





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)
     __lowercase  :   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')

     __lowercase  :   List[Any]						      =tf.keras.preprocessing.image.ImageDataGenerator(
         rescale=1.0 / 255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
     )

     __lowercase  :   str						      =tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 255)

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

     __lowercase  :   int						      =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

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











import unittest

from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import (
         LiltForQuestionAnswering,
         LiltForSequenceClassification,
         LiltForTokenClassification,
         LiltModel,
     )
     from transformers.models.lilt.modeling_lilt import LILT_PRETRAINED_MODEL_ARCHIVE_LIST

class      A       :



      def __init__(	self:						Union[str, Any]		,  _lowerCAmelCase:						str		,  _lowerCAmelCase:						Tuple=13		,  _lowerCAmelCase:						Tuple=7		,  _lowerCAmelCase:						str=True		,  _lowerCAmelCase:						Optional[Any]=True		,  _lowerCAmelCase:						Union[str, Any]=True		,  _lowerCAmelCase:						Dict=True		,  _lowerCAmelCase:						List[Any]=99		,  _lowerCAmelCase:						str=24		,  _lowerCAmelCase:						List[str]=2		,  _lowerCAmelCase:						List[str]=6		,  _lowerCAmelCase:						Optional[int]=37		,  _lowerCAmelCase:						Dict="gelu"		,  _lowerCAmelCase:						Dict=0.1		,  _lowerCAmelCase:						List[str]=0.1		,  _lowerCAmelCase:						List[str]=512		,  _lowerCAmelCase:						List[str]=16		,  _lowerCAmelCase:						List[str]=2		,  _lowerCAmelCase:						Tuple=0.02		,  _lowerCAmelCase:						List[str]=3		,  _lowerCAmelCase:						List[str]=None		,  _lowerCAmelCase:						Optional[int]=1000		,  )						->    Tuple:
         '''simple docstring'''

         UpperCAmelCase_											=parent
         UpperCAmelCase_											=batch_size
         UpperCAmelCase_											=seq_length
         UpperCAmelCase_											=is_training
         UpperCAmelCase_											=use_input_mask
         UpperCAmelCase_											=use_token_type_ids
         UpperCAmelCase_											=use_labels
         UpperCAmelCase_											=vocab_size
         UpperCAmelCase_											=hidden_size
         UpperCAmelCase_											=num_hidden_layers
         UpperCAmelCase_											=num_attention_heads
         UpperCAmelCase_											=intermediate_size
         UpperCAmelCase_											=hidden_act
         UpperCAmelCase_											=hidden_dropout_prob
         UpperCAmelCase_											=attention_probs_dropout_prob
         UpperCAmelCase_											=max_position_embeddings
         UpperCAmelCase_											=type_vocab_size
         UpperCAmelCase_											=type_sequence_label_size
         UpperCAmelCase_											=initializer_range
         UpperCAmelCase_											=num_labels
         UpperCAmelCase_											=scope
         UpperCAmelCase_											=range_bbox



      def 	lowerCAmelCase__    (	self:						List[Any]       )						->    int:
         '''simple docstring'''

         UpperCAmelCase_											=ids_tensor([self.batch_size, self.seq_length]		,  self.vocab_size       )

         UpperCAmelCase_											=ids_tensor([self.batch_size, self.seq_length, 4]		,  self.range_bbox       )
         # Ensure that bbox is legal
         for i in range(bbox.shape[0]       ):
            for j in range(bbox.shape[1]       ):
               if bbox[i, j, 3] < bbox[i, j, 1]:
                  UpperCAmelCase_											=bbox[i, j, 3]
                  UpperCAmelCase_											=bbox[i, j, 1]
                  UpperCAmelCase_											=t
               if bbox[i, j, 2] < bbox[i, j, 0]:
                  UpperCAmelCase_											=bbox[i, j, 2]
                  UpperCAmelCase_											=bbox[i, j, 0]
                  UpperCAmelCase_											=t

         UpperCAmelCase_											=None
         if self.use_input_mask:
            UpperCAmelCase_											=ids_tensor([self.batch_size, self.seq_length]		,  vocab_size=2       )

         UpperCAmelCase_											=None
         if self.use_token_type_ids:
            UpperCAmelCase_											=ids_tensor([self.batch_size, self.seq_length]		,  self.type_vocab_size       )

         UpperCAmelCase_											=None
         UpperCAmelCase_											=None
         if self.use_labels:
            UpperCAmelCase_											=ids_tensor([self.batch_size]		,  self.type_sequence_label_size       )
            UpperCAmelCase_											=ids_tensor([self.batch_size, self.seq_length]		,  self.num_labels       )

