"""simple docstring"""



from typing import List

import numpy as np





def  SCREAMING_SNAKE_CASE							(  snake_case):
     __snake_case				=				{key: len(UpperCamelCase__) for key, value in gen_kwargs.items() if isinstance(UpperCamelCase__,   UpperCamelCase__)}
     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.'''
              ))
     __snake_case				=				max(lists_lengths.values(),   default=0)
     return max(1,   UpperCamelCase__)





def  SCREAMING_SNAKE_CASE							(  snake_case,   snake_case):
     __snake_case				=				[]
     for group_idx in range(UpperCamelCase__):
          __snake_case				=				num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
          if num_shards_to_add == 0:
               break
          __snake_case				=				shards_indices_per_group[-1].stop if shards_indices_per_group else 0
          __snake_case				=				range(UpperCamelCase__,   start + num_shards_to_add)
          shards_indices_per_group.append(UpperCamelCase__)
     return shards_indices_per_group





def  SCREAMING_SNAKE_CASE							(  snake_case,   snake_case):
     __snake_case				=				_number_of_shards_in_gen_kwargs(UpperCamelCase__)
     if num_shards == 1:
          return [dict(UpperCamelCase__)]
     else:
          __snake_case				=				_distribute_shards(num_shards=UpperCamelCase__,   max_num_jobs=UpperCamelCase__)
          return [
              {
                  key: [value[shard_idx] for shard_idx in shard_indices_per_group[group_idx]]
                  if isinstance(UpperCamelCase__,   UpperCamelCase__)
                  else value
                  for key, value in gen_kwargs.items()
              }
              for group_idx in range(len(UpperCamelCase__))
          ]





def  SCREAMING_SNAKE_CASE							(  snake_case):
     return {
         key: [value for gen_kwargs in gen_kwargs_list for value in gen_kwargs[key]]
         if isinstance(gen_kwargs_list[0][key],   UpperCamelCase__)
         else gen_kwargs_list[0][key]
         for key in gen_kwargs_list[0]
     }





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

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase  ={
    "configuration_luke": ["LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP", "LukeConfig"],
    "tokenization_luke": ["LukeTokenizer"],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase  =[
        "LUKE_PRETRAINED_MODEL_ARCHIVE_LIST",
        "LukeForEntityClassification",
        "LukeForEntityPairClassification",
        "LukeForEntitySpanClassification",
        "LukeForMultipleChoice",
        "LukeForQuestionAnswering",
        "LukeForSequenceClassification",
        "LukeForTokenClassification",
        "LukeForMaskedLM",
        "LukeModel",
        "LukePreTrainedModel",
    ]


if TYPE_CHECKING:
    from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
    from .tokenization_luke import LukeTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_luke import (
            LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
            LukeForEntityClassification,
            LukeForEntityPairClassification,
            LukeForEntitySpanClassification,
            LukeForMaskedLM,
            LukeForMultipleChoice,
            LukeForQuestionAnswering,
            LukeForSequenceClassification,
            LukeForTokenClassification,
            LukeModel,
            LukePreTrainedModel,
        )

else:
    import sys

    lowerCamelCase  =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


