

import json
import os
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging


__A      		=     logging.get_logger(__name__)


__A      		=     {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}

# See all LED models at https://huggingface.co/models?filter=LED
__A      		=     {
    '''vocab_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json''',
    },
}

__A      		=     {
    '''allenai/led-base-16384''': 1_63_84,
}



@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def 				snake_case_()							->     int:



  """simple docstring"""






  _snake_case						=							(
      list(range(ord('''!'''					)			,	ord('''~'''					) + 1					)					) + list(range(ord('''¡'''					)			,	ord('''¬'''					) + 1					)					) + list(range(ord('''®'''					)			,	ord('''ÿ'''					) + 1					)					)
  )
  _snake_case						=							bs[:]
  _snake_case						=							0
  for b in range(2**8					):
    if b not in bs:
      bs.append(_UpperCamelCase					)
      cs.append(2**8 + n					)
      n += 1
  _snake_case						=							[chr(_UpperCamelCase					) for n in cs]
  return dict(zip(_UpperCamelCase			,	_UpperCamelCase					)					)



def 				snake_case_(_UpperCamelCase					)							->     str:



  """simple docstring"""






  _snake_case						=							set()
  _snake_case						=							word[0]
  for char in word[1:]:
    pairs.add((prev_char, char)					)
    _snake_case						=							char
  return pairs



class   lowercase_   (  __lowercase					):
      UpperCamelCase_      :		Dict										=    VOCAB_FILES_NAMES
      UpperCamelCase_      :		List[str]										=    PRETRAINED_VOCAB_FILES_MAP
      UpperCamelCase_      :		List[str]										=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCamelCase_      :		Union[str, Any]										=    ["input_ids", "attention_mask"]



      def __init__(      self				:    Tuple      , A__				:    List[str]      , A__				:    Dict      , A__				:    Tuple="replace"      , A__				:    List[str]="<s>"      , A__				:    Union[str, Any]="</s>"      , A__				:    List[str]="</s>"      , A__				:    str="<s>"      , A__				:    Any="<unk>"      , A__				:    Dict="<pad>"      , A__				:    List[Any]="<mask>"      , A__				:    Any=False      , **A__				:    str      , )   ->					int:
        _snake_case						=							AddedToken(A__      , lstrip=A__      , rstrip=A__		) if isinstance(A__      , A__		) else bos_token
        _snake_case						=							AddedToken(A__      , lstrip=A__      , rstrip=A__		) if isinstance(A__      , A__		) else eos_token
        _snake_case						=							AddedToken(A__      , lstrip=A__      , rstrip=A__		) if isinstance(A__      , A__		) else sep_token
        _snake_case						=							AddedToken(A__      , lstrip=A__      , rstrip=A__		) if isinstance(A__      , A__		) else cls_token
        _snake_case						=							AddedToken(A__      , lstrip=A__      , rstrip=A__		) if isinstance(A__      , A__		) else unk_token
        _snake_case						=							AddedToken(A__      , lstrip=A__      , rstrip=A__		) if isinstance(A__      , A__		) else pad_token

        # Mask token behave like a normal word, i.e. include the space before it
        _snake_case						=							AddedToken(A__      , lstrip=A__      , rstrip=A__		) if isinstance(A__      , A__		) else mask_token

        super().__init__(
            errors=A__      , bos_token=A__      , eos_token=A__      , unk_token=A__      , sep_token=A__      , cls_token=A__      , pad_token=A__      , mask_token=A__      , add_prefix_space=A__      , **A__      , )

        with open(A__      , encoding='''utf-8'''		) as vocab_handle:
          _snake_case						=							json.load(A__		)
        _snake_case						=							{v: k for k, v in self.encoder.items()}
        _snake_case						=							errors  # how to handle errors in decoding
        _snake_case						=							bytes_to_unicode()
        _snake_case						=							{v: k for k, v in self.byte_encoder.items()}
        with open(A__      , encoding='''utf-8'''		) as merges_handle:
          _snake_case						=							merges_handle.read().split('''\n'''		)[1:-1]
        _snake_case						=							[tuple(merge.split()		) for merge in bpe_merges]
        _snake_case						=							dict(zip(A__      , range(len(A__		)		)		)		)
        _snake_case						=							{}
        _snake_case						=							add_prefix_space

        # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
        _snake_case						=							re.compile(R'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'''		)



