



"""simple docstring"""






import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase__      =    logging.get_logger(__name__)






class 	__snake_case	(     _lowercase):
  snake_case__    :       str        =						"encoder-decoder"
  snake_case__    :       Optional[int]        =						True




  def __init__(			self					:						Any						, **__lowerCAmelCase					:						Any		):






         """simple docstring"""






         super().__init__(**__lowerCAmelCase		)
         assert (
             "encoder" in kwargs and "decoder" in kwargs
         ), "Config has to be initialized with encoder and decoder config"
         _lowerCamelCase						:    Optional[Any]         =			kwargs.pop('''encoder'''		)
         _lowerCamelCase						:    Dict         =			encoder_config.pop('''model_type'''		)
         _lowerCamelCase						:    int         =			kwargs.pop('''decoder'''		)
         _lowerCamelCase						:    Tuple         =			decoder_config.pop('''model_type'''		)

         from ..auto.configuration_auto import AutoConfig

         _lowerCamelCase						:    List[Any]         =			AutoConfig.for_model(__lowerCAmelCase						, **__lowerCAmelCase		)
         _lowerCamelCase						:    List[Any]         =			AutoConfig.for_model(__lowerCAmelCase						, **__lowerCAmelCase		)
         _lowerCamelCase						:    Any         =			True




  @classmethod
  def 						SCREAMING_SNAKE_CASE    (			cls					:						Any						, __lowerCAmelCase					:						PretrainedConfig						, __lowerCAmelCase					:						PretrainedConfig						, **__lowerCAmelCase					:						List[str]		):






         """simple docstring"""






         logger.info('''Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'''		)
         _lowerCamelCase						:    List[str]         =			True
         _lowerCamelCase						:    int         =			True

         return cls(encoder=encoder_config.to_dict()						, decoder=decoder_config.to_dict()						, **__lowerCAmelCase		)




  def 						SCREAMING_SNAKE_CASE    (			self					:						Dict		):






         """simple docstring"""






         _lowerCamelCase						:    Optional[Any]         =			copy.deepcopy(self.__dict__		)
         _lowerCamelCase						:    Dict         =			self.encoder.to_dict()
         _lowerCamelCase						:    List[str]         =			self.decoder.to_dict()
         _lowerCamelCase						:    Optional[Any]         =			self.__class__.model_type
         return output







"""simple docstring"""






import timeit

import numpy as np

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






def 	snake_case_  (				A_ :		Optional[int]						):


       '''simple docstring'''





       def wrapper(*A_ :		int,    **A_ :		List[str]						):
              _lowerCamelCase						:    Tuple         =			timeit.default_timer()
              _lowerCamelCase						:    Optional[Any]         =			func(*A_,    **A_						)
              _lowerCamelCase						:    Tuple         =			timeit.default_timer() - starttime
              return delta

       _lowerCamelCase						:    Optional[Any]         =			func.__name__

       return wrapper






def 	snake_case_  (				A_ :		dict,    A_ :		List[str]=1_00,    A_ :		Optional[Any]=None						):


       '''simple docstring'''





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

              dummy_data.append((i, example)						)

       return dummy_data







def 	snake_case_  (				A_ :		List[str],    A_ :		Optional[int],    A_ :		Optional[int]=1_00,    A_ :		Optional[Any]=None						):


       '''simple docstring'''





       _lowerCamelCase						:    Optional[int]         =			generate_examples(A_,    num_examples=A_,    seq_shapes=A_						)

       with ArrowWriter(features=A_,    path=A_						) as writer:
              for key, record in dummy_data:
                     _lowerCamelCase						:    Optional[Any]         =			features.encode_example(A_						)
                     writer.write(A_						)

              _lowerCamelCase      ,   _lowerCamelCase						:    Dict         =			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}.'''						)

       _lowerCamelCase						:    List[Any]         =			datasets.Dataset.from_file(filename=A_,    info=datasets.DatasetInfo(features=A_						)						)

       return dataset


