






import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


a		:       Tuple			     = get_tests_dir("fixtures/test_sentencepiece_no_bos.model")
@require_sentencepiece
@require_tokenizers
class     a    ( lowercase__							,       unittest.TestCase   ):



      """simple docstring"""


      a  : Optional[Any]							       =	PegasusTokenizer
      a  : List[Any]							       =	PegasusTokenizerFast
      a  : Tuple							       =	True
      a  : Dict							       =	True
      def 	UpperCAmelCase      (      self					:			Union[str, Any]					)							->      Tuple:
        super().setUp()

        # We have a SentencePiece fixture for testing
        __UpperCAmelCase							:	List[Any]           =  PegasusTokenizer(__lowercase					)
        tokenizer.save_pretrained(self.tmpdirname					)
      @cached_property
      def 	UpperCAmelCase      (      self					:			Union[str, Any]					)							->      Optional[Any]:
        return PegasusTokenizer.from_pretrained("""google/pegasus-large"""					)
      def 	UpperCAmelCase      (      self					:			Optional[Any]		,      **__lowercase					:			str					)							->      PegasusTokenizer:
        return PegasusTokenizer.from_pretrained(self.tmpdirname		,      **__lowercase					)
      def 	UpperCAmelCase      (      self					:			Optional[Any]		,      __lowercase					:			str					)							->      Tuple:
        return ("This is a test", "This is a test")
      def 	UpperCAmelCase      (      self					:			Dict					)							->      List[str]:
        __UpperCAmelCase							:	List[Any]           =  """</s>"""
        __UpperCAmelCase							:	Union[str, Any]           =  1

        self.assertEqual(self.get_tokenizer()._convert_token_to_id(__lowercase					)		,      __lowercase					)
        self.assertEqual(self.get_tokenizer()._convert_id_to_token(__lowercase					)		,      __lowercase					)
      def 	UpperCAmelCase      (      self					:			Dict					)							->      Union[str, Any]:
        __UpperCAmelCase							:	int           =  list(self.get_tokenizer().get_vocab().keys()					)

        self.assertEqual(vocab_keys[0]		,      """<pad>"""					)
        self.assertEqual(vocab_keys[1]		,      """</s>"""					)
        self.assertEqual(vocab_keys[-1]		,      """v"""					)
        self.assertEqual(len(__lowercase					)		,      1103					)
      def 	UpperCAmelCase      (      self					:			Any					)							->      List[str]:
        self.assertEqual(self.get_tokenizer().vocab_size		,      1103					)
      def 	UpperCAmelCase      (      self					:			Optional[int]					)							->      Union[str, Any]:
        __UpperCAmelCase							:	Union[str, Any]           =  self.rust_tokenizer_class.from_pretrained(self.tmpdirname					)
        __UpperCAmelCase							:	Dict           =  self.tokenizer_class.from_pretrained(self.tmpdirname					)
        __UpperCAmelCase							:	str           =  (
            """Let's see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important"""
            """ </s> <pad> <pad> <pad>"""
        )
        __UpperCAmelCase							:	Optional[int]           =  rust_tokenizer([raw_input_str]		,      return_tensors=__lowercase		,      add_special_tokens=__lowercase					).input_ids[0]
        __UpperCAmelCase							:	int           =  py_tokenizer([raw_input_str]		,      return_tensors=__lowercase		,      add_special_tokens=__lowercase					).input_ids[0]
        self.assertListEqual(__lowercase		,      __lowercase					)
      def 	UpperCAmelCase      (      self					:			str					)							->      Dict:
        __UpperCAmelCase							:	Tuple           =  self._large_tokenizer
        # <mask_1> masks whole sentence while <mask_2> masks single word
        __UpperCAmelCase							:	List[Any]           =  """<mask_1> To ensure a <mask_2> flow of bank resolutions."""
        __UpperCAmelCase							:	Optional[int]           =  [2, 413, 615, 114, 3, 1971, 113, 1679, 10710, 107, 1]
        __UpperCAmelCase							:	Union[str, Any]           =  tokenizer([raw_input_str]		,      return_tensors=__lowercase					).input_ids[0]
        self.assertListEqual(__lowercase		,      __lowercase					)
      def 	UpperCAmelCase      (      self					:			List[str]					)							->      Optional[Any]:
        __UpperCAmelCase							:	str           =  self._large_tokenizer
        # The tracebacks for the following asserts are **better** without messages or self.assertEqual
        assert tokenizer.vocab_size == 96103
        assert tokenizer.pad_token_id == 0
        assert tokenizer.eos_token_id == 1
        assert tokenizer.offset == 103
        assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105
        assert tokenizer.unk_token == "<unk>"
        assert tokenizer.model_max_length == 1024
        __UpperCAmelCase							:	Union[str, Any]           =  """To ensure a smooth flow of bank resolutions."""
        __UpperCAmelCase							:	Dict           =  [413, 615, 114, 2291, 1971, 113, 1679, 10710, 107, 1]
        __UpperCAmelCase							:	Any           =  tokenizer([raw_input_str]		,      return_tensors=__lowercase					).input_ids[0]
        self.assertListEqual(__lowercase		,      __lowercase					)
        assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3]					) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]
      @require_torch
      def 	UpperCAmelCase      (      self					:			str					)							->      Optional[Any]:
        __UpperCAmelCase							:	Optional[int]           =  ["""This is going to be way too long.""" * 150, """short example"""]
        __UpperCAmelCase							:	List[str]           =  ["""not super long but more than 5 tokens""", """tiny"""]
        __UpperCAmelCase							:	int           =  self._large_tokenizer(__lowercase		,      padding=__lowercase		,      truncation=__lowercase		,      return_tensors="""pt"""					)
        __UpperCAmelCase							:	List[Any]           =  self._large_tokenizer(
            text_target=__lowercase		,      max_length=5		,      padding=__lowercase		,      truncation=__lowercase		,      return_tensors="""pt"""					)

