code: 
  
  
  
  
  
  import argparse
  
  import requests
  import torch
  from PIL import Image
  
  from transformers import (
      CLIPSegConfig,
      CLIPSegForImageSegmentation,
      CLIPSegProcessor,
      CLIPSegTextConfig,
      CLIPSegVisionConfig,
      CLIPTokenizer,
      ViTImageProcessor,
  )
  
  
  
  
  def 						A_			(  lowercase_  )			->						int:
         _snake_case      :							List[Any]     				=						CLIPSegTextConfig()
         _snake_case      :							Dict     				=						CLIPSegVisionConfig(patch_size=16  )
  
         _snake_case      :							Optional[Any]     				=						True if '''refined''' in model_name else False
         _snake_case      :							int     				=						16 if '''rd16''' in model_name else 64
  
         _snake_case      :							Union[str, Any]     				=						CLIPSegConfig.from_text_vision_configs(
             lowercase_      ,		lowercase_      ,		use_complex_transposed_convolution=lowercase_      ,		reduce_dim=lowercase_      ,		)
         return config
  
  
  
  
  def 						A_			(  lowercase_  )			->						Union[str, Any]:
         # update prefixes
         if "clip_model" in name:
                _snake_case      :							Optional[int]     				=						name.replace('''clip_model'''      ,		'''clip'''  )
         if "transformer" in name:
                if "visual" in name:
                       _snake_case      :							List[str]     				=						name.replace('''visual.transformer'''      ,		'''vision_model'''  )
                else:
                       _snake_case      :							str     				=						name.replace('''transformer'''      ,		'''text_model'''  )
         if "resblocks" in name:
                _snake_case      :							List[str]     				=						name.replace('''resblocks'''      ,		'''encoder.layers'''  )
         if "ln_1" in name:
                _snake_case      :							str     				=						name.replace('''ln_1'''      ,		'''layer_norm1'''  )
         if "ln_2" in name:
                _snake_case      :							List[str]     				=						name.replace('''ln_2'''      ,		'''layer_norm2'''  )
         if "c_fc" in name:
                _snake_case      :							Optional[int]     				=						name.replace('''c_fc'''      ,		'''fc1'''  )
         if "c_proj" in name:
                _snake_case      :							List[Any]     				=						name.replace('''c_proj'''      ,		'''fc2'''  )
         if "attn" in name and "self" not in name:
                _snake_case      :							List[Any]     				=						name.replace('''attn'''      ,		'''self_attn'''  )
         # text encoder
         if "token_embedding" in name:
                _snake_case      :							List[str]     				=						name.replace('''token_embedding'''      ,		'''text_model.embeddings.token_embedding'''  )
         if "positional_embedding" in name and "visual" not in name:
                _snake_case      :							List[Any]     				=						name.replace('''positional_embedding'''      ,		'''text_model.embeddings.position_embedding.weight'''  )
         if "ln_final" in name:
                _snake_case      :							Dict     				=						name.replace('''ln_final'''      ,		'''text_model.final_layer_norm'''  )
         # vision encoder
         if "visual.class_embedding" in name:
                _snake_case      :							Tuple     				=						name.replace('''visual.class_embedding'''      ,		'''vision_model.embeddings.class_embedding'''  )
         if "visual.conv1" in name:
                _snake_case      :							Optional[Any]     				=						name.replace('''visual.conv1'''      ,		'''vision_model.embeddings.patch_embedding'''  )
         if "visual.positional_embedding" in name:
                _snake_case      :							Dict     				=						name.replace('''visual.positional_embedding'''      ,		'''vision_model.embeddings.position_embedding.weight'''  )
         if "visual.ln_pre" in name:
                _snake_case      :							Optional[int]     				=						name.replace('''visual.ln_pre'''      ,		'''vision_model.pre_layrnorm'''  )
         if "visual.ln_post" in name:
                _snake_case      :							str     				=						name.replace('''visual.ln_post'''      ,		'''vision_model.post_layernorm'''  )
         # projection layers
         if "visual.proj" in name:
                _snake_case      :							str     				=						name.replace('''visual.proj'''      ,		'''visual_projection.weight'''  )
         if "text_projection" in name:
                _snake_case      :							List[Any]     				=						name.replace('''text_projection'''      ,		'''text_projection.weight'''  )
         # decoder
         if "trans_conv" in name:
                _snake_case      :							List[str]     				=						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:
                _snake_case      :							List[str]     				=						'''decoder.''' + name
         if "blocks" in name:
                _snake_case      :							Tuple     				=						name.replace('''blocks'''      ,		'''decoder.layers'''  )
         if "linear1" in name:
                _snake_case      :							Union[str, Any]     				=						name.replace('''linear1'''      ,		'''mlp.fc1'''  )
         if "linear2" in name:
                _snake_case      :							Any     				=						name.replace('''linear2'''      ,		'''mlp.fc2'''  )
         if "norm1" in name and "layer_" not in name:
                _snake_case      :							Any     				=						name.replace('''norm1'''      ,		'''layer_norm1'''  )
         if "norm2" in name and "layer_" not in name:
                _snake_case      :							List[Any]     				=						name.replace('''norm2'''      ,		'''layer_norm2'''  )
  
