
"""simple docstring"""


import os
import unittest

from transformers import MobileBertTokenizer, MobileBertTokenizerFast
from transformers.models.bert.tokenization_bert import (
    VOCAB_FILES_NAMES,
    BasicTokenizer,
    WordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english



@require_tokenizers
class   __a  (      _lowerCAmelCase    ,     unittest.TestCase	):
       UpperCamelCase_		:					Union[str, Any]   =			MobileBertTokenizer
       UpperCamelCase_		:					Any   =			MobileBertTokenizerFast
       UpperCamelCase_		:					Union[str, Any]   =			True
       UpperCamelCase_		:					Union[str, Any]   =			True
       UpperCamelCase_		:					Optional[Any]   =			filter_non_english
       UpperCamelCase_		:					Union[str, Any]   =			'''google/mobilebert-uncased'''






       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[Any]			)->  Union[str, Any]:







              """simple docstring"""
              super().setUp()

              UpperCamelCase					 =		[
                  "[UNK]",
                  "[CLS]",
                  "[SEP]",
                  "[PAD]",
                  "[MASK]",
                  "want",
                  "##want",
                  "##ed",
                  "wa",
                  "un",
                  "runn",
                  "##ing",
                  ",",
                  "low",
                  "lowest",
              ]
              UpperCamelCase					 =		os.path.join(self.tmpdirname       ,      VOCAB_FILES_NAMES["vocab_file"]			)
              with open(self.vocab_file       ,      "w"       ,      encoding="utf-8"			) as vocab_writer:
                     vocab_writer.write("".join([x + "\n" for x in vocab_tokens]			)			)

              UpperCamelCase					 =		[
                  (tokenizer_def[0], self.pre_trained_model_path, tokenizer_def[2])  # else the 'google/' prefix is stripped
                  for tokenizer_def in self.tokenizers_list
              ]






       def 	_SCREAMING_SNAKE_CASE       (  self :							Tuple       ,      UpperCAmelCase_ :							str			)->  List[Any]:







              """simple docstring"""
              UpperCamelCase					 =		"UNwant\u00E9d,running"
              UpperCamelCase					 =		"unwanted, running"
              return input_text, output_text






       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[int]			)->  List[Any]:







              """simple docstring"""
              UpperCamelCase					 =		self.tokenizer_class(self.vocab_file			)

              UpperCamelCase					 =		tokenizer.tokenize("UNwant\u00E9d,running"			)
              self.assertListEqual(UpperCAmelCase_       ,      ["un", "##want", "##ed", ",", "runn", "##ing"]			)
              self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase_			)       ,      [9, 6, 7, 12, 10, 11]			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Dict			)->  int:







              """simple docstring"""
              if not self.test_rust_tokenizer:
                     return

              UpperCamelCase					 =		self.get_tokenizer()
              UpperCamelCase					 =		self.get_rust_tokenizer()

              UpperCamelCase					 =		"UNwant\u00E9d,running"

              UpperCamelCase					 =		tokenizer.tokenize(UpperCAmelCase_			)
              UpperCamelCase					 =		rust_tokenizer.tokenize(UpperCAmelCase_			)
              self.assertListEqual(UpperCAmelCase_       ,      UpperCAmelCase_			)

              UpperCamelCase					 =		tokenizer.encode(UpperCAmelCase_       ,      add_special_tokens=UpperCAmelCase_			)
              UpperCamelCase					 =		rust_tokenizer.encode(UpperCAmelCase_       ,      add_special_tokens=UpperCAmelCase_			)
              self.assertListEqual(UpperCAmelCase_       ,      UpperCAmelCase_			)

              UpperCamelCase					 =		self.get_rust_tokenizer()
              UpperCamelCase					 =		tokenizer.encode(UpperCAmelCase_			)
              UpperCamelCase					 =		rust_tokenizer.encode(UpperCAmelCase_			)
              self.assertListEqual(UpperCAmelCase_       ,      UpperCAmelCase_			)

              # With lower casing
              UpperCamelCase					 =		self.get_tokenizer(do_lower_case=UpperCAmelCase_			)
              UpperCamelCase					 =		self.get_rust_tokenizer(do_lower_case=UpperCAmelCase_			)

              UpperCamelCase					 =		"UNwant\u00E9d,running"

              UpperCamelCase					 =		tokenizer.tokenize(UpperCAmelCase_			)
              UpperCamelCase					 =		rust_tokenizer.tokenize(UpperCAmelCase_			)
              self.assertListEqual(UpperCAmelCase_       ,      UpperCAmelCase_			)

