



"""simple docstring"""
import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCAmelCase     =			logging.get_logger(__name__)

_UpperCAmelCase     =			{
    """asapp/sew-tiny-100k""": """https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json""",
    # See all SEW models at https://huggingface.co/models?filter=sew
}
class 					a	(  UpperCAmelCase__       ):
  UpperCamelCase	:				int     						=					'sew'



  def __init__(						self :					Any       ,		lowerCAmelCase :					Tuple=32       ,		lowerCAmelCase :					Any=768       ,		lowerCAmelCase :					str=12       ,		lowerCAmelCase :					str=12       ,		lowerCAmelCase :					List[Any]=3072       ,		lowerCAmelCase :					int=2       ,		lowerCAmelCase :					str="gelu"       ,		lowerCAmelCase :					int=0.1       ,		lowerCAmelCase :					Dict=0.1       ,		lowerCAmelCase :					Union[str, Any]=0.1       ,		lowerCAmelCase :					str=0.0       ,		lowerCAmelCase :					str=0.1       ,		lowerCAmelCase :					Optional[Any]=0.1       ,		lowerCAmelCase :					Optional[Any]=0.0_2       ,		lowerCAmelCase :					Optional[Any]=1E-5       ,		lowerCAmelCase :					Dict="group"       ,		lowerCAmelCase :					List[str]="gelu"       ,		lowerCAmelCase :					Any=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512)       ,		lowerCAmelCase :					str=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)       ,		lowerCAmelCase :					List[str]=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)       ,		lowerCAmelCase :					Optional[int]=False       ,		lowerCAmelCase :					List[Any]=128       ,		lowerCAmelCase :					int=16       ,		lowerCAmelCase :					List[str]=True       ,		lowerCAmelCase :					Optional[Any]=0.0_5       ,		lowerCAmelCase :					Optional[Any]=10       ,		lowerCAmelCase :					Union[str, Any]=2       ,		lowerCAmelCase :					List[Any]=0.0       ,		lowerCAmelCase :					str=10       ,		lowerCAmelCase :					int=0       ,		lowerCAmelCase :					Dict="mean"       ,		lowerCAmelCase :					Any=False       ,		lowerCAmelCase :					Dict=False       ,		lowerCAmelCase :					Optional[Any]=256       ,		lowerCAmelCase :					Dict=0       ,		lowerCAmelCase :					Union[str, Any]=1       ,		lowerCAmelCase :					Tuple=2       ,		**lowerCAmelCase :					Dict       ,		)     ->					str:


     '''simple docstring'''




     super().__init__(**lowerCAmelCase       ,		pad_token_id=lowerCAmelCase       ,		bos_token_id=lowerCAmelCase       ,		eos_token_id=lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: Dict     			=hidden_size
     SCREAMING_SNAKE_CASE_: Dict     			=feat_extract_norm
     SCREAMING_SNAKE_CASE_: Dict     			=feat_extract_activation
     SCREAMING_SNAKE_CASE_: Tuple     			=list(lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: str     			=list(lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: str     			=list(lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: Optional[int]     			=conv_bias
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=num_conv_pos_embeddings
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=num_conv_pos_embedding_groups
     SCREAMING_SNAKE_CASE_: List[Any]     			=len(self.conv_dim		)
     SCREAMING_SNAKE_CASE_: Any     			=num_hidden_layers
     SCREAMING_SNAKE_CASE_: int     			=intermediate_size
     SCREAMING_SNAKE_CASE_: Tuple     			=squeeze_factor
     SCREAMING_SNAKE_CASE_: Dict     			=hidden_act
     SCREAMING_SNAKE_CASE_: Tuple     			=num_attention_heads
     SCREAMING_SNAKE_CASE_: List[Any]     			=hidden_dropout
     SCREAMING_SNAKE_CASE_: int     			=attention_dropout
     SCREAMING_SNAKE_CASE_: Dict     			=activation_dropout
     SCREAMING_SNAKE_CASE_: List[str]     			=feat_proj_dropout
     SCREAMING_SNAKE_CASE_: Dict     			=final_dropout
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=layerdrop
     SCREAMING_SNAKE_CASE_: Any     			=layer_norm_eps
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=initializer_range
     SCREAMING_SNAKE_CASE_: int     			=vocab_size

