






'''simple docstring'''



from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


__UpperCamelCase   :							str         =   logging.get_logger(__name__)

__UpperCamelCase   :							List[str]         =   {
    """EleutherAI/gpt-j-6B""": """https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json""",
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}
class 				__SCREAMING_SNAKE_CASE							(				_lowerCAmelCase  ):
      __a	       ="gptj"
      __a	       ={
          "max_position_embeddings": "n_positions",
          "hidden_size": "n_embd",
          "num_attention_heads": "n_head",
          "num_hidden_layers": "n_layer",
      }




      def __init__(	self			, lowerCamelCase=5_0400			, lowerCamelCase=2048			, lowerCamelCase=4096			, lowerCamelCase=28			, lowerCamelCase=16			, lowerCamelCase=64			, lowerCamelCase=None			, lowerCamelCase="gelu_new"			, lowerCamelCase=0.0			, lowerCamelCase=0.0			, lowerCamelCase=0.0			, lowerCamelCase=1e-5			, lowerCamelCase=0.02			, lowerCamelCase=True			, lowerCamelCase=5_0256			, lowerCamelCase=5_0256			, lowerCamelCase=False			, **lowerCamelCase			, )				->Any:



         '''simple docstring'''
         __a					  =     vocab_size
         __a					  =     n_positions
         __a					  =     n_embd
         __a					  =     n_layer
         __a					  =     n_head
         __a					  =     n_inner
         __a					  =     rotary_dim
         __a					  =     activation_function
         __a					  =     resid_pdrop
         __a					  =     embd_pdrop
         __a					  =     attn_pdrop
         __a					  =     layer_norm_epsilon
         __a					  =     initializer_range
         __a					  =     use_cache

         __a					  =     bos_token_id
         __a					  =     eos_token_id

         super().__init__(
             bos_token_id=lowerCamelCase			, eos_token_id=lowerCamelCase			, tie_word_embeddings=lowerCamelCase			, **lowerCamelCase   )







class 				__SCREAMING_SNAKE_CASE							(				_lowerCAmelCase  ):




      def __init__(	self			, lowerCamelCase			, lowerCamelCase = "default"			, lowerCamelCase = None			, lowerCamelCase = False			, )				->Tuple:



         '''simple docstring'''
         super().__init__(lowerCamelCase			, task=lowerCamelCase			, patching_specs=lowerCamelCase			, use_past=lowerCamelCase   )
         if not getattr(self._config			, 'pad_token_id'			, lowerCamelCase   ):
            # TODO: how to do that better?
            __a					  =     0




      @property
      def 			__UpperCamelCase				(	self   )				->Mapping[str, Mapping[int, str]]:



         '''simple docstring'''
         __a					  =     OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}   )
         if self.use_past:
            self.fill_with_past_key_values_(lowerCamelCase			, direction='inputs'   )
            __a					  =     {0: 'batch', 1: 'past_sequence + sequence'}
         else:
            __a					  =     {0: 'batch', 1: 'sequence'}

         return common_inputs




      @property
      def 			__UpperCamelCase				(	self   )				->int:



         '''simple docstring'''
         return self._config.n_layer




      @property
      def 			__UpperCamelCase				(	self   )				->int:



         '''simple docstring'''
         return self._config.n_head




      def 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase = -1			, lowerCamelCase = -1			, lowerCamelCase = False			, lowerCamelCase = None			, )				->Mapping[str, Any]:



         '''simple docstring'''
         __a					  =     super(lowerCamelCase			, self   ).generate_dummy_inputs(
             lowerCamelCase			, batch_size=lowerCamelCase			, seq_length=lowerCamelCase			, is_pair=lowerCamelCase			, framework=lowerCamelCase   )

         # We need to order the input in the way they appears in the forward()
         __a					  =     OrderedDict({'input_ids': common_inputs['input_ids']}   )

         # Need to add the past_keys
         if self.use_past:
            if not is_torch_available():
               raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'   )
            else:
               import torch

               __a  ,      __a					  =     common_inputs['input_ids'].shape
               # Not using the same length for past_key_values
               __a					  =     seqlen + 2
               __a					  =     (
                   batch,
                   self.num_attention_heads,
                   past_key_values_length,
                   self._config.hidden_size // self.num_attention_heads,
               )
               __a					  =     [
                   (torch.zeros(lowerCamelCase   ), torch.zeros(lowerCamelCase   )) for _ in range(self.num_layers   )
               ]

         __a					  =     common_inputs['attention_mask']
         if self.use_past:
            __a					  =     ordered_inputs['attention_mask'].dtype
            __a					  =     torch.cat(
                [ordered_inputs['attention_mask'], torch.ones(lowerCamelCase			, lowerCamelCase			, dtype=lowerCamelCase   )]			, dim=1   )

         return ordered_inputs






      @property
      def 			__UpperCamelCase				(	self   )				->int:



         '''simple docstring'''
         return 13







'''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)
 __UpperCamelCase   :							Union[str, Any]         =   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')

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

 __UpperCamelCase   :							Union[str, Any]         =   tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 255)

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

 __UpperCamelCase   :							str         =   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

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