




import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
			from transformers.pipelines.conversational import Conversation


UpperCAmelCase__				     = logging.get_logger(__name__)

UpperCAmelCase__				     = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

UpperCAmelCase__				     = {
    "tokenizer_file": {
        "EleutherAI/gpt-neox-20b": "https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json",
    },
}

UpperCAmelCase__				     = {
    "gpt-neox-20b": 2048,
}







class a (     __SCREAMING_SNAKE_CASE  ):
						"""simple docstring"""

						UpperCamelCase_	:       List[str]       	=		VOCAB_FILES_NAMES
						UpperCamelCase_	:       Optional[Any]       	=		PRETRAINED_VOCAB_FILES_MAP
						UpperCamelCase_	:       Tuple       	=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						UpperCamelCase_	:       int       	=		['input_ids', 'attention_mask']






						def __init__( self :     int				,							lowerCamelCase__ :     Optional[int]=None				,							lowerCamelCase__ :     Union[str, Any]=None				,							lowerCamelCase__ :     str=None				,							lowerCamelCase__ :     Optional[int]="<|endoftext|>"				,							lowerCamelCase__ :     Dict="<|endoftext|>"				,							lowerCamelCase__ :     Tuple="<|endoftext|>"				,							lowerCamelCase__ :     Optional[int]=False				,							**lowerCamelCase__ :     List[Any]				,							)    ->						str:
													"""simple docstring"""


													super().__init__(
													    lowerCamelCase__				,							lowerCamelCase__				,							tokenizer_file=lowerCamelCase__				,							unk_token=lowerCamelCase__				,							bos_token=lowerCamelCase__				,							eos_token=lowerCamelCase__				,							add_prefix_space=lowerCamelCase__				,							**lowerCamelCase__				,							)

													__lowercase      =    json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()				)
													if pre_tok_state.get('''add_prefix_space'''				,							lowerCamelCase__				) != add_prefix_space:
																				__lowercase      =    getattr(lowerCamelCase__				,							pre_tok_state.pop('''type'''				)				)
																				__lowercase      =    add_prefix_space
																				__lowercase      =    pre_tok_class(**lowerCamelCase__				)

													__lowercase      =    add_prefix_space






						def     UpperCAmelCase_      ( self :     str				,							lowerCamelCase__ :     str				,							lowerCamelCase__ :     Optional[str] = None				)    ->						Tuple[str]:
													"""simple docstring"""


													__lowercase      =    self._tokenizer.model.save(lowerCamelCase__				,							name=lowerCamelCase__				)
													return tuple(lowerCamelCase__				)






						def     UpperCAmelCase_      ( self :     Dict				,							lowerCamelCase__ :     "Conversation"				)    ->						List[int]:
													"""simple docstring"""


													__lowercase      =    []
													for is_user, text in conversation.iter_texts():
																				input_ids.extend(self.encode(lowerCamelCase__				,							add_special_tokens=lowerCamelCase__				) + [self.eos_token_id]				)

													if len(lowerCamelCase__				) > self.model_max_length:
																				__lowercase      =    input_ids[-self.model_max_length :]
													return input_ids












import cva
import numpy as np







class a :
						"""simple docstring"""

						def __init__( self :     Dict				,							lowerCamelCase__ :     float				,							lowerCamelCase__ :     int				)    ->						Dict:
													"""simple docstring"""


													if k in (0.0_4, 0.0_6):
																				__lowercase      =    k
																				__lowercase      =    window_size
													else:
																				raise ValueError('''invalid k value'''				)






						def __str__( self :     int				)    ->						str:
													"""simple docstring"""


													return str(self.k				)






						def     UpperCAmelCase_      ( self :     Dict				,							lowerCamelCase__ :     str				)    ->						tuple[cva.Mat, list[list[int]]]:
													"""simple docstring"""


													__lowercase      =    cva.imread(lowerCamelCase__				,							0				)
													__lowercase     ,     __lowercase      =    img.shape
													__lowercase      =    []
													__lowercase      =    img.copy()
													__lowercase      =    cva.cvtColor(lowerCamelCase__				,							cva.COLOR_GRAY2RGB				)
													__lowercase     ,     __lowercase      =    np.gradient(lowerCamelCase__				)
													__lowercase      =    dx**2
													__lowercase      =    dy**2
													__lowercase      =    dx * dy
													__lowercase      =    0.0_4
													__lowercase      =    self.window_size // 2
													for y in range(lowerCamelCase__				,							h - offset				):
																				for x in range(lowerCamelCase__				,							w - offset				):
																											__lowercase      =    ixx[
																											    y - offset : y + offset + 1, x - offset : x + offset + 1
																											].sum()
																											__lowercase      =    iyy[
																											    y - offset : y + offset + 1, x - offset : x + offset + 1
																											].sum()
																											__lowercase      =    ixy[
																											    y - offset : y + offset + 1, x - offset : x + offset + 1
																											].sum()

																											__lowercase      =    (wxx * wyy) - (wxy**2)
																											__lowercase      =    wxx + wyy
																											__lowercase      =    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__":
			UpperCAmelCase__				     = HarrisCorner(0.04, 3)
			UpperCAmelCase__     ,							UpperCAmelCase__				     = edge_detect.detect("path_to_image")
			cva.imwrite("detect.png", color_img)






