





'''simple docstring'''


from __future__ import annotations

import unittest

from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
		import tensorflow as tf

		from transformers import TFAutoModelForSeqaSeqLM, TFMBartForConditionalGeneration, TFMBartModel






@require_tf
class       UpperCamelCase__  :
	lowerCAmelCase__					:			Union[str, Any]								=						MBartConfig
	lowerCAmelCase__					:			Union[str, Any]								=						{}
	lowerCAmelCase__					:			List[str]								=						"gelu"







	def __init__(				self			:   Union[str, Any] , lowerCamelCase			:   Dict , lowerCamelCase			:   str=1_3 , lowerCamelCase			:   Dict=7 , lowerCamelCase			:   int=True , lowerCamelCase			:   str=False , lowerCamelCase			:   Dict=9_9 , lowerCamelCase			:   Dict=3_2 , lowerCamelCase			:   str=2 , lowerCamelCase			:   Optional[Any]=4 , lowerCamelCase			:   Dict=3_7 , lowerCamelCase			:   str=0.1 , lowerCamelCase			:   List[str]=0.1 , lowerCamelCase			:   str=2_0 , lowerCamelCase			:   Optional[int]=2 , lowerCamelCase			:   Optional[Any]=1 , lowerCamelCase			:   Union[str, Any]=0 , ):




				'''simple docstring'''
				a__     = parent
				a__     = batch_size
				a__     = seq_length
				a__     = is_training
				a__     = use_labels
				a__     = vocab_size
				a__     = hidden_size
				a__     = num_hidden_layers
				a__     = num_attention_heads
				a__     = intermediate_size
				a__     = hidden_dropout_prob
				a__     = attention_probs_dropout_prob
				a__     = max_position_embeddings
				a__     = eos_token_id
				a__     = pad_token_id
				a__     = bos_token_id







	def 						__a   (				self			:   Optional[int]		):




				'''simple docstring'''
				a__     = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size		)
				a__     = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size		) , 1		)
				a__     = tf.concat([input_ids, eos_tensor] , axis=1		)

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

				a__     = self.config_cls(
				    vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , **self.config_updates , )
				a__     = prepare_mbart_inputs_dict(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase		)
				return config, inputs_dict







	def 						__a   (				self			:   Tuple , lowerCamelCase			:   int , lowerCamelCase			:   List[Any]		):




				'''simple docstring'''
				a__     = TFMBartModel(config=_lowerCamelCase		).get_decoder()
				a__     = inputs_dict["input_ids"]

				a__     = input_ids[:1, :]
				a__     = inputs_dict["attention_mask"][:1, :]
				a__     = inputs_dict["head_mask"]
				a__     = 1

				# first forward pass
				a__     = model(_lowerCamelCase , attention_mask=_lowerCamelCase , head_mask=_lowerCamelCase , use_cache=_lowerCamelCase		)

				a__   ,				a__     = outputs.to_tuple()
				a__     = past_key_values[1]







def    _lowerCamelCase  (__lowerCamelCase  :     Any					,  __lowerCamelCase  :     Any					,  __lowerCamelCase  :     Optional[int]					,  __lowerCamelCase  :     List[str]=None					,  __lowerCamelCase  :     Any=None					,  __lowerCamelCase  :     List[Any]=None					,  __lowerCamelCase  :     List[Any]=None					,  __lowerCamelCase  :     List[str]=None					,  )   ->    Optional[int]:

			'''simple docstring'''





			if attention_mask is None:
						a__     = tf.cast(tf.math.not_equal(_lowerCAmelCase					,  config.pad_token_id     )					,  tf.inta     )
			if decoder_attention_mask is None:
						a__     = tf.concat(
						    [
						        tf.ones(decoder_input_ids[:, :1].shape					,  dtype=tf.inta     ),
						        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]					,  config.pad_token_id     )					,  tf.inta     ),
						    ]					,  axis=-1					,  )
			if head_mask is None:
						a__     = tf.ones((config.encoder_layers, config.encoder_attention_heads)     )
			if decoder_head_mask is None:
						a__     = tf.ones((config.decoder_layers, config.decoder_attention_heads)     )
			if cross_attn_head_mask is None:
						a__     = tf.ones((config.decoder_layers, config.decoder_attention_heads)     )
			return {
			    "input_ids": input_ids,
			    "decoder_input_ids": decoder_input_ids,
			    "attention_mask": attention_mask,
			    "decoder_attention_mask": decoder_attention_mask,
			    "head_mask": head_mask,
			    "decoder_head_mask": decoder_head_mask,
			    "cross_attn_head_mask": cross_attn_head_mask,
			}






