





'''simple docstring'''


import json
import os
from typing import Optional, Tuple

import regex as re

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase_   :   List[str]       =					logging.get_logger(__name__)

lowerCAmelCase_   :   Union[str, Any]       =					{
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
}

lowerCAmelCase_   :   Tuple       =					{
    "vocab_file": {"ctrl": "https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-vocab.json"},
    "merges_file": {"ctrl": "https://raw.githubusercontent.com/salesforce/ctrl/master/ctrl-merges.txt"},
}

lowerCAmelCase_   :   Tuple       =					{
    "ctrl": 256,
}

lowerCAmelCase_   :   List[Any]       =					{
    "Pregnancy": 168629,
    "Christianity": 7675,
    "Explain": 106423,
    "Fitness": 63440,
    "Saving": 63163,
    "Ask": 27171,
    "Ass": 95985,
    "Joke": 163509,
    "Questions": 45622,
    "Thoughts": 49605,
    "Retail": 52342,
    "Feminism": 164338,
    "Writing": 11992,
    "Atheism": 192263,
    "Netflix": 48616,
    "Computing": 39639,
    "Opinion": 43213,
    "Alone": 44967,
    "Funny": 58917,
    "Gaming": 40358,
    "Human": 4088,
    "India": 1331,
    "Joker": 77138,
    "Diet": 36206,
    "Legal": 11859,
    "Norman": 4939,
    "Tip": 72689,
    "Weight": 52343,
    "Movies": 46273,
    "Running": 23425,
    "Science": 2090,
    "Horror": 37793,
    "Confession": 60572,
    "Finance": 12250,
    "Politics": 16360,
    "Scary": 191985,
    "Support": 12654,
    "Technologies": 32516,
    "Teenage": 66160,
    "Event": 32769,
    "Learned": 67460,
    "Notion": 182770,
    "Wikipedia": 37583,
    "Books": 6665,
    "Extract": 76050,
    "Confessions": 102701,
    "Conspiracy": 75932,
    "Links": 63674,
    "Narcissus": 150425,
    "Relationship": 54766,
    "Relationships": 134796,
    "Reviews": 41671,
    "News": 4256,
    "Translation": 26820,
    "multilingual": 128406,
}







def    _lowerCamelCase  (__lowerCamelCase  :     List[Any]     )   ->    str:
			a__     = set()
			a__     = word[0]
			for char in word[1:]:
						pairs.add((prev_char, char)     )
						a__     = char

			a__     = set(__lowerCamelCase     )
			return pairs






class       UpperCamelCase__  (		__lowerCAmelCase     ):
	lowerCAmelCase__					:			Union[str, Any]								=						VOCAB_FILES_NAMES
	lowerCAmelCase__					:			List[str]								=						PRETRAINED_VOCAB_FILES_MAP
	lowerCAmelCase__					:			List[Any]								=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowerCAmelCase__					:			List[Any]								=						CONTROL_CODES







	def __init__(				self			:   Any , lowerCamelCase			:   Any , lowerCamelCase			:   Tuple , lowerCamelCase			:   int="<unk>" , **lowerCamelCase			:   List[str]		):




				'''simple docstring'''
				super().__init__(unk_token=lowerCamelCase , **lowerCamelCase		)

				with open(lowerCamelCase , encoding="utf-8"		) as vocab_handle:
							a__     = json.load(lowerCamelCase		)
				a__     = {v: k for k, v in self.encoder.items()}
				with open(lowerCamelCase , encoding="utf-8"		) as merges_handle:
							a__     = merges_handle.read().split("\n"		)[1:-1]
				a__     = [tuple(merge.split()		) for merge in merges]
				a__     = dict(zip(lowerCamelCase , range(len(lowerCamelCase		)		)		)		)
				a__     = {}







	@property
	def 						__a   (				self			:   Union[str, Any]		):




				'''simple docstring'''
				return len(self.encoder		)







	def 						__a   (				self			:   List[str]		):




				'''simple docstring'''
				return dict(self.encoder , **self.added_tokens_encoder		)







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




				'''simple docstring'''
				if token in self.cache:
							return self.cache[token]
				a__     = tuple(lowerCamelCase		)
				a__     = tuple(list(word[:-1]		) + [word[-1] + "</w>"]		)
				a__     = get_pairs(lowerCamelCase		)

				if not pairs:
							return token

				while True:
							a__     = min(lowerCamelCase , key=lambda lowerCamelCase		: self.bpe_ranks.get(lowerCamelCase , float("inf"		)		)		)
							if bigram not in self.bpe_ranks:
										break
							a__   ,				a__     = bigram
							a__     = []
							a__     = 0
							while i < len(lowerCamelCase		):
										try:
													a__     = word.index(lowerCamelCase , lowerCamelCase		)
										except ValueError:
													new_word.extend(word[i:]		)
													break
										else:
													new_word.extend(word[i:j]		)
													a__     = j

										if word[i] == first and i < len(lowerCamelCase		) - 1 and word[i + 1] == second:
													new_word.append(first + second		)
													i += 2
										else:
													new_word.append(word[i]		)
													i += 1
							a__     = tuple(lowerCamelCase		)
							a__     = new_word
							if len(lowerCamelCase		) == 1:
										break
							else:
										a__     = get_pairs(lowerCamelCase		)
				a__     = "@@ ".join(lowerCamelCase		)
				a__     = word[:-4]
				a__     = word
				return word







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




				'''simple docstring'''
				a__     = []

				a__     = re.findall(r"\S+\n?" , lowerCamelCase		)

				for token in words:
							split_tokens.extend(list(self.bpe(lowerCamelCase		).split(" "		)		)		)
				return split_tokens







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




				'''simple docstring'''
				return self.encoder.get(lowerCamelCase , self.encoder.get(self.unk_token		)		)







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




				'''simple docstring'''
				return self.decoder.get(lowerCamelCase , self.unk_token		)







	def 						__a   (				self			:   int , lowerCamelCase			:   Tuple		):




				'''simple docstring'''
				a__     = " ".join(lowerCamelCase		).replace("@@ " , ""		).strip()
				return out_string







	def 						__a   (				self			:   Optional[int] , lowerCamelCase			:   str , lowerCamelCase			:   Optional[str] = None		):




				'''simple docstring'''
				if not os.path.isdir(lowerCamelCase		):
							logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''		)
							return
				a__     = os.path.join(
				    lowerCamelCase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)
				a__     = os.path.join(
				    lowerCamelCase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]		)

				with open(lowerCamelCase , "w" , encoding="utf-8"		) as f:
							f.write(json.dumps(self.encoder , indent=2 , sort_keys=lowerCamelCase , ensure_ascii=lowerCamelCase		) + "\n"		)

				a__     = 0
				with open(lowerCamelCase , "w" , encoding="utf-8"		) as writer:
							writer.write("#version: 0.2\n"		)
							for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda lowerCamelCase		: kv[1]		):
										if index != token_index:
													logger.warning(
													    F'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
													    " Please check that the tokenizer is not corrupted!"		)
													a__     = token_index
										writer.write(" ".join(lowerCamelCase		) + "\n"		)
										index += 1

				return vocab_file, merge_file

	# def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
	#     filtered_tokens = ' '.join(self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens))
	#     tokens_generated_so_far = re.sub('(@@ )', '', string=filtered_tokens)
	#     tokens_generated_so_far = re.sub('(@@ ?$)', '', string=tokens_generated_so_far)
	#     return ''.join(tokens_generated_so_far)










'''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"



