
import itertools
import random
import unittest

import numpy as np

from transformers import EncodecFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
					import torch


lowerCamelCase__        =						random.Random()
def 					lowerCAmelCase__     (    SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_=1.0			,				SCREAMING_SNAKE_CASE_=None			,				SCREAMING_SNAKE_CASE_=None	)							->						Union[str, Any]:


			if rng is None:
						lowerCAmelCase__ : Union[str, Any]   =						global_rng

			lowerCAmelCase__ : 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
class 	A__				(	unittest.TestCase      ):






				def __init__(		self   : Optional[int]       ,	a   : Optional[Any]       ,	a   : Tuple=7       ,	a   : int=400       ,	a   : List[str]=2_000       ,	a   : List[str]=1       ,	a   : Union[str, Any]=0.0       ,	a   : List[str]=24_000       ,	a   : str=True       ,	):






							'''simple docstring'''







							lowerCAmelCase__ : List[Any]   =						parent
							lowerCAmelCase__ : Any   =						batch_size
							lowerCAmelCase__ : Any   =						min_seq_length
							lowerCAmelCase__ : List[str]   =						max_seq_length
							lowerCAmelCase__ : Optional[int]   =						(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
							lowerCAmelCase__ : Union[str, Any]   =						feature_size
							lowerCAmelCase__ : List[Any]   =						padding_value
							lowerCAmelCase__ : List[Any]   =						sampling_rate
							lowerCAmelCase__ : List[str]   =						return_attention_mask






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






							'''simple docstring'''







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






				def 				_lowerCamelCase						(		self   : int       ,	a   : List[Any]=False       ,	a   : Dict=False  ):






							'''simple docstring'''







							def _flatten(a   : Optional[Any]  ):
										return list(itertools.chain(*lowerCamelCase__  )  )

							if equal_length:
										lowerCAmelCase__ : Union[str, Any]   =						floats_list((self.batch_size, self.max_seq_length)  )
							else:
										# make sure that inputs increase in size
										lowerCAmelCase__ : Dict   =						[
										    _flatten(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__ : str   =						[np.asarray(lowerCamelCase__  ) for x in audio_inputs]

							return audio_inputs







@require_torch
class 	A__				(	lowerCAmelCase_		,  unittest.TestCase      ):
				lowercase            =    EncodecFeatureExtractor






				def 				_lowerCamelCase						(		self   : List[Any]  ):






							'''simple docstring'''







							lowerCAmelCase__ : Any   =						EnCodecFeatureExtractionTester(self  )






				def 				_lowerCamelCase						(		self   : Dict  ):






							'''simple docstring'''







							lowerCAmelCase__ : List[Any]   =						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
							# create three inputs of length 800, 1000, and 1200
							lowerCAmelCase__ : Optional[Any]   =						[floats_list((1, x)  )[0] for x in range(800       ,	1_400       ,	200  )]
							lowerCAmelCase__ : str   =						[np.asarray(lowerCamelCase__  ) for audio_input in audio_inputs]

							# Test not batched input
							lowerCAmelCase__ : int   =						feat_extract(audio_inputs[0]       ,	return_tensors='np'  ).input_values
							lowerCAmelCase__ : Any   =						feat_extract(np_audio_inputs[0]       ,	return_tensors='np'  ).input_values
							self.assertTrue(np.allclose(lowerCamelCase__       ,	lowerCamelCase__       ,	atol=1E-3  )  )

							# Test batched
							lowerCAmelCase__ : Dict   =						feat_extract(lowerCamelCase__       ,	padding=lowerCamelCase__       ,	return_tensors='np'  ).input_values
							lowerCAmelCase__ : int   =						feat_extract(lowerCamelCase__       ,	padding=lowerCamelCase__       ,	return_tensors='np'  ).input_values
							for enc_seq_a, enc_seq_a in zip(lowerCamelCase__       ,	lowerCamelCase__  ):
										self.assertTrue(np.allclose(lowerCamelCase__       ,	lowerCamelCase__       ,	atol=1E-3  )  )






				def 				_lowerCamelCase						(		self   : Tuple  ):






							'''simple docstring'''







							lowerCAmelCase__ : Any   =						self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
							lowerCAmelCase__ : str   =						np.random.rand(100  ).astype(np.floataa  )
							lowerCAmelCase__ : List[str]   =						np_audio_inputs.tolist()

							for inputs in [py_audio_inputs, np_audio_inputs]:
										lowerCAmelCase__ : int   =						feature_extractor.pad([{'input_values': inputs}]       ,	return_tensors='np'  )
										self.assertTrue(np_processed.input_values.dtype == np.floataa  )
										lowerCAmelCase__ : int   =						feature_extractor.pad([{'input_values': inputs}]       ,	return_tensors='pt'  )
										self.assertTrue(pt_processed.input_values.dtype == torch.floataa  )






				def 				_lowerCamelCase						(		self   : Any       ,	a   : Optional[int]  ):






