

import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow


if is_tf_available():
						import tensorflow as tf

if is_tensorflow_text_available():
						from transformers.models.bert import TFBertTokenizer


_snake_case	=						["bert-base-uncased", "bert-base-cased"]
_snake_case	=						"hf-internal-testing/tiny-bert-tf-only"

if is_tf_available():



						class 						UpperCAmelCase_					( tf.keras.Model):







													def __init__(     self,    __a):

																				'''simple docstring'''

																				super().__init__()
																				_lowerCAmelCase  :							Dict				     =					tokenizer
																				_lowerCAmelCase  :							Optional[int]				     =					AutoConfig.from_pretrained(__a)
																				_lowerCAmelCase  :							Any				     =					TFAutoModel.from_config(__a)







													def      snake_case__   (     self,    __a):

																				'''simple docstring'''

																				_lowerCAmelCase  :							Union[str, Any]				     =					self.tokenizer(__a)
																				_lowerCAmelCase  :							Tuple				     =					self.bert(**__a)
																				return out["pooler_output"]



@require_tf
@require_tensorflow_text
class 						UpperCAmelCase_					( unittest.TestCase):







							def      snake_case__   (     self):

														'''simple docstring'''

														super().setUp()

														_lowerCAmelCase  :							Optional[int]				     =					[
														    BertTokenizer.from_pretrained(__a) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
														]  # repeat for when fast_bert_tokenizer=false
														_lowerCAmelCase  :							Any				     =					[TFBertTokenizer.from_pretrained(__a) for checkpoint in TOKENIZER_CHECKPOINTS] + [
														    TFBertTokenizer.from_pretrained(__a,    use_fast_bert_tokenizer=__a)
														    for checkpoint in TOKENIZER_CHECKPOINTS
														]
														assert len(self.tokenizers) == len(self.tf_tokenizers)

														_lowerCAmelCase  :							Optional[Any]				     =					[
														    "This is a straightforward English test sentence.",
														    "This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.",
														    "Now we're going to add some Chinese: 一 二 三 一二三",
														    "And some much more rare Chinese: 齉 堃 齉堃",
														    "Je vais aussi écrire en français pour tester les accents",
														    "Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ",
														]
														_lowerCAmelCase  :							Any				     =					list(zip(self.test_sentences,    self.test_sentences[::-1]))







							def      snake_case__   (     self):

														'''simple docstring'''

														for tokenizer, tf_tokenizer in zip(self.tokenizers,    self.tf_tokenizers):
																					for test_inputs in (self.test_sentences, self.paired_sentences):
																												_lowerCAmelCase  :							str				     =					tokenizer(__a,    return_tensors="tf",    padding="longest")
																												_lowerCAmelCase  :							str				     =					tf_tokenizer(__a)

																												for key in python_outputs.keys():
																																			self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape))
																																			self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key],    tf.intaa) == tf_outputs[key]))







							@slow
							def      snake_case__   (     self):

														'''simple docstring'''

														for tf_tokenizer in self.tf_tokenizers:
																					_lowerCAmelCase  :							Optional[Any]				     =					tf_tokenizer(self.paired_sentences)
																					_lowerCAmelCase  :							Union[str, Any]				     =					tf_tokenizer(
																					    text=[sentence[0] for sentence in self.paired_sentences],    text_pair=[sentence[1] for sentence in self.paired_sentences],    )
																					for key in merged_outputs.keys():
																												self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key],    tf.intaa) == separated_outputs[key]))







							@slow
							def      snake_case__   (     self):

														'''simple docstring'''

														for tf_tokenizer in self.tf_tokenizers:
																					_lowerCAmelCase  :							Optional[int]				     =					tf.function(__a)
																					for test_inputs in (self.test_sentences, self.paired_sentences):
																												_lowerCAmelCase  :							Optional[Any]				     =					tf.constant(__a)
																												_lowerCAmelCase  :							str				     =					compiled_tokenizer(__a)
																												_lowerCAmelCase  :							str				     =					tf_tokenizer(__a)

																												for key in eager_outputs.keys():
																																			self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]))







							@slow
							def      snake_case__   (     self):

														'''simple docstring'''

														for tf_tokenizer in self.tf_tokenizers:
																					_lowerCAmelCase  :							str				     =					ModelToSave(tokenizer=__a)
																					_lowerCAmelCase  :							Dict				     =					tf.convert_to_tensor(self.test_sentences)
																					_lowerCAmelCase  :							str				     =					model(__a)  # Build model with some sample inputs
																					with TemporaryDirectory() as tempdir:
																												_lowerCAmelCase  :							Union[str, Any]				     =					Path(__a) / "saved.model"
																												model.save(__a)
																												_lowerCAmelCase  :							Dict				     =					tf.keras.models.load_model(__a)
																					_lowerCAmelCase  :							int				     =					loaded_model(__a)
																					# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
																					self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output)),    1E-5)





import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey






def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							if (ksize % 2) == 0:
														_lowerCAmelCase  :							str				     =					ksize + 1
							_lowerCAmelCase  :							List[str]				     =					np.zeros((ksize, ksize)       ,				dtype=np.floataa						)

							# each value
							for y in range(_lowerCamelCase						):
														for x in range(_lowerCamelCase						):
																					# distance from center
																					_lowerCAmelCase  :							int				     =					x - ksize // 2
																					_lowerCAmelCase  :							Dict				     =					y - ksize // 2

																					# degree to radiant
																					_lowerCAmelCase  :							List[Any]				     =					theta / 180 * np.pi
																					_lowerCAmelCase  :							int				     =					np.cos(_theta						)
																					_lowerCAmelCase  :							Optional[int]				     =					np.sin(_theta						)

																					# get kernel x
																					_lowerCAmelCase  :							int				     =					cos_theta * px + sin_theta * py

																					# get kernel y
																					_lowerCAmelCase  :							str				     =					-sin_theta * px + cos_theta * py

																					# fill kernel
																					_lowerCAmelCase  :							Union[str, Any]				     =					np.exp(
																					    -(_x**2 + gamma**2 * _y**2) / (2 * sigma**2)						) * np.cos(2 * np.pi * _x / lambd + psi						)

							return gabor


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						# read original image
						_snake_case	=						imread("../image_data/lena.jpg")
						# turn image in gray scale value
						_snake_case	=						cvtColor(img, COLOR_BGR2GRAY)

						# Apply multiple Kernel to detect edges
						_snake_case	=						np.zeros(gray.shape[:2])
						for theta in [0, 30, 60, 90, 120, 150]:
												_snake_case	=						gabor_filter_kernel(10, 8, theta, 10, 0, 0)
												out += filteraD(gray, CV_8UC3, kernel_aa)
						_snake_case	=						out / out.max() * 255
						_snake_case	=						out.astype(np.uinta)

						imshow("Original", gray)
						imshow("Gabor filter with 20x20 mask and 6 directions", out)

						waitKey(0)
