code: 
  '''simple docstring'''
  
  
  
  from collections import OrderedDict
  from typing import Mapping
  
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfig
  from ...utils import logging
  
  
  __A          =    logging.get_logger(__name__)
  
  __A          =    {
      "mnaylor/mega-base-wikitext": "https://huggingface.co/mnaylor/mega-base-wikitext/resolve/main/config.json",
  }
  
  
  
  
  
  class 			A							(       __UpperCAmelCase				):
  			lowerCamelCase							:							List[str]       		=  """mega"""
  
  
  
  			def __init__(					self       ,     lowerCamelCase__=30_522       ,     lowerCamelCase__=128       ,     lowerCamelCase__=4       ,     lowerCamelCase__=256       ,     lowerCamelCase__=16       ,     lowerCamelCase__=True       ,     lowerCamelCase__=64       ,     lowerCamelCase__=False       ,     lowerCamelCase__=-1       ,     lowerCamelCase__=None       ,     lowerCamelCase__=True       ,     lowerCamelCase__="scalenorm"       ,     lowerCamelCase__=True       ,     lowerCamelCase__="silu"       ,     lowerCamelCase__="softmax"       ,     lowerCamelCase__=0.1       ,     lowerCamelCase__=0.1       ,     lowerCamelCase__=0.1       ,     lowerCamelCase__=False       ,     lowerCamelCase__=True       ,     lowerCamelCase__=256       ,     lowerCamelCase__=True       ,     lowerCamelCase__=0.1       ,     lowerCamelCase__=2_048       ,     lowerCamelCase__=False       ,     lowerCamelCase__=2       ,     lowerCamelCase__=0.02       ,     lowerCamelCase__=0.2       ,     lowerCamelCase__=0.02       ,     lowerCamelCase__=1.0       ,     lowerCamelCase__=1       ,     lowerCamelCase__=0       ,     lowerCamelCase__=2       ,     lowerCamelCase__="rotary"       ,     lowerCamelCase__=None       ,     lowerCamelCase__=True       ,     lowerCamelCase__=True       ,     **lowerCamelCase__       ,     )			->		Optional[Any]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									super().__init__(pad_token_id=lowerCamelCase__       ,     bos_token_id=lowerCamelCase__       ,     eos_token_id=lowerCamelCase__       ,     **lowerCamelCase__       )
  
  									lowercase__							 =       vocab_size
  									lowercase__							 =       hidden_size
  									lowercase__							 =       num_hidden_layers
  									lowercase__							 =       activation
  									lowercase__							 =       attention_activation
  									lowercase__							 =       intermediate_size
  									lowercase__							 =       ema_projection_size
  									lowercase__							 =       bidirectional
  									lowercase__							 =       shared_representation_size
  									lowercase__							 =       use_chunking
  									lowercase__							 =       chunk_size
  									lowercase__							 =       truncation
  									lowercase__							 =       normalize_before_mega
  									lowercase__							 =       normalization_type
  									lowercase__							 =       norm_affine
  									lowercase__							 =       dropout_prob
  									lowercase__							 =       hidden_dropout_prob
  									lowercase__							 =       attention_probs_dropout_prob
  									lowercase__							 =       use_feature_dropout
  									lowercase__							 =       use_normalized_ffn
  									lowercase__							 =       nffn_hidden_size
  									lowercase__							 =       normalize_before_ffn
  									lowercase__							 =       nffn_activation_dropout_prob
  									lowercase__							 =       max_positions
  									lowercase__							 =       add_token_type_embeddings
  									lowercase__							 =       type_vocab_size
  									lowercase__							 =       initializer_range
  									lowercase__							 =       ema_delta_alpha_range
  									lowercase__							 =       ema_beta_range
  									lowercase__							 =       ema_gamma_omega_range
  									lowercase__							 =       relative_positional_bias
  									lowercase__							 =       use_cache
  									lowercase__							 =       classifier_dropout
  									lowercase__							 =       add_lm_hidden_dense_layer
  									lowercase__							 =       1  # not used but required by Hugging Face
  
  
  
  
  
  class 			A							(       __UpperCAmelCase				):
  
  
  
