





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

_lowerCAmelCase 				=     random.Random()



def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase=1.0  ,_lowerCAmelCase=None  ,_lowerCAmelCase=None     ):
   '''simple docstring'''


   if rng is None:
      A_     :				Dict			      =	global_rng

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

   return values

class    _UpperCAmelCase							(		unittest.TestCase     ):

      '''simple docstring'''





      def __init__(		self      ,       a__      ,       a__=7      ,       a__=400      ,       a__=2000      ,       a__=2048      ,       a__=128      ,       a__=1      ,       a__=512      ,       a__=30      ,       a__=44100      ,       ):
         A_     :				Any			      =	parent
         A_     :				Tuple			      =	batch_size
         A_     :				Optional[Any]			      =	min_seq_length
         A_     :				Dict			      =	max_seq_length
         A_     :				List[str]			      =	(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
         A_     :				Optional[Any]			      =	spectrogram_length
         A_     :				List[str]			      =	feature_size
         A_     :				List[Any]			      =	num_audio_channels
         A_     :				Optional[int]			      =	hop_length
         A_     :				Tuple			      =	chunk_length
         A_     :				List[str]			      =	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      ,       a__=False      ,       a__=False       ):
         def _flatten(a__       ):
            return list(itertools.chain(*a__       )       )

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






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

      '''simple docstring'''





      a						       =							TvltFeatureExtractor
      def 		_lowerCamelCase      (		self       ):
         A_     :				Optional[Any]			      =	TvltFeatureExtractionTester(self       )
      def 		_lowerCamelCase      (		self       ):
         A_     :				int			      =	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 		_lowerCamelCase      (		self       ):
         A_     :				Dict			      =	self.feature_extraction_class(**self.feat_extract_dict       )

         with tempfile.TemporaryDirectory() as tmpdirname:
            A_     :				Optional[Any]			      =	feat_extract_first.save_pretrained(a__       )[0]
            check_json_file_has_correct_format(a__       )
            A_     :				Any			      =	self.feature_extraction_class.from_pretrained(a__       )

         A_     :				Tuple			      =	feat_extract_first.to_dict()
         A_     :				str			      =	feat_extract_second.to_dict()
         A_     :				str			      =	dict_first.pop("""mel_filters"""       )
         A_     :				List[str]			      =	dict_second.pop("""mel_filters"""       )
         self.assertTrue(np.allclose(a__      ,       a__       )       )
         self.assertEqual(a__      ,       a__       )
      def 		_lowerCamelCase      (		self       ):
         A_     :				str			      =	self.feature_extraction_class(**self.feat_extract_dict       )

         with tempfile.TemporaryDirectory() as tmpdirname:
            A_     :				Union[str, Any]			      =	os.path.join(a__      ,       """feat_extract.json"""       )
            feat_extract_first.to_json_file(a__       )
            A_     :				Optional[Any]			      =	self.feature_extraction_class.from_json_file(a__       )

         A_     :				str			      =	feat_extract_first.to_dict()
         A_     :				int			      =	feat_extract_second.to_dict()
         A_     :				List[str]			      =	dict_first.pop("""mel_filters"""       )
         A_     :				Tuple			      =	dict_second.pop("""mel_filters"""       )
         self.assertTrue(np.allclose(a__      ,       a__       )       )
         self.assertEqual(a__      ,       a__       )
      def 		_lowerCamelCase      (		self       ):
         # Initialize feature_extractor
         A_     :				str			      =	self.feature_extraction_class(**self.feat_extract_dict       )

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

         # Test not batched input
         A_     :				Tuple			      =	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
         A_     :				Union[str, Any]			      =	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
         A_     :				Tuple			      =	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.
         A_     :				List[str]			      =	[floats_list((1, x)       )[0] for x in (800, 800, 800)]
         A_     :				List[Any]			      =	np.asarray(a__       )
         A_     :				List[Any]			      =	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 		_lowerCamelCase      (		self      ,       a__       ):
         A_     :				Tuple			      =	load_dataset("""hf-internal-testing/librispeech_asr_dummy"""      ,       """clean"""      ,       split="""validation"""       )
         # automatic decoding with librispeech
         A_     :				Any			      =	ds.sort("""id"""       ).select(range(a__       )       )[:num_samples]["""audio"""]

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






      def 		_lowerCamelCase      (		self       ):
         A_     :				str			      =	self._load_datasamples(1       )
         A_     :				Optional[Any]			      =	TvltFeatureExtractor()
         A_     :				Dict			      =	feature_extractor(a__      ,       return_tensors="""pt"""       ).audio_values

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

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













from typing import List

from .keymap import KEYMAP, get_character



def     _lowerCAmelCase		(  _lowerCAmelCase     ):
   '''simple docstring'''


   def decorator(_lowerCAmelCase     ):
      A_     :				List[Any]			      =	getattr(_lowerCAmelCase  ,"""handle_key"""  ,[]     )
      handle += [key]
      setattr(_lowerCAmelCase  ,"""handle_key"""  ,_lowerCAmelCase     )
      return func

   return decorator



def     _lowerCAmelCase		(  *_lowerCAmelCase     ):
   '''simple docstring'''


   def decorator(_lowerCAmelCase     ):
      A_     :				Tuple			      =	getattr(_lowerCAmelCase  ,"""handle_key"""  ,[]     )
      handle += keys
      setattr(_lowerCAmelCase  ,"""handle_key"""  ,_lowerCAmelCase     )
      return func

   return decorator

class    _UpperCAmelCase							(		_lowerCamelCase     ):
      def __new__(		cls      ,       a__      ,       a__      ,       a__       ):
         A_     :				Any			      =	super().__new__(cls      ,       a__      ,       a__      ,       a__       )
         if not hasattr(a__      ,       """key_handler"""       ):
            setattr(a__      ,       """key_handler"""      ,       {}       )
         setattr(a__      ,       """handle_input"""      ,       KeyHandler.handle_input       )

         for value in attrs.values():
            A_     :				Tuple			      =	getattr(a__      ,       """handle_key"""      ,       []       )
            for key in handled_keys:
               A_     :				Optional[Any]			      =	value
         return new_cls






      @staticmethod
      def 		_lowerCamelCase      (		cls       ):
         A_     :				List[str]			      =	get_character()
         if char != KEYMAP["undefined"]:
            A_     :				str			      =	ord(a__       )
         A_     :				List[str]			      =	cls.key_handler.get(a__       )
         if handler:
            A_     :				Optional[int]			      =	char
            return handler(cls       )
         else:
            return None






def     _lowerCAmelCase		(  cls     ):
   '''simple docstring'''


   return KeyHandler(cls.__name__  ,cls.__bases__  ,cls.__dict__.copy()     )