     if (
         (len(self.conv_stride		) != self.num_feat_extract_layers)
         or (len(self.conv_kernel		) != self.num_feat_extract_layers)
         or (len(self.conv_dim		) != self.num_feat_extract_layers)
     ):
        raise ValueError(
            """Configuration for convolutional layers is incorrect."""
            """It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"""
            f'''but is `len(config.conv_dim) = {len(self.conv_dim		)}`, `len(config.conv_stride)'''
            f'''= {len(self.conv_stride		)}`, `len(config.conv_kernel) = {len(self.conv_kernel		)}`.'''		)

     # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
     SCREAMING_SNAKE_CASE_: Dict     			=apply_spec_augment
     SCREAMING_SNAKE_CASE_: Optional[int]     			=mask_time_prob
     SCREAMING_SNAKE_CASE_: Optional[int]     			=mask_time_length
     SCREAMING_SNAKE_CASE_: Dict     			=mask_time_min_masks
     SCREAMING_SNAKE_CASE_: Dict     			=mask_feature_prob
     SCREAMING_SNAKE_CASE_: Tuple     			=mask_feature_length
     SCREAMING_SNAKE_CASE_: Tuple     			=mask_feature_min_masks

     # ctc loss
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=ctc_loss_reduction
     SCREAMING_SNAKE_CASE_: List[Any]     			=ctc_zero_infinity

     # sequence classification
     SCREAMING_SNAKE_CASE_: str     			=use_weighted_layer_sum
     SCREAMING_SNAKE_CASE_: List[str]     			=classifier_proj_size





  @property
  def 	lowerCamelCase__       (						self :					Optional[int]		)     ->					Union[str, Any]:


     '''simple docstring'''




     return functools.reduce(operator.mul       ,		self.conv_stride       ,		1		)








"""simple docstring"""
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple

import yaml
class 					a	(  yaml.SafeLoader       ):



  def 	lowerCamelCase__       (						self :					int       ,		lowerCAmelCase :					List[str]		)     ->					Optional[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Any     			=[self.constructed_objects[key_node] for key_node, _ in node.value]
     SCREAMING_SNAKE_CASE_: Any     			=[tuple(lowerCAmelCase		) if isinstance(lowerCAmelCase       ,		lowerCAmelCase		) else key for key in keys]
     SCREAMING_SNAKE_CASE_: Dict     			=Counter(lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: List[str]     			=[key for key in counter if counter[key] > 1]
     if duplicate_keys:
        raise TypeError(f'''Got duplicate yaml keys: {duplicate_keys}'''		)





  def 	lowerCamelCase__       (						self :					Tuple       ,		lowerCAmelCase :					str       ,		lowerCAmelCase :					Optional[int]=False		)     ->					int:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Tuple     			=super().construct_mapping(lowerCAmelCase       ,		deep=lowerCAmelCase		)
     self._check_no_duplicates_on_constructed_node(lowerCAmelCase		)
     return mapping







def      __magic_name__				(							lowercase ):
   SCREAMING_SNAKE_CASE_: Dict     			=list(readme_content.splitlines() )
   if full_content and full_content[0] == "---" and "---" in full_content[1:]:
      SCREAMING_SNAKE_CASE_: Union[str, Any]     			=full_content[1:].index("""---""" ) + 1
      SCREAMING_SNAKE_CASE_: List[str]     			="""\n""".join(full_content[1:sep_idx] )
      return yamlblock, "\n".join(full_content[sep_idx + 1 :] )

   return None, "\n".join(lowercase )
class 					a	(  UpperCAmelCase__       ):
  # class attributes
  UpperCamelCase	:				Tuple     						=					{'train_eval_index'}  # train-eval-index in the YAML metadata



  @classmethod
  def 	lowerCamelCase__       (						cls :					List[Any]       ,		lowerCAmelCase :					Path		)     ->					"DatasetMetadata":


     '''simple docstring'''




     with open(lowerCAmelCase       ,		encoding="""utf-8"""		) as readme_file:
        SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: Union[str, Any]     			=_split_yaml_from_readme(readme_file.read()		)
     if yaml_string is not None:
        return cls.from_yaml_string(lowerCAmelCase		)
     else:
        return cls()



  def 	lowerCamelCase__       (						self :					Any       ,		lowerCAmelCase :					Path		)     ->					List[str]:


     '''simple docstring'''




     if path.exists():
        with open(lowerCAmelCase       ,		encoding="""utf-8"""		) as readme_file:
           SCREAMING_SNAKE_CASE_: str     			=readme_file.read()
     else:
        SCREAMING_SNAKE_CASE_: str     			=None
     SCREAMING_SNAKE_CASE_: Tuple     			=self._to_readme(lowerCAmelCase		)
     with open(lowerCAmelCase       ,		"""w"""       ,		encoding="""utf-8"""		) as readme_file:
        readme_file.write(lowerCAmelCase		)



  def 	lowerCamelCase__       (						self :					Optional[Any]       ,		lowerCAmelCase :					Optional[str] = None		)     ->					str:


     '''simple docstring'''




     if readme_content is not None:
        SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: List[str]     			=_split_yaml_from_readme(lowerCAmelCase		)
        SCREAMING_SNAKE_CASE_: List[str]     			="""---\n""" + self.to_yaml_string() + """---\n""" + content
     else:
        SCREAMING_SNAKE_CASE_: List[Any]     			="""---\n""" + self.to_yaml_string() + """---\n"""
     return full_content



  @classmethod
  def 	lowerCamelCase__       (						cls :					Optional[int]       ,		lowerCAmelCase :					str		)     ->					"DatasetMetadata":


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: int     			=yaml.load(lowerCAmelCase       ,		Loader=_NoDuplicateSafeLoader		) or {}

     # Convert the YAML keys to DatasetMetadata fields
     SCREAMING_SNAKE_CASE_: List[Any]     			={
         (key.replace("""-"""       ,		"""_"""		) if key.replace("""-"""       ,		"""_"""		) in cls._FIELDS_WITH_DASHES else key): value
         for key, value in metadata_dict.items()
     }
     return cls(**lowerCAmelCase		)





  def 	lowerCamelCase__       (						self :					Dict		)     ->					str:


     '''simple docstring'''




     return yaml.safe_dump(
         {
             (key.replace("""_"""       ,		"""-"""		) if key in self._FIELDS_WITH_DASHES else key): value
             for key, value in self.items()
         }       ,		sort_keys=lowerCAmelCase       ,		allow_unicode=lowerCAmelCase       ,		encoding="""utf-8"""       ,		).decode("""utf-8"""		)







_UpperCAmelCase     =			{
    """image-classification""": [],
    """translation""": [],
    """image-segmentation""": [],
    """fill-mask""": [],
    """automatic-speech-recognition""": [],
    """token-classification""": [],
    """sentence-similarity""": [],
    """audio-classification""": [],
    """question-answering""": [],
    """summarization""": [],
    """zero-shot-classification""": [],
    """table-to-text""": [],
    """feature-extraction""": [],
    """other""": [],
    """multiple-choice""": [],
    """text-classification""": [],
    """text-to-image""": [],
    """text2text-generation""": [],
    """zero-shot-image-classification""": [],
    """tabular-classification""": [],
    """tabular-regression""": [],
    """image-to-image""": [],
    """tabular-to-text""": [],
    """unconditional-image-generation""": [],
    """text-retrieval""": [],
    """text-to-speech""": [],
    """object-detection""": [],
    """audio-to-audio""": [],
    """text-generation""": [],
    """conversational""": [],
    """table-question-answering""": [],
    """visual-question-answering""": [],
    """image-to-text""": [],
    """reinforcement-learning""": [],
    """voice-activity-detection""": [],
    """time-series-forecasting""": [],
    """document-question-answering""": [],
}


if __name__ == "__main__":
   from argparse import ArgumentParser

   _UpperCAmelCase     =			ArgumentParser(usage="""Validate the yaml metadata block of a README.md file.""")
   ap.add_argument("""readme_filepath""")
   _UpperCAmelCase     =			ap.parse_args()

   _UpperCAmelCase     =			Path(args.readme_filepath)
   _UpperCAmelCase     =			DatasetMetadata.from_readme(readme_filepath)
   print(dataset_metadata)
   dataset_metadata.to_readme(readme_filepath)



