





'''simple docstring'''





from typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_vision_available():
     from PIL import Image

     from ..image_utils import load_image

if is_torch_available():
     import torch

     from transformers.modeling_outputs import BaseModelOutput

     from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING

_UpperCAmelCase        =				logging.get_logger(__name__)



@add_end_docstrings(__a     )
class 							_UpperCamelCase					(	__a     ):

       def __init__(		self: List[str]					,   **_SCREAMING_SNAKE_CASE: Optional[Any]		)       -> Any:




              """simple docstring"""


              super().__init__(**snake_case__		)

              if self.framework == "tf":
                     raise ValueError(f'''The {self.__class__} is only available in PyTorch.'''		)

              requires_backends(self					,   "vision"		)
              self.check_model_type(snake_case__		)

       def __call__(		self: List[Any]					,   _SCREAMING_SNAKE_CASE: Union[str, "Image.Image", List[Dict[str, Any]]]					,   _SCREAMING_SNAKE_CASE: Union[str, List[str]] = None					,   **_SCREAMING_SNAKE_CASE: List[Any]					,   )       -> Union[str, Any]:




              """simple docstring"""


              if "text_queries" in kwargs:
                     UpperCamelCase_          =  kwargs.pop("text_queries"		)

              if isinstance(snake_case__					,   (str, Image.Image)		):
                     UpperCamelCase_          =  {"image": image, "candidate_labels": candidate_labels}
              else:
                     UpperCamelCase_          =  image
              UpperCamelCase_          =  super().__call__(snake_case__					,   **snake_case__		)
              return results

       def lowercase (		self: List[str]					,   **_SCREAMING_SNAKE_CASE: Dict		)       -> int:




              """simple docstring"""


              UpperCamelCase_          =  {}
              if "threshold" in kwargs:
                     UpperCamelCase_          =  kwargs["threshold"]
              if "top_k" in kwargs:
                     UpperCamelCase_          =  kwargs["top_k"]
              return {}, {}, postprocess_params

       def lowercase (		self: Any					,   _SCREAMING_SNAKE_CASE: List[str]		)       -> str:




              """simple docstring"""


              UpperCamelCase_          =  load_image(inputs["image"]		)
              UpperCamelCase_          =  inputs["candidate_labels"]
              if isinstance(snake_case__					,   snake_case__		):
                     UpperCamelCase_          =  candidate_labels.split(","		)

              UpperCamelCase_          =  torch.tensor([[image.height, image.width]]					,   dtype=torch.intaa		)
              for i, candidate_label in enumerate(snake_case__		):
                     UpperCamelCase_          =  self.tokenizer(snake_case__					,   return_tensors=self.framework		)
                     UpperCamelCase_          =  self.image_processor(snake_case__					,   return_tensors=self.framework		)
                     yield {
                         "is_last": i == len(snake_case__		) - 1,
                         "target_size": target_size,
                         "candidate_label": candidate_label,
                         **text_inputs,
                         **image_features,
                     }

       def lowercase (		self: Dict					,   _SCREAMING_SNAKE_CASE: Optional[int]		)       -> List[str]:




              """simple docstring"""


              UpperCamelCase_          =  model_inputs.pop("target_size"		)
              UpperCamelCase_          =  model_inputs.pop("candidate_label"		)
              UpperCamelCase_          =  model_inputs.pop("is_last"		)

              UpperCamelCase_          =  self.model(**snake_case__		)

              UpperCamelCase_          =  {"target_size": target_size, "candidate_label": candidate_label, "is_last": is_last, **outputs}
              return model_outputs

       def lowercase (		self: Union[str, Any]					,   _SCREAMING_SNAKE_CASE: Optional[Any]					,   _SCREAMING_SNAKE_CASE: int=0.1					,   _SCREAMING_SNAKE_CASE: int=None		)       -> Optional[Any]:




              """simple docstring"""


