

'''simple docstring'''




from typing import Optional, Union

from ...file_utils import TensorType
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, TruncationStrategy



class 				lowercase_		(						_UpperCamelCase       ):

  '''simple docstring'''





  __lowerCAmelCase    :       Tuple			     =      'MarkupLMFeatureExtractor'
  __lowerCAmelCase    :       int			     =      ('MarkupLMTokenizer', 'MarkupLMTokenizerFast')
  __lowerCAmelCase    :       str			     =      True
  def __call__(   self      ,       a_=None      ,       a_=None      ,       a_=None      ,       a_=None      ,       a_=None      ,       a_ = True      ,       a_ = False      ,       a_ = None      ,       a_ = None      ,       a_ = 0      ,       a_ = None      ,       a_ = None      ,       a_ = None      ,       a_ = False      ,       a_ = False      ,       a_ = False      ,       a_ = False      ,       a_ = True      ,       a_ = None      ,       **a_      ,       )							-> BatchEncoding:


    """simple docstring"""
    # first, create nodes and xpaths
    if self.parse_html:
      if html_strings is None:
        raise ValueError('Make sure to pass HTML strings in case `parse_html` is set to `True`'   )

      if nodes is not None or xpaths is not None or node_labels is not None:
        raise ValueError(
            'Please don\'t pass nodes, xpaths nor node labels in case `parse_html` is set to `True`'   )

      UpperCAmelCase  							=		self.feature_extractor(_SCREAMING_SNAKE_CASE   )
      UpperCAmelCase  							=		features['nodes']
      UpperCAmelCase  							=		features['xpaths']
    else:
      if html_strings is not None:
        raise ValueError('You have passed HTML strings but `parse_html` is set to `False`.'   )
      if nodes is None or xpaths is None:
        raise ValueError('Make sure to pass nodes and xpaths in case `parse_html` is set to `False`'   )

        # # second, apply the tokenizer
    if questions is not None and self.parse_html:
      if isinstance(_SCREAMING_SNAKE_CASE      ,       _SCREAMING_SNAKE_CASE   ):
        UpperCAmelCase  							=		[questions]  # add batch dimension (as the feature extractor always adds a batch dimension)

    UpperCAmelCase  							=		self.tokenizer(
        text=questions if questions is not None else nodes      ,       text_pair=nodes if questions is not None else None      ,       xpaths=_SCREAMING_SNAKE_CASE      ,       node_labels=_SCREAMING_SNAKE_CASE      ,       add_special_tokens=_SCREAMING_SNAKE_CASE      ,       padding=_SCREAMING_SNAKE_CASE      ,       truncation=_SCREAMING_SNAKE_CASE      ,       max_length=_SCREAMING_SNAKE_CASE      ,       stride=_SCREAMING_SNAKE_CASE      ,       pad_to_multiple_of=_SCREAMING_SNAKE_CASE      ,       return_token_type_ids=_SCREAMING_SNAKE_CASE      ,       return_attention_mask=_SCREAMING_SNAKE_CASE      ,       return_overflowing_tokens=_SCREAMING_SNAKE_CASE      ,       return_special_tokens_mask=_SCREAMING_SNAKE_CASE      ,       return_offsets_mapping=_SCREAMING_SNAKE_CASE      ,       return_length=_SCREAMING_SNAKE_CASE      ,       verbose=_SCREAMING_SNAKE_CASE      ,       return_tensors=_SCREAMING_SNAKE_CASE      ,       **_SCREAMING_SNAKE_CASE      ,       )

    return encoded_inputs
  def   snake_case_	(   self      ,       *a_      ,       **a_   )							-> Tuple:


    """simple docstring"""
    return self.tokenizer.batch_decode(*_SCREAMING_SNAKE_CASE      ,       **_SCREAMING_SNAKE_CASE   )
  def   snake_case_	(   self      ,       *a_      ,       **a_   )							-> int:


    """simple docstring"""
    return self.tokenizer.decode(*_SCREAMING_SNAKE_CASE      ,       **_SCREAMING_SNAKE_CASE   )





