



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


__snake_case	:				Tuple  					=			logging.get_logger(__name__)

__snake_case	:				Union[str, Any]  					=			{
    "google/pegasus-x-base": "https://huggingface.co/google/pegasus-x-base/resolve/main/config.json",
    "google/pegasus-x-large": "https://huggingface.co/google/pegasus-x-large/resolve/main/config.json",
    # See all PEGASUS-X models at https://huggingface.co/models?filter=pegasus-x
}

class       UpperCamelCase  (					SCREAMING_SNAKE_CASE__				):





     """simple docstring"""






     _lowerCamelCase					: List[str]				   ='pegasus_x'
     _lowerCamelCase					: Dict				   =['past_key_values']
     _lowerCamelCase					: List[str]				   ={'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}





     def __init__(					self :							Optional[int]	,      _lowerCamelCase :							Tuple=9_6_1_0_3	,      _lowerCamelCase :							Any=1_6_3_8_4	,      _lowerCamelCase :							Optional[int]=1_6	,      _lowerCamelCase :							List[str]=4_0_9_6	,      _lowerCamelCase :							int=1_6	,      _lowerCamelCase :							Tuple=1_6	,      _lowerCamelCase :							Union[str, Any]=4_0_9_6	,      _lowerCamelCase :							Optional[Any]=1_6	,      _lowerCamelCase :							int=0.0	,      _lowerCamelCase :							Dict=0.0	,      _lowerCamelCase :							Any=True	,      _lowerCamelCase :							Optional[int]=True	,      _lowerCamelCase :							Tuple="gelu"	,      _lowerCamelCase :							Union[str, Any]=1_0_2_4	,      _lowerCamelCase :							Optional[Any]=0.1	,      _lowerCamelCase :							int=0.0	,      _lowerCamelCase :							int=0.0	,      _lowerCamelCase :							Optional[int]=0.02	,      _lowerCamelCase :							Union[str, Any]=0	,      _lowerCamelCase :							Optional[int]=True	,      _lowerCamelCase :							str=0	,      _lowerCamelCase :							Tuple=1	,      _lowerCamelCase :							Optional[Any]=1	,      _lowerCamelCase :							Tuple=3_2	,      _lowerCamelCase :							Dict=5_1_2	,      _lowerCamelCase :							Tuple=True	,      **_lowerCamelCase :							Tuple	,      ):
          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

          A__				=   num_global_tokens
          A__				=   block_size
          A__				=   stagger_local_blocks

          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 :							Dict ):
          return self.encoder_attention_heads





     @property
     def 						A__  (					self :							List[str] ):
          return self.d_model








"""simple docstring"""







import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


__snake_case	:				Optional[Any]  					=			500_000

__snake_case					,  __snake_case	:				Any  					=			os.path.split(__file__)
__snake_case	:				Optional[Any]  					=			os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))



@get_duration
def   a_	(			__a      ):
     A__				=   dataset.select(range(0		,							len(__a      )		,							2      )      )



@get_duration
def   a_	(			__a      ):
     A__				=   dataset.sort('''numbers'''      )



@get_duration
def   a_	(			__a      ):
     A__				=   dataset.shuffle()



@get_duration
def   a_	(			__a      ):
     A__				=   dataset.train_test_split(0.1      )



@get_duration
def   a_	(			__a		,							__a=10      ):
     for shard_id in range(__a      ):
          A__				=   dataset.shard(__a		,							__a      )



def   a_	(			):
     A__				=   {'''num examples''': SPEED_TEST_N_EXAMPLES}
     A__				=   (select, sort, shuffle, train_test_split, shard)
     with tempfile.TemporaryDirectory() as tmp_dir:
          print('''generating dataset'''      )
          A__				=   datasets.Features({'''text''': datasets.Value('''string'''      ), '''numbers''': datasets.Value('''float32'''      )}      )
          A__				=   generate_example_dataset(
              os.path.join(__a		,							'''dataset.arrow'''      )		,							__a		,							num_examples=__a      )
          print('''Functions'''      )
          for func in functions:
               print(func.__name__      )
               A__				=   func(__a      )

     with open(__a		,							'''wb'''      ) as f:
          f.write(json.dumps(__a      ).encode('''utf-8'''      )      )


if __name__ == "__main__":  # useful to run the profiler
 benchmark_indices_mapping()



