

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)
  _SCREAMING_SNAKE_CASE  :						str         =    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')

  _SCREAMING_SNAKE_CASE  :						Optional[int]         =    tf.keras.preprocessing.image.ImageDataGenerator(
      rescale=1.0 / 255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
  )

  _SCREAMING_SNAKE_CASE  :						Dict         =    tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 255)

  _SCREAMING_SNAKE_CASE  :						Union[str, Any]         =    train_datagen.flow_from_directory(
      '''dataset/training_set''', target_size=(64, 64), batch_size=32, class_mode='''binary'''
  )

  _SCREAMING_SNAKE_CASE  :						Dict         =    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

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





from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging


if TYPE_CHECKING:
       from ...onnx.config import PatchingSpec
       from ...tokenization_utils_base import PreTrainedTokenizerBase


_SCREAMING_SNAKE_CASE  :						Any         =    logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE  :						List[str]         =    {
    '''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json''',
    '''allenai/longformer-large-4096''': '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json''',
    '''allenai/longformer-large-4096-finetuned-triviaqa''': (
        '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json'''
    ),
    '''allenai/longformer-base-4096-extra.pos.embd.only''': (
        '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json'''
    ),
    '''allenai/longformer-large-4096-extra.pos.embd.only''': (
        '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json'''
    ),
}






class  UpperCAmelCase__	( A__ ):






  """simple docstring"""


  a     =      "longformer"






  def __init__(							self		:	int			,     __lowerCamelCase		:	Union[List[int], int] = 512			,     __lowerCamelCase		:	int = 2			,     __lowerCamelCase		:	int = 1			,     __lowerCamelCase		:	int = 0			,     __lowerCamelCase		:	int = 2			,     __lowerCamelCase		:	int = 3_0522			,     __lowerCamelCase		:	int = 768			,     __lowerCamelCase		:	int = 12			,     __lowerCamelCase		:	int = 12			,     __lowerCamelCase		:	int = 3072			,     __lowerCamelCase		:	str = "gelu"			,     __lowerCamelCase		:	float = 0.1			,     __lowerCamelCase		:	float = 0.1			,     __lowerCamelCase		:	int = 512			,     __lowerCamelCase		:	int = 2			,     __lowerCamelCase		:	float = 0.02			,     __lowerCamelCase		:	float = 1e-12			,     __lowerCamelCase		:	bool = False			,     **__lowerCamelCase		:	Tuple			,     )	->							List[Any]:
       super().__init__(pad_token_id=__lowerCamelCase			,     **__lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					attention_window
       SCREAMING_SNAKE_CASE__					=					sep_token_id
       SCREAMING_SNAKE_CASE__					=					bos_token_id
       SCREAMING_SNAKE_CASE__					=					eos_token_id
       SCREAMING_SNAKE_CASE__					=					vocab_size
       SCREAMING_SNAKE_CASE__					=					hidden_size
       SCREAMING_SNAKE_CASE__					=					num_hidden_layers
       SCREAMING_SNAKE_CASE__					=					num_attention_heads
       SCREAMING_SNAKE_CASE__					=					hidden_act
       SCREAMING_SNAKE_CASE__					=					intermediate_size
       SCREAMING_SNAKE_CASE__					=					hidden_dropout_prob
       SCREAMING_SNAKE_CASE__					=					attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE__					=					max_position_embeddings
       SCREAMING_SNAKE_CASE__					=					type_vocab_size
       SCREAMING_SNAKE_CASE__					=					initializer_range
       SCREAMING_SNAKE_CASE__					=					layer_norm_eps
       SCREAMING_SNAKE_CASE__					=					onnx_export






class  UpperCAmelCase__	( A__ ):






  """simple docstring"""


  def __init__(							self		:	Dict			,     __lowerCamelCase		:	"PretrainedConfig"			,     __lowerCamelCase		:	str = "default"			,     __lowerCamelCase		:	"List[PatchingSpec]" = None      )	->							List[str]:
       super().__init__(__lowerCamelCase			,     __lowerCamelCase			,     __lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					True






  @property
  def    lowercase_	(							self		:	Optional[Any]      )	->							Mapping[str, Mapping[int, str]]:
       if self.task == "multiple-choice":
            SCREAMING_SNAKE_CASE__					=					{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
       else:
            SCREAMING_SNAKE_CASE__					=					{0: '''batch''', 1: '''sequence'''}
       return OrderedDict(
           [
               ('''input_ids''', dynamic_axis),
               ('''attention_mask''', dynamic_axis),
               ('''global_attention_mask''', dynamic_axis),
           ]      )






  @property
  def    lowercase_	(							self		:	Optional[int]      )	->							Mapping[str, Mapping[int, str]]:
       SCREAMING_SNAKE_CASE__					=					super().outputs
       if self.task == "default":
            SCREAMING_SNAKE_CASE__					=					{0: '''batch'''}
       return outputs






  @property
  def    lowercase_	(							self		:	Optional[int]      )	->							float:
       return 1e-4






  @property
  def    lowercase_	(							self		:	Union[str, Any]      )	->							int:
       # needs to be >= 14 to support tril operator
       return max(super().default_onnx_opset			,     14      )







  def    lowercase_	(							self		:	Dict			,     __lowerCamelCase		:	"PreTrainedTokenizerBase"			,     __lowerCamelCase		:	int = -1			,     __lowerCamelCase		:	int = -1			,     __lowerCamelCase		:	bool = False			,     __lowerCamelCase		:	Optional[TensorType] = None			,     )	->							Mapping[str, Any]:
       SCREAMING_SNAKE_CASE__					=					super().generate_dummy_inputs(
           preprocessor=__lowerCamelCase			,     batch_size=__lowerCamelCase			,     seq_length=__lowerCamelCase			,     is_pair=__lowerCamelCase			,     framework=__lowerCamelCase      )
       import torch

       # for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
       # makes the export fail randomly
       SCREAMING_SNAKE_CASE__					=					torch.zeros_like(inputs['''input_ids''']      )
       # make every second token global
       SCREAMING_SNAKE_CASE__					=					1

       return inputs