							'''simple docstring'''







							from datasets import load_dataset

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

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






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






							'''simple docstring'''







							lowerCAmelCase__ : int   =						torch.tensor(
							    [2.3804E-03, 2.0752E-03, 1.9836E-03, 2.1057E-03, 1.6174E-03,
							     3.0518E-04, 9.1553E-05, 3.3569E-04, 9.7656E-04, 1.8311E-03,
							     2.0142E-03, 2.1057E-03, 1.7395E-03, 4.5776E-04, -3.9673E-04,
							     4.5776E-04, 1.0071E-03, 9.1553E-05, 4.8828E-04, 1.1597E-03,
							     7.3242E-04, 9.4604E-04, 1.8005E-03, 1.8311E-03, 8.8501E-04,
							     4.2725E-04, 4.8828E-04, 7.3242E-04, 1.0986E-03, 2.1057E-03]  )
							# fmt: on
							lowerCAmelCase__ : Optional[int]   =						self._load_datasamples(1  )
							lowerCAmelCase__ : List[str]   =						EncodecFeatureExtractor()
							lowerCAmelCase__ : Optional[int]   =						feature_extractor(lowerCamelCase__       ,	return_tensors='pt'  ).input_values
							self.assertEquals(input_values.shape       ,	(1, 1, 93_680)  )
							self.assertTrue(torch.allclose(input_values[0, 0, :30]       ,	lowerCamelCase__       ,	atol=1E-6  )  )






				def 				_lowerCamelCase						(		self   : List[str]  ):






							'''simple docstring'''







							lowerCAmelCase__ : Dict   =						torch.tensor(
							    [2.3804E-03, 2.0752E-03, 1.9836E-03, 2.1057E-03, 1.6174E-03,
							     3.0518E-04, 9.1553E-05, 3.3569E-04, 9.7656E-04, 1.8311E-03,
							     2.0142E-03, 2.1057E-03, 1.7395E-03, 4.5776E-04, -3.9673E-04,
							     4.5776E-04, 1.0071E-03, 9.1553E-05, 4.8828E-04, 1.1597E-03,
							     7.3242E-04, 9.4604E-04, 1.8005E-03, 1.8311E-03, 8.8501E-04,
							     4.2725E-04, 4.8828E-04, 7.3242E-04, 1.0986E-03, 2.1057E-03]  )
							# fmt: on
							lowerCAmelCase__ : Dict   =						self._load_datasamples(1  )
							lowerCAmelCase__ : Optional[Any]   =						[np.tile(input_audio[0][None]       ,	reps=(2, 1)  )]
							input_audio[0][1] *= 0.5
							lowerCAmelCase__ : Dict   =						EncodecFeatureExtractor(feature_size=2  )
							lowerCAmelCase__ : str   =						feature_extractor(lowerCamelCase__       ,	return_tensors='pt'  ).input_values
							self.assertEquals(input_values.shape       ,	(1, 2, 93_680)  )
							self.assertTrue(torch.allclose(input_values[0, 0, :30]       ,	lowerCamelCase__       ,	atol=1E-6  )  )
							self.assertTrue(torch.allclose(input_values[0, 1, :30]       ,	EXPECTED_INPUT_VALUES * 0.5       ,	atol=1E-6  )  )






				def 				_lowerCamelCase						(		self   : Dict  ):






							'''simple docstring'''







							lowerCAmelCase__ : int   =						self._load_datasamples(2  )
							# would be easier if the stride was like
							lowerCAmelCase__ : List[str]   =						EncodecFeatureExtractor(feature_size=1       ,	chunk_length_s=1       ,	overlap=0.0_1  )

							# pad and trunc raise an error ?
							with self.assertRaisesRegex(
							    lowerCamelCase__       ,	'^Both padding and truncation were set. Make sure you only set one.$'       ,	):
										lowerCAmelCase__ : Optional[Any]   =						feature_extractor(
										    lowerCamelCase__       ,	padding='max_length'       ,	truncation=lowerCamelCase__       ,	return_tensors='pt'  ).input_values

