




import logging
import os
import sys
from dataclasses import dataclass, field
from typing import List, Optional

import torch
from datasets import Dataset
from torch import nn
from tqdm.auto import tqdm

from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
    utils,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


UpperCAmelCase__						:       Union[str, Any]							=    '\nDistills an NLI-based zero-shot classifier to a smaller, more efficient model with a fixed set of candidate class\nnames. Useful for speeding up zero-shot classification in cases where labeled training data is not available, but\nwhen only a single fixed set of classes is needed. Takes a teacher NLI model, student classifier model, unlabeled\ndataset, and set of K possible class names. Yields a single classifier with K outputs corresponding to the provided\nclass names.\n'

UpperCAmelCase__						:       Optional[Any]							=    logging.getLogger(__name__)

@dataclass
class      UpperCAmelCase						:
   __SCREAMING_SNAKE_CASE		:							Optional[str]							=					field(
       default="roberta-large-mnli"   ,					metadata={"help": "The NLI/zero-shot teacher model to be distilled."}   )
   __SCREAMING_SNAKE_CASE		:							Optional[str]							=					field(
       default="This example is {}."   ,					metadata={
           "help": (
               "Template used to turn class names into mock hypotheses for teacher NLI model. Must include {{}}"
               "where class name is inserted."
           )
       }   ,					)
   __SCREAMING_SNAKE_CASE		:							Optional[int]							=					field(
       default=32   ,					metadata={"help": "Batch size for generating teacher predictions."}   )
   __SCREAMING_SNAKE_CASE		:							Optional[bool]							=					field(
       default=A   ,					metadata={
           "help": (
               "Allow multiple classes to be true rather than forcing them to sum to 1 (sometimes called"
               "multi-class multi-label classification)."
           )
       }   ,					)
   __SCREAMING_SNAKE_CASE		:							Optional[float]							=					field(
       default=1.0   ,					metadata={"help": "Temperature applied to teacher softmax for distillation."}   )

@dataclass
class      UpperCAmelCase						:
   __SCREAMING_SNAKE_CASE		:							Optional[str]							=					field(
       default="distilbert-base-uncased"   ,					metadata={"help": "The NLI/zero-shot teacher model to be distilled."}   )

@dataclass
class      UpperCAmelCase						:
   __SCREAMING_SNAKE_CASE		:							str							=					field(metadata={"help": "Text file with one unlabeled instance per line."}   )
   __SCREAMING_SNAKE_CASE		:							str							=					field(metadata={"help": "Text file with one class name per line."}   )
   __SCREAMING_SNAKE_CASE		:							bool							=					field(
       default=A   ,					metadata={"help": "Whether to use one of the fast tokenizer (backed by the Rust tokenizers library) or not."}   ,					)

@dataclass
class      UpperCAmelCase						(			A   ):
   __SCREAMING_SNAKE_CASE		:							Optional[str]							=					field(
       default=A   ,					metadata={"help": "The output directory where the model predictions and checkpoints will be written."}   ,					)
   __SCREAMING_SNAKE_CASE		:							int							=					field(
       default=32   ,					metadata={"help": "Batch size per GPU/TPU core/CPU for training."}   )
   __SCREAMING_SNAKE_CASE		:							int							=					field(
       default=1_28   ,					metadata={"help": "Batch size per GPU/TPU core/CPU for evaluation."}   )
   __SCREAMING_SNAKE_CASE		:							float							=					field(default=1.0   ,					metadata={"help": "Total number of training epochs to perform."}   )
   __SCREAMING_SNAKE_CASE		:							bool							=					field(default=A   ,					metadata={"help": "Whether to run training of student model."}   )
   __SCREAMING_SNAKE_CASE		:							bool							=					field(
       default=A   ,					metadata={
           "help": (
               "Whether to evaluate the agreement of the final student predictions and the teacher predictions"
               "after training."
           )
       }   ,					)
   __SCREAMING_SNAKE_CASE		:							Optional[int]							=					field(
       default=0   ,					metadata={
           "help": (
               "Limit the total amount of checkpoints."
               "Deletes the older checkpoints in the output_dir. Default is 0 (no checkpoints)."
           )
       }   ,					)

class      UpperCAmelCase						(			A   ):






   def  _a						(	self   :       Optional[Any]						,					UpperCamelCase_   :       int						,					UpperCamelCase_   :       Any						,					UpperCamelCase_   :       Dict=False     )->			Tuple:
     _lowercase: Union[str, Any]						     =    inputs['labels']
     _lowercase: Any						     =    model(inputs['input_ids']						,					attention_mask=inputs['attention_mask']     )
     _lowercase: int						     =    outputs[0]

