
'''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 PreTrainedTokenizer
from ...utils import logging


lowercase    :							Union[str, Any]      =					logging.get_logger(__name__)

lowercase    :							str      =					"▁"

lowercase    :							Any      =					{
    "vocab_file": "vocab.json",
    "spm_file": "sentencepiece.bpe.model",
}

lowercase    :							Tuple      =					{
    "vocab_file": {
        "facebook/s2t-small-librispeech-asr": (
            "https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/vocab.json"
        ),
    },
    "spm_file": {
        "facebook/s2t-small-librispeech-asr": (
            "https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/sentencepiece.bpe.model"
        )
    },
}

lowercase    :							Dict      =					{
    "facebook/s2t-small-librispeech-asr": 1024,
}

lowercase    :							Tuple      =					["pt", "fr", "ru", "nl", "ro", "it", "es", "de"]

lowercase    :							Any      =					{"mustc": MUSTC_LANGS}


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

							__lowercase      		=       []


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





								"""simple docstring"""






								_snake_case   						=		{} if sp_model_kwargs is None else sp_model_kwargs

								super().__init__(
								    bos_token=lowerCAmelCase_    ,   eos_token=lowerCAmelCase_    ,   unk_token=lowerCAmelCase_    ,   pad_token=lowerCAmelCase_    ,   do_upper_case=lowerCAmelCase_    ,   do_lower_case=lowerCAmelCase_    ,   tgt_lang=lowerCAmelCase_    ,   lang_codes=lowerCAmelCase_    ,   sp_model_kwargs=self.sp_model_kwargs    ,   **lowerCAmelCase_    ,   )
								_snake_case   						=		do_upper_case
								_snake_case   						=		do_lower_case

								_snake_case   						=		load_json(lowerCAmelCase_  )
								_snake_case   						=		{v: k for k, v in self.encoder.items()}
								_snake_case   						=		spm_file
								_snake_case   						=		load_spm(lowerCAmelCase_    ,   self.sp_model_kwargs  )

								if lang_codes is not None:
									_snake_case   						=		lang_codes
									_snake_case   						=		LANGUAGES[lang_codes]
									_snake_case   						=		[F'<lang:{lang}>' for lang in self.langs]
									_snake_case   						=		{lang: self.sp_model.PieceToId(F'<lang:{lang}>'  ) for lang in self.langs}

									_snake_case   						=		self.lang_tokens
									_snake_case   						=		tgt_lang if tgt_lang is not None else self.langs[0]

									self.set_tgt_lang_special_tokens(self._tgt_lang  )
								else:
									_snake_case   						=		{}


							@property
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								return len(self.encoder  )


							@property
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								return self._tgt_lang


							@tgt_lang.setter
							def  lowerCamelCase				(       self    ,   lowerCAmelCase_  ):





								"""simple docstring"""






								_snake_case   						=		new_tgt_lang
								self.set_tgt_lang_special_tokens(lowerCAmelCase_  )


							def  lowerCamelCase				(       self    ,   lowerCAmelCase_  ):





								"""simple docstring"""






								_snake_case   						=		self.lang_code_to_id[tgt_lang]
								_snake_case   						=		[lang_code_id]


							def  lowerCamelCase				(       self    ,   lowerCAmelCase_  ):





								"""simple docstring"""






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


							def  lowerCamelCase				(       self    ,   lowerCAmelCase_  ):





								"""simple docstring"""






								return self.encoder.get(lowerCAmelCase_    ,   self.encoder[self.unk_token]  )


							def  lowerCamelCase				(       self    ,   lowerCAmelCase_  ):





								"""simple docstring"""






								return self.decoder.get(lowerCAmelCase_    ,   self.unk_token  )


							def  lowerCamelCase				(       self    ,   lowerCAmelCase_  ):





								"""simple docstring"""






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


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





								"""simple docstring"""






								if token_ids_a is None:
									return self.prefix_tokens + token_ids_a + [self.eos_token_id]
								# 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.eos_token_id]


							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_  )

								_snake_case   						=		[1] * len(self.prefix_tokens  )
								_snake_case   						=		[1]
								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  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		self.encoder.copy()
								vocab.update(self.added_tokens_encoder  )
								return vocab


							def __getstate__(       self  ):





								"""simple docstring"""






								_snake_case   						=		self.__dict__.copy()
								_snake_case   						=		None
								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   						=		load_spm(self.spm_file    ,   self.sp_model_kwargs  )




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





								"""simple docstring"""






								_snake_case   						=		Path(lowerCAmelCase_  )
								assert save_dir.is_dir(), F'{save_directory} should be a directory'
								_snake_case   						=		save_dir / (
								    (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['vocab_file']
								)
								_snake_case   						=		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:
										_snake_case   						=		self.sp_model.serialized_model_proto()
										fi.write(lowerCAmelCase_  )

								return (str(lowerCAmelCase_  ), str(lowerCAmelCase_  ))







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A	)       ->	sentencepiece.SentencePieceProcessor:
	_snake_case   						=		sentencepiece.SentencePieceProcessor(**__A	)
	spm.Load(str(__A	)	)
	return spm







def 				SCREAMING_SNAKE_CASE__	(   __A	)       ->	Union[Dict, List]:
	with open(__A			,				'r'	) as f:
		return json.load(__A	)







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A	)       ->	None:
	with open(__A			,				'w'	) as f:
		json.dump(__A			,				__A			,				indent=2	)



'''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()

