
'''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),
									    ]       )








'''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






