



'''simple docstring'''


from typing import List

import numpy as np







def 				__lowerCamelCase				(   A__    )       ->	int:






   """simple docstring"""
   UpperCamelCase					=  {key: len(A__    ) for key, value in gen_kwargs.items() if isinstance(A__    ,						A__    )}
   if len(set(lists_lengths.values()    )    ) > 1:
      raise RuntimeError(
          (
              'Sharding is ambiguous for this dataset: '
              + 'we found several data sources lists of different lengths, and we don\'t know over which list we should parallelize:\n'
              + '\n'.join(F"""\t- key {key} has length {length}""" for key, length in lists_lengths.items()    )
              + '\nTo fix this, check the \'gen_kwargs\' and make sure to use lists only for data sources, '
              + 'and use tuples otherwise. In the end there should only be one single list, or several lists with the same length.'
          )    )
   UpperCamelCase					=  max(lists_lengths.values()    ,						default=0    )
   return max(1    ,						A__    )







def 				__lowerCamelCase				(   A__    ,						A__    )       ->	List[range]:






   """simple docstring"""
   UpperCamelCase					=  []
   for group_idx in range(A__    ):
      UpperCamelCase					=  num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
      if num_shards_to_add == 0:
         break
      UpperCamelCase					=  shards_indices_per_group[-1].stop if shards_indices_per_group else 0
      UpperCamelCase					=  range(A__    ,						start + num_shards_to_add    )
      shards_indices_per_group.append(A__    )
   return shards_indices_per_group







def 				__lowerCamelCase				(   A__    ,						A__    )       ->	List[dict]:






   """simple docstring"""
   UpperCamelCase					=  _number_of_shards_in_gen_kwargs(A__    )
   if num_shards == 1:
      return [dict(A__    )]
   else:
      UpperCamelCase					=  _distribute_shards(num_shards=A__    ,						max_num_jobs=A__    )
      return [
          {
              key: [value[shard_idx] for shard_idx in shard_indices_per_group[group_idx]]
              if isinstance(A__    ,						A__    )
              else value
              for key, value in gen_kwargs.items()
          }
          for group_idx in range(len(A__    )    )
      ]







def 				__lowerCamelCase				(   A__    )       ->	dict:






   """simple docstring"""
   return {
       key: [value for gen_kwargs in gen_kwargs_list for value in gen_kwargs[key]]
       if isinstance(gen_kwargs_list[0][key]    ,						A__    )
       else gen_kwargs_list[0][key]
       for key in gen_kwargs_list[0]
   }







def 				__lowerCamelCase				(   A__    ,						A__    )       ->	dict:






   """simple docstring"""
   UpperCamelCase					=  {len(A__    ) for value in gen_kwargs.values() if isinstance(A__    ,						A__    )}
   UpperCamelCase					=  {}
   for size in list_sizes:
      UpperCamelCase					=  list(range(A__    )    )
      rng.shuffle(indices_per_size[size]    )
   # Now let's copy the gen_kwargs and shuffle the lists based on their sizes
   UpperCamelCase					=  dict(A__    )
   for key, value in shuffled_kwargs.items():
      if isinstance(A__    ,						A__    ):
         UpperCamelCase					=  [value[i] for i in indices_per_size[len(A__    )]]
   return shuffled_kwargs








"""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_ =			{
    """roberta-base""": """https://huggingface.co/roberta-base/resolve/main/config.json""",
    """roberta-large""": """https://huggingface.co/roberta-large/resolve/main/config.json""",
    """roberta-large-mnli""": """https://huggingface.co/roberta-large-mnli/resolve/main/config.json""",
    """distilroberta-base""": """https://huggingface.co/distilroberta-base/resolve/main/config.json""",
    """roberta-base-openai-detector""": """https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json""",
    """roberta-large-openai-detector""": """https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json""",
}






class 		__snake_case					(		SCREAMING_SNAKE_CASE__     ):





 """simple docstring"""


 _lowerCamelCase        =     """roberta"""

 def __init__(    self					,						__lowerCamelCase=5_0265					,						__lowerCamelCase=768					,						__lowerCamelCase=12					,						__lowerCamelCase=12					,						__lowerCamelCase=3072					,						__lowerCamelCase="gelu"					,						__lowerCamelCase=0.1					,						__lowerCamelCase=0.1					,						__lowerCamelCase=512					,						__lowerCamelCase=2					,						__lowerCamelCase=0.0_2					,						__lowerCamelCase=1e-1_2					,						__lowerCamelCase=1					,						__lowerCamelCase=0					,						__lowerCamelCase=2					,						__lowerCamelCase="absolute"					,						__lowerCamelCase=True					,						__lowerCamelCase=None					,						**__lowerCamelCase					,						):



     '''simple docstring'''




     super().__init__(pad_token_id=__lowerCamelCase					,						bos_token_id=__lowerCamelCase					,						eos_token_id=__lowerCamelCase					,						**__lowerCamelCase  )

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






class 		__snake_case					(		SCREAMING_SNAKE_CASE__     ):





 """simple docstring"""


 @property
 def 					UpperCamelCase__(    self  ):



     '''simple docstring'''




     if self.task == "multiple-choice":
         __A    :						List[Any]						 =     {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
     else:
         __A    :						Optional[Any]						 =     {0: '''batch''', 1: '''sequence'''}
     return OrderedDict(
         [
             ('''input_ids''', dynamic_axis),
             ('''attention_mask''', dynamic_axis),
         ]  )