  			@property
  			def A__ (					self       )			->		Mapping[str, Mapping[int, str]]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									if self.task == "multiple-choice":
  															lowercase__							 =       {0: """batch""", 1: """choice""", 2: """sequence"""}
  									else:
  															lowercase__							 =       {0: """batch""", 1: """sequence"""}
  									return OrderedDict(
  									    [
  									        ("""input_ids""", dynamic_axis),
  									        ("""attention_mask""", dynamic_axis),
  									    ]       )
  
  
  
  
  
  
  
style_context: 
  '''simple docstring'''
  
  
  
  import bisect
  import copy
  from collections import defaultdict
  
  import numpy as np
  from torch.utils.data import BatchSampler, Sampler
  
  from utils import logger
  
  
  
  
  
  def  _A		(							lowercase__		,		lowercase__	):
  						lowercase__							 =       copy.deepcopy(lowercase__	)
  						lowercase__							 =       sorted(lowercase__	)
  						lowercase__							 =       [bisect.bisect_right(lowercase__		,		lowercase__	) for y in x]
  						return quantized
  
  
  
  
  
  def  _A		(							lowercase__		,		lowercase__=0	):
  						lowercase__							 =       np.arange(start=3		,		stop=lowercase__		,		step=4	).tolist() if k > 0 else [10]
  						lowercase__							 =       _quantize(lowercase__		,		lowercase__	)
  						# count number of elements per group
  						lowercase__							 =       np.unique(lowercase__		,		return_counts=lowercase__	)[1]
  						lowercase__							 =       [0] + bins + [np.inf]
  						logger.info("""Using {} as bins for aspect lengths quantization""".format(lowercase__	)	)
  						logger.info("""Count of instances per bin: {}""".format(lowercase__	)	)
  						return groups
  
  
  
  
  
  
  
  class 			A							(       __UpperCAmelCase				):
  
  
  
  			def __init__(					self       ,     lowerCamelCase__       ,     lowerCamelCase__       ,     lowerCamelCase__       )			->		Optional[int]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									if not isinstance(lowerCamelCase__       ,     lowerCamelCase__       ):
  															raise ValueError(
  															    """sampler should be an instance of torch.utils.data.Sampler, but got sampler={}""".format(lowerCamelCase__       )       )
  									lowercase__							 =       sampler
  									lowercase__							 =       group_ids
  									lowercase__							 =       batch_size
  
  
  
  			def __iter__(					self       )			->		List[str]:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									lowercase__							 =       defaultdict(lowerCamelCase__       )
  									lowercase__							 =       defaultdict(lowerCamelCase__       )
  
  									lowercase__							 =       0
  									for idx in self.sampler:
  															lowercase__							 =       self.group_ids[idx]
  															buffer_per_group[group_id].append(lowerCamelCase__       )
  															samples_per_group[group_id].append(lowerCamelCase__       )
  															if len(buffer_per_group[group_id]       ) == self.batch_size:
  																					yield buffer_per_group[group_id]  # TODO
  																					num_batches += 1
  																					del buffer_per_group[group_id]
  															assert len(buffer_per_group[group_id]       ) < self.batch_size
  
  									# now we have run out of elements that satisfy
  									# the group criteria, let's return the remaining
  									# elements so that the size of the sampler is
  									# deterministic
  									lowercase__							 =       len(self       )
  									lowercase__							 =       expected_num_batches - num_batches
  									if num_remaining > 0:
  															# for the remaining batches, group the batches by similar lengths
  															lowercase__							 =       []
  															for group_id, idxs in sorted(buffer_per_group.items()       ,     key=lambda lowerCamelCase__       : x[0]       ):
  																					batch_idx.extend(lowerCamelCase__       )
  																					if len(lowerCamelCase__       ) >= self.batch_size:
  																											yield batch_idx[: self.batch_size]
  																											lowercase__							 =       batch_idx[self.batch_size :]
  																											num_remaining -= 1
  															if len(lowerCamelCase__       ) > 0:
  																					yield batch_idx
  																					num_remaining -= 1
  									assert num_remaining == 0
  
  
  
  			def __len__(					self       )			->		Any:
  
  
  
  
  									'''simple docstring'''
  
  
  
  									return (len(self.sampler       ) + self.batch_size - 1) // self.batch_size
  
  
  
  
  
  
  