






"""simple docstring"""







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			:    Optional[int]				 =		random.Random()





def 						lowercase__(A ,		A=1.0 ,		A=None ,		A=None						)    ->Optional[Any]:




						"""simple docstring"""



						if rng is None:
												lowercase__    :				int=	global_rng

						lowercase__    :				int=	[]
						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	,  snake_case__	,  snake_case__=7	,  snake_case__=400	,  snake_case__=2000	,  snake_case__=2048	,  snake_case__=128	,  snake_case__=1	,  snake_case__=512	,  snake_case__=30	,  snake_case__=44100	,  ):
												'''simple docstring'''
												lowercase__    :				int=	parent
												lowercase__    :				Tuple=	batch_size
												lowercase__    :				str=	min_seq_length
												lowercase__    :				Any=	max_seq_length
												lowercase__    :				List[str]=	(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
												lowercase__    :				Dict=	spectrogram_length
												lowercase__    :				Optional[Any]=	feature_size
												lowercase__    :				Optional[Any]=	num_audio_channels
												lowercase__    :				Tuple=	hop_length
												lowercase__    :				Dict=	chunk_length
												lowercase__    :				List[str]=	sampling_rate




						def 		UpperCAmelCase_	(       self      ):
												'''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 		UpperCAmelCase_	(       self	,  snake_case__=False	,  snake_case__=False      ):
												'''simple docstring'''
												def _flatten(snake_case__      ):
																		return list(itertools.chain(*snake_case__      )      )

												if equal_length:
																		lowercase__    :				Any=	[floats_list((self.max_seq_length, self.feature_size)      ) for _ in range(self.batch_size      )]
												else:
																		# make sure that inputs increase in size
																		lowercase__    :				str=	[
																		    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__    :				int=	[np.asarray(snake_case__      ) for x in speech_inputs]
												return speech_inputs


@require_torch
@require_torchaudio
class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__  ,  unittest.TestCase							):




						"""simple docstring"""




						__lowerCamelCase    	= TvltFeatureExtractor




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__    :				Any=	TvltFeatureExtractionTester(self      )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__    :				Optional[int]=	self.feature_extraction_class(**self.feat_extract_dict      )
												self.assertTrue(hasattr(snake_case__	,  "spectrogram_length"      )      )
												self.assertTrue(hasattr(snake_case__	,  "feature_size"      )      )
												self.assertTrue(hasattr(snake_case__	,  "num_audio_channels"      )      )
												self.assertTrue(hasattr(snake_case__	,  "hop_length"      )      )
												self.assertTrue(hasattr(snake_case__	,  "chunk_length"      )      )
												self.assertTrue(hasattr(snake_case__	,  "sampling_rate"      )      )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__    :				Any=	self.feature_extraction_class(**self.feat_extract_dict      )

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

												lowercase__    :				Any=	feat_extract_first.to_dict()
												lowercase__    :				List[Any]=	feat_extract_second.to_dict()
												lowercase__    :				List[Any]=	dict_first.pop("mel_filters"      )
												lowercase__    :				Any=	dict_second.pop("mel_filters"      )
												self.assertTrue(np.allclose(snake_case__	,  snake_case__      )      )
												self.assertEqual(snake_case__	,  snake_case__      )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__    :				Tuple=	self.feature_extraction_class(**self.feat_extract_dict      )

												with tempfile.TemporaryDirectory() as tmpdirname:
																		lowercase__    :				List[Any]=	os.path.join(snake_case__	,  "feat_extract.json"      )
																		feat_extract_first.to_json_file(snake_case__      )
																		lowercase__    :				Optional[int]=	self.feature_extraction_class.from_json_file(snake_case__      )

												lowercase__    :				List[str]=	feat_extract_first.to_dict()
												lowercase__    :				List[str]=	feat_extract_second.to_dict()
												lowercase__    :				str=	dict_first.pop("mel_filters"      )
												lowercase__    :				Optional[int]=	dict_second.pop("mel_filters"      )
												self.assertTrue(np.allclose(snake_case__	,  snake_case__      )      )
												self.assertEqual(snake_case__	,  snake_case__      )




