

import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


_SCREAMING_SNAKE_CASE					:  int             =      {
    '''text_branch''': '''text_model''',
    '''audio_branch''': '''audio_model.audio_encoder''',
    '''attn''': '''attention.self''',
    '''self.proj''': '''output.dense''',
    '''attention.self_mask''': '''attn_mask''',
    '''mlp.fc1''': '''intermediate.dense''',
    '''mlp.fc2''': '''output.dense''',
    '''norm1''': '''layernorm_before''',
    '''norm2''': '''layernorm_after''',
    '''bn0''': '''batch_norm''',
}

_SCREAMING_SNAKE_CASE					:  Any             =      AutoFeatureExtractor.from_pretrained('''laion/clap-htsat-unfused''', truncation='''rand_trunc''')
def     UpperCAmelCase_       (						_A				,		_A=False    ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   create_model(
         '''HTSAT-tiny'''				,		'''roberta'''				,		_A				,		precision='''fp32'''				,		device='''cuda:0''' if torch.cuda.is_available() else '''cpu'''				,		enable_fusion=_A				,		fusion_type='''aff_2d''' if enable_fusion else None				,		)
     return model, model_cfg
def     UpperCAmelCase_       (						_A    ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   {}

     SCREAMING_SNAKE_CASE__   =   R'''.*sequential.(\d+).*'''
     SCREAMING_SNAKE_CASE__   =   R'''.*_projection.(\d+).*'''

     for key, value in state_dict.items():
          # check if any key needs to be modified
          for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
               if key_to_modify in key:
                    SCREAMING_SNAKE_CASE__   =   key.replace(_A				,		_A    )

          if re.match(_A				,		_A    ):
               # replace sequential layers with list
               SCREAMING_SNAKE_CASE__   =   re.match(_A				,		_A    ).group(1    )

               SCREAMING_SNAKE_CASE__   =   key.replace(F'''sequential.{sequential_layer}.'''				,		F'''layers.{int(_A    )//3}.linear.'''    )
          elif re.match(_A				,		_A    ):
               SCREAMING_SNAKE_CASE__   =   int(re.match(_A				,		_A    ).group(1    )    )

               # Because in CLAP they use `nn.Sequential`...
               SCREAMING_SNAKE_CASE__   =   1 if projecton_layer == 0 else 2

               SCREAMING_SNAKE_CASE__   =   key.replace(F'''_projection.{projecton_layer}.'''				,		F'''_projection.linear{transformers_projection_layer}.'''    )

          if "audio" and "qkv" in key:
               # split qkv into query key and value
               SCREAMING_SNAKE_CASE__   =   value
               SCREAMING_SNAKE_CASE__   =   mixed_qkv.size(0    ) // 3

               SCREAMING_SNAKE_CASE__   =   mixed_qkv[:qkv_dim]
               SCREAMING_SNAKE_CASE__   =   mixed_qkv[qkv_dim : qkv_dim * 2]
               SCREAMING_SNAKE_CASE__   =   mixed_qkv[qkv_dim * 2 :]

               SCREAMING_SNAKE_CASE__   =   query_layer
               SCREAMING_SNAKE_CASE__   =   key_layer
               SCREAMING_SNAKE_CASE__   =   value_layer
          else:
               SCREAMING_SNAKE_CASE__   =   value

     return model_state_dict
def     UpperCAmelCase_       (						_A				,		_A				,		_A				,		_A=False    ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   init_clap(_A				,		enable_fusion=_A    )

     clap_model.eval()
     SCREAMING_SNAKE_CASE__   =   clap_model.state_dict()
     SCREAMING_SNAKE_CASE__   =   rename_state_dict(_A    )

     SCREAMING_SNAKE_CASE__   =   ClapConfig()
     SCREAMING_SNAKE_CASE__   =   enable_fusion
     SCREAMING_SNAKE_CASE__   =   ClapModel(_A    )