         return name
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_  )			->						int:
         for key in orig_state_dict.copy().keys():
                _snake_case      :							Tuple     				=						orig_state_dict.pop(lowercase_  )
  
                if key.startswith('''clip_model'''  ) and "attn.in_proj" in key:
                       _snake_case      :							Tuple     				=						key.split('''.'''  )
                       if "visual" in key:
                              _snake_case      :							Dict     				=						int(key_split[4]  )
                              _snake_case      :							int     				=						config.vision_config.hidden_size
                              _snake_case      :							Any     				=						'''vision_model'''
                       else:
                              _snake_case      :							List[Any]     				=						int(key_split[3]  )
                              _snake_case      :							Optional[int]     				=						config.text_config.hidden_size
                              _snake_case      :							Union[str, Any]     				=						'''text_model'''
  
                       if "weight" in key:
                              _snake_case      :							Dict     				=						val[:dim, :]
                              _snake_case      :							str     				=						val[
                                  dim : dim * 2, :
                              ]
                              _snake_case      :							Tuple     				=						val[-dim:, :]
                       else:
                              _snake_case      :							Optional[int]     				=						val[:dim]
                              _snake_case      :							Optional[int]     				=						val[dim : dim * 2]
                              _snake_case      :							str     				=						val[-dim:]
                elif "self_attn" in key and "out_proj" not in key:
                       _snake_case      :							List[Any]     				=						key.split('''.'''  )
                       _snake_case      :							List[str]     				=						int(key_split[1]  )
                       _snake_case      :							Dict     				=						config.reduce_dim
                       if "weight" in key:
                              _snake_case      :							Optional[int]     				=						val[:dim, :]
                              _snake_case      :							Any     				=						val[dim : dim * 2, :]
                              _snake_case      :							Any     				=						val[-dim:, :]
                       else:
                              _snake_case      :							Any     				=						val[:dim]
                              _snake_case      :							Tuple     				=						val[dim : dim * 2]
                              _snake_case      :							Dict     				=						val[-dim:]
                else:
                       _snake_case      :							int     				=						rename_key(lowercase_  )
                       if "visual_projection" in new_name or "text_projection" in new_name:
                              _snake_case      :							Dict     				=						val.T
                       _snake_case      :							Dict     				=						val
  
         return orig_state_dict
  
  
  
  
  def 						A_			(  )			->						int:
         _snake_case      :							Union[str, Any]     				=						'''http://images.cocodataset.org/val2017/000000039769.jpg'''
         _snake_case      :							Any     				=						Image.open(requests.get(lowercase_      ,		stream=lowercase_  ).raw  )
         return image
  