      @property
      # Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
      def 						UpperCamelCase_		(      self				:    List[Any]		)   ->					List[Any]:
        return len(self.encoder		)



      def 						UpperCamelCase_		(      self				:    Union[str, Any]		)   ->					List[Any]:
        return dict(self.encoder      , **self.added_tokens_encoder		)



      def 						UpperCamelCase_		(      self				:    Optional[int]      , A__				:    Optional[int]		)   ->					Tuple:
        if token in self.cache:
          return self.cache[token]
        _snake_case						=							tuple(A__		)
        _snake_case						=							get_pairs(A__		)

        if not pairs:
          return token

        while True:
          _snake_case						=							min(A__      , key=lambda A__		: self.bpe_ranks.get(A__      , float('''inf'''		)		)		)
          if bigram not in self.bpe_ranks:
            break
          _snake_case,					_snake_case						=							bigram
          _snake_case						=							[]
          _snake_case						=							0
          while i < len(A__		):
            try:
              _snake_case						=							word.index(A__      , A__		)
            except ValueError:
              new_word.extend(word[i:]		)
              break
            else:
              new_word.extend(word[i:j]		)
              _snake_case						=							j

            if word[i] == first and i < len(A__		) - 1 and word[i + 1] == second:
              new_word.append(first + second		)
              i += 2
            else:
              new_word.append(word[i]		)
              i += 1
          _snake_case						=							tuple(A__		)
          _snake_case						=							new_word
          if len(A__		) == 1:
            break
          else:
            _snake_case						=							get_pairs(A__		)
        _snake_case						=							''' '''.join(A__		)
        _snake_case						=							word
        return word



      def 						UpperCamelCase_		(      self				:    Optional[Any]      , A__				:    List[Any]		)   ->					Optional[Any]:
        _snake_case						=							[]
        for token in re.findall(self.pat      , A__		):
          _snake_case						=							''''''.join(
              self.byte_encoder[b] for b in token.encode('''utf-8'''		)		)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
          bpe_tokens.extend(bpe_token for bpe_token in self.bpe(A__		).split(''' '''		)		)
        return bpe_tokens



      def 						UpperCamelCase_		(      self				:    List[Any]      , A__				:    Union[str, Any]		)   ->					Union[str, Any]:
        return self.encoder.get(A__      , self.encoder.get(self.unk_token		)		)



      def 						UpperCamelCase_		(      self				:    Any      , A__				:    int		)   ->					Union[str, Any]:
        return self.decoder.get(A__		)



      def 						UpperCamelCase_		(      self				:    Optional[int]      , A__				:    Optional[Any]		)   ->					int:
        _snake_case						=							''''''.join(A__		)
        _snake_case						=							bytearray([self.byte_decoder[c] for c in text]		).decode('''utf-8'''      , errors=self.errors		)
        return text



      def 						UpperCamelCase_		(      self				:    Dict      , A__				:    str      , A__				:    Optional[str] = None		)   ->					Tuple[str]:
        if not os.path.isdir(A__		):
          logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""		)
          return
        _snake_case						=							os.path.join(
            A__      , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']		)
        _snake_case						=							os.path.join(
            A__      , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']		)

        with open(A__      , '''w'''      , encoding='''utf-8'''		) as f:
          f.write(json.dumps(self.encoder      , indent=2      , sort_keys=A__      , ensure_ascii=A__		) + '''\n'''		)

        _snake_case						=							0
        with open(A__      , '''w'''      , encoding='''utf-8'''		) as writer:
          writer.write('''#version: 0.2\n'''		)
          for bpe_tokens, token_index in sorted(self.bpe_ranks.items()      , key=lambda A__		: kv[1]		):
            if index != token_index:
              logger.warning(
                  f"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
                  ''' Please check that the tokenizer is not corrupted!'''		)
              _snake_case						=							token_index
            writer.write(''' '''.join(A__		) + '''\n'''		)
            index += 1

        return vocab_file, merge_file



      def 						UpperCamelCase_		(      self				:    Optional[int]      , A__				:    List[int]      , A__				:    Optional[List[int]] = None		)   ->					List[int]:
        if token_ids_a is None:
          return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
        _snake_case						=							[self.cls_token_id]
        _snake_case						=							[self.sep_token_id]
        return cls + token_ids_a + sep + sep + token_ids_a + sep



      def 						UpperCamelCase_		(      self				:    str      , A__				:    List[int]      , A__				:    Optional[List[int]] = None      , A__				:    bool = False		)   ->					List[int]:
        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 [1] + ([0] * len(A__		)) + [1]
        return [1] + ([0] * len(A__		)) + [1, 1] + ([0] * len(A__		)) + [1]



      def 						UpperCamelCase_		(      self				:    Dict      , A__				:    List[int]      , A__				:    Optional[List[int]] = None		)   ->					List[int]:
        _snake_case						=							[self.sep_token_id]
        _snake_case						=							[self.cls_token_id]

