





'''simple docstring'''





import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
     _UpperCAmelCase        =				'pt'
elif is_tf_available():
     _UpperCAmelCase        =				'tf'
else:
     _UpperCAmelCase        =				'jax'



class 							_UpperCamelCase					(	lowerCAmelCase_  ,						unittest.TestCase     ):
       _UpperCamelCase				:					Union[str, Any]         =   PerceiverTokenizer
       _UpperCamelCase				:					Optional[int]         =   False

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




              """simple docstring"""


              super().setUp()
              UpperCamelCase_          =  PerceiverTokenizer()
              tokenizer.save_pretrained(self.tmpdirname		)

       @cached_property
       def lowercase (		self: List[Any]		)       -> List[Any]:




              """simple docstring"""


              return PerceiverTokenizer.from_pretrained("deepmind/language-perceiver"		)

       def lowercase (		self: Any					,   **_SCREAMING_SNAKE_CASE: Any		)       -> PerceiverTokenizer:




              """simple docstring"""


              return self.tokenizer_class.from_pretrained(self.tmpdirname					,   **_SCREAMING_SNAKE_CASE		)

       def lowercase (		self: List[Any]					,   _SCREAMING_SNAKE_CASE: List[Any]					,   _SCREAMING_SNAKE_CASE: Optional[Any]=False					,   _SCREAMING_SNAKE_CASE: int=20					,   _SCREAMING_SNAKE_CASE: str=5		)       -> Tuple[str, list]:




              """simple docstring"""


              UpperCamelCase_          =  []
              for i in range(len(_SCREAMING_SNAKE_CASE		)		):
                     try:
                            UpperCamelCase_          =  tokenizer.decode([i]					,   clean_up_tokenization_spaces=_SCREAMING_SNAKE_CASE		)
                     except UnicodeDecodeError:
                            pass
                     toks.append((i, tok)		)

              UpperCamelCase_          =  list(filter(lambda _SCREAMING_SNAKE_CASE		: re.match(R"^[ a-zA-Z]+$"					,   t[1]		)					,   _SCREAMING_SNAKE_CASE		)		)
              UpperCamelCase_          =  list(filter(lambda _SCREAMING_SNAKE_CASE		: [t[0]] == tokenizer.encode(t[1]					,   add_special_tokens=_SCREAMING_SNAKE_CASE		)					,   _SCREAMING_SNAKE_CASE		)		)
              if max_length is not None and len(_SCREAMING_SNAKE_CASE		) > max_length:
                     UpperCamelCase_          =  toks[:max_length]
              if min_length is not None and len(_SCREAMING_SNAKE_CASE		) < min_length and len(_SCREAMING_SNAKE_CASE		) > 0:
                     while len(_SCREAMING_SNAKE_CASE		) < min_length:
                            UpperCamelCase_          =  toks + toks
        # toks_str = [t[1] for t in toks]
              UpperCamelCase_          =  [t[0] for t in toks]

              # Ensure consistency
              UpperCamelCase_          =  tokenizer.decode(_SCREAMING_SNAKE_CASE					,   clean_up_tokenization_spaces=_SCREAMING_SNAKE_CASE		)
              if " " not in output_txt and len(_SCREAMING_SNAKE_CASE		) > 1:
                     UpperCamelCase_          =  (
                         tokenizer.decode([toks_ids[0]]					,   clean_up_tokenization_spaces=_SCREAMING_SNAKE_CASE		)
                         + " "
                         + tokenizer.decode(toks_ids[1:]					,   clean_up_tokenization_spaces=_SCREAMING_SNAKE_CASE		)
                     )
              if with_prefix_space:
                     UpperCamelCase_          =  " " + output_txt
              UpperCamelCase_          =  tokenizer.encode(_SCREAMING_SNAKE_CASE					,   add_special_tokens=_SCREAMING_SNAKE_CASE		)
              return output_txt, output_ids

       def lowercase (		self: List[str]		)       -> List[str]:




              """simple docstring"""


              UpperCamelCase_          =  self.perceiver_tokenizer
              UpperCamelCase_          =  "Unicode €."
              UpperCamelCase_          =  tokenizer(_SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  [4, 91, 116, 111, 105, 117, 106, 107, 38, 232, 136, 178, 52, 5]
              self.assertEqual(encoded["input_ids"]					,   _SCREAMING_SNAKE_CASE		)

