





from math import acos, sin
from typing import List, Tuple, Union

import numpy as np
import torch
from PIL import Image

from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel







class   _UpperCamelCase  (       A_  ):







					'''simple docstring'''

					lowerCamelCase     :     Dict			    =       ['vqvae']





					def __init__(						self    :  Dict ,					__lowercase    :  AutoencoderKL ,					__lowercase    :  UNetaDConditionModel ,					__lowercase    :  Mel ,					__lowercase    :  Union[DDIMScheduler, DDPMScheduler] ,					):






						'''simple docstring'''







						super().__init__()
						self.register_modules(unet=__lowercase ,					scheduler=__lowercase ,					mel=__lowercase ,					vqvae=__lowercase		)





					def        SCREAMING_SNAKE_CASE							(						self    :  List[Any]		):






						'''simple docstring'''







						return 50 if isinstance(self.scheduler ,					__lowercase		) else 10_00





					@torch.no_grad()
					def __call__(						self    :  str ,					__lowercase    :  int = 1 ,					__lowercase    :  str = None ,					__lowercase    :  np.ndarray = None ,					__lowercase    :  int = 0 ,					__lowercase    :  int = 0 ,					__lowercase    :  int = None ,					__lowercase    :  torch.Generator = None ,					__lowercase    :  float = 0 ,					__lowercase    :  float = 0 ,					__lowercase    :  torch.Generator = None ,					__lowercase    :  float = 0 ,					__lowercase    :  torch.Tensor = None ,					__lowercase    :  torch.Tensor = None ,					__lowercase    :  Any=True ,					):






						'''simple docstring'''







						UpperCAmelCase_								=     steps or self.get_default_steps()
						self.scheduler.set_timesteps(__lowercase		)
						UpperCAmelCase_								=     step_generator or generator
						# For backwards compatibility
						if type(self.unet.config.sample_size		) == int:
							UpperCAmelCase_								=     (self.unet.config.sample_size, self.unet.config.sample_size)
						if noise is None:
							UpperCAmelCase_								=     randn_tensor(
							    (
							        batch_size,
							        self.unet.config.in_channels,
							        self.unet.config.sample_size[0],
							        self.unet.config.sample_size[1],
							    ) ,					generator=__lowercase ,					device=self.device ,					)
						UpperCAmelCase_								=     noise
						UpperCAmelCase_								=     None

						if audio_file is not None or raw_audio is not None:
							self.mel.load_audio(__lowercase ,					__lowercase		)
							UpperCAmelCase_								=     self.mel.audio_slice_to_image(__lowercase		)
							UpperCAmelCase_								=     np.frombuffer(input_image.tobytes() ,					dtype="""uint8"""		).reshape(
							    (input_image.height, input_image.width)		)
							UpperCAmelCase_								=     (input_image / 2_55) * 2 - 1
							UpperCAmelCase_								=     torch.tensor(input_image[np.newaxis, :, :] ,					dtype=torch.float		).to(self.device		)

							if self.vqvae is not None:
								UpperCAmelCase_								=     self.vqvae.encode(torch.unsqueeze(__lowercase ,					0		)		).latent_dist.sample(
								    generator=__lowercase		)[0]
								UpperCAmelCase_								=     self.vqvae.config.scaling_factor * input_images

							if start_step > 0:
								UpperCAmelCase_								=     self.scheduler.add_noise(__lowercase ,					__lowercase ,					self.scheduler.timesteps[start_step - 1]		)

