
import collections
import os
from typing import List, Optional, Tuple

from transformers.utils import is_jieba_available, requires_backends


if is_jieba_available():
		import jieba

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


__SCREAMING_SNAKE_CASE      : Union[str, Any]       =  logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE      : Optional[int]       =  {"""vocab_file""": """vocab.txt"""}

__SCREAMING_SNAKE_CASE      : Any       =  {
    """vocab_file""": {
        """openbmb/cpm-ant-10b""": """https://huggingface.co/openbmb/cpm-ant-10b/blob/main/vocab.txt""",
    },
}

__SCREAMING_SNAKE_CASE      : Optional[int]       =  {
    """openbmb/cpm-ant-10b""": 10_24,
}
def      UpperCAmelCase__				(							__magic_name__    :  Tuple	):


					'''simple docstring'''

					lowerCAmelCase		:       Tuple							=  collections.OrderedDict()
					with open(SCREAMING_SNAKE_CASE__				,	'''r'''				,	encoding='''utf-8'''	) as reader:
										lowerCAmelCase		:       int							=  reader.readlines()
					for index, token in enumerate(SCREAMING_SNAKE_CASE__	):
										lowerCAmelCase		:       str							=  token.rstrip('''\n'''	)
										lowerCAmelCase		:       Dict							=  index
					return vocab


class 		__magic_name__ (    _lowerCAmelCase     ):





		def __init__(				self     :						str    ,	lowerCamelCase__     :						List[Any]    ,	lowerCamelCase__     :						List[str]="<unk>"    ,	lowerCamelCase__     :						List[Any]=2_0_0					):
							lowerCAmelCase		:       str							=  vocab
							lowerCAmelCase		:       Optional[Any]							=  unk_token
							lowerCAmelCase		:       Tuple							=  max_input_chars_per_word





		def    _A				(				self     :						str    ,	lowerCamelCase__     :						List[Any]					):
							lowerCAmelCase		:       Union[str, Any]							=  list(lowerCamelCase__					)
							if len(lowerCamelCase__					) > self.max_input_chars_per_word:
												return [self.unk_token]

							lowerCAmelCase		:       Dict							=  0
							lowerCAmelCase		:       List[str]							=  []
							while start < len(lowerCamelCase__					):
												lowerCAmelCase		:       str							=  len(lowerCamelCase__					)
												lowerCAmelCase		:       int							=  None
												while start < end:
																	lowerCAmelCase		:       Optional[int]							=  ''''''.join(chars[start:end]					)
																	if substr in self.vocab:
																						lowerCAmelCase		:       Union[str, Any]							=  substr
																						break
																	end -= 1
												if cur_substr is None:
																	sub_tokens.append(self.unk_token					)
																	start += 1
												else:
																	sub_tokens.append(lowerCamelCase__					)
																	lowerCAmelCase		:       Dict							=  end

							return sub_tokens



class 		__magic_name__ (    _lowerCAmelCase     ):
		_lowerCAmelCase		=						VOCAB_FILES_NAMES
		_lowerCAmelCase		=						PRETRAINED_VOCAB_FILES_MAP
		_lowerCAmelCase		=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		_lowerCAmelCase		=						["input_ids", "attention_mask"]
		_lowerCAmelCase		=						False





		def __init__(				self     :						Any    ,	lowerCamelCase__     :						int    ,	lowerCamelCase__     :						List[Any]="<d>"    ,	lowerCamelCase__     :						List[str]="</d>"    ,	lowerCamelCase__     :						Optional[Any]="<s>"    ,	lowerCamelCase__     :						Dict="</s>"    ,	lowerCamelCase__     :						Tuple="<pad>"    ,	lowerCamelCase__     :						str="<unk>"    ,	lowerCamelCase__     :						Tuple="</n>"    ,	lowerCamelCase__     :						Optional[int]="</_>"    ,	lowerCamelCase__     :						Optional[Any]="left"    ,	**lowerCamelCase__     :						Tuple    ,	):
							requires_backends(self    ,	['''jieba''']					)
							super().__init__(
							    bod_token=lowerCamelCase__    ,	eod_token=lowerCamelCase__    ,	bos_token=lowerCamelCase__    ,	eos_token=lowerCamelCase__    ,	pad_token=lowerCamelCase__    ,	unk_token=lowerCamelCase__    ,	line_token=lowerCamelCase__    ,	space_token=lowerCamelCase__    ,	padding_side=lowerCamelCase__    ,	**lowerCamelCase__    ,	)
							lowerCAmelCase		:       Optional[int]							=  bod_token
							lowerCAmelCase		:       int							=  eod_token
							lowerCAmelCase		:       List[Any]							=  load_vocab(lowerCamelCase__					)
							lowerCAmelCase		:       Optional[Any]							=  self.encoder[space_token]
							lowerCAmelCase		:       List[Any]							=  self.encoder[line_token]

							del self.encoder[space_token]
							del self.encoder[line_token]

							lowerCAmelCase		:       Optional[Any]							=  collections.OrderedDict(sorted(self.encoder.items()    ,	key=lambda lowerCamelCase__					: x[1]					)					)
							lowerCAmelCase		:       Optional[int]							=  {v: k for k, v in self.encoder.items()}

							lowerCAmelCase		:       Optional[int]							=  WordpieceTokenizer(vocab=self.encoder    ,	unk_token=self.unk_token					)