  
  
  
  def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_      ,		lowercase_  )			->						List[Any]:
         _snake_case      :							Optional[Any]     				=						get_clipseg_config(lowercase_  )
         _snake_case      :							List[Any]     				=						CLIPSegForImageSegmentation(lowercase_  )
         model.eval()
  
         _snake_case      :							List[str]     				=						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
         _snake_case      :							Union[str, Any]     				=						convert_state_dict(lowercase_      ,		lowercase_  )
         _snake_case				,    _snake_case      :							Optional[Any]     				=						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}'''  )
  
         _snake_case      :							str     				=						ViTImageProcessor(size=352  )
         _snake_case      :							Any     				=						CLIPTokenizer.from_pretrained('''openai/clip-vit-base-patch32'''  )
         _snake_case      :							Dict     				=						CLIPSegProcessor(image_processor=lowercase_      ,		tokenizer=lowercase_  )
  
         _snake_case      :							Optional[int]     				=						prepare_img()
         _snake_case      :							Optional[int]     				=						['''a glass''', '''something to fill''', '''wood''', '''a jar''']
  
         _snake_case      :							int     				=						processor(text=lowercase_      ,		images=[image] * len(lowercase_  )      ,		padding='''max_length'''      ,		return_tensors='''pt'''  )
  
         with torch.no_grad():
                _snake_case      :							Any     				=						model(**lowercase_  )
  
         # verify values
         _snake_case      :							Any     				=						torch.tensor([0.11_10, -0.18_82, 0.16_45]  )
         _snake_case      :							Optional[Any]     				=						torch.tensor([0.26_92, -0.71_97, -0.13_28]  )
         if model_name == "clipseg-rd64-refined":
                _snake_case      :							str     				=						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":
                _snake_case      :							Dict     				=						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":
                _snake_case      :							str     				=						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)
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  import unittest
  
  from transformers import MarkupLMConfig, is_torch_available
  from transformers.testing_utils import require_torch, slow, torch_device
  from transformers.utils import cached_property
  
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, ids_tensor
  from ...test_pipeline_mixin import PipelineTesterMixin
  
  
  if is_torch_available():
     import torch
  
     from transformers import (
         MarkupLMForQuestionAnswering,
         MarkupLMForSequenceClassification,
         MarkupLMForTokenClassification,
         MarkupLMModel,
     )
  
  # TODO check dependencies
  from transformers import MarkupLMFeatureExtractor, MarkupLMProcessor, MarkupLMTokenizer
  
  class 				A    :
  
  
  
        def __init__(					self     ,  lowercase_     ,  lowercase_=13     ,  lowercase_=7     ,  lowercase_=True     ,  lowercase_=True     ,  lowercase_=True     ,  lowercase_=True     ,  lowercase_=99     ,  lowercase_=32     ,  lowercase_=5     ,  lowercase_=4     ,  lowercase_=37     ,  lowercase_="gelu"     ,  lowercase_=0.1     ,  lowercase_=0.1     ,  lowercase_=512     ,  lowercase_=16     ,  lowercase_=2     ,  lowercase_=0.02     ,  lowercase_=3     ,  lowercase_=None     ,  lowercase_=20     ,  lowercase_=30     ,  lowercase_=2     ,  lowercase_=2     ,  lowercase_=10     ,  )      ->   Optional[int]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Union[str, Any]     				=						parent
               _snake_case      :							List[str]     				=						batch_size
               _snake_case      :							str     				=						seq_length
               _snake_case      :							str     				=						is_training
               _snake_case      :							Optional[Any]     				=						use_input_mask
               _snake_case      :							Any     				=						use_token_type_ids
               _snake_case      :							str     				=						use_labels
               _snake_case      :							int     				=						vocab_size
               _snake_case      :							Tuple     				=						hidden_size
               _snake_case      :							str     				=						num_hidden_layers
               _snake_case      :							str     				=						num_attention_heads
               _snake_case      :							Optional[int]     				=						intermediate_size
               _snake_case      :							Tuple     				=						hidden_act
               _snake_case      :							Any     				=						hidden_dropout_prob
               _snake_case      :							Tuple     				=						attention_probs_dropout_prob
               _snake_case      :							str     				=						max_position_embeddings
               _snake_case      :							List[Any]     				=						type_vocab_size
               _snake_case      :							str     				=						type_sequence_label_size
               _snake_case      :							Any     				=						initializer_range
               _snake_case      :							Optional[Any]     				=						num_labels
               _snake_case      :							Union[str, Any]     				=						scope
               _snake_case      :							int     				=						max_xpath_tag_unit_embeddings
               _snake_case      :							Any     				=						max_xpath_subs_unit_embeddings
               _snake_case      :							Union[str, Any]     				=						tag_pad_id
               _snake_case      :							Union[str, Any]     				=						subs_pad_id
               _snake_case      :							int     				=						max_depth
  
