
'''simple docstring'''
import os

import pytest
import yaml

from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict







@pytest.mark.parametrize(
    """files"""			,     [
        ["""full:README.md""", """dataset_infos.json"""],
        ["""empty:README.md""", """dataset_infos.json"""],
        ["""dataset_infos.json"""],
        ["""full:README.md"""],
    ]			,     )
def        A_     (						__SCREAMING_SNAKE_CASE						:  Optional[Any]			,     __SCREAMING_SNAKE_CASE						:  Union[str, Any] )				->       List[str]:





  """simple docstring"""

  __A		:     Union[str, Any]												=		tmp_path_factory.mktemp("""dset_infos_dir""" )
  if "full:README.md" in files:
    with open(dataset_infos_dir / """README.md"""			,     """w""" ) as f:
      f.write("""---\ndataset_info:\n  dataset_size: 42\n---""" )
  if "empty:README.md" in files:
    with open(dataset_infos_dir / """README.md"""			,     """w""" ) as f:
      f.write("""""" )
    # we want to support dataset_infos.json for backward compatibility
  if "dataset_infos.json" in files:
    with open(dataset_infos_dir / """dataset_infos.json"""			,     """w""" ) as f:
      f.write("""{\"default\": {\"dataset_size\": 42}}""" )
  __A		:     Union[str, Any]												=		DatasetInfosDict.from_directory(__SCREAMING_SNAKE_CASE )
  assert dataset_infos
  assert dataset_infos["default"].dataset_size == 42







@pytest.mark.parametrize(
    """dataset_info"""			,     [
        DatasetInfo(),
        DatasetInfo(
            description="""foo"""			,     features=Features({"""a""": Value("""int32""" )} )			,     builder_name="""builder"""			,     config_name="""config"""			,     version="""1.0.0"""			,     splits=[{"""name""": """train"""}]			,     download_size=42			,     ),
    ]			,     )
def        A_     (						__SCREAMING_SNAKE_CASE						:  Optional[int]			,     __SCREAMING_SNAKE_CASE						:  List[str] )				->       Optional[int]:





  """simple docstring"""

  __A		:     str												=		str(__SCREAMING_SNAKE_CASE )
  dataset_info.write_to_directory(__SCREAMING_SNAKE_CASE )
  __A		:     Union[str, Any]												=		DatasetInfo.from_directory(__SCREAMING_SNAKE_CASE )
  assert dataset_info == reloaded
  assert os.path.exists(os.path.join(__SCREAMING_SNAKE_CASE			,     """dataset_info.json""" ) )







def        A_     (						)				->       List[str]:





  """simple docstring"""

  __A		:     int												=		DatasetInfo(
      description="""foo"""			,     citation="""bar"""			,     homepage="""https://foo.bar"""			,     license="""CC0"""			,     features=Features({"""a""": Value("""int32""" )} )			,     post_processed={}			,     supervised_keys=()			,     task_templates=[]			,     builder_name="""builder"""			,     config_name="""config"""			,     version="""1.0.0"""			,     splits=[{"""name""": """train""", """num_examples""": 42}]			,     download_checksums={}			,     download_size=1337			,     post_processing_size=442			,     dataset_size=1234			,     size_in_bytes=1337 + 442 + 1234			,     )
  __A		:     Dict												=		dataset_info._to_yaml_dict()
  assert sorted(__SCREAMING_SNAKE_CASE ) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML )
  for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
    assert key in dataset_info_yaml_dict
    assert isinstance(dataset_info_yaml_dict[key]			,     (list, dict, int, str) )
  __A		:     Optional[int]												=		yaml.safe_dump(__SCREAMING_SNAKE_CASE )
  __A		:     Union[str, Any]												=		yaml.safe_load(__SCREAMING_SNAKE_CASE )
  assert dataset_info_yaml_dict == reloaded







def        A_     (						)				->       int:





  """simple docstring"""

  __A		:     Any												=		DatasetInfo()
  __A		:     Tuple												=		dataset_info._to_yaml_dict()
  assert dataset_info_yaml_dict == {}







@pytest.mark.parametrize(
    """dataset_infos_dict"""			,     [
        DatasetInfosDict(),
        DatasetInfosDict({"""default""": DatasetInfo()} ),
        DatasetInfosDict({"""my_config_name""": DatasetInfo()} ),
        DatasetInfosDict(
            {
                """default""": DatasetInfo(
                    description="""foo"""			,     features=Features({"""a""": Value("""int32""" )} )			,     builder_name="""builder"""			,     config_name="""config"""			,     version="""1.0.0"""			,     splits=[{"""name""": """train"""}]			,     download_size=42			,     )
            } ),
        DatasetInfosDict(
            {
                """v1""": DatasetInfo(dataset_size=42 ),
                """v2""": DatasetInfo(dataset_size=1337 ),
            } ),
    ]			,     )
def        A_     (						__SCREAMING_SNAKE_CASE						:  List[Any]			,     __SCREAMING_SNAKE_CASE						:  str )				->       Dict:





  """simple docstring"""