         UpperCAmelCase_											=self.get_config()

         return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels



      def 	lowerCAmelCase__    (	self:						List[str]       )						->    Any:
         '''simple docstring'''

         return LiltConfig(
             vocab_size=self.vocab_size		,  hidden_size=self.hidden_size		,  num_hidden_layers=self.num_hidden_layers		,  num_attention_heads=self.num_attention_heads		,  intermediate_size=self.intermediate_size		,  hidden_act=self.hidden_act		,  hidden_dropout_prob=self.hidden_dropout_prob		,  attention_probs_dropout_prob=self.attention_probs_dropout_prob		,  max_position_embeddings=self.max_position_embeddings		,  type_vocab_size=self.type_vocab_size		,  initializer_range=self.initializer_range		,  )



      def 	lowerCAmelCase__    (	self:						List[Any]		,  _lowerCAmelCase:						str		,  _lowerCAmelCase:						List[str]		,  _lowerCAmelCase:						Union[str, Any]		,  _lowerCAmelCase:						Union[str, Any]		,  _lowerCAmelCase:						List[Any]		,  _lowerCAmelCase:						Tuple		,  _lowerCAmelCase:						Optional[int]		,  )						->    Union[str, Any]:
         '''simple docstring'''

         UpperCAmelCase_											=LiltModel(config=_lowerCAmelCase       )
         model.to(_lowerCAmelCase       )
         model.eval()
         UpperCAmelCase_											=model(_lowerCAmelCase		,  bbox=_lowerCAmelCase		,  attention_mask=_lowerCAmelCase		,  token_type_ids=_lowerCAmelCase       )
         UpperCAmelCase_											=model(_lowerCAmelCase		,  bbox=_lowerCAmelCase		,  token_type_ids=_lowerCAmelCase       )
         UpperCAmelCase_											=model(_lowerCAmelCase		,  bbox=_lowerCAmelCase       )

         self.parent.assertEqual(result.last_hidden_state.shape		,  (self.batch_size, self.seq_length, self.hidden_size)       )
         self.parent.assertEqual(result.pooler_output.shape		,  (self.batch_size, self.hidden_size)       )



      def 	lowerCAmelCase__    (	self:						Optional[Any]		,  _lowerCAmelCase:						Union[str, Any]		,  _lowerCAmelCase:						int		,  _lowerCAmelCase:						str		,  _lowerCAmelCase:						Tuple		,  _lowerCAmelCase:						List[str]		,  _lowerCAmelCase:						Optional[Any]		,  _lowerCAmelCase:						Tuple		,  )						->    Tuple:
         '''simple docstring'''

         UpperCAmelCase_											=self.num_labels
         UpperCAmelCase_											=LiltForTokenClassification(config=_lowerCAmelCase       )
         model.to(_lowerCAmelCase       )
         model.eval()
         UpperCAmelCase_											=model(
             _lowerCAmelCase		,  bbox=_lowerCAmelCase		,  attention_mask=_lowerCAmelCase		,  token_type_ids=_lowerCAmelCase		,  labels=_lowerCAmelCase       )
         self.parent.assertEqual(result.logits.shape		,  (self.batch_size, self.seq_length, self.num_labels)       )



      def 	lowerCAmelCase__    (	self:						List[Any]		,  _lowerCAmelCase:						Optional[Any]		,  _lowerCAmelCase:						List[str]		,  _lowerCAmelCase:						List[str]		,  _lowerCAmelCase:						List[str]		,  _lowerCAmelCase:						Tuple		,  _lowerCAmelCase:						int		,  _lowerCAmelCase:						Union[str, Any]		,  )						->    Optional[Any]:
         '''simple docstring'''

         UpperCAmelCase_											=LiltForQuestionAnswering(config=_lowerCAmelCase       )
         model.to(_lowerCAmelCase       )
         model.eval()
         UpperCAmelCase_											=model(
             _lowerCAmelCase		,  bbox=_lowerCAmelCase		,  attention_mask=_lowerCAmelCase		,  token_type_ids=_lowerCAmelCase		,  start_positions=_lowerCAmelCase		,  end_positions=_lowerCAmelCase		,  )
         self.parent.assertEqual(result.start_logits.shape		,  (self.batch_size, self.seq_length)       )
         self.parent.assertEqual(result.end_logits.shape		,  (self.batch_size, self.seq_length)       )






      def 	lowerCAmelCase__    (	self:						List[Any]       )						->    Optional[int]:
         '''simple docstring'''

