

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)
					lowerCAmelCase_       	=	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=1_28, 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')

					lowerCAmelCase_       	=	tf.keras.preprocessing.image.ImageDataGenerator(
					    rescale=1.0 / 2_55, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
					)

					lowerCAmelCase_       	=	tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 2_55)

					lowerCAmelCase_       	=	train_datagen.flow_from_directory(
					    '''dataset/training_set''', target_size=(64, 64), batch_size=32, class_mode='''binary'''
					)

					lowerCAmelCase_       	=	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

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



'''simple docstring'''

import unittest

from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, 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.models.trocr.modeling_trocr import TrOCRDecoder, TrOCRForCausalLM
@require_torch
class   __lowerCAmelCase						:





      '''simple docstring'''






      def __init__(self    :    str   ,						UpperCamelCase    :    Tuple   ,						UpperCamelCase    :    Optional[int]=99   ,						UpperCamelCase    :    Optional[int]=13   ,						UpperCamelCase    :    Tuple=16   ,						UpperCamelCase    :    Union[str, Any]=7   ,						UpperCamelCase    :    List[Any]=True   ,						UpperCamelCase    :    List[str]=True   ,						UpperCamelCase    :    str=True   ,						UpperCamelCase    :    Tuple=False   ,						UpperCamelCase    :    str=True   ,						UpperCamelCase    :    Tuple=2   ,						UpperCamelCase    :    Optional[int]=32   ,						UpperCamelCase    :    Any=4   ,						UpperCamelCase    :    Optional[int]=4   ,						UpperCamelCase    :    Tuple=30   ,						UpperCamelCase    :    str=0   ,						UpperCamelCase    :    Tuple=1   ,						UpperCamelCase    :    List[Any]=2   ,						UpperCamelCase    :    str=None   ,						):

             '''simple docstring'''







             lowercase__      			= parent
             lowercase__      			= batch_size
             lowercase__      			= decoder_seq_length
             # For common tests
             lowercase__      			= self.decoder_seq_length
             lowercase__      			= is_training
             lowercase__      			= use_attention_mask
             lowercase__      			= use_labels

             lowercase__      			= vocab_size
             lowercase__      			= d_model
             lowercase__      			= d_model
             lowercase__      			= decoder_layers
             lowercase__      			= decoder_layers
             lowercase__      			= decoder_ffn_dim
             lowercase__      			= decoder_attention_heads
             lowercase__      			= decoder_attention_heads
             lowercase__      			= eos_token_id
             lowercase__      			= bos_token_id
             lowercase__      			= pad_token_id
             lowercase__      			= decoder_start_token_id
             lowercase__      			= use_cache
             lowercase__      			= max_position_embeddings

             lowercase__      			= None
             lowercase__      			= decoder_seq_length
             lowercase__      			= 2
             lowercase__      			= 1



      def       UpperCamelCase__		(self    :    str ):

             '''simple docstring'''







             lowercase__      			= ids_tensor([self.batch_size, self.decoder_seq_length]   ,						self.vocab_size )

             lowercase__      			= None
             if self.use_attention_mask:
                    lowercase__      			= ids_tensor([self.batch_size, self.decoder_seq_length]   ,						vocab_size=2 )

             lowercase__      			= None
             if self.use_labels:
                    lowercase__      			= ids_tensor([self.batch_size, self.decoder_seq_length]   ,						self.vocab_size )

             lowercase__      			= TrOCRConfig(
                 vocab_size=self.vocab_size   ,						d_model=self.d_model   ,						decoder_layers=self.decoder_layers   ,						decoder_ffn_dim=self.decoder_ffn_dim   ,						decoder_attention_heads=self.decoder_attention_heads   ,						eos_token_id=self.eos_token_id   ,						bos_token_id=self.bos_token_id   ,						use_cache=self.use_cache   ,						pad_token_id=self.pad_token_id   ,						decoder_start_token_id=self.decoder_start_token_id   ,						max_position_embeddings=self.max_position_embeddings   ,						)

             return (config, input_ids, attention_mask, lm_labels)



      def       UpperCamelCase__		(self    :    Tuple   ,						UpperCamelCase    :    List[Any]   ,						UpperCamelCase    :    Optional[Any]   ,						UpperCamelCase    :    Tuple   ,						UpperCamelCase    :    List[str]   ,						):

