






import itertools
import random
import unittest

import numpy as np

from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


_a      	=  random.Random()

if is_torch_available():
		import torch


def __A    (      __lowerCAmelCase ,		__lowerCAmelCase=1.0 ,		__lowerCAmelCase=None ,		__lowerCAmelCase=None	)->  Union[str, Any]:





						"""simple docstring"""



						if rng is None:
												_UpperCAmelCase									=			global_rng

						_UpperCAmelCase									=			[]
						for batch_idx in range(shape[0]	):
												values.append([]	)
												for _ in range(shape[1]	):
																		values[-1].append(rng.random() * scale	)

						return values





class __lowerCamelCase (      unittest.TestCase):





	"""simple docstring"""






	def __init__(							self       ,    UpperCAmelCase       ,    UpperCAmelCase=7       ,    UpperCAmelCase=400       ,    UpperCAmelCase=2000       ,    UpperCAmelCase=1       ,    UpperCAmelCase=0.0       ,    UpperCAmelCase=1_6000       ,    UpperCAmelCase=True       ,    UpperCAmelCase=True       ,    ):
							"""simple docstring"""







							_UpperCAmelCase									=			parent
							_UpperCAmelCase									=			batch_size
							_UpperCAmelCase									=			min_seq_length
							_UpperCAmelCase									=			max_seq_length
							_UpperCAmelCase									=			(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
							_UpperCAmelCase									=			feature_size
							_UpperCAmelCase									=			padding_value
							_UpperCAmelCase									=			sampling_rate
							_UpperCAmelCase									=			return_attention_mask
							_UpperCAmelCase									=			do_normalize

	def        UpperCamelCase    (							self    ):
							"""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,
							    "do_normalize": self.do_normalize,
							}

	def        UpperCamelCase    (							self       ,    UpperCAmelCase=False       ,    UpperCAmelCase=False    ):
							"""simple docstring"""







							def _flatten(UpperCAmelCase    ):
													return list(itertools.chain(*UpperCAmelCase    )    )

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

							return speech_inputs





@require_torch
@require_torchaudio
class __lowerCamelCase (      snake_case__     ,       unittest.TestCase):





	"""simple docstring"""






	UpperCamelCase__								=      ASTFeatureExtractor

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			ASTFeatureExtractionTester(self    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







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

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

							# Test batched
							_UpperCAmelCase									=			feat_extract(UpperCAmelCase       ,    padding=UpperCAmelCase       ,    return_tensors='np'    ).input_values
							_UpperCAmelCase									=			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    )    )

							# Test 2-D numpy arrays are batched.
							_UpperCAmelCase									=			[floats_list((1, x)    )[0] for x in (800, 800, 800)]
							_UpperCAmelCase									=			np.asarray(UpperCAmelCase    )
							_UpperCAmelCase									=			feat_extract(UpperCAmelCase       ,    return_tensors='np'    ).input_values
							_UpperCAmelCase									=			feat_extract(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    )    )

	@require_torch
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							import torch

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

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

	def        UpperCamelCase    (							self       ,    UpperCAmelCase    ):
							"""simple docstring"""







							from datasets import load_dataset

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

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

	@require_torch
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			torch.tensor(
							    [-0.98_94, -1.27_76, -0.90_66, -1.27_76, -0.93_49, -1.26_09, -1.03_86, -1.27_76,
							     -1.15_61, -1.27_76, -1.20_52, -1.27_23, -1.21_90, -1.21_32, -1.27_76, -1.11_33,
							     -1.19_53, -1.13_43, -1.15_84, -1.22_03, -1.17_70, -1.24_74, -1.23_81, -1.19_36,
							     -0.92_70, -0.83_17, -0.80_49, -0.77_06, -0.75_65, -0.78_69]    )
							# fmt: on

							_UpperCAmelCase									=			self._load_datasamples(1    )
							_UpperCAmelCase									=			ASTFeatureExtractor()
							_UpperCAmelCase									=			feature_extractor(UpperCAmelCase       ,    return_tensors='pt'    ).input_values
							self.assertEquals(input_values.shape       ,    (1, 1024, 128)    )
							self.assertTrue(torch.allclose(input_values[0, 0, :30]       ,    UpperCAmelCase       ,    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      A__							(     __magic_name__	):
				lowercase			    =  42
				lowercase			    =  42






				def __init__(						self					:      Any    ,     a					:      UNetaDModel    ,     a					:      ScoreSdeVeScheduler   ):






							'''simple docstring'''







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






				@torch.no_grad()
				def __call__(						self					:      List[str]    ,     a					:      int = 1    ,     a					:      int = 2_000    ,     a					:      Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,     a					:      Optional[str] = "pil"    ,     a					:      bool = True    ,     **a					:      int    ,     ):






							'''simple docstring'''







							lowerCAmelCase__      :      List[str]         =		self.unet.config.sample_size
							lowerCAmelCase__      :      Union[str, Any]         =		(batch_size, 3, img_size, img_size)

							lowerCAmelCase__      :      Tuple         =		self.unet

							lowerCAmelCase__      :      Optional[Any]         =		randn_tensor(a    ,     generator=a   ) * self.scheduler.init_noise_sigma
							lowerCAmelCase__      :      int         =		sample.to(self.device   )

							self.scheduler.set_timesteps(a   )
							self.scheduler.set_sigmas(a   )

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

										# correction step
										for _ in range(self.scheduler.config.correct_steps   ):
													lowerCAmelCase__      :      Optional[Any]         =		self.unet(a    ,     a   ).sample
													lowerCAmelCase__      :      Dict         =		self.scheduler.step_correct(a    ,     a    ,     generator=a   ).prev_sample

										# prediction step
										lowerCAmelCase__      :      Optional[int]         =		model(a    ,     a   ).sample
										lowerCAmelCase__      :      Optional[Any]         =		self.scheduler.step_pred(a    ,     a    ,     a    ,     generator=a   )

										lowerCAmelCase__      ,				lowerCAmelCase__      :      str         =		output.prev_sample, output.prev_sample_mean

							lowerCAmelCase__      :      Any         =		sample_mean.clamp(0    ,     1   )
							lowerCAmelCase__      :      List[str]         =		sample.cpu().permute(0    ,     2    ,     3    ,     1   ).numpy()
							if output_type == "pil":
										lowerCAmelCase__      :      int         =		self.numpy_to_pil(a   )

							if not return_dict:
										return (sample,)

							return ImagePipelineOutput(images=a   )