code: 
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_flax_available,
      is_tf_available,
      is_torch_available,
  )
  
  
  snake_case_    						=				{
      '''auto_factory''': ['''get_values'''],
      '''configuration_auto''': ['''ALL_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CONFIG_MAPPING''', '''MODEL_NAMES_MAPPING''', '''AutoConfig'''],
      '''feature_extraction_auto''': ['''FEATURE_EXTRACTOR_MAPPING''', '''AutoFeatureExtractor'''],
      '''image_processing_auto''': ['''IMAGE_PROCESSOR_MAPPING''', '''AutoImageProcessor'''],
      '''processing_auto''': ['''PROCESSOR_MAPPING''', '''AutoProcessor'''],
      '''tokenization_auto''': ['''TOKENIZER_MAPPING''', '''AutoTokenizer'''],
  }
  
  try:
         if not is_torch_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         snake_case_    						=				[
             '''MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING''',
             '''MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING''',
             '''MODEL_FOR_AUDIO_XVECTOR_MAPPING''',
             '''MODEL_FOR_BACKBONE_MAPPING''',
             '''MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING''',
             '''MODEL_FOR_CAUSAL_LM_MAPPING''',
             '''MODEL_FOR_CTC_MAPPING''',
             '''MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING''',
             '''MODEL_FOR_DEPTH_ESTIMATION_MAPPING''',
             '''MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING''',
             '''MODEL_FOR_IMAGE_SEGMENTATION_MAPPING''',
             '''MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING''',
             '''MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING''',
             '''MODEL_FOR_MASKED_LM_MAPPING''',
             '''MODEL_FOR_MASK_GENERATION_MAPPING''',
             '''MODEL_FOR_MULTIPLE_CHOICE_MAPPING''',
             '''MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING''',
             '''MODEL_FOR_OBJECT_DETECTION_MAPPING''',
             '''MODEL_FOR_PRETRAINING_MAPPING''',
             '''MODEL_FOR_QUESTION_ANSWERING_MAPPING''',
             '''MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING''',
             '''MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING''',
             '''MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING''',
             '''MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING''',
             '''MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING''',
             '''MODEL_FOR_TEXT_ENCODING_MAPPING''',
             '''MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING''',
             '''MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING''',
             '''MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING''',
             '''MODEL_FOR_VISION_2_SEQ_MAPPING''',
             '''MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING''',
             '''MODEL_MAPPING''',
             '''MODEL_WITH_LM_HEAD_MAPPING''',
             '''MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING''',
             '''MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING''',
             '''AutoModel''',
             '''AutoBackbone''',
             '''AutoModelForAudioClassification''',
             '''AutoModelForAudioFrameClassification''',
             '''AutoModelForAudioXVector''',
             '''AutoModelForCausalLM''',
             '''AutoModelForCTC''',
             '''AutoModelForDepthEstimation''',
             '''AutoModelForImageClassification''',
             '''AutoModelForImageSegmentation''',
             '''AutoModelForInstanceSegmentation''',
             '''AutoModelForMaskGeneration''',
             '''AutoModelForTextEncoding''',
             '''AutoModelForMaskedImageModeling''',
             '''AutoModelForMaskedLM''',
             '''AutoModelForMultipleChoice''',
             '''AutoModelForNextSentencePrediction''',
             '''AutoModelForObjectDetection''',
             '''AutoModelForPreTraining''',
             '''AutoModelForQuestionAnswering''',
             '''AutoModelForSemanticSegmentation''',
             '''AutoModelForSeq2SeqLM''',
             '''AutoModelForSequenceClassification''',
             '''AutoModelForSpeechSeq2Seq''',
             '''AutoModelForTableQuestionAnswering''',
             '''AutoModelForTokenClassification''',
             '''AutoModelForUniversalSegmentation''',
             '''AutoModelForVideoClassification''',
             '''AutoModelForVision2Seq''',
             '''AutoModelForVisualQuestionAnswering''',
             '''AutoModelForDocumentQuestionAnswering''',
             '''AutoModelWithLMHead''',
             '''AutoModelForZeroShotImageClassification''',
             '''AutoModelForZeroShotObjectDetection''',
         ]
  
