





'''simple docstring'''


import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer


if TYPE_CHECKING:
		from transformers.pipelines.conversational import Conversation


lowerCAmelCase_   :   Dict       =					logging.get_logger(__name__)

lowerCAmelCase_   :   Optional[int]       =					{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

lowerCAmelCase_   :   List[str]       =					{
    "vocab_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/vocab.json",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/vocab.json",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/vocab.json",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/vocab.json",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/vocab.json",
    },
    "merges_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/merges.txt",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/merges.txt",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/merges.txt",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/merges.txt",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/tokenizer.json",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/tokenizer.json",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/tokenizer.json",
    },
}

lowerCAmelCase_   :   List[Any]       =					{
    "gpt2": 1024,
    "gpt2-medium": 1024,
    "gpt2-large": 1024,
    "gpt2-xl": 1024,
    "distilgpt2": 1024,
}






class       UpperCamelCase__  (		__lowerCAmelCase     ):
	lowerCAmelCase__					:			Any								=						VOCAB_FILES_NAMES
	lowerCAmelCase__					:			Any								=						PRETRAINED_VOCAB_FILES_MAP
	lowerCAmelCase__					:			Dict								=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowerCAmelCase__					:			Union[str, Any]								=						["input_ids", "attention_mask"]
	lowerCAmelCase__					:			str								=						GPTaTokenizer







	def __init__(				self			:   Optional[int] , lowerCamelCase			:   str=None , lowerCamelCase			:   str=None , lowerCamelCase			:   str=None , lowerCamelCase			:   str="<|endoftext|>" , lowerCamelCase			:   str="<|endoftext|>" , lowerCamelCase			:   Union[str, Any]="<|endoftext|>" , lowerCamelCase			:   Any=False , **lowerCamelCase			:   List[str] , ):




				'''simple docstring'''
				super().__init__(
				    lowerCamelCase , lowerCamelCase , tokenizer_file=lowerCamelCase , unk_token=lowerCamelCase , bos_token=lowerCamelCase , eos_token=lowerCamelCase , add_prefix_space=lowerCamelCase , **lowerCamelCase , )

				a__     = kwargs.pop("add_bos_token" , lowerCamelCase		)

				a__     = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()		)
				if pre_tok_state.get("add_prefix_space" , lowerCamelCase		) != add_prefix_space:
							a__     = getattr(lowerCamelCase , pre_tok_state.pop("type"		)		)
							a__     = add_prefix_space
							a__     = pre_tok_class(**lowerCamelCase		)

				a__     = add_prefix_space







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




				'''simple docstring'''
				a__     = kwargs.get("is_split_into_words" , lowerCamelCase		)
				assert self.add_prefix_space or not is_split_into_words, (
				    F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
				    "to use it with pretokenized inputs."
				)

				return super()._batch_encode_plus(*lowerCamelCase , **lowerCamelCase		)







	def 						__a   (				self			:   Dict , *lowerCamelCase			:   Any , **lowerCamelCase			:   str		):




				'''simple docstring'''
				a__     = kwargs.get("is_split_into_words" , lowerCamelCase		)

				assert self.add_prefix_space or not is_split_into_words, (
				    F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
				    "to use it with pretokenized inputs."
				)

				return super()._encode_plus(*lowerCamelCase , **lowerCamelCase		)







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




				'''simple docstring'''
				a__     = self._tokenizer.model.save(lowerCamelCase , name=lowerCamelCase		)
				return tuple(lowerCamelCase		)







	def 						__a   (				self			:   Dict , lowerCamelCase			:   "Conversation"		):




				'''simple docstring'''
				a__     = []
				for is_user, text in conversation.iter_texts():
							input_ids.extend(self.encode(lowerCamelCase , add_special_tokens=lowerCamelCase		) + [self.eos_token_id]		)

				if len(lowerCamelCase		) > self.model_max_length:
							a__     = input_ids[-self.model_max_length :]
				return input_ids










'''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"



