
from typing import Mapping

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


UpperCAmelCase__	:  List[str]      =   logging.get_logger(__name__)

UpperCAmelCase__	:  List[str]      =   {
    '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 UpperCAmelCase     (		SCREAMING_SNAKE_CASE__     ):





			'''simple docstring'''
			__UpperCamelCase	:					Tuple				       =					'''longt5'''
			__UpperCamelCase	:					Union[str, Any]				       =					['''past_key_values''']
			__UpperCamelCase	:					int				       =					{'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}


			def __init__(      self  :							str    ,    lowerCAmelCase_  :							Optional[int]=3_2_1_2_8    ,    lowerCAmelCase_  :							List[Any]=5_1_2    ,    lowerCAmelCase_  :							Dict=6_4    ,    lowerCAmelCase_  :							Tuple=2_0_4_8    ,    lowerCAmelCase_  :							Tuple=6    ,    lowerCAmelCase_  :							Dict=None    ,    lowerCAmelCase_  :							str=8    ,    lowerCAmelCase_  :							Tuple=1_2_7    ,    lowerCAmelCase_  :							Optional[int]=1_6    ,    lowerCAmelCase_  :							Dict=3_2    ,    lowerCAmelCase_  :							int=1_2_8    ,    lowerCAmelCase_  :							List[Any]=0.1    ,    lowerCAmelCase_  :							Union[str, Any]=1e-6    ,    lowerCAmelCase_  :							Optional[int]=1.0    ,    lowerCAmelCase_  :							int="relu"    ,    lowerCAmelCase_  :							Optional[int]=True    ,    lowerCAmelCase_  :							int="local"    ,    lowerCAmelCase_  :							Dict=True    ,    lowerCAmelCase_  :							Dict=0    ,    lowerCAmelCase_  :							Union[str, Any]=1    ,    **lowerCAmelCase_  :							Any    ,    ):

						"""simple docstring"""
						_A:						List[str]	    = vocab_size
						_A:						Tuple	    = d_model
						_A:						List[str]	    = d_kv
						_A:						List[str]	    = d_ff
						_A:						int	    = num_layers
						# default = symmetry
						_A:						List[str]	    = num_decoder_layers if num_decoder_layers is not None else self.num_layers
						_A:						int	    = num_heads
						_A:						Optional[int]	    = local_radius
						_A:						str	    = global_block_size
						_A:						int	    = relative_attention_num_buckets
						_A:						int	    = relative_attention_max_distance
						_A:						List[str]	    = dropout_rate
						_A:						Optional[int]	    = layer_norm_epsilon
						_A:						Dict	    = initializer_factor
						_A:						Optional[int]	    = feed_forward_proj
						_A:						Any	    = encoder_attention_type
						_A:						Optional[int]	    = use_cache

						_A:						List[str]	    = self.feed_forward_proj.split('''-'''  )
						_A:						Any	    = act_info[-1]
						_A:						List[Any]	    = act_info[0] == '''gated'''

						if len(lowerCAmelCase_  ) > 1 and act_info[0] != "gated" or len(lowerCAmelCase_  ) > 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":
									_A:						str	    = '''gelu_new'''

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






class UpperCAmelCase     (		SCREAMING_SNAKE_CASE__     ):





			'''simple docstring'''


			@property
			def 				__magic_name__       (      self  :							List[str]  ):

						"""simple docstring"""
						_A:						Tuple	    = {
						    '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
						    '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
						}
						if self.use_past:
									_A:						Tuple	    = '''past_encoder_sequence + sequence'''
									_A:						int	    = {0: '''batch'''}
									_A:						str	    = {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
						else:
									_A:						List[str]	    = {0: '''batch''', 1: '''decoder_sequence'''}
									_A:						Dict	    = {0: '''batch''', 1: '''decoder_sequence'''}

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

						return common_inputs


			@property
			def 				__magic_name__       (      self  :							Dict  ):

						"""simple docstring"""
						return 1_3





UpperCAmelCase__	:  str      =   1000






def 						lowerCamelCase__							(      a   )   ->     float:
   return x * x






def 						lowerCamelCase__							(      a	,	a	,	a	,	a = 4   )   ->     float:
   assert callable(
       a   ), f"""the function(object) passed should be callable your input : {function}"""
   assert isinstance(a	,	(float, int)   ), f"""a should be float or integer your input : {a}"""
   assert isinstance(function(a   )	,	(float, int)   ), (
       "the function should return integer or float return type of your function, "
       f"""{type(a   )}"""
   )
   assert isinstance(a	,	(float, int)   ), f"""b should be float or integer your input : {b}"""
   assert (
       isinstance(a	,	a   ) and precision > 0
   ), f"""precision should be positive integer your input : {precision}"""

   # just applying the formula of simpson for approximate integration written in
   # mentioned article in first comment of this file and above this function

   _A:						int	    = (b - a) / N_STEPS
   _A:						Any	    = function(a   ) + function(a   )

   for i in range(1	,	a   ):
      _A:						List[Any]	    = a + h * i
      result += function(a   ) * (4 if i % 2 else 2)

   result *= h / 3
   return round(a	,	a   )


if __name__ == "__main__":
   import doctest

   doctest.testmod()