@require_tf
class       UpperCamelCase__  (		__UpperCAmelCase	,__UpperCAmelCase	,unittest.TestCase     ):
	lowerCAmelCase__					:			Tuple								=						(TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
	lowerCAmelCase__					:			int								=						(TFMBartForConditionalGeneration,) if is_tf_available() else ()
	lowerCAmelCase__					:			Tuple								=						(
	    {
	        "conversational": TFMBartForConditionalGeneration,
	        "feature-extraction": TFMBartModel,
	        "summarization": TFMBartForConditionalGeneration,
	        "text2text-generation": TFMBartForConditionalGeneration,
	        "translation": TFMBartForConditionalGeneration,
	    }
	    if is_tf_available()
	    else {}
	)
	lowerCAmelCase__					:			Any								=						True
	lowerCAmelCase__					:			Optional[int]								=						False
	lowerCAmelCase__					:			Tuple								=						False







	def 						__a   (				self			:   int , lowerCamelCase			:   Union[str, Any] , lowerCamelCase			:   Union[str, Any] , lowerCamelCase			:   List[str] , lowerCamelCase			:   Any , lowerCamelCase			:   str		):




				'''simple docstring'''
				if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
							# Exception encountered when calling layer '...'
							return True

				return False







	def 						__a   (				self			:   Optional[Any]		):




				'''simple docstring'''
				a__     = TFMBartModelTester(self		)
				a__     = ConfigTester(self , config_class=_lowerCamelCase		)







	def 						__a   (				self			:   Tuple		):




				'''simple docstring'''
				self.config_tester.run_common_tests()







	def 						__a   (				self			:   Optional[Any]		):




				'''simple docstring'''
				a__     = self.model_tester.prepare_config_and_inputs_for_common()
				self.model_tester.check_decoder_model_past_large_inputs(*_lowerCamelCase		)






@require_sentencepiece
@require_tokenizers
@require_tf
class       UpperCamelCase__  (		unittest.TestCase     ):
	lowerCAmelCase__					:			Optional[int]								=						[
	    " UN Chief Says There Is No Military Solution in Syria",
	]
	lowerCAmelCase__					:			List[str]								=						[
	    "Şeful ONU declară că nu există o soluţie militară în Siria",
	]
	lowerCAmelCase__					:			str								=						"facebook/mbart-large-en-ro"







	@cached_property
	def 						__a   (				self			:   List[Any]		):




				'''simple docstring'''
				return AutoTokenizer.from_pretrained(self.model_name		)







	@cached_property
	def 						__a   (				self			:   str		):




				'''simple docstring'''
				a__     = TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name		)
				return model







	def 						__a   (				self			:   Dict , **lowerCamelCase			:   Optional[Any]		):




				'''simple docstring'''
				a__     = self.translate_src_text(**_lowerCamelCase		)
				self.assertListEqual(self.expected_text , _lowerCamelCase		)







	def 						__a   (				self			:   Union[str, Any] , **lowerCamelCase			:   Optional[Any]		):




				'''simple docstring'''
				a__     = self.tokenizer(self.src_text , **_lowerCamelCase , return_tensors="tf"		)
				a__     = self.model.generate(
				    model_inputs.input_ids , attention_mask=model_inputs.attention_mask , num_beams=2		)
				a__     = self.tokenizer.batch_decode(_lowerCamelCase , skip_special_tokens=_lowerCamelCase		)
				return generated_words







	@slow
	def 						__a   (				self			:   List[str]		):




				'''simple docstring'''
				self._assert_generated_batch_equal_expected()










'''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)
		lowerCAmelCase_   :   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')

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

		lowerCAmelCase_   :   List[Any]       =					tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 255)

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

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



