




import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
							from .tokenization_big_bird import BigBirdTokenizer
else:
							UpperCAmelCase_		:					Dict          =  None

UpperCAmelCase_		:					Optional[Any]          =  logging.get_logger(__name__)
UpperCAmelCase_		:					Optional[int]          =  {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

UpperCAmelCase_		:					Dict          =  {
    "vocab_file": {
        "google/bigbird-roberta-base": "https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model",
        "google/bigbird-roberta-large": (
            "https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"
        ),
        "google/bigbird-base-trivia-itc": (
            "https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"
        ),
    },
    "tokenizer_file": {
        "google/bigbird-roberta-base": (
            "https://huggingface.co/google/bigbird-roberta-base/resolve/main/tokenizer.json"
        ),
        "google/bigbird-roberta-large": (
            "https://huggingface.co/google/bigbird-roberta-large/resolve/main/tokenizer.json"
        ),
        "google/bigbird-base-trivia-itc": (
            "https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/tokenizer.json"
        ),
    },
}

UpperCAmelCase_		:					Optional[int]          =  {
    "google/bigbird-roberta-base": 4096,
    "google/bigbird-roberta-large": 4096,
    "google/bigbird-base-trivia-itc": 4096,
}


UpperCAmelCase_		:					Any          =  "▁"




class 		__A   (      UpperCamelCase__      ):
						UpperCamelCase      =       VOCAB_FILES_NAMES
						UpperCamelCase      =       PRETRAINED_VOCAB_FILES_MAP
						UpperCamelCase      =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						UpperCamelCase      =       BigBirdTokenizer
						UpperCamelCase      =       ["""input_ids""", """attention_mask"""]
						UpperCamelCase      =       []

						def __init__(		self :List[str]     ,			__snake_case :List[Any]=None     ,			__snake_case :Tuple=None     ,			__snake_case :Dict="<unk>"     ,			__snake_case :Union[str, Any]="<s>"     ,			__snake_case :List[Any]="</s>"     ,			__snake_case :List[Any]="<pad>"     ,			__snake_case :Optional[Any]="[SEP]"     ,			__snake_case :Union[str, Any]="[MASK]"     ,			__snake_case :Dict="[CLS]"     ,			**__snake_case :Tuple     ,			):
										'''simple docstring'''

										__magic_name__						:						Dict										=AddedToken(__snake_case     ,			lstrip=__snake_case     ,			rstrip=__snake_case			) if isinstance(__snake_case     ,			__snake_case			) else bos_token
										__magic_name__						:						Dict										=AddedToken(__snake_case     ,			lstrip=__snake_case     ,			rstrip=__snake_case			) if isinstance(__snake_case     ,			__snake_case			) else eos_token
										__magic_name__						:						Optional[Any]										=AddedToken(__snake_case     ,			lstrip=__snake_case     ,			rstrip=__snake_case			) if isinstance(__snake_case     ,			__snake_case			) else unk_token
										__magic_name__						:						Optional[Any]										=AddedToken(__snake_case     ,			lstrip=__snake_case     ,			rstrip=__snake_case			) if isinstance(__snake_case     ,			__snake_case			) else pad_token
										__magic_name__						:						Union[str, Any]										=AddedToken(__snake_case     ,			lstrip=__snake_case     ,			rstrip=__snake_case			) if isinstance(__snake_case     ,			__snake_case			) else cls_token
										__magic_name__						:						str										=AddedToken(__snake_case     ,			lstrip=__snake_case     ,			rstrip=__snake_case			) if isinstance(__snake_case     ,			__snake_case			) else sep_token

										# Mask token behave like a normal word, i.e. include the space before it
										__magic_name__						:						Union[str, Any]										=AddedToken(__snake_case     ,			lstrip=__snake_case     ,			rstrip=__snake_case			) if isinstance(__snake_case     ,			__snake_case			) else mask_token

										super().__init__(
										    __snake_case     ,			tokenizer_file=__snake_case     ,			bos_token=__snake_case     ,			eos_token=__snake_case     ,			unk_token=__snake_case     ,			sep_token=__snake_case     ,			pad_token=__snake_case     ,			cls_token=__snake_case     ,			mask_token=__snake_case     ,			**__snake_case     ,			)

										__magic_name__						:						List[str]										=vocab_file
										__magic_name__						:						List[str]										=False if not self.vocab_file else True

						def      A__    (		self :Dict     ,			__snake_case :List[int]     ,			__snake_case :Optional[List[int]] = None			):
										'''simple docstring'''

										__magic_name__						:						Dict										=[self.sep_token_id]
										__magic_name__						:						str										=[self.cls_token_id]
										if token_ids_a is None:
														return cls + token_ids_a + sep
										return cls + token_ids_a + sep + token_ids_a + sep

						def      A__    (		self :int     ,			__snake_case :List[int]     ,			__snake_case :Optional[List[int]] = None     ,			__snake_case :bool = False			):
										'''simple docstring'''

										if already_has_special_tokens:
														if token_ids_a is not None:
																		raise ValueError(
																		    """You should not supply a second sequence if the provided sequence of """
																		    """ids is already formatted with special tokens for the model."""			)
														return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

										if token_ids_a is None:
														return [1] + ([0] * len(__snake_case			)) + [1]
										return [1] + ([0] * len(__snake_case			)) + [1] + ([0] * len(__snake_case			)) + [1]

						def      A__    (		self :Tuple     ,			__snake_case :List[int]     ,			__snake_case :Optional[List[int]] = None			):
										'''simple docstring'''

