






"""simple docstring"""





from collections import OrderedDict
from typing import Mapping

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


UpperCamelCase					=     logging.get_logger(__name__)

UpperCamelCase					=     {
    """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 						_a  (		lowercase_       ):





   '''simple docstring'''






   UpperCamelCase__     	=							SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
   UpperCamelCase__     	=							"""squeezebert"""





   def __init__(       self					, UpperCAmelCase_=30_522					, UpperCAmelCase_=768					, UpperCAmelCase_=12					, UpperCAmelCase_=12					, UpperCAmelCase_=3_072					, UpperCAmelCase_="gelu"					, UpperCAmelCase_=0.1					, UpperCAmelCase_=0.1					, UpperCAmelCase_=512					, UpperCAmelCase_=2					, UpperCAmelCase_=0.02					, UpperCAmelCase_=1E-1_2					, UpperCAmelCase_=0					, UpperCAmelCase_=768					, UpperCAmelCase_=4					, UpperCAmelCase_=4					, UpperCAmelCase_=4					, UpperCAmelCase_=1					, UpperCAmelCase_=4					, UpperCAmelCase_=4					, **UpperCAmelCase_					, )			->   int:




          '''simple docstring'''




          super().__init__(pad_token_id=UpperCAmelCase_					, **UpperCAmelCase_)

          lowercase__:							Union[str, Any]         =     vocab_size
          lowercase__:							int         =     hidden_size
          lowercase__:							int         =     num_hidden_layers
          lowercase__:							List[str]         =     num_attention_heads
          lowercase__:							Any         =     hidden_act
          lowercase__:							List[str]         =     intermediate_size
          lowercase__:							Any         =     hidden_dropout_prob
          lowercase__:							List[Any]         =     attention_probs_dropout_prob
          lowercase__:							Optional[int]         =     max_position_embeddings
          lowercase__:							List[Any]         =     type_vocab_size
          lowercase__:							Union[str, Any]         =     initializer_range
          lowercase__:							str         =     layer_norm_eps
          lowercase__:							Optional[Any]         =     embedding_size
          lowercase__:							Optional[int]         =     q_groups
          lowercase__:							List[str]         =     k_groups
          lowercase__:							Union[str, Any]         =     v_groups
          lowercase__:							str         =     post_attention_groups
          lowercase__:							Optional[int]         =     intermediate_groups
          lowercase__:							List[Any]         =     output_groups







class 						_a  (		lowercase_       ):





   '''simple docstring'''






   @property
   def __lowercase							(       self)			->   Mapping[str, Mapping[int, str]]:




          '''simple docstring'''




          if self.task == "multiple-choice":
                 lowercase__:							Optional[int]         =     {0: "batch", 1: "choice", 2: "sequence"}
          else:
                 lowercase__:							Optional[Any]         =     {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 							A(    snake_case_						):
       """simple docstring"""





       lowercase__:							Dict         =     quantum_engine.allocate_qubit()
       H | qubit
       Measure | qubit
       return int(snake_case_						)


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

  # initialises a new quantum backend
  UpperCamelCase					=     projectq.MainEngine()

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

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

  print("""Random numbers""", random_numbers_list)






