



'''simple docstring'''





import timeit

import numpy as np

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




def lowerCAmelCase						(__A):



					"""simple docstring"""



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

					_a											=						func.__name__

					return wrapper




def lowerCAmelCase						(__A						,			__A=100						,			__A=None):



					"""simple docstring"""



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

										dummy_data.append((i, example))

					return dummy_data




def lowerCAmelCase						(__A						,			__A						,			__A=100						,			__A=None):



					"""simple docstring"""



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

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

										_a											=						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}.''')

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

					return dataset








'''simple docstring'''





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


lowercase_	 =						logging.get_logger(__name__)

lowercase_	 =						{
    "google/pegasus-large": "https://huggingface.co/google/pegasus-large/resolve/main/config.json",
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}






class __A    (						A		):



 '''simple docstring'''







 __lowerCamelCase					: Union[str, Any]    = 'pegasus'
 __lowerCamelCase					: Tuple    = ['past_key_values']
 __lowerCamelCase					: List[Any]    = {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}



 def __init__(self				,     A=50_265				,     A=1_024				,     A=12				,     A=4_096				,     A=16				,     A=12				,     A=4_096				,     A=16				,     A=0.0				,     A=0.0				,     A=True				,     A=True				,     A="gelu"				,     A=1_024				,     A=0.1				,     A=0.0				,     A=0.0				,     A=0.02				,     A=0				,     A=False				,     A=0				,     A=1				,     A=1				,     **A				,     )					->  str:


      """simple docstring"""



      _a											=						vocab_size
      _a											=						max_position_embeddings
      _a											=						d_model
      _a											=						encoder_ffn_dim
      _a											=						encoder_layers
      _a											=						encoder_attention_heads
      _a											=						decoder_ffn_dim
      _a											=						decoder_layers
      _a											=						decoder_attention_heads
      _a											=						dropout
      _a											=						attention_dropout
      _a											=						activation_dropout
      _a											=						activation_function
      _a											=						init_std
      _a											=						encoder_layerdrop
      _a											=						decoder_layerdrop
      _a											=						use_cache
      _a											=						encoder_layers
      _a											=						scale_embedding  # scale factor will be sqrt(d_model) if True
      super().__init__(
          pad_token_id=A				,     eos_token_id=A				,     is_encoder_decoder=A				,     decoder_start_token_id=A				,     forced_eos_token_id=A				,     **A				,     )



 @property
 def       a__						(self )					->  int:


      """simple docstring"""



      return self.encoder_attention_heads



 @property
 def       a__						(self )					->  int:


      """simple docstring"""



      return self.d_model



