






'''simple docstring'''
import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD




def 		__SCREAMING_SNAKE_CASE					(  lowerCamelCase							):


   """simple docstring"""




   def wrapper(*lowerCamelCase						,		**lowerCamelCase							):
      lowercase_	:    Dict           =     timeit.default_timer()
      lowercase_	:    int           =     func(*lowerCamelCase						,		**lowerCamelCase							)
      lowercase_	:    Any           =     timeit.default_timer() - starttime
      return delta

   lowercase_	:    Tuple           =     func.__name__

   return wrapper




def 		__SCREAMING_SNAKE_CASE					(  lowerCamelCase						,		lowerCamelCase=100						,		lowerCamelCase=None							):


   """simple docstring"""




   lowercase_	:    Any           =     []
   lowercase_	:    Optional[int]           =     seq_shapes or {}
   for i in range(lowerCamelCase							):
      lowercase_	:    Union[str, Any]           =     {}
      for col_id, (k, v) in enumerate(features.items()							):
         if isinstance(lowerCamelCase						,		_ArrayXD							):
            lowercase_	:    Any           =     np.random.rand(*v.shape							).astype(v.dtype							)
         elif isinstance(lowerCamelCase						,		datasets.Value							):
            if v.dtype == "string":
               lowercase_	:    str           =     "The small grey turtle was surprisingly fast when challenged."
            else:
               lowercase_	:    List[Any]           =     np.random.randint(10						,		size=1							).astype(v.dtype							).item()
         elif isinstance(lowerCamelCase						,		datasets.Sequence							):
            while isinstance(lowerCamelCase						,		datasets.Sequence							):
               lowercase_	:    int           =     v.feature
            lowercase_	:    int           =     seq_shapes[k]
            lowercase_	:    Dict           =     np.random.rand(*lowerCamelCase							).astype(v.dtype							)
         lowercase_	:    Dict           =     data

      dummy_data.append((i, example)							)

   return dummy_data





def 		__SCREAMING_SNAKE_CASE					(  lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase=100						,		lowerCamelCase=None							):


   """simple docstring"""




   lowercase_	:    Any           =     generate_examples(lowerCamelCase						,		num_examples=lowerCamelCase						,		seq_shapes=lowerCamelCase							)

   with ArrowWriter(features=lowerCamelCase						,		path=lowerCamelCase							) as writer:
      for key, record in dummy_data:
         lowercase_	:    List[Any]           =     features.encode_example(lowerCamelCase							)
         writer.write(lowerCamelCase							)

      lowercase_	:    Tuple           =     writer.finalize()

   if not num_final_examples == num_examples:
      raise ValueError(
          F"""Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}."""							)

   lowercase_	:    List[str]           =     datasets.Dataset.from_file(filename=lowerCamelCase						,		info=datasets.DatasetInfo(features=lowerCamelCase							)							)

   return dataset












'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


UpperCamelCase__ =			{
    'configuration_encodec': [
        'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'EncodecConfig',
    ],
    'feature_extraction_encodec': ['EncodecFeatureExtractor'],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 UpperCamelCase__ =			[
     'ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST',
     'EncodecModel',
     'EncodecPreTrainedModel',
 ]

if TYPE_CHECKING:
 from .configuration_encodec import (
     ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
     EncodecConfig,
 )
 from .feature_extraction_encodec import EncodecFeatureExtractor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_encodec import (
      ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
      EncodecModel,
      EncodecPreTrainedModel,
  )

else:
 import sys

 UpperCamelCase__ =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




