"""simple docstring"""


import itertools
import random
import unittest

import numpy as np

from transformers import BatchFeature, SpeechTaFeatureExtractor
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


__UpperCamelCase									=			random.Random()







def 					UpperCAmelCase    (		UpperCAmelCase						,					UpperCAmelCase=1.0						,					UpperCAmelCase=None						,					UpperCAmelCase=None					)				->      Optional[Any]:
     if rng is None:
          snake_case_					       =					global_rng

     snake_case_					       =					[]
     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  UpperCamelCase					(				unittest.TestCase   ):







       def __init__( self,							lowerCAmelCase__,							lowerCAmelCase__=7,							lowerCAmelCase__=400,							lowerCAmelCase__=2000,							lowerCAmelCase__=1,							lowerCAmelCase__=0.0,							lowerCAmelCase__=1_6000,							lowerCAmelCase__=True,							lowerCAmelCase__=80,							lowerCAmelCase__=16,							lowerCAmelCase__=64,							lowerCAmelCase__="hann_window",							lowerCAmelCase__=80,							lowerCAmelCase__=7600,							lowerCAmelCase__=1e-10,							lowerCAmelCase__=True,							)      ->      Optional[int]:
            snake_case_					       =					parent
            snake_case_					       =					batch_size
            snake_case_					       =					min_seq_length
            snake_case_					       =					max_seq_length
            snake_case_					       =					(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
            snake_case_					       =					feature_size
            snake_case_					       =					padding_value
            snake_case_					       =					sampling_rate
            snake_case_					       =					do_normalize
            snake_case_					       =					num_mel_bins
            snake_case_					       =					hop_length
            snake_case_					       =					win_length
            snake_case_					       =					win_function
            snake_case_					       =					fmin
            snake_case_					       =					fmax
            snake_case_					       =					mel_floor
            snake_case_					       =					return_attention_mask







       def 		a_						( self)      ->      Dict:
            return {
                "feature_size": self.feature_size,
                "padding_value": self.padding_value,
                "sampling_rate": self.sampling_rate,
                "do_normalize": self.do_normalize,
                "num_mel_bins": self.num_mel_bins,
                "hop_length": self.hop_length,
                "win_length": self.win_length,
                "win_function": self.win_function,
                "fmin": self.fmin,
                "fmax": self.fmax,
                "mel_floor": self.mel_floor,
                "return_attention_mask": self.return_attention_mask,
            }







       def 		a_						( self,							lowerCAmelCase__=False,							lowerCAmelCase__=False)      ->      Union[str, Any]:
            def _flatten(lowerCAmelCase__):
                 return list(itertools.chain(*lowerCAmelCase__))

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

            return speech_inputs







       def 		a_						( self,							lowerCAmelCase__=False,							lowerCAmelCase__=False)      ->      int:
            if equal_length:
                 snake_case_					       =					[floats_list((self.max_seq_length, self.num_mel_bins)) for _ in range(self.batch_size)]
            else:
                 # make sure that inputs increase in size
                 snake_case_					       =					[
                     floats_list((x, self.num_mel_bins))
                     for x in range(self.min_seq_length,							self.max_seq_length,							self.seq_length_diff)
                 ]

            if numpify:
                 snake_case_					       =					[np.asarray(lowerCAmelCase__) for x in speech_inputs]

            return speech_inputs




@require_torch
class  UpperCamelCase					(				lowerCAmelCase__    ,		unittest.TestCase   ):
       SCREAMING_SNAKE_CASE_			=      SpeechTaFeatureExtractor







       def 		a_						( self)      ->      Optional[int]:
            snake_case_					       =					SpeechTaFeatureExtractionTester(self)







       def 		a_						( self,							lowerCAmelCase__)      ->      Tuple:
            self.assertTrue(np.all(np.mean(lowerCAmelCase__,							axis=0) < 1e-3))
            self.assertTrue(np.all(np.abs(np.var(lowerCAmelCase__,							axis=0) - 1) < 1e-3))







       def 		a_						( self)      ->      Any:
            # Tests that all call wrap to encode_plus and batch_encode_plus
            snake_case_					       =					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
            # create three inputs of length 800, 1000, and 1200
            snake_case_					       =					[floats_list((1, x))[0] for x in range(800,							1400,							200)]
            snake_case_					       =					[np.asarray(lowerCAmelCase__) for speech_input in speech_inputs]

