


'''simple docstring'''






from typing import List

import numpy as np






def 		_A			(lowerCAmelCase__					:dict						)				->					int:




     '''simple docstring'''



     _a      =							{key: len(_lowercase						) for key, value in gen_kwargs.items() if isinstance(_lowercase			,  _lowercase						)}
     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.'
              )						)
     _a      =							max(lists_lengths.values()			,  default=0						)
     return max(1			,  _lowercase						)






def 		_A			(lowerCAmelCase__					:int			,  lowerCAmelCase__					:int						)				->					List[range]:




     '''simple docstring'''



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






def 		_A			(lowerCAmelCase__					:dict			,  lowerCAmelCase__					:int						)				->					List[dict]:




     '''simple docstring'''



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






def 		_A			(lowerCAmelCase__					:List[dict]						)				->					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]			,  _lowercase						)
         else gen_kwargs_list[0][key]
         for key in gen_kwargs_list[0]
     }






def 		_A			(lowerCAmelCase__					:np.random.Generator			,  lowerCAmelCase__					:dict						)				->					dict:




     '''simple docstring'''



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





'''simple docstring'''






from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


a_   :      List[Any]				    =      {"configuration_gpt_neox": ["GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoXConfig"]}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :      List[str]				    =      ["GPTNeoXTokenizerFast"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :      List[Any]				    =      [
     "GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST",
     "GPTNeoXForCausalLM",
     "GPTNeoXForQuestionAnswering",
     "GPTNeoXForSequenceClassification",
     "GPTNeoXForTokenClassification",
     "GPTNeoXLayer",
     "GPTNeoXModel",
     "GPTNeoXPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_gpt_neox import (
      GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
      GPTNeoXForCausalLM,
      GPTNeoXForQuestionAnswering,
      GPTNeoXForSequenceClassification,
      GPTNeoXForTokenClassification,
      GPTNeoXLayer,
      GPTNeoXModel,
      GPTNeoXPreTrainedModel,
  )


else:
 import sys

 a_   :      Optional[Any]				    =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