              # decoding
              UpperCamelCase_          =  tokenizer.decode(_SCREAMING_SNAKE_CASE		)
              self.assertEqual(_SCREAMING_SNAKE_CASE					,   "[CLS]Unicode €.[SEP]"		)

              UpperCamelCase_          =  tokenizer("e è é ê ë"		)
              UpperCamelCase_          =  [4, 107, 38, 201, 174, 38, 201, 175, 38, 201, 176, 38, 201, 177, 5]
              self.assertEqual(encoded["input_ids"]					,   _SCREAMING_SNAKE_CASE		)
              # decoding
              UpperCamelCase_          =  tokenizer.decode(_SCREAMING_SNAKE_CASE		)
              self.assertEqual(_SCREAMING_SNAKE_CASE					,   "[CLS]e è é ê ë[SEP]"		)

              # encode/decode, but with `encode` instead of `__call__`
              self.assertEqual(tokenizer.decode(tokenizer.encode("e è é ê ë"		)		)					,   "[CLS]e è é ê ë[SEP]"		)

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




              """simple docstring"""


              UpperCamelCase_          =  self.perceiver_tokenizer
              UpperCamelCase_          =  ["A long paragraph for summarization.", "Another paragraph for summarization."]
              # fmt: off
              UpperCamelCase_          =  [4, 71, 38, 114, 117, 116, 109, 38, 118, 103, 120, 103, 109, 120, 103, 118, 110, 38, 108, 117, 120, 38, 121, 123, 115, 115, 103, 120, 111, 128, 103, 122, 111, 117, 116, 52, 5, 0]
              # fmt: on
              UpperCamelCase_          =  tokenizer(_SCREAMING_SNAKE_CASE					,   padding=_SCREAMING_SNAKE_CASE					,   return_tensors=_SCREAMING_SNAKE_CASE		)
              self.assertIsInstance(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)

              if FRAMEWORK != "jax":
                     UpperCamelCase_          =  list(batch.input_ids.numpy()[0]		)
              else:
                     UpperCamelCase_          =  list(batch.input_ids.tolist()[0]		)

              self.assertListEqual(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)

              self.assertEqual((2, 38)					,   batch.input_ids.shape		)
              self.assertEqual((2, 38)					,   batch.attention_mask.shape		)

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




              """simple docstring"""


              UpperCamelCase_          =  self.perceiver_tokenizer
              UpperCamelCase_          =  ["A long paragraph for summarization.", "Another paragraph for summarization."]
              UpperCamelCase_          =  tokenizer(_SCREAMING_SNAKE_CASE					,   padding=_SCREAMING_SNAKE_CASE					,   return_tensors=_SCREAMING_SNAKE_CASE		)
              # check if input_ids are returned and no decoder_input_ids
              self.assertIn("input_ids"					,   _SCREAMING_SNAKE_CASE		)
              self.assertIn("attention_mask"					,   _SCREAMING_SNAKE_CASE		)
              self.assertNotIn("decoder_input_ids"					,   _SCREAMING_SNAKE_CASE		)
              self.assertNotIn("decoder_attention_mask"					,   _SCREAMING_SNAKE_CASE		)

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




              """simple docstring"""


              UpperCamelCase_          =  self.perceiver_tokenizer
              UpperCamelCase_          =  [
                  "Summary of the text.",
                  "Another summary.",
              ]
              UpperCamelCase_          =  tokenizer(
                  text_target=_SCREAMING_SNAKE_CASE					,   max_length=32					,   padding="max_length"					,   truncation=_SCREAMING_SNAKE_CASE					,   return_tensors=_SCREAMING_SNAKE_CASE		)
              self.assertEqual(32					,   targets["input_ids"].shape[1]		)

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




              """simple docstring"""


              UpperCamelCase_          =  self.get_tokenizers()
              for tokenizer in tokenizers:
                     with self.subTest(f'''{tokenizer.__class__.__name__}'''		):
                            self.assertNotEqual(tokenizer.model_max_length					,   42		)