  try:
         if not is_tf_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         snake_case_    						=				[
             '''TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING''',
             '''TF_MODEL_FOR_CAUSAL_LM_MAPPING''',
             '''TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING''',
             '''TF_MODEL_FOR_MASK_GENERATION_MAPPING''',
             '''TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING''',
             '''TF_MODEL_FOR_MASKED_LM_MAPPING''',
             '''TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING''',
             '''TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING''',
             '''TF_MODEL_FOR_PRETRAINING_MAPPING''',
             '''TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING''',
             '''TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING''',
             '''TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING''',
             '''TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING''',
             '''TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING''',
             '''TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING''',
             '''TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING''',
             '''TF_MODEL_FOR_TEXT_ENCODING_MAPPING''',
             '''TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING''',
             '''TF_MODEL_FOR_VISION_2_SEQ_MAPPING''',
             '''TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING''',
             '''TF_MODEL_MAPPING''',
             '''TF_MODEL_WITH_LM_HEAD_MAPPING''',
             '''TFAutoModel''',
             '''TFAutoModelForAudioClassification''',
             '''TFAutoModelForCausalLM''',
             '''TFAutoModelForImageClassification''',
             '''TFAutoModelForMaskedImageModeling''',
             '''TFAutoModelForMaskedLM''',
             '''TFAutoModelForMaskGeneration''',
             '''TFAutoModelForMultipleChoice''',
             '''TFAutoModelForNextSentencePrediction''',
             '''TFAutoModelForPreTraining''',
             '''TFAutoModelForDocumentQuestionAnswering''',
             '''TFAutoModelForQuestionAnswering''',
             '''TFAutoModelForSemanticSegmentation''',
             '''TFAutoModelForSeq2SeqLM''',
             '''TFAutoModelForSequenceClassification''',
             '''TFAutoModelForSpeechSeq2Seq''',
             '''TFAutoModelForTableQuestionAnswering''',
             '''TFAutoModelForTextEncoding''',
             '''TFAutoModelForTokenClassification''',
             '''TFAutoModelForVision2Seq''',
             '''TFAutoModelForZeroShotImageClassification''',
             '''TFAutoModelWithLMHead''',
         ]
  
  try:
         if not is_flax_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         snake_case_    						=				[
             '''FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING''',
             '''FLAX_MODEL_FOR_CAUSAL_LM_MAPPING''',
             '''FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING''',
             '''FLAX_MODEL_FOR_MASKED_LM_MAPPING''',
             '''FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING''',
             '''FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING''',
             '''FLAX_MODEL_FOR_PRETRAINING_MAPPING''',
             '''FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING''',
             '''FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING''',
             '''FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING''',
             '''FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING''',
             '''FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING''',
             '''FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING''',
             '''FLAX_MODEL_MAPPING''',
             '''FlaxAutoModel''',
             '''FlaxAutoModelForCausalLM''',
             '''FlaxAutoModelForImageClassification''',
             '''FlaxAutoModelForMaskedLM''',
             '''FlaxAutoModelForMultipleChoice''',
             '''FlaxAutoModelForNextSentencePrediction''',
             '''FlaxAutoModelForPreTraining''',
             '''FlaxAutoModelForQuestionAnswering''',
             '''FlaxAutoModelForSeq2SeqLM''',
             '''FlaxAutoModelForSequenceClassification''',
             '''FlaxAutoModelForSpeechSeq2Seq''',
             '''FlaxAutoModelForTokenClassification''',
             '''FlaxAutoModelForVision2Seq''',
         ]
  