  
  
        def    __a (					self					)      ->   Tuple:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							List[Any]     				=						ids_tensor([self.batch_size, self.seq_length]     ,  self.vocab_size					)
  
               _snake_case      :							Any     				=						ids_tensor(
                   [self.batch_size, self.seq_length, self.max_depth]     ,  self.max_xpath_tag_unit_embeddings					)
  
               _snake_case      :							Tuple     				=						ids_tensor(
                   [self.batch_size, self.seq_length, self.max_depth]     ,  self.max_xpath_subs_unit_embeddings					)
  
               _snake_case      :							Optional[int]     				=						None
               if self.use_input_mask:
                      _snake_case      :							Optional[int]     				=						ids_tensor([self.batch_size, self.seq_length]     ,  vocab_size=2					)
  
               _snake_case      :							int     				=						None
               if self.use_token_type_ids:
                      _snake_case      :							List[Any]     				=						ids_tensor([self.batch_size, self.seq_length]     ,  self.type_vocab_size					)
  
               _snake_case      :							List[Any]     				=						None
               _snake_case      :							int     				=						None
               if self.use_labels:
                      _snake_case      :							Optional[int]     				=						ids_tensor([self.batch_size]     ,  self.type_sequence_label_size					)
                      _snake_case      :							Optional[int]     				=						ids_tensor([self.batch_size, self.seq_length]     ,  self.num_labels					)
  
               _snake_case      :							Tuple     				=						self.get_config()
  
               return (
                   config,
                   input_ids,
                   xpath_tags_seq,
                   xpath_subs_seq,
                   token_type_ids,
                   input_mask,
                   sequence_labels,
                   token_labels,
               )
  
  
  
        def    __a (					self					)      ->   Tuple:
  
  
  
               '''simple docstring'''
  
               return MarkupLMConfig(
                   vocab_size=self.vocab_size     ,  hidden_size=self.hidden_size     ,  num_hidden_layers=self.num_hidden_layers     ,  num_attention_heads=self.num_attention_heads     ,  intermediate_size=self.intermediate_size     ,  hidden_act=self.hidden_act     ,  hidden_dropout_prob=self.hidden_dropout_prob     ,  attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,  max_position_embeddings=self.max_position_embeddings     ,  type_vocab_size=self.type_vocab_size     ,  initializer_range=self.initializer_range     ,  max_xpath_tag_unit_embeddings=self.max_xpath_tag_unit_embeddings     ,  max_xpath_subs_unit_embeddings=self.max_xpath_subs_unit_embeddings     ,  tag_pad_id=self.tag_pad_id     ,  subs_pad_id=self.subs_pad_id     ,  max_depth=self.max_depth     ,  )
  
