





import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
lowerCAmelCase_   =   logging.get_logger(__name__)

lowerCAmelCase_   =   {
    "post_extract_proj": "feature_projection.projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.k_proj": "encoder.layers.*.attention.k_proj",
    "self_attn.v_proj": "encoder.layers.*.attention.v_proj",
    "self_attn.q_proj": "encoder.layers.*.attention.q_proj",
    "self_attn.out_proj": "encoder.layers.*.attention.out_proj",
    "self_attn_layer_norm": "encoder.layers.*.layer_norm",
    "fc1": "encoder.layers.*.feed_forward.intermediate_dense",
    "fc2": "encoder.layers.*.feed_forward.output_dense",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "encoder.layer_norm",
    "mask_emb": "masked_spec_embed",
}




def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_      ,		lowercase_      ,		lowercase_  )			->						Tuple:







       for attribute in key.split('''.'''  ):
              _snake_case      :							List[str]     				=						getattr(_A      ,		_A  )

       if weight_type is not None:
              _snake_case      :							Dict     				=						getattr(_A      ,		_A  ).shape
       else:
              _snake_case      :							int     				=						hf_pointer.shape

       assert hf_shape == value.shape, (
           f'''Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be'''
           f''' {value.shape} for {full_name}'''
       )

       if weight_type == "weight":
              _snake_case      :							Optional[Any]     				=						value
       elif weight_type == "weight_g":
              _snake_case      :							Optional[Any]     				=						value
       elif weight_type == "weight_v":
              _snake_case      :							str     				=						value
       elif weight_type == "bias":
              _snake_case      :							Any     				=						value
       else:
              _snake_case      :							Optional[Any]     				=						value

       logger.info(f'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.'''  )




def 						A_			(  lowercase_      ,		lowercase_  )			->						Optional[int]:
       _snake_case      :							Optional[int]     				=						[]
       _snake_case      :							int     				=						fairseq_model.state_dict()

       _snake_case      :							Tuple     				=						hf_model.feature_extractor

       for name, value in fairseq_dict.items():
              _snake_case      :							Any     				=						False
              if "conv_layers" in name:
                     load_conv_layer(
                         _A      ,		_A      ,		_A      ,		_A      ,		hf_model.config.feat_extract_norm == '''group'''      ,		)
                     _snake_case      :							List[str]     				=						True
              else:
                     for key, mapped_key in MAPPING.items():
                            _snake_case      :							Tuple     				=						mapped_key

                            if key in name:
                                   _snake_case      :							int     				=						True
                                   if "*" in mapped_key:
                                          _snake_case      :							str     				=						name.split(_A  )[0].split('''.'''  )[-2]
                                          _snake_case      :							Dict     				=						mapped_key.replace('''*'''      ,		_A  )
                                   if "weight_g" in name:
                                          _snake_case      :							List[str]     				=						'''weight_g'''
                                   elif "weight_v" in name:
                                          _snake_case      :							Tuple     				=						'''weight_v'''
                                   elif "weight" in name:
                                          _snake_case      :							Any     				=						'''weight'''
                                   elif "bias" in name:
                                          _snake_case      :							str     				=						'''bias'''
                                   else:
                                          _snake_case      :							Optional[int]     				=						None
                                   set_recursively(_A      ,		_A      ,		_A      ,		_A      ,		_A  )
                            continue
              if not is_used:
                     unused_weights.append(_A  )

       logger.warning(f'''Unused weights: {unused_weights}'''  )




def 						A_			(  lowercase_      ,		lowercase_      ,		lowercase_      ,		lowercase_      ,		lowercase_  )			->						List[Any]:
       _snake_case      :							Tuple     				=						full_name.split('''conv_layers.'''  )[-1]
       _snake_case      :							Optional[int]     				=						name.split('''.'''  )
       _snake_case      :							List[Any]     				=						int(items[0]  )
       _snake_case      :							int     				=						int(items[1]  )

       if type_id == 0:
              if "bias" in name:
                     assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
                         f'''{full_name} has size {value.shape}, but'''
                         f''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
                     )
                     _snake_case      :							Optional[int]     				=						value
                     logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''  )
              elif "weight" in name:
                     assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
                         f'''{full_name} has size {value.shape}, but'''
                         f''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
                     )
                     _snake_case      :							Any     				=						value
                     logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''  )
       elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
              if "bias" in name:
                     assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
                         f'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
                         " found."
                     )
                     _snake_case      :							int     				=						value
                     logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''  )
              elif "weight" in name:
                     assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
                         f'''{full_name} has size {value.shape}, but'''
                         f''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
                     )
                     _snake_case      :							Optional[Any]     				=						value
                     logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''  )
       else:
              unused_weights.append(_A  )




def 						A_			(  lowercase_  )			->						Optional[Any]:
       _snake_case      :							Optional[Any]     				=						HubertConfig()
       _snake_case      :							str     				=						model.config

       _snake_case      :							List[Any]     				=						fs_config.activation_dropout
       _snake_case      :							Optional[Any]     				=						False
       _snake_case      :							int     				=						fs_config.attention_dropout
       _snake_case      :							int     				=						False
       _snake_case      :							Dict     				=						eval(fs_config.extractor_conv_feature_layers  )
       _snake_case      :							Optional[Any]     				=						[x[0] for x in conv_layers]
       _snake_case      :							Optional[int]     				=						[x[1] for x in conv_layers]
       _snake_case      :							Optional[Any]     				=						[x[2] for x in conv_layers]
       _snake_case      :							List[Any]     				=						'''gelu'''
       _snake_case      :							List[str]     				=						'''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
       _snake_case      :							List[Any]     				=						False
       _snake_case      :							List[Any]     				=						0.0
       _snake_case      :							str     				=						0.0
       _snake_case      :							Union[str, Any]     				=						fs_config.activation_fn
       _snake_case      :							Union[str, Any]     				=						fs_config.dropout
       _snake_case      :							Dict     				=						fs_config.encoder_embed_dim
       _snake_case      :							List[Any]     				=						0.02
       _snake_case      :							List[Any]     				=						fs_config.encoder_ffn_embed_dim
       _snake_case      :							List[Any]     				=						1E-5
       _snake_case      :							int     				=						0.0
       _snake_case      :							Tuple     				=						fs_config.encoder_attention_heads
       _snake_case      :							List[Any]     				=						fs_config.conv_pos_groups
       _snake_case      :							Tuple     				=						fs_config.conv_pos
       _snake_case      :							int     				=						len(_A  )
       _snake_case      :							Optional[int]     				=						fs_config.encoder_layers

       return config




@torch.no_grad()
def 						A_			(  lowercase_      ,		lowercase_=None  )			->						int:
       _snake_case      :							Optional[int]     				=						distilhubert().model.model

       if config_path is not None:
              _snake_case      :							int     				=						HubertConfig.from_pretrained(_A  )
       else:
              _snake_case      :							str     				=						convert_config(_A  )
       _snake_case      :							List[str]     				=						model.eval()

       _snake_case      :							List[str]     				=						WavaVecaFeatureExtractor(
           feature_size=1      ,		sampling_rate=16000      ,		padding_value=0      ,		do_normalize=_A      ,		return_attention_mask=_A      ,		)
       _snake_case      :							str     				=						HubertModel(_A  )

       recursively_load_weights(_A      ,		_A  )

       feature_extractor.save_pretrained(_A  )
       hf_model.save_pretrained(_A  )


if __name__ == "__main__":
       lowerCAmelCase_   =   argparse.ArgumentParser()
       parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
       parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
       lowerCAmelCase_   =   parser.parse_args()
       convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)













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					)					)






