




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 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_=1.0					,     lowercase_=None					,     lowercase_=None      )       ->	List[str]:

    """simple docstring"""


    if rng is None:
        __UpperCamelCase  		=						global_rng

    __UpperCamelCase  		=						[]
    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					: Union[str, Any] ,       snake_case					: Tuple ,       snake_case					: str=7 ,       snake_case					: Tuple=400 ,       snake_case					: Any=2000 ,       snake_case					: int=2048 ,       snake_case					: Optional[int]=128 ,       snake_case					: Tuple=1 ,       snake_case					: Dict=512 ,       snake_case					: Union[str, Any]=30 ,       snake_case					: Optional[int]=44100 ,       ):
          __UpperCamelCase  		=						parent
          __UpperCamelCase  		=						batch_size
          __UpperCamelCase  		=						min_seq_length
          __UpperCamelCase  		=						max_seq_length
          __UpperCamelCase  		=						(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
          __UpperCamelCase  		=						spectrogram_length
          __UpperCamelCase  		=						feature_size
          __UpperCamelCase  		=						num_audio_channels
          __UpperCamelCase  		=						hop_length
          __UpperCamelCase  		=						chunk_length
          __UpperCamelCase  		=						sampling_rate







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

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







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

      """simple docstring"""

      lowerCAmelCase__			: Union[str, Any]    =					TvltFeatureExtractor







      def       snake_case	(     self					: Union[str, Any]				):
          __UpperCamelCase  		=						TvltFeatureExtractionTester(self				)







      def       snake_case	(     self					: Union[str, Any]				):
          __UpperCamelCase  		=						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       snake_case	(     self					: Any				):
          __UpperCamelCase  		=						self.feature_extraction_class(**self.feat_extract_dict				)

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

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







      def       snake_case	(     self					: Optional[int]				):
          __UpperCamelCase  		=						self.feature_extraction_class(**self.feat_extract_dict				)

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

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







      def       snake_case	(     self					: int				):
          # Initialize feature_extractor
          __UpperCamelCase  		=						self.feature_extraction_class(**self.feat_extract_dict				)

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

          # Test not batched input
          __UpperCamelCase  		=						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
          __UpperCamelCase  		=						feature_extractor(__UpperCamelCase ,       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
          __UpperCamelCase  		=						feature_extractor(
              __UpperCamelCase ,       return_tensors='''np''' ,       sampling_rate=44100 ,       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.
          __UpperCamelCase  		=						[floats_list((1, x)				)[0] for x in (800, 800, 800)]
          __UpperCamelCase  		=						np.asarray(__UpperCamelCase				)
          __UpperCamelCase  		=						feature_extractor(__UpperCamelCase ,       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       snake_case	(     self					: Dict ,       snake_case					: Optional[Any]				):
          __UpperCamelCase  		=						load_dataset('''hf-internal-testing/librispeech_asr_dummy''' ,       '''clean''' ,       split='''validation'''				)
          # automatic decoding with librispeech
          __UpperCamelCase  		=						ds.sort('''id'''				).select(range(__UpperCamelCase				)				)[:num_samples]['''audio''']

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







      def       snake_case	(     self					: Dict				):
          __UpperCamelCase  		=						self._load_datasamples(1				)
          __UpperCamelCase  		=						TvltFeatureExtractor()
          __UpperCamelCase  		=						feature_extractor(__UpperCamelCase ,       return_tensors='''pt'''				).audio_values

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

          __UpperCamelCase  		=						torch.tensor([[-0.30_32, -0.27_08], [-0.44_34, -0.40_07]]				)
          self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2] ,       __UpperCamelCase ,       atol=1E-4				)				)












import os
import time

import numpy as np
import onnxruntime as ort


a_   		=     "1"
a_   		=     "0"
a_   		=     "1"

a_   		=     ort.SessionOptions()
a_   		=     ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print("Create inference session...")
a_   		=     ["TensorrtExecutionProvider", "CUDAExecutionProvider"]
a_   		=     ort.InferenceSession("model.onnx", sess_options=sess_opt, providers=execution_provider)
a_   		=     ort.RunOptions()

a_   		=     128
a_   		=     1
a_   		=     np.ones((batch, sequence), dtype=np.intaa)
a_   		=     np.ones((batch, sequence), dtype=np.intaa)
a_   		=     np.ones((batch, sequence), dtype=np.intaa)

print("Warm up phase...")
sess.run(
    None,
    {
        sess.get_inputs()[0].name: input_ids,
        sess.get_inputs()[1].name: attention_mask,
        sess.get_inputs()[2].name: token_type_ids,
    },
    run_options=run_opt,
)

print("Start inference...")
a_   		=     time.time()
a_   		=     2000
a_   		=     {}
for iter in range(max_iters):
 a_   		=     sess.run(
     None,
     {
         sess.get_inputs()[0].name: input_ids,
         sess.get_inputs()[1].name: attention_mask,
         sess.get_inputs()[2].name: token_type_ids,
     },
     run_options=run_opt,
 )
print("Average Inference Time = {:.3f} ms".format((time.time() - start_time) * 1000 / max_iters))






