


import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding


class 			_a				(     UpperCamelCase__     ):
							_lowercase      :     str     =					['''image_processor''', '''tokenizer''']
							_lowercase      :     Any     =					'''CLIPImageProcessor'''
							_lowercase      :     str     =					('''XLMRobertaTokenizer''', '''XLMRobertaTokenizerFast''')

							def __init__(				self:      str       ,		UpperCamelCase_:      Any=None       ,		UpperCamelCase_:      Optional[int]=None       ,		**UpperCamelCase_:      List[str]				)     ->  Dict:





									"""simple docstring"""







									lowercase__				= None
									if "feature_extractor" in kwargs:
											warnings.warn(
											    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
											    ''' instead.'''       ,		UpperCamelCase_       ,		)
											lowercase__				= kwargs.pop('''feature_extractor'''				)

									lowercase__				= image_processor if image_processor is not None else feature_extractor
									if image_processor is None:
											raise ValueError('''You need to specify an `image_processor`.'''				)
									if tokenizer is None:
											raise ValueError('''You need to specify a `tokenizer`.'''				)

									super().__init__(UpperCamelCase_       ,		UpperCamelCase_				)

							def __call__(				self:      int       ,		UpperCamelCase_:      Optional[Any]=None       ,		UpperCamelCase_:      Tuple=None       ,		UpperCamelCase_:      Tuple=None       ,		**UpperCamelCase_:      Union[str, Any]				)     ->  Tuple:





									"""simple docstring"""







									if text is None and images is None:
											raise ValueError('''You have to specify either text or images. Both cannot be none.'''				)

									if text is not None:
											lowercase__				= self.tokenizer(UpperCamelCase_       ,		return_tensors=UpperCamelCase_       ,		**UpperCamelCase_				)

									if images is not None:
											lowercase__				= self.image_processor(UpperCamelCase_       ,		return_tensors=UpperCamelCase_       ,		**UpperCamelCase_				)

									if text is not None and images is not None:
											lowercase__				= image_features.pixel_values
											return encoding
									elif text is not None:
											return encoding
									else:
											return BatchEncoding(data=dict(**UpperCamelCase_				)       ,		tensor_type=UpperCamelCase_				)

							def  lowerCamelCase_					(				self:      Any       ,		*UpperCamelCase_:      Optional[Any]       ,		**UpperCamelCase_:      int				)     ->  Union[str, Any]:





									"""simple docstring"""







									return self.tokenizer.batch_decode(*UpperCamelCase_       ,		**UpperCamelCase_				)

							def  lowerCamelCase_					(				self:      int       ,		*UpperCamelCase_:      str       ,		**UpperCamelCase_:      List[Any]				)     ->  Optional[Any]:





									"""simple docstring"""







									return self.tokenizer.decode(*UpperCamelCase_       ,		**UpperCamelCase_				)


							@property
							def  lowerCamelCase_					(				self:      Optional[int]				)     ->  Optional[Any]:





									"""simple docstring"""







									lowercase__				= self.tokenizer.model_input_names
									lowercase__				= self.image_processor.model_input_names
									return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)







'''simple docstring'''
from typing import List

import numpy as np






def A__       (	UpperCAmelCase_				):
						_UpperCamelCase   :						Any												=  {key: len(UpperCAmelCase_				) for key, value in gen_kwargs.items() if isinstance(UpperCAmelCase_    ,    UpperCAmelCase_				)}
						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   :						Tuple												=  max(lists_lengths.values()    ,    default=0				)
						return max(1    ,    UpperCAmelCase_				)






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_				):
						_UpperCamelCase   :						Dict												=  []
						for group_idx in range(UpperCAmelCase_				):
												_UpperCamelCase   :						int												=  num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
												if num_shards_to_add == 0:
																		break
												_UpperCamelCase   :						Tuple												=  shards_indices_per_group[-1].stop if shards_indices_per_group else 0
												_UpperCamelCase   :						Tuple												=  range(UpperCAmelCase_    ,    start + num_shards_to_add				)
												shards_indices_per_group.append(UpperCAmelCase_				)
						return shards_indices_per_group






def A__       (	UpperCAmelCase_    ,    UpperCAmelCase_				):
						_UpperCamelCase   :						Tuple												=  _number_of_shards_in_gen_kwargs(UpperCAmelCase_				)
						if num_shards == 1:
												return [dict(UpperCAmelCase_				)]
						else:
												_UpperCamelCase   :						str												=  _distribute_shards(num_shards=UpperCAmelCase_    ,    max_num_jobs=UpperCAmelCase_				)
												return [
												    {
												        key: [value[shard_idx] for shard_idx in shard_indices_per_group[group_idx]]
												        if isinstance(UpperCAmelCase_    ,    UpperCAmelCase_				)
												        else value
												        for key, value in gen_kwargs.items()
												    }
												    for group_idx in range(len(UpperCAmelCase_				)				)
												]






def A__       (	UpperCAmelCase_				):
						return {
						    key: [value for gen_kwargs in gen_kwargs_list for value in gen_kwargs[key]]
						    if isinstance(gen_kwargs_list[0][key]    ,    UpperCAmelCase_				)
						    else gen_kwargs_list[0][key]
						    for key in gen_kwargs_list[0]
						}






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


