






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


UpperCAmelCase__				     = random.Random()

if is_torch_available():
			import torch







def 		_A(       UpperCamelCase__							:			Tuple				,       UpperCamelCase__							:			Optional[int]=1.0				,       UpperCamelCase__							:			str=None				,       UpperCamelCase__							:			Union[str, Any]=None				)    -> Tuple:
							'''simple docstring'''


							if rng is None:
														__lowercase      =    global_rng

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

							return values







class a (     unittest.TestCase  ):
						"""simple docstring"""

						def __init__( self :     str				,							lowerCamelCase__ :     Optional[Any]				,							lowerCamelCase__ :     Any=7				,							lowerCamelCase__ :     List[Any]=400				,							lowerCamelCase__ :     List[Any]=2_000				,							lowerCamelCase__ :     Optional[int]=1				,							lowerCamelCase__ :     Optional[int]=0.0				,							lowerCamelCase__ :     str=16_000				,							lowerCamelCase__ :     int=True				,							lowerCamelCase__ :     List[str]=True				,							)    ->						Optional[Any]:
													"""simple docstring"""


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






						def     UpperCAmelCase_      ( self :     Optional[Any]				)    ->						int:
													"""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 :     int				,							lowerCamelCase__ :     Tuple=False				,							lowerCamelCase__ :     Union[str, Any]=False				)    ->						Optional[Any]:
													"""simple docstring"""


													def _flatten(lowerCamelCase__ :     Any				):
																				return list(itertools.chain(*lowerCamelCase_				)				)

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

													return speech_inputs







@require_torch
@require_torchaudio
class a (     a__       ,						unittest.TestCase  ):
						"""simple docstring"""

						UpperCamelCase_	:       Optional[int]       	=		ASTFeatureExtractor






						def     UpperCAmelCase_      ( self :     List[Any]				)    ->						Optional[int]:
													"""simple docstring"""


													__lowercase      =    ASTFeatureExtractionTester(self				)






						def     UpperCAmelCase_      ( self :     Optional[int]				)    ->						str:
													"""simple docstring"""


													__lowercase      =    self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()				)
													# create three inputs of length 800, 1000, and 1200
													__lowercase      =    [floats_list((1, x)				)[0] for x in range(800				,							1_400				,							200				)]
													__lowercase      =    [np.asarray(lowerCamelCase_				) for speech_input in speech_inputs]

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

													# Test batched
													__lowercase      =    feat_extract(lowerCamelCase_				,							padding=lowerCamelCase_				,							return_tensors='''np'''				).input_values
													__lowercase      =    feat_extract(lowerCamelCase_				,							padding=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.
													__lowercase      =    [floats_list((1, x)				)[0] for x in (800, 800, 800)]
													__lowercase      =    np.asarray(lowerCamelCase_				)
													__lowercase      =    feat_extract(lowerCamelCase_				,							return_tensors='''np'''				).input_values
													__lowercase      =    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				)				)






						@require_torch
						def     UpperCAmelCase_      ( self :     Dict				)    ->						List[str]:
													"""simple docstring"""


													import torch

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

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






						def     UpperCAmelCase_      ( self :     Tuple				,							lowerCamelCase__ :     List[str]				)    ->						List[Any]:
													"""simple docstring"""


													from datasets import load_dataset

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

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






						@require_torch
						def     UpperCAmelCase_      ( self :     List[str]				)    ->						Any:
													"""simple docstring"""


													__lowercase      =    torch.tensor(
													    [-0.9_8_9_4, -1.2_7_7_6, -0.9_0_6_6, -1.2_7_7_6, -0.9_3_4_9, -1.2_6_0_9, -1.0_3_8_6, -1.2_7_7_6,
													     -1.1_5_6_1, -1.2_7_7_6, -1.2_0_5_2, -1.2_7_2_3, -1.2_1_9_0, -1.2_1_3_2, -1.2_7_7_6, -1.1_1_3_3,
													     -1.1_9_5_3, -1.1_3_4_3, -1.1_5_8_4, -1.2_2_0_3, -1.1_7_7_0, -1.2_4_7_4, -1.2_3_8_1, -1.1_9_3_6,
													     -0.9_2_7_0, -0.8_3_1_7, -0.8_0_4_9, -0.7_7_0_6, -0.7_5_6_5, -0.7_8_6_9]				)
													# fmt: on

