






'''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							={
    "facebook/data2vec-text-base": "https://huggingface.co/data2vec/resolve/main/config.json",
}




class     A    (      SCREAMING_SNAKE_CASE__						):






 """simple docstring"""





 __a			:						Optional[Any]      =						'''data2vec-text'''
 def __init__(							self			,     __lowerCAmelCase=3_05_22			,     __lowerCAmelCase=7_68			,     __lowerCAmelCase=12			,     __lowerCAmelCase=12			,     __lowerCAmelCase=30_72			,     __lowerCAmelCase="gelu"			,     __lowerCAmelCase=0.1			,     __lowerCAmelCase=0.1			,     __lowerCAmelCase=5_12			,     __lowerCAmelCase=2			,     __lowerCAmelCase=0.02			,     __lowerCAmelCase=1E-12			,     __lowerCAmelCase=1			,     __lowerCAmelCase=0			,     __lowerCAmelCase=2			,     __lowerCAmelCase="absolute"			,     __lowerCAmelCase=True			,     __lowerCAmelCase=None			,     **__lowerCAmelCase			,     ):
       super().__init__(pad_token_id=__lowerCAmelCase			,     bos_token_id=__lowerCAmelCase			,     eos_token_id=__lowerCAmelCase			,     **__lowerCAmelCase )

       UpperCamelCase_				:   Tuple					    =							vocab_size
       UpperCamelCase_				:   List[Any]					    =							hidden_size
       UpperCamelCase_				:   Dict					    =							num_hidden_layers
       UpperCamelCase_				:   Tuple					    =							num_attention_heads
       UpperCamelCase_				:   Dict					    =							hidden_act
       UpperCamelCase_				:   Optional[Any]					    =							intermediate_size
       UpperCamelCase_				:   Dict					    =							hidden_dropout_prob
       UpperCamelCase_				:   List[str]					    =							attention_probs_dropout_prob
       UpperCamelCase_				:   Union[str, Any]					    =							max_position_embeddings
       UpperCamelCase_				:   List[Any]					    =							type_vocab_size
       UpperCamelCase_				:   Tuple					    =							initializer_range
       UpperCamelCase_				:   Tuple					    =							layer_norm_eps
       UpperCamelCase_				:   Any					    =							position_embedding_type
       UpperCamelCase_				:   str					    =							use_cache
       UpperCamelCase_				:   Union[str, Any]					    =							classifier_dropout






class     A    (      SCREAMING_SNAKE_CASE__						):






 """simple docstring"""





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














'''simple docstring'''

from itertools import count



def 							snake_case					(     a_							: int = 50	)       ->				int:





      """simple docstring"""

      UpperCamelCase_				:   int					    =							[1] * min_block_length

      for n in count(a_	):
            fill_count_functions.append(1	)

            for block_length in range(a_    ,   n + 1	):
                  for block_start in range(n - block_length	):
                        fill_count_functions[n] += fill_count_functions[
                            n - block_start - block_length - 1
                        ]

                  fill_count_functions[n] += 1

            if fill_count_functions[n] > 1_000_000:
                  break

      return n


if __name__ == "__main__":
      print(f"{solution() = }")






