


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

snake_case							:       Optional[Any]										=	random.Random()
def 							__lowerCamelCase		(					UpperCAmelCase_		:						int					,    UpperCAmelCase_		:						str=1.0					,    UpperCAmelCase_		:						Any=None					,    UpperCAmelCase_		:						str=None		):




      """simple docstring"""





      if rng is None:
            a		:Any									=			global_rng

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

      return values







class 						_snake_case    ( unittest.TestCase				):




 def __init__(						self   ,    _lowerCamelCase   ,    _lowerCamelCase=7   ,    _lowerCamelCase=400   ,    _lowerCamelCase=2000   ,    _lowerCamelCase=2048   ,    _lowerCamelCase=128   ,    _lowerCamelCase=1   ,    _lowerCamelCase=512   ,    _lowerCamelCase=30   ,    _lowerCamelCase=4_4100   ,    ):
       a		:Dict									=			parent
       a		:Optional[int]									=			batch_size
       a		:Tuple									=			min_seq_length
       a		:Dict									=			max_seq_length
       a		:Optional[int]									=			(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
       a		:Optional[int]									=			spectrogram_length
       a		:int									=			feature_size
       a		:Union[str, Any]									=			num_audio_channels
       a		:int									=			hop_length
       a		:Any									=			chunk_length
       a		:Optional[Any]									=			sampling_rate




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

       if equal_length:
             a		:List[str]									=			[floats_list((self.max_seq_length, self.feature_size)       ) for _ in range(self.batch_size       )]
       else:
             # make sure that inputs increase in size
             a		:Any									=			[
                 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		:str									=			[np.asarray(_lowerCamelCase       ) for x in speech_inputs]
       return speech_inputs







@require_torch
@require_torchaudio
class 						_snake_case    ( _snake_case		,       unittest.TestCase				):
 SCREAMING_SNAKE_CASE__ =			TvltFeatureExtractor




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:str									=			TvltFeatureExtractionTester(self       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Optional[int]									=			self.feature_extraction_class(**self.feat_extract_dict       )
       self.assertTrue(hasattr(_lowerCamelCase   ,    '''spectrogram_length'''       )       )
       self.assertTrue(hasattr(_lowerCamelCase   ,    '''feature_size'''       )       )
       self.assertTrue(hasattr(_lowerCamelCase   ,    '''num_audio_channels'''       )       )
       self.assertTrue(hasattr(_lowerCamelCase   ,    '''hop_length'''       )       )
       self.assertTrue(hasattr(_lowerCamelCase   ,    '''chunk_length'''       )       )
       self.assertTrue(hasattr(_lowerCamelCase   ,    '''sampling_rate'''       )       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:List[Any]									=			self.feature_extraction_class(**self.feat_extract_dict       )

       with tempfile.TemporaryDirectory() as tmpdirname:
             a		:Tuple									=			feat_extract_first.save_pretrained(_lowerCamelCase       )[0]
             check_json_file_has_correct_format(_lowerCamelCase       )
             a		:Dict									=			self.feature_extraction_class.from_pretrained(_lowerCamelCase       )

       a		:List[Any]									=			feat_extract_first.to_dict()
       a		:Union[str, Any]									=			feat_extract_second.to_dict()
       a		:Dict									=			dict_first.pop('''mel_filters'''       )
       a		:Any									=			dict_second.pop('''mel_filters'''       )
       self.assertTrue(np.allclose(_lowerCamelCase   ,    _lowerCamelCase       )       )
       self.assertEqual(_lowerCamelCase   ,    _lowerCamelCase       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Optional[Any]									=			self.feature_extraction_class(**self.feat_extract_dict       )

       with tempfile.TemporaryDirectory() as tmpdirname:
             a		:List[Any]									=			os.path.join(_lowerCamelCase   ,    '''feat_extract.json'''       )
             feat_extract_first.to_json_file(_lowerCamelCase       )
             a		:Any									=			self.feature_extraction_class.from_json_file(_lowerCamelCase       )

       a		:str									=			feat_extract_first.to_dict()
       a		:str									=			feat_extract_second.to_dict()
       a		:Optional[int]									=			dict_first.pop('''mel_filters'''       )
       a		:Any									=			dict_second.pop('''mel_filters'''       )
       self.assertTrue(np.allclose(_lowerCamelCase   ,    _lowerCamelCase       )       )
       self.assertEqual(_lowerCamelCase   ,    _lowerCamelCase       )




