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 :     str          =  random.Random()




def 				_snake_case     (       lowerCAmelCase							:       Dict       ,			lowerCAmelCase							:       Any=1.0       ,			lowerCAmelCase							:       Tuple=None       ,			lowerCAmelCase							:       Optional[int]=None     ):
		"""simple docstring"""







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

		SCREAMING_SNAKE_CASE_  :		Dict    				=   []
		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				:	str,_A				:	Any,_A				:	Optional[Any]=7,_A				:	List[Any]=400,_A				:	List[str]=2000,_A				:	Any=1,_A				:	str=0.0,_A				:	int=2_4000,_A				:	Dict=True,):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   parent
							SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   batch_size
							SCREAMING_SNAKE_CASE_  :		Dict    				=   min_seq_length
							SCREAMING_SNAKE_CASE_  :		Any    				=   max_seq_length
							SCREAMING_SNAKE_CASE_  :		List[Any]    				=   (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
							SCREAMING_SNAKE_CASE_  :		int    				=   feature_size
							SCREAMING_SNAKE_CASE_  :		Tuple    				=   padding_value
							SCREAMING_SNAKE_CASE_  :		int    				=   sampling_rate
							SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   return_attention_mask

					def     __UpperCamelCase       (   self				:	Tuple   ):







							"""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     __UpperCamelCase       (   self				:	Tuple,_A				:	Dict=False,_A				:	Dict=False   ):







							"""simple docstring"""






							def _flatten(_A				:	List[str]   ):
									return list(itertools.chain(*snake_case_   )   )

							if equal_length:
									SCREAMING_SNAKE_CASE_  :		List[Any]    				=   floats_list((self.batch_size, self.max_seq_length)   )
							else:
									# make sure that inputs increase in size
									SCREAMING_SNAKE_CASE_  :		List[Any]    				=   [
									    _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:
									SCREAMING_SNAKE_CASE_  :		str    				=   [np.asarray(snake_case_   ) for x in audio_inputs]

							return audio_inputs







@require_torch
class 		a__						( _a   ,		unittest.TestCase ):
					A     	=							EncodecFeatureExtractor

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







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   EnCodecFeatureExtractionTester(self   )

					def     __UpperCamelCase       (   self				:	Optional[Any]   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		List[Any]    				=   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
							# create three inputs of length 800, 1000, and 1200
							SCREAMING_SNAKE_CASE_  :		List[str]    				=   [floats_list((1, x)   )[0] for x in range(800,1400,200   )]
							SCREAMING_SNAKE_CASE_  :		Any    				=   [np.asarray(snake_case_   ) for audio_input in audio_inputs]

							# Test not batched input
							SCREAMING_SNAKE_CASE_  :		List[str]    				=   feat_extract(audio_inputs[0],return_tensors="np"   ).input_values
							SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   feat_extract(np_audio_inputs[0],return_tensors="np"   ).input_values
							self.assertTrue(np.allclose(snake_case_,snake_case_,atol=1E-3   )   )

							# Test batched
							SCREAMING_SNAKE_CASE_  :		List[str]    				=   feat_extract(snake_case_,padding=snake_case_,return_tensors="np"   ).input_values
							SCREAMING_SNAKE_CASE_  :		Dict    				=   feat_extract(snake_case_,padding=snake_case_,return_tensors="np"   ).input_values
							for enc_seq_a, enc_seq_a in zip(snake_case_,snake_case_   ):
									self.assertTrue(np.allclose(snake_case_,snake_case_,atol=1E-3   )   )

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







							"""simple docstring"""






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

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

					def     __UpperCamelCase       (   self				:	int,_A				:	str   ):







							"""simple docstring"""






							from datasets import load_dataset

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

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

					def     __UpperCamelCase       (   self				:	Optional[int]   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   torch.tensor(
							    [2.38_04E-03, 2.07_52E-03, 1.98_36E-03, 2.10_57E-03, 1.61_74E-03,
							     3.05_18E-04, 9.15_53E-05, 3.35_69E-04, 9.76_56E-04, 1.83_11E-03,
							     2.01_42E-03, 2.10_57E-03, 1.73_95E-03, 4.57_76E-04, -3.96_73E-04,
							     4.57_76E-04, 1.00_71E-03, 9.15_53E-05, 4.88_28E-04, 1.15_97E-03,
							     7.32_42E-04, 9.46_04E-04, 1.80_05E-03, 1.83_11E-03, 8.85_01E-04,
							     4.27_25E-04, 4.88_28E-04, 7.32_42E-04, 1.09_86E-03, 2.10_57E-03]   )
							# fmt: on
							SCREAMING_SNAKE_CASE_  :		str    				=   self._load_datasamples(1   )
							SCREAMING_SNAKE_CASE_  :		Tuple    				=   EncodecFeatureExtractor()
							SCREAMING_SNAKE_CASE_  :		List[str]    				=   feature_extractor(snake_case_,return_tensors="pt"   ).input_values
							self.assertEquals(input_values.shape,(1, 1, 9_3680)   )
							self.assertTrue(torch.allclose(input_values[0, 0, :30],snake_case_,atol=1E-6   )   )

