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

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


lowerCAmelCase__:    Dict      = logging.get_logger(__name__)

lowerCAmelCase__:    str      = '▁'

lowerCAmelCase__:    List[str]      = {'vocab_file': 'prophetnet.tokenizer'}

lowerCAmelCase__:    Any      = {
    'vocab_file': {
        'microsoft/xprophetnet-large-wiki100-cased': (
            'https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer'
        ),
    }
}

lowerCAmelCase__:    Union[str, Any]      = {
    'microsoft/xprophetnet-large-wiki100-cased': {'do_lower_case': False},
}

lowerCAmelCase__:    Dict      = {
    'microsoft/xprophetnet-large-wiki100-cased': 512,
}



def   __SCREAMING_SNAKE_CASE (		SCREAMING_SNAKE_CASE )		-> Optional[int]:
		SCREAMING_SNAKE_CASE_					:	Any		  =			collections.OrderedDict()
		with open(SCREAMING_SNAKE_CASE ,    'r' ,    encoding='utf-8' ) as reader:
				SCREAMING_SNAKE_CASE_					:	str		  =			reader.readlines()
		for index, token in enumerate(SCREAMING_SNAKE_CASE ):
				SCREAMING_SNAKE_CASE_					:	Union[str, Any]		  =			token.rstrip('\n' )
				SCREAMING_SNAKE_CASE_					:	Optional[int]		  =			index
		return vocab

class 							snake_case_ (				snake_case_    ):
			__lowerCamelCase							:     str						=			VOCAB_FILES_NAMES
			__lowerCamelCase							:     List[str]						=			PRETRAINED_VOCAB_FILES_MAP
			__lowerCamelCase							:     Any						=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__lowerCamelCase							:     int						=			['input_ids', 'attention_mask']





			def __init__(		self      ,						__lowerCAmelCase      ,						__lowerCAmelCase="[SEP]"      ,						__lowerCAmelCase="[SEP]"      ,						__lowerCAmelCase="[SEP]"      ,						__lowerCAmelCase="[UNK]"      ,						__lowerCAmelCase="[PAD]"      ,						__lowerCAmelCase="[CLS]"      ,						__lowerCAmelCase="[MASK]"      ,						__lowerCAmelCase = None      ,						**__lowerCAmelCase      ,						):
					SCREAMING_SNAKE_CASE_					:	str		  =			{} if sp_model_kwargs is None else sp_model_kwargs

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

					try:
							import sentencepiece as spm
					except ImportError:
							logger.warning(
							    'You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'
							    ' pip install sentencepiece'      )
							raise

					SCREAMING_SNAKE_CASE_					:	Tuple		  =			spm.SentencePieceProcessor(**self.sp_model_kwargs      )
					self.sp_model.Load(str(A_      )      )
					SCREAMING_SNAKE_CASE_					:	Optional[int]		  =			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'

					# put special tokens and [unused] tokens into the vocab
					SCREAMING_SNAKE_CASE_					:	Dict		  =			{'[PAD]': 0, '[CLS]': 1, '[SEP]': 2, '[UNK]': 3, '[MASK]': 4}

					for i in range(10      ):
							SCREAMING_SNAKE_CASE_					:	Dict		  =			F'[unused{i}]'
							SCREAMING_SNAKE_CASE_					:	List[Any]		  =			5 + i

					# The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
					SCREAMING_SNAKE_CASE_					:	Union[str, Any]		  =			12
					SCREAMING_SNAKE_CASE_					:	Tuple		  =			{v: k for k, v in self.fairseq_tokens_to_ids.items()}
					for k in self.fairseq_tokens_to_ids.keys():
							self.unique_no_split_tokens.append(A_      )





			def __getstate__(		self      ):
					SCREAMING_SNAKE_CASE_					:	Any		  =			self.__dict__.copy()
					SCREAMING_SNAKE_CASE_					:	Dict		  =			None
					return state





			def __setstate__(		self      ,						__lowerCAmelCase      ):
					SCREAMING_SNAKE_CASE_					:	List[str]		  =			d
					try:
							import sentencepiece as spm
					except ImportError:
							logger.warning(
							    'You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'
							    ' pip install sentencepiece'      )
							raise

					# for backward compatibility
					if not hasattr(self      ,						'sp_model_kwargs'      ):
							SCREAMING_SNAKE_CASE_					:	List[str]		  =			{}

					SCREAMING_SNAKE_CASE_					:	Optional[Any]		  =			spm.SentencePieceProcessor(**self.sp_model_kwargs      )
					self.sp_model.Load(self.vocab_file      )





			def 						__A						(		self      ,						__lowerCAmelCase      ,						__lowerCAmelCase = None      ,						__lowerCAmelCase = False      ):






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

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





			def 						__A						(		self      ,						__lowerCAmelCase      ,						__lowerCAmelCase = None      ):
					SCREAMING_SNAKE_CASE_					:	str		  =			[self.sep_token_id]

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





			@property
			def 						__A						(		self      ):
					return len(self.sp_model      ) + self.fairseq_offset





			def 						__A						(		self      ):
					SCREAMING_SNAKE_CASE_					:	str		  =			{self.convert_ids_to_tokens(A_      ): i for i in range(self.vocab_size      )}
					vocab.update(self.added_tokens_encoder      )
					return vocab





			def 						__A						(		self      ,						__lowerCAmelCase      ):
					return self.sp_model.encode(A_      ,						out_type=A_      )





			def 						__A						(		self      ,						__lowerCAmelCase      ):






					if token in self.fairseq_tokens_to_ids:
							return self.fairseq_tokens_to_ids[token]
					SCREAMING_SNAKE_CASE_					:	Dict		  =			self.sp_model.PieceToId(A_      )

					# 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      ,						__lowerCAmelCase      ):






					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      ,						__lowerCAmelCase      ):
					SCREAMING_SNAKE_CASE_					:	Dict		  =			''.join(A_      ).replace(A_      ,						' '      ).strip()
					return out_string





			def 						__A						(		self      ,						__lowerCAmelCase      ,						__lowerCAmelCase = None      ):






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

					if os.path.abspath(self.vocab_file      ) != os.path.abspath(A_      ) and os.path.isfile(self.vocab_file      ):
							copyfile(self.vocab_file      ,						A_      )
					elif not os.path.isfile(self.vocab_file      ):
							with open(A_      ,						'wb'      ) as fi:
									SCREAMING_SNAKE_CASE_					:	List[Any]		  =			self.sp_model.serialized_model_proto()
									fi.write(A_      )

					return (out_vocab_file,)






			def 						__A						(		self      ,						__lowerCAmelCase      ,						__lowerCAmelCase = None      ):






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








'''simple docstring'''
import colorsys

from PIL import Image  # type: ignore







def 		A_(     A						:		float		,     A						:		float		,     A						:		int):
							UpperCamelCase   			=     x
							UpperCamelCase   			=     y
							for step in range(A):  # noqa: B007
														UpperCamelCase   			=     a * a - b * b + x
														UpperCamelCase   			=     2 * a * b + y
														UpperCamelCase   			=     a_new

														# divergence happens for all complex number with an absolute value
														# greater than 4
														if a * a + b * b > 4:
																					break
							return step / (max_step - 1)







def 		A_(     A						:		float):
							if distance == 1:
														return (0, 0, 0)
							else:
														return (255, 255, 255)







def 		A_(     A						:		float):
							if distance == 1:
														return (0, 0, 0)
							else:
														return tuple(round(i * 255) for i in colorsys.hsv_to_rgb(A		,     1		,     1))







def 		A_(     A						:		int = 800		,     A						:		int = 600		,     A						:		float = -0.6		,     A						:		float = 0		,     A						:		float = 3.2		,     A						:		int = 50		,     A						:		bool = True		,     ):
							UpperCamelCase   			=     Image.new('RGB'		,     (image_width, image_height))
							UpperCamelCase   			=     img.load()

							# loop through the image-coordinates
							for image_x in range(A):
														for image_y in range(A):
																					# determine the figure-coordinates based on the image-coordinates
																					UpperCamelCase   			=     figure_width / image_width * image_height
																					UpperCamelCase   			=     figure_center_x + (image_x / image_width - 0.5) * figure_width
																					UpperCamelCase   			=     figure_center_y + (image_y / image_height - 0.5) * figure_height

																					UpperCamelCase   			=     get_distance(A		,     A		,     A)

																					# color the corresponding pixel based on the selected coloring-function
																					if use_distance_color_coding:
																												UpperCamelCase   			=     get_color_coded_rgb(A)
																					else:
																												UpperCamelCase   			=     get_black_and_white_rgb(A)

							return img


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						# colored version, full figure
						lowerCAmelCase					:    Any           = get_image()

						# uncomment for colored version, different section, zoomed in
						# img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
						# figure_width = 0.8)

						# uncomment for black and white version, full figure
						# img = get_image(use_distance_color_coding = False)

						# uncomment to save the image
						# img.save("mandelbrot.png")

						img.show()