  
  if TYPE_CHECKING:
         from .auto_factory import get_values
         from .configuration_auto import ALL_PRETRAINED_CONFIG_ARCHIVE_MAP, CONFIG_MAPPING, MODEL_NAMES_MAPPING, AutoConfig
         from .feature_extraction_auto import FEATURE_EXTRACTOR_MAPPING, AutoFeatureExtractor
         from .image_processing_auto import IMAGE_PROCESSOR_MAPPING, AutoImageProcessor
         from .processing_auto import PROCESSOR_MAPPING, AutoProcessor
         from .tokenization_auto import TOKENIZER_MAPPING, AutoTokenizer
  
         try:
                if not is_torch_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .modeling_auto import (
                    MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
                    MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING,
                    MODEL_FOR_AUDIO_XVECTOR_MAPPING,
                    MODEL_FOR_BACKBONE_MAPPING,
                    MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING,
                    MODEL_FOR_CAUSAL_LM_MAPPING,
                    MODEL_FOR_CTC_MAPPING,
                    MODEL_FOR_DEPTH_ESTIMATION_MAPPING,
                    MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING,
                    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
                    MODEL_FOR_IMAGE_SEGMENTATION_MAPPING,
                    MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING,
                    MODEL_FOR_MASK_GENERATION_MAPPING,
                    MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
                    MODEL_FOR_MASKED_LM_MAPPING,
                    MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
                    MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
                    MODEL_FOR_OBJECT_DETECTION_MAPPING,
                    MODEL_FOR_PRETRAINING_MAPPING,
                    MODEL_FOR_QUESTION_ANSWERING_MAPPING,
                    MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
                    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
                    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
                    MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
                    MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
                    MODEL_FOR_TEXT_ENCODING_MAPPING,
                    MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
                    MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING,
                    MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
                    MODEL_FOR_VISION_2_SEQ_MAPPING,
                    MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING,
                    MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING,
                    MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING,
                    MODEL_MAPPING,
                    MODEL_WITH_LM_HEAD_MAPPING,
                    AutoBackbone,
                    AutoModel,
                    AutoModelForAudioClassification,
                    AutoModelForAudioFrameClassification,
                    AutoModelForAudioXVector,
                    AutoModelForCausalLM,
                    AutoModelForCTC,
                    AutoModelForDepthEstimation,
                    AutoModelForDocumentQuestionAnswering,
                    AutoModelForImageClassification,
                    AutoModelForImageSegmentation,
                    AutoModelForInstanceSegmentation,
                    AutoModelForMaskedImageModeling,
                    AutoModelForMaskedLM,
                    AutoModelForMaskGeneration,
                    AutoModelForMultipleChoice,
                    AutoModelForNextSentencePrediction,
                    AutoModelForObjectDetection,
                    AutoModelForPreTraining,
                    AutoModelForQuestionAnswering,
                    AutoModelForSemanticSegmentation,
                    AutoModelForSeqaSeqLM,
                    AutoModelForSequenceClassification,
                    AutoModelForSpeechSeqaSeq,
                    AutoModelForTableQuestionAnswering,
                    AutoModelForTextEncoding,
                    AutoModelForTokenClassification,
                    AutoModelForUniversalSegmentation,
                    AutoModelForVideoClassification,
                    AutoModelForVisionaSeq,
                    AutoModelForVisualQuestionAnswering,
                    AutoModelForZeroShotImageClassification,
                    AutoModelForZeroShotObjectDetection,
                    AutoModelWithLMHead,
                )
  