         UpperCAmelCase_											=self.prepare_config_and_inputs()
         (
             (
             UpperCAmelCase_
         )						,       (
             UpperCAmelCase_
         )						,       (
             UpperCAmelCase_
         )						,       (
             UpperCAmelCase_
         )						,       (
             UpperCAmelCase_
         )						,       (
             UpperCAmelCase_
         )						,       (
             UpperCAmelCase_
         )						,       
         )											=config_and_inputs
         UpperCAmelCase_											={
             "input_ids": input_ids,
             "bbox": bbox,
             "token_type_ids": token_type_ids,
             "attention_mask": input_mask,
         }
         return config, inputs_dict

@require_torch
class      A       (	__lowercase		,      __lowercase		,      __lowercase		,      unittest.TestCase						):
      _snake_case							     =(
          (
              LiltModel,
              LiltForSequenceClassification,
              LiltForTokenClassification,
              LiltForQuestionAnswering,
          )
          if is_torch_available()
          else ()
      )
      _snake_case							     =(
          {
              '''feature-extraction''': LiltModel,
              '''question-answering''': LiltForQuestionAnswering,
              '''text-classification''': LiltForSequenceClassification,
              '''token-classification''': LiltForTokenClassification,
              '''zero-shot''': LiltForSequenceClassification,
          }
          if is_torch_available()
          else {}
      )
      _snake_case							     =False
      _snake_case							     =False



      def 	lowerCAmelCase__    (	self:						Any		,  _lowerCAmelCase:						Any		,  _lowerCAmelCase:						Dict		,  _lowerCAmelCase:						List[Any]		,  _lowerCAmelCase:						Tuple		,  _lowerCAmelCase:						Optional[Any]       )						->    Tuple:
         '''simple docstring'''

         return True



      def 	lowerCAmelCase__    (	self:						Any       )						->    List[Any]:
         '''simple docstring'''

         UpperCAmelCase_											=LiltModelTester(self       )
         UpperCAmelCase_											=ConfigTester(self		,  config_class=_lowerCAmelCase		,  hidden_size=37       )



      def 	lowerCAmelCase__    (	self:						Union[str, Any]       )						->    List[Any]:
         '''simple docstring'''

         self.config_tester.run_common_tests()



      def 	lowerCAmelCase__    (	self:						Optional[Any]       )						->    str:
         '''simple docstring'''

         UpperCAmelCase_											=self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						List[Any]       )						->    int:
         '''simple docstring'''

         UpperCAmelCase_											=self.model_tester.prepare_config_and_inputs()
         for type in ["absolute", "relative_key", "relative_key_query"]:
            UpperCAmelCase_											=type
            self.model_tester.create_and_check_model(*_lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						Any       )						->    Tuple:
         '''simple docstring'''

         UpperCAmelCase_											=self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*_lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						Dict       )						->    Optional[Any]:
         '''simple docstring'''

         UpperCAmelCase_											=self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(*_lowerCAmelCase       )






      @slow
      def 	lowerCAmelCase__    (	self:						Tuple       )						->    int:
         '''simple docstring'''

         for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            UpperCAmelCase_											=LiltModel.from_pretrained(_lowerCAmelCase       )
            self.assertIsNotNone(_lowerCAmelCase       )

@require_torch
@slow
class      A       (	unittest.TestCase						):



      def 	lowerCAmelCase__    (	self:						str       )						->    Optional[Any]:
         '''simple docstring'''

         UpperCAmelCase_											=LiltModel.from_pretrained("SCUT-DLVCLab/lilt-roberta-en-base"       ).to(_lowerCAmelCase       )

         UpperCAmelCase_											=torch.tensor([[1, 2]]		,  device=_lowerCAmelCase       )
         UpperCAmelCase_											=torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]]		,  device=_lowerCAmelCase       )

         # forward pass
         with torch.no_grad():
            UpperCAmelCase_											=model(input_ids=_lowerCAmelCase		,  bbox=_lowerCAmelCase       )

         UpperCAmelCase_											=torch.Size([1, 2, 768]       )
         UpperCAmelCase_											=torch.tensor(
             [[-0.06_53, 0.09_50, -0.00_61], [-0.05_45, 0.09_26, -0.03_24]]		,  device=_lowerCAmelCase		,  )

         self.assertTrue(outputs.last_hidden_state.shape		,  _lowerCAmelCase       )
         self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :, :3]		,  _lowerCAmelCase		,  atol=1e-3       )       )