              UpperCamelCase_          =  []
              for model_output in model_outputs:
                     UpperCamelCase_          =  model_output["candidate_label"]
                     UpperCamelCase_          =  BaseModelOutput(snake_case__		)
                     UpperCamelCase_          =  self.image_processor.post_process_object_detection(
                         outputs=snake_case__					,   threshold=snake_case__					,   target_sizes=model_output["target_size"]		)[0]

                     for index in outputs["scores"].nonzero():
                            UpperCamelCase_          =  outputs["scores"][index].item()
                            UpperCamelCase_          =  self._get_bounding_box(outputs["boxes"][index][0]		)

                            UpperCamelCase_          =  {"score": score, "label": label, "box": box}
                            results.append(snake_case__		)

              UpperCamelCase_          =  sorted(snake_case__					,   key=lambda _SCREAMING_SNAKE_CASE		: x["score"]					,   reverse=snake_case__		)
              if top_k:
                     UpperCamelCase_          =  results[:top_k]

              return results



       def lowercase (		self: List[Any]					,   _SCREAMING_SNAKE_CASE: "torch.Tensor"		)       -> Any:




              """simple docstring"""


              if self.framework != "pt":
                     raise ValueError("The ZeroShotObjectDetectionPipeline is only available in PyTorch."		)
              UpperCamelCase_       , UpperCamelCase_       , UpperCamelCase_       , UpperCamelCase_          =  box.int().tolist()
              UpperCamelCase_          =  {
                  "xmin": xmin,
                  "ymin": ymin,
                  "xmax": xmax,
                  "ymax": ymax,
              }
              return bbox








import numpy as np

# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models

if __name__ == "__main__":
     # Initialising the CNN
     # (Sequential- Building the model layer by layer)
     _UpperCAmelCase        =				models.Sequential()

     # Step 1 - Convolution
     # Here 64,64 is the length & breadth of dataset images and 3 is for the RGB channel
     # (3,3) is the kernel size (filter matrix)
     classifier.add(
         layers.ConvaD(3_2, (3, 3), input_shape=(6_4, 6_4, 3), activation='relu')
     )

     # Step 2 - Pooling
     classifier.add(layers.MaxPoolingaD(pool_size=(2, 2)))

     # Adding a second convolutional layer
     classifier.add(layers.ConvaD(3_2, (3, 3), activation='relu'))
     classifier.add(layers.MaxPoolingaD(pool_size=(2, 2)))

     # Step 3 - Flattening
     classifier.add(layers.Flatten())

     # Step 4 - Full connection
     classifier.add(layers.Dense(units=1_2_8, activation='relu'))
     classifier.add(layers.Dense(units=1, activation='sigmoid'))

     # Compiling the CNN
     classifier.compile(
         optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']
     )

     # Part 2 - Fitting the CNN to the images

     # Load Trained model weights

     # from keras.models import load_model
     # regressor=load_model('cnn.h5')

     _UpperCAmelCase        =				tf.keras.preprocessing.image.ImageDataGenerator(
         rescale=1.0 / 2_5_5, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
     )

     _UpperCAmelCase        =				tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 2_5_5)

     _UpperCAmelCase        =				train_datagen.flow_from_directory(
         'dataset/training_set', target_size=(6_4, 6_4), batch_size=3_2, class_mode='binary'
     )

     _UpperCAmelCase        =				test_datagen.flow_from_directory(
         'dataset/test_set', target_size=(6_4, 6_4), batch_size=3_2, class_mode='binary'
     )

     classifier.fit_generator(
         training_set, steps_per_epoch=5, epochs=3_0, validation_data=test_set
     )

     classifier.save('cnn.h5')

     # Part 3 - Making new predictions

     _UpperCAmelCase        =				tf.keras.preprocessing.image.load_img(
         'dataset/single_prediction/image.png', target_size=(6_4, 6_4)
     )
     _UpperCAmelCase        =				tf.keras.preprocessing.image.img_to_array(test_image)
     _UpperCAmelCase        =				np.expand_dims(test_image, axis=0)
     _UpperCAmelCase        =				classifier.predict(test_image)
     # training_set.class_indices
     if result[0][0] == 0:
          _UpperCAmelCase        =				'Normal'
     if result[0][0] == 1:
          _UpperCAmelCase        =				'Abnormality detected'