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







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Any    				=   torch.tensor(
							    [2.38_04E-03, 2.07_52E-03, 1.98_36E-03, 2.10_57E-03, 1.61_74E-03,
							     3.05_18E-04, 9.15_53E-05, 3.35_69E-04, 9.76_56E-04, 1.83_11E-03,
							     2.01_42E-03, 2.10_57E-03, 1.73_95E-03, 4.57_76E-04, -3.96_73E-04,
							     4.57_76E-04, 1.00_71E-03, 9.15_53E-05, 4.88_28E-04, 1.15_97E-03,
							     7.32_42E-04, 9.46_04E-04, 1.80_05E-03, 1.83_11E-03, 8.85_01E-04,
							     4.27_25E-04, 4.88_28E-04, 7.32_42E-04, 1.09_86E-03, 2.10_57E-03]   )
							# fmt: on
							SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   self._load_datasamples(1   )
							SCREAMING_SNAKE_CASE_  :		Any    				=   [np.tile(input_audio[0][None],reps=(2, 1)   )]
							input_audio[0][1] *= 0.5
							SCREAMING_SNAKE_CASE_  :		Dict    				=   EncodecFeatureExtractor(feature_size=2   )
							SCREAMING_SNAKE_CASE_  :		List[Any]    				=   feature_extractor(snake_case_,return_tensors="pt"   ).input_values
							self.assertEquals(input_values.shape,(1, 2, 9_3680)   )
							self.assertTrue(torch.allclose(input_values[0, 0, :30],snake_case_,atol=1E-6   )   )
							self.assertTrue(torch.allclose(input_values[0, 1, :30],EXPECTED_INPUT_VALUES * 0.5,atol=1E-6   )   )






					def     __UpperCamelCase       (   self				:	Optional[int]   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		str    				=   self._load_datasamples(2   )
							# would be easier if the stride was like
							SCREAMING_SNAKE_CASE_  :		int    				=   EncodecFeatureExtractor(feature_size=1,chunk_length_s=1,overlap=0.01   )

							# pad and trunc raise an error ?
							with self.assertRaisesRegex(
							    snake_case_,"^Both padding and truncation were set. Make sure you only set one.$",):
									SCREAMING_SNAKE_CASE_  :		List[str]    				=   feature_extractor(
									    snake_case_,padding="max_length",truncation=snake_case_,return_tensors="pt"   ).input_values

							# truncate to chunk
							SCREAMING_SNAKE_CASE_  :		Any    				=   feature_extractor(snake_case_,truncation=snake_case_,return_tensors="pt"   ).input_values
							self.assertEquals(truncated_outputs.shape,(2, 1, 7_1520)   )  # 2 chunks

							# force truncate to max_length
							SCREAMING_SNAKE_CASE_  :		Tuple    				=   feature_extractor(
							    snake_case_,truncation=snake_case_,max_length=4_8000,return_tensors="pt"   ).input_values
							self.assertEquals(truncated_outputs.shape,(2, 1, 4_8000)   )

							# pad to chunk
							SCREAMING_SNAKE_CASE_  :		Dict    				=   feature_extractor(snake_case_,padding=snake_case_,return_tensors="pt"   ).input_values
							self.assertEquals(padded_outputs.shape,(2, 1, 9_5280)   )

							# pad to chunk
							SCREAMING_SNAKE_CASE_  :		Any    				=   feature_extractor(snake_case_,return_tensors="pt"   ).input_values
							self.assertEquals(truncated_outputs.shape,(2, 1, 9_5280)   )

							# force pad to max length
							SCREAMING_SNAKE_CASE_  :		Any    				=   feature_extractor(
							    snake_case_,padding="max_length",max_length=10_0000,return_tensors="pt"   ).input_values
							self.assertEquals(truncated_outputs.shape,(2, 1, 10_0000)   )

							# force no pad
							with self.assertRaisesRegex(
							    snake_case_,"^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$",):
									SCREAMING_SNAKE_CASE_  :		List[Any]    				=   feature_extractor(snake_case_,padding=snake_case_,return_tensors="pt"   ).input_values

							SCREAMING_SNAKE_CASE_  :		Any    				=   feature_extractor(input_audio[0],padding=snake_case_,return_tensors="pt"   ).input_values
							self.assertEquals(truncated_outputs.shape,(1, 1, 9_3680)   )

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

							SCREAMING_SNAKE_CASE_  :		Tuple    				=   feature_extractor(input_audio[0],padding=snake_case_,return_tensors="pt"   ).input_values
							self.assertEquals(truncated_outputs.shape,(1, 1, 9_3680)   )

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

							SCREAMING_SNAKE_CASE_  :		List[Any]    				=   feature_extractor(input_audio[0],padding=snake_case_,return_tensors="pt"   ).input_values
							self.assertEquals(truncated_outputs.shape,(1, 1, 9_3680)   )

from ..utils import DummyObject, requires_backends






class 		a__						( metaclass=A__ ):
					A     	=							['tensorflow_text']

					def __init__(   self				:	List[str],*_A				:	Optional[Any],**_A				:	int   ):







							"""simple docstring"""






							requires_backends(self,["tensorflow_text"]   )