		@property
		def    _A				(				self     :						Dict					):
							return self.encoder[self.bod_token]





		@property
		def    _A				(				self     :						Tuple					):
							return self.encoder[self.eod_token]





		@property
		def    _A				(				self     :						str					):
							return self.encoder["\n"]





		@property
		def    _A				(				self     :						str					):
							return len(self.encoder					)





		def    _A				(				self     :						List[Any]					):
							return dict(self.encoder    ,	**self.added_tokens_encoder					)





		def    _A				(				self     :						Optional[Any]    ,	lowerCamelCase__     :						Optional[int]					):
							lowerCAmelCase		:       Any							=  []
							for x in jieba.cut(lowerCamelCase__    ,	cut_all=lowerCamelCase__					):
												output_tokens.extend(self.wordpiece_tokenizer.tokenize(lowerCamelCase__					)					)
							return output_tokens





		def    _A				(				self     :						Any    ,	lowerCamelCase__     :						Optional[Any]    ,	**lowerCamelCase__     :						Optional[Any]					):
							lowerCAmelCase		:       int							=  [i for i in token_ids if i >= 0]
							lowerCAmelCase		:       List[str]							=  [
							    x for x in token_ids if x != self.pad_token_id and x != self.eos_token_id and x != self.bos_token_id
							]
							return super()._decode(lowerCamelCase__    ,	**lowerCamelCase__					)





		def    _A				(				self     :						List[Any]    ,	lowerCamelCase__     :						Union[str, Any]					):
							return token in self.encoder





		def    _A				(				self     :						int    ,	lowerCamelCase__     :						List[Any]					):
							return "".join(lowerCamelCase__					)





		def    _A				(				self     :						Tuple    ,	lowerCamelCase__     :						Union[str, Any]					):
							return self.encoder.get(lowerCamelCase__    ,	self.encoder.get(self.unk_token					)					)





		def    _A				(				self     :						str    ,	lowerCamelCase__     :						int					):
							return self.decoder.get(lowerCamelCase__    ,	self.unk_token					)





		def    _A				(				self     :						Tuple    ,	lowerCamelCase__     :						List[str]    ,	lowerCamelCase__     :						Tuple = None					):
							if os.path.isdir(lowerCamelCase__					):
												lowerCAmelCase		:       Tuple							=  os.path.join(
												    lowerCamelCase__    ,	(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']					)
							else:
												lowerCAmelCase		:       Dict							=  (filename_prefix + '''-''' if filename_prefix else '''''') + save_directory
							lowerCAmelCase		:       Dict							=  0
							if " " in self.encoder:
												lowerCAmelCase		:       Tuple							=  self.encoder[''' ''']
												del self.encoder[" "]
							if "\n" in self.encoder:
												lowerCAmelCase		:       Union[str, Any]							=  self.encoder['''\n''']
												del self.encoder["\n"]
							lowerCAmelCase		:       Optional[Any]							=  collections.OrderedDict(sorted(self.encoder.items()    ,	key=lambda lowerCamelCase__					: x[1]					)					)
							with open(lowerCamelCase__    ,	'''w'''    ,	encoding='''utf-8'''					) as writer:
												for token, token_index in self.encoder.items():
																	if index != token_index:
																						logger.warning(
																						    f'''Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.'''
																						    ''' Please check that the vocabulary is not corrupted!'''					)
																						lowerCAmelCase		:       str							=  token_index
																	writer.write(token + '''\n'''					)
																	index += 1
							return (vocab_file,)





		def    _A				(				self     :						Dict    ,	lowerCamelCase__     :						Optional[Any]    ,	lowerCamelCase__     :						Optional[int] = None					):
							if token_ids_a is None:
												return [self.bos_token_id] + token_ids_a
							return [self.bos_token_id] + token_ids_a + [self.bos_token_id] + token_ids_a





		def    _A				(				self     :						Union[str, Any]    ,	lowerCamelCase__     :						Dict    ,	lowerCamelCase__     :						Optional[Any] = None    ,	lowerCamelCase__     :						Union[str, Any] = False					):

							if already_has_special_tokens:
												return super().get_special_tokens_mask(
												    token_ids_a=lowerCamelCase__    ,	token_ids_a=lowerCamelCase__    ,	already_has_special_tokens=lowerCamelCase__					)

							if token_ids_a is not None:
												return [1] + ([0] * len(lowerCamelCase__					)) + [1] + ([0] * len(lowerCamelCase__					))
							return [1] + ([0] * len(lowerCamelCase__					))








'''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"""