     _lowercase: int						     =    -torch.sum(target_p * logits.log_softmax(dim=-1     )						,					axis=-1     ).mean()

     if return_outputs:
       return loss, outputs

     return loss


def 		_lowerCamelCase				(					UpperCamelCase							)			->		List[str]:





  """simple docstring"""





  _lowercase: List[Any]						     =    []
  with open(UpperCamelCase					,     'r'							) as f:
    for line in f:
      _lowercase: Optional[int]						     =    line.strip()
      if len(UpperCamelCase							) > 0:
        lines.append(UpperCamelCase							)
  return lines


def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase							)			->		List[Any]:





  """simple docstring"""





  _lowercase: List[Any]						     =    []
  _lowercase: Any						     =    []
  for example in examples:
    for name in class_names:
      premises.append(UpperCamelCase							)
      hypotheses.append(hypothesis_template.format(UpperCamelCase							)							)
  return premises, hypotheses


def 		_lowerCamelCase				(					UpperCamelCase							)			->		Dict:





  """simple docstring"""





  for label, ind in config.labelaid.items():
    if label.lower().startswith('entail'							):
      return ind
  logger.warning('Could not identify entailment dimension from teacher config label2id. Setting to -1.'							)
  return -1


def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     )			->		List[Any]:





  """simple docstring"""





  _lowercase: str						     =    AutoModelForSequenceClassification.from_pretrained(UpperCamelCase							)
  _lowercase: List[Any]						     =    model.config
  if not no_cuda and torch.cuda.is_available():
    _lowercase: List[Any]						     =    nn.DataParallel(model.cuda()							)
    batch_size *= len(model.device_ids							)
  _lowercase: Optional[Any]						     =    AutoTokenizer.from_pretrained(UpperCamelCase					,     use_fast=UpperCamelCase							)

  _lowercase     ,					_lowercase: Any						     =    get_premise_hypothesis_pairs(UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase							)
  _lowercase: Optional[int]						     =    []

  for i in tqdm(range(0					,     len(UpperCamelCase							)					,     UpperCamelCase							)							):
    _lowercase: List[str]						     =    premises[i : i + batch_size]
    _lowercase: Optional[int]						     =    hypotheses[i : i + batch_size]

    _lowercase: Any						     =    tokenizer(
        UpperCamelCase					,     UpperCamelCase					,     padding=UpperCamelCase					,     truncation='only_first'					,     return_tensors='pt'					,     )

    with torch.cuda.amp.autocast(enabled=UpperCamelCase							):
      with torch.no_grad():
        _lowercase: Optional[Any]						     =    model(**UpperCamelCase							)
    logits.append(outputs.logits.detach().cpu().float()							)

  _lowercase: Union[str, Any]						     =    get_entailment_id(UpperCamelCase							)
  _lowercase: Tuple						     =    -1 if entail_id == 0 else 0
  _lowercase: str						     =    torch.cat(UpperCamelCase					,     dim=0							)  # N*K x 3
  _lowercase: Dict						     =    logits.reshape(len(UpperCamelCase							)					,     len(UpperCamelCase							)					,     -1							)[..., [contr_id, entail_id]]  # N x K x 2

  if multi_label:
    # softmax over (contr, entail) logits for each class independently
    _lowercase: List[str]						     =    (nli_logits / temperature).softmax(-1							)
  else:
    # softmax over entail logits across classes s.t. class probabilities sum to 1.
    _lowercase: int						     =    (nli_logits / temperature).softmax(1							)

  return nli_prob[..., 1]  # N x K


def 		_lowerCamelCase				(					)			->		int:





  """simple docstring"""





