




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   :			List[str]          = random.Random()





def 						__A						(      __lowerCamelCase				,     __lowerCamelCase=1.0				,     __lowerCamelCase=None				,     __lowerCamelCase=None       ) ->			List[Any]:
	if rng is None:
		a									=				global_rng

	a									=				[]
	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 						__lowerCAmelCase      (      unittest.TestCase					):
							def __init__(		self		:Dict						, __magic_name__		:List[str]						, __magic_name__		:Dict=7						, __magic_name__		:Optional[Any]=400						, __magic_name__		:Dict=2000						, __magic_name__		:int=1						, __magic_name__		:Optional[int]=0.0						, __magic_name__		:Dict=1_6000						, __magic_name__		:Dict=True						, __magic_name__		:List[Any]=80						, __magic_name__		:Dict=16						, __magic_name__		:Union[str, Any]=64						, __magic_name__		:Union[str, Any]="hann_window"						, __magic_name__		:Optional[int]=80						, __magic_name__		:Dict=7600						, __magic_name__		:Any=1E-1_0						, __magic_name__		:Optional[int]=True						, ):







								'''simple docstring'''







								a									=				parent
								a									=				batch_size
								a									=				min_seq_length
								a									=				max_seq_length
								a									=				(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
								a									=				feature_size
								a									=				padding_value
								a									=				sampling_rate
								a									=				do_normalize
								a									=				num_mel_bins
								a									=				hop_length
								a									=				win_length
								a									=				win_function
								a									=				fmin
								a									=				fmax
								a									=				mel_floor
								a									=				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,
								    "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  lowerCamelCase__				(		self		:Dict						, __magic_name__		:Optional[int]=False						, __magic_name__		:List[Any]=False							):







								'''simple docstring'''







								def _flatten(__magic_name__		:Optional[int]							):
									return list(itertools.chain(*__magic_name__							)							)

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

								return speech_inputs





							def  lowerCamelCase__				(		self		:List[Any]						, __magic_name__		:Optional[int]=False						, __magic_name__		:Optional[Any]=False							):







								'''simple docstring'''







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

								return speech_inputs







@require_torch
class 						__lowerCAmelCase      (      __magic_name__ ,			unittest.TestCase					):
							UpperCamelCase__   	=    SpeechTaFeatureExtractor
							def  lowerCamelCase__				(		self		:Union[str, Any]							):







								'''simple docstring'''







								a									=				SpeechTaFeatureExtractionTester(self							)
							def  lowerCamelCase__				(		self		:Tuple						, __magic_name__		:Dict							):







								'''simple docstring'''







								self.assertTrue(np.all(np.mean(__magic_name__						, axis=0							) < 1E-3							)							)
								self.assertTrue(np.all(np.abs(np.var(__magic_name__						, axis=0							) - 1							) < 1E-3							)							)
							def  lowerCamelCase__				(		self		:Optional[int]							):







								'''simple docstring'''







								a									=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
								# create three inputs of length 800, 1000, and 1200
								a									=				[floats_list((1, x)							)[0] for x in range(800						, 1400						, 200							)]
								a									=				[np.asarray(__magic_name__							) for speech_input in speech_inputs]

								# Test not batched input
								a									=				feat_extract(speech_inputs[0]						, return_tensors="""np"""							).input_values
								a									=				feat_extract(np_speech_inputs[0]						, return_tensors="""np"""							).input_values
								self.assertTrue(np.allclose(__magic_name__						, __magic_name__						, atol=1E-3							)							)

								# Test batched
								a									=				feat_extract(__magic_name__						, return_tensors="""np"""							).input_values
								a									=				feat_extract(__magic_name__						, return_tensors="""np"""							).input_values
								for enc_seq_a, enc_seq_a in zip(__magic_name__						, __magic_name__							):
									self.assertTrue(np.allclose(__magic_name__						, __magic_name__						, atol=1E-3							)							)
							def  lowerCamelCase__				(		self		:List[str]							):