        assert batch.input_ids.shape == (2, 1024)
        assert batch.attention_mask.shape == (2, 1024)
        assert targets["input_ids"].shape == (2, 5)
        assert len(__lowercase					) == 2  # input_ids, attention_mask.




      @slow
      def 	UpperCAmelCase      (      self					:			Dict					)							->      Tuple:
        # fmt: off
        __UpperCAmelCase							:	Optional[Any]           =  {"""input_ids""": [[38979, 143, 18485, 606, 130, 26669, 87686, 121, 54189, 1129, 111, 26669, 87686, 121, 9114, 14787, 121, 13249, 158, 592, 956, 121, 14621, 31576, 143, 62613, 108, 9688, 930, 43430, 11562, 62613, 304, 108, 11443, 897, 108, 9314, 17415, 63399, 108, 11443, 7614, 18316, 118, 4284, 7148, 12430, 143, 1400, 25703, 158, 111, 4284, 7148, 11772, 143, 21297, 1064, 158, 122, 204, 3506, 1754, 1133, 14787, 1581, 115, 33224, 4482, 111, 1355, 110, 29173, 317, 50833, 108, 20147, 94665, 111, 77198, 107, 1], [110, 62613, 117, 638, 112, 1133, 121, 20098, 1355, 79050, 13872, 135, 1596, 53541, 1352, 141, 13039, 5542, 124, 302, 518, 111, 268, 2956, 115, 149, 4427, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [139, 1235, 2799, 18289, 17780, 204, 109, 9474, 1296, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
        # fmt: on

        self.tokenizer_integration_test_util(
            expected_encoding=__lowercase		,      model_name="""google/bigbird-pegasus-large-arxiv"""		,      revision="""ba85d0851d708441f91440d509690f1ab6353415"""		,      )


@require_sentencepiece
@require_tokenizers
class     a    ( lowercase__							,       unittest.TestCase   ):



      """simple docstring"""


      a  : Any							       =	PegasusTokenizer
      a  : List[Any]							       =	PegasusTokenizerFast
      a  : Optional[int]							       =	True
      a  : List[Any]							       =	True
      def 	UpperCAmelCase      (      self					:			int					)							->      Any:
        super().setUp()

        # We have a SentencePiece fixture for testing
        __UpperCAmelCase							:	Optional[Any]           =  PegasusTokenizer(__lowercase		,      offset=0		,      mask_token_sent=__lowercase		,      mask_token="""[MASK]"""					)
        tokenizer.save_pretrained(self.tmpdirname					)
      @cached_property
      def 	UpperCAmelCase      (      self					:			Union[str, Any]					)							->      int:
        return PegasusTokenizer.from_pretrained("""google/bigbird-pegasus-large-arxiv"""					)
      def 	UpperCAmelCase      (      self					:			Dict		,      **__lowercase					:			List[str]					)							->      PegasusTokenizer:
        return PegasusTokenizer.from_pretrained(self.tmpdirname		,      **__lowercase					)
      def 	UpperCAmelCase      (      self					:			Optional[int]		,      __lowercase					:			str					)							->      str:
        return ("This is a test", "This is a test")
      def 	UpperCAmelCase      (      self					:			str					)							->      Dict:
        __UpperCAmelCase							:	int           =  self.rust_tokenizer_class.from_pretrained(self.tmpdirname					)
        __UpperCAmelCase							:	str           =  self.tokenizer_class.from_pretrained(self.tmpdirname					)
        __UpperCAmelCase							:	str           =  (
            """Let's see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>"""
            """ <pad> <pad> <pad>"""
        )
        __UpperCAmelCase							:	str           =  rust_tokenizer([raw_input_str]		,      return_tensors=__lowercase		,      add_special_tokens=__lowercase					).input_ids[0]
        __UpperCAmelCase							:	Union[str, Any]           =  py_tokenizer([raw_input_str]		,      return_tensors=__lowercase		,      add_special_tokens=__lowercase					).input_ids[0]
        self.assertListEqual(__lowercase		,      __lowercase					)
      @require_torch
      def 	UpperCAmelCase      (      self					:			List[Any]					)							->      List[Any]:
        __UpperCAmelCase							:	Any           =  ["""This is going to be way too long.""" * 1000, """short example"""]
        __UpperCAmelCase							:	Union[str, Any]           =  ["""not super long but more than 5 tokens""", """tiny"""]
        __UpperCAmelCase							:	Optional[int]           =  self._large_tokenizer(__lowercase		,      padding=__lowercase		,      truncation=__lowercase		,      return_tensors="""pt"""					)
        __UpperCAmelCase							:	Dict           =  self._large_tokenizer(
            text_target=__lowercase		,      max_length=5		,      padding=__lowercase		,      truncation=__lowercase		,      return_tensors="""pt"""					)

