


from typing import List

import numpy as np





def 						_lowerCAmelCase							(     _lowerCAmelCase			)    ->						int:


						'''simple docstring'''






						__snake_case				=		{key: len(_lowerCAmelCase			) for key, value in gen_kwargs.items() if isinstance(_lowerCAmelCase   ,						_lowerCAmelCase			)}
						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   ,						_lowerCAmelCase			)





def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase			)    ->						List[range]:


						'''simple docstring'''






						__snake_case				=		[]
						for group_idx in range(_lowerCAmelCase			):
												__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(_lowerCAmelCase   ,						start + num_shards_to_add			)
												shards_indices_per_group.append(_lowerCAmelCase			)
						return shards_indices_per_group





def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase			)    ->						List[dict]:


						'''simple docstring'''






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





def 						_lowerCAmelCase							(     _lowerCAmelCase			)    ->						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]   ,						_lowerCAmelCase			)
						    else gen_kwargs_list[0][key]
						    for key in gen_kwargs_list[0]
						}





def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase			)    ->						dict:


						'''simple docstring'''






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









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A				:       List[str]      =  {
    'configuration_time_series_transformer': [
        'TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'TimeSeriesTransformerConfig',
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A				:       str      =  [
	    'TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'TimeSeriesTransformerForPrediction',
	    'TimeSeriesTransformerModel',
	    'TimeSeriesTransformerPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_time_series_transformer import (
	    TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    TimeSeriesTransformerConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_time_series_transformer import (
		    TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TimeSeriesTransformerForPrediction,
		    TimeSeriesTransformerModel,
		    TimeSeriesTransformerPreTrainedModel,
		)

else:
	import sys

	A				:       str      =  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





