


from typing import Mapping

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


snake_case_  :     Optional[Any]							  =							logging.get_logger(__name__)

snake_case_  :     Dict							  =							{
    '''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__							(						__lowerCamelCase					):
						UpperCAmelCase     =  "longt5"
						UpperCAmelCase     =  ["past_key_values"]
						UpperCAmelCase     =  {"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}




						def __init__(    self   : int			,      _a   : Any=3_2128			,      _a   : Optional[Any]=512			,      _a   : List[str]=64			,      _a   : Any=2048			,      _a   : List[str]=6			,      _a   : List[str]=None			,      _a   : Union[str, Any]=8			,      _a   : Tuple=127			,      _a   : Dict=16			,      _a   : str=32			,      _a   : Union[str, Any]=128			,      _a   : Optional[int]=0.1			,      _a   : List[Any]=1E-6			,      _a   : List[Any]=1.0			,      _a   : str="relu"			,      _a   : Dict=True			,      _a   : Any="local"			,      _a   : Optional[int]=True			,      _a   : Any=0			,      _a   : Tuple=1			,      **_a   : List[Any]			,      )							->       Tuple:


										"""simple docstring"""





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

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

										if len(_UpperCamelCase					) > 1 and act_info[0] != "gated" or len(_UpperCamelCase					) > 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":
														_SCREAMING_SNAKE_CASE     					='''gelu_new'''

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


class   A__							(						__lowerCamelCase					):




						@property
						def 							__UpperCamelCase			(    self   : List[Any]					)							->       Mapping[str, Mapping[int, str]]:


										"""simple docstring"""





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

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

										return common_inputs




						@property
						def 							__UpperCamelCase			(    self   : Union[str, Any]					)							->       int:


										"""simple docstring"""





										return 13



import math

class   A__							:




						def __init__(    self   : Optional[int]			,      _a   : int			,      _a   : List[Any]			,      _a   : int					)							->       Optional[int]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=x
										_SCREAMING_SNAKE_CASE     					=y
										_SCREAMING_SNAKE_CASE     					=z




						def __repr__(    self   : List[str]					)							->       str:


										"""simple docstring"""





										return f"Point({self.x}, {self.y}, {self.z})"







def   lowerCamelCase(				a__		,a__):
				return math.sqrt(abs((b.x - a.x) ** 2 + (b.y - a.y) ** 2 + (b.z - a.z) ** 2))


if __name__ == "__main__":
							import doctest

							doctest.testmod()