



'''simple docstring'''







from transformers import HfArgumentParser, PyTorchBenchmark, PyTorchBenchmarkArguments







def  SCREAMING_SNAKE_CASE__ (				):
		SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      HfArgumentParser(lowerCAmelCase__     )
		try:
				SCREAMING_SNAKE_CASE_						:int										=      parser.parse_args_into_dataclasses()[0]
		except ValueError as e:
				SCREAMING_SNAKE_CASE_						:Tuple										=      'Arg --no_{0} is no longer used, please use --no-{0} instead.'
				SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      ' '.join(str(lowerCAmelCase__     ).split(' '     )[:-1]     )
				SCREAMING_SNAKE_CASE_						:int										=      ''
				SCREAMING_SNAKE_CASE_						:str										=      eval(str(lowerCAmelCase__     ).split(' '     )[-1]     )
				SCREAMING_SNAKE_CASE_						:Optional[int]										=      []
				for arg in depreciated_args:
						# arg[2:] removes '--'
						if arg[2:] in PyTorchBenchmarkArguments.deprecated_args:
								# arg[5:] removes '--no_'
								full_error_msg += arg_error_msg.format(arg[5:]     )
						else:
								wrong_args.append(lowerCAmelCase__     )
				if len(lowerCAmelCase__     ) > 0:
						SCREAMING_SNAKE_CASE_						:List[str]										=      full_error_msg + begin_error_msg + str(lowerCAmelCase__     )
				raise ValueError(lowerCAmelCase__     )

		SCREAMING_SNAKE_CASE_						:str										=      PyTorchBenchmark(args=lowerCAmelCase__     )
		benchmark.run()


if __name__ == "__main__":
							main()

from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


lowerCAmelCase__      		=					logging.get_logger(__name__)

lowerCAmelCase__      		=					{
    'google/long-t5-local-base': 'https://huggingface.co/google/long-t5-local-base/blob/main/config.json',
    'google/long-t5-local-large': 'https://huggingface.co/google/long-t5-local-large/blob/main/config.json',
    'google/long-t5-tglobal-base': 'https://huggingface.co/google/long-t5-tglobal-base/blob/main/config.json',
    'google/long-t5-tglobal-large': 'https://huggingface.co/google/long-t5-tglobal-large/blob/main/config.json',
}
class 							a_					(		SCREAMING_SNAKE_CASE		):






 '''simple docstring'''






 UpperCAmelCase_      					=   'longt5'
 UpperCAmelCase_      					=   ['past_key_values']
 UpperCAmelCase_      					=   {'hidden_size': 'd_model', 'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}




 def __init__(     self   :    Optional[Any]     ,       lowercase__   :    Union[str, Any]=32_128     ,       lowercase__   :    Union[str, Any]=512     ,       lowercase__   :    List[Any]=64     ,       lowercase__   :    Tuple=2_048     ,       lowercase__   :    List[Any]=6     ,       lowercase__   :    Any=None     ,       lowercase__   :    List[str]=8     ,       lowercase__   :    str=127     ,       lowercase__   :    Any=16     ,       lowercase__   :    str=32     ,       lowercase__   :    str=128     ,       lowercase__   :    Union[str, Any]=0.1     ,       lowercase__   :    List[Any]=1e-6     ,       lowercase__   :    Optional[Any]=1.0     ,       lowercase__   :    str="relu"     ,       lowercase__   :    List[str]=True     ,       lowercase__   :    Any="local"     ,       lowercase__   :    str=True     ,       lowercase__   :    Union[str, Any]=0     ,       lowercase__   :    Optional[int]=1     ,       **lowercase__   :    str     ,       ):







        '''simple docstring'''



        lowerCAmelCase__	      =   vocab_size
        lowerCAmelCase__	      =   d_model
        lowerCAmelCase__	      =   d_kv
        lowerCAmelCase__	      =   d_ff
        lowerCAmelCase__	      =   num_layers
        # default = symmetry
        lowerCAmelCase__	      =   num_decoder_layers if num_decoder_layers is not None else self.num_layers
        lowerCAmelCase__	      =   num_heads
        lowerCAmelCase__	      =   local_radius
        lowerCAmelCase__	      =   global_block_size
        lowerCAmelCase__	      =   relative_attention_num_buckets
        lowerCAmelCase__	      =   relative_attention_max_distance
        lowerCAmelCase__	      =   dropout_rate
        lowerCAmelCase__	      =   layer_norm_epsilon
        lowerCAmelCase__	      =   initializer_factor
        lowerCAmelCase__	      =   feed_forward_proj
        lowerCAmelCase__	      =   encoder_attention_type
        lowerCAmelCase__	      =   use_cache

        lowerCAmelCase__	      =   self.feed_forward_proj.split('-')
        lowerCAmelCase__	      =   act_info[-1]
        lowerCAmelCase__	      =   act_info[0] == 'gated'

        if len(lowercase__) > 1 and act_info[0] != "gated" or len(lowercase__) > 2:
               raise ValueError(
                   F"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
                   'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
                   '\'gated-gelu\' or \'relu\'')

        # for backwards compatibility
        if feed_forward_proj == "gated-gelu":
               lowerCAmelCase__	      =   'gelu_new'

        super().__init__(
            pad_token_id=lowercase__     ,       eos_token_id=lowercase__     ,       is_encoder_decoder=lowercase__     ,       **lowercase__     ,       )






class 							a_					(		SCREAMING_SNAKE_CASE		):






 '''simple docstring'''






 @property
 def 						__snake_case		(     self   :    str):







        '''simple docstring'''



        lowerCAmelCase__	      =   {
            'input_ids': {0: 'batch', 1: 'encoder_sequence'},
            'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
        }
        if self.use_past:
               lowerCAmelCase__	      =   'past_encoder_sequence + sequence'
               lowerCAmelCase__	      =   {0: 'batch'}
               lowerCAmelCase__	      =   {0: 'batch', 1: 'past_decoder_sequence + sequence'}
        else:
               lowerCAmelCase__	      =   {0: 'batch', 1: 'decoder_sequence'}
               lowerCAmelCase__	      =   {0: 'batch', 1: 'decoder_sequence'}

        if self.use_past:
               self.fill_with_past_key_values_(lowercase__     ,       direction='inputs')

        return common_inputs




 @property
 def 						__snake_case		(     self   :    Any):







        '''simple docstring'''



        return 13