            # Test not batched input
            snake_case_					       =					feat_extract(speech_inputs[0],							return_tensors='np').input_values
            snake_case_					       =					feat_extract(np_speech_inputs[0],							return_tensors='np').input_values
            self.assertTrue(np.allclose(lowerCAmelCase__,							lowerCAmelCase__,							atol=1e-3))

            # Test batched
            snake_case_					       =					feat_extract(lowerCAmelCase__,							return_tensors='np').input_values
            snake_case_					       =					feat_extract(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 		a_						( self)      ->      List[Any]:
            snake_case_					       =					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
            snake_case_					       =					[floats_list((1, x))[0] for x in range(800,							1400,							200)]

            snake_case_					       =					['longest', 'max_length', 'do_not_pad']
            snake_case_					       =					[None, 1600, None]
            for max_length, padding in zip(lowerCAmelCase__,							lowerCAmelCase__):
                 snake_case_					       =					feat_extract(lowerCAmelCase__,							padding=lowerCAmelCase__,							max_length=lowerCAmelCase__,							return_tensors='np')
                 snake_case_					       =					processed.input_values

                 self._check_zero_mean_unit_variance(input_values[0][:800])
                 self.assertTrue(input_values[0][800:].sum() < 1e-6)
                 self._check_zero_mean_unit_variance(input_values[1][:1000])
                 self.assertTrue(input_values[0][1000:].sum() < 1e-6)
                 self._check_zero_mean_unit_variance(input_values[2][:1200])







       def 		a_						( self)      ->      str:
            snake_case_					       =					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
            snake_case_					       =					range(800,							1400,							200)
            snake_case_					       =					[floats_list((1, x))[0] for x in lengths]

            snake_case_					       =					['longest', 'max_length', 'do_not_pad']
            snake_case_					       =					[None, 1600, None]

            for max_length, padding in zip(lowerCAmelCase__,							lowerCAmelCase__):
                 snake_case_					       =					feat_extract(lowerCAmelCase__,							max_length=lowerCAmelCase__,							padding=lowerCAmelCase__)
                 snake_case_					       =					processed.input_values

                 self._check_zero_mean_unit_variance(input_values[0][:800])
                 self._check_zero_mean_unit_variance(input_values[1][:1000])
                 self._check_zero_mean_unit_variance(input_values[2][:1200])







       def 		a_						( self)      ->      List[Any]:
            snake_case_					       =					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
            snake_case_					       =					[floats_list((1, x))[0] for x in range(800,							1400,							200)]
            snake_case_					       =					feat_extract(
                lowerCAmelCase__,							truncation=lowerCAmelCase__,							max_length=1000,							padding='max_length',							return_tensors='np')
            snake_case_					       =					processed.input_values

            self._check_zero_mean_unit_variance(input_values[0, :800])
            self._check_zero_mean_unit_variance(input_values[1])
            self._check_zero_mean_unit_variance(input_values[2])







       def 		a_						( self)      ->      Dict:
            snake_case_					       =					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
            snake_case_					       =					[floats_list((1, x))[0] for x in range(800,							1400,							200)]
            snake_case_					       =					feat_extract(
                lowerCAmelCase__,							truncation=lowerCAmelCase__,							max_length=1000,							padding='longest',							return_tensors='np')
            snake_case_					       =					processed.input_values

            self._check_zero_mean_unit_variance(input_values[0, :800])
            self._check_zero_mean_unit_variance(input_values[1, :1000])
            self._check_zero_mean_unit_variance(input_values[2])

            # make sure that if max_length < longest -> then pad to max_length
            self.assertTrue(input_values.shape == (3, 1000))

            snake_case_					       =					[floats_list((1, x))[0] for x in range(800,							1400,							200)]
            snake_case_					       =					feat_extract(
                lowerCAmelCase__,							truncation=lowerCAmelCase__,							max_length=2000,							padding='longest',							return_tensors='np')
            snake_case_					       =					processed.input_values

            self._check_zero_mean_unit_variance(input_values[0, :800])
            self._check_zero_mean_unit_variance(input_values[1, :1000])
            self._check_zero_mean_unit_variance(input_values[2])