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



      def 						UpperCamelCase_		(      self				:    Any      , A__				:    Dict      , A__				:    Optional[int]=False      , **A__				:    Tuple		)   ->					List[str]:
        _snake_case						=							kwargs.pop('''add_prefix_space'''      , self.add_prefix_space		)
        if (is_split_into_words or add_prefix_space) and (len(A__		) > 0 and not text[0].isspace()):
          _snake_case						=							''' ''' + text
        return (text, kwargs)







      def 						UpperCamelCase_		(      self				:    Any      , A__				:    Union[Dict[str, EncodedInput], BatchEncoding]      , A__				:    Optional[int] = None      , A__				:    PaddingStrategy = PaddingStrategy.DO_NOT_PAD      , A__				:    Optional[int] = None      , A__				:    Optional[bool] = None      , )   ->					dict:
        _snake_case						=							super()._pad(
            encoded_inputs=A__      , max_length=A__      , padding_strategy=A__      , pad_to_multiple_of=A__      , return_attention_mask=A__      , )

        # Load from model defaults
        if return_attention_mask is None:
          _snake_case						=							'''attention_mask''' in self.model_input_names

        if return_attention_mask and "global_attention_mask" in encoded_inputs:
          _snake_case						=							encoded_inputs[self.model_input_names[0]]
          # `global_attention_mask` need to have the same length as other (sequential) inputs.
          _snake_case						=							len(encoded_inputs['''global_attention_mask''']		) != len(A__		)

          if needs_to_be_padded:
            _snake_case						=							len(A__		) - len(encoded_inputs['''global_attention_mask''']		)

            if self.padding_side == "right":
              # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
              _snake_case						=							(
                  encoded_inputs['''global_attention_mask'''] + [-1] * difference
              )
            elif self.padding_side == "left":
              _snake_case						=							[-1] * difference + encoded_inputs[
                  '''global_attention_mask'''
              ]
            else:
              raise ValueError('''Invalid padding strategy:''' + str(self.padding_side		)		)

        return encoded_inputs




import itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
 import torch

if is_datasets_available():
 from datasets import load_dataset

__A      		=     random.Random()



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase=1.0			,	_UpperCamelCase=None			,	_UpperCamelCase=None					)							->     Optional[int]:



  """simple docstring"""






  if rng is None:
    _snake_case						=							global_rng

  _snake_case						=							[]
  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					):



      def __init__(      self				:    List[Any]      , A__				:    List[Any]      , A__				:    int=7      , A__				:    Tuple=400      , A__				:    int=2000      , A__				:    Any=2048      , A__				:    List[Any]=128      , A__				:    Optional[int]=1      , A__				:    Optional[Any]=512      , A__				:    Any=30      , A__				:    Any=44100      , )   ->					int:
        _snake_case						=							parent
        _snake_case						=							batch_size
        _snake_case						=							min_seq_length
        _snake_case						=							max_seq_length
        _snake_case						=							(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
        _snake_case						=							spectrogram_length
        _snake_case						=							feature_size
        _snake_case						=							num_audio_channels
        _snake_case						=							hop_length
        _snake_case						=							chunk_length
        _snake_case						=							sampling_rate



      def 						UpperCamelCase_		(      self				:    str		)   ->					Optional[int]:
        return {
            "spectrogram_length": self.spectrogram_length,
            "feature_size": self.feature_size,
            "num_audio_channels": self.num_audio_channels,
            "hop_length": self.hop_length,
            "chunk_length": self.chunk_length,
            "sampling_rate": self.sampling_rate,
        }







      def 						UpperCamelCase_		(      self				:    Any      , A__				:    Any=False      , A__				:    List[str]=False		)   ->					Tuple:
        def _flatten(A__				:    List[str]		):
          return list(itertools.chain(*A__		)		)

        if equal_length:
          _snake_case						=							[floats_list((self.max_seq_length, self.feature_size)		) for _ in range(self.batch_size		)]
        else:
          # make sure that inputs increase in size
          _snake_case						=							[
              floats_list((x, self.feature_size)		)
              for x in range(self.min_seq_length      , self.max_seq_length      , self.seq_length_diff		)
          ]
        if numpify:
          _snake_case						=							[np.asarray(A__		) for x in speech_inputs]
        return speech_inputs



