'''simple docstring'''
import itertools
import random
import unittest

import numpy as np

from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


a=  random.Random()

if is_torch_available():
  import torch







def  _UpperCamelCase (     _a	:	int				,     _a	:	Dict=1.0				,     _a	:	Any=None				,     _a	:	List[Any]=None			):




    """simple docstring"""



    if rng is None:
        __UpperCamelCase	:	int		    =  global_rng

    __UpperCamelCase	:	Union[str, Any]		    =  []
    for batch_idx in range(shape[0]			):
        values.append([]			)
        for _ in range(shape[1]			):
            values[-1].append(rng.random() * scale			)

    return values






class 			__lowercase		(						unittest.TestCase						):







     """simple docstring"""






     def __init__(		self     ,			_lowerCamelCase     ,			_lowerCamelCase=7     ,			_lowerCamelCase=4_0_0     ,			_lowerCamelCase=2_0_0_0     ,			_lowerCamelCase=1     ,			_lowerCamelCase=0.0     ,			_lowerCamelCase=1_6_0_0_0     ,			_lowerCamelCase=True     ,			_lowerCamelCase=True     ,			):
         __UpperCamelCase	:	int		    =  parent
         __UpperCamelCase	:	Any		    =  batch_size
         __UpperCamelCase	:	Any		    =  min_seq_length
         __UpperCamelCase	:	Dict		    =  max_seq_length
         __UpperCamelCase	:	List[str]		    =  (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
         __UpperCamelCase	:	List[str]		    =  feature_size
         __UpperCamelCase	:	str		    =  padding_value
         __UpperCamelCase	:	Dict		    =  sampling_rate
         __UpperCamelCase	:	int		    =  return_attention_mask
         __UpperCamelCase	:	Union[str, Any]		    =  do_normalize







     def lowerCAmelCase     (		self    ):
         return {
             "feature_size": self.feature_size,
             "padding_value": self.padding_value,
             "sampling_rate": self.sampling_rate,
             "return_attention_mask": self.return_attention_mask,
             "do_normalize": self.do_normalize,
         }







     def lowerCAmelCase     (		self     ,			_lowerCamelCase=False     ,			_lowerCamelCase=False    ):
         def _flatten(_lowerCamelCase    ):
             return list(itertools.chain(*_lowerCamelCase    )    )

         if equal_length:
             __UpperCamelCase	:	List[str]		    =  floats_list((self.batch_size, self.max_seq_length)    )
         else:
             # make sure that inputs increase in size
             __UpperCamelCase	:	Optional[Any]		    =  [
                 _flatten(floats_list((x, self.feature_size)    )    )
                 for x in range(self.min_seq_length     ,			self.max_seq_length     ,			self.seq_length_diff    )
             ]

         if numpify:
             __UpperCamelCase	:	str		    =  [np.asarray(_lowerCamelCase    ) for x in speech_inputs]

         return speech_inputs






@require_torch
@require_torchaudio
class 			__lowercase		(						_lowerCamelCase			,							unittest.TestCase						):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		   =						ASTFeatureExtractor







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Tuple		    =  ASTFeatureExtractionTester(self    )







     def lowerCAmelCase     (		self    ):
         # Tests that all call wrap to encode_plus and batch_encode_plus
         __UpperCamelCase	:	List[Any]		    =  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
         # create three inputs of length 800, 1000, and 1200
         __UpperCamelCase	:	int		    =  [floats_list((1, x)    )[0] for x in range(8_0_0     ,			1_4_0_0     ,			2_0_0    )]
         __UpperCamelCase	:	Union[str, Any]		    =  [np.asarray(_lowerCamelCase    ) for speech_input in speech_inputs]

         # Test not batched input
         __UpperCamelCase	:	Optional[Any]		    =  feat_extract(speech_inputs[0]     ,			return_tensors='np'    ).input_values
         __UpperCamelCase	:	Optional[Any]		    =  feat_extract(np_speech_inputs[0]     ,			return_tensors='np'    ).input_values
         self.assertTrue(np.allclose(_lowerCamelCase     ,			_lowerCamelCase     ,			atol=1E-3    )    )

         # Test batched
         __UpperCamelCase	:	Optional[int]		    =  feat_extract(_lowerCamelCase     ,			padding=_lowerCamelCase     ,			return_tensors='np'    ).input_values
         __UpperCamelCase	:	str		    =  feat_extract(_lowerCamelCase     ,			padding=_lowerCamelCase     ,			return_tensors='np'    ).input_values
         for enc_seq_a, enc_seq_a in zip(_lowerCamelCase     ,			_lowerCamelCase    ):
             self.assertTrue(np.allclose(_lowerCamelCase     ,			_lowerCamelCase     ,			atol=1E-3    )    )