            # make sure that if max_length > longest -> then pad to longest
            self.assertTrue(input_values.shape == (3, 1200))







       def 		a_						( self)      ->      Dict:
            snake_case_					       =					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
            snake_case_					       =					np.random.rand(100).astype(np.floataa)
            snake_case_					       =					np_speech_inputs.tolist()

            for inputs in [py_speech_inputs, np_speech_inputs]:
                 snake_case_					       =					feature_extractor.pad([{'input_values': inputs}],							return_tensors='np')
                 self.assertTrue(np_processed.input_values.dtype == np.floataa)
                 snake_case_					       =					feature_extractor.pad([{'input_values': inputs}],							return_tensors='pt')
                 self.assertTrue(pt_processed.input_values.dtype == torch.floataa)







       def 		a_						( self)      ->      int:
            # Tests that all call wrap to encode_plus and batch_encode_plus
            snake_case_					       =					self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
            # create three inputs of length 800, 1000, and 1200
            snake_case_					       =					[floats_list((1, x))[0] for x in range(800,							1400,							200)]
            snake_case_					       =					[np.asarray(lowerCAmelCase__) for speech_input in speech_inputs]

            # Test feature size
            snake_case_					       =					feature_extractor(audio_target=lowerCAmelCase__,							padding=lowerCAmelCase__,							return_tensors='np').input_values
            self.assertTrue(input_values.ndim == 3)
            self.assertTrue(input_values.shape[-1] == feature_extractor.num_mel_bins)

            # Test not batched input
            snake_case_					       =					feature_extractor(speech_inputs[0],							return_tensors='np').input_values
            snake_case_					       =					feature_extractor(np_speech_inputs[0],							return_tensors='np').input_values
            self.assertTrue(np.allclose(lowerCAmelCase__,							lowerCAmelCase__,							atol=1e-3))

            # Test batched
            snake_case_					       =					feature_extractor(lowerCAmelCase__,							return_tensors='np').input_values
            snake_case_					       =					feature_extractor(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))

            # Test 2-D numpy arrays are batched.
            snake_case_					       =					[floats_list((1, x))[0] for x in (800, 800, 800)]
            snake_case_					       =					np.asarray(lowerCAmelCase__)
            snake_case_					       =					feature_extractor(lowerCAmelCase__,							return_tensors='np').input_values
            snake_case_					       =					feature_extractor(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 		a_						( self)      ->      Tuple:
            snake_case_					       =					self.feat_extract_tester.prepare_inputs_for_target()
            snake_case_					       =					self.feature_extraction_class(**self.feat_extract_dict)
            snake_case_					       =					feat_extract.model_input_names[0]

            snake_case_					       =					BatchFeature({input_name: speech_inputs})

            self.assertTrue(all(len(lowerCAmelCase__) == len(lowerCAmelCase__) for x, y in zip(lowerCAmelCase__,							processed_features[input_name])))

            snake_case_					       =					self.feat_extract_tester.prepare_inputs_for_target(equal_length=lowerCAmelCase__)
            snake_case_					       =					BatchFeature({input_name: speech_inputs},							tensor_type='np')

            snake_case_					       =					processed_features[input_name]

            if len(batch_features_input.shape) < 3:
                 snake_case_					       =					batch_features_input[:, :, None]

            self.assertTrue(
                batch_features_input.shape
                == (self.feat_extract_tester.batch_size, len(speech_inputs[0]), self.feat_extract_tester.num_mel_bins))







       @require_torch
       def 		a_						( self)      ->      Tuple:
            snake_case_					       =					self.feat_extract_tester.prepare_inputs_for_target(equal_length=lowerCAmelCase__)
            snake_case_					       =					self.feature_extraction_class(**self.feat_extract_dict)
            snake_case_					       =					feat_extract.model_input_names[0]

            snake_case_					       =					BatchFeature({input_name: speech_inputs},							tensor_type='pt')

            snake_case_					       =					processed_features[input_name]

            if len(batch_features_input.shape) < 3:
                 snake_case_					       =					batch_features_input[:, :, None]

            self.assertTrue(
                batch_features_input.shape
                == (self.feat_extract_tester.batch_size, len(speech_inputs[0]), self.feat_extract_tester.num_mel_bins))







       @require_torch
       def 		a_						( self)      ->      Optional[Any]:
            snake_case_					       =					self.feature_extraction_class(**self.feat_extract_dict)
            snake_case_					       =					self.feat_extract_tester.prepare_inputs_for_target()
            snake_case_					       =					feat_extract.model_input_names[0]

            snake_case_					       =					BatchFeature({input_name: speech_inputs})

            snake_case_					       =					feat_extract.num_mel_bins  # hack!

