

from typing import List

import numpy as np





def      A__					(			__A  :						Optional[Any]			)					->Optional[Any]:
      __A    ={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.'''
                )			)
      __A    =max(lists_lengths.values()     ,				default=0			)
      return max(1     ,				__A			)





def      A__					(			__A  :						List[str]     ,				__A  :						List[str]			)					->Union[str, Any]:
      __A    =[]
      for group_idx in range(__A			):
            __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(__A     ,				start + num_shards_to_add			)
            shards_indices_per_group.append(__A			)
      return shards_indices_per_group





def      A__					(			__A  :						Optional[Any]     ,				__A  :						Any			)					->List[str]:
      __A    =_number_of_shards_in_gen_kwargs(__A			)
      if num_shards == 1:
            return [dict(__A			)]
      else:
            __A    =_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      A__					(			__A  :						Dict			)					->Dict:
      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      A__					(			__A  :						Optional[Any]     ,				__A  :						int			)					->Union[str, Any]:
      __A    ={len(__A			) for value in gen_kwargs.values() if isinstance(__A     ,				__A			)}
      __A    ={}
      for size in list_sizes:
            __A    =list(range(__A			)			)
            rng.shuffle(indices_per_size[size]			)
      # Now let's copy the gen_kwargs and shuffle the lists based on their sizes
      __A    =dict(__A			)
      for key, value in shuffled_kwargs.items():
            if isinstance(__A     ,				__A			):
                  __A    =[value[i] for i in indices_per_size[len(__A			)]]
      return shuffled_kwargs








'''simple docstring'''


from typing import TYPE_CHECKING

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


_lowercase  =			{
    """configuration_biogpt""": ["""BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BioGptConfig"""],
    """tokenization_biogpt""": ["""BioGptTokenizer"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase  =			[
	    """BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """BioGptForCausalLM""",
	    """BioGptForTokenClassification""",
	    """BioGptForSequenceClassification""",
	    """BioGptModel""",
	    """BioGptPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
	from .tokenization_biogpt import BioGptTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_biogpt import (
		    BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    BioGptForCausalLM,
		    BioGptForSequenceClassification,
		    BioGptForTokenClassification,
		    BioGptModel,
		    BioGptPreTrainedModel,
		)


else:
	import sys

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

