

_UpperCamelCase 			=		"\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"

_UpperCamelCase 			=		[{"type": "code", "content": INSTALL_CONTENT}]
_UpperCamelCase 			=		{
    "{processor_class}": "FakeProcessorClass",
    "{model_class}": "FakeModelClass",
    "{object_class}": "FakeObjectClass",
}





import itertools
import os
import random
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers import is_speech_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_speech_available():
 from transformers import WhisperFeatureExtractor

if is_torch_available():
 import torch

_UpperCamelCase 			=		random.Random()







def 		_lowercase (     lowercase__   ,	lowercase__=1.0   ,	lowercase__=None   ,	lowercase__=None						):
      if rng is None:
            __lowerCAmelCase					:						Any					=					global_rng

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

      return values







@require_torch
@require_torchaudio
class   __lowercase      (unittest.TestCase ):
 def __init__(							self      ,							A_      ,							A_=7      ,							A_=400      ,							A_=2000      ,							A_=10      ,							A_=160      ,							A_=8      ,							A_=0.0      ,							A_=4000      ,							A_=False      ,							A_=True      ,							)					->List[str]:



       '''simple docstring'''





       __lowerCAmelCase					:						Optional[int]					=					parent
       __lowerCAmelCase					:						Dict					=					batch_size
       __lowerCAmelCase					:						str					=					min_seq_length
       __lowerCAmelCase					:						int					=					max_seq_length
       __lowerCAmelCase					:						Any					=					(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
       __lowerCAmelCase					:						Any					=					padding_value
       __lowerCAmelCase					:						str					=					sampling_rate
       __lowerCAmelCase					:						Optional[Any]					=					return_attention_mask
       __lowerCAmelCase					:						Optional[Any]					=					do_normalize
       __lowerCAmelCase					:						Optional[Any]					=					feature_size
       __lowerCAmelCase					:						Optional[int]					=					chunk_length
       __lowerCAmelCase					:						Optional[Any]					=					hop_length
 def 	UpperCamelCase__  (							self					)					->Optional[Any]:



       '''simple docstring'''





       return {
           "feature_size": self.feature_size,
           "hop_length": self.hop_length,
           "chunk_length": self.chunk_length,
           "padding_value": self.padding_value,
           "sampling_rate": self.sampling_rate,
           "return_attention_mask": self.return_attention_mask,
           "do_normalize": self.do_normalize,
       }





 def 	UpperCamelCase__  (							self      ,							A_=False      ,							A_=False					)					->Optional[Any]:



       '''simple docstring'''





       def _flatten(A_					):
             return list(itertools.chain(*A_					)					)

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







@require_torch
@require_torchaudio
class   __lowercase      (_UpperCAmelCase		,   unittest.TestCase ):
 _UpperCamelCase			  =  WhisperFeatureExtractor if is_speech_available() else None
 def 	UpperCamelCase__  (							self					)					->Optional[int]:



       '''simple docstring'''





       __lowerCAmelCase					:						Tuple					=					WhisperFeatureExtractionTester(self					)
 def 	UpperCamelCase__  (							self					)					->int:



       '''simple docstring'''





       __lowerCAmelCase					:						str					=					self.feature_extraction_class(**self.feat_extract_dict					)

       with tempfile.TemporaryDirectory() as tmpdirname:
             __lowerCAmelCase					:						List[str]					=					feat_extract_first.save_pretrained(A_					)[0]
             check_json_file_has_correct_format(A_					)
             __lowerCAmelCase					:						int					=					self.feature_extraction_class.from_pretrained(A_					)

       __lowerCAmelCase					:						Dict					=					feat_extract_first.to_dict()
       __lowerCAmelCase					:						Union[str, Any]					=					feat_extract_second.to_dict()
       __lowerCAmelCase					:						Union[str, Any]					=					feat_extract_first.mel_filters
       __lowerCAmelCase					:						Dict					=					feat_extract_second.mel_filters
       self.assertTrue(np.allclose(A_      ,							A_					)					)
       self.assertEqual(A_      ,							A_					)
 def 	UpperCamelCase__  (							self					)					->str:



       '''simple docstring'''





       __lowerCAmelCase					:						str					=					self.feature_extraction_class(**self.feat_extract_dict					)

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

       __lowerCAmelCase					:						List[str]					=					feat_extract_first.to_dict()
       __lowerCAmelCase					:						Tuple					=					feat_extract_second.to_dict()
       __lowerCAmelCase					:						Any					=					feat_extract_first.mel_filters
       __lowerCAmelCase					:						List[str]					=					feat_extract_second.mel_filters
       self.assertTrue(np.allclose(A_      ,							A_					)					)
       self.assertEqual(A_      ,							A_					)
 def 	UpperCamelCase__  (							self					)					->List[Any]:



       '''simple docstring'''





       __lowerCAmelCase					:						List[str]					=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
       # create three inputs of length 800, 1000, and 1200
       __lowerCAmelCase					:						Dict					=					[floats_list((1, x)					)[0] for x in range(800      ,							1400      ,							200					)]
       __lowerCAmelCase					:						Dict					=					[np.asarray(A_					) for speech_input in speech_inputs]

       # Test feature size
       __lowerCAmelCase					:						Tuple					=					feature_extractor(A_      ,							padding='''max_length'''      ,							return_tensors='''np'''					).input_features
       self.assertTrue(input_features.ndim == 3					)
       self.assertTrue(input_features.shape[-1] == feature_extractor.nb_max_frames					)
       self.assertTrue(input_features.shape[-2] == feature_extractor.feature_size					)