            snake_case_					       =					feat_extract.pad(lowerCAmelCase__,							padding='longest',							return_tensors='np')[input_name]
            snake_case_					       =					feat_extract.pad(lowerCAmelCase__,							padding='longest',							return_tensors='pt')[input_name]

            self.assertTrue(abs(input_np.astype(np.floataa).sum() - input_pt.numpy().astype(np.floataa).sum()) < 1e-2)







       def 		a_						( self)      ->      Union[str, Any]:
            snake_case_					       =					self.feat_extract_dict
            snake_case_					       =					True
            snake_case_					       =					self.feature_extraction_class(**lowerCAmelCase__)
            snake_case_					       =					self.feat_extract_tester.prepare_inputs_for_target()
            snake_case_					       =					[len(lowerCAmelCase__) for x in speech_inputs]
            snake_case_					       =					feat_extract.model_input_names[0]

            snake_case_					       =					BatchFeature({input_name: speech_inputs})

            snake_case_					       =					feat_extract.num_mel_bins  # hack!

            snake_case_					       =					feat_extract.pad(lowerCAmelCase__,							padding='longest',							return_tensors='np')
            self.assertIn('attention_mask',							lowerCAmelCase__)
            self.assertListEqual(list(processed.attention_mask.shape),							list(processed[input_name].shape[:2]))
            self.assertListEqual(processed.attention_mask.sum(-1).tolist(),							lowerCAmelCase__)







       def 		a_						( self)      ->      Any:
            snake_case_					       =					self.feat_extract_dict
            snake_case_					       =					True
            snake_case_					       =					self.feature_extraction_class(**lowerCAmelCase__)
            snake_case_					       =					self.feat_extract_tester.prepare_inputs_for_target()
            snake_case_					       =					[len(lowerCAmelCase__) for x in speech_inputs]
            snake_case_					       =					feat_extract.model_input_names[0]

            snake_case_					       =					BatchFeature({input_name: speech_inputs})
            snake_case_					       =					min(lowerCAmelCase__)

            snake_case_					       =					feat_extract.num_mel_bins  # hack!

            snake_case_					       =					feat_extract.pad(
                lowerCAmelCase__,							padding='max_length',							max_length=lowerCAmelCase__,							truncation=lowerCAmelCase__,							return_tensors='np')
            self.assertIn('attention_mask',							lowerCAmelCase__)
            self.assertListEqual(
                list(processed_pad.attention_mask.shape),							[processed_pad[input_name].shape[0], max_length])
            self.assertListEqual(
                processed_pad.attention_mask[:, :max_length].sum(-1).tolist(),							[max_length for x in speech_inputs])







       def 		a_						( self,							lowerCAmelCase__)      ->      List[str]:
            from datasets import load_dataset

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

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







       def 		a_						( self)      ->      List[str]:
            # fmt: off
            snake_case_					       =					torch.tensor(
                [2.3_804e-03, 2.0_752e-03, 1.9_836e-03, 2.1_057e-03, 1.6_174e-03,
                 3.0_518e-04, 9.1_553e-05, 3.3_569e-04, 9.7_656e-04, 1.8_311e-03,
                 2.0_142e-03, 2.1_057e-03, 1.7_395e-03, 4.5_776e-04, -3.9_673e-04,
                 4.5_776e-04, 1.0_071e-03, 9.1_553e-05, 4.8_828e-04, 1.1_597e-03,
                 7.3_242e-04, 9.4_604e-04, 1.8_005e-03, 1.8_311e-03, 8.8_501e-04,
                 4.2_725e-04, 4.8_828e-04, 7.3_242e-04, 1.0_986e-03, 2.1_057e-03])
            # fmt: on

            snake_case_					       =					self._load_datasamples(1)
            snake_case_					       =					SpeechTaFeatureExtractor()
            snake_case_					       =					feature_extractor(lowerCAmelCase__,							return_tensors='pt').input_values
            self.assertEquals(input_values.shape,							(1, 9_3680))
            self.assertTrue(torch.allclose(input_values[0, :30],							lowerCAmelCase__,							atol=1e-6))