								'''simple docstring'''







								a									=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
								a									=				[floats_list((1, x)							)[0] for x in range(800						, 1400						, 200							)]

								a									=				["""longest""", """max_length""", """do_not_pad"""]
								a									=				[None, 1600, None]
								for max_length, padding in zip(__magic_name__						, __magic_name__							):
									a									=				feat_extract(__magic_name__						, padding=__magic_name__						, max_length=__magic_name__						, return_tensors="""np"""							)
									a									=				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  lowerCamelCase__				(		self		:Union[str, Any]							):







								'''simple docstring'''







								a									=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
								a									=				range(800						, 1400						, 200							)
								a									=				[floats_list((1, x)							)[0] for x in lengths]

								a									=				["""longest""", """max_length""", """do_not_pad"""]
								a									=				[None, 1600, None]

								for max_length, padding in zip(__magic_name__						, __magic_name__							):
									a									=				feat_extract(__magic_name__						, max_length=__magic_name__						, padding=__magic_name__							)
									a									=				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  lowerCamelCase__				(		self		:Optional[Any]							):







								'''simple docstring'''







								a									=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
								a									=				[floats_list((1, x)							)[0] for x in range(800						, 1400						, 200							)]
								a									=				feat_extract(
								    __magic_name__						, truncation=__magic_name__						, max_length=1000						, padding="""max_length"""						, return_tensors="""np"""							)
								a									=				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  lowerCamelCase__				(		self		:Optional[Any]							):







								'''simple docstring'''







								a									=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
								a									=				[floats_list((1, x)							)[0] for x in range(800						, 1400						, 200							)]
								a									=				feat_extract(
								    __magic_name__						, truncation=__magic_name__						, max_length=1000						, padding="""longest"""						, return_tensors="""np"""							)
								a									=				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)							)

								a									=				[floats_list((1, x)							)[0] for x in range(800						, 1400						, 200							)]
								a									=				feat_extract(
								    __magic_name__						, truncation=__magic_name__						, max_length=2000						, padding="""longest"""						, return_tensors="""np"""							)
								a									=				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  lowerCamelCase__				(		self		:Optional[Any]							):







								'''simple docstring'''







								a									=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
								a									=				np.random.rand(100							).astype(np.floataa							)
								a									=				np_speech_inputs.tolist()

								for inputs in [py_speech_inputs, np_speech_inputs]:
									a									=				feature_extractor.pad([{"""input_values""": inputs}]						, return_tensors="""np"""							)
									self.assertTrue(np_processed.input_values.dtype == np.floataa							)
									a									=				feature_extractor.pad([{"""input_values""": inputs}]						, return_tensors="""pt"""							)
									self.assertTrue(pt_processed.input_values.dtype == torch.floataa							)
							def  lowerCamelCase__				(		self		:Union[str, Any]							):







								'''simple docstring'''







								a									=				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
								# create three inputs of length 800, 1000, and 1200
								a									=				[floats_list((1, x)							)[0] for x in range(800						, 1400						, 200							)]
								a									=				[np.asarray(__magic_name__							) for speech_input in speech_inputs]

								# Test feature size
								a									=				feature_extractor(audio_target=__magic_name__						, padding=__magic_name__						, 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
								a									=				feature_extractor(speech_inputs[0]						, return_tensors="""np"""							).input_values
								a									=				feature_extractor(np_speech_inputs[0]						, return_tensors="""np"""							).input_values
								self.assertTrue(np.allclose(__magic_name__						, __magic_name__						, atol=1E-3							)							)

								# Test batched
								a									=				feature_extractor(__magic_name__						, return_tensors="""np"""							).input_values
								a									=				feature_extractor(__magic_name__						, return_tensors="""np"""							).input_values
								for enc_seq_a, enc_seq_a in zip(__magic_name__						, __magic_name__							):
									self.assertTrue(np.allclose(__magic_name__						, __magic_name__						, atol=1E-3							)							)

								# Test 2-D numpy arrays are batched.
								a									=				[floats_list((1, x)							)[0] for x in (800, 800, 800)]
								a									=				np.asarray(__magic_name__							)
								a									=				feature_extractor(__magic_name__						, return_tensors="""np"""							).input_values
								a									=				feature_extractor(__magic_name__						, return_tensors="""np"""							).input_values
								for enc_seq_a, enc_seq_a in zip(__magic_name__						, __magic_name__							):
									self.assertTrue(np.allclose(__magic_name__						, __magic_name__						, atol=1E-3							)							)
							def  lowerCamelCase__				(		self		:Optional[Any]							):







