


'''simple docstring'''



import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging


_lowercase      :      str            =					logging.get_logger(__name__)

_lowercase      :      int            =					"""▁"""

_lowercase      :      Any            =					{
    """vocab_file""": """vocab.json""",
    """spm_file""": """sentencepiece.bpe.model""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

_lowercase      :      int            =					{
    """vocab_file""": {
        """facebook/m2m100_418M""": """https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json""",
        """facebook/m2m100_1.2B""": """https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json""",
    },
    """spm_file""": {
        """facebook/m2m100_418M""": """https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model""",
        """facebook/m2m100_1.2B""": """https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model""",
    },
    """tokenizer_config_file""": {
        """facebook/m2m100_418M""": """https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json""",
        """facebook/m2m100_1.2B""": """https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json""",
    },
}

_lowercase      :      Tuple            =					{
    """facebook/m2m100_418M""": 1024,
}

# fmt: off
_lowercase      :      Optional[int]            =					{
    """m2m100""": ["""af""", """am""", """ar""", """ast""", """az""", """ba""", """be""", """bg""", """bn""", """br""", """bs""", """ca""", """ceb""", """cs""", """cy""", """da""", """de""", """el""", """en""", """es""", """et""", """fa""", """ff""", """fi""", """fr""", """fy""", """ga""", """gd""", """gl""", """gu""", """ha""", """he""", """hi""", """hr""", """ht""", """hu""", """hy""", """id""", """ig""", """ilo""", """is""", """it""", """ja""", """jv""", """ka""", """kk""", """km""", """kn""", """ko""", """lb""", """lg""", """ln""", """lo""", """lt""", """lv""", """mg""", """mk""", """ml""", """mn""", """mr""", """ms""", """my""", """ne""", """nl""", """no""", """ns""", """oc""", """or""", """pa""", """pl""", """ps""", """pt""", """ro""", """ru""", """sd""", """si""", """sk""", """sl""", """so""", """sq""", """sr""", """ss""", """su""", """sv""", """sw""", """ta""", """th""", """tl""", """tn""", """tr""", """uk""", """ur""", """uz""", """vi""", """wo""", """xh""", """yi""", """yo""", """zh""", """zu"""],
    """wmt21""": ["""en""", """ha""", """is""", """ja""", """cs""", """ru""", """zh""", """de"""]
}






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

				__magic_name__    :				List[int]  		=	[]
				__magic_name__    :				List[int]  		=	[]




				def __init__(		self      :			List[Any]				,   lowerCAmelCase      :			int				,   lowerCAmelCase      :			Dict				,   lowerCAmelCase      :			List[str]=None				,   lowerCAmelCase      :			List[str]=None				,   lowerCAmelCase      :			Any="<s>"				,   lowerCAmelCase      :			Optional[int]="</s>"				,   lowerCAmelCase      :			List[str]="</s>"				,   lowerCAmelCase      :			str="<pad>"				,   lowerCAmelCase      :			Any="<unk>"				,   lowerCAmelCase      :			List[str]="m2m100"				,   lowerCAmelCase      :			Optional[Dict[str, Any]] = None				,   lowerCAmelCase      :			List[Any]=8				,   **lowerCAmelCase      :			Dict				,   )->					None:

										"""simple docstring"""




										UpperCAmelCase					    =     {} if sp_model_kwargs is None else sp_model_kwargs

										UpperCAmelCase					    =     language_codes
										UpperCAmelCase					    =     FAIRSEQ_LANGUAGE_CODES[language_codes]
										UpperCAmelCase					    =     {lang_code: F"""__{lang_code}__""" for lang_code in fairseq_language_code}

