

"""simple docstring"""




from typing import Mapping

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


__A     :    Optional[int]    =		logging.get_logger(__name__)

__A     :    Union[str, Any]    =		{
    '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 (   _A						):
		SCREAMING_SNAKE_CASE								=    "longt5"
		SCREAMING_SNAKE_CASE								=    ["past_key_values"]
		SCREAMING_SNAKE_CASE								=    {"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}


		def __init__(self :		List[Any]     ,     __SCREAMING_SNAKE_CASE :		Optional[Any]=3_2_1_2_8     ,     __SCREAMING_SNAKE_CASE :		str=5_1_2     ,     __SCREAMING_SNAKE_CASE :		List[str]=6_4     ,     __SCREAMING_SNAKE_CASE :		Optional[Any]=2_0_4_8     ,     __SCREAMING_SNAKE_CASE :		str=6     ,     __SCREAMING_SNAKE_CASE :		Tuple=None     ,     __SCREAMING_SNAKE_CASE :		Union[str, Any]=8     ,     __SCREAMING_SNAKE_CASE :		Any=1_2_7     ,     __SCREAMING_SNAKE_CASE :		int=1_6     ,     __SCREAMING_SNAKE_CASE :		Dict=3_2     ,     __SCREAMING_SNAKE_CASE :		Dict=1_2_8     ,     __SCREAMING_SNAKE_CASE :		str=0.1     ,     __SCREAMING_SNAKE_CASE :		Tuple=1E-6     ,     __SCREAMING_SNAKE_CASE :		str=1.0     ,     __SCREAMING_SNAKE_CASE :		str="relu"     ,     __SCREAMING_SNAKE_CASE :		List[Any]=True     ,     __SCREAMING_SNAKE_CASE :		Any="local"     ,     __SCREAMING_SNAKE_CASE :		int=True     ,     __SCREAMING_SNAKE_CASE :		Optional[int]=0     ,     __SCREAMING_SNAKE_CASE :		List[Any]=1     ,     **__SCREAMING_SNAKE_CASE :		List[Any]     ,     ):
							A			=					vocab_size
							A			=					d_model
							A			=					d_kv
							A			=					d_ff
							A			=					num_layers
							# default = symmetry
							A			=					num_decoder_layers if num_decoder_layers is not None else self.num_layers
							A			=					num_heads
							A			=					local_radius
							A			=					global_block_size
							A			=					relative_attention_num_buckets
							A			=					relative_attention_max_distance
							A			=					dropout_rate
							A			=					layer_norm_epsilon
							A			=					initializer_factor
							A			=					feed_forward_proj
							A			=					encoder_attention_type
							A			=					use_cache

							A			=					self.feed_forward_proj.split("-")
							A			=					act_info[-1]
							A			=					act_info[0] == "gated"

							if len(__SCREAMING_SNAKE_CASE) > 1 and act_info[0] != "gated" or len(__SCREAMING_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":
												A			=					"gelu_new"

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





class 			__UpperCamelCase (   _A						):


		@property
		def 					SCREAMING_SNAKE_CASE__   (self :		Optional[Any]):
							A			=					{
							    "input_ids": {0: "batch", 1: "encoder_sequence"},
							    "attention_mask": {0: "batch", 1: "encoder_sequence"},
							}
							if self.use_past:
												A			=					"past_encoder_sequence + sequence"
												A			=					{0: "batch"}
												A			=					{0: "batch", 1: "past_decoder_sequence + sequence"}
							else:
												A			=					{0: "batch", 1: "decoder_sequence"}
												A			=					{0: "batch", 1: "decoder_sequence"}

							if self.use_past:
												self.fill_with_past_key_values_(__SCREAMING_SNAKE_CASE     ,     direction="inputs")

							return common_inputs


		@property
		def 					SCREAMING_SNAKE_CASE__   (self :		str):
							return 1_3








"""simple docstring"""




import doctest

import projectq
from projectq.ops import H, Measure
def    __SCREAMING_SNAKE_CASE						(						lowercase__      ):
					"""simple docstring"""
					A			=					quantum_engine.allocate_qubit()
					H | qubit
					Measure | qubit
					return int(lowercase__      )


if __name__ == "__main__":
	doctest.testmod()

	# initialises a new quantum backend
	__A     :    Dict    =		projectq.MainEngine()

	# Generate a list of 10 random numbers
	__A     :    Any    =		[get_random_number(quantum_engine) for _ in range(10)]

	# Flushes the quantum engine from memory
	quantum_engine.flush()

	print('Random numbers', random_numbers_list)