  _lowercase: Any						     =    HfArgumentParser(
      (DataTrainingArguments, TeacherModelArguments, StudentModelArguments, DistillTrainingArguments)					,     description=UpperCamelCase					,     )

  if len(sys.argv							) == 2 and sys.argv[1].endswith('.json'							):
    # If we pass only one argument to the script and it's the path to a json file,
    # let's parse it to get our arguments.
    _lowercase     ,					_lowercase     ,					_lowercase     ,					_lowercase: Dict						     =    parser.parse_json_file(
        json_file=os.path.abspath(sys.argv[1]							)							)
  else:
    _lowercase     ,					_lowercase     ,					_lowercase     ,					_lowercase: Union[str, Any]						     =    parser.parse_args_into_dataclasses()

  # Detecting last checkpoint.
  _lowercase: Optional[int]						     =    None
  if os.path.isdir(training_args.output_dir							) and training_args.do_train and not training_args.overwrite_output_dir:
    _lowercase: List[Any]						     =    get_last_checkpoint(training_args.output_dir							)
    if last_checkpoint is None and len(os.listdir(training_args.output_dir							)							) > 0:
      raise ValueError(
          F'Output directory ({training_args.output_dir}) already exists and is not empty. '
          'Use --overwrite_output_dir to overcome.'							)
    elif last_checkpoint is not None:
      logger.info(
          F'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
          'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'							)

    # Setup logging
  logging.basicConfig(
      format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'					,     datefmt='%m/%d/%Y %H:%M:%S'					,     handlers=[logging.StreamHandler(sys.stdout							)]					,     )
  logger.setLevel(logging.INFO if is_main_process(training_args.local_rank							) else logging.WARN							)

  # Log on each process the small summary:
  logger.warning(
      F'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'
      + F'distributed training: {bool(training_args.local_rank != -1							)}, 16-bits training: {training_args.fpaa}'							)
  # Set the verbosity to info of the Transformers logger (on main process only):
  if is_main_process(training_args.local_rank							):
    utils.logging.set_verbosity_info()
    utils.logging.enable_default_handler()
    utils.logging.enable_explicit_format()

  if training_args.local_rank != -1:
    raise ValueError('Distributed training is not currently supported.'							)
  if training_args.tpu_num_cores is not None:
    raise ValueError('TPU acceleration is not currently supported.'							)

  logger.info(F'Training/evaluation parameters {training_args}'							)

  # Set seed before initializing model.
  set_seed(training_args.seed							)

  # 1. read in data
  _lowercase: Union[str, Any]						     =    read_lines(data_args.data_file							)
  _lowercase: List[str]						     =    read_lines(data_args.class_names_file							)

  # 2. get teacher predictions and load into dataset
  logger.info('Generating predictions from zero-shot teacher model'							)
  _lowercase: List[Any]						     =    get_teacher_predictions(
      teacher_args.teacher_name_or_path					,     UpperCamelCase					,     UpperCamelCase					,     teacher_args.hypothesis_template					,     teacher_args.teacher_batch_size					,     teacher_args.temperature					,     teacher_args.multi_label					,     data_args.use_fast_tokenizer					,     training_args.no_cuda					,     training_args.fpaa					,     )
  _lowercase: Dict						     =    Dataset.from_dict(
      {
          'text': examples,
          'labels': teacher_soft_preds,
      }							)

  # 3. create student
  logger.info('Initializing student model'							)
  _lowercase: Optional[int]						     =    AutoModelForSequenceClassification.from_pretrained(
      student_args.student_name_or_path					,     num_labels=len(UpperCamelCase							)							)
  _lowercase: Optional[int]						     =    AutoTokenizer.from_pretrained(student_args.student_name_or_path					,     use_fast=data_args.use_fast_tokenizer							)
  _lowercase: Any						     =    dict(enumerate(UpperCamelCase							)							)
  _lowercase: List[Any]						     =    {label: i for i, label in enumerate(UpperCamelCase							)}

  # 4. train student on teacher predictions
  _lowercase: Optional[int]						     =    dataset.map(UpperCamelCase					,     input_columns='text'							)
  dataset.set_format('torch'							)

  def compute_metrics(UpperCamelCase					,     UpperCamelCase=False							):
    _lowercase: Dict						     =    p.predictions.argmax(-1							)
    _lowercase: Dict						     =    p.label_ids.argmax(-1							)  # "label_ids" are actually distributions
    return {"agreement": (preds == proxy_labels).mean().item()}

