


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

__lowerCAmelCase		       =							random.Random()




def    snake_case_     (  snake_case			,							snake_case=1.0			,							snake_case=None			,							snake_case=None     )  ->				Any:
       if rng is None:
              lowercase__:   List[Any]  			=   global_rng

       lowercase__:   Optional[Any]  			=   []
       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   __a			(    unittest.TestCase      ):
 def __init__(   self				, lowerCAmelCase__				, lowerCAmelCase__=7				, lowerCAmelCase__=400				, lowerCAmelCase__=2_000				, lowerCAmelCase__=10				, lowerCAmelCase__=160				, lowerCAmelCase__=8				, lowerCAmelCase__=0.0				, lowerCAmelCase__=4_000				, lowerCAmelCase__=False				, lowerCAmelCase__=True				, )					->				Dict:




        '''simple docstring'''
        lowercase__:   Any  			=   parent
        lowercase__:   Tuple  			=   batch_size
        lowercase__:   Any  			=   min_seq_length
        lowercase__:   Dict  			=   max_seq_length
        lowercase__:   str  			=   (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
        lowercase__:   Tuple  			=   padding_value
        lowercase__:   Dict  			=   sampling_rate
        lowercase__:   Any  			=   return_attention_mask
        lowercase__:   Tuple  			=   do_normalize
        lowercase__:   Optional[Any]  			=   feature_size
        lowercase__:   int  			=   chunk_length
        lowercase__:   str  			=   hop_length
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				List[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 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__=False				, lowerCAmelCase__=False )					->				Dict:




        '''simple docstring'''




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

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





@require_torch
@require_torchaudio
class   __a			(    _lowerCAmelCase       ,       unittest.TestCase      ):
 __lowercase :							Dict        = WhisperFeatureExtractor if is_speech_available() else None
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				int:




        '''simple docstring'''
        lowercase__:   Optional[Any]  			=   WhisperFeatureExtractionTester(self )
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				str:




        '''simple docstring'''
        lowercase__:   Union[str, Any]  			=   self.feature_extraction_class(**self.feat_extract_dict )

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

        lowercase__:   Optional[int]  			=   feat_extract_first.to_dict()
        lowercase__:   Optional[Any]  			=   feat_extract_second.to_dict()
        lowercase__:   str  			=   feat_extract_first.mel_filters
        lowercase__:   Union[str, Any]  			=   feat_extract_second.mel_filters
        self.assertTrue(np.allclose(lowerCAmelCase__				, lowerCAmelCase__ ) )
        self.assertEqual(lowerCAmelCase__				, lowerCAmelCase__ )
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Tuple:




        '''simple docstring'''
        lowercase__:   str  			=   self.feature_extraction_class(**self.feat_extract_dict )

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

        lowercase__:   Optional[Any]  			=   feat_extract_first.to_dict()
        lowercase__:   Dict  			=   feat_extract_second.to_dict()
        lowercase__:   Union[str, Any]  			=   feat_extract_first.mel_filters
        lowercase__:   str  			=   feat_extract_second.mel_filters
        self.assertTrue(np.allclose(lowerCAmelCase__				, lowerCAmelCase__ ) )
        self.assertEqual(lowerCAmelCase__				, lowerCAmelCase__ )
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Optional[Any]:




        '''simple docstring'''
        lowercase__:   Optional[int]  			=   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
        # create three inputs of length 800, 1000, and 1200
        lowercase__:   str  			=   [floats_list((1, x) )[0] for x in range(800				, 1_400				, 200 )]
        lowercase__:   Any  			=   [np.asarray(lowerCAmelCase__ ) for speech_input in speech_inputs]

        # Test feature size
        lowercase__:   Optional[int]  			=   feature_extractor(lowerCAmelCase__				, 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
        lowercase__:   List[str]  			=   feature_extractor(speech_inputs[0]				, return_tensors='np' ).input_features
        lowercase__:   Dict  			=   feature_extractor(np_speech_inputs[0]				, return_tensors='np' ).input_features
        self.assertTrue(np.allclose(lowerCAmelCase__				, lowerCAmelCase__				, atol=1E-3 ) )

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

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

        # Test truncation required
        lowercase__:   Union[str, Any]  			=   [floats_list((1, x) )[0] for x in range(200				, (feature_extractor.n_samples + 500)				, 200 )]
        lowercase__:   Dict  			=   [np.asarray(lowerCAmelCase__ ) for speech_input in speech_inputs]

        lowercase__:   List[Any]  			=   [x[: feature_extractor.n_samples] for x in speech_inputs]
        lowercase__:   List[str]  			=   [np.asarray(lowerCAmelCase__ ) for speech_input in speech_inputs_truncated]

        lowercase__:   int  			=   feature_extractor(lowerCAmelCase__				, return_tensors='np' ).input_features
        lowercase__:   Dict  			=   feature_extractor(lowerCAmelCase__				, return_tensors='np' ).input_features
        for enc_seq_a, enc_seq_a in zip(lowerCAmelCase__				, lowerCAmelCase__ ):
               self.assertTrue(np.allclose(lowerCAmelCase__				, lowerCAmelCase__				, atol=1E-3 ) )
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Optional[int]:




