





from collections import OrderedDict
from typing import Mapping

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


lowerCamelCase__				  =   logging.get_logger(__name__)

lowerCamelCase__				  =   {
    "junnyu/roformer_chinese_small": "https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json",
    "junnyu/roformer_chinese_base": "https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json",
    "junnyu/roformer_chinese_char_small": (
        "https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json"
    ),
    "junnyu/roformer_chinese_char_base": (
        "https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json"
    ),
    "junnyu/roformer_small_discriminator": (
        "https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json"
    ),
    "junnyu/roformer_small_generator": (
        "https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json"
    ),
    # See all RoFormer models at https://huggingface.co/models?filter=roformer
}



class      __SCREAMING_SNAKE_CASE    (						_UpperCamelCase   ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__							:Dict	     =					"roformer"

  def __init__(		self   :       Tuple					,   __a   :       List[str]=5_0000					,   __a   :       Union[str, Any]=None					,   __a   :       Tuple=768					,   __a   :       Union[str, Any]=12					,   __a   :       Optional[Any]=12					,   __a   :       List[Any]=3072					,   __a   :       Dict="gelu"					,   __a   :       Optional[Any]=0.1					,   __a   :       Dict=0.1					,   __a   :       int=1536					,   __a   :       Union[str, Any]=2					,   __a   :       Optional[int]=0.02					,   __a   :       int=1e-1_2					,   __a   :       List[str]=0					,   __a   :       Any=False					,   __a   :       str=True					,   **__a   :       Union[str, Any]					,   )		->				Optional[int]:
        super().__init__(pad_token_id=__a					,   **__a	)

        _UpperCamelCase      :	Any						=       vocab_size
        _UpperCamelCase      :	Optional[Any]						=       hidden_size if embedding_size is None else embedding_size
        _UpperCamelCase      :	str						=       hidden_size
        _UpperCamelCase      :	Optional[Any]						=       num_hidden_layers
        _UpperCamelCase      :	List[str]						=       num_attention_heads
        _UpperCamelCase      :	Optional[Any]						=       hidden_act
        _UpperCamelCase      :	Union[str, Any]						=       intermediate_size
        _UpperCamelCase      :	List[str]						=       hidden_dropout_prob
        _UpperCamelCase      :	Tuple						=       attention_probs_dropout_prob
        _UpperCamelCase      :	Any						=       max_position_embeddings
        _UpperCamelCase      :	Optional[Any]						=       type_vocab_size
        _UpperCamelCase      :	List[str]						=       initializer_range
        _UpperCamelCase      :	int						=       layer_norm_eps
        _UpperCamelCase      :	Optional[int]						=       rotary_value
        _UpperCamelCase      :	int						=       use_cache





class      __SCREAMING_SNAKE_CASE    (						_UpperCamelCase   ):







  '''simple docstring'''


  @property
  def    __SCREAMING_SNAKE_CASE				(		self   :       str	)		->				Mapping[str, Mapping[int, str]]:
        if self.task == "multiple-choice":
              _UpperCamelCase      :	List[Any]						=       {0: "batch", 1: "choice", 2: "sequence"}
        else:
              _UpperCamelCase      :	int						=       {0: "batch", 1: "sequence"}
        _UpperCamelCase      :	str						=       {0: "batch", 1: "sequence"}
        return OrderedDict(
            [
                ("input_ids", dynamic_axis),
                ("attention_mask", dynamic_axis),
                ("token_type_ids", dynamic_axis),
            ]	)







"""simple docstring"""


def 					lowercase__    (					lowercase_		,lowercase_			)					->		None:




      """simple docstring"""

      _UpperCamelCase      :	List[Any]						=       len(lowercase_			)
      print("The following activities are selected:"			)

      # The first activity is always selected
      _UpperCamelCase      :	List[Any]						=       0
      print(lowercase_		,end=","			)

      # Consider rest of the activities
      for j in range(lowercase_			):
            # If this activity has start time greater than
            # or equal to the finish time of previously
            # selected activity, then select it
            if start[j] >= finish[i]:
                  print(lowercase_		,end=","			)
                  _UpperCamelCase      :	Optional[Any]						=       j


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  lowerCamelCase__				  =   [1, 3, 0, 5, 8, 5]
  lowerCamelCase__				  =   [2, 4, 6, 7, 9, 9]
  print_max_activities(start, finish)