  
  
        def    __a (					self     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  )      ->   Optional[Any]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Optional[Any]     				=						MarkupLMModel(config=lowercase_					)
               model.to(lowercase_					)
               model.eval()
               print('''Configs:'''     ,  model.config.tag_pad_id     ,  model.config.subs_pad_id					)
               _snake_case      :							int     				=						model(lowercase_     ,  attention_mask=lowercase_     ,  token_type_ids=lowercase_					)
               _snake_case      :							Dict     				=						model(lowercase_     ,  token_type_ids=lowercase_					)
               _snake_case      :							str     				=						model(lowercase_					)
               self.parent.assertEqual(result.last_hidden_state.shape     ,  (self.batch_size, self.seq_length, self.hidden_size)					)
               self.parent.assertEqual(result.pooler_output.shape     ,  (self.batch_size, self.hidden_size)					)
  
  
  
        def    __a (					self     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  )      ->   Any:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Optional[Any]     				=						self.num_labels
               _snake_case      :							List[str]     				=						MarkupLMForSequenceClassification(lowercase_					)
               model.to(lowercase_					)
               model.eval()
               _snake_case      :							Optional[int]     				=						model(
                   lowercase_     ,  xpath_tags_seq=lowercase_     ,  xpath_subs_seq=lowercase_     ,  attention_mask=lowercase_     ,  token_type_ids=lowercase_     ,  labels=lowercase_     ,  )
               self.parent.assertEqual(result.logits.shape     ,  (self.batch_size, self.num_labels)					)
  
  
  
        def    __a (					self     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  )      ->   Optional[Any]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							List[Any]     				=						self.num_labels
               _snake_case      :							Dict     				=						MarkupLMForTokenClassification(config=lowercase_					)
               model.to(lowercase_					)
               model.eval()
               _snake_case      :							List[str]     				=						model(
                   lowercase_     ,  xpath_tags_seq=lowercase_     ,  xpath_subs_seq=lowercase_     ,  attention_mask=lowercase_     ,  token_type_ids=lowercase_     ,  labels=lowercase_     ,  )
               self.parent.assertEqual(result.logits.shape     ,  (self.batch_size, self.seq_length, self.num_labels)					)
  
  
  
        def    __a (					self     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  )      ->   Tuple:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Optional[int]     				=						MarkupLMForQuestionAnswering(config=lowercase_					)
               model.to(lowercase_					)
               model.eval()
               _snake_case      :							Optional[int]     				=						model(
                   lowercase_     ,  xpath_tags_seq=lowercase_     ,  xpath_subs_seq=lowercase_     ,  attention_mask=lowercase_     ,  token_type_ids=lowercase_     ,  start_positions=lowercase_     ,  end_positions=lowercase_     ,  )
               self.parent.assertEqual(result.start_logits.shape     ,  (self.batch_size, self.seq_length)					)
               self.parent.assertEqual(result.end_logits.shape     ,  (self.batch_size, self.seq_length)					)
  
  
  
        def    __a (					self					)      ->   Optional[Any]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Optional[int]     				=						self.prepare_config_and_inputs()
               (
                   (
                   _snake_case
               )				,    (
                   _snake_case
               )				,    (
                   _snake_case
               )				,    (
                   _snake_case
               )				,    (
                   _snake_case
               )				,    (
                   _snake_case
               )				,    (
                   _snake_case
               )				,    (
                   _snake_case
               )				,    
               )      :							Tuple     				=						config_and_inputs
               _snake_case      :							str     				=						{
                   '''input_ids''': input_ids,
                   '''xpath_tags_seq''': xpath_tags_seq,
                   '''xpath_subs_seq''': xpath_subs_seq,
                   '''token_type_ids''': token_type_ids,
                   '''attention_mask''': input_mask,
               }
               return config, inputs_dict
  
