
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

__SCREAMING_SNAKE_CASE      : Tuple       =  logging.get_logger(__name__)


@add_end_docstrings(_lowerCAmelCase     )
class 		__magic_name__ (    _lowerCAmelCase     ):





		def __init__(				self     :						Dict    ,	**lowerCamelCase__     :						Optional[int]					):
							super().__init__(**lowerCamelCase__					)

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

							requires_backends(self    ,	'''vision'''					)
							self.check_model_type(lowerCamelCase__					)





		def __call__(				self     :						Optional[Any]    ,	lowerCamelCase__     :						Tuple    ,	lowerCamelCase__     :						Dict = None    ,	**lowerCamelCase__     :						Optional[int]    ,	):
							if "text_queries" in kwargs:
												lowerCAmelCase		:       str							=  kwargs.pop('''text_queries'''					)

							if isinstance(lowerCamelCase__    ,	(str, Image.Image)					):
												lowerCAmelCase		:       Union[str, Any]							=  {'''image''': image, '''candidate_labels''': candidate_labels}
							else:
												lowerCAmelCase		:       Union[str, Any]							=  image
							lowerCAmelCase		:       Union[str, Any]							=  super().__call__(lowerCamelCase__    ,	**lowerCamelCase__					)
							return results





		def    _A				(				self     :						List[Any]    ,	**lowerCamelCase__     :						Optional[Any]					):
							lowerCAmelCase		:       Union[str, Any]							=  {}
							if "threshold" in kwargs:
												lowerCAmelCase		:       List[Any]							=  kwargs['''threshold''']
							if "top_k" in kwargs:
												lowerCAmelCase		:       List[str]							=  kwargs['''top_k''']
							return {}, {}, postprocess_params





		def    _A				(				self     :						str    ,	lowerCamelCase__     :						Dict					):
							lowerCAmelCase		:       Any							=  load_image(inputs['''image''']					)
							lowerCAmelCase		:       Optional[int]							=  inputs['''candidate_labels''']
							if isinstance(lowerCamelCase__    ,	lowerCamelCase__					):
												lowerCAmelCase		:       Dict							=  candidate_labels.split(''','''					)

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





		def    _A				(				self     :						List[Any]    ,	lowerCamelCase__     :						str					):
							lowerCAmelCase		:       Optional[Any]							=  model_inputs.pop('''target_size'''					)
							lowerCAmelCase		:       Optional[int]							=  model_inputs.pop('''candidate_label'''					)
							lowerCAmelCase		:       Tuple							=  model_inputs.pop('''is_last'''					)

							lowerCAmelCase		:       Tuple							=  self.model(**lowerCamelCase__					)

							lowerCAmelCase		:       List[Any]							=  {'''target_size''': target_size, '''candidate_label''': candidate_label, '''is_last''': is_last, **outputs}
							return model_outputs





		def    _A				(				self     :						str    ,	lowerCamelCase__     :						List[str]    ,	lowerCamelCase__     :						int=0.1    ,	lowerCamelCase__     :						Optional[int]=None					):
							lowerCAmelCase		:       Any							=  []
							for model_output in model_outputs:
												lowerCAmelCase		:       Any							=  model_output['''candidate_label''']
												lowerCAmelCase		:       List[Any]							=  BaseModelOutput(lowerCamelCase__					)
												lowerCAmelCase		:       Dict							=  self.image_processor.post_process_object_detection(
												    outputs=lowerCamelCase__    ,	threshold=lowerCamelCase__    ,	target_sizes=model_output['''target_size''']					)[0]

												for index in outputs["scores"].nonzero():
																	lowerCAmelCase		:       int							=  outputs['''scores'''][index].item()
																	lowerCAmelCase		:       Dict							=  self._get_bounding_box(outputs['''boxes'''][index][0]					)

																	lowerCAmelCase		:       Any							=  {'''score''': score, '''label''': label, '''box''': box}
																	results.append(lowerCamelCase__					)

							lowerCAmelCase		:       Any							=  sorted(lowerCamelCase__    ,	key=lambda lowerCamelCase__					: x["score"]    ,	reverse=lowerCamelCase__					)
							if top_k:
												lowerCAmelCase		:       str							=  results[:top_k]

							return results





		def    _A				(				self     :						Union[str, Any]    ,	lowerCamelCase__     :						int					):
							if self.framework != "pt":
												raise ValueError('''The ZeroShotObjectDetectionPipeline is only available in PyTorch.'''					)
							lowerCAmelCase						,				lowerCAmelCase						,				lowerCAmelCase						,				lowerCAmelCase		:       Dict							=  box.int().tolist()
							lowerCAmelCase		:       str							=  {
							    '''xmin''': xmin,
							    '''ymin''': ymin,
							    '''xmax''': xmax,
							    '''ymax''': ymax,
							}
							return bbox








'''simple docstring'''



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   :							Union[str, Any]         =   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(32, (3, 3), input_shape=(64, 64, 3), activation="""relu""")
 )

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

 # Adding a second convolutional layer
 classifier.add(layers.ConvaD(32, (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=128, 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   :							Optional[Any]         =   tf.keras.preprocessing.image.ImageDataGenerator(
     rescale=1.0 / 255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
 )

 __UpperCamelCase   :							Union[str, Any]         =   tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 255)

 __UpperCamelCase   :							Any         =   train_datagen.flow_from_directory(
     """dataset/training_set""", target_size=(64, 64), batch_size=32, class_mode="""binary"""
 )

 __UpperCamelCase   :							str         =   test_datagen.flow_from_directory(
     """dataset/test_set""", target_size=(64, 64), batch_size=32, class_mode="""binary"""
 )

 classifier.fit_generator(
     training_set, steps_per_epoch=5, epochs=30, validation_data=test_set
 )

 classifier.save("""cnn.h5""")

 # Part 3 - Making new predictions

 __UpperCamelCase   :							Optional[Any]         =   tf.keras.preprocessing.image.load_img(
     """dataset/single_prediction/image.png""", target_size=(64, 64)
 )
 __UpperCamelCase   :							List[Any]         =   tf.keras.preprocessing.image.img_to_array(test_image)
 __UpperCamelCase   :							Dict         =   np.expand_dims(test_image, axis=0)
 __UpperCamelCase   :							Optional[Any]         =   classifier.predict(test_image)
 # training_set.class_indices
 if result[0][0] == 0:
  __UpperCamelCase   :							Optional[int]         =   """Normal"""
 if result[0][0] == 1:
  __UpperCamelCase   :							Optional[int]         =   """Abnormality detected"""