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												# Initialize feature_extractor
												lowercase__    :				int=	self.feature_extraction_class(**self.feat_extract_dict      )

												# create three inputs of length 800, 1000, and 1200
												lowercase__    :				List[Any]=	[floats_list((1, x)      )[0] for x in range(800	,  1400	,  200      )]
												lowercase__    :				Optional[int]=	[np.asarray(snake_case__      ) for speech_input in speech_inputs]

												# Test not batched input
												lowercase__    :				Tuple=	feature_extractor(np_speech_inputs[0]	,  return_tensors="np"	,  sampling_rate=44100      ).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__    :				Optional[int]=	feature_extractor(snake_case__	,  return_tensors="np"	,  sampling_rate=44100      ).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__    :				List[str]=	feature_extractor(
												    snake_case__	,  return_tensors="np"	,  sampling_rate=44100	,  mask_audio=snake_case__      ).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__    :				Dict=	[floats_list((1, x)      )[0] for x in (800, 800, 800)]
												lowercase__    :				Optional[Any]=	np.asarray(snake_case__      )
												lowercase__    :				List[str]=	feature_extractor(snake_case__	,  return_tensors="np"	,  sampling_rate=44100      ).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 		UpperCAmelCase_	(       self	,  snake_case__      ):
												'''simple docstring'''
												lowercase__    :				Optional[int]=	load_dataset("hf-internal-testing/librispeech_asr_dummy"	,  "clean"	,  split="validation"      )
												# automatic decoding with librispeech
												lowercase__    :				Optional[Any]=	ds.sort("id"      ).select(range(snake_case__      )      )[:num_samples]["audio"]

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




						def 		UpperCAmelCase_	(       self      ):
												'''simple docstring'''
												lowercase__    :				Dict=	self._load_datasamples(1      )
												lowercase__    :				Any=	TvltFeatureExtractor()
												lowercase__    :				Any=	feature_extractor(snake_case__	,  return_tensors="pt"      ).audio_values

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

												lowercase__    :				Optional[int]=	torch.tensor([[-0.30_32, -0.27_08], [-0.44_34, -0.40_07]]      )
												self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]	,  snake_case__	,  atol=1e-4      )      )














"""simple docstring"""







from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class    __UpperCAmelCase(       SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						def __init__(       self	,  snake_case__	,  snake_case__	,  snake_case__	,  snake_case__ = None	,  ):
												'''simple docstring'''
												super().__init__()
												self.register_modules(transformer=snake_case__	,  vae=snake_case__	,  scheduler=snake_case__      )

												# create a imagenet -> id dictionary for easier use
												lowercase__    :				int=	{}
												if idalabel is not None:
																		for key, value in idalabel.items():
																								for label in value.split(","      ):
																														lowercase__    :				Tuple=	int(snake_case__      )
																		lowercase__    :				Union[str, Any]=	dict(sorted(self.labels.items()      )      )




						def 		UpperCAmelCase_	(       self	,  snake_case__      ):
												'''simple docstring'''

												if not isinstance(snake_case__	,  snake_case__      ):
																		lowercase__    :				List[Any]=	list(snake_case__      )

												for l in label:
																		if l not in self.labels:
																								raise ValueError(
																								    F'''{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}.'''      )

												return [self.labels[l] for l in label]




						@torch.no_grad()
						def __call__(       self	,  snake_case__	,  snake_case__ = 4.0	,  snake_case__ = None	,  snake_case__ = 50	,  snake_case__ = "pil"	,  snake_case__ = True	,  ):
												'''simple docstring'''
												lowercase__    :				List[Any]=	len(snake_case__      )
												lowercase__    :				Optional[int]=	self.transformer.config.sample_size
												lowercase__    :				List[str]=	self.transformer.config.in_channels

												lowercase__    :				Any=	randn_tensor(
												    shape=(batch_size, latent_channels, latent_size, latent_size)	,  generator=snake_case__	,  device=self.device	,  dtype=self.transformer.dtype	,  )
												lowercase__    :				Any=	torch.cat([latents] * 2      ) if guidance_scale > 1 else latents

												lowercase__    :				Tuple=	torch.tensor(snake_case__	,  device=self.device      ).reshape(-1      )
												lowercase__    :				Any=	torch.tensor([1000] * batch_size	,  device=self.device      )
												lowercase__    :				Tuple=	torch.cat([class_labels, class_null]	,  0      ) if guidance_scale > 1 else class_labels