							# truncate to chunk
							lowerCAmelCase__ : List[str]   =						feature_extractor(lowerCamelCase__       ,	truncation=lowerCamelCase__       ,	return_tensors='pt'  ).input_values
							self.assertEquals(truncated_outputs.shape       ,	(2, 1, 71_520)  )  # 2 chunks

							# force truncate to max_length
							lowerCAmelCase__ : Any   =						feature_extractor(
							    lowerCamelCase__       ,	truncation=lowerCamelCase__       ,	max_length=48_000       ,	return_tensors='pt'  ).input_values
							self.assertEquals(truncated_outputs.shape       ,	(2, 1, 48_000)  )

							# pad to chunk
							lowerCAmelCase__ : Union[str, Any]   =						feature_extractor(lowerCamelCase__       ,	padding=lowerCamelCase__       ,	return_tensors='pt'  ).input_values
							self.assertEquals(padded_outputs.shape       ,	(2, 1, 95_280)  )

							# pad to chunk
							lowerCAmelCase__ : Dict   =						feature_extractor(lowerCamelCase__       ,	return_tensors='pt'  ).input_values
							self.assertEquals(truncated_outputs.shape       ,	(2, 1, 95_280)  )

							# force pad to max length
							lowerCAmelCase__ : str   =						feature_extractor(
							    lowerCamelCase__       ,	padding='max_length'       ,	max_length=100_000       ,	return_tensors='pt'  ).input_values
							self.assertEquals(truncated_outputs.shape       ,	(2, 1, 100_000)  )

							# force no pad
							with self.assertRaisesRegex(
							    lowerCamelCase__       ,	'^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'       ,	):
										lowerCAmelCase__ : Dict   =						feature_extractor(lowerCamelCase__       ,	padding=lowerCamelCase__       ,	return_tensors='pt'  ).input_values

							lowerCAmelCase__ : Dict   =						feature_extractor(input_audio[0]       ,	padding=lowerCamelCase__       ,	return_tensors='pt'  ).input_values
							self.assertEquals(truncated_outputs.shape       ,	(1, 1, 93_680)  )

							# no pad if no chunk_length_s
							lowerCAmelCase__ : Tuple   =						None
							with self.assertRaisesRegex(
							    lowerCamelCase__       ,	'^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'       ,	):
										lowerCAmelCase__ : List[str]   =						feature_extractor(lowerCamelCase__       ,	padding=lowerCamelCase__       ,	return_tensors='pt'  ).input_values

							lowerCAmelCase__ : Tuple   =						feature_extractor(input_audio[0]       ,	padding=lowerCamelCase__       ,	return_tensors='pt'  ).input_values
							self.assertEquals(truncated_outputs.shape       ,	(1, 1, 93_680)  )

							# no pad if no overlap
							lowerCAmelCase__ : Dict   =						2
							lowerCAmelCase__ : Optional[int]   =						None
							with self.assertRaisesRegex(
							    lowerCamelCase__       ,	'^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$'       ,	):
										lowerCAmelCase__ : int   =						feature_extractor(lowerCamelCase__       ,	padding=lowerCamelCase__       ,	return_tensors='pt'  ).input_values

							lowerCAmelCase__ : Optional[Any]   =						feature_extractor(input_audio[0]       ,	padding=lowerCamelCase__       ,	return_tensors='pt'  ).input_values
							self.assertEquals(truncated_outputs.shape       ,	(1, 1, 93_680)  )






import unittest
from dataclasses import dataclass
from typing import Optional

from transformers.utils import ModelOutput

@dataclass
class 						UpperCamelCase__					( lowerCAmelCase_  ):



     '''simple docstring'''

     __snake_case       :      float
     __snake_case       :      Optional[float]						      =     None
     __snake_case       :      Optional[float]						      =     None

class 						UpperCamelCase__					( unittest.TestCase  ):



     '''simple docstring'''

     def 							SCREAMING_SNAKE_CASE__    (				self		:       Any			)	->						Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		ModelOutputTest(a=30			)
           self.assertEqual(x.a		,30			)
           self.assertIsNone(x.b			)
           self.assertIsNone(x.c			)
           with self.assertRaises(lowerCamelCase__			):
                 SCREAMING_SNAKE_CASE						=		x.d






     def 							SCREAMING_SNAKE_CASE__    (				self		:       Optional[Any]			)	->						Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		ModelOutputTest(a=30		,b=10			)
           self.assertEqual(x[0]		,30			)
           self.assertEqual(x[1]		,10			)
           self.assertEqual(x[:2]		,(30, 10)			)
           self.assertEqual(x[:]		,(30, 10)			)