  _lowercase: Any						     =    DistillationTrainer(
      model=UpperCamelCase					,     tokenizer=UpperCamelCase					,     args=UpperCamelCase					,     train_dataset=UpperCamelCase					,     compute_metrics=UpperCamelCase					,     )

  if training_args.do_train:
    logger.info('Training student model on teacher predictions'							)
    trainer.train()

  if training_args.do_eval:
    _lowercase: Union[str, Any]						     =    trainer.evaluate(eval_dataset=UpperCamelCase							)['eval_agreement']
    logger.info(F'Agreement of student and teacher predictions: {agreement * 100:0.2f}%'							)

  trainer.save_model()


if __name__ == "__main__":
   main()






import argparse

import requests
import torch
from PIL import Image

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


def 		_lowerCamelCase				(					UpperCamelCase							)			->		str:





  """simple docstring"""





  _lowercase: Any						     =    CLIPSegTextConfig()
  _lowercase: Optional[Any]						     =    CLIPSegVisionConfig(patch_size=16							)

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

  _lowercase: Any						     =    CLIPSegConfig.from_text_vision_configs(
      UpperCamelCase					,     UpperCamelCase					,     use_complex_transposed_convolution=UpperCamelCase					,     reduce_dim=UpperCamelCase					,     )
  return config


def 		_lowerCamelCase				(					UpperCamelCase							)			->		Any:





  """simple docstring"""





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

  return name


def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase							)			->		Optional[Any]:





  """simple docstring"""





  for key in orig_state_dict.copy().keys():
    _lowercase: int						     =    orig_state_dict.pop(UpperCamelCase							)

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

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

  return orig_state_dict


def 		_lowerCamelCase				(					)			->		Optional[Any]:





  """simple docstring"""





  _lowercase: Any						     =    'http://images.cocodataset.org/val2017/000000039769.jpg'
  _lowercase: str						     =    Image.open(requests.get(UpperCamelCase					,     stream=UpperCamelCase							).raw							)
  return image


def 		_lowerCamelCase				(					UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase					,     UpperCamelCase							)			->		Optional[int]:





  """simple docstring"""





  _lowercase: Optional[Any]						     =    get_clipseg_config(UpperCamelCase							)
  _lowercase: Optional[Any]						     =    CLIPSegForImageSegmentation(UpperCamelCase							)
  model.eval()

  _lowercase: Dict						     =    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
  _lowercase: List[str]						     =    convert_state_dict(UpperCamelCase					,     UpperCamelCase							)
  _lowercase     ,					_lowercase: List[Any]						     =    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}'							)

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

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

  _lowercase: str						     =    processor(text=UpperCamelCase					,     images=[image] * len(UpperCamelCase							)					,     padding='max_length'					,     return_tensors='pt'							)

  with torch.no_grad():
    _lowercase: Optional[Any]						     =    model(**UpperCamelCase							)

  # verify values
  _lowercase: int						     =    torch.tensor([0.1_110, -0.1_882, 0.1_645]							)
  _lowercase: Tuple						     =    torch.tensor([0.2_692, -0.7_197, -0.1_328]							)
  if model_name == "clipseg-rd64-refined":
    _lowercase: str						     =    torch.tensor(
        [[-10.0_407, -9.9_431, -10.2_646], [-9.9_751, -9.7_064, -9.9_586], [-9.6_891, -9.5_645, -9.9_618]]							)
  elif model_name == "clipseg-rd64":
    _lowercase: Dict						     =    torch.tensor(
        [[-7.2_877, -7.2_711, -7.2_463], [-7.2_652, -7.2_780, -7.2_520], [-7.2_239, -7.2_204, -7.2_001]]							)
  elif model_name == "clipseg-rd16":
    _lowercase: Optional[int]						     =    torch.tensor(
        [[-6.3_955, -6.4_055, -6.4_151], [-6.3_911, -6.4_033, -6.4_100], [-6.3_474, -6.3_702, -6.3_762]]							)
  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__						:       int							=    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)