												# set step values
												self.scheduler.set_timesteps(snake_case__      )

												for t in self.progress_bar(self.scheduler.timesteps      ):
																		if guidance_scale > 1:
																								lowercase__    :				List[str]=	latent_model_input[: len(snake_case__      ) // 2]
																								lowercase__    :				int=	torch.cat([half, half]	,  dim=0      )
																		lowercase__    :				Union[str, Any]=	self.scheduler.scale_model_input(snake_case__	,  snake_case__      )

																		lowercase__    :				Optional[int]=	t
																		if not torch.is_tensor(snake_case__      ):
																								# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
																								# This would be a good case for the `match` statement (Python 3.10+)
																								lowercase__    :				List[str]=	latent_model_input.device.type == "mps"
																								if isinstance(snake_case__	,  snake_case__      ):
																														lowercase__    :				int=	torch.floataa if is_mps else torch.floataa
																								else:
																														lowercase__    :				Dict=	torch.intaa if is_mps else torch.intaa
																								lowercase__    :				Tuple=	torch.tensor([timesteps]	,  dtype=snake_case__	,  device=latent_model_input.device      )
																		elif len(timesteps.shape      ) == 0:
																								lowercase__    :				Dict=	timesteps[None].to(latent_model_input.device      )
																		# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
																		lowercase__    :				int=	timesteps.expand(latent_model_input.shape[0]      )
																		# predict noise model_output
																		lowercase__    :				Union[str, Any]=	self.transformer(
																		    snake_case__	,  timestep=snake_case__	,  class_labels=snake_case__      ).sample

																		# perform guidance
																		if guidance_scale > 1:
																								lowercase__,     lowercase__    :				Tuple=	noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
																								lowercase__,     lowercase__    :				Union[str, Any]=	torch.split(snake_case__	,  len(snake_case__      ) // 2	,  dim=0      )

																								lowercase__    :				str=	uncond_eps + guidance_scale * (cond_eps - uncond_eps)
																								lowercase__    :				Dict=	torch.cat([half_eps, half_eps]	,  dim=0      )

																								lowercase__    :				Optional[int]=	torch.cat([eps, rest]	,  dim=1      )

																		# learned sigma
																		if self.transformer.config.out_channels // 2 == latent_channels:
																								lowercase__,     lowercase__    :				Union[str, Any]=	torch.split(snake_case__	,  snake_case__	,  dim=1      )
																		else:
																								lowercase__    :				int=	noise_pred

																		# compute previous image: x_t -> x_t-1
																		lowercase__    :				List[Any]=	self.scheduler.step(snake_case__	,  snake_case__	,  snake_case__      ).prev_sample

												if guidance_scale > 1:
																		lowercase__,     lowercase__    :				Any=	latent_model_input.chunk(2	,  dim=0      )
												else:
																		lowercase__    :				str=	latent_model_input

												lowercase__    :				Dict=	1 / self.vae.config.scaling_factor * latents
												lowercase__    :				Any=	self.vae.decode(snake_case__      ).sample

												lowercase__    :				Tuple=	(samples / 2 + 0.5).clamp(0	,  1      )

												# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
												lowercase__    :				List[Any]=	samples.cpu().permute(0	,  2	,  3	,  1      ).float().numpy()

												if output_type == "pil":
																		lowercase__    :				Optional[Any]=	self.numpy_to_pil(snake_case__      )

												if not return_dict:
																		return (samples,)

												return ImagePipelineOutput(images=snake_case__      )