										__magic_name__						:						Union[str, Any]										=[self.sep_token_id]
										__magic_name__						:						Optional[int]										=[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			) * [0] + len(token_ids_a + sep			) * [1]


						def      A__    (		self :Dict     ,			__snake_case :str     ,			__snake_case :Optional[str] = None			):
										'''simple docstring'''

										if not self.can_save_slow_tokenizer:
														raise ValueError(
														    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
														    """tokenizer."""			)

										if not os.path.isdir(__snake_case			):
														logger.error(f"Vocabulary path ({save_directory}) should be a directory"			)
														return
										__magic_name__						:						Dict										=os.path.join(
										    __snake_case     ,			(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]			)

										if os.path.abspath(self.vocab_file			) != os.path.abspath(__snake_case			):
														copyfile(self.vocab_file     ,			__snake_case			)

										return (out_vocab_file,)











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							:			str         =			random.Random()





def 				__lowerCAmelCase(       _SCREAMING_SNAKE_CASE							,     _SCREAMING_SNAKE_CASE=1.0							,     _SCREAMING_SNAKE_CASE=None							,     _SCREAMING_SNAKE_CASE=None				)							-> Union[str, Any]:




    """simple docstring"""




    if rng is None:
        _A     =							global_rng

    _A     =							[]
    for batch_idx in range(shape[0]				):
        values.append([]				)
        for _ in range(shape[1]				):
            values[-1].append(rng.random() * scale				)

    return values







class      lowerCamelCase(						unittest.TestCase	):
     '''simple docstring'''


     def __init__(    self				,     snake_case_				,     snake_case_=7				,     snake_case_=400				,     snake_case_=2000				,     snake_case_=2048				,     snake_case_=128				,     snake_case_=1				,     snake_case_=512				,     snake_case_=30				,     snake_case_=4_4100				,     ):
         _A     =							parent
         _A     =							batch_size
         _A     =							min_seq_length
         _A     =							max_seq_length
         _A     =							(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
         _A     =							spectrogram_length
         _A     =							feature_size
         _A     =							num_audio_channels
         _A     =							hop_length
         _A     =							chunk_length
         _A     =							sampling_rate




     def        lowerCAmelCase__		(    self							):
         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        lowerCAmelCase__		(    self				,     snake_case_=False				,     snake_case_=False							):
         def _flatten(snake_case_							):
             return list(itertools.chain(*snake_case_							)							)

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







@require_torch
@require_torchaudio
class      lowerCamelCase(						__snake_case			, unittest.TestCase	):
     '''simple docstring'''


     __magic_name__   =			TvltFeatureExtractor




     def        lowerCAmelCase__		(    self							):
         _A     =							TvltFeatureExtractionTester(self							)




     def        lowerCAmelCase__		(    self							):
         _A     =							self.feature_extraction_class(**self.feat_extract_dict							)
         self.assertTrue(hasattr(snake_case_				,     'spectrogram_length'							)							)
         self.assertTrue(hasattr(snake_case_				,     'feature_size'							)							)
         self.assertTrue(hasattr(snake_case_				,     'num_audio_channels'							)							)
         self.assertTrue(hasattr(snake_case_				,     'hop_length'							)							)
         self.assertTrue(hasattr(snake_case_				,     'chunk_length'							)							)
         self.assertTrue(hasattr(snake_case_				,     'sampling_rate'							)							)




     def        lowerCAmelCase__		(    self							):
         _A     =							self.feature_extraction_class(**self.feat_extract_dict							)

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

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




     def        lowerCAmelCase__		(    self							):
         _A     =							self.feature_extraction_class(**self.feat_extract_dict							)

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

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




     def        lowerCAmelCase__		(    self							):
         # Initialize feature_extractor
         _A     =							self.feature_extraction_class(**self.feat_extract_dict							)

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

         # Test not batched input
         _A     =							feature_extractor(np_speech_inputs[0]				,     return_tensors='np'				,     sampling_rate=4_4100							).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
         _A     =							feature_extractor(snake_case_				,     return_tensors='np'				,     sampling_rate=4_4100							).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
         _A     =							feature_extractor(
             snake_case_				,     return_tensors='np'				,     sampling_rate=4_4100				,     mask_audio=snake_case_							).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.
         _A     =							[floats_list((1, x)							)[0] for x in (800, 800, 800)]
         _A     =							np.asarray(snake_case_							)
         _A     =							feature_extractor(snake_case_				,     return_tensors='np'				,     sampling_rate=4_4100							).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        lowerCAmelCase__		(    self				,     snake_case_							):
         _A     =							load_dataset('hf-internal-testing/librispeech_asr_dummy'				,     'clean'				,     split='validation'							)
         # automatic decoding with librispeech
         _A     =							ds.sort('id'							).select(range(snake_case_							)							)[:num_samples]['audio']

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




     def        lowerCAmelCase__		(    self							):
         _A     =							self._load_datasamples(1							)
         _A     =							TvltFeatureExtractor()
         _A     =							feature_extractor(snake_case_				,     return_tensors='pt'							).audio_values

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

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