  __A		:     List[Any]												=		str(__SCREAMING_SNAKE_CASE )
  dataset_infos_dict.write_to_directory(__SCREAMING_SNAKE_CASE )
  __A		:     Any												=		DatasetInfosDict.from_directory(__SCREAMING_SNAKE_CASE )

  # the config_name of the dataset_infos_dict take over the attribute
  for config_name, dataset_info in dataset_infos_dict.items():
    __A		:     int												=		config_name
    # the yaml representation doesn't include fields like description or citation
    # so we just test that we can recover what we can from the yaml
    __A		:     str												=		DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict() )
  assert dataset_infos_dict == reloaded

  if dataset_infos_dict:
    assert os.path.exists(os.path.join(__SCREAMING_SNAKE_CASE			,     """README.md""" ) )





'''simple docstring'''
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__   :		Dict 		=logging.get_logger(__name__)







class  __A				( _SCREAMING_SNAKE_CASE			):
 lowerCamelCase				       =['''input_values''', '''padding_mask''']
 def __init__(   self			:    Optional[int]				,   lowerCamelCase			:    int = 1				,   lowerCamelCase			:    int = 2_40_00				,   lowerCamelCase			:    float = 0.0				,   lowerCamelCase			:    float = None				,   lowerCamelCase			:    float = None				,   **lowerCamelCase			:    Dict				,   ):



   """simple docstring"""





   super().__init__(feature_size=lowerCamelCase				,   sampling_rate=lowerCamelCase				,   padding_value=lowerCamelCase				,   **lowerCamelCase       )
   __A		:     str												=		chunk_length_s
   __A		:     str												=		overlap
 @property
 def 		lowercase_(   self			:    List[str]       ):



   """simple docstring"""





   if self.chunk_length_s is None:
     return None
   else:
     return int(self.chunk_length_s * self.sampling_rate       )
 @property
 def 		lowercase_(   self			:    List[str]       ):



   """simple docstring"""





   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			:    List[str]				,   lowerCamelCase			:    Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]				,   lowerCamelCase			:    Optional[Union[bool, str, PaddingStrategy]] = None				,   lowerCamelCase			:    Optional[bool] = False				,   lowerCamelCase			:    Optional[int] = None				,   lowerCamelCase			:    Optional[Union[str, TensorType]] = None				,   lowerCamelCase			:    Optional[int] = None				,   ):



   """simple docstring"""





   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
     __A		:     List[Any]												=		True

   __A		:     List[str]												=		bool(
       isinstance(lowerCamelCase				,   (list, tuple)       ) and (isinstance(raw_audio[0]				,   (np.ndarray, tuple, list)       ))       )

   if is_batched:
     __A		:     Optional[Any]												=		[np.asarray(lowerCamelCase				,   dtype=np.floataa       ).T for audio in raw_audio]
   elif not is_batched and not isinstance(lowerCamelCase				,   np.ndarray       ):
     __A		:     Union[str, Any]												=		np.asarray(lowerCamelCase				,   dtype=np.floataa       )
   elif isinstance(lowerCamelCase				,   np.ndarray       ) and raw_audio.dtype is np.dtype(np.floataa       ):
     __A		:     Optional[Any]												=		raw_audio.astype(np.floataa       )

   # always return batch
   if not is_batched:
     __A		:     Tuple												=		[np.asarray(lowerCamelCase       ).T]

   # verify inputs are valid
   for idx, example in enumerate(lowerCamelCase       ):
     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"       )

   __A		:     Optional[Any]												=		None
   __A		:     Dict												=		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:
       __A		:     Optional[Any]												=		min(array.shape[0] for array in raw_audio       )
       __A		:     Dict												=		int(np.floor(max_length / self.chunk_stride       )       )
       __A		:     Union[str, Any]												=		(nb_step - 1) * self.chunk_stride + self.chunk_length
     elif padding:
       __A		:     int												=		max(array.shape[0] for array in raw_audio       )
       __A		:     Optional[Any]												=		int(np.ceil(max_length / self.chunk_stride       )       )
       __A		:     Any												=		(nb_step - 1) * self.chunk_stride + self.chunk_length
       __A		:     Optional[int]												=		"""max_length"""
     else:
       __A		:     Optional[Any]												=		input_values

        # normal padding on batch
   if padded_inputs is None:
     __A		:     Tuple												=		self.pad(
         lowerCamelCase				,   max_length=lowerCamelCase				,   truncation=lowerCamelCase				,   padding=lowerCamelCase				,   return_attention_mask=lowerCamelCase				,   )
     if padding:
       __A		:     List[Any]												=		padded_inputs.pop("""attention_mask"""       )

   __A		:     List[Any]												=		[]
   for example in padded_inputs.pop("""input_values"""       ):
     if self.feature_size == 1:
       __A		:     Optional[Any]												=		example[..., None]
     input_values.append(example.T       )

   __A		:     str												=		input_values
   if return_tensors is not None:
     __A		:     Any												=		padded_inputs.convert_to_tensors(lowerCamelCase       )

   return padded_inputs



