






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	:		Tuple										=   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	:		int										=   {
    'gpt2': 10_24,
    'gpt2-medium': 10_24,
    'gpt2-large': 10_24,
    'gpt2-xl': 10_24,
    'distilgpt2': 10_24,
}







class  _lowercase   (			_A						):
      _a							:						str										=							VOCAB_FILES_NAMES
      _a							:						List[Any]										=							PRETRAINED_VOCAB_FILES_MAP
      _a							:						Union[str, Any]										=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _a							:						List[Any]										=							['''input_ids''', '''attention_mask''']
      _a							:						Tuple										=							GPTaTokenizer
      def __init__(     self				,			a=None				,			a=None				,			a=None				,			a="<|endoftext|>"				,			a="<|endoftext|>"				,			a="<|endoftext|>"				,			a=False				,			**a				,			):
       super().__init__(
           A__				,			A__				,			tokenizer_file=A__				,			unk_token=A__				,			bos_token=A__				,			eos_token=A__				,			add_prefix_space=A__				,			**A__				,			)

       snake_case__  :    Union[str, Any]							       =kwargs.pop("""add_bos_token"""				,			A__   )

       snake_case__  :    Union[str, Any]							       =json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()   )
       if pre_tok_state.get("""add_prefix_space"""				,			A__   ) != add_prefix_space:
        snake_case__  :    Union[str, Any]							       =getattr(A__				,			pre_tok_state.pop("""type"""   )   )
        snake_case__  :    Tuple							       =add_prefix_space
        snake_case__  :    Dict							       =pre_tok_class(**A__   )

       snake_case__  :    Dict							       =add_prefix_space
      def 						lowercase__					(     self				,			*a				,			**a   ):
       snake_case__  :    Any							       =kwargs.get("""is_split_into_words"""				,			A__   )
       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(*A__				,			**A__   )
      def 						lowercase__					(     self				,			*a				,			**a   ):
       snake_case__  :    Optional[Any]							       =kwargs.get("""is_split_into_words"""				,			A__   )

       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(*A__				,			**A__   )
      def 						lowercase__					(     self				,			a				,			a = None   ):
       snake_case__  :    Optional[int]							       =self._tokenizer.model.save(A__				,			name=A__   )
       return tuple(A__   )



      def 						lowercase__					(     self				,			a   ):
       snake_case__  :    Optional[Any]							       =[]
       for is_user, text in conversation.iter_texts():
        input_ids.extend(self.encode(A__				,			add_special_tokens=A__   ) + [self.eos_token_id]   )

       if len(A__   ) > self.model_max_length:
        snake_case__  :    str							       =input_ids[-self.model_max_length :]
       return input_ids













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

       __lowerCamelCase	:		Dict										=   tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 2_55)

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

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





