
"""simple docstring"""




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


A__   :						Optional[int] 							=     random.Random()



def 				a__			(					lowerCAmelCase						:							Any							,     lowerCAmelCase						:							Union[str, Any]=1.0							,     lowerCAmelCase						:							Optional[Any]=None							,     lowerCAmelCase						:							str=None		):






     '''simple docstring'''
     if rng is None:
          UpperCAmelCase__				:			Tuple							=    global_rng

     UpperCAmelCase__				:			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
class 	_lowercase    (							unittest.TestCase					):


     '''simple docstring'''





     def __init__(   self			,      __UpperCamelCase			,      __UpperCamelCase=7			,      __UpperCamelCase=4_00			,      __UpperCamelCase=20_00			,      __UpperCamelCase=1			,      __UpperCamelCase=0.0			,      __UpperCamelCase=2_40_00			,      __UpperCamelCase=True			,      )->       Tuple:
          UpperCAmelCase__				:			Optional[Any]							=    parent
          UpperCAmelCase__				:			Optional[Any]							=    batch_size
          UpperCAmelCase__				:			int							=    min_seq_length
          UpperCAmelCase__				:			Tuple							=    max_seq_length
          UpperCAmelCase__				:			Optional[Any]							=    (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
          UpperCAmelCase__				:			List[Any]							=    feature_size
          UpperCAmelCase__				:			Dict							=    padding_value
          UpperCAmelCase__				:			Optional[int]							=    sampling_rate
          UpperCAmelCase__				:			List[str]							=    return_attention_mask



     def 			lowerCAmelCase__							(   self	)->       Optional[Any]:
          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			,      __UpperCamelCase=False			,      __UpperCamelCase=False	)->       Dict:



          def _flatten(__UpperCamelCase	):
               return list(itertools.chain(*UpperCAmelCase_	)	)

          if equal_length:
               UpperCAmelCase__				:			Optional[int]							=    floats_list((self.batch_size, self.max_seq_length)	)
          else:
               # make sure that inputs increase in size
               UpperCAmelCase__				:			int							=    [
                   _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:
               UpperCAmelCase__				:			Union[str, Any]							=    [np.asarray(UpperCAmelCase_	) for x in audio_inputs]

          return audio_inputs







@require_torch
class 	_lowercase    (							snake_case__						, unittest.TestCase					):


     '''simple docstring'''





     _A		    =				EncodecFeatureExtractor



     def 			lowerCAmelCase__							(   self	)->       Optional[int]:
          UpperCAmelCase__				:			Tuple							=    EnCodecFeatureExtractionTester(self	)



     def 			lowerCAmelCase__							(   self	)->       Optional[int]:
          UpperCAmelCase__				:			List[str]							=    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
          # create three inputs of length 800, 1000, and 1200
          UpperCAmelCase__				:			Union[str, Any]							=    [floats_list((1, x)	)[0] for x in range(8_00			,      14_00			,      2_00	)]
          UpperCAmelCase__				:			List[Any]							=    [np.asarray(UpperCAmelCase_	) for audio_input in audio_inputs]

          # Test not batched input
          UpperCAmelCase__				:			Dict							=    feat_extract(audio_inputs[0]			,      return_tensors="np"	).input_values
          UpperCAmelCase__				:			Optional[Any]							=    feat_extract(np_audio_inputs[0]			,      return_tensors="np"	).input_values
          self.assertTrue(np.allclose(UpperCAmelCase_			,      UpperCAmelCase_			,      atol=1E-3	)	)

          # Test batched
          UpperCAmelCase__				:			Union[str, Any]							=    feat_extract(UpperCAmelCase_			,      padding=UpperCAmelCase_			,      return_tensors="np"	).input_values
          UpperCAmelCase__				:			Optional[Any]							=    feat_extract(UpperCAmelCase_			,      padding=UpperCAmelCase_			,      return_tensors="np"	).input_values
          for enc_seq_a, enc_seq_a in zip(UpperCAmelCase_			,      UpperCAmelCase_	):
               self.assertTrue(np.allclose(UpperCAmelCase_			,      UpperCAmelCase_			,      atol=1E-3	)	)



     def 			lowerCAmelCase__							(   self	)->       Tuple:
          UpperCAmelCase__				:			Dict							=    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()	)
          UpperCAmelCase__				:			str							=    np.random.rand(1_00	).astype(np.floataa	)
          UpperCAmelCase__				:			List[str]							=    np_audio_inputs.tolist()

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



     def 			lowerCAmelCase__							(   self			,      __UpperCamelCase	)->       Dict:
          from datasets import load_dataset

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

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



     def 			lowerCAmelCase__							(   self	)->       List[Any]:
          UpperCAmelCase__				:			str							=    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
          UpperCAmelCase__				:			List[str]							=    self._load_datasamples(1	)
          UpperCAmelCase__				:			Any							=    EncodecFeatureExtractor()
          UpperCAmelCase__				:			str							=    feature_extractor(UpperCAmelCase_			,      return_tensors="pt"	).input_values
          self.assertEquals(input_values.shape			,      (1, 1, 9_36_80)	)
          self.assertTrue(torch.allclose(input_values[0, 0, :30]			,      UpperCAmelCase_			,      atol=1E-6	)	)



