




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

import numpy as np
import torch
from utils_hans import HansDataset, InputFeatures, hans_processors, hans_tasks_num_labels

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import is_main_process


UpperCAmelCase__						:       int							=    logging.getLogger(__name__)

@dataclass
class      UpperCAmelCase						:
   __SCREAMING_SNAKE_CASE		:							int							=					field(
       metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}   )
   __SCREAMING_SNAKE_CASE		:							Any							=					field(
       default=_SCREAMING_SNAKE_CASE   ,					metadata={"help": "Pretrained config name or path if not the same as model_name"}   )
   __SCREAMING_SNAKE_CASE		:							Dict							=					field(
       default=_SCREAMING_SNAKE_CASE   ,					metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}   )
   __SCREAMING_SNAKE_CASE		:							Tuple							=					field(
       default=_SCREAMING_SNAKE_CASE   ,					metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}   ,					)

@dataclass
class      UpperCAmelCase						:
   __SCREAMING_SNAKE_CASE		:							Any							=					field(
       metadata={"help": "The name of the task to train selected in the list: " + ", ".join(hans_processors.keys()   )}   )
   __SCREAMING_SNAKE_CASE		:							Union[str, Any]							=					field(
       metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}   )
   __SCREAMING_SNAKE_CASE		:							List[str]							=					field(
       default=1_28   ,					metadata={
           "help": (
               "The maximum total input sequence length after tokenization. Sequences longer "
               "than this will be truncated, sequences shorter will be padded."
           )
       }   ,					)
   __SCREAMING_SNAKE_CASE		:							int							=					field(
       default=_SCREAMING_SNAKE_CASE   ,					metadata={"help": "Overwrite the cached training and evaluation sets"}   )


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





  """simple docstring"""





  _lowercase: Tuple						     =    default_data_collator(UpperCamelCase							)
  _lowercase: Tuple						     =    batch.pop('pairID'					,     UpperCamelCase							)
  return batch


def 		_lowerCamelCase				(					)			->		Union[str, Any]:





  """simple docstring"""





  _lowercase: int						     =    HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)							)
  _lowercase     ,					_lowercase     ,					_lowercase: Union[str, Any]						     =    parser.parse_args_into_dataclasses()

  if (
      os.path.exists(training_args.output_dir							)
      and os.listdir(training_args.output_dir							)
      and training_args.do_train
      and not training_args.overwrite_output_dir
  ):
    raise ValueError(
        F'Output directory ({training_args.output_dir}) already exists and is not empty. Use'
        ' --overwrite_output_dir to overcome.'							)

  # Setup logging
  logging.basicConfig(
      format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'					,     datefmt='%m/%d/%Y %H:%M:%S'					,     level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN					,     )
  logger.warning(
      'Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'					,     training_args.local_rank					,     training_args.device					,     training_args.n_gpu					,     bool(training_args.local_rank != -1							)					,     training_args.fpaa					,     )
  # Set the verbosity to info of the Transformers logger (on main process only):
  if is_main_process(training_args.local_rank							):
    transformers.utils.logging.set_verbosity_info()
    transformers.utils.logging.enable_default_handler()
    transformers.utils.logging.enable_explicit_format()
  logger.info('Training/evaluation parameters %s'					,     UpperCamelCase							)

  # Set seed
  set_seed(training_args.seed							)

  try:
    _lowercase: str						     =    hans_tasks_num_labels[data_args.task_name]
  except KeyError:
    raise ValueError('Task not found: %s' % (data_args.task_name)							)

  # Load pretrained model and tokenizer
  #
  # Distributed training:
  # The .from_pretrained methods guarantee that only one local process can concurrently
  # download model & vocab.

  _lowercase: List[Any]						     =    AutoConfig.from_pretrained(
      model_args.config_name if model_args.config_name else model_args.model_name_or_path					,     num_labels=UpperCamelCase					,     finetuning_task=data_args.task_name					,     cache_dir=model_args.cache_dir					,     )
  _lowercase: int						     =    AutoTokenizer.from_pretrained(
      model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path					,     cache_dir=model_args.cache_dir					,     )
  _lowercase: List[str]						     =    AutoModelForSequenceClassification.from_pretrained(
      model_args.model_name_or_path					,     from_tf=bool('.ckpt' in model_args.model_name_or_path							)					,     config=UpperCamelCase					,     cache_dir=model_args.cache_dir					,     )

  # Get datasets
  _lowercase: str						     =    (
      HansDataset(
          data_dir=data_args.data_dir					,     tokenizer=UpperCamelCase					,     task=data_args.task_name					,     max_seq_length=data_args.max_seq_length					,     overwrite_cache=data_args.overwrite_cache					,     )
      if training_args.do_train
      else None
  )
  _lowercase: Optional[Any]						     =    (
      HansDataset(
          data_dir=data_args.data_dir					,     tokenizer=UpperCamelCase					,     task=data_args.task_name					,     max_seq_length=data_args.max_seq_length					,     overwrite_cache=data_args.overwrite_cache					,     evaluate=UpperCamelCase					,     )
      if training_args.do_eval
      else None
  )

  # Initialize our Trainer
  _lowercase: Optional[Any]						     =    Trainer(
      model=UpperCamelCase					,     args=UpperCamelCase					,     train_dataset=UpperCamelCase					,     eval_dataset=UpperCamelCase					,     data_collator=UpperCamelCase					,     )

  # Training
  if training_args.do_train:
    trainer.train(
        model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path							) else None							)
    trainer.save_model()
    # For convenience, we also re-save the tokenizer to the same directory,
    # so that you can share your model easily on huggingface.co/models =)
    if trainer.is_world_master():
      tokenizer.save_pretrained(training_args.output_dir							)

    # Evaluation
  if training_args.do_eval:
    logger.info('*** Evaluate ***'							)

    _lowercase: List[Any]						     =    trainer.predict(UpperCamelCase							)
    _lowercase: Dict						     =    output.predictions
    _lowercase: Union[str, Any]						     =    np.argmax(UpperCamelCase					,     axis=1							)

    _lowercase: Optional[int]						     =    [ex.pairID for ex in eval_dataset]
    _lowercase: int						     =    os.path.join(training_args.output_dir					,     'hans_predictions.txt'							)
    _lowercase: List[Any]						     =    eval_dataset.get_labels()
    if trainer.is_world_master():
      with open(UpperCamelCase					,     'w'							) as writer:
        writer.write('pairID,gold_label\n'							)
        for pid, pred in zip(UpperCamelCase					,     UpperCamelCase							):
          writer.write('ex' + str(UpperCamelCase							) + ',' + label_list[int(UpperCamelCase							)] + '\n'							)

    trainer._log(output.metrics							)


def 		_lowerCamelCase				(					UpperCamelCase							)			->		Dict:





  """simple docstring"""





  main()


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)
