






import itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
							import torch

if is_datasets_available():
							from datasets import load_dataset

A__      :     Dict       =					random.Random()






def     a     (		lowerCamelCase_						,  lowerCamelCase_=1.0						,  lowerCamelCase_=None						,  lowerCamelCase_=None							):






				'''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  _UpperCAmelCase   (			unittest.TestCase      ):

				"""simple docstring"""




				def __init__(	self		:       Dict,    lowerCamelCase		:       List[Any],    lowerCamelCase		:       str=7,    lowerCamelCase		:       int=400,    lowerCamelCase		:       Optional[Any]=2_000,    lowerCamelCase		:       List[Any]=2_048,    lowerCamelCase		:       str=128,    lowerCamelCase		:       Union[str, Any]=1,    lowerCamelCase		:       int=512,    lowerCamelCase		:       Any=30,    lowerCamelCase		:       int=44_100,    ):

								'''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__  					=     spectrogram_length
								lowercase__  					=     feature_size
								lowercase__  					=     num_audio_channels
								lowercase__  					=     hop_length
								lowercase__  					=     chunk_length
								lowercase__  					=     sampling_rate




				def 							lowercase__  (	self		:       List[str]    ):

								'''simple docstring'''
								return {
								    "spectrogram_length": self.spectrogram_length,
								    "feature_size": self.feature_size,
								    "num_audio_channels": self.num_audio_channels,
								    "hop_length": self.hop_length,
								    "chunk_length": self.chunk_length,
								    "sampling_rate": self.sampling_rate,
								}




				def 							lowercase__  (	self		:       str,    lowerCamelCase		:       int=False,    lowerCamelCase		:       Any=False    ):

								'''simple docstring'''
								def _flatten(lowerCamelCase		:       Optional[Any]    ):
												return list(itertools.chain(*lowerCamelCase    )    )

								if equal_length:
												lowercase__  					=     [floats_list((self.max_seq_length, self.feature_size)    ) for _ in range(self.batch_size    )]
								else:
												# make sure that inputs increase in size
												lowercase__  					=     [
												    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  _UpperCAmelCase   (			A__						,unittest.TestCase      ):

				"""simple docstring"""




				lowercase__											=			TvltFeatureExtractor




				def 							lowercase__  (	self		:       Any    ):

								'''simple docstring'''
								lowercase__  					=     TvltFeatureExtractionTester(self    )




				def 							lowercase__  (	self		:       Optional[int]    ):

								'''simple docstring'''
								lowercase__  					=     self.feature_extraction_class(**self.feat_extract_dict    )
								self.assertTrue(hasattr(lowerCamelCase,    '''spectrogram_length'''    )    )
								self.assertTrue(hasattr(lowerCamelCase,    '''feature_size'''    )    )
								self.assertTrue(hasattr(lowerCamelCase,    '''num_audio_channels'''    )    )
								self.assertTrue(hasattr(lowerCamelCase,    '''hop_length'''    )    )
								self.assertTrue(hasattr(lowerCamelCase,    '''chunk_length'''    )    )
								self.assertTrue(hasattr(lowerCamelCase,    '''sampling_rate'''    )    )




				def 							lowercase__  (	self		:       List[str]    ):

								'''simple docstring'''
								lowercase__  					=     self.feature_extraction_class(**self.feat_extract_dict    )

								with tempfile.TemporaryDirectory() as tmpdirname:
												lowercase__  					=     feat_extract_first.save_pretrained(lowerCamelCase    )[0]
												check_json_file_has_correct_format(lowerCamelCase    )
												lowercase__  					=     self.feature_extraction_class.from_pretrained(lowerCamelCase    )

								lowercase__  					=     feat_extract_first.to_dict()
								lowercase__  					=     feat_extract_second.to_dict()
								lowercase__  					=     dict_first.pop('''mel_filters'''    )
								lowercase__  					=     dict_second.pop('''mel_filters'''    )
								self.assertTrue(np.allclose(lowerCamelCase,    lowerCamelCase    )    )
								self.assertEqual(lowerCamelCase,    lowerCamelCase    )




				def 							lowercase__  (	self		:       Optional[int]    ):

