





from typing import List, Union

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


_lowerCamelCase							=     logging.get_logger(__name__)

_lowerCamelCase							=     {
    'allenai/led-base-16384': 'https://huggingface.co/allenai/led-base-16384/resolve/main/config.json',
    # See all LED models at https://huggingface.co/models?filter=led
}




class 					UpperCamelCase_    (       UpperCamelCase__      ):
							lowerCamelCase_          =   "led"
							lowerCamelCase_          =   {
							    "num_attention_heads": "encoder_attention_heads",
							    "hidden_size": "d_model",
							    "attention_probs_dropout_prob": "attention_dropout",
							    "initializer_range": "init_std",
							}


							def __init__(							self      :Union[str, Any]							,			__A      :Optional[int]=5_0265							,			__A      :Dict=1_6384							,			__A      :int=1024							,			__A      :Tuple=12							,			__A      :List[Any]=4096							,			__A      :List[Any]=16							,			__A      :Tuple=12							,			__A      :Union[str, Any]=4096							,			__A      :List[str]=16							,			__A      :str=0.0							,			__A      :List[Any]=0.0							,			__A      :Tuple=True							,			__A      :Union[str, Any]=True							,			__A      :Dict="gelu"							,			__A      :Dict=1024							,			__A      :str=0.1							,			__A      :Tuple=0.0							,			__A      :Union[str, Any]=0.0							,			__A      :Union[str, Any]=0.0_2							,			__A      :Union[str, Any]=2							,			__A      :Any=0.0							,			__A      :Optional[Any]=1							,			__A      :Union[str, Any]=0							,			__A      :Union[str, Any]=2							,			__A      :Union[List[int], int] = 512							,			**__A      :Tuple							,			)				->				Optional[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= vocab_size
									SCREAMING_SNAKE_CASE__								= max_encoder_position_embeddings
									SCREAMING_SNAKE_CASE__								= max_decoder_position_embeddings
									SCREAMING_SNAKE_CASE__								= d_model
									SCREAMING_SNAKE_CASE__								= encoder_ffn_dim
									SCREAMING_SNAKE_CASE__								= encoder_layers
									SCREAMING_SNAKE_CASE__								= encoder_attention_heads
									SCREAMING_SNAKE_CASE__								= decoder_ffn_dim
									SCREAMING_SNAKE_CASE__								= decoder_layers
									SCREAMING_SNAKE_CASE__								= decoder_attention_heads
									SCREAMING_SNAKE_CASE__								= dropout
									SCREAMING_SNAKE_CASE__								= attention_dropout
									SCREAMING_SNAKE_CASE__								= activation_dropout
									SCREAMING_SNAKE_CASE__								= activation_function
									SCREAMING_SNAKE_CASE__								= init_std
									SCREAMING_SNAKE_CASE__								= encoder_layerdrop
									SCREAMING_SNAKE_CASE__								= decoder_layerdrop
									SCREAMING_SNAKE_CASE__								= classifier_dropout
									SCREAMING_SNAKE_CASE__								= use_cache
									SCREAMING_SNAKE_CASE__								= encoder_layers
									SCREAMING_SNAKE_CASE__								= attention_window

									super().__init__(
									    pad_token_id=__A							,			bos_token_id=__A							,			eos_token_id=__A							,			is_encoder_decoder=__A							,			decoder_start_token_id=__A							,			**__A							,			)






import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


_lowerCamelCase							=     500000

_lowerCamelCase			,    _lowerCamelCase							=     os.path.split(__file__)
_lowerCamelCase							=     os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))






@get_duration
def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							datasets.Dataset      ):
		SCREAMING_SNAKE_CASE__								= dataset.select(range(0   ,				len(UpperCamelCase__      )   ,				2      )      )






@get_duration
def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							datasets.Dataset      ):
		SCREAMING_SNAKE_CASE__								= dataset.sort("""numbers"""      )






@get_duration
def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							datasets.Dataset      ):
		SCREAMING_SNAKE_CASE__								= dataset.shuffle()






@get_duration
def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							datasets.Dataset      ):
		SCREAMING_SNAKE_CASE__								= dataset.train_test_split(0.1      )






@get_duration
def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							datasets.Dataset   ,				UpperCamelCase__:							Tuple=10      ):
		for shard_id in range(UpperCamelCase__      ):
				SCREAMING_SNAKE_CASE__								= dataset.shard(UpperCamelCase__   ,				UpperCamelCase__      )






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

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


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