


"""simple docstring"""





from typing import List

import numpy as np




def 	UpperCAmelCase     (		A	:						Dict       ):

		'''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 	UpperCAmelCase     (		A	:						str     ,				A	:						Optional[int]       ):

		'''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 	UpperCAmelCase     (		A	:						Tuple     ,				A	:						List[str]       ):

		'''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 	UpperCAmelCase     (		A	:						Tuple       ):

		'''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 	UpperCAmelCase     (		A	:						Dict     ,				A	:						List[str]       ):

		'''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 typing import TYPE_CHECKING

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


lowercase  							=					{
    '''configuration_mvp''': ['''MVP_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MvpConfig''', '''MvpOnnxConfig'''],
    '''tokenization_mvp''': ['''MvpTokenizer'''],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase  							=					['''MvpTokenizerFast''']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase  							=					[
					    '''MVP_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''MvpForCausalLM''',
					    '''MvpForConditionalGeneration''',
					    '''MvpForQuestionAnswering''',
					    '''MvpForSequenceClassification''',
					    '''MvpModel''',
					    '''MvpPreTrainedModel''',
					]

if TYPE_CHECKING:
					from .configuration_mvp import MVP_PRETRAINED_CONFIG_ARCHIVE_MAP, MvpConfig, MvpOnnxConfig
					from .tokenization_mvp import MvpTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_mvp_fast import MvpTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_mvp import (
										    MVP_PRETRAINED_MODEL_ARCHIVE_LIST,
										    MvpForCausalLM,
										    MvpForConditionalGeneration,
										    MvpForQuestionAnswering,
										    MvpForSequenceClassification,
										    MvpModel,
										    MvpPreTrainedModel,
										)

else:
					import sys

					lowercase  							=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