     # ignore the spectrogram embedding layer
     model.load_state_dict(_A				,		strict=_A    )

     model.save_pretrained(_A    )
     transformers_config.save_pretrained(_A    )


if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE					:  List[str]             =      argparse.ArgumentParser()
       parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
       parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
       parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
       parser.add_argument('''--enable_fusion''', action='''store_true''', help='''Whether to enable fusion or not''')
       _SCREAMING_SNAKE_CASE					:  List[Any]             =      parser.parse_args()

       convert_clap_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.enable_fusion)





import datasets

from .evaluate import evaluate


_SCREAMING_SNAKE_CASE					:  Union[str, Any]             =      '''\
@inproceedings{Rajpurkar2016SQuAD10,
  title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},
  author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},
  booktitle={EMNLP},
  year={2016}
}
'''

_SCREAMING_SNAKE_CASE					:  Dict             =      '''
This metric wrap the official scoring script for version 1 of the Stanford Question Answering Dataset (SQuAD).

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by
crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,
from the corresponding reading passage, or the question might be unanswerable.
'''

_SCREAMING_SNAKE_CASE					:  str             =      '''
Computes SQuAD scores (F1 and EM).
Args:
    predictions: List of question-answers dictionaries with the following key-values:
        - \'id\': id of the question-answer pair as given in the references (see below)
        - \'prediction_text\': the text of the answer
    references: List of question-answers dictionaries with the following key-values:
        - \'id\': id of the question-answer pair (see above),
        - \'answers\': a Dict in the SQuAD dataset format
            {
                \'text\': list of possible texts for the answer, as a list of strings
                \'answer_start\': list of start positions for the answer, as a list of ints
            }
            Note that answer_start values are not taken into account to compute the metric.
Returns:
    \'exact_match\': Exact match (the normalized answer exactly match the gold answer)
    \'f1\': The F-score of predicted tokens versus the gold answer
Examples:

    >>> predictions = [{\'prediction_text\': \'1976\', \'id\': \'56e10a3be3433e1400422b22\'}]
    >>> references = [{\'answers\': {\'answer_start\': [97], \'text\': [\'1976\']}, \'id\': \'56e10a3be3433e1400422b22\'}]
    >>> squad_metric = datasets.load_metric("squad")
    >>> results = squad_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'exact_match\': 100.0, \'f1\': 100.0}
'''






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,					_KWARGS_DESCRIPTION	)
class 	UpperCAmelCase__		(	datasets.Metric	):






  """simple docstring"""


  def 				lowercase_		(							self						:		List[Any] )		->							Optional[int]:
       return datasets.MetricInfo(
           description=_DESCRIPTION     ,       citation=_CITATION     ,       inputs_description=_KWARGS_DESCRIPTION     ,       features=datasets.Features(
               {
                   '''predictions''': {'''id''': datasets.Value('''string''' ), '''prediction_text''': datasets.Value('''string''' )},
                   '''references''': {
                       '''id''': datasets.Value('''string''' ),
                       '''answers''': datasets.features.Sequence(
                           {
                               '''text''': datasets.Value('''string''' ),
                               '''answer_start''': datasets.Value('''int32''' ),
                           } ),
                   },
               } )     ,       codebase_urls=['''https://rajpurkar.github.io/SQuAD-explorer/''']     ,       reference_urls=['''https://rajpurkar.github.io/SQuAD-explorer/''']     ,       )







  def 				lowercase_		(							self						:		Optional[Any]     ,       __lowerCamelCase						:		Dict     ,       __lowerCamelCase						:		List[Any] )		->							Optional[int]:
       SCREAMING_SNAKE_CASE__   =   {prediction['''id''']: prediction['''prediction_text'''] for prediction in predictions}
       SCREAMING_SNAKE_CASE__   =   [
           {
               '''paragraphs''': [
                   {
                       '''qas''': [
                           {
                               '''answers''': [{'''text''': answer_text} for answer_text in ref['''answers''']['''text''']],
                               '''id''': ref['''id'''],
                           }
                           for ref in references
                       ]
                   }
               ]
           }
       ]
       SCREAMING_SNAKE_CASE__   =   evaluate(dataset=__lowerCamelCase     ,       predictions=__lowerCamelCase )
       return score