              UpperCamelCase					 =		tokenizer.encode(UpperCAmelCase_       ,      add_special_tokens=UpperCAmelCase_			)
              UpperCamelCase					 =		rust_tokenizer.encode(UpperCAmelCase_       ,      add_special_tokens=UpperCAmelCase_			)
              self.assertListEqual(UpperCAmelCase_       ,      UpperCAmelCase_			)

              UpperCamelCase					 =		self.get_rust_tokenizer()
              UpperCamelCase					 =		tokenizer.encode(UpperCAmelCase_			)
              UpperCamelCase					 =		rust_tokenizer.encode(UpperCAmelCase_			)
              self.assertListEqual(UpperCAmelCase_       ,      UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							int			)->  Union[str, Any]:







              """simple docstring"""
              UpperCamelCase					 =		BasicTokenizer()

              self.assertListEqual(tokenizer.tokenize("ah\u535A\u63A8zz"			)       ,      ["ah", "\u535A", "\u63A8", "zz"]			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Dict			)->  Optional[Any]:







              """simple docstring"""
              UpperCamelCase					 =		BasicTokenizer(do_lower_case=UpperCAmelCase_			)

              self.assertListEqual(
                  tokenizer.tokenize(" \tHeLLo!how  \n Are yoU?  "			)       ,      ["hello", "!", "how", "are", "you", "?"]			)
              self.assertListEqual(tokenizer.tokenize("H\u00E9llo"			)       ,      ["hello"]			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Tuple			)->  Dict:







              """simple docstring"""
              UpperCamelCase					 =		BasicTokenizer(do_lower_case=UpperCAmelCase_       ,      strip_accents=UpperCAmelCase_			)

              self.assertListEqual(
                  tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "			)       ,      ["hällo", "!", "how", "are", "you", "?"]			)
              self.assertListEqual(tokenizer.tokenize("H\u00E9llo"			)       ,      ["h\u00E9llo"]			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							List[Any]			)->  List[Any]:







              """simple docstring"""
              UpperCamelCase					 =		BasicTokenizer(do_lower_case=UpperCAmelCase_       ,      strip_accents=UpperCAmelCase_			)

              self.assertListEqual(
                  tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "			)       ,      ["hallo", "!", "how", "are", "you", "?"]			)
              self.assertListEqual(tokenizer.tokenize("H\u00E9llo"			)       ,      ["hello"]			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							List[str]			)->  List[Any]:







              """simple docstring"""
              UpperCamelCase					 =		BasicTokenizer(do_lower_case=UpperCAmelCase_			)

              self.assertListEqual(
                  tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "			)       ,      ["hallo", "!", "how", "are", "you", "?"]			)
              self.assertListEqual(tokenizer.tokenize("H\u00E9llo"			)       ,      ["hello"]			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Any			)->  Optional[Any]:







              """simple docstring"""
              UpperCamelCase					 =		BasicTokenizer(do_lower_case=UpperCAmelCase_			)

              self.assertListEqual(
                  tokenizer.tokenize(" \tHeLLo!how  \n Are yoU?  "			)       ,      ["HeLLo", "!", "how", "Are", "yoU", "?"]			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							str			)->  Dict:







              """simple docstring"""
              UpperCamelCase					 =		BasicTokenizer(do_lower_case=UpperCAmelCase_       ,      strip_accents=UpperCAmelCase_			)

              self.assertListEqual(
                  tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "			)       ,      ["HäLLo", "!", "how", "Are", "yoU", "?"]			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Dict			)->  Dict:







              """simple docstring"""
              UpperCamelCase					 =		BasicTokenizer(do_lower_case=UpperCAmelCase_       ,      strip_accents=UpperCAmelCase_			)

              self.assertListEqual(
                  tokenizer.tokenize(" \tHäLLo!how  \n Are yoU?  "			)       ,      ["HaLLo", "!", "how", "Are", "yoU", "?"]			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							str			)->  str:







              """simple docstring"""
              UpperCamelCase					 =		BasicTokenizer(do_lower_case=UpperCAmelCase_       ,      never_split=["[UNK]"]			)

              self.assertListEqual(
                  tokenizer.tokenize(" \tHeLLo!how  \n Are yoU? [UNK]"			)       ,      ["HeLLo", "!", "how", "Are", "yoU", "?", "[UNK]"]			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Union[str, Any]			)->  str:







              """simple docstring"""
              UpperCamelCase					 =		["[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn", "##ing"]

              UpperCamelCase					 =		{}
              for i, token in enumerate(UpperCAmelCase_			):
                     UpperCamelCase					 =		i
              UpperCamelCase					 =		WordpieceTokenizer(vocab=UpperCAmelCase_       ,      unk_token="[UNK]"			)

              self.assertListEqual(tokenizer.tokenize(""			)       ,      []			)

              self.assertListEqual(tokenizer.tokenize("unwanted running"			)       ,      ["un", "##want", "##ed", "runn", "##ing"]			)

              self.assertListEqual(tokenizer.tokenize("unwantedX running"			)       ,      ["[UNK]", "runn", "##ing"]			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Any			)->  Union[str, Any]:







              """simple docstring"""
              self.assertTrue(_is_whitespace(" "			)			)
              self.assertTrue(_is_whitespace("\t"			)			)
              self.assertTrue(_is_whitespace("\r"			)			)
              self.assertTrue(_is_whitespace("\n"			)			)
              self.assertTrue(_is_whitespace("\u00A0"			)			)

              self.assertFalse(_is_whitespace("A"			)			)
              self.assertFalse(_is_whitespace("-"			)			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[int]			)->  int:







              """simple docstring"""
              self.assertTrue(_is_control("\u0005"			)			)

              self.assertFalse(_is_control("A"			)			)
              self.assertFalse(_is_control(" "			)			)
              self.assertFalse(_is_control("\t"			)			)
              self.assertFalse(_is_control("\r"			)			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[Any]			)->  List[str]:







              """simple docstring"""
              self.assertTrue(_is_punctuation("-"			)			)
              self.assertTrue(_is_punctuation("$"			)			)
              self.assertTrue(_is_punctuation("`"			)			)
              self.assertTrue(_is_punctuation("."			)			)

              self.assertFalse(_is_punctuation("A"			)			)
              self.assertFalse(_is_punctuation(" "			)			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Any			)->  Union[str, Any]:







              """simple docstring"""
              UpperCamelCase					 =		self.get_tokenizer()
              UpperCamelCase					 =		self.get_rust_tokenizer()

              # Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
              self.assertListEqual([tokenizer.tokenize(UpperCAmelCase_			) for t in ["Test", "\xad", "test"]]       ,      [["[UNK]"], [], ["[UNK]"]]			)

              self.assertListEqual(
                  [rust_tokenizer.tokenize(UpperCAmelCase_			) for t in ["Test", "\xad", "test"]]       ,      [["[UNK]"], [], ["[UNK]"]]			)






       @slow
       def 	_SCREAMING_SNAKE_CASE       (  self :							List[Any]			)->  List[Any]:







              """simple docstring"""
              UpperCamelCase					 =		self.tokenizer_class.from_pretrained("google/mobilebert-uncased"			)

              UpperCamelCase					 =		tokenizer.encode("sequence builders"       ,      add_special_tokens=UpperCAmelCase_			)
              UpperCamelCase					 =		tokenizer.encode("multi-sequence build"       ,      add_special_tokens=UpperCAmelCase_			)

              UpperCamelCase					 =		tokenizer.build_inputs_with_special_tokens(UpperCAmelCase_			)
              UpperCamelCase					 =		tokenizer.build_inputs_with_special_tokens(UpperCAmelCase_       ,      UpperCAmelCase_			)

              assert encoded_sentence == [101] + text + [102]
              assert encoded_pair == [101] + text + [102] + text_a + [102]






       def 	_SCREAMING_SNAKE_CASE       (  self :							Tuple			)->  Any:







              """simple docstring"""
              for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                     with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"			):
                            UpperCamelCase					 =		self.rust_tokenizer_class.from_pretrained(UpperCAmelCase_       ,      **UpperCAmelCase_			)

                            UpperCamelCase					 =		f"A, naïve {tokenizer_r.mask_token} AllenNLP sentence."
                            UpperCamelCase					 =		tokenizer_r.encode_plus(
                                UpperCAmelCase_       ,      return_attention_mask=UpperCAmelCase_       ,      return_token_type_ids=UpperCAmelCase_       ,      return_offsets_mapping=UpperCAmelCase_       ,      add_special_tokens=UpperCAmelCase_       ,      )