             '''simple docstring'''







             lowercase__      			= True
             lowercase__      			= TrOCRDecoder(config=UpperCamelCase ).to(UpperCamelCase ).eval()
             lowercase__      			= input_ids[:2]

             input_ids[input_ids == 0] += 1
             # first forward pass
             lowercase__      			= model(UpperCamelCase   ,						use_cache=UpperCamelCase )
             lowercase__      			= model(UpperCamelCase )
             lowercase__      			= model(UpperCamelCase   ,						use_cache=UpperCamelCase )

             self.parent.assertTrue(len(UpperCamelCase ) == len(UpperCamelCase ) )
             self.parent.assertTrue(len(UpperCamelCase ) == len(UpperCamelCase ) + 1 )

             lowercase__      			= outputs['''past_key_values''']

             # create hypothetical next token and extent to next_input_ids
             lowercase__      			= ids_tensor((2, 1)   ,						config.vocab_size - 1 ) + 1

             # append to next input_ids and
             lowercase__      			= torch.cat([input_ids, next_tokens]   ,						dim=-1 )

             lowercase__      			= model(UpperCamelCase )['''last_hidden_state''']
             lowercase__      			= model(UpperCamelCase   ,						past_key_values=UpperCamelCase )['''last_hidden_state''']

             # select random slice
             lowercase__      			= ids_tensor((1,)   ,						output_from_past.shape[-1] ).item()
             lowercase__      			= output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
             lowercase__      			= output_from_past[:, 0, random_slice_idx].detach()

             # test that outputs are equal for slice
             assert torch.allclose(UpperCamelCase   ,						UpperCamelCase   ,						atol=1E-3 )






      def       UpperCamelCase__		(self    :    Optional[Any] ):

             '''simple docstring'''







             lowercase__      			= self.prepare_config_and_inputs()
             lowercase__			,lowercase__			,lowercase__			,lowercase__      			= config_and_inputs

             lowercase__      			= {'''input_ids''': input_ids, '''attention_mask''': attention_mask}
             return config, inputs_dict






@require_torch
class   __lowerCAmelCase						(lowercase_  ,     lowercase_  ,     lowercase_  ,     unittest.TestCase     ):





      '''simple docstring'''






      lowerCAmelCase__					:     List[str]  					=							(TrOCRDecoder, TrOCRForCausalLM) if is_torch_available() else ()
      lowerCAmelCase__					:     List[Any]  					=							(TrOCRForCausalLM,) if is_torch_available() else ()
      lowerCAmelCase__					:     Optional[Any]  					=							{"""text-generation""": TrOCRForCausalLM} if is_torch_available() else {}
      lowerCAmelCase__					:     Optional[Any]  					=							True
      lowerCAmelCase__					:     List[str]  					=							False



      def       UpperCamelCase__		(self    :    Any ):

             '''simple docstring'''







             lowercase__      			= TrOCRStandaloneDecoderModelTester(self   ,						is_training=UpperCamelCase )
             lowercase__      			= ConfigTester(self   ,						config_class=UpperCamelCase )



      def       UpperCamelCase__		(self    :    List[str] ):

             '''simple docstring'''







             pass



      def       UpperCamelCase__		(self    :    Optional[int] ):

             '''simple docstring'''







             pass



      def       UpperCamelCase__		(self    :    Any ):

             '''simple docstring'''







             pass



      def       UpperCamelCase__		(self    :    Any ):

             '''simple docstring'''







             self.config_tester.run_common_tests()



      def       UpperCamelCase__		(self    :    Union[str, Any] ):

             '''simple docstring'''







             lowercase__      			= self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_decoder_model_past(*UpperCamelCase )



      def       UpperCamelCase__		(self    :    Optional[int] ):

             '''simple docstring'''







             return






      @unittest.skip('''The model doesn\'t support left padding''' )  # and it's not used enough to be worth fixing :)
      def       UpperCamelCase__		(self    :    List[str] ):

             '''simple docstring'''







             pass