													__lowercase      =    self._load_datasamples(1				)
													__lowercase      =    ASTFeatureExtractor()
													__lowercase      =    feature_extractor(lowerCamelCase_				,							return_tensors='''pt'''				).input_values
													self.assertEquals(input_values.shape				,							(1, 1_024, 128)				)
													self.assertTrue(torch.allclose(input_values[0, 0, :30]				,							lowerCamelCase_				,							atol=1e-4				)				)












from typing import List, Optional, Tuple, Union

import torch

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







class a (     __SCREAMING_SNAKE_CASE  ):
						"""simple docstring"""

						UpperCamelCase_	:       UNetaDModel
						UpperCamelCase_	:       KarrasVeScheduler






						def __init__( self :     Tuple				,							lowerCamelCase__ :     UNetaDModel				,							lowerCamelCase__ :     KarrasVeScheduler				)    ->						Optional[Any]:
													"""simple docstring"""


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






						@torch.no_grad()
						def __call__( self :     str				,							lowerCamelCase__ :     int = 1				,							lowerCamelCase__ :     int = 50				,							lowerCamelCase__ :     Optional[Union[torch.Generator, List[torch.Generator]]] = None				,							lowerCamelCase__ :     Optional[str] = "pil"				,							lowerCamelCase__ :     bool = True				,							**lowerCamelCase__ :     int				,							)    ->						Union[Tuple, ImagePipelineOutput]:
													"""simple docstring"""


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

													__lowercase      =    self.unet

													# sample x_0 ~ N(0, sigma_0^2 * I)
													__lowercase      =    randn_tensor(lowerCamelCase__				,							generator=lowerCamelCase__				,							device=self.device				) * self.scheduler.init_noise_sigma

													self.scheduler.set_timesteps(lowerCamelCase__				)

													for t in self.progress_bar(self.scheduler.timesteps				):
																				# here sigma_t == t_i from the paper
																				__lowercase      =    self.scheduler.schedule[t]
																				__lowercase      =    self.scheduler.schedule[t - 1] if t > 0 else 0

																				# 1. Select temporarily increased noise level sigma_hat
																				# 2. Add new noise to move from sample_i to sample_hat
																				__lowercase     ,     __lowercase      =    self.scheduler.add_noise_to_input(lowerCamelCase__				,							lowerCamelCase__				,							generator=lowerCamelCase__				)

																				# 3. Predict the noise residual given the noise magnitude `sigma_hat`
																				# The model inputs and output are adjusted by following eq. (213) in [1].
																				__lowercase      =    (sigma_hat / 2) * model((sample_hat + 1) / 2				,							sigma_hat / 2				).sample

																				# 4. Evaluate dx/dt at sigma_hat
																				# 5. Take Euler step from sigma to sigma_prev
																				__lowercase      =    self.scheduler.step(lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				)

																				if sigma_prev != 0:
																											# 6. Apply 2nd order correction
																											# The model inputs and output are adjusted by following eq. (213) in [1].
																											__lowercase      =    (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2				,							sigma_prev / 2				).sample
																											__lowercase      =    self.scheduler.step_correct(
																											    lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				,							step_output.prev_sample				,							step_output['''derivative''']				,							)
																				__lowercase      =    step_output.prev_sample

													__lowercase      =    (sample / 2 + 0.5).clamp(0				,							1				)
													__lowercase      =    sample.cpu().permute(0				,							2				,							3				,							1				).numpy()
													if output_type == "pil":
																				__lowercase      =    self.numpy_to_pil(lowerCamelCase__				)

													if not return_dict:
																				return (image,)

													return ImagePipelineOutput(images=lowerCamelCase__				)