								'''simple docstring'''
								lowercase__  					=     self.feature_extraction_class(**self.feat_extract_dict    )

								with tempfile.TemporaryDirectory() as tmpdirname:
												lowercase__  					=     os.path.join(lowerCamelCase,    '''feat_extract.json'''    )
												feat_extract_first.to_json_file(lowerCamelCase    )
												lowercase__  					=     self.feature_extraction_class.from_json_file(lowerCamelCase    )

								lowercase__  					=     feat_extract_first.to_dict()
								lowercase__  					=     feat_extract_second.to_dict()
								lowercase__  					=     dict_first.pop('''mel_filters'''    )
								lowercase__  					=     dict_second.pop('''mel_filters'''    )
								self.assertTrue(np.allclose(lowerCamelCase,    lowerCamelCase    )    )
								self.assertEqual(lowerCamelCase,    lowerCamelCase    )




				def 							lowercase__  (	self		:       Union[str, Any]    ):

								'''simple docstring'''
								# Initialize feature_extractor
								lowercase__  					=     self.feature_extraction_class(**self.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__  					=     feature_extractor(np_speech_inputs[0],    return_tensors='''np''',    sampling_rate=44_100    ).audio_values

								self.assertTrue(encoded_audios.ndim == 4    )
								self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size    )
								self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length    )
								self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels    )

								# Test batched
								lowercase__  					=     feature_extractor(lowerCamelCase,    return_tensors='''np''',    sampling_rate=44_100    ).audio_values

								self.assertTrue(encoded_audios.ndim == 4    )
								self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size    )
								self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length    )
								self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels    )

								# Test audio masking
								lowercase__  					=     feature_extractor(
								    lowerCamelCase,    return_tensors='''np''',    sampling_rate=44_100,    mask_audio=lowerCamelCase    ).audio_values

								self.assertTrue(encoded_audios.ndim == 4    )
								self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size    )
								self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length    )
								self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels    )

								# Test 2-D numpy arrays are batched.
								lowercase__  					=     [floats_list((1, x)    )[0] for x in (800, 800, 800)]
								lowercase__  					=     np.asarray(lowerCamelCase    )
								lowercase__  					=     feature_extractor(lowerCamelCase,    return_tensors='''np''',    sampling_rate=44_100    ).audio_values
								self.assertTrue(encoded_audios.ndim == 4    )
								self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size    )
								self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length    )
								self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels    )




				def 							lowercase__  (	self		:       Any,    lowerCamelCase		:       Dict    ):

								'''simple docstring'''
								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]




				def 							lowercase__  (	self		:       Tuple    ):

								'''simple docstring'''
								lowercase__  					=     self._load_datasamples(1    )
								lowercase__  					=     TvltFeatureExtractor()
								lowercase__  					=     feature_extractor(lowerCamelCase,    return_tensors='''pt'''    ).audio_values

								self.assertEquals(audio_values.shape,    (1, 1, 192, 128)    )

								lowercase__  					=     torch.tensor([[-0.3032, -0.2708], [-0.4434, -0.4007]]    )
								self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2],    lowerCamelCase,    atol=1E-4    )    )












from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


A__      :     Union[str, Any]       =					transforms.Compose(
    [
        transforms.Resize((2_56, 2_56)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)






def     a     (		lowerCamelCase_							):






				'''simple docstring'''
				if isinstance(lowerCamelCase_						,  torch.Tensor							):
								return image
				elif isinstance(lowerCamelCase_						,  PIL.Image.Image							):
								lowercase__  					=     [image]

				lowercase__  					=     [trans(img.convert('''RGB'''							)							) for img in image]
				lowercase__  					=     torch.stack(lowerCamelCase_							)
				return image




class  _UpperCAmelCase   (			A__      ):

				"""simple docstring"""




				def __init__(	self		:       Tuple,    lowerCamelCase		:       Dict,    lowerCamelCase		:       str    ):

								'''simple docstring'''
								super().__init__()

								# make sure scheduler can always be converted to DDIM
								lowercase__  					=     DDIMScheduler.from_config(scheduler.config    )

								self.register_modules(unet=lowerCamelCase,    scheduler=lowerCamelCase    )




				def 							lowercase__  (	self		:       int,    lowerCamelCase		:       Optional[int]    ):