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       # Initialize feature_extractor
       a		:Optional[int]									=			self.feature_extraction_class(**self.feat_extract_dict       )

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

       # Test not batched input
       a		:Dict									=			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		:Any									=			feature_extractor(_lowerCamelCase   ,    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		:Tuple									=			feature_extractor(
           _lowerCamelCase   ,    return_tensors='''np'''   ,    sampling_rate=4_4100   ,    mask_audio=_lowerCamelCase       ).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		:Union[str, Any]									=			[floats_list((1, x)       )[0] for x in (800, 800, 800)]
       a		:Dict									=			np.asarray(_lowerCamelCase       )
       a		:Tuple									=			feature_extractor(_lowerCamelCase   ,    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 			SCREAMING_SNAKE_CASE__							(						self   ,    _lowerCamelCase       ):
       a		:List[Any]									=			load_dataset('''hf-internal-testing/librispeech_asr_dummy'''   ,    '''clean'''   ,    split='''validation'''       )
       # automatic decoding with librispeech
       a		:List[Any]									=			ds.sort('''id'''       ).select(range(_lowerCamelCase       )       )[:num_samples]['''audio''']

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




 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       a		:Union[str, Any]									=			self._load_datasamples(1       )
       a		:Any									=			TvltFeatureExtractor()
       a		:Tuple									=			feature_extractor(_lowerCamelCase   ,    return_tensors='''pt'''       ).audio_values

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

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









import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case							:       Optional[int]										=	logging.get_logger(__name__)

snake_case							:       Optional[int]										=	{
    '''microsoft/unispeech-large-1500h-cv''': (
        '''https://huggingface.co/microsoft/unispeech-large-1500h-cv/resolve/main/config.json'''
    ),
    # See all UniSpeech models at https://huggingface.co/models?filter=unispeech
}







class 						_snake_case    ( _snake_case				):
 SCREAMING_SNAKE_CASE__ =			'unispeech'




