_name == "clipseg-rd64-refined":
    _A           =    torch.tensor(
        [[-10.04_07, -9.94_31, -10.26_46], [-9.97_51, -9.70_64, -9.95_86], [-9.68_91, -9.56_45, -9.96_18]]							)
  elif model_name == "clipseg-rd64":
    _A           =    torch.tensor(
        [[-7.28_77, -7.27_11, -7.24_63], [-7.26_52, -7.27_80, -7.25_20], [-7.22_39, -7.22_04, -7.20_01]]							)
  elif model_name == "clipseg-rd16":
    _A           =    torch.tensor(
        [[-6.39_55, -6.40_55, -6.41_51], [-6.39_11, -6.40_33, -6.41_00], [-6.34_74, -6.37_02, -6.37_62]]							)
  else:
    raise ValueError(F'''Model name {model_name} not supported.'''							)

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

  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__":
    __lowerCamelCase  				=					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."""
    )

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




from sklearn.metrics import accuracy_score

import datasets


__lowerCamelCase  				=					"""
Accuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:
Accuracy = (TP + TN) / (TP + TN + FP + FN)
 Where:
TP: True positive
TN: True negative
FP: False positive
FN: False negative
"""


__lowerCamelCase  				=					"""
Args:
    predictions (`list` of `int`): Predicted labels.
    references (`list` of `int`): Ground truth labels.
    normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.
    sample_weight (`list` of `float`): Sample weights Defaults to None.

Returns:
    accuracy (`float` or `int`): Accuracy score. Minimum possible value is 0. Maximum possible value is 1.0, or the number of examples input, if `normalize` is set to `True`.. A higher score means higher accuracy.

Examples:

    Example 1-A simple example
        >>> accuracy_metric = datasets.load_metric(\"accuracy\")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])
        >>> print(results)
        {'accuracy': 0.5}

    Example 2-The same as Example 1, except with `normalize` set to `False`.
        >>> accuracy_metric = datasets.load_metric(\"accuracy\")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)
        >>> print(results)
        {'accuracy': 3.0}

    Example 3-The same as Example 1, except with `sample_weight` set.
        >>> accuracy_metric = datasets.load_metric(\"accuracy\")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], sample_weight=[0.5, 2, 0.7, 0.5, 9, 0.4])
        >>> print(results)
        {'accuracy': 0.8778625954198473}
"""


__lowerCamelCase  				=					"""
@article{scikit-learn,
  title={Scikit-learn: Machine Learning in {P}ython},
  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
  journal={Journal of Machine Learning Research},
  volume={12},
  pages={2825--2830},
  year={2011}
}
"""



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION					,_KWARGS_DESCRIPTION				)
class 		_snake_case      (			datasets.Metric				):





 """simple docstring"""



 def lowercase_		(						self					)	->							Optional[Any]:




   """simple docstring"""







   return datasets.MetricInfo(
       description=_DESCRIPTION	,    citation=_CITATION	,    inputs_description=_KWARGS_DESCRIPTION	,    features=datasets.Features(
           {
               '''predictions''': datasets.Sequence(datasets.Value('''int32'''					)					),
               '''references''': datasets.Sequence(datasets.Value('''int32'''					)					),
           }
           if self.config_name == '''multilabel'''
           else {
               '''predictions''': datasets.Value('''int32'''					),
               '''references''': datasets.Value('''int32'''					),
           }					)	,    reference_urls=['''https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html''']	,    )



 def lowercase_		(						self	,    a	,    a	,    a=True	,    a=None					)	->							Dict:




   """simple docstring"""







   return {
       "accuracy": float(
           accuracy_score(a	,    a	,    normalize=a	,    sample_weight=a					)					)
   }