






'''simple docstring'''

from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE__     =       {
    'configuration_layoutlmv2': ['LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LayoutLMv2Config'],
    'processing_layoutlmv2': ['LayoutLMv2Processor'],
    'tokenization_layoutlmv2': ['LayoutLMv2Tokenizer'],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__     =       ['LayoutLMv2TokenizerFast']

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__     =       ['LayoutLMv2FeatureExtractor']
					SCREAMING_SNAKE_CASE__     =       ['LayoutLMv2ImageProcessor']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__     =       [
					    'LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'LayoutLMv2ForQuestionAnswering',
					    'LayoutLMv2ForSequenceClassification',
					    'LayoutLMv2ForTokenClassification',
					    'LayoutLMv2Layer',
					    'LayoutLMv2Model',
					    'LayoutLMv2PreTrainedModel',
					]

if TYPE_CHECKING:
					from .configuration_layoutlmva import LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMvaConfig
					from .processing_layoutlmva import LayoutLMvaProcessor
					from .tokenization_layoutlmva import LayoutLMvaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor, LayoutLMvaImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_layoutlmva import (
										    LAYOUTLMV2_PRETRAINED_MODEL_ARCHIVE_LIST,
										    LayoutLMvaForQuestionAnswering,
										    LayoutLMvaForSequenceClassification,
										    LayoutLMvaForTokenClassification,
										    LayoutLMvaLayer,
										    LayoutLMvaModel,
										    LayoutLMvaPreTrainedModel,
										)
else:
					import sys

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








'''simple docstring'''

def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase   )->			list:
      UpperCamelCase      = word.split()

      def justify(__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase   ) -> str:
            UpperCamelCase      = max_width - width
            UpperCamelCase      = len(__UpperCamelCase   )
            if len(__UpperCamelCase   ) == 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:
                  UpperCamelCase      = 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]
                  UpperCamelCase      = spaces_to_insert_between_words * [
                      overall_spaces_count // spaces_to_insert_between_words
                  ]
                  UpperCamelCase      = (
                      overall_spaces_count % spaces_to_insert_between_words
                  )
                  # distribute spaces via round robin to the left words
                  for i in range(__UpperCamelCase   ):
                        num_spaces_between_words_list[i] += 1
                  UpperCamelCase      = []
                  for i in range(__UpperCamelCase   ):
                        # 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(__UpperCamelCase   )

      UpperCamelCase      = []
      UpperCamelCase      = []
      UpperCamelCase      = 0
      for word in words:
            if width + len(__UpperCamelCase   ) + len(__UpperCamelCase   ) <= 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(__UpperCamelCase   )
                  width += len(__UpperCamelCase   )
            else:
                  # justify the line and add it to result
                  answer.append(justify(__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase   )   )
                  # reset new line and new width
                  UpperCamelCase  ,UpperCamelCase      = [word], len(__UpperCamelCase   )
      UpperCamelCase      = max_width - width - len(__UpperCamelCase   )
      answer.append(""" """.join(__UpperCamelCase   ) + (remaining_spaces + 1) * """ """   )
      return answer


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

     testmod()
