

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

lowercase        =  random.Random()



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Any			,				UpperCAmelCase__				:      Any=1.0			,				UpperCAmelCase__				:      str=None			,				UpperCAmelCase__				:      Dict=None       )  ->    List[str]:
							if rng is None:
														lowerCamelCase_							  =					global_rng

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

							return values





class       __A(				unittest.TestCase       ):

					def __init__(	self					:     Union[str, Any]   ,					__UpperCamelCase					:     Tuple   ,					__UpperCamelCase					:     str=7   ,					__UpperCamelCase					:     Tuple=4_0_0   ,					__UpperCamelCase					:     Any=2_0_0_0   ,					__UpperCamelCase					:     int=2_0_4_8   ,					__UpperCamelCase					:     Optional[int]=1_2_8   ,					__UpperCamelCase					:     Tuple=1   ,					__UpperCamelCase					:     Dict=5_1_2   ,					__UpperCamelCase					:     Union[str, Any]=3_0   ,					__UpperCamelCase					:     Optional[int]=4_4_1_0_0   ,					):
												lowerCamelCase_							  =					parent
												lowerCamelCase_							  =					batch_size
												lowerCamelCase_							  =					min_seq_length
												lowerCamelCase_							  =					max_seq_length
												lowerCamelCase_							  =					(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
												lowerCamelCase_							  =					spectrogram_length
												lowerCamelCase_							  =					feature_size
												lowerCamelCase_							  =					num_audio_channels
												lowerCamelCase_							  =					hop_length
												lowerCamelCase_							  =					chunk_length
												lowerCamelCase_							  =					sampling_rate

					def    lowercase__      (	self					:     Tuple   ):
												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    lowercase__      (	self					:     Optional[int]   ,					__UpperCamelCase					:     int=False   ,					__UpperCamelCase					:     Tuple=False   ):
												def _flatten(__UpperCamelCase					:     Any   ):
																			return list(itertools.chain(*__UpperCamelCase   )   )

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





@require_torch
@require_torchaudio
class       __A(				UpperCAmelCase     ,		unittest.TestCase       ):
					SCREAMING_SNAKE_CASE			    =    TvltFeatureExtractor

					def    lowercase__      (	self					:     Union[str, Any]   ):
												lowerCamelCase_							  =					TvltFeatureExtractionTester(self   )

					def    lowercase__      (	self					:     Union[str, Any]   ):
												lowerCamelCase_							  =					self.feature_extraction_class(**self.feat_extract_dict   )
												self.assertTrue(hasattr(__UpperCamelCase   ,					"""spectrogram_length"""   )   )
												self.assertTrue(hasattr(__UpperCamelCase   ,					"""feature_size"""   )   )
												self.assertTrue(hasattr(__UpperCamelCase   ,					"""num_audio_channels"""   )   )
												self.assertTrue(hasattr(__UpperCamelCase   ,					"""hop_length"""   )   )
												self.assertTrue(hasattr(__UpperCamelCase   ,					"""chunk_length"""   )   )
												self.assertTrue(hasattr(__UpperCamelCase   ,					"""sampling_rate"""   )   )

					def    lowercase__      (	self					:     Any   ):
												lowerCamelCase_							  =					self.feature_extraction_class(**self.feat_extract_dict   )

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

												lowerCamelCase_							  =					feat_extract_first.to_dict()
												lowerCamelCase_							  =					feat_extract_second.to_dict()
												lowerCamelCase_							  =					dict_first.pop("""mel_filters"""   )
												lowerCamelCase_							  =					dict_second.pop("""mel_filters"""   )
												self.assertTrue(np.allclose(__UpperCamelCase   ,					__UpperCamelCase   )   )
												self.assertEqual(__UpperCamelCase   ,					__UpperCamelCase   )

					def    lowercase__      (	self					:     Optional[int]   ):
												lowerCamelCase_							  =					self.feature_extraction_class(**self.feat_extract_dict   )

												with tempfile.TemporaryDirectory() as tmpdirname:
																			lowerCamelCase_							  =					os.path.join(__UpperCamelCase   ,					"""feat_extract.json"""   )
																			feat_extract_first.to_json_file(__UpperCamelCase   )
																			lowerCamelCase_							  =					self.feature_extraction_class.from_json_file(__UpperCamelCase   )

												lowerCamelCase_							  =					feat_extract_first.to_dict()
												lowerCamelCase_							  =					feat_extract_second.to_dict()
												lowerCamelCase_							  =					dict_first.pop("""mel_filters"""   )
												lowerCamelCase_							  =					dict_second.pop("""mel_filters"""   )
												self.assertTrue(np.allclose(__UpperCamelCase   ,					__UpperCamelCase   )   )
												self.assertEqual(__UpperCamelCase   ,					__UpperCamelCase   )

					def    lowercase__      (	self					:     int   ):
												# Initialize feature_extractor
												lowerCamelCase_							  =					self.feature_extraction_class(**self.feat_extract_dict   )

												# create three inputs of length 800, 1000, and 1200
												lowerCamelCase_							  =					[floats_list((1, x)   )[0] for x in range(8_0_0   ,					1_4_0_0   ,					2_0_0   )]
												lowerCamelCase_							  =					[np.asarray(__UpperCamelCase   ) for speech_input in speech_inputs]