        assert batch.input_ids.shape == (2, 4096)
        assert batch.attention_mask.shape == (2, 4096)
        assert targets["input_ids"].shape == (2, 5)
        assert len(__lowercase					) == 2  # input_ids, attention_mask.




      def 	UpperCAmelCase      (      self					:			List[str]					)							->      int:
        __UpperCAmelCase							:	List[str]           =  (
            """This is an example string that is used to test the original TF implementation against the HF"""
            """ implementation"""
        )

        __UpperCAmelCase							:	Any           =  self._large_tokenizer(__lowercase					).input_ids

        self.assertListEqual(
            __lowercase		,      [182, 117, 142, 587, 4211, 120, 117, 263, 112, 804, 109, 856, 25016, 3137, 464, 109, 26955, 3137, 1]		,      )










import cva
import numpy as np
class     a    :



      """simple docstring"""


      def __init__(      self					:			Optional[Any]		,      __lowercase					:			float		,      __lowercase					:			int					)							->      List[Any]:

        if k in (0.04, 0.06):
          __UpperCAmelCase							:	int           =  k
          __UpperCAmelCase							:	List[str]           =  window_size
        else:
          raise ValueError("""invalid k value"""					)
      def __str__(      self					:			Union[str, Any]					)							->      str:
        return str(self.k					)




      def 	UpperCAmelCase      (      self					:			Optional[int]		,      __lowercase					:			str					)							->      tuple[cva.Mat, list[list[int]]]:
        __UpperCAmelCase							:	Optional[Any]           =  cva.imread(__lowercase		,      0					)
        __UpperCAmelCase      , __UpperCAmelCase							:	List[Any]           =  img.shape
        __UpperCAmelCase							:	list[list[int]]           =  []
        __UpperCAmelCase							:	Any           =  img.copy()
        __UpperCAmelCase							:	Tuple           =  cva.cvtColor(__lowercase		,      cva.COLOR_GRAY2RGB					)
        __UpperCAmelCase      , __UpperCAmelCase							:	List[str]           =  np.gradient(__lowercase					)
        __UpperCAmelCase							:	int           =  dx**2
        __UpperCAmelCase							:	Any           =  dy**2
        __UpperCAmelCase							:	Any           =  dx * dy
        __UpperCAmelCase							:	Optional[int]           =  0.04
        __UpperCAmelCase							:	List[str]           =  self.window_size // 2
        for y in range(__lowercase		,      h - offset					):
          for x in range(__lowercase		,      w - offset					):
            __UpperCAmelCase							:	Tuple           =  ixx[
                y - offset : y + offset + 1, x - offset : x + offset + 1
            ].sum()
            __UpperCAmelCase							:	List[Any]           =  iyy[
                y - offset : y + offset + 1, x - offset : x + offset + 1
            ].sum()
            __UpperCAmelCase							:	Optional[int]           =  ixy[
                y - offset : y + offset + 1, x - offset : x + offset + 1
            ].sum()

            __UpperCAmelCase							:	int           =  (wxx * wyy) - (wxy**2)
            __UpperCAmelCase							:	Any           =  wxx + wyy
            __UpperCAmelCase							:	Optional[Any]           =  det - k * (trace**2)
            # Can change the value
            if r > 0.5:
              corner_list.append([x, y, r]					)
              color_img.itemset((y, x, 0)		,      0					)
              color_img.itemset((y, x, 1)		,      0					)
              color_img.itemset((y, x, 2)		,      255					)
        return color_img, corner_list


if __name__ == "__main__":
       a		:       int			     = HarrisCorner(0.04, 3)
       a     ,a		:       Any			     = edge_detect.detect("path_to_image")
       cva.imwrite("detect.png", color_img)