  
  @require_torch
  class 				A    (__UpperCAmelCase       ,__UpperCAmelCase       ,unittest.TestCase				):
        _SCREAMING_SNAKE_CASE					=							(
            (
                MarkupLMModel,
                MarkupLMForSequenceClassification,
                MarkupLMForTokenClassification,
                MarkupLMForQuestionAnswering,
            )
            if is_torch_available()
            else None
        )
        _SCREAMING_SNAKE_CASE					=							(
            {
                """feature-extraction""": MarkupLMModel,
                """question-answering""": MarkupLMForQuestionAnswering,
                """text-classification""": MarkupLMForSequenceClassification,
                """token-classification""": MarkupLMForTokenClassification,
                """zero-shot""": MarkupLMForSequenceClassification,
            }
            if is_torch_available()
            else {}
        )
  
  
  
        def    __a (					self     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_     ,  lowercase_					)      ->   List[str]:
  
  
  
               '''simple docstring'''
  
               return True
  
  
  
        def    __a (					self					)      ->   Optional[Any]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Any     				=						MarkupLMModelTester(self					)
               _snake_case      :							Dict     				=						ConfigTester(self     ,  config_class=lowercase_     ,  hidden_size=37					)
  
  
  
        def    __a (					self					)      ->   Optional[int]:
  
  
  
               '''simple docstring'''
  
               self.config_tester.run_common_tests()
  
  
  
        def    __a (					self					)      ->   List[Any]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Union[str, Any]     				=						self.model_tester.prepare_config_and_inputs()
               self.model_tester.create_and_check_model(*lowercase_					)
  
  
  
        def    __a (					self					)      ->   Optional[Any]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Optional[int]     				=						self.model_tester.prepare_config_and_inputs()
               self.model_tester.create_and_check_for_sequence_classification(*lowercase_					)
  
  
  
        def    __a (					self					)      ->   Any:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Optional[Any]     				=						self.model_tester.prepare_config_and_inputs()
               self.model_tester.create_and_check_for_token_classification(*lowercase_					)
  
  
  
        def    __a (					self					)      ->   str:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Optional[int]     				=						self.model_tester.prepare_config_and_inputs()
               self.model_tester.create_and_check_for_question_answering(*lowercase_					)
  
  
  
  
  def 						A_			(  )			->						Any:
         _snake_case      :							Tuple     				=						'''
      <!DOCTYPE html>
      <html>
      <head>
      <title>Page Title</title>
      </head>
      <body>
  
      <h1>This is a Heading</h1>
      <p>This is a paragraph.</p>
  
      </body>
      </html>
      '''
  
         return html_string
  
  @require_torch
  class 				A    (unittest.TestCase				):
  
  
  
        @cached_property
        def    __a (					self					)      ->   int:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							List[str]     				=						MarkupLMFeatureExtractor()
               _snake_case      :							Any     				=						MarkupLMTokenizer.from_pretrained('''microsoft/markuplm-base'''					)
  
               return MarkupLMProcessor(lowercase_     ,  lowercase_					)
  
  
  
        @slow
        def    __a (					self					)      ->   Optional[int]:
  
  
  
               '''simple docstring'''
  
               _snake_case      :							Any     				=						MarkupLMModel.from_pretrained('''microsoft/markuplm-base'''					).to(lowercase_					)
  
               _snake_case      :							Tuple     				=						self.default_processor
  
               _snake_case      :							Union[str, Any]     				=						processor(prepare_html_string()     ,  return_tensors='''pt'''					)
               _snake_case      :							List[Any]     				=						inputs.to(lowercase_					)
  
               # forward pass
               with torch.no_grad():
                      _snake_case      :							List[str]     				=						model(**lowercase_					)
  
               # verify the last hidden states
               _snake_case      :							Union[str, Any]     				=						torch.Size([1, 14, 768]					)
               self.assertEqual(outputs.last_hidden_state.shape     ,  lowercase_					)
  
               _snake_case      :							List[str]     				=						torch.tensor(
                   [[0.0675, -0.0052, 0.5001], [-0.2281, 0.0802, 0.2192], [-0.0583, -0.3311, 0.1185]]					).to(lowercase_					)
  
               self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]     ,  lowercase_     ,  atol=1E-4					)					)
  
  
  
  
  
  
  