        # Now let's start the test
              UpperCamelCase_          =  self.get_tokenizers()
              for tokenizer in tokenizers:
                     with self.subTest(f'''{tokenizer.__class__.__name__}'''		):
                            # Isolate this from the other tests because we save additional tokens/etc
                            UpperCamelCase_          =  tempfile.mkdtemp()

                            UpperCamelCase_          =  " He is very happy, UNwant\u00E9d,running"
                            UpperCamelCase_          =  tokenizer.encode(_SCREAMING_SNAKE_CASE					,   add_special_tokens=_SCREAMING_SNAKE_CASE		)
                            tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE		)

                            UpperCamelCase_          =  tokenizer.__class__.from_pretrained(_SCREAMING_SNAKE_CASE		)
                            UpperCamelCase_          =  after_tokenizer.encode(_SCREAMING_SNAKE_CASE					,   add_special_tokens=_SCREAMING_SNAKE_CASE		)
                            self.assertListEqual(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)

                            shutil.rmtree(_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_          =  self.get_tokenizers(model_max_length=42		)
              for tokenizer in tokenizers:
                     with self.subTest(f'''{tokenizer.__class__.__name__}'''		):
                            # Isolate this from the other tests because we save additional tokens/etc
                            UpperCamelCase_          =  tempfile.mkdtemp()

                            UpperCamelCase_          =  " He is very happy, UNwant\u00E9d,running"
                            tokenizer.add_tokens(["bim", "bambam"]		)
                            UpperCamelCase_          =  tokenizer.additional_special_tokens
                            additional_special_tokens.append("new_additional_special_token"		)
                            tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens}		)
                            UpperCamelCase_          =  tokenizer.encode(_SCREAMING_SNAKE_CASE					,   add_special_tokens=_SCREAMING_SNAKE_CASE		)
                            tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE		)

                            UpperCamelCase_          =  tokenizer.__class__.from_pretrained(_SCREAMING_SNAKE_CASE		)
                            UpperCamelCase_          =  after_tokenizer.encode(_SCREAMING_SNAKE_CASE					,   add_special_tokens=_SCREAMING_SNAKE_CASE		)
                            self.assertListEqual(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)
                            self.assertIn("new_additional_special_token"					,   after_tokenizer.additional_special_tokens		)
                            self.assertEqual(after_tokenizer.model_max_length					,   42		)

                            UpperCamelCase_          =  tokenizer.__class__.from_pretrained(_SCREAMING_SNAKE_CASE					,   model_max_length=43		)
                            self.assertEqual(tokenizer.model_max_length					,   43		)

                            shutil.rmtree(_SCREAMING_SNAKE_CASE		)

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




              """simple docstring"""


              UpperCamelCase_          =  []
              if self.test_slow_tokenizer:
                     tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())		)

              if self.test_rust_tokenizer:
                     tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())		)

              for tokenizer_class, tokenizer_utils in tokenizer_list:
                     with tempfile.TemporaryDirectory() as tmp_dir:
                            tokenizer_utils.save_pretrained(_SCREAMING_SNAKE_CASE		)

                            with open(os.path.join(_SCREAMING_SNAKE_CASE					,   "special_tokens_map.json"		)					,   encoding="utf-8"		) as json_file:
                                   UpperCamelCase_          =  json.load(_SCREAMING_SNAKE_CASE		)

                            with open(os.path.join(_SCREAMING_SNAKE_CASE					,   "tokenizer_config.json"		)					,   encoding="utf-8"		) as json_file:
                                   UpperCamelCase_          =  json.load(_SCREAMING_SNAKE_CASE		)

                            UpperCamelCase_          =  [f'''<extra_id_{i}>''' for i in range(125		)]

                            UpperCamelCase_          =  added_tokens_extra_ids + [
                                "an_additional_special_token"
                            ]
                            UpperCamelCase_          =  added_tokens_extra_ids + [
                                "an_additional_special_token"
                            ]

                            with open(os.path.join(_SCREAMING_SNAKE_CASE					,   "special_tokens_map.json"		)					,   "w"					,   encoding="utf-8"		) as outfile:
                                   json.dump(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)
                            with open(os.path.join(_SCREAMING_SNAKE_CASE					,   "tokenizer_config.json"		)					,   "w"					,   encoding="utf-8"		) as outfile:
                                   json.dump(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)