 def __init__(						self   ,    _lowerCamelCase=32   ,    _lowerCamelCase=768   ,    _lowerCamelCase=12   ,    _lowerCamelCase=12   ,    _lowerCamelCase=3072   ,    _lowerCamelCase="gelu"   ,    _lowerCamelCase=0.1   ,    _lowerCamelCase=0.1   ,    _lowerCamelCase=0.1   ,    _lowerCamelCase=0.0   ,    _lowerCamelCase=0.0   ,    _lowerCamelCase=0.1   ,    _lowerCamelCase=0.1   ,    _lowerCamelCase=0.02   ,    _lowerCamelCase=1e-5   ,    _lowerCamelCase="group"   ,    _lowerCamelCase="gelu"   ,    _lowerCamelCase=(512, 512, 512, 512, 512, 512, 512)   ,    _lowerCamelCase=(5, 2, 2, 2, 2, 2, 2)   ,    _lowerCamelCase=(10, 3, 3, 3, 3, 2, 2)   ,    _lowerCamelCase=False   ,    _lowerCamelCase=128   ,    _lowerCamelCase=16   ,    _lowerCamelCase=False   ,    _lowerCamelCase=True   ,    _lowerCamelCase=0.05   ,    _lowerCamelCase=10   ,    _lowerCamelCase=2   ,    _lowerCamelCase=0.0   ,    _lowerCamelCase=10   ,    _lowerCamelCase=0   ,    _lowerCamelCase=320   ,    _lowerCamelCase=2   ,    _lowerCamelCase=0.1   ,    _lowerCamelCase=100   ,    _lowerCamelCase=256   ,    _lowerCamelCase=256   ,    _lowerCamelCase=0.1   ,    _lowerCamelCase="mean"   ,    _lowerCamelCase=False   ,    _lowerCamelCase=False   ,    _lowerCamelCase=256   ,    _lowerCamelCase=80   ,    _lowerCamelCase=0   ,    _lowerCamelCase=1   ,    _lowerCamelCase=2   ,    _lowerCamelCase=0.5   ,    **_lowerCamelCase   ,    ):
       super().__init__(**_lowerCamelCase   ,    pad_token_id=_lowerCamelCase   ,    bos_token_id=_lowerCamelCase   ,    eos_token_id=_lowerCamelCase       )
       a		:Any									=			hidden_size
       a		:str									=			feat_extract_norm
       a		:List[Any]									=			feat_extract_activation
       a		:Tuple									=			list(_lowerCamelCase       )
       a		:Any									=			list(_lowerCamelCase       )
       a		:List[Any]									=			list(_lowerCamelCase       )
       a		:Union[str, Any]									=			conv_bias
       a		:str									=			num_conv_pos_embeddings
       a		:str									=			num_conv_pos_embedding_groups
       a		:Tuple									=			len(self.conv_dim       )
       a		:int									=			num_hidden_layers
       a		:Any									=			intermediate_size
       a		:Optional[Any]									=			hidden_act
       a		:Tuple									=			num_attention_heads
       a		:Any									=			hidden_dropout
       a		:Any									=			attention_dropout
       a		:Optional[Any]									=			activation_dropout
       a		:Optional[Any]									=			feat_proj_dropout
       a		:Any									=			final_dropout
       a		:int									=			layerdrop
       a		:int									=			layer_norm_eps
       a		:Dict									=			initializer_range
       a		:Dict									=			num_ctc_classes
       a		:Optional[Any]									=			vocab_size
       a		:str									=			do_stable_layer_norm
       a		:Tuple									=			use_weighted_layer_sum
       a		:Any									=			classifier_proj_size

       if (
           (len(self.conv_stride       ) != self.num_feat_extract_layers)
           or (len(self.conv_kernel       ) != self.num_feat_extract_layers)
           or (len(self.conv_dim       ) != self.num_feat_extract_layers)
       ):
             raise ValueError(
                 '''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
                 ''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
                 F''' {len(self.conv_dim       )}`, `len(config.conv_stride) = {len(self.conv_stride       )}`,'''
                 F''' `len(config.conv_kernel) = {len(self.conv_kernel       )}`.'''       )

       # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
       a		:List[Any]									=			apply_spec_augment
       a		:Any									=			mask_time_prob
       a		:Union[str, Any]									=			mask_time_length
       a		:str									=			mask_time_min_masks
       a		:Tuple									=			mask_feature_prob
       a		:Dict									=			mask_feature_length
       a		:int									=			mask_feature_min_masks

       # parameters for pretraining with codevector quantized representations
       a		:Union[str, Any]									=			num_codevectors_per_group
       a		:Dict									=			num_codevector_groups
       a		:List[Any]									=			contrastive_logits_temperature
       a		:Union[str, Any]									=			feat_quantizer_dropout
       a		:Optional[Any]									=			num_negatives
       a		:Tuple									=			codevector_dim
       a		:Optional[Any]									=			proj_codevector_dim
       a		:Union[str, Any]									=			diversity_loss_weight

       # ctc loss
       a		:List[Any]									=			ctc_loss_reduction
       a		:Union[str, Any]									=			ctc_zero_infinity

       # pretraining loss
       a		:int									=			replace_prob




 @property
 def 			SCREAMING_SNAKE_CASE__							(						self       ):
       return functools.reduce(operator.mul   ,    self.conv_stride   ,    1       )