                            UpperCamelCase					 =		tokenizer_r.do_lower_case if hasattr(UpperCAmelCase_       ,      "do_lower_case"			) else False
                            UpperCamelCase					 =		(
                                [
                                    ((0, 0), tokenizer_r.cls_token),
                                    ((0, 1), "A"),
                                    ((1, 2), ","),
                                    ((3, 5), "na"),
                                    ((5, 6), "##ï"),
                                    ((6, 8), "##ve"),
                                    ((9, 15), tokenizer_r.mask_token),
                                    ((16, 21), "Allen"),
                                    ((21, 23), "##NL"),
                                    ((23, 24), "##P"),
                                    ((25, 33), "sentence"),
                                    ((33, 34), "."),
                                    ((0, 0), tokenizer_r.sep_token),
                                ]
                                if not do_lower_case
                                else [
                                    ((0, 0), tokenizer_r.cls_token),
                                    ((0, 1), "a"),
                                    ((1, 2), ","),
                                    ((3, 8), "naive"),
                                    ((9, 15), tokenizer_r.mask_token),
                                    ((16, 21), "allen"),
                                    ((21, 23), "##nl"),
                                    ((23, 24), "##p"),
                                    ((25, 33), "sentence"),
                                    ((33, 34), "."),
                                    ((0, 0), tokenizer_r.sep_token),
                                ]
                            )

                            self.assertEqual(
                                [e[1] for e in expected_results]       ,      tokenizer_r.convert_ids_to_tokens(tokens["input_ids"]			)			)
                            self.assertEqual([e[0] for e in expected_results]       ,      tokens["offset_mapping"]			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							List[str]			)->  Tuple:







              """simple docstring"""
              UpperCamelCase					 =		["的", "人", "有"]
              UpperCamelCase					 =		"".join(UpperCAmelCase_			)
              for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                     with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"			):
                            UpperCamelCase					 =		True
                            UpperCamelCase					 =		self.tokenizer_class.from_pretrained(UpperCAmelCase_       ,      **UpperCAmelCase_			)
                            UpperCamelCase					 =		self.rust_tokenizer_class.from_pretrained(UpperCAmelCase_       ,      **UpperCAmelCase_			)

                            UpperCamelCase					 =		tokenizer_p.encode(UpperCAmelCase_       ,      add_special_tokens=UpperCAmelCase_			)
                            UpperCamelCase					 =		tokenizer_r.encode(UpperCAmelCase_       ,      add_special_tokens=UpperCAmelCase_			)

                            UpperCamelCase					 =		tokenizer_r.convert_ids_to_tokens(UpperCAmelCase_			)
                            UpperCamelCase					 =		tokenizer_p.convert_ids_to_tokens(UpperCAmelCase_			)

                            # it is expected that each Chinese character is not preceded by "##"
                            self.assertListEqual(UpperCAmelCase_       ,      UpperCAmelCase_			)
                            self.assertListEqual(UpperCAmelCase_       ,      UpperCAmelCase_			)

                            UpperCamelCase					 =		False
                            UpperCamelCase					 =		self.rust_tokenizer_class.from_pretrained(UpperCAmelCase_       ,      **UpperCAmelCase_			)
                            UpperCamelCase					 =		self.tokenizer_class.from_pretrained(UpperCAmelCase_       ,      **UpperCAmelCase_			)

                            UpperCamelCase					 =		tokenizer_r.encode(UpperCAmelCase_       ,      add_special_tokens=UpperCAmelCase_			)
                            UpperCamelCase					 =		tokenizer_p.encode(UpperCAmelCase_       ,      add_special_tokens=UpperCAmelCase_			)

                            UpperCamelCase					 =		tokenizer_r.convert_ids_to_tokens(UpperCAmelCase_			)
                            UpperCamelCase					 =		tokenizer_p.convert_ids_to_tokens(UpperCAmelCase_			)

                            # it is expected that only the first Chinese character is not preceded by "##".
                            UpperCamelCase					 =		[
                                f"##{token}" if idx != 0 else token for idx, token in enumerate(UpperCAmelCase_			)
                            ]
                            self.assertListEqual(UpperCAmelCase_       ,      UpperCAmelCase_			)
                            self.assertListEqual(UpperCAmelCase_       ,      UpperCAmelCase_			)







"""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)
      SCREAMING_SNAKE_CASE      =     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')

      SCREAMING_SNAKE_CASE      =     tf.keras.preprocessing.image.ImageDataGenerator(
          rescale=1.0 / 255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
      )

      SCREAMING_SNAKE_CASE      =     tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 255)

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

      SCREAMING_SNAKE_CASE      =     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

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