                            # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
                            # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
                            # "special_tokens_map.json" files
                            UpperCamelCase_          =  tokenizer_class.from_pretrained(
                                _SCREAMING_SNAKE_CASE					,   )
                            self.assertIn(
                                "an_additional_special_token"					,   tokenizer_without_change_in_init.additional_special_tokens		)
                            self.assertEqual(
                                ["an_additional_special_token"]					,   tokenizer_without_change_in_init.convert_ids_to_tokens(
                                    tokenizer_without_change_in_init.convert_tokens_to_ids(["an_additional_special_token"]		)		)					,   )

                            # Now we test that we can change the value of additional_special_tokens in the from_pretrained
                            UpperCamelCase_          =  added_tokens_extra_ids + [AddedToken("a_new_additional_special_token"					,   lstrip=_SCREAMING_SNAKE_CASE		)]
                            UpperCamelCase_          =  tokenizer_class.from_pretrained(
                                _SCREAMING_SNAKE_CASE					,   additional_special_tokens=_SCREAMING_SNAKE_CASE					,   )

                            self.assertIn("a_new_additional_special_token"					,   tokenizer.additional_special_tokens		)
                            self.assertEqual(
                                ["a_new_additional_special_token"]					,   tokenizer.convert_ids_to_tokens(
                                    tokenizer.convert_tokens_to_ids(["a_new_additional_special_token"]		)		)					,   )

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




              """simple docstring"""


              UpperCamelCase_          =  self.perceiver_tokenizer
              self.assertEqual(tokenizer.decode([178]		)					,   "�"		)

       def lowercase (		self: str		)       -> Optional[int]:




              """simple docstring"""


              pass

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




              """simple docstring"""


              pass

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




              """simple docstring"""


              pass

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




              """simple docstring"""


              pass



       def lowercase (		self: Tuple		)       -> List[Any]:




              """simple docstring"""


              UpperCamelCase_          =  self.get_tokenizers(fast=_SCREAMING_SNAKE_CASE					,   do_lower_case=_SCREAMING_SNAKE_CASE		)
              for tokenizer in tokenizers:
                     with self.subTest(f'''{tokenizer.__class__.__name__}'''		):
                            UpperCamelCase_          =  ["[CLS]", "t", "h", "i", "s", " ", "i", "s", " ", "a", " ", "t", "e", "s", "t", "[SEP]"]
                            UpperCamelCase_          =  tokenizer.convert_tokens_to_string(_SCREAMING_SNAKE_CASE		)

                            self.assertIsInstance(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)








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        =				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(3_2, (3, 3), input_shape=(6_4, 6_4, 3), activation='relu')
     )

     # Step 2 - Pooling
     classifier.add(layers.MaxPoolingaD(pool_size=(2, 2)))

     # Adding a second convolutional layer
     classifier.add(layers.ConvaD(3_2, (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_2_8, 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        =				tf.keras.preprocessing.image.ImageDataGenerator(
         rescale=1.0 / 2_5_5, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
     )

     _UpperCAmelCase        =				tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 2_5_5)

     _UpperCAmelCase        =				train_datagen.flow_from_directory(
         'dataset/training_set', target_size=(6_4, 6_4), batch_size=3_2, class_mode='binary'
     )

     _UpperCAmelCase        =				test_datagen.flow_from_directory(
         'dataset/test_set', target_size=(6_4, 6_4), batch_size=3_2, class_mode='binary'
     )

     classifier.fit_generator(
         training_set, steps_per_epoch=5, epochs=3_0, validation_data=test_set
     )

     classifier.save('cnn.h5')

     # Part 3 - Making new predictions

     _UpperCAmelCase        =				tf.keras.preprocessing.image.load_img(
         'dataset/single_prediction/image.png', target_size=(6_4, 6_4)
     )
     _UpperCAmelCase        =				tf.keras.preprocessing.image.img_to_array(test_image)
     _UpperCAmelCase        =				np.expand_dims(test_image, axis=0)
     _UpperCAmelCase        =				classifier.predict(test_image)
     # training_set.class_indices
     if result[0][0] == 0:
          _UpperCAmelCase        =				'Normal'
     if result[0][0] == 1:
          _UpperCAmelCase        =				'Abnormality detected'