										UpperCAmelCase					    =     kwargs.get('''additional_special_tokens'''				,   []				)
										kwargs["additional_special_tokens"] += [
										    self.get_lang_token(lowerCAmelCase				)
										    for lang_code in fairseq_language_code
										    if self.get_lang_token(lowerCAmelCase				) not in kwargs["additional_special_tokens"]
										]

										super().__init__(
										    src_lang=lowerCAmelCase				,   tgt_lang=lowerCAmelCase				,   bos_token=lowerCAmelCase				,   eos_token=lowerCAmelCase				,   sep_token=lowerCAmelCase				,   unk_token=lowerCAmelCase				,   pad_token=lowerCAmelCase				,   language_codes=lowerCAmelCase				,   sp_model_kwargs=self.sp_model_kwargs				,   num_madeup_words=lowerCAmelCase				,   **lowerCAmelCase				,   )

										UpperCAmelCase					    =     vocab_file
										UpperCAmelCase					    =     load_json(lowerCAmelCase				)
										UpperCAmelCase					    =     {v: k for k, v in self.encoder.items()}
										UpperCAmelCase					    =     spm_file
										UpperCAmelCase					    =     load_spm(lowerCAmelCase				,   self.sp_model_kwargs				)

										UpperCAmelCase					    =     len(self.encoder				)

										UpperCAmelCase					    =     {
										    self.get_lang_token(lowerCAmelCase				): self.encoder_size + i for i, lang_code in enumerate(lowerCAmelCase				)
										}
										UpperCAmelCase					    =     {lang_code: self.encoder_size + i for i, lang_code in enumerate(lowerCAmelCase				)}
										UpperCAmelCase					    =     {v: k for k, v in self.lang_token_to_id.items()}

										UpperCAmelCase					    =     src_lang if src_lang is not None else '''en'''
										UpperCAmelCase					    =     tgt_lang
										UpperCAmelCase					    =     self.get_lang_id(self._src_lang				)
										self.set_src_lang_special_tokens(self._src_lang				)

										UpperCAmelCase					    =     num_madeup_words




				@property
				def 					a__(		self      :			Tuple				)->					int:

										"""simple docstring"""




										return len(self.encoder				) + len(self.lang_token_to_id				)




				@property
				def 					a__(		self      :			int				)->					str:

										"""simple docstring"""




										return self._src_lang




				@src_lang.setter
				def 					a__(		self      :			Any				,   lowerCAmelCase      :			str				)->					None:

										"""simple docstring"""




										UpperCAmelCase					    =     new_src_lang
										self.set_src_lang_special_tokens(self._src_lang				)




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

										"""simple docstring"""




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




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

										"""simple docstring"""




										if token in self.lang_token_to_id:
																return self.lang_token_to_id[token]
										return self.encoder.get(lowerCAmelCase				,   self.encoder[self.unk_token]				)




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

										"""simple docstring"""




										if index in self.id_to_lang_token:
																return self.id_to_lang_token[index]
										return self.decoder.get(lowerCAmelCase				,   self.unk_token				)




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

										"""simple docstring"""




										UpperCAmelCase					    =     []
										UpperCAmelCase					    =     ''''''
										for token in tokens:
																# make sure that special tokens are not decoded using sentencepiece model
																if token in self.all_special_tokens:
																						out_string += self.sp_model.decode(lowerCAmelCase				) + token
																						UpperCAmelCase					    =     []
																else:
																						current_sub_tokens.append(lowerCAmelCase				)
										out_string += self.sp_model.decode(lowerCAmelCase				)
										return out_string.strip()




				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				)

										UpperCAmelCase					    =     [1] * len(self.prefix_tokens				)
										UpperCAmelCase					    =     [1] * len(self.suffix_tokens				)
										if token_ids_a is None:
																return prefix_ones + ([0] * len(lowerCAmelCase				)) + suffix_ones
										return prefix_ones + ([0] * len(lowerCAmelCase				)) + ([0] * len(lowerCAmelCase				)) + suffix_ones




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

										"""simple docstring"""




										if token_ids_a is None:
																return self.prefix_tokens + token_ids_a + self.suffix_tokens
										# We don't expect to process pairs, but leave the pair logic for API consistency
										return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens




				def 					a__(		self      :			List[Any]				)->					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 __getstate__(		self      :			Any				)->					Dict:

										"""simple docstring"""




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




				def __setstate__(		self      :			Tuple				,   lowerCAmelCase      :			Dict				)->					None:

										"""simple docstring"""




										UpperCAmelCase					    =     d

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

										UpperCAmelCase					    =     load_spm(self.spm_file				,   self.sp_model_kwargs				)




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

										"""simple docstring"""




										UpperCAmelCase					    =     Path(lowerCAmelCase				)
										if not save_dir.is_dir():
																raise OSError(F"""{save_directory} should be a directory"""				)
										UpperCAmelCase					    =     save_dir / (
										    (filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''vocab_file''']
										)
										UpperCAmelCase					    =     save_dir / (
										    (filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''spm_file''']
										)

										save_json(self.encoder				,   lowerCAmelCase				)

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

										return (str(lowerCAmelCase				), str(lowerCAmelCase				))




				def 					a__(		self      :			Optional[int]				,   lowerCAmelCase      :			List[str]				,   lowerCAmelCase      :			str = "en"				,   lowerCAmelCase      :			Optional[List[str]] = None				,   lowerCAmelCase      :			str = "ro"				,   **lowerCAmelCase      :			Dict				,   )->					BatchEncoding:

										"""simple docstring"""




										UpperCAmelCase					    =     src_lang
										UpperCAmelCase					    =     tgt_lang
										self.set_src_lang_special_tokens(self.src_lang				)
										return super().prepare_seqaseq_batch(lowerCAmelCase				,   lowerCAmelCase				,   **lowerCAmelCase				)




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

										"""simple docstring"""




										if src_lang is None or tgt_lang is None:
																raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model'''				)
										UpperCAmelCase					    =     src_lang
										UpperCAmelCase					    =     self(lowerCAmelCase				,   add_special_tokens=lowerCAmelCase				,   **lowerCAmelCase				)
										UpperCAmelCase					    =     self.get_lang_id(lowerCAmelCase				)
										UpperCAmelCase					    =     tgt_lang_id
										return inputs




				def 					a__(		self      :			Dict				)->					Dict:

										"""simple docstring"""




										self.set_src_lang_special_tokens(self.src_lang				)




				def 					a__(		self      :			str				)->					Dict:

										"""simple docstring"""




										self.set_tgt_lang_special_tokens(self.tgt_lang				)




				def 					a__(		self      :			Dict				,   lowerCAmelCase      :			str				)->					None:

										"""simple docstring"""




										UpperCAmelCase					    =     self.get_lang_token(lowerCAmelCase				)
										UpperCAmelCase					    =     self.lang_token_to_id[lang_token]
										UpperCAmelCase					    =     [self.cur_lang_id]
										UpperCAmelCase					    =     [self.eos_token_id]




				def 					a__(		self      :			List[str]				,   lowerCAmelCase      :			str				)->					None:

										"""simple docstring"""




										UpperCAmelCase					    =     self.get_lang_token(lowerCAmelCase				)
										UpperCAmelCase					    =     self.lang_token_to_id[lang_token]
										UpperCAmelCase					    =     [self.cur_lang_id]
										UpperCAmelCase					    =     [self.eos_token_id]




				def 					a__(		self      :			Dict				,   lowerCAmelCase      :			str				)->					str:

										"""simple docstring"""




										return self.lang_code_to_token[lang]





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

										"""simple docstring"""




										UpperCAmelCase					    =     self.get_lang_token(lowerCAmelCase				)
										return self.lang_token_to_id[lang_token]







def 				lowerCamelCase__				(	A       :      str				,       A       :      Dict[str, Any]					):




						'''simple docstring'''






						UpperCAmelCase					    =     sentencepiece.SentencePieceProcessor(**A					)
						spm.Load(str(A					)					)
						return spm


def 				lowerCamelCase__				(	A       :      str					):




						'''simple docstring'''






						with open(A				,       '''r'''					) as f:
												return json.load(A					)




def 				lowerCamelCase__				(	A       :      Optional[Any]				,       A       :      str					):




						'''simple docstring'''






						with open(A				,       '''w'''					) as f:
												json.dump(A				,       A				,       indent=2					)









'''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)