								'''simple docstring'''







								a									=				self.feat_extract_tester.prepare_inputs_for_target()
								a									=				self.feature_extraction_class(**self.feat_extract_dict							)
								a									=				feat_extract.model_input_names[0]

								a									=				BatchFeature({input_name: speech_inputs}							)

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

								a									=				self.feat_extract_tester.prepare_inputs_for_target(equal_length=__magic_name__							)
								a									=				BatchFeature({input_name: speech_inputs}						, tensor_type="""np"""							)

								a									=				processed_features[input_name]

								if len(batch_features_input.shape							) < 3:
									a									=				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  lowerCamelCase__				(		self		:Optional[Any]							):







								'''simple docstring'''







								a									=				self.feat_extract_tester.prepare_inputs_for_target(equal_length=__magic_name__							)
								a									=				self.feature_extraction_class(**self.feat_extract_dict							)
								a									=				feat_extract.model_input_names[0]

								a									=				BatchFeature({input_name: speech_inputs}						, tensor_type="""pt"""							)

								a									=				processed_features[input_name]

								if len(batch_features_input.shape							) < 3:
									a									=				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  lowerCamelCase__				(		self		:Optional[Any]							):







								'''simple docstring'''







								a									=				self.feature_extraction_class(**self.feat_extract_dict							)
								a									=				self.feat_extract_tester.prepare_inputs_for_target()
								a									=				feat_extract.model_input_names[0]

								a									=				BatchFeature({input_name: speech_inputs}							)

								a									=				feat_extract.num_mel_bins  # hack!

								a									=				feat_extract.pad(__magic_name__						, padding="""longest"""						, return_tensors="""np"""							)[input_name]
								a									=				feat_extract.pad(__magic_name__						, 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  lowerCamelCase__				(		self		:Dict							):







								'''simple docstring'''







								a									=				self.feat_extract_dict
								a									=				True
								a									=				self.feature_extraction_class(**__magic_name__							)
								a									=				self.feat_extract_tester.prepare_inputs_for_target()
								a									=				[len(__magic_name__							) for x in speech_inputs]
								a									=				feat_extract.model_input_names[0]

								a									=				BatchFeature({input_name: speech_inputs}							)

								a									=				feat_extract.num_mel_bins  # hack!

								a									=				feat_extract.pad(__magic_name__						, padding="""longest"""						, return_tensors="""np"""							)
								self.assertIn("""attention_mask"""						, __magic_name__							)
								self.assertListEqual(list(processed.attention_mask.shape							)						, list(processed[input_name].shape[:2]							)							)
								self.assertListEqual(processed.attention_mask.sum(-1							).tolist()						, __magic_name__							)
							def  lowerCamelCase__				(		self		:Dict							):







								'''simple docstring'''







								a									=				self.feat_extract_dict
								a									=				True
								a									=				self.feature_extraction_class(**__magic_name__							)
								a									=				self.feat_extract_tester.prepare_inputs_for_target()
								a									=				[len(__magic_name__							) for x in speech_inputs]
								a									=				feat_extract.model_input_names[0]

								a									=				BatchFeature({input_name: speech_inputs}							)
								a									=				min(__magic_name__							)

								a									=				feat_extract.num_mel_bins  # hack!

								a									=				feat_extract.pad(
								    __magic_name__						, padding="""max_length"""						, max_length=__magic_name__						, truncation=__magic_name__						, return_tensors="""np"""							)
								self.assertIn("""attention_mask"""						, __magic_name__							)
								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  lowerCamelCase__				(		self		:str						, __magic_name__		:Tuple							):







								'''simple docstring'''







								from datasets import load_dataset

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

								return [x["array"] for x in speech_samples]
							def  lowerCamelCase__				(		self		:Optional[int]							):







