

import timeit

import numpy as np

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







def 			snake_case__				(   lowerCAmelCase_			):





     """simple docstring"""







     def wrapper(*lowerCAmelCase_,    **lowerCAmelCase_			):
          SCREAMING_SNAKE_CASE      						=timeit.default_timer()
          SCREAMING_SNAKE_CASE      						=func(*_lowerCamelCase,    **_lowerCamelCase			)
          SCREAMING_SNAKE_CASE      						=timeit.default_timer() - starttime
          return delta

     SCREAMING_SNAKE_CASE      						=func.__name__

     return wrapper







def 			snake_case__				(   lowerCAmelCase_,    lowerCAmelCase_=100,    lowerCAmelCase_=None			):





     """simple docstring"""







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

          dummy_data.append((i, example)			)

     return dummy_data







def 			snake_case__				(   lowerCAmelCase_,    lowerCAmelCase_,    lowerCAmelCase_=100,    lowerCAmelCase_=None			):





     """simple docstring"""







     SCREAMING_SNAKE_CASE      						=generate_examples(_lowerCamelCase,    num_examples=_lowerCamelCase,    seq_shapes=_lowerCamelCase			)

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

          SCREAMING_SNAKE_CASE      						=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}.'			)

     SCREAMING_SNAKE_CASE      						=datasets.Dataset.from_file(filename=_lowerCamelCase,    info=datasets.DatasetInfo(features=_lowerCamelCase			)			)

     return dataset


"""simple docstring"""




from math import ceil





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Union[str, Any]			,_lowerCamelCase						:       Union[str, Any]	)       ->      int:
  _lowerCAmelCase						:       Dict  			=     list(range(0			,_lowerCamelCase	)	)

  _lowerCAmelCase						:       Tuple  			=     [item for sublist in list(device_map.values()	) for item in sublist]

  # Duplicate check
  _lowerCAmelCase						:       Union[str, Any]  			=     []
  for i in device_map_blocks:
    if device_map_blocks.count(_lowerCamelCase	) > 1 and i not in duplicate_blocks:
      duplicate_blocks.append(_lowerCamelCase	)
    # Missing blocks
  _lowerCAmelCase						:       int  			=     [i for i in blocks if i not in device_map_blocks]
  _lowerCAmelCase						:       List[Any]  			=     [i for i in device_map_blocks if i not in blocks]

  if len(_lowerCamelCase	) != 0:
    raise ValueError(
        """Duplicate attention blocks specified in device_map. Attention blocks must be specified to one device."""
        """ These attention blocks were specified more than once: """ + str(_lowerCamelCase	)	)
  if len(_lowerCamelCase	) != 0:
    raise ValueError(
        """There are attention blocks for this model that are not specified in the device_map. Add these attention """
        """blocks to a device on the device_map: """ + str(_lowerCamelCase	)	)
  if len(_lowerCamelCase	) != 0:
    raise ValueError(
        """The device_map contains more attention blocks than this model has. Remove these from the device_map:"""
        + str(_lowerCamelCase	)	)






def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       int			,_lowerCamelCase						:       Tuple	)       ->      str:
  _lowerCAmelCase						:       Optional[Any]  			=     list(range(_lowerCamelCase	)	)
  _lowerCAmelCase						:       Optional[Any]  			=     int(ceil(n_layers / len(_lowerCamelCase	)	)	)
  _lowerCAmelCase						:       Optional[int]  			=     [layers[i : i + n_blocks] for i in range(0			,_lowerCamelCase			,_lowerCamelCase	)]

  return dict(zip(_lowerCamelCase			,_lowerCamelCase	)	)