         # Test 2-D numpy arrays are batched.
         __UpperCamelCase	:	Dict		    =  [floats_list((1, x)    )[0] for x in (8_0_0, 8_0_0, 8_0_0)]
         __UpperCamelCase	:	List[str]		    =  np.asarray(_lowerCamelCase    )
         __UpperCamelCase	:	List[str]		    =  feat_extract(_lowerCamelCase     ,			return_tensors='np'    ).input_values
         __UpperCamelCase	:	Optional[int]		    =  feat_extract(_lowerCamelCase     ,			return_tensors='np'    ).input_values
         for enc_seq_a, enc_seq_a in zip(_lowerCamelCase     ,			_lowerCamelCase    ):
             self.assertTrue(np.allclose(_lowerCamelCase     ,			_lowerCamelCase     ,			atol=1E-3    )    )







     @require_torch
     def lowerCAmelCase     (		self    ):
         import torch

         __UpperCamelCase	:	Tuple		    =  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()    )
         __UpperCamelCase	:	Dict		    =  np.random.rand(1_0_0    ).astype(np.floataa    )
         __UpperCamelCase	:	List[Any]		    =  np_speech_inputs.tolist()

         for inputs in [py_speech_inputs, np_speech_inputs]:
             __UpperCamelCase	:	Optional[Any]		    =  feature_extractor.pad([{'input_values': inputs}]     ,			return_tensors='np'    )
             self.assertTrue(np_processed.input_values.dtype == np.floataa    )
             __UpperCamelCase	:	Optional[Any]		    =  feature_extractor.pad([{'input_values': inputs}]     ,			return_tensors='pt'    )
             self.assertTrue(pt_processed.input_values.dtype == torch.floataa    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         from datasets import load_dataset

         __UpperCamelCase	:	str		    =  load_dataset('hf-internal-testing/librispeech_asr_dummy'     ,			'clean'     ,			split='validation'    )
         # automatic decoding with librispeech
         __UpperCamelCase	:	Optional[Any]		    =  ds.sort('id'    ).select(range(_lowerCamelCase    )    )[:num_samples]['audio']

         return [x["array"] for x in speech_samples]







     @require_torch
     def lowerCAmelCase     (		self    ):
         # fmt: off
         __UpperCamelCase	:	Any		    =  torch.tensor(
             [-0.9_8_9_4, -1.2_7_7_6, -0.9_0_6_6, -1.2_7_7_6, -0.9_3_4_9, -1.2_6_0_9, -1.0_3_8_6, -1.2_7_7_6,
              -1.1_5_6_1, -1.2_7_7_6, -1.2_0_5_2, -1.2_7_2_3, -1.2_1_9_0, -1.2_1_3_2, -1.2_7_7_6, -1.1_1_3_3,
              -1.1_9_5_3, -1.1_3_4_3, -1.1_5_8_4, -1.2_2_0_3, -1.1_7_7_0, -1.2_4_7_4, -1.2_3_8_1, -1.1_9_3_6,
              -0.9_2_7_0, -0.8_3_1_7, -0.8_0_4_9, -0.7_7_0_6, -0.7_5_6_5, -0.7_8_6_9]    )
         # fmt: on

         __UpperCamelCase	:	Optional[Any]		    =  self._load_datasamples(1    )
         __UpperCamelCase	:	List[str]		    =  ASTFeatureExtractor()
         __UpperCamelCase	:	Optional[Any]		    =  feature_extractor(_lowerCamelCase     ,			return_tensors='pt'    ).input_values
         self.assertEquals(input_values.shape     ,			(1, 1_0_2_4, 1_2_8)    )
         self.assertTrue(torch.allclose(input_values[0, 0, :3_0]     ,			_lowerCamelCase     ,			atol=1E-4    )    )




'''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


a=  logging.get_logger(__name__)

a=  '''▁'''

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

a=  {
    '''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'''
        )
    },
}

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

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

a=  {'''mustc''': MUSTC_LANGS}






class 			__lowercase		(						_lowerCamelCase						):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		   =						VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE__		   =						PRETRAINED_VOCAB_FILES_MAP
     SCREAMING_SNAKE_CASE__		   =						MAX_MODEL_INPUT_SIZES
     SCREAMING_SNAKE_CASE__		   =						['''input_ids''', '''attention_mask''']

