

import gc
import threading
import time

import psutil
import torch






class _UpperCamelCase				:
			'''simple docstring'''







			def __init__(      self				:			Optional[Any