								'''simple docstring'''
								if strength < 0 or strength > 1:
												raise ValueError(F"""The value of strength should in [0.0, 1.0] but is {strength}"""    )




				def 							lowercase__  (	self		:       Optional[int],    lowerCamelCase		:       Union[str, Any],    lowerCamelCase		:       Dict,    lowerCamelCase		:       List[str]    ):

								'''simple docstring'''
								# get the original timestep using init_timestep
								lowercase__  					=     min(int(num_inference_steps * strength    ),    lowerCamelCase    )

								lowercase__  					=     max(num_inference_steps - init_timestep,    0    )
								lowercase__  					=     self.scheduler.timesteps[t_start:]

								return timesteps, num_inference_steps - t_start




				def 							lowercase__  (	self		:       Optional[Any],    lowerCamelCase		:       List[Any],    lowerCamelCase		:       int,    lowerCamelCase		:       Dict,    lowerCamelCase		:       Any,    lowerCamelCase		:       Optional[Any],    lowerCamelCase		:       Any=None    ):

								'''simple docstring'''
								if not isinstance(lowerCamelCase,    (torch.Tensor, PIL.Image.Image, list)    ):
												raise ValueError(
												    F"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(lowerCamelCase    )}"""    )

								lowercase__  					=     image.to(device=lowerCamelCase,    dtype=lowerCamelCase    )

								if isinstance(lowerCamelCase,    lowerCamelCase    ) and len(lowerCamelCase    ) != batch_size:
												raise ValueError(
												    F"""You have passed a list of generators of length {len(lowerCamelCase    )}, but requested an effective batch"""
												    F""" size of {batch_size}. Make sure the batch size matches the length of the generators."""    )

								lowercase__  					=     init_latents.shape
								lowercase__  					=     randn_tensor(lowerCamelCase,    generator=lowerCamelCase,    device=lowerCamelCase,    dtype=lowerCamelCase    )

								# get latents
								print('''add noise to latents at timestep''',    lowerCamelCase    )
								lowercase__  					=     self.scheduler.add_noise(lowerCamelCase,    lowerCamelCase,    lowerCamelCase    )
								lowercase__  					=     init_latents

								return latents




				@torch.no_grad()
				def __call__(	self		:       Dict,    lowerCamelCase		:       Union[torch.FloatTensor, PIL.Image.Image] = None,    lowerCamelCase		:       float = 0.8,    lowerCamelCase		:       int = 1,    lowerCamelCase		:       Optional[Union[torch.Generator, List[torch.Generator]]] = None,    lowerCamelCase		:       float = 0.0,    lowerCamelCase		:       int = 50,    lowerCamelCase		:       Optional[bool] = None,    lowerCamelCase		:       Optional[str] = "pil",    lowerCamelCase		:       bool = True,    ):

								'''simple docstring'''
								self.check_inputs(lowerCamelCase    )

								# 2. Preprocess image
								lowercase__  					=     preprocess(lowerCamelCase    )

								# 3. set timesteps
								self.scheduler.set_timesteps(lowerCamelCase,    device=self.device    )
								lowercase__					,		lowercase__  					=     self.get_timesteps(lowerCamelCase,    lowerCamelCase,    self.device    )
								lowercase__  					=     timesteps[:1].repeat(lowerCamelCase    )

								# 4. Prepare latent variables
								lowercase__  					=     self.prepare_latents(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    self.unet.dtype,    self.device,    lowerCamelCase    )
								lowercase__  					=     latents

								# 5. Denoising loop
								for t in self.progress_bar(lowerCamelCase    ):
												# 1. predict noise model_output
												lowercase__  					=     self.unet(lowerCamelCase,    lowerCamelCase    ).sample

												# 2. predict previous mean of image x_t-1 and add variance depending on eta
												# eta corresponds to η in paper and should be between [0, 1]
												# do x_t -> x_t-1
												lowercase__  					=     self.scheduler.step(
												    lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    eta=lowerCamelCase,    use_clipped_model_output=lowerCamelCase,    generator=lowerCamelCase,    ).prev_sample

								lowercase__  					=     (image / 2 + 0.5).clamp(0,    1    )
								lowercase__  					=     image.cpu().permute(0,    2,    3,    1    ).numpy()
								if output_type == "pil":
												lowercase__  					=     self.numpy_to_pil(lowerCamelCase    )

								if not return_dict:
												return (image, latent_timestep.item())

								return ImagePipelineOutput(images=lowerCamelCase    )




