


"""simple docstring"""






import numpy as np
from PIL import Image




def        A_					(					UpperCAmelCase__				)     ->     np.array:
   a	, a	, a   :	Union[str, Any]						=    rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
   return 0.2_9_8_9 * r + 0.5_8_7_0 * g + 0.1_1_4_0 * b




def        A_					(					UpperCAmelCase__				)     ->     np.array:
   return (gray > 127) & (gray <= 255)




def        A_					(					UpperCAmelCase__       ,   UpperCAmelCase__				)     ->     np.array:
   a   :	Optional[Any]						=    np.zeros_like(UpperCAmelCase__				)
   a   :	str						=    np.zeros(
       (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)				)

   # Copy image to padded image
   a   :	str						=    image

   # Iterate over image & apply kernel
   for x in range(image.shape[1]				):
      for y in range(image.shape[0]				):
         a   :	Union[str, Any]						=    (
             kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
         ).sum()
         a   :	str						=    int(summation == 5				)
   return output


# kernel to be applied
SCREAMING_SNAKE_CASE__				:  Dict						= np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])

if __name__ == "__main__":
 # read original image
 SCREAMING_SNAKE_CASE__				:  Tuple						= np.array(Image.open(R"..\image_data\lena.jpg"))
 # Apply erosion operation to a binary image
 SCREAMING_SNAKE_CASE__				:  int						= erosion(grayabinary(rgbagray(image)), structuring_element)
 # Save the output image
 SCREAMING_SNAKE_CASE__				:  Any						= Image.fromarray(output).convert("RGB")
 pil_img.save("result_erosion.png")









"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE__				:  int						= {
    "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:
 SCREAMING_SNAKE_CASE__				:  Optional[int]						= [
     "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:
 SCREAMING_SNAKE_CASE__				:  int						= [
     "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:
 SCREAMING_SNAKE_CASE__				:  Optional[int]						= [
     "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

 SCREAMING_SNAKE_CASE__				:  Optional[int]						= _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





