



from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


__A   :							int										=   logging.get_logger(__name__)
class 			_a (					lowerCAmelCase):
			"""simple docstring"""
			UpperCamelCase__			      =	["""input_values""", """padding_mask"""]



			def __init__(		self     :    str				,   __UpperCamelCase     :    int = 1				,   __UpperCamelCase     :    int = 2_4_0_0_0				,   __UpperCamelCase     :    float = 0.0				,   __UpperCamelCase     :    float = None				,   __UpperCamelCase     :    float = None				,   **__UpperCamelCase     :    List[Any]				,   )->Optional[int]:
							super().__init__(feature_size=__UpperCamelCase				,   sampling_rate=__UpperCamelCase				,   padding_value=__UpperCamelCase				,   **__UpperCamelCase   )
							_UpperCAmelCase					=    chunk_length_s
							_UpperCAmelCase					=    overlap



			@property
			def  lowercase__						(		self     :    int   )->Optional[int]:
							if self.chunk_length_s is None:
											return None
							else:
											return int(self.chunk_length_s * self.sampling_rate   )



			@property
			def  lowercase__						(		self     :    Union[str, Any]   )->Optional[int]:
							if self.chunk_length_s is None or self.overlap is None:
											return None
							else:
											return max(1				,   int((1.0 - self.overlap) * self.chunk_length   )   )



			def __call__(		self     :    Optional[Any]				,   __UpperCamelCase     :    Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]				,   __UpperCamelCase     :    Optional[Union[bool, str, PaddingStrategy]] = None				,   __UpperCamelCase     :    Optional[bool] = False				,   __UpperCamelCase     :    Optional[int] = None				,   __UpperCamelCase     :    Optional[Union[str, TensorType]] = None				,   __UpperCamelCase     :    Optional[int] = None				,   )->BatchFeature:
							if sampling_rate is not None:
											if sampling_rate != self.sampling_rate:
															raise ValueError(
															    F'The model corresponding to this feature extractor: {self} was trained using a sampling rate of'
															    F' {self.sampling_rate}. Please make sure that the provided audio input was sampled with'
															    F' {self.sampling_rate} and not {sampling_rate}.'   )
							else:
											logger.warning(
											    '''It is strongly recommended to pass the `sampling_rate` argument to this function. '''
											    '''Failing to do so can result in silent errors that might be hard to debug.'''   )

							if padding and truncation:
											raise ValueError('''Both padding and truncation were set. Make sure you only set one.'''   )
							elif padding is None:
											# by default let's pad the inputs
											_UpperCAmelCase					=    True

							_UpperCAmelCase					=    bool(
							    isinstance(__UpperCamelCase				,   (list, tuple)   ) and (isinstance(raw_audio[0]				,   (np.ndarray, tuple, list)   ))   )

							if is_batched:
											_UpperCAmelCase					=    [np.asarray(__UpperCamelCase				,   dtype=np.floataa   ).T for audio in raw_audio]
							elif not is_batched and not isinstance(__UpperCamelCase				,   np.ndarray   ):
											_UpperCAmelCase					=    np.asarray(__UpperCamelCase				,   dtype=np.floataa   )
							elif isinstance(__UpperCamelCase				,   np.ndarray   ) and raw_audio.dtype is np.dtype(np.floataa   ):
											_UpperCAmelCase					=    raw_audio.astype(np.floataa   )

							# always return batch
							if not is_batched:
											_UpperCAmelCase					=    [np.asarray(__UpperCamelCase   ).T]

							# verify inputs are valid
							for idx, example in enumerate(__UpperCamelCase   ):
											if example.ndim > 2:
															raise ValueError(F'Expected input shape (channels, length) but got shape {example.shape}'   )
											if self.feature_size == 1 and example.ndim != 1:
															raise ValueError(F'Expected mono audio but example has {example.shape[-1]} channels'   )
											if self.feature_size == 2 and example.shape[-1] != 2:
															raise ValueError(F'Expected stereo audio but example has {example.shape[-1]} channels'   )

							_UpperCAmelCase					=    None
							_UpperCAmelCase					=    BatchFeature({'''input_values''': raw_audio}   )
							if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
											if truncation:
															_UpperCAmelCase					=    min(array.shape[0] for array in raw_audio   )
															_UpperCAmelCase					=    int(np.floor(max_length / self.chunk_stride   )   )
															_UpperCAmelCase					=    (nb_step - 1) * self.chunk_stride + self.chunk_length
											elif padding:
															_UpperCAmelCase					=    max(array.shape[0] for array in raw_audio   )
															_UpperCAmelCase					=    int(np.ceil(max_length / self.chunk_stride   )   )
															_UpperCAmelCase					=    (nb_step - 1) * self.chunk_stride + self.chunk_length
															_UpperCAmelCase					=    '''max_length'''
											else:
															_UpperCAmelCase					=    input_values

        # normal padding on batch
							if padded_inputs is None:
											_UpperCAmelCase					=    self.pad(
											    __UpperCamelCase				,   max_length=__UpperCamelCase				,   truncation=__UpperCamelCase				,   padding=__UpperCamelCase				,   return_attention_mask=__UpperCamelCase				,   )
											if padding:
															_UpperCAmelCase					=    padded_inputs.pop('''attention_mask'''   )

							_UpperCAmelCase					=    []
							for example in padded_inputs.pop('''input_values'''   ):
											if self.feature_size == 1:
															_UpperCAmelCase					=    example[..., None]
											input_values.append(example.T   )