							UpperCAmelCase_								=     (
							    self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
							)
							UpperCAmelCase_								=     int(mask_start_secs * pixels_per_second		)
							UpperCAmelCase_								=     int(mask_end_secs * pixels_per_second		)
							UpperCAmelCase_								=     self.scheduler.add_noise(__lowercase ,					__lowercase ,					torch.tensor(self.scheduler.timesteps[start_step:]		)		)

						for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]		)		):
							if isinstance(self.unet ,					__lowercase		):
								UpperCAmelCase_								=     self.unet(__lowercase ,					__lowercase ,					__lowercase		)["""sample"""]
							else:
								UpperCAmelCase_								=     self.unet(__lowercase ,					__lowercase		)["""sample"""]

							if isinstance(self.scheduler ,					__lowercase		):
								UpperCAmelCase_								=     self.scheduler.step(
								    model_output=__lowercase ,					timestep=__lowercase ,					sample=__lowercase ,					eta=__lowercase ,					generator=__lowercase ,					)["""prev_sample"""]
							else:
								UpperCAmelCase_								=     self.scheduler.step(
								    model_output=__lowercase ,					timestep=__lowercase ,					sample=__lowercase ,					generator=__lowercase ,					)["""prev_sample"""]

							if mask is not None:
								if mask_start > 0:
									UpperCAmelCase_								=     mask[:, step, :, :mask_start]
								if mask_end > 0:
									UpperCAmelCase_								=     mask[:, step, :, -mask_end:]

						if self.vqvae is not None:
							# 0.18215 was scaling factor used in training to ensure unit variance
							UpperCAmelCase_								=     1 / self.vqvae.config.scaling_factor * images
							UpperCAmelCase_								=     self.vqvae.decode(__lowercase		)["""sample"""]

						UpperCAmelCase_								=     (images / 2 + 0.5).clamp(0 ,					1		)
						UpperCAmelCase_								=     images.cpu().permute(0 ,					2 ,					3 ,					1		).numpy()
						UpperCAmelCase_								=     (images * 2_55).round().astype("""uint8"""		)
						UpperCAmelCase_								=     list(
						    (Image.fromarray(_[:, :, 0]		) for _ in images)
						    if images.shape[3] == 1
						    else (Image.fromarray(__lowercase ,					mode="""RGB"""		).convert("""L"""		) for _ in images)		)

						UpperCAmelCase_								=     [self.mel.image_to_audio(__lowercase		) for _ in images]
						if not return_dict:
							return images, (self.mel.get_sample_rate(), audios)

						return BaseOutput(**AudioPipelineOutput(np.array(__lowercase		)[:, np.newaxis, :]		) ,					**ImagePipelineOutput(__lowercase		)		)





					@torch.no_grad()
					def        SCREAMING_SNAKE_CASE							(						self    :  List[Any] ,					__lowercase    :  List[Image.Image] ,					__lowercase    :  int = 50		):






						'''simple docstring'''







						assert isinstance(self.scheduler ,					__lowercase		)
						self.scheduler.set_timesteps(__lowercase		)
						UpperCAmelCase_								=     np.array(
						    [np.frombuffer(image.tobytes() ,					dtype="""uint8"""		).reshape((1, image.height, image.width)		) for image in images]		)
						UpperCAmelCase_								=     (sample / 2_55) * 2 - 1
						UpperCAmelCase_								=     torch.Tensor(__lowercase		).to(self.device		)

						for t in self.progress_bar(torch.flip(self.scheduler.timesteps ,					(0,)		)		):
							UpperCAmelCase_								=     t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
							UpperCAmelCase_								=     self.scheduler.alphas_cumprod[t]
							UpperCAmelCase_								=     (
							    self.scheduler.alphas_cumprod[prev_timestep]
							    if prev_timestep >= 0
							    else self.scheduler.final_alpha_cumprod
							)
							UpperCAmelCase_								=     1 - alpha_prod_t
							UpperCAmelCase_								=     self.unet(__lowercase ,					__lowercase		)["""sample"""]
							UpperCAmelCase_								=     (1 - alpha_prod_t_prev) ** 0.5 * model_output
							UpperCAmelCase_								=     (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
							UpperCAmelCase_								=     sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

						return sample





					@staticmethod
					def        SCREAMING_SNAKE_CASE							(						__lowercase    :  torch.Tensor ,					__lowercase    :  torch.Tensor ,					__lowercase    :  float		):






						'''simple docstring'''