								'''simple docstring'''







								a									=				torch.tensor(
								    [2.3_8_0_4E-0_3, 2.0_7_5_2E-0_3, 1.9_8_3_6E-0_3, 2.1_0_5_7E-0_3, 1.6_1_7_4E-0_3,
								     3.0_5_1_8E-0_4, 9.1_5_5_3E-0_5, 3.3_5_6_9E-0_4, 9.7_6_5_6E-0_4, 1.8_3_1_1E-0_3,
								     2.0_1_4_2E-0_3, 2.1_0_5_7E-0_3, 1.7_3_9_5E-0_3, 4.5_7_7_6E-0_4, -3.9_6_7_3E-0_4,
								     4.5_7_7_6E-0_4, 1.0_0_7_1E-0_3, 9.1_5_5_3E-0_5, 4.8_8_2_8E-0_4, 1.1_5_9_7E-0_3,
								     7.3_2_4_2E-0_4, 9.4_6_0_4E-0_4, 1.8_0_0_5E-0_3, 1.8_3_1_1E-0_3, 8.8_5_0_1E-0_4,
								     4.2_7_2_5E-0_4, 4.8_8_2_8E-0_4, 7.3_2_4_2E-0_4, 1.0_9_8_6E-0_3, 2.1_0_5_7E-0_3]							)
								# fmt: on

								a									=				self._load_datasamples(1							)
								a									=				SpeechTaFeatureExtractor()
								a									=				feature_extractor(__magic_name__						, return_tensors="""pt"""							).input_values
								self.assertEquals(input_values.shape						, (1, 9_3680)							)
								self.assertTrue(torch.allclose(input_values[0, :30]						, __magic_name__						, atol=1E-6							)							)





							def  lowerCamelCase__				(		self		:List[str]							):







								'''simple docstring'''







								a									=				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

								a									=				self._load_datasamples(1							)
								a									=				SpeechTaFeatureExtractor()
								a									=				feature_extractor(audio_target=__magic_name__						, return_tensors="""pt"""							).input_values
								self.assertEquals(input_values.shape						, (1, 366, 80)							)
								self.assertTrue(torch.allclose(input_values[0, 0, :30]						, __magic_name__						, atol=1E-4							)							)







from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class 						__lowerCAmelCase      (      __magic_name__					):
							UpperCamelCase__   	=    42
							UpperCamelCase__   	=    42
							def __init__(		self		:Any						, __magic_name__		:UNetaDModel						, __magic_name__		:ScoreSdeVeScheduler							):







								'''simple docstring'''







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





							@torch.no_grad()
							def __call__(		self		:List[str]						, __magic_name__		:int = 1						, __magic_name__		:int = 2000						, __magic_name__		:Optional[Union[torch.Generator, List[torch.Generator]]] = None						, __magic_name__		:Optional[str] = "pil"						, __magic_name__		:bool = True						, **__magic_name__		:Union[str, Any]						, ):







								'''simple docstring'''







								a									=				self.unet.config.sample_size
								a									=				(batch_size, 3, img_size, img_size)

								a									=				self.unet

								a									=				randn_tensor(__magic_name__						, generator=__magic_name__							) * self.scheduler.init_noise_sigma
								a									=				sample.to(self.device							)

								self.scheduler.set_timesteps(__magic_name__							)
								self.scheduler.set_sigmas(__magic_name__							)

								for i, t in enumerate(self.progress_bar(self.scheduler.timesteps							)							):
									a									=				self.scheduler.sigmas[i] * torch.ones(shape[0]						, device=self.device							)

									# correction step
									for _ in range(self.scheduler.config.correct_steps							):
										a									=				self.unet(__magic_name__						, __magic_name__							).sample
										a									=				self.scheduler.step_correct(__magic_name__						, __magic_name__						, generator=__magic_name__							).prev_sample

									# prediction step
									a									=				model(__magic_name__						, __magic_name__							).sample
									a									=				self.scheduler.step_pred(__magic_name__						, __magic_name__						, __magic_name__						, generator=__magic_name__							)

									a							,					a									=				output.prev_sample, output.prev_sample_mean

								a									=				sample_mean.clamp(0						, 1							)
								a									=				sample.cpu().permute(0						, 2						, 3						, 1							).numpy()
								if output_type == "pil":
									a									=				self.numpy_to_pil(__magic_name__							)

								if not return_dict:
									return (sample,)

								return ImagePipelineOutput(images=__magic_name__							)