     def 			lowerCAmelCase__							(   self	)->       Optional[int]:
          UpperCAmelCase__				:			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
          UpperCAmelCase__				:			Optional[Any]							=    self._load_datasamples(1	)
          UpperCAmelCase__				:			Tuple							=    [np.tile(input_audio[0][None]			,      reps=(2, 1)	)]
          input_audio[0][1] *= 0.5
          UpperCAmelCase__				:			Tuple							=    EncodecFeatureExtractor(feature_size=2	)
          UpperCAmelCase__				:			str							=    feature_extractor(UpperCAmelCase_			,      return_tensors="pt"	).input_values
          self.assertEquals(input_values.shape			,      (1, 2, 9_36_80)	)
          self.assertTrue(torch.allclose(input_values[0, 0, :30]			,      UpperCAmelCase_			,      atol=1E-6	)	)
          self.assertTrue(torch.allclose(input_values[0, 1, :30]			,      EXPECTED_INPUT_VALUES * 0.5			,      atol=1E-6	)	)



     def 			lowerCAmelCase__							(   self	)->       Optional[Any]:
          UpperCAmelCase__				:			Union[str, Any]							=    self._load_datasamples(2	)
          # would be easier if the stride was like
          UpperCAmelCase__				:			Any							=    EncodecFeatureExtractor(feature_size=1			,      chunk_length_s=1			,      overlap=0.01	)

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

          # truncate to chunk
          UpperCAmelCase__				:			int							=    feature_extractor(UpperCAmelCase_			,      truncation=UpperCAmelCase_			,      return_tensors="pt"	).input_values
          self.assertEquals(truncated_outputs.shape			,      (2, 1, 7_15_20)	)  # 2 chunks

          # force truncate to max_length
          UpperCAmelCase__				:			str							=    feature_extractor(
              UpperCAmelCase_			,      truncation=UpperCAmelCase_			,      max_length=4_80_00			,      return_tensors="pt"	).input_values
          self.assertEquals(truncated_outputs.shape			,      (2, 1, 4_80_00)	)

          # pad to chunk
          UpperCAmelCase__				:			str							=    feature_extractor(UpperCAmelCase_			,      padding=UpperCAmelCase_			,      return_tensors="pt"	).input_values
          self.assertEquals(padded_outputs.shape			,      (2, 1, 9_52_80)	)

          # pad to chunk
          UpperCAmelCase__				:			str							=    feature_extractor(UpperCAmelCase_			,      return_tensors="pt"	).input_values
          self.assertEquals(truncated_outputs.shape			,      (2, 1, 9_52_80)	)

          # force pad to max length
          UpperCAmelCase__				:			int							=    feature_extractor(
              UpperCAmelCase_			,      padding="max_length"			,      max_length=10_00_00			,      return_tensors="pt"	).input_values
          self.assertEquals(truncated_outputs.shape			,      (2, 1, 10_00_00)	)

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

          UpperCAmelCase__				:			Optional[int]							=    feature_extractor(input_audio[0]			,      padding=UpperCAmelCase_			,      return_tensors="pt"	).input_values
          self.assertEquals(truncated_outputs.shape			,      (1, 1, 9_36_80)	)

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

          UpperCAmelCase__				:			Dict							=    feature_extractor(input_audio[0]			,      padding=UpperCAmelCase_			,      return_tensors="pt"	).input_values
          self.assertEquals(truncated_outputs.shape			,      (1, 1, 9_36_80)	)

          # no pad if no overlap
          UpperCAmelCase__				:			Tuple							=    2
          UpperCAmelCase__				:			Union[str, Any]							=    None
          with self.assertRaisesRegex(
              UpperCAmelCase_			,      "^Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length.$"			,      ):
               UpperCAmelCase__				:			List[Any]							=    feature_extractor(UpperCAmelCase_			,      padding=UpperCAmelCase_			,      return_tensors="pt"	).input_values

          UpperCAmelCase__				:			Any							=    feature_extractor(input_audio[0]			,      padding=UpperCAmelCase_			,      return_tensors="pt"	).input_values
          self.assertEquals(truncated_outputs.shape			,      (1, 1, 9_36_80)	)







"""simple docstring"""
from typing import Any





class   __A  :





							'''simple docstring'''

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



														"""simple docstring"""



														snake_case_     							=		[]
														snake_case_     							=		0



							def __str__(					self				:   List[str]							)       ->Union[str, Any]:



														"""simple docstring"""



														snake_case_     							=		"""<""" + str(self.stack							)[1:-1] + """>"""
														return printed



							def 		lowerCAmelCase							(					self				:   List[Any]		,							UpperCAmelCase_				:   Any							)       ->None:



														"""simple docstring"""



														self.stack.append(UpperCAmelCase_							)
														snake_case_     							=		self.length + 1



							def 		lowerCAmelCase							(					self				:   List[str]							)       ->Any:



														"""simple docstring"""



														self.rotate(1							)
														snake_case_     							=		self.stack[self.length - 1]
														snake_case_     							=		self.stack[:-1]
														self.rotate(self.length - 1							)
														snake_case_     							=		self.length - 1
														return dequeued



							def 		lowerCAmelCase							(					self				:   List[str]		,							UpperCAmelCase_				:   int							)       ->None:



														"""simple docstring"""



														for _ in range(UpperCAmelCase_							):
																					snake_case_     							=		self.stack[0]
																					snake_case_     							=		self.stack[1:]
																					self.put(UpperCAmelCase_							)
																					snake_case_     							=		self.length - 1



							def 		lowerCAmelCase							(					self				:   Dict							)       ->Any:



														"""simple docstring"""



														snake_case_     							=		self.get()
														self.put(UpperCAmelCase_							)
														self.rotate(self.length - 1							)
														return front



							def 		lowerCAmelCase							(					self				:   Optional[Any]							)       ->int:



														"""simple docstring"""



														return self.length