						UpperCAmelCase_								=     acos(torch.dot(torch.flatten(__lowercase		) ,					torch.flatten(__lowercase		)		) / torch.norm(__lowercase		) / torch.norm(__lowercase		)		)
						return sin((1 - alpha) * theta		) * xa / sin(__lowercase		) + sin(alpha * theta		) * xa / sin(__lowercase		)








import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


UpperCamelCase__   : Tuple					  = 5_00_00
UpperCamelCase__   : Any					  = 50_00

UpperCamelCase__  ,			UpperCamelCase__   : Tuple					  = os.path.split(__file__)
UpperCamelCase__   : Union[str, Any]					  = os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))




@get_duration
def    A_(			A				,      A   ):
	for i in range(A   ):
		UpperCAmelCase_								=     dataset[i]




@get_duration
def    A_(			A				,      A				,      A   ):
	for i in range(0				,      len(A   )				,      A   ):
		UpperCAmelCase_								=     dataset[i : i + batch_size]




@get_duration
def    A_(			A				,      A				,      A   ):
	with dataset.formatted_as(type=A   ):
		for i in range(A   ):
			UpperCAmelCase_								=     dataset[i]




@get_duration
def    A_(			A				,      A				,      A				,      A   ):
	with dataset.formatted_as(type=A   ):
		for i in range(0				,      A				,      A   ):
			UpperCAmelCase_								=     dataset[i : i + batch_size]




def    A_(			):
	UpperCAmelCase_								=     {"""num examples""": SPEED_TEST_N_EXAMPLES}
	UpperCAmelCase_								=     [
	    (read, {"""length""": SMALL_TEST}),
	    (read, {"""length""": SPEED_TEST_N_EXAMPLES}),
	    (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 10}),
	    (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 100}),
	    (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 1000}),
	    (read_formatted, {"""type""": """numpy""", """length""": SMALL_TEST}),
	    (read_formatted, {"""type""": """pandas""", """length""": SMALL_TEST}),
	    (read_formatted, {"""type""": """torch""", """length""": SMALL_TEST}),
	    (read_formatted, {"""type""": """tensorflow""", """length""": SMALL_TEST}),
	    (read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 10}),
	    (read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 1000}),
	]

	UpperCAmelCase_								=     [
	    (read, {"""length""": SMALL_TEST}),
	    (read, {"""length""": SPEED_TEST_N_EXAMPLES}),
	    (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 10}),
	    (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 100}),
	    (read_batch, {"""length""": SPEED_TEST_N_EXAMPLES, """batch_size""": 1000}),
	    (read_formatted, {"""type""": """numpy""", """length""": SMALL_TEST}),
	    (read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 10}),
	    (read_formatted_batch, {"""type""": """numpy""", """length""": SMALL_TEST, """batch_size""": 1000}),
	]
	with tempfile.TemporaryDirectory() as tmp_dir:
		print("""generating dataset"""   )
		UpperCAmelCase_								=     datasets.Features(
		    {"""list""": datasets.Sequence(datasets.Value("""float32"""   )   ), """numbers""": datasets.Value("""float32"""   )}   )
		UpperCAmelCase_								=     generate_example_dataset(
		    os.path.join(A				,      """dataset.arrow"""   )				,      A				,      num_examples=A				,      seq_shapes={"""list""": (100,)}				,      )
		print("""first set of iterations"""   )
		for func, kwargs in functions:
			print(func.__name__				,      str(A   )   )
			UpperCAmelCase_								=     func(A				,      **A   )

		print("""shuffling dataset"""   )
		UpperCAmelCase_								=     dataset.shuffle()
		print("""Second set of iterations (after shuffling"""   )
		for func, kwargs in functions_shuffled:
			print("""shuffled """				,      func.__name__				,      str(A   )   )
			UpperCAmelCase_								=     func(
			    A				,      **A   )

	with open(A				,      """wb"""   ) as f:
		f.write(json.dumps(A   ).encode("""utf-8"""   )   )


if __name__ == "__main__":  # useful to run the profiler
				benchmark_iterating()