         try:
                if not is_tf_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .modeling_tf_auto import (
                    TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
                    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
                    TF_MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING,
                    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
                    TF_MODEL_FOR_MASK_GENERATION_MAPPING,
                    TF_MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
                    TF_MODEL_FOR_MASKED_LM_MAPPING,
                    TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
                    TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
                    TF_MODEL_FOR_PRETRAINING_MAPPING,
                    TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
                    TF_MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
                    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
                    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
                    TF_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
                    TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
                    TF_MODEL_FOR_TEXT_ENCODING_MAPPING,
                    TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
                    TF_MODEL_FOR_VISION_2_SEQ_MAPPING,
                    TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING,
                    TF_MODEL_MAPPING,
                    TF_MODEL_WITH_LM_HEAD_MAPPING,
                    TFAutoModel,
                    TFAutoModelForAudioClassification,
                    TFAutoModelForCausalLM,
                    TFAutoModelForDocumentQuestionAnswering,
                    TFAutoModelForImageClassification,
                    TFAutoModelForMaskedImageModeling,
                    TFAutoModelForMaskedLM,
                    TFAutoModelForMaskGeneration,
                    TFAutoModelForMultipleChoice,
                    TFAutoModelForNextSentencePrediction,
                    TFAutoModelForPreTraining,
                    TFAutoModelForQuestionAnswering,
                    TFAutoModelForSemanticSegmentation,
                    TFAutoModelForSeqaSeqLM,
                    TFAutoModelForSequenceClassification,
                    TFAutoModelForSpeechSeqaSeq,
                    TFAutoModelForTableQuestionAnswering,
                    TFAutoModelForTextEncoding,
                    TFAutoModelForTokenClassification,
                    TFAutoModelForVisionaSeq,
                    TFAutoModelForZeroShotImageClassification,
                    TFAutoModelWithLMHead,
                )
  
         try:
                if not is_flax_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .modeling_flax_auto import (
                    FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
                    FLAX_MODEL_FOR_CAUSAL_LM_MAPPING,
                    FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
                    FLAX_MODEL_FOR_MASKED_LM_MAPPING,
                    FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
                    FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
                    FLAX_MODEL_FOR_PRETRAINING_MAPPING,
                    FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
                    FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
                    FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
                    FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING,
                    FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
                    FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING,
                    FLAX_MODEL_MAPPING,
                    FlaxAutoModel,
                    FlaxAutoModelForCausalLM,
                    FlaxAutoModelForImageClassification,
                    FlaxAutoModelForMaskedLM,
                    FlaxAutoModelForMultipleChoice,
                    FlaxAutoModelForNextSentencePrediction,
                    FlaxAutoModelForPreTraining,
                    FlaxAutoModelForQuestionAnswering,
                    FlaxAutoModelForSeqaSeqLM,
                    FlaxAutoModelForSequenceClassification,
                    FlaxAutoModelForSpeechSeqaSeq,
                    FlaxAutoModelForTokenClassification,
                    FlaxAutoModelForVisionaSeq,
                )
  
  else:
         import sys
  
         snake_case_    						=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
  
  
style_context: 
  
  from cva import destroyAllWindows, imread, imshow, waitKey
  
  def 			snake_case__    (					SCREAMING_SNAKE_CASE_	:					int			,  SCREAMING_SNAKE_CASE_	:					int    ):
  
  
  
       '''simple docstring'''
  
  
  
  
  
       lowercase__   ,				lowercase__					:     Optional[int]   = img.shape[0], img.shape[1]
  
       def to_grayscale(SCREAMING_SNAKE_CASE_	:					Union[str, Any]			,  SCREAMING_SNAKE_CASE_	:					List[Any]			,  SCREAMING_SNAKE_CASE_	:					Union[str, Any]    ):
            return 0.2126 * red + 0.587 * green + 0.114 * blue
  
       def normalize(SCREAMING_SNAKE_CASE_	:					int    ):
            return min(SCREAMING_SNAKE_CASE_			,  255    )
  
       for i in range(SCREAMING_SNAKE_CASE_    ):
            for j in range(SCREAMING_SNAKE_CASE_    ):
                 lowercase__					:     str   = int(to_grayscale(*img[i][j]    )    )
                 lowercase__					:     Dict   = [
                     normalize(SCREAMING_SNAKE_CASE_    ),
                     normalize(greyscale + factor    ),
                     normalize(greyscale + 2 * factor    ),
                 ]
  
       return img
  
  
  if __name__ == "__main__":
         # read original image
         snake_case_    						=				{
             percentage: imread('''image_data/lena.jpg''', 1) for percentage in (10, 20, 30, 40)
         }
  
         for percentage, img in images.items():
                make_sepia(img, percentage)
  
         for percentage, img in images.items():
                imshow(F'''Original image with sepia (factor: {percentage})''', img)
  
         waitKey(0)
         destroyAllWindows()
  
  