
'''simple docstring'''







import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
					from .tokenization_ta import TaTokenizer
else:
					lowercase    :							Dict      =					None


lowercase    :							Tuple      =					logging.get_logger(__name__)

lowercase    :							List[Any]      =					{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

lowercase    :							Any      =					{
    "vocab_file": {
        "t5-small": "https://huggingface.co/t5-small/resolve/main/spiece.model",
        "t5-base": "https://huggingface.co/t5-base/resolve/main/spiece.model",
        "t5-large": "https://huggingface.co/t5-large/resolve/main/spiece.model",
        "t5-3b": "https://huggingface.co/t5-3b/resolve/main/spiece.model",
        "t5-11b": "https://huggingface.co/t5-11b/resolve/main/spiece.model",
    },
    "tokenizer_file": {
        "t5-small": "https://huggingface.co/t5-small/resolve/main/tokenizer.json",
        "t5-base": "https://huggingface.co/t5-base/resolve/main/tokenizer.json",
        "t5-large": "https://huggingface.co/t5-large/resolve/main/tokenizer.json",
        "t5-3b": "https://huggingface.co/t5-3b/resolve/main/tokenizer.json",
        "t5-11b": "https://huggingface.co/t5-11b/resolve/main/tokenizer.json",
    },
}


# TODO(PVP) - this should be removed in Transformers v5
lowercase    :							Union[str, Any]      =					{
    "t5-small": 512,
    "t5-base": 512,
    "t5-large": 512,
    "t5-3b": 512,
    "t5-11b": 512,
}


class 						__UpperCAmelCase      (					lowercase__						):
							__lowercase      		=       VOCAB_FILES_NAMES
							__lowercase      		=       PRETRAINED_VOCAB_FILES_MAP
							__lowercase      		=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							__lowercase      		=       ['input_ids', 'attention_mask']
							__lowercase      		=       TaTokenizer

							__lowercase      		=       []


							def __init__(       self    ,   lowerCAmelCase_=None    ,   lowerCAmelCase_=None    ,   lowerCAmelCase_="</s>"    ,   lowerCAmelCase_="<unk>"    ,   lowerCAmelCase_="<pad>"    ,   lowerCAmelCase_=1_00    ,   lowerCAmelCase_=None    ,   **lowerCAmelCase_    ,   ):





								"""simple docstring"""






								if extra_ids > 0 and additional_special_tokens is None:
									_snake_case   						=		[F'<extra_id_{i}>' for i in range(__lowercase  )]
								elif extra_ids > 0 and additional_special_tokens is not None:
									# Check that we have the right number of extra special tokens
									_snake_case   						=		len(set(filter(lambda lowerCAmelCase_  : bool('extra_id_' in str(__lowercase  )  )    ,   __lowercase  )  )  )
									if extra_tokens != extra_ids:
										raise ValueError(
										    F'Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'
										    ' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'
										    ' tokens'  )

								super().__init__(
								    __lowercase    ,   tokenizer_file=__lowercase    ,   eos_token=__lowercase    ,   unk_token=__lowercase    ,   pad_token=__lowercase    ,   extra_ids=__lowercase    ,   additional_special_tokens=__lowercase    ,   **__lowercase    ,   )

								_snake_case   						=		vocab_file
								_snake_case   						=		False if not self.vocab_file else True
								_snake_case   						=		extra_ids


							@staticmethod
							def  lowerCamelCase				(       lowerCAmelCase_    ,   lowerCAmelCase_    ,   lowerCAmelCase_  ):





								"""simple docstring"""






								if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
									_snake_case   						=		TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
									if init_max_model_length is not None and init_max_model_length != max_model_length:
										return init_max_model_length
									elif init_max_model_length is None:
										warnings.warn(
										    'This tokenizer was incorrectly instantiated with a model max length of'
										    F' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'
										    ' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'
										    ' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'
										    F' {pretrained_model_name_or_path} automatically truncating your input to'
										    F' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'
										    F' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'
										    ' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'
										    ' instantiate this tokenizer with `model_max_length` set to your preferred value.'    ,   __lowercase    ,   )

								return max_model_length


							def  lowerCamelCase				(       self    ,   lowerCAmelCase_    ,   lowerCAmelCase_ = None  ):





								"""simple docstring"""






								if not self.can_save_slow_tokenizer:
									raise ValueError(
									    'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
									    'tokenizer.'  )

								if not os.path.isdir(__lowercase  ):
									logger.error(F'Vocabulary path ({save_directory}) should be a directory'  )
									return
								_snake_case   						=		os.path.join(
								    __lowercase    ,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']  )

								if os.path.abspath(self.vocab_file  ) != os.path.abspath(__lowercase  ):
									copyfile(self.vocab_file    ,   __lowercase  )
									logger.info(F'Copy vocab file to {out_vocab_file}'  )

								return (out_vocab_file,)


							def  lowerCamelCase				(       self    ,   lowerCAmelCase_    ,   lowerCAmelCase_ = None  ):





								"""simple docstring"""






								_snake_case   						=		token_ids_a + [self.eos_token_id]
								if token_ids_a is None:
									return self.prefix_tokens + token_ids_a
								else:
									_snake_case   						=		token_ids_a + [self.eos_token_id]
									return self.prefix_tokens + token_ids_a + token_ids_a


							def  lowerCamelCase				(       self    ,   lowerCAmelCase_    ,   lowerCAmelCase_ = None  ):





								"""simple docstring"""






								_snake_case   						=		[self.eos_token_id]

								if token_ids_a is None:
									return len(token_ids_a + eos  ) * [0]
								return len(token_ids_a + eos + token_ids_a + eos  ) * [0]


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								return list(
								    set(filter(lambda lowerCAmelCase_  : bool(re.search(R'<extra_id_\d+>'    ,   __lowercase  )  ) is not None    ,   self.additional_special_tokens  )  )  )




							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								return [self.convert_tokens_to_ids(__lowercase  ) for token in self.get_sentinel_tokens()]









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)