       def 		a_						( self)      ->      List[str]:
            # fmt: off
            snake_case_					       =					torch.tensor(
                [-2.6870, -3.0104, -3.1356, -3.5352, -3.0044, -3.0353, -3.4719, -3.6777,
                 -3.1520, -2.9435, -2.6553, -2.8795, -2.9944, -2.5921, -3.0279, -3.0386,
                 -3.0864, -3.1291, -3.2353, -2.7444, -2.6831, -2.7287, -3.1761, -3.1571,
                 -3.2726, -3.0582, -3.1007, -3.4533, -3.4695, -3.0998])
            # fmt: on

            snake_case_					       =					self._load_datasamples(1)
            snake_case_					       =					SpeechTaFeatureExtractor()
            snake_case_					       =					feature_extractor(audio_target=lowerCAmelCase__,							return_tensors='pt').input_values
            self.assertEquals(input_values.shape,							(1, 366, 80))
            self.assertTrue(torch.allclose(input_values[0, 0, :30],							lowerCAmelCase__,							atol=1e-4))





'''simple docstring'''
from typing import List, Optional, Tuple, Union

import torch

from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline


UpperCamelCase__    					=      logging.get_logger(__name__)  # pylint: disable=invalid-name




class 			lowerCamelCase_   (	__a      ):






      def __init__(					self				:    Any						,						_A				:    int						,						_A				:    str ):


          '''simple docstring'''


          super().__init__()
          self.register_modules(unet=_A						,						scheduler=_A )






      @torch.no_grad()
      def __call__(					self				:    Union[str, Any]						,						_A				:    int = 1						,						_A				:    int = 100						,						_A				:    Optional[Union[torch.Generator, List[torch.Generator]]] = None						,						_A				:    Optional[float] = None						,						_A				:    bool = True						,						):


          '''simple docstring'''


          if audio_length_in_s is None:
              UpperCAmelCase__			:		List[str]													=				self.unet.config.sample_size / self.unet.config.sample_rate

          UpperCAmelCase__			:		Dict													=				audio_length_in_s * self.unet.config.sample_rate

          UpperCAmelCase__			:		Union[str, Any]													=				2 ** len(self.unet.up_blocks )
          if sample_size < 3 * down_scale_factor:
              raise ValueError(
                  f"""{audio_length_in_s} is too small. Make sure it's bigger or equal to"""
                  f""" {3 * down_scale_factor / self.unet.config.sample_rate}.""" )

          UpperCAmelCase__			:		Optional[Any]													=				int(_A )
          if sample_size % down_scale_factor != 0:
              UpperCAmelCase__			:		List[str]													=				(
                  (audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
              ) * down_scale_factor
              logger.info(
                  f"""{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled"""
                  f""" by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising"""
                  ''' process.''' )
          UpperCAmelCase__			:		Union[str, Any]													=				int(_A )

          UpperCAmelCase__			:		Any													=				next(iter(self.unet.parameters() ) ).dtype
          UpperCAmelCase__			:		Union[str, Any]													=				(batch_size, self.unet.config.in_channels, sample_size)
          if isinstance(_A						,						_A ) and len(_A ) != batch_size:
              raise ValueError(
                  f"""You have passed a list of generators of length {len(_A )}, but requested an effective batch"""
                  f""" size of {batch_size}. Make sure the batch size matches the length of the generators.""" )

          UpperCAmelCase__			:		int													=				randn_tensor(_A						,						generator=_A						,						device=self.device						,						dtype=_A )

          # set step values
          self.scheduler.set_timesteps(_A						,						device=audio.device )
          UpperCAmelCase__			:		Union[str, Any]													=				self.scheduler.timesteps.to(_A )

          for t in self.progress_bar(self.scheduler.timesteps ):
              # 1. predict noise model_output
              UpperCAmelCase__			:		Any													=				self.unet(_A						,						_A ).sample

              # 2. compute previous image: x_t -> t_t-1
              UpperCAmelCase__			:		Union[str, Any]													=				self.scheduler.step(_A						,						_A						,						_A ).prev_sample

          UpperCAmelCase__			:		Any													=				audio.clamp(-1						,						1 ).float().cpu().numpy()

          UpperCAmelCase__			:		List[str]													=				audio[:, :, :original_sample_size]

          if not return_dict:
              return (audio,)

          return AudioPipelineOutput(audios=_A )
