






from typing import TYPE_CHECKING

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


__A      =				{
    'configuration_data2vec_audio': ['DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Data2VecAudioConfig'],
    'configuration_data2vec_text': [
        'DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Data2VecTextConfig',
        'Data2VecTextOnnxConfig',
    ],
    'configuration_data2vec_vision': [
        'DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'Data2VecVisionConfig',
        'Data2VecVisionOnnxConfig',
    ],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__A      =				[
				    'DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'Data2VecAudioForAudioFrameClassification',
				    'Data2VecAudioForCTC',
				    'Data2VecAudioForSequenceClassification',
				    'Data2VecAudioForXVector',
				    'Data2VecAudioModel',
				    'Data2VecAudioPreTrainedModel',
				]
				__A      =				[
				    'DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'Data2VecTextForCausalLM',
				    'Data2VecTextForMaskedLM',
				    'Data2VecTextForMultipleChoice',
				    'Data2VecTextForQuestionAnswering',
				    'Data2VecTextForSequenceClassification',
				    'Data2VecTextForTokenClassification',
				    'Data2VecTextModel',
				    'Data2VecTextPreTrainedModel',
				]
				__A      =				[
				    'DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'Data2VecVisionForImageClassification',
				    'Data2VecVisionForMaskedImageModeling',
				    'Data2VecVisionForSemanticSegmentation',
				    'Data2VecVisionModel',
				    'Data2VecVisionPreTrainedModel',
				]

if is_tf_available():
				__A      =				[
				    'TFData2VecVisionForImageClassification',
				    'TFData2VecVisionForSemanticSegmentation',
				    'TFData2VecVisionModel',
				    'TFData2VecVisionPreTrainedModel',
				]

if TYPE_CHECKING:
				from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
				from .configuration_dataavec_text import (
				    DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    DataaVecTextConfig,
				    DataaVecTextOnnxConfig,
				)
				from .configuration_dataavec_vision import (
				    DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    DataaVecVisionConfig,
				    DataaVecVisionOnnxConfig,
				)

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_dataavec_audio import (
								    DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
								    DataaVecAudioForAudioFrameClassification,
								    DataaVecAudioForCTC,
								    DataaVecAudioForSequenceClassification,
								    DataaVecAudioForXVector,
								    DataaVecAudioModel,
								    DataaVecAudioPreTrainedModel,
								)
								from .modeling_dataavec_text import (
								    DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    DataaVecTextForCausalLM,
								    DataaVecTextForMaskedLM,
								    DataaVecTextForMultipleChoice,
								    DataaVecTextForQuestionAnswering,
								    DataaVecTextForSequenceClassification,
								    DataaVecTextForTokenClassification,
								    DataaVecTextModel,
								    DataaVecTextPreTrainedModel,
								)
								from .modeling_dataavec_vision import (
								    DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
								    DataaVecVisionForImageClassification,
								    DataaVecVisionForMaskedImageModeling,
								    DataaVecVisionForSemanticSegmentation,
								    DataaVecVisionModel,
								    DataaVecVisionPreTrainedModel,
								)
				if is_tf_available():
								from .modeling_tf_dataavec_vision import (
								    TFDataaVecVisionForImageClassification,
								    TFDataaVecVisionForSemanticSegmentation,
								    TFDataaVecVisionModel,
								    TFDataaVecVisionPreTrainedModel,
								)

else:
				import sys

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











def      __A					(     _lowercase					,			_lowercase ):

					'''simple docstring'''







					_A								= word.split()

					def justify(_lowercase					,			_lowercase					,			_lowercase ) -> str:
										_A								= max_width - width
										_A								= len(_lowercase )
										if len(_lowercase ) == 1:
															# if there is only word in line
															# just insert overall_spaces_count for the remainder of line
															return line[0] + " " * overall_spaces_count
										else:
															_A								= words_count - 1
															# num_spaces_between_words_list[i] : tells you to insert
															# num_spaces_between_words_list[i] spaces
															# after word on line[i]
															_A								= spaces_to_insert_between_words * [
															    overall_spaces_count // spaces_to_insert_between_words
															]
															_A								= (
															    overall_spaces_count % spaces_to_insert_between_words
															)
															# distribute spaces via round robin to the left words
															for i in range(_lowercase ):
																				num_spaces_between_words_list[i] += 1
															_A								= []
															for i in range(_lowercase ):
																				# add the word
																				aligned_words_list.append(line[i] )
																				# add the spaces to insert
																				aligned_words_list.append(num_spaces_between_words_list[i] * ''' ''' )
															# just add the last word to the sentence
															aligned_words_list.append(line[-1] )
															# join the aligned words list to form a justified line
															return "".join(_lowercase )

					_A								= []
					_A								= []
					_A								= 0
					for word in words:
										if width + len(_lowercase ) + len(_lowercase ) <= max_width:
															# keep adding words until we can fill out max_width
															# width = sum of length of all words (without overall_spaces_count)
															# len(word) = length of current word
															# len(line) = number of overall_spaces_count to insert between words
															line.append(_lowercase )
															width += len(_lowercase )
										else:
															# justify the line and add it to result
															answer.append(justify(_lowercase					,			_lowercase					,			_lowercase ) )
															# reset new line and new width
															_A							,_A								= [word], len(_lowercase )
					_A								= max_width - width - len(_lowercase )
					answer.append(''' '''.join(_lowercase ) + (remaining_spaces + 1) * ''' ''' )
					return answer


if __name__ == "__main__":
				from doctest import testmod

				testmod()



