

from typing import List

import numpy as np





def a(lowercase__	):





	'''simple docstring'''
	snake_case_      		= {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.'
		    )	)
	snake_case_      		= max(lists_lengths.values()			, default=0	)
	return max(1			, _A	)





def a(lowercase__			, lowercase__	):





	'''simple docstring'''
	snake_case_      		= []
	for group_idx in range(_A	):
		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(_A			, start + num_shards_to_add	)
		shards_indices_per_group.append(_A	)
	return shards_indices_per_group





def a(lowercase__			, lowercase__	):





	'''simple docstring'''
	snake_case_      		= _number_of_shards_in_gen_kwargs(_A	)
	if num_shards == 1:
		return [dict(_A	)]
	else:
		snake_case_      		= _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(lowercase__	):





	'''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 a(lowercase__			, lowercase__	):





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



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


A											=     {
    'configuration_perceiver': ['PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PerceiverConfig', 'PerceiverOnnxConfig'],
    'tokenization_perceiver': ['PerceiverTokenizer'],
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A											=     ['PerceiverFeatureExtractor']
		A											=     ['PerceiverImageProcessor']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A											=     [
		    'PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'PerceiverForImageClassificationConvProcessing',
		    'PerceiverForImageClassificationFourier',
		    'PerceiverForImageClassificationLearned',
		    'PerceiverForMaskedLM',
		    'PerceiverForMultimodalAutoencoding',
		    'PerceiverForOpticalFlow',
		    'PerceiverForSequenceClassification',
		    'PerceiverLayer',
		    'PerceiverModel',
		    'PerceiverPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_perceiver import PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP, PerceiverConfig, PerceiverOnnxConfig
		from .tokenization_perceiver import PerceiverTokenizer

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_perceiver import PerceiverFeatureExtractor
				from .image_processing_perceiver import PerceiverImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_perceiver import (
				    PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    PerceiverForImageClassificationConvProcessing,
				    PerceiverForImageClassificationFourier,
				    PerceiverForImageClassificationLearned,
				    PerceiverForMaskedLM,
				    PerceiverForMultimodalAutoencoding,
				    PerceiverForOpticalFlow,
				    PerceiverForSequenceClassification,
				    PerceiverLayer,
				    PerceiverModel,
				    PerceiverPreTrainedModel,
				)

else:
		import sys

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