       # Test not batched input
       __lowerCAmelCase					:						Dict					=					feature_extractor(speech_inputs[0]      ,							return_tensors='''np'''					).input_features
       __lowerCAmelCase					:						List[str]					=					feature_extractor(np_speech_inputs[0]      ,							return_tensors='''np'''					).input_features
       self.assertTrue(np.allclose(A_      ,							A_      ,							atol=1e-3					)					)

       # Test batched
       __lowerCAmelCase					:						Union[str, Any]					=					feature_extractor(A_      ,							return_tensors='''np'''					).input_features
       __lowerCAmelCase					:						List[Any]					=					feature_extractor(A_      ,							return_tensors='''np'''					).input_features
       for enc_seq_a, enc_seq_a in zip(A_      ,							A_					):
             self.assertTrue(np.allclose(A_      ,							A_      ,							atol=1e-3					)					)

       # Test 2-D numpy arrays are batched.
       __lowerCAmelCase					:						int					=					[floats_list((1, x)					)[0] for x in (800, 800, 800)]
       __lowerCAmelCase					:						Optional[int]					=					np.asarray(A_					)
       __lowerCAmelCase					:						Dict					=					feature_extractor(A_      ,							return_tensors='''np'''					).input_features
       __lowerCAmelCase					:						Any					=					feature_extractor(A_      ,							return_tensors='''np'''					).input_features
       for enc_seq_a, enc_seq_a in zip(A_      ,							A_					):
             self.assertTrue(np.allclose(A_      ,							A_      ,							atol=1e-3					)					)

       # Test truncation required
       __lowerCAmelCase					:						Optional[int]					=					[floats_list((1, x)					)[0] for x in range(200      ,							(feature_extractor.n_samples + 500)      ,							200					)]
       __lowerCAmelCase					:						Dict					=					[np.asarray(A_					) for speech_input in speech_inputs]

       __lowerCAmelCase					:						Tuple					=					[x[: feature_extractor.n_samples] for x in speech_inputs]
       __lowerCAmelCase					:						Optional[int]					=					[np.asarray(A_					) for speech_input in speech_inputs_truncated]

       __lowerCAmelCase					:						Any					=					feature_extractor(A_      ,							return_tensors='''np'''					).input_features
       __lowerCAmelCase					:						List[str]					=					feature_extractor(A_      ,							return_tensors='''np'''					).input_features
       for enc_seq_a, enc_seq_a in zip(A_      ,							A_					):
             self.assertTrue(np.allclose(A_      ,							A_      ,							atol=1e-3					)					)
 def 	UpperCamelCase__  (							self					)					->Dict:



       '''simple docstring'''





       import torch

       __lowerCAmelCase					:						Tuple					=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
       __lowerCAmelCase					:						List[Any]					=					np.random.rand(100      ,							32					).astype(np.floataa					)
       __lowerCAmelCase					:						Any					=					np_speech_inputs.tolist()

       for inputs in [py_speech_inputs, np_speech_inputs]:
             __lowerCAmelCase					:						Tuple					=					feature_extractor.pad([{'''input_features''': inputs}]      ,							return_tensors='''np'''					)
             self.assertTrue(np_processed.input_features.dtype == np.floataa					)
             __lowerCAmelCase					:						int					=					feature_extractor.pad([{'''input_features''': inputs}]      ,							return_tensors='''pt'''					)
             self.assertTrue(pt_processed.input_features.dtype == torch.floataa					)
 def 	UpperCamelCase__  (							self      ,							A_					)					->str:



       '''simple docstring'''





       __lowerCAmelCase					:						Any					=					load_dataset('''hf-internal-testing/librispeech_asr_dummy'''      ,							'''clean'''      ,							split='''validation'''					)
       # automatic decoding with librispeech
       __lowerCAmelCase					:						Union[str, Any]					=					ds.sort('''id'''					).select(range(A_					)					)[:num_samples]['''audio''']

       return [x["array"] for x in speech_samples]
 def 	UpperCamelCase__  (							self					)					->int:



       '''simple docstring'''





       __lowerCAmelCase					:						Optional[int]					=					torch.tensor(
           [
               0.1_193, -0.0_946, -0.1_098, -0.0_196, 0.0_225, -0.0_690, -0.1_736, 0.0_951,
               0.0_971, -0.0_817, -0.0_702, 0.0_162, 0.0_260, 0.0_017, -0.0_192, -0.1_678,
               0.0_709, -0.1_867, -0.0_655, -0.0_274, -0.0_234, -0.1_884, -0.0_516, -0.0_554,
               -0.0_274, -0.1_425, -0.1_423, 0.0_837, 0.0_377, -0.0_854
           ]					)
       # fmt: on

       __lowerCAmelCase					:						int					=					self._load_datasamples(1					)
       __lowerCAmelCase					:						Any					=					WhisperFeatureExtractor()
       __lowerCAmelCase					:						Optional[Any]					=					feature_extractor(A_      ,							return_tensors='''pt'''					).input_features
       self.assertEqual(input_features.shape      ,							(1, 80, 3000)					)
       self.assertTrue(torch.allclose(input_features[0, 0, :30]      ,							A_      ,							atol=1e-4					)					)





 def 	UpperCamelCase__  (							self					)					->Any:



       '''simple docstring'''





       __lowerCAmelCase					:						Tuple					=					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
       __lowerCAmelCase					:						str					=					self._load_datasamples(1					)[0]
       __lowerCAmelCase					:						Optional[Any]					=					((audio - audio.min()) / (audio.max() - audio.min())) * 6_5535  # Rescale to [0, 65535] to show issue
       __lowerCAmelCase					:						Union[str, Any]					=					feat_extract.zero_mean_unit_var_norm([audio]      ,							attention_mask=A_					)[0]

       self.assertTrue(np.all(np.mean(A_					) < 1e-3					)					)
       self.assertTrue(np.all(np.abs(np.var(A_					) - 1					) < 1e-3					)					)


