



'''simple docstring'''






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__:       int							     =      {
    "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     SCREAMING_SNAKE_CASE(							A__      ):







	"""simple docstring"""






	lowerCamelCase__      			=	"""longt5"""
	lowerCamelCase__      			=	["""past_key_values"""]
	lowerCamelCase__      			=	{"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}







	def __init__(						self      :					Optional[int]		,      __snake_case      :					List[str]=32128		,      __snake_case      :					int=512		,      __snake_case      :					Any=64		,      __snake_case      :					Tuple=2048		,      __snake_case      :					Dict=6		,      __snake_case      :					Optional[int]=None		,      __snake_case      :					List[str]=8		,      __snake_case      :					Any=127		,      __snake_case      :					List[Any]=16		,      __snake_case      :					Any=32		,      __snake_case      :					Tuple=128		,      __snake_case      :					List[Any]=0.1		,      __snake_case      :					Optional[int]=1E-6		,      __snake_case      :					Any=1.0		,      __snake_case      :					Dict="relu"		,      __snake_case      :					Any=True		,      __snake_case      :					str="local"		,      __snake_case      :					List[Any]=True		,      __snake_case      :					List[str]=0		,      __snake_case      :					Optional[Any]=1		,      **__snake_case      :					Any		,      )      ->		Tuple:
								UpperCAmelCase			:	Optional[Any]										=		vocab_size
								UpperCAmelCase			:	Optional[Any]										=		d_model
								UpperCAmelCase			:	str										=		d_kv
								UpperCAmelCase			:	List[Any]										=		d_ff
								UpperCAmelCase			:	Union[str, Any]										=		num_layers
								# default = symmetry
								UpperCAmelCase			:	Union[str, Any]										=		num_decoder_layers if num_decoder_layers is not None else self.num_layers
								UpperCAmelCase			:	List[Any]										=		num_heads
								UpperCAmelCase			:	Any										=		local_radius
								UpperCAmelCase			:	int										=		global_block_size
								UpperCAmelCase			:	Optional[int]										=		relative_attention_num_buckets
								UpperCAmelCase			:	Optional[Any]										=		relative_attention_max_distance
								UpperCAmelCase			:	Dict										=		dropout_rate
								UpperCAmelCase			:	List[str]										=		layer_norm_epsilon
								UpperCAmelCase			:	Any										=		initializer_factor
								UpperCAmelCase			:	List[str]										=		feed_forward_proj
								UpperCAmelCase			:	Any										=		encoder_attention_type
								UpperCAmelCase			:	List[Any]										=		use_cache

								UpperCAmelCase			:	int										=		self.feed_forward_proj.split('''-'''		)
								UpperCAmelCase			:	Tuple										=		act_info[-1]
								UpperCAmelCase			:	Tuple										=		act_info[0] == '''gated'''

								if len(__snake_case		) > 1 and act_info[0] != "gated" or len(__snake_case		) > 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":
															UpperCAmelCase			:	Optional[int]										=		'''gelu_new'''

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

class     SCREAMING_SNAKE_CASE(							A__      ):







	"""simple docstring"""






	@property
	def 					A     (						self      :					Tuple		)      ->		Mapping[str, Mapping[int, str]]:
								UpperCAmelCase			:	Union[str, Any]										=		{
								    '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
								    '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
								}
								if self.use_past:
															UpperCAmelCase			:	Tuple										=		'''past_encoder_sequence + sequence'''
															UpperCAmelCase			:	Tuple										=		{0: '''batch'''}
															UpperCAmelCase			:	Tuple										=		{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
								else:
															UpperCAmelCase			:	Optional[Any]										=		{0: '''batch''', 1: '''decoder_sequence'''}
															UpperCAmelCase			:	Optional[Any]										=		{0: '''batch''', 1: '''decoder_sequence'''}

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

								return common_inputs







	@property
	def 					A     (						self      :					Optional[Any]		)      ->		int:
								return 13










'''simple docstring'''






import re


def 			snake_case_					( _lowerCAmelCase  :						List[str]  )				->       Dict:
							def docstring_decorator(_lowerCAmelCase  :						List[str]  ):
														UpperCAmelCase			:	str										=		fn.__doc__
														UpperCAmelCase			:	List[str]										=		func_doc.split('''\n'''  )
														UpperCAmelCase			:	Optional[Any]										=		0
														while i < len(_lowerCAmelCase  ) and re.search(R'''^\s*Examples?:\s*$'''       ,				lines[i]  ) is None:
																					i += 1
														if i < len(_lowerCAmelCase  ):
																					UpperCAmelCase			:	Union[str, Any]										=		example_docstring
																					UpperCAmelCase			:	Tuple										=		'''\n'''.join(_lowerCAmelCase  )
														else:
																					raise ValueError(
																					    f"""The function {fn} should have an empty 'Examples:' in its docstring as placeholder, """
																					    f"""current docstring is:\n{func_doc}"""  )
														UpperCAmelCase			:	Any										=		func_doc
														return fn

							return docstring_decorator