     SCREAMING_SNAKE_CASE__		   =						[]







     def __init__(		self     ,			_lowerCamelCase     ,			_lowerCamelCase     ,			_lowerCamelCase="<s>"     ,			_lowerCamelCase="</s>"     ,			_lowerCamelCase="<pad>"     ,			_lowerCamelCase="<unk>"     ,			_lowerCamelCase=False     ,			_lowerCamelCase=False     ,			_lowerCamelCase=None     ,			_lowerCamelCase=None     ,			_lowerCamelCase = None     ,			**_lowerCamelCase     ,			):
         __UpperCamelCase	:	List[Any]		    =  {} 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     ,			)
         __UpperCamelCase	:	Union[str, Any]		    =  do_upper_case
         __UpperCamelCase	:	Dict		    =  do_lower_case

         __UpperCamelCase	:	List[str]		    =  load_json(_lowerCamelCase    )
         __UpperCamelCase	:	List[Any]		    =  {v: k for k, v in self.encoder.items()}
         __UpperCamelCase	:	int		    =  spm_file
         __UpperCamelCase	:	List[Any]		    =  load_spm(_lowerCamelCase     ,			self.sp_model_kwargs    )

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

             __UpperCamelCase	:	str		    =  self.lang_tokens
             __UpperCamelCase	:	str		    =  tgt_lang if tgt_lang is not None else self.langs[0]

             self.set_tgt_lang_special_tokens(self._tgt_lang    )
         else:
             __UpperCamelCase	:	Dict		    =  {}







     @property
     def lowerCAmelCase     (		self    ):
         return len(self.encoder    )







     @property
     def lowerCAmelCase     (		self    ):
         return self._tgt_lang







     @tgt_lang.setter
     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	Optional[int]		    =  new_tgt_lang
         self.set_tgt_lang_special_tokens(_lowerCamelCase    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	int		    =  self.lang_code_to_id[tgt_lang]
         __UpperCamelCase	:	List[str]		    =  [lang_code_id]







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         return self.sp_model.encode(_lowerCamelCase     ,			out_type=_lowerCamelCase    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         return self.encoder.get(_lowerCamelCase     ,			self.encoder[self.unk_token]    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         return self.decoder.get(_lowerCamelCase     ,			self.unk_token    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	Optional[Any]		    =  []
         __UpperCamelCase	:	List[str]		    =  ''
         for token in tokens:
             # make sure that special tokens are not decoded using sentencepiece model
             if token in self.all_special_tokens:
                 __UpperCamelCase	:	int		    =  self.sp_model.decode(_lowerCamelCase    )
                 out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
                 __UpperCamelCase	:	int		    =  []
             else:
                 current_sub_tokens.append(_lowerCamelCase    )
         __UpperCamelCase	:	str		    =  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    ):
         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    ):

         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	:	Union[str, Any]		    =  [1] * len(self.prefix_tokens    )
         __UpperCamelCase	:	Any		    =  [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    ):
         __UpperCamelCase	:	Union[str, Any]		    =  self.encoder.copy()
         vocab.update(self.added_tokens_encoder    )
         return vocab







     def __getstate__(		self    ):
         __UpperCamelCase	:	int		    =  self.__dict__.copy()
         __UpperCamelCase	:	Dict		    =  None
         return state







     def __setstate__(		self     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	List[str]		    =  d

         # for backward compatibility
         if not hasattr(self     ,			'sp_model_kwargs'    ):
             __UpperCamelCase	:	Optional[int]		    =  {}

         __UpperCamelCase	:	Union[str, Any]		    =  load_spm(self.spm_file     ,			self.sp_model_kwargs    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase = None    ):
         __UpperCamelCase	:	List[str]		    =  Path(_lowerCamelCase    )
         assert save_dir.is_dir(), f"""{save_directory} should be a directory"""
         __UpperCamelCase	:	Optional[int]		    =  save_dir / (
             (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['vocab_file']
         )
         __UpperCamelCase	:	Union[str, Any]		    =  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	:	Dict		    =  self.sp_model.serialized_model_proto()
                 fi.write(_lowerCamelCase    )

         return (str(_lowerCamelCase    ), str(_lowerCamelCase    ))







def  _UpperCamelCase (     _a	:	str				,     _a	:	Dict[str, Any]			):




    """simple docstring"""



    __UpperCamelCase	:	List[Any]		    =  sentencepiece.SentencePieceProcessor(**_a			)
    spm.Load(str(_a			)			)
    return spm







def  _UpperCamelCase (     _a	:	str			):




    """simple docstring"""



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







def  _UpperCamelCase (     _a	:	Any				,     _a	:	str			):




    """simple docstring"""



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



