
'''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    :							Optional[int]      =					"▁"

lowercase    :							Any      =					{"vocab_file": "sentencepiece.bpe.model"}

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

lowercase    :							Tuple      =					{
    "facebook/xglm-564M": 2048,
}


class 						__UpperCAmelCase      (					_lowerCamelCase						):
							__lowercase      		=       VOCAB_FILES_NAMES
							__lowercase      		=       PRETRAINED_VOCAB_FILES_MAP
							__lowercase      		=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							__lowercase      		=       ["""input_ids""", """attention_mask"""]


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





								"""simple docstring"""






								_snake_case   						=		{} if sp_model_kwargs is None else sp_model_kwargs

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

								_snake_case   						=		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_    ,   )

								_snake_case   						=		spm.SentencePieceProcessor(**self.sp_model_kwargs  )
								self.sp_model.Load(str(lowerCAmelCase_  )  )
								_snake_case   						=		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
								_snake_case   						=		1

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

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

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


							def __getstate__(       self  ):





								"""simple docstring"""






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


							def __setstate__(       self    ,   lowerCAmelCase_  ):





								"""simple docstring"""






								_snake_case   						=		d

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

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


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





								"""simple docstring"""






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


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





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





								"""simple docstring"""






								_snake_case   						=		[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  lowerCamelCase				(       self  ):





								"""simple docstring"""






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


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






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


							def  lowerCamelCase				(       self    ,   lowerCAmelCase_  ):





								"""simple docstring"""






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


							def  lowerCamelCase				(       self    ,   lowerCAmelCase_  ):





								"""simple docstring"""






								if token in self.fairseq_tokens_to_ids:
									return self.fairseq_tokens_to_ids[token]
								_snake_case   						=		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  lowerCamelCase				(       self    ,   lowerCAmelCase_  ):





								"""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  lowerCamelCase				(       self    ,   lowerCAmelCase_  ):





								"""simple docstring"""






								_snake_case   						=		''.join(lowerCAmelCase_  ).replace(lowerCAmelCase_    ,   ' '  ).strip()
								return out_string




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





								"""simple docstring"""






								if not os.path.isdir(lowerCAmelCase_  ):
									logger.error(F'Vocabulary path ({save_directory}) should be a directory'  )
									return
								_snake_case   						=		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:
										_snake_case   						=		self.sp_model.serialized_model_proto()
										fi.write(lowerCAmelCase_  )

								return (out_vocab_file,)



'''simple docstring'''







from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np


class 						__UpperCAmelCase      (					_lowerCamelCase						):


							def  lowerCamelCase				(       self    ,   lowerCAmelCase_  ):





								"""simple docstring"""






								return 0.0







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A	)       ->	tuple[int | float, int | float]:
	_snake_case   						=		min([-20, np.min(fft_results[1 : samplerate // 2 - 1]	)]	)
	_snake_case   						=		max([20, np.max(fft_results[1 : samplerate // 2 - 1]	)]	)
	return lowest, highest







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A	)       ->	None:
	_snake_case   						=		512
	_snake_case   						=		[1] + [0] * (size - 1)
	_snake_case   						=		[filter_type.process(__A	) for item in inputs]

	_snake_case   						=		[0] * (samplerate - size)  # zero-padding
	outputs += filler
	_snake_case   						=		np.abs(np.fft.fft(__A	)	)
	_snake_case   						=		20 * np.logaa(__A	)

	# Frequencies on log scale from 24 to nyquist frequency
	plt.xlim(24			,				samplerate / 2 - 1	)
	plt.xlabel('Frequency (Hz)'	)
	plt.xscale('log'	)

	# Display within reasonable bounds
	_snake_case   						=		get_bounds(__A			,				__A	)
	plt.ylim(max([-80, bounds[0]]	)			,				min([80, bounds[1]]	)	)
	plt.ylabel('Gain (dB)'	)

	plt.plot(__A	)
	plt.show()







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A	)       ->	None:
	_snake_case   						=		512
	_snake_case   						=		[1] + [0] * (size - 1)
	_snake_case   						=		[filter_type.process(__A	) for item in inputs]

	_snake_case   						=		[0] * (samplerate - size)  # zero-padding
	outputs += filler
	_snake_case   						=		np.angle(np.fft.fft(__A	)	)

	# Frequencies on log scale from 24 to nyquist frequency
	plt.xlim(24			,				samplerate / 2 - 1	)
	plt.xlabel('Frequency (Hz)'	)
	plt.xscale('log'	)

	plt.ylim(-2 * pi			,				2 * pi	)
	plt.ylabel('Phase shift (Radians)'	)
	plt.plot(np.unwrap(__A			,				-2 * pi	)	)
	plt.show()