@require_torch
@require_torchaudio
class   lowercase_   (  __lowercase       ,							unittest.TestCase					):
      UpperCamelCase_      :		Optional[int]										=    TvltFeatureExtractor



      def 						UpperCamelCase_		(      self				:    Dict		)   ->					List[str]:
        _snake_case						=							TvltFeatureExtractionTester(self		)



      def 						UpperCamelCase_		(      self				:    int		)   ->					Optional[int]:
        _snake_case						=							self.feature_extraction_class(**self.feat_extract_dict		)
        self.assertTrue(hasattr(A__      , '''spectrogram_length'''		)		)
        self.assertTrue(hasattr(A__      , '''feature_size'''		)		)
        self.assertTrue(hasattr(A__      , '''num_audio_channels'''		)		)
        self.assertTrue(hasattr(A__      , '''hop_length'''		)		)
        self.assertTrue(hasattr(A__      , '''chunk_length'''		)		)
        self.assertTrue(hasattr(A__      , '''sampling_rate'''		)		)



      def 						UpperCamelCase_		(      self				:    Any		)   ->					Union[str, Any]:
        _snake_case						=							self.feature_extraction_class(**self.feat_extract_dict		)

        with tempfile.TemporaryDirectory() as tmpdirname:
          _snake_case						=							feat_extract_first.save_pretrained(A__		)[0]
          check_json_file_has_correct_format(A__		)
          _snake_case						=							self.feature_extraction_class.from_pretrained(A__		)

        _snake_case						=							feat_extract_first.to_dict()
        _snake_case						=							feat_extract_second.to_dict()
        _snake_case						=							dict_first.pop('''mel_filters'''		)
        _snake_case						=							dict_second.pop('''mel_filters'''		)
        self.assertTrue(np.allclose(A__      , A__		)		)
        self.assertEqual(A__      , A__		)



      def 						UpperCamelCase_		(      self				:    int		)   ->					Union[str, Any]:
        _snake_case						=							self.feature_extraction_class(**self.feat_extract_dict		)

        with tempfile.TemporaryDirectory() as tmpdirname:
          _snake_case						=							os.path.join(A__      , '''feat_extract.json'''		)
          feat_extract_first.to_json_file(A__		)
          _snake_case						=							self.feature_extraction_class.from_json_file(A__		)

        _snake_case						=							feat_extract_first.to_dict()
        _snake_case						=							feat_extract_second.to_dict()
        _snake_case						=							dict_first.pop('''mel_filters'''		)
        _snake_case						=							dict_second.pop('''mel_filters'''		)
        self.assertTrue(np.allclose(A__      , A__		)		)
        self.assertEqual(A__      , A__		)



      def 						UpperCamelCase_		(      self				:    Union[str, Any]		)   ->					Any:
        # Initialize feature_extractor
        _snake_case						=							self.feature_extraction_class(**self.feat_extract_dict		)

        # create three inputs of length 800, 1000, and 1200
        _snake_case						=							[floats_list((1, x)		)[0] for x in range(800      , 1400      , 200		)]
        _snake_case						=							[np.asarray(A__		) for speech_input in speech_inputs]

        # Test not batched input
        _snake_case						=							feature_extractor(np_speech_inputs[0]      , return_tensors='''np'''      , sampling_rate=44100		).audio_values

        self.assertTrue(encoded_audios.ndim == 4		)
        self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size		)
        self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length		)
        self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels		)

        # Test batched
        _snake_case						=							feature_extractor(A__      , return_tensors='''np'''      , sampling_rate=44100		).audio_values

        self.assertTrue(encoded_audios.ndim == 4		)
        self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size		)
        self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length		)
        self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels		)

        # Test audio masking
        _snake_case						=							feature_extractor(
            A__      , return_tensors='''np'''      , sampling_rate=44100      , mask_audio=A__		).audio_values

        self.assertTrue(encoded_audios.ndim == 4		)
        self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size		)
        self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length		)
        self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels		)

        # Test 2-D numpy arrays are batched.
        _snake_case						=							[floats_list((1, x)		)[0] for x in (800, 800, 800)]
        _snake_case						=							np.asarray(A__		)
        _snake_case						=							feature_extractor(A__      , return_tensors='''np'''      , sampling_rate=44100		).audio_values
        self.assertTrue(encoded_audios.ndim == 4		)
        self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size		)
        self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length		)
        self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels		)



      def 						UpperCamelCase_		(      self				:    Optional[Any]      , A__				:    Any		)   ->					Optional[int]:
        _snake_case						=							load_dataset('''hf-internal-testing/librispeech_asr_dummy'''      , '''clean'''      , split='''validation'''		)
        # automatic decoding with librispeech
        _snake_case						=							ds.sort('''id'''		).select(range(A__		)		)[:num_samples]['''audio''']

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







      def 						UpperCamelCase_		(      self				:    List[str]		)   ->					Optional[Any]:
        _snake_case						=							self._load_datasamples(1		)
        _snake_case						=							TvltFeatureExtractor()
        _snake_case						=							feature_extractor(A__      , return_tensors='''pt'''		).audio_values

        self.assertEquals(audio_values.shape      , (1, 1, 192, 128)		)

        _snake_case						=							torch.tensor([[-0.3032, -0.2708], [-0.4434, -0.4007]]		)
        self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]      , A__      , atol=1e-4		)		)