        '''simple docstring'''
        import torch

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

        for inputs in [py_speech_inputs, np_speech_inputs]:
               lowercase__:   Any  			=   feature_extractor.pad([{'input_features': inputs}]				, return_tensors='np' )
               self.assertTrue(np_processed.input_features.dtype == np.floataa )
               lowercase__:   Dict  			=   feature_extractor.pad([{'input_features': inputs}]				, return_tensors='pt' )
               self.assertTrue(pt_processed.input_features.dtype == torch.floataa )
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__ )					->				List[str]:




        '''simple docstring'''
        lowercase__:   Optional[int]  			=   load_dataset('hf-internal-testing/librispeech_asr_dummy'				, 'clean'				, split='validation' )
        # automatic decoding with librispeech
        lowercase__:   List[str]  			=   ds.sort('id' ).select(range(lowerCAmelCase__ ) )[:num_samples]["audio"]

        return [x["array"] for x in speech_samples]
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				List[str]:




        '''simple docstring'''
        lowercase__:   Optional[int]  			=   torch.tensor(
            [
                0.1_1_9_3, -0.0_9_4_6, -0.1_0_9_8, -0.0_1_9_6, 0.0_2_2_5, -0.0_6_9_0, -0.1_7_3_6, 0.0_9_5_1,
                0.0_9_7_1, -0.0_8_1_7, -0.0_7_0_2, 0.0_1_6_2, 0.0_2_6_0, 0.0_0_1_7, -0.0_1_9_2, -0.1_6_7_8,
                0.0_7_0_9, -0.1_8_6_7, -0.0_6_5_5, -0.0_2_7_4, -0.0_2_3_4, -0.1_8_8_4, -0.0_5_1_6, -0.0_5_5_4,
                -0.0_2_7_4, -0.1_4_2_5, -0.1_4_2_3, 0.0_8_3_7, 0.0_3_7_7, -0.0_8_5_4
            ] )
        # fmt: on

        lowercase__:   Any  			=   self._load_datasamples(1 )
        lowercase__:   List[Any]  			=   WhisperFeatureExtractor()
        lowercase__:   str  			=   feature_extractor(lowerCAmelCase__				, return_tensors='pt' ).input_features
        self.assertEqual(input_features.shape				, (1, 80, 3_000) )
        self.assertTrue(torch.allclose(input_features[0, 0, :30]				, lowerCAmelCase__				, atol=1E-4 ) )
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Dict:




        '''simple docstring'''
        lowercase__:   List[str]  			=   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
        lowercase__:   Union[str, Any]  			=   self._load_datasamples(1 )[0]
        lowercase__:   Optional[int]  			=   ((audio - audio.min()) / (audio.max() - audio.min())) * 65_535  # Rescale to [0, 65535] to show issue
        lowercase__:   Optional[Any]  			=   feat_extract.zero_mean_unit_var_norm([audio]				, attention_mask=lowerCAmelCase__ )[0]

        self.assertTrue(np.all(np.mean(lowerCAmelCase__ ) < 1E-3 ) )
        self.assertTrue(np.all(np.abs(np.var(lowerCAmelCase__ ) - 1 ) < 1E-3 ) )









'''simple docstring'''


import pytest

from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def 							UpperCAmelCase_  (						__lowerCamelCase					:					List[str] ):
   monkeypatch.setattr("datasets.utils.deprecation_utils._emitted_deprecation_warnings"	,set() )
@pytest.fixture
def 							UpperCAmelCase_  (						__lowerCamelCase					:					Any ):
   class       a_   :





        def __init__( self		:					int	,      lowercase		:					int						):





           """simple docstring"""




           lowercase_			:Optional[Any]    			= metric_id







   class       a_   :
        __A      =			[MetricMock(_lowerCAmelCase							) for metric_id in ["accuracy", "mse", "precision", "codeparrot/apps_metric"]]





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





           """simple docstring"""




           return self._metrics







   monkeypatch.setattr("datasets.inspect.huggingface_hub"	,HfhMock() )


@pytest.mark.parametrize(
    "func, args"	,[(load_metric, ("metrics/mse",)), (list_metrics, ()), (inspect_metric, ("metrics/mse", "tmp_path"))] )
def 							UpperCAmelCase_  (						__lowerCamelCase					:					Union[str, Any]	,__lowerCamelCase					:					int	,__lowerCamelCase					:					Optional[int]	,__lowerCamelCase					:					Union[str, Any]	,__lowerCamelCase					:					Tuple ):
   if "tmp_path" in args:
      lowercase_			:Union[str, Any]    			= tuple(arg if arg != "tmp_path" else tmp_path for arg in args )
   with pytest.warns(__lowerCamelCase	,match="https://huggingface.co/docs/evaluate" ):
      func(*__lowerCamelCase )