												# Test not batched input
												lowerCamelCase_							  =					feature_extractor(np_speech_inputs[0]   ,					return_tensors="""np"""   ,					sampling_rate=4_4_1_0_0   ).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
												lowerCamelCase_							  =					feature_extractor(__UpperCamelCase   ,					return_tensors="""np"""   ,					sampling_rate=4_4_1_0_0   ).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
												lowerCamelCase_							  =					feature_extractor(
												    __UpperCamelCase   ,					return_tensors="""np"""   ,					sampling_rate=4_4_1_0_0   ,					mask_audio=__UpperCamelCase   ).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.
												lowerCamelCase_							  =					[floats_list((1, x)   )[0] for x in (8_0_0, 8_0_0, 8_0_0)]
												lowerCamelCase_							  =					np.asarray(__UpperCamelCase   )
												lowerCamelCase_							  =					feature_extractor(__UpperCamelCase   ,					return_tensors="""np"""   ,					sampling_rate=4_4_1_0_0   ).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    lowercase__      (	self					:     Dict   ,					__UpperCamelCase					:     Optional[Any]   ):
												lowerCamelCase_							  =					load_dataset("""hf-internal-testing/librispeech_asr_dummy"""   ,					"""clean"""   ,					split="""validation"""   )
												# automatic decoding with librispeech
												lowerCamelCase_							  =					ds.sort("""id"""   ).select(range(__UpperCamelCase   )   )[:num_samples]["""audio"""]

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



					def    lowercase__      (	self					:     Dict   ):
												lowerCamelCase_							  =					self._load_datasamples(1   )
												lowerCamelCase_							  =					TvltFeatureExtractor()
												lowerCamelCase_							  =					feature_extractor(__UpperCamelCase   ,					return_tensors="""pt"""   ).audio_values

												self.assertEquals(audio_values.shape   ,					(1, 1, 1_9_2, 1_2_8)   )

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







import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mobilebert import MobileBertTokenizer


lowercase        =  logging.get_logger(__name__)

lowercase        =  {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

lowercase        =  {
    '''vocab_file''': {'''mobilebert-uncased''': '''https://huggingface.co/google/mobilebert-uncased/resolve/main/vocab.txt'''},
    '''tokenizer_file''': {
        '''mobilebert-uncased''': '''https://huggingface.co/google/mobilebert-uncased/resolve/main/tokenizer.json'''
    },
}

lowercase        =  {'''mobilebert-uncased''': 5_1_2}


lowercase        =  {}





class       __A(				UpperCAmelCase       ):
					SCREAMING_SNAKE_CASE			    =    VOCAB_FILES_NAMES
					SCREAMING_SNAKE_CASE			    =    PRETRAINED_VOCAB_FILES_MAP
					SCREAMING_SNAKE_CASE			    =    PRETRAINED_INIT_CONFIGURATION
					SCREAMING_SNAKE_CASE			    =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					SCREAMING_SNAKE_CASE			    =    MobileBertTokenizer

					def __init__(	self					:     Union[str, Any]   ,					__UpperCamelCase					:     str=None   ,					__UpperCamelCase					:     str=None   ,					__UpperCamelCase					:     Dict=True   ,					__UpperCamelCase					:     Any="[UNK]"   ,					__UpperCamelCase					:     str="[SEP]"   ,					__UpperCamelCase					:     Dict="[PAD]"   ,					__UpperCamelCase					:     List[str]="[CLS]"   ,					__UpperCamelCase					:     Any="[MASK]"   ,					__UpperCamelCase					:     Any=True   ,					__UpperCamelCase					:     int=None   ,					**__UpperCamelCase					:     Dict   ,					):
												super().__init__(
												    __UpperCamelCase   ,					tokenizer_file=__UpperCamelCase   ,					do_lower_case=__UpperCamelCase   ,					unk_token=__UpperCamelCase   ,					sep_token=__UpperCamelCase   ,					pad_token=__UpperCamelCase   ,					cls_token=__UpperCamelCase   ,					mask_token=__UpperCamelCase   ,					tokenize_chinese_chars=__UpperCamelCase   ,					strip_accents=__UpperCamelCase   ,					**__UpperCamelCase   ,					)

												lowerCamelCase_							  =					json.loads(self.backend_tokenizer.normalizer.__getstate__()   )
												if (
												    normalizer_state.get("""lowercase"""   ,					__UpperCamelCase   ) != do_lower_case
												    or normalizer_state.get("""strip_accents"""   ,					__UpperCamelCase   ) != strip_accents
												    or normalizer_state.get("""handle_chinese_chars"""   ,					__UpperCamelCase   ) != tokenize_chinese_chars
												):
																			lowerCamelCase_							  =					getattr(__UpperCamelCase   ,					normalizer_state.pop("""type"""   )   )
																			lowerCamelCase_							  =					do_lower_case
																			lowerCamelCase_							  =					strip_accents
																			lowerCamelCase_							  =					tokenize_chinese_chars
																			lowerCamelCase_							  =					normalizer_class(**__UpperCamelCase   )

												lowerCamelCase_							  =					do_lower_case

					def    lowercase__      (	self					:     List[str]   ,					__UpperCamelCase					:     Union[str, Any]   ,					__UpperCamelCase					:     Dict=None   ):
												lowerCamelCase_							  =					[self.cls_token_id] + token_ids_a + [self.sep_token_id]

												if token_ids_a:
																			output += token_ids_a + [self.sep_token_id]

												return output

					def    lowercase__      (	self					:     Tuple   ,					__UpperCamelCase					:     List[int]   ,					__UpperCamelCase					:     Optional[List[int]] = None   ):
												lowerCamelCase_							  =					[self.sep_token_id]
												lowerCamelCase_							  =					[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    lowercase__      (	self					:     List[str]   ,					__UpperCamelCase					:     str   ,					__UpperCamelCase					:     Optional[str] = None   ):
												lowerCamelCase_							  =					self._tokenizer.model.save(__UpperCamelCase   ,					name=__UpperCamelCase   )
												return tuple(__UpperCamelCase   )





