

import argparse

import requests
import torch
from PIL import Image

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





def 				__a							(       __A						):
    SCREAMING_SNAKE_CASE__         =    CLIPSegTextConfig()
    SCREAMING_SNAKE_CASE__         =    CLIPSegVisionConfig(patch_size=16						)

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

    SCREAMING_SNAKE_CASE__         =    CLIPSegConfig.from_text_vision_configs(
        __A			,							__A			,							use_complex_transposed_convolution=__A			,							reduce_dim=__A			,							)
    return config





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

    return name





def 				__a							(       __A			,							__A						):
    for key in orig_state_dict.copy().keys():
        SCREAMING_SNAKE_CASE__         =    orig_state_dict.pop(__A						)

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

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

    return orig_state_dict





def 				__a							(       ):
    SCREAMING_SNAKE_CASE__         =    "http://images.cocodataset.org/val2017/000000039769.jpg"
    SCREAMING_SNAKE_CASE__         =    Image.open(requests.get(__A			,							stream=__A						).raw						)
    return image





def 				__a							(       __A			,							__A			,							__A			,							__A						):
    SCREAMING_SNAKE_CASE__         =    get_clipseg_config(__A						)
    SCREAMING_SNAKE_CASE__         =    CLIPSegForImageSegmentation(__A						)
    model.eval()

    SCREAMING_SNAKE_CASE__         =    torch.load(__A			,							map_location="cpu"						)

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

    # rename some keys
    SCREAMING_SNAKE_CASE__         =    convert_state_dict(__A			,							__A						)
    SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__         =    model.load_state_dict(__A			,							strict=__A						)

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

    SCREAMING_SNAKE_CASE__         =    ViTImageProcessor(size=352						)
    SCREAMING_SNAKE_CASE__         =    CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32"						)
    SCREAMING_SNAKE_CASE__         =    CLIPSegProcessor(image_processor=__A			,							tokenizer=__A						)

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

    SCREAMING_SNAKE_CASE__         =    processor(text=__A			,							images=[image] * len(__A						)			,							padding="max_length"			,							return_tensors="pt"						)

    with torch.no_grad():
        SCREAMING_SNAKE_CASE__         =    model(**__A						)

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

    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__										=      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__										=      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


A__										=      "\nAccuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:\nAccuracy = (TP + TN) / (TP + TN + FP + FN)\n Where:\nTP: True positive\nTN: True negative\nFP: False positive\nFN: False negative\n"


A__										=      "\nArgs:\n    predictions (`list` of `int`): Predicted labels.\n    references (`list` of `int`): Ground truth labels.\n    normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.\n    sample_weight (`list` of `float`): Sample weights Defaults to None.\n\nReturns:\n    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.\n\nExamples:\n\n    Example 1-A simple example\n        >>> accuracy_metric = datasets.load_metric(\"accuracy\")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])\n        >>> print(results)\n        {'accuracy': 0.5}\n\n    Example 2-The same as Example 1, except with `normalize` set to `False`.\n        >>> accuracy_metric = datasets.load_metric(\"accuracy\")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)\n        >>> print(results)\n        {'accuracy': 3.0}\n\n    Example 3-The same as Example 1, except with `sample_weight` set.\n        >>> accuracy_metric = datasets.load_metric(\"accuracy\")\n        >>> 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])\n        >>> print(results)\n        {'accuracy': 0.8778625954198473}\n"


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







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


  """simple docstring"""



  def   _UpperCamelCase     (						self     :					int						)       ->			List[str]:
      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   _UpperCamelCase     (						self     :					Dict  ,	A_     :					List[Any]  ,	A_     :					Tuple  ,	A_     :					Optional[Any]=True  ,	A_     :					Tuple=None						)       ->			Any:
      return {
          "accuracy": float(
              accuracy_score(A_  ,	A_  ,	normalize=A_  ,	sample_weight=A_						)						)
      }


