'''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          =  logging.get_logger(__name__)

__lowercase          =  '''▁'''

__lowercase          =  {
    '''vocab_file''': '''vocab.json''',
    '''spm_file''': '''sentencepiece.bpe.model''',
}

__lowercase          =  {
    '''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          =  {
    '''facebook/s2t-small-librispeech-asr''': 1_0_2_4,
}

__lowercase          =  ['''pt''', '''fr''', '''ru''', '''nl''', '''ro''', '''it''', '''es''', '''de''']

__lowercase          =  {'''mustc''': MUSTC_LANGS}






class 				a__(					lowerCAmelCase__	):


			'''simple docstring'''






			UpperCAmelCase_			:     str      =       VOCAB_FILES_NAMES
			UpperCAmelCase_			:     Any      =       PRETRAINED_VOCAB_FILES_MAP
			UpperCAmelCase_			:     Union[str, Any]      =       MAX_MODEL_INPUT_SIZES
			UpperCAmelCase_			:     Tuple      =       ['''input_ids''', '''attention_mask''']

			UpperCAmelCase_			:     List[int]      =       []



			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"""


					lowerCAmelCase   					=  {} 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       ,    )
					lowerCAmelCase   					=  do_upper_case
					lowerCAmelCase   					=  do_lower_case

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

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

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

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



			@property
			def 							a_       (   self):




					"""simple docstring"""


					return len(self.encoder)



			@property
			def 							a_       (   self):




					"""simple docstring"""


					return self._tgt_lang



			@tgt_lang.setter
			def 							a_       (   self       ,    __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase   					=  new_tgt_lang
					self.set_tgt_lang_special_tokens(__lowerCAmelCase)



			def 							a_       (   self       ,    __lowerCAmelCase):




					"""simple docstring"""


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



			def 							a_       (   self       ,    __lowerCAmelCase):




					"""simple docstring"""


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



			def 							a_       (   self       ,    __lowerCAmelCase):




					"""simple docstring"""


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



			def 							a_       (   self       ,    __lowerCAmelCase):




					"""simple docstring"""


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



			def 							a_       (   self       ,    __lowerCAmelCase):




					"""simple docstring"""


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



			def 							a_       (   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 							a_       (   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)

					lowerCAmelCase   					=  [1] * len(self.prefix_tokens)
					lowerCAmelCase   					=  [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 							a_       (   self):




					"""simple docstring"""


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



			def __getstate__(   self):




					"""simple docstring"""


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



			def __setstate__(   self       ,    __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase   					=  d

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

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







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




					"""simple docstring"""


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

					return (str(__lowerCAmelCase), str(__lowerCAmelCase))







def 				snake_case__							(			_A:					str      ,   _A:					Dict[str, Any] )    ->	sentencepiece.SentencePieceProcessor:

		'''simple docstring'''


		lowerCAmelCase   					=  sentencepiece.SentencePieceProcessor(**_A )
		spm.Load(str(_A ) )
		return spm






def 				snake_case__							(			_A:					str )    ->	Union[Dict, List]:

		'''simple docstring'''


		with open(_A      ,   """r""" ) as f:
				return json.load(_A )






def 				snake_case__							(			_A:					Dict      ,   _A:					str )    ->	None:

		'''simple docstring'''


		with open(_A      ,   """w""" ) as f:
				json.dump(_A      ,   _A      ,   indent=2 )





'''simple docstring'''




import math

import flax.linen as nn
import jax.numpy as jnp






def 				snake_case__							(			_A:					jnp.ndarray      ,   _A:					int      ,   _A:					float = 1      ,   _A:					float = 1      ,   _A:					float = 1.0e4      ,   _A:					bool = False      ,   _A:					float = 1.0      ,   )    ->	jnp.ndarray:

		'''simple docstring'''


		assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
		assert embedding_dim % 2 == 0, f"Embedding dimension {embedding_dim} should be even"
		lowerCAmelCase   					=  float(embedding_dim // 2 )
		lowerCAmelCase   					=  math.log(max_timescale / min_timescale ) / (num_timescales - freq_shift)
		lowerCAmelCase   					=  min_timescale * jnp.exp(jnp.arange(_A      ,   dtype=jnp.floataa ) * -log_timescale_increment )
		lowerCAmelCase   					=  jnp.expand_dims(_A      ,   1 ) * jnp.expand_dims(_A      ,   0 )

		# scale embeddings
		lowerCAmelCase   					=  scale * emb

		if flip_sin_to_cos:
				lowerCAmelCase   					=  jnp.concatenate([jnp.cos(_A ), jnp.sin(_A )]      ,   axis=1 )
		else:
				lowerCAmelCase   					=  jnp.concatenate([jnp.sin(_A ), jnp.cos(_A )]      ,   axis=1 )
		lowerCAmelCase   					=  jnp.reshape(_A      ,   [jnp.shape(_A )[0], embedding_dim] )
		return signal






class 				a__(					nn.Module	):


			'''simple docstring'''






			UpperCAmelCase_			:     int      =       3_2
			UpperCAmelCase_			:     jnp.dtype      =       jnp.floataa



			@nn.compact
			def __call__(   self       ,    __lowerCAmelCase):




					"""simple docstring"""


					lowerCAmelCase   					=  nn.Dense(self.time_embed_dim       ,    dtype=self.dtype       ,    name="""linear_1""")(__lowerCAmelCase)
					lowerCAmelCase   					=  nn.silu(__lowerCAmelCase)
					lowerCAmelCase   					=  nn.Dense(self.time_embed_dim       ,    dtype=self.dtype       ,    name="""linear_2""")(__lowerCAmelCase)
					return temb







class 				a__(					nn.Module	):


			'''simple docstring'''






			UpperCAmelCase_			:     int      =       3_2
			UpperCAmelCase_			:     bool      =       False
			UpperCAmelCase_			:     float      =       1



			@nn.compact
			def __call__(   self       ,    __lowerCAmelCase):




					"""simple docstring"""


					return get_sinusoidal_embeddings(
					    __lowerCAmelCase       ,    embedding_dim=self.dim       ,    flip_sin_to_cos=self.flip_sin_to_cos       ,    freq_shift=self.freq_shift)