							_UpperCAmelCase					=    input_values
							if return_tensors is not None:
											_UpperCAmelCase					=    padded_inputs.convert_to_tensors(__UpperCamelCase   )

							return padded_inputs








"""simple docstring"""


from collections import Counter
from pathlib import Path
from typing import Optional, Tuple

import yaml
class 			_a (					yaml.SafeLoader):
			"""simple docstring"""



			def  lowercase__						(		self     :    List[str]				,   __UpperCamelCase     :    Any   )->List[Any]:
							_UpperCAmelCase					=    [self.constructed_objects[key_node] for key_node, _ in node.value]
							_UpperCAmelCase					=    [tuple(__UpperCamelCase   ) if isinstance(__UpperCamelCase				,   __UpperCamelCase   ) else key for key in keys]
							_UpperCAmelCase					=    Counter(__UpperCamelCase   )
							_UpperCAmelCase					=    [key for key in counter if counter[key] > 1]
							if duplicate_keys:
											raise TypeError(F'Got duplicate yaml keys: {duplicate_keys}'   )



			def  lowercase__						(		self     :    List[str]				,   __UpperCamelCase     :    Optional[Any]				,   __UpperCamelCase     :    str=False   )->Dict:
							_UpperCAmelCase					=    super().construct_mapping(__UpperCamelCase				,   deep=__UpperCamelCase   )
							self._check_no_duplicates_on_constructed_node(__UpperCamelCase   )
							return mapping







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						str				):






				'''simple docstring'''



				_UpperCAmelCase					=    list(readme_content.splitlines()				)
				if full_content and full_content[0] == "---" and "---" in full_content[1:]:
								_UpperCAmelCase					=    full_content[1:].index('''---'''				) + 1
								_UpperCAmelCase					=    '''\n'''.join(full_content[1:sep_idx]				)
								return yamlblock, "\n".join(full_content[sep_idx + 1 :]				)

				return None, "\n".join(_SCREAMING_SNAKE_CASE				)
class 			_a (					lowerCAmelCase):
			"""simple docstring"""
			# class attributes
			UpperCamelCase__			      =	{"""train_eval_index"""}  # train-eval-index in the YAML metadata



			@classmethod
			def  lowercase__						(		cls     :    List[Any]				,   __UpperCamelCase     :    Path   )->"DatasetMetadata":
							with open(__UpperCamelCase				,   encoding='''utf-8'''   ) as readme_file:
											_UpperCAmelCase		,						_UpperCAmelCase					=    _split_yaml_from_readme(readme_file.read()   )
							if yaml_string is not None:
											return cls.from_yaml_string(__UpperCamelCase   )
							else:
											return cls()



			def  lowercase__						(		self     :    Tuple				,   __UpperCamelCase     :    Path   )->List[Any]:
							if path.exists():
											with open(__UpperCamelCase				,   encoding='''utf-8'''   ) as readme_file:
															_UpperCAmelCase					=    readme_file.read()
							else:
											_UpperCAmelCase					=    None
							_UpperCAmelCase					=    self._to_readme(__UpperCamelCase   )
							with open(__UpperCamelCase				,   '''w'''				,   encoding='''utf-8'''   ) as readme_file:
											readme_file.write(__UpperCamelCase   )



			def  lowercase__						(		self     :    List[str]				,   __UpperCamelCase     :    Optional[str] = None   )->str:
							if readme_content is not None:
											_UpperCAmelCase		,						_UpperCAmelCase					=    _split_yaml_from_readme(__UpperCamelCase   )
											_UpperCAmelCase					=    '''---\n''' + self.to_yaml_string() + '''---\n''' + content
							else:
											_UpperCAmelCase					=    '''---\n''' + self.to_yaml_string() + '''---\n'''
							return full_content



			@classmethod
			def  lowercase__						(		cls     :    str				,   __UpperCamelCase     :    str   )->"DatasetMetadata":
							_UpperCAmelCase					=    yaml.load(__UpperCamelCase				,   Loader=_NoDuplicateSafeLoader   ) or {}

							# Convert the YAML keys to DatasetMetadata fields
							_UpperCAmelCase					=    {
							    (key.replace('''-'''				,   '''_'''   ) if key.replace('''-'''				,   '''_'''   ) in cls._FIELDS_WITH_DASHES else key): value
							    for key, value in metadata_dict.items()
							}
							return cls(**__UpperCamelCase   )



			def  lowercase__						(		self     :    str   )->str:
							return yaml.safe_dump(
							    {
							        (key.replace('''_'''				,   '''-'''   ) if key in self._FIELDS_WITH_DASHES else key): value
							        for key, value in self.items()
							    }				,   sort_keys=__UpperCamelCase				,   allow_unicode=__UpperCamelCase				,   encoding='''utf-8'''				,   ).decode('''utf-8'''   )



__A   :							str										=   {
    "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

					__A   :							str										=   ArgumentParser(usage="Validate the yaml metadata block of a README.md file.")
					ap.add_argument("readme_filepath")
					__A   :							Union[str, Any]										=   ap.parse_args()

					__A   :							Dict										=   Path(args.readme_filepath)
					__A   :							Tuple										=   DatasetMetadata.from_readme(readme_filepath)
					print(dataset_metadata)
					dataset_metadata.to_readme(readme_filepath)



