

"""simple docstring"""




from collections import OrderedDict
from typing import Mapping

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


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

__A     :    Union[str, Any]    =		{
    'squeezebert/squeezebert-uncased': (
        'https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/config.json'
    ),
    'squeezebert/squeezebert-mnli': 'https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/config.json',
    'squeezebert/squeezebert-mnli-headless': (
        'https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/config.json'
    ),
}





class 			__UpperCamelCase (   _A						):
		SCREAMING_SNAKE_CASE								=    SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
		SCREAMING_SNAKE_CASE								=    "squeezebert"


		def __init__(self :		Optional[int]     ,     __SCREAMING_SNAKE_CASE :		Tuple=3_0_5_2_2     ,     __SCREAMING_SNAKE_CASE :		Any=7_6_8     ,     __SCREAMING_SNAKE_CASE :		Optional[Any]=1_2     ,     __SCREAMING_SNAKE_CASE :		Optional[int]=1_2     ,     __SCREAMING_SNAKE_CASE :		Tuple=3_0_7_2     ,     __SCREAMING_SNAKE_CASE :		int="gelu"     ,     __SCREAMING_SNAKE_CASE :		List[str]=0.1     ,     __SCREAMING_SNAKE_CASE :		Dict=0.1     ,     __SCREAMING_SNAKE_CASE :		List[Any]=5_1_2     ,     __SCREAMING_SNAKE_CASE :		int=2     ,     __SCREAMING_SNAKE_CASE :		Optional[int]=0.0_2     ,     __SCREAMING_SNAKE_CASE :		Optional[Any]=1E-12     ,     __SCREAMING_SNAKE_CASE :		Optional[Any]=0     ,     __SCREAMING_SNAKE_CASE :		Any=7_6_8     ,     __SCREAMING_SNAKE_CASE :		Tuple=4     ,     __SCREAMING_SNAKE_CASE :		Tuple=4     ,     __SCREAMING_SNAKE_CASE :		Dict=4     ,     __SCREAMING_SNAKE_CASE :		Any=1     ,     __SCREAMING_SNAKE_CASE :		Tuple=4     ,     __SCREAMING_SNAKE_CASE :		List[Any]=4     ,     **__SCREAMING_SNAKE_CASE :		List[Any]     ,     ):
							super().__init__(pad_token_id=__SCREAMING_SNAKE_CASE     ,     **__SCREAMING_SNAKE_CASE)

							A			=					vocab_size
							A			=					hidden_size
							A			=					num_hidden_layers
							A			=					num_attention_heads
							A			=					hidden_act
							A			=					intermediate_size
							A			=					hidden_dropout_prob
							A			=					attention_probs_dropout_prob
							A			=					max_position_embeddings
							A			=					type_vocab_size
							A			=					initializer_range
							A			=					layer_norm_eps
							A			=					embedding_size
							A			=					q_groups
							A			=					k_groups
							A			=					v_groups
							A			=					post_attention_groups
							A			=					intermediate_groups
							A			=					output_groups





class 			__UpperCamelCase (   _A						):


		@property
		def 					SCREAMING_SNAKE_CASE__   (self :		Dict):
							if self.task == "multiple-choice":
												A			=					{0: "batch", 1: "choice", 2: "sequence"}
							else:
												A			=					{0: "batch", 1: "sequence"}
							return OrderedDict(
							    [
							        ("input_ids", dynamic_axis),
							        ("attention_mask", dynamic_axis),
							        ("token_type_ids", dynamic_axis),
							    ])








"""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)