           SCREAMING_SNAKE_CASE						=		ModelOutputTest(a=30		,c=10			)
           self.assertEqual(x[0]		,30			)
           self.assertEqual(x[1]		,10			)
           self.assertEqual(x[:2]		,(30, 10)			)
           self.assertEqual(x[:]		,(30, 10)			)






     def 							SCREAMING_SNAKE_CASE__    (				self		:       str			)	->						Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		ModelOutputTest(a=30		,b=10			)
           self.assertEqual(x["""a"""]		,30			)
           self.assertEqual(x["""b"""]		,10			)
           with self.assertRaises(lowerCamelCase__			):
                 SCREAMING_SNAKE_CASE						=		x["""c"""]

           SCREAMING_SNAKE_CASE						=		ModelOutputTest(a=30		,c=10			)
           self.assertEqual(x["""a"""]		,30			)
           self.assertEqual(x["""c"""]		,10			)
           with self.assertRaises(lowerCamelCase__			):
                 SCREAMING_SNAKE_CASE						=		x["""b"""]






     def 							SCREAMING_SNAKE_CASE__    (				self		:       List[Any]			)	->						Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		ModelOutputTest(a=30			)
           self.assertEqual(list(x.keys()			)		,["""a"""]			)
           self.assertEqual(list(x.values()			)		,[30]			)
           self.assertEqual(list(x.items()			)		,[("""a""", 30)]			)
           self.assertEqual(list(lowerCamelCase__			)		,["""a"""]			)

           SCREAMING_SNAKE_CASE						=		ModelOutputTest(a=30		,b=10			)
           self.assertEqual(list(x.keys()			)		,["""a""", """b"""]			)
           self.assertEqual(list(x.values()			)		,[30, 10]			)
           self.assertEqual(list(x.items()			)		,[("""a""", 30), ("""b""", 10)]			)
           self.assertEqual(list(lowerCamelCase__			)		,["""a""", """b"""]			)

           SCREAMING_SNAKE_CASE						=		ModelOutputTest(a=30		,c=10			)
           self.assertEqual(list(x.keys()			)		,["""a""", """c"""]			)
           self.assertEqual(list(x.values()			)		,[30, 10]			)
           self.assertEqual(list(x.items()			)		,[("""a""", 30), ("""c""", 10)]			)
           self.assertEqual(list(lowerCamelCase__			)		,["""a""", """c"""]			)

           with self.assertRaises(lowerCamelCase__			):
                 SCREAMING_SNAKE_CASE						=		x.update({"""d""": 20}			)
           with self.assertRaises(lowerCamelCase__			):
                 del x["a"]
           with self.assertRaises(lowerCamelCase__			):
                 SCREAMING_SNAKE_CASE						=		x.pop("""a"""			)
           with self.assertRaises(lowerCamelCase__			):
                 SCREAMING_SNAKE_CASE						=		x.setdefault("""d"""		,32			)






     def 							SCREAMING_SNAKE_CASE__    (				self		:       int			)	->						List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		ModelOutputTest(a=30			)
           SCREAMING_SNAKE_CASE						=		10
           self.assertEqual(x.a		,10			)
           self.assertEqual(x["""a"""]		,10			)






     def 							SCREAMING_SNAKE_CASE__    (				self		:       List[str]			)	->						Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		ModelOutputTest(a=30			)
           SCREAMING_SNAKE_CASE						=		10
           self.assertEqual(x.a		,10			)
           self.assertEqual(x["""a"""]		,10			)






     def 							SCREAMING_SNAKE_CASE__    (				self		:       List[Any]			)	->						Optional[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		ModelOutputTest({"""a""": 30, """b""": 10}			)
           self.assertEqual(list(x.keys()			)		,["""a""", """b"""]			)
           self.assertEqual(x.a		,30			)
           self.assertEqual(x.b		,10			)






     def 							SCREAMING_SNAKE_CASE__    (				self		:       Optional[int]			)	->						Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE						=		ModelOutputTest([("""a""", 30), ("""b""", 10)]			)
           self.assertEqual(list(x.keys()			)		,["""a""", """b"""]			)
           self.assertEqual(x.a		,30			)
           self.assertEqual(x.b		,10			)

           with self.assertRaises(lowerCamelCase__			):
                 SCREAMING_SNAKE_CASE						=		ModelOutputTest([("""a""", 30), (10, 10)]			)

           SCREAMING_SNAKE_CASE						=		ModelOutputTest(a=(30, 30)			)
           self.assertEqual(list(x.keys()			)		,["""a"""]			)
           self.assertEqual(x.a		,(30, 30)			)