  @property
  def   snake_case_	(   self   )							-> Tuple:


    """simple docstring"""
    UpperCAmelCase  							=		self.tokenizer.model_input_names
    return tokenizer_input_names



'''simple docstring'''




import argparse

import requests
import torch
from PIL import Image

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





def      _UpperCAmelCase    (							_UpperCamelCase    :	Dict   )	->      Union[str, 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(
        _UpperCamelCase,   _UpperCamelCase,   use_complex_transposed_convolution=_UpperCamelCase,   reduce_dim=_UpperCamelCase,   )
    return config





def      _UpperCAmelCase    (							_UpperCamelCase    :	Union[str, Any]   )	->      Dict:
    # 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    (							_UpperCamelCase    :	int,   _UpperCamelCase    :	Dict   )	->      Union[str, Any]:
    for key in orig_state_dict.copy().keys():
        A_     =   orig_state_dict.pop(_UpperCamelCase   )

        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(_UpperCamelCase   )
            if "visual_projection" in new_name or "text_projection" in new_name:
                A_     =   val.T
            A_     =   val

    return orig_state_dict





def      _UpperCAmelCase    (							)	->      Tuple:
    A_     =   '''http://images.cocodataset.org/val2017/000000039769.jpg'''
    A_     =   Image.open(requests.get(_UpperCamelCase,   stream=_UpperCamelCase   ).raw   )
    return image





def      _UpperCAmelCase    (							_UpperCamelCase    :	Tuple,   _UpperCamelCase    :	int,   _UpperCamelCase    :	List[Any],   _UpperCamelCase    :	str   )	->      List[str]:
    A_     =   get_clipseg_config(_UpperCamelCase   )
    A_     =   CLIPSegForImageSegmentation(_UpperCamelCase   )
    model.eval()

    A_     =   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
    A_     =   convert_state_dict(_UpperCamelCase,   _UpperCamelCase   )
    A_   ,A_     =   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}'''   )

    A_     =   ViTImageProcessor(size=3_52   )
    A_     =   CLIPTokenizer.from_pretrained('''openai/clip-vit-base-patch32'''   )
    A_     =   CLIPSegProcessor(image_processor=_UpperCamelCase,   tokenizer=_UpperCamelCase   )

    A_     =   prepare_img()
    A_     =   ['''a glass''', '''something to fill''', '''wood''', '''a jar''']

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

    with torch.no_grad():
        A_     =   model(**_UpperCamelCase   )

    # verify values
    A_     =   torch.tensor([0.1_1_1_0, -0.1_8_8_2, 0.1_6_4_5]   )
    A_     =   torch.tensor([0.2_6_9_2, -0.7_1_9_7, -0.1_3_2_8]   )
    if model_name == "clipseg-rd64-refined":
        A_     =   torch.tensor(
            [[-1_0.0_4_0_7, -9.9_4_3_1, -1_0.2_6_4_6], [-9.9_7_5_1, -9.7_0_6_4, -9.9_5_8_6], [-9.6_8_9_1, -9.5_6_4_5, -9.9_6_1_8]]   )
    elif model_name == "clipseg-rd64":
        A_     =   torch.tensor(
            [[-7.2_8_7_7, -7.2_7_1_1, -7.2_4_6_3], [-7.2_6_5_2, -7.2_7_8_0, -7.2_5_2_0], [-7.2_2_3_9, -7.2_2_0_4, -7.2_0_0_1]]   )
    elif model_name == "clipseg-rd16":
        A_     =   torch.tensor(
            [[-6.3_9_5_5, -6.4_0_5_5, -6.4_1_5_1], [-6.3_9_1_1, -6.4_0_3_3, -6.4_1_0_0], [-6.3_4_7_4, -6.3_7_0_2, -6.3_7_6_2]]   )
    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__":
  __snake_case   :      Dict						    =  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.'
  )

  __snake_case   :      str						    =  parser.parse_args()
  convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)






