


'''simple docstring'''



import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_lowercase      :      int            =					logging.get_logger(__name__)

_lowercase      :      Tuple            =					"""▁"""

_lowercase      :      List[str]            =					{"""vocab_file""": """sentencepiece.bpe.model"""}

_lowercase      :      Optional[int]            =					{
    """vocab_file""": {
        """facebook/xglm-564M""": """https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model""",
    }
}

_lowercase      :      Any            =					{
    """facebook/xglm-564M""": 2048,
}






class 	UpperCamelCase__(       lowerCAmelCase	):
				__magic_name__    :				int  		=	VOCAB_FILES_NAMES
				__magic_name__    :				List[str]  		=	PRETRAINED_VOCAB_FILES_MAP
				__magic_name__    :				Optional[Any]  		=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__magic_name__    :				str  		=	["input_ids", "attention_mask"]




				def __init__(		self      :			int				,   lowerCAmelCase      :			Optional[int]				,   lowerCAmelCase      :			str="<s>"				,   lowerCAmelCase      :			Optional[Any]="</s>"				,   lowerCAmelCase      :			Optional[int]="</s>"				,   lowerCAmelCase      :			List[Any]="<s>"				,   lowerCAmelCase      :			Tuple="<unk>"				,   lowerCAmelCase      :			List[str]="<pad>"				,   lowerCAmelCase      :			Optional[Dict[str, Any]] = None				,   **lowerCAmelCase      :			List[Any]				,   )->					None:

										"""simple docstring"""




										UpperCAmelCase					    =     {} if sp_model_kwargs is None else sp_model_kwargs

										# Compatibility with the original tokenizer
										UpperCAmelCase					    =     7
										UpperCAmelCase					    =     [F"""<madeupword{i}>""" for i in range(self.num_madeup_words				)]

										UpperCAmelCase					    =     kwargs.get('''additional_special_tokens'''				,   []				)
										kwargs["additional_special_tokens"] += [
										    word for word in madeup_words if word not in kwargs["additional_special_tokens"]
										]

										super().__init__(
										    bos_token=lowerCAmelCase				,   eos_token=lowerCAmelCase				,   unk_token=lowerCAmelCase				,   sep_token=lowerCAmelCase				,   cls_token=lowerCAmelCase				,   pad_token=lowerCAmelCase				,   sp_model_kwargs=self.sp_model_kwargs				,   **lowerCAmelCase				,   )

										UpperCAmelCase					    =     spm.SentencePieceProcessor(**self.sp_model_kwargs				)
										self.sp_model.Load(str(lowerCAmelCase				)				)
										UpperCAmelCase					    =     vocab_file

										# Original fairseq vocab and spm vocab must be "aligned":
										# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
										# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
										# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
										# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

										# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
										UpperCAmelCase					    =     1

										# Mimic fairseq token-to-id alignment for the first 4 token
										UpperCAmelCase					    =     {'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

										UpperCAmelCase					    =     len(self.sp_model				)
										UpperCAmelCase					    =     {F"""<madeupword{i}>""": sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words				)}
										self.fairseq_tokens_to_ids.update(lowerCAmelCase				)

										UpperCAmelCase					    =     {v: k for k, v in self.fairseq_tokens_to_ids.items()}




				def __getstate__(		self      :			Tuple				)->					Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase					    =     self.__dict__.copy()
										UpperCAmelCase					    =     None
										UpperCAmelCase					    =     self.sp_model.serialized_model_proto()
										return state




				def __setstate__(		self      :			Optional[Any]				,   lowerCAmelCase      :			Optional[int]				)->					Union[str, Any]:

										"""simple docstring"""




										UpperCAmelCase					    =     d

										# for backward compatibility
										if not hasattr(self				,   '''sp_model_kwargs'''				):
																UpperCAmelCase					    =     {}

										UpperCAmelCase					    =     spm.SentencePieceProcessor(**self.sp_model_kwargs				)
										self.sp_model.LoadFromSerializedProto(self.sp_model_proto				)




				def 					a__(		self      :			Any				,   lowerCAmelCase      :			List[int]				,   lowerCAmelCase      :			Optional[List[int]] = None				)->					List[int]:

										"""simple docstring"""




										if token_ids_a is None:
																return [self.sep_token_id] + token_ids_a
										UpperCAmelCase					    =     [self.sep_token_id]
										return sep + token_ids_a + sep + sep + token_ids_a




				def 					a__(		self      :			Dict				,   lowerCAmelCase      :			List[int]				,   lowerCAmelCase      :			Optional[List[int]] = None				,   lowerCAmelCase      :			bool = False				)->					List[int]:

										"""simple docstring"""




										if already_has_special_tokens:
																return super().get_special_tokens_mask(
																    token_ids_a=lowerCAmelCase				,   token_ids_a=lowerCAmelCase				,   already_has_special_tokens=lowerCAmelCase				)

										if token_ids_a is None:
																return [1] + ([0] * len(lowerCAmelCase				))
										return [1] + ([0] * len(lowerCAmelCase				)) + [1, 1] + ([0] * len(lowerCAmelCase				))




				def 					a__(		self      :			Tuple				,   lowerCAmelCase      :			List[int]				,   lowerCAmelCase      :			Optional[List[int]] = None				)->					List[int]:

										"""simple docstring"""




										UpperCAmelCase					    =     [self.sep_token_id]

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




				@property
				def 					a__(		self      :			int				)->					Optional[int]:

										"""simple docstring"""




										return len(self.sp_model				) + self.fairseq_offset + self.num_madeup_words




				def 					a__(		self      :			str				)->					Dict:

										"""simple docstring"""




										UpperCAmelCase					    =     {self.convert_ids_to_tokens(lowerCAmelCase				): i for i in range(self.vocab_size				)}
										vocab.update(self.added_tokens_encoder				)
										return vocab




				def 					a__(		self      :			str				,   lowerCAmelCase      :			str				)->					List[str]:

										"""simple docstring"""




										return self.sp_model.encode(lowerCAmelCase				,   out_type=lowerCAmelCase				)




				def 					a__(		self      :			Optional[Any]				,   lowerCAmelCase      :			Optional[int]				)->					int:

										"""simple docstring"""




										if token in self.fairseq_tokens_to_ids:
																return self.fairseq_tokens_to_ids[token]
										UpperCAmelCase					    =     self.sp_model.PieceToId(lowerCAmelCase				)

										# Need to return unknown token if the SP model returned 0
										return spm_id + self.fairseq_offset if spm_id else self.unk_token_id




				def 					a__(		self      :			int				,   lowerCAmelCase      :			int				)->					Tuple:

										"""simple docstring"""




										if index in self.fairseq_ids_to_tokens:
																return self.fairseq_ids_to_tokens[index]
										return self.sp_model.IdToPiece(index - self.fairseq_offset				)




				def 					a__(		self      :			Optional[int]				,   lowerCAmelCase      :			List[str]				)->					List[Any]:

										"""simple docstring"""




										UpperCAmelCase					    =     ''''''.join(lowerCAmelCase				).replace(lowerCAmelCase				,   ''' '''				).strip()
										return out_string





				def 					a__(		self      :			Optional[int]				,   lowerCAmelCase      :			str				,   lowerCAmelCase      :			Optional[str] = None				)->					Tuple[str]:

										"""simple docstring"""




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

										if os.path.abspath(self.vocab_file				) != os.path.abspath(lowerCAmelCase				) and os.path.isfile(self.vocab_file				):
																copyfile(self.vocab_file				,   lowerCAmelCase				)
										elif not os.path.isfile(self.vocab_file				):
																with open(lowerCAmelCase				,   '''wb'''				) as fi:
																						UpperCAmelCase					    =     self.sp_model.serialized_model_proto()
																						fi.write(lowerCAmelCase				)

										return (out_vocab_file,)









'''simple docstring'''



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


def 				lowerCamelCase__				(	A       :      int				,       A       :      int				,       A       :      int				,       A       :      int				,       A       :      int				,       A       :      int					):




						'''simple docstring'''






						if (ksize % 2) == 0:
												UpperCAmelCase					    =     ksize + 1
						UpperCAmelCase					    =     np.zeros((ksize, ksize)				,       dtype=np.floataa					)

						# each value
						for y in range(A					):
												for x in range(A					):
																		# distance from center
																		UpperCAmelCase					    =     x - ksize // 2
																		UpperCAmelCase					    =     y - ksize // 2

																		# degree to radiant
																		UpperCAmelCase					    =     theta / 1_80 * np.pi
																		UpperCAmelCase					    =     np.cos(_theta					)
																		UpperCAmelCase					    =     np.sin(_theta					)

																		# get kernel x
																		UpperCAmelCase					    =     cos_theta * px + sin_theta * py

																		# get kernel y
																		UpperCAmelCase					    =     -sin_theta * px + cos_theta * py

																		# fill kernel
																		UpperCAmelCase					    =     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
	_lowercase      :      Tuple            =					imread("""../image_data/lena.jpg""")
	# turn image in gray scale value
	_lowercase      :      int            =					cvtColor(img, COLOR_BGR2GRAY)

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

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

	waitKey(0)





