





import timeit

import numpy as np

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




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		)    ->       Any:
  def wrapper(*_SCREAMING_SNAKE_CASE		,						**_SCREAMING_SNAKE_CASE		):
    lowercase__           =  timeit.default_timer()
    lowercase__           =  func(*_SCREAMING_SNAKE_CASE		,						**_SCREAMING_SNAKE_CASE		)
    lowercase__           =  timeit.default_timer() - starttime
    return delta

  lowercase__           =  func.__name__

  return wrapper




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE=100		,						_SCREAMING_SNAKE_CASE=None		)    ->       int:
  lowercase__           =  []
  lowercase__           =  seq_shapes or {}
  for i in range(_SCREAMING_SNAKE_CASE		):
    lowercase__           =  {}
    for col_id, (k, v) in enumerate(features.items()		):
      if isinstance(_SCREAMING_SNAKE_CASE		,						_ArrayXD		):
        lowercase__           =  np.random.rand(*v.shape		).astype(v.dtype		)
      elif isinstance(_SCREAMING_SNAKE_CASE		,						datasets.Value		):
        if v.dtype == "string":
          lowercase__           =  'The small grey turtle was surprisingly fast when challenged.'
        else:
          lowercase__           =  np.random.randint(10		,						size=1		).astype(v.dtype		).item()
      elif isinstance(_SCREAMING_SNAKE_CASE		,						datasets.Sequence		):
        while isinstance(_SCREAMING_SNAKE_CASE		,						datasets.Sequence		):
          lowercase__           =  v.feature
        lowercase__           =  seq_shapes[k]
        lowercase__           =  np.random.rand(*_SCREAMING_SNAKE_CASE		).astype(v.dtype		)
      lowercase__           =  data

    dummy_data.append((i, example)		)

  return dummy_data




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE=100		,						_SCREAMING_SNAKE_CASE=None		)    ->       Any:
  lowercase__           =  generate_examples(_SCREAMING_SNAKE_CASE		,						num_examples=_SCREAMING_SNAKE_CASE		,						seq_shapes=_SCREAMING_SNAKE_CASE		)

  with ArrowWriter(features=_SCREAMING_SNAKE_CASE		,						path=_SCREAMING_SNAKE_CASE		) as writer:
    for key, record in dummy_data:
      lowercase__           =  features.encode_example(_SCREAMING_SNAKE_CASE		)
      writer.write(_SCREAMING_SNAKE_CASE		)

    lowercase__      ,   lowercase__           =  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__           =  datasets.Dataset.from_file(filename=_SCREAMING_SNAKE_CASE		,						info=datasets.DatasetInfo(features=_SCREAMING_SNAKE_CASE		)		)

  return dataset









from typing import Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class     SCREAMING_SNAKE_CASE       (UpperCAmelCase				):


   def __init__(   self      :    List[str]      ,					a      :    Callable      ,					a      :    Optional[Features] = None      ,					a      :    str = None      ,					a      :    bool = False      ,					a      :    bool = False      ,					a      :    Optional[dict] = None      ,					a      :    Optional[int] = None      ,					**a      :    str      ,					)->       Tuple:





     """simple docstring"""







     super().__init__(
         features=a      ,					cache_dir=a      ,					keep_in_memory=a      ,					streaming=a      ,					num_proc=a      ,					**a      ,					)
     lowercase__           =  Generator(
         cache_dir=a      ,					features=a      ,					generator=a      ,					gen_kwargs=a      ,					**a      ,					)


   def 	SCREAMING_SNAKE_CASE_							(   self      :    Any	)->       Dict:





     """simple docstring"""







     if self.streaming:
       lowercase__           =  self.builder.as_streaming_dataset(split='train'	)
     # Build regular (map-style) dataset
     else:
       lowercase__           =  None
       lowercase__           =  None
       lowercase__           =  None
       lowercase__           =  None

       self.builder.download_and_prepare(
           download_config=a      ,					download_mode=a      ,					verification_mode=a      ,					base_path=a      ,					num_proc=self.num_proc      ,					)
       lowercase__           =  self.builder.as_dataset(
           split='train'      ,					verification_mode=a      ,					in_memory=self.keep_in_memory	)
     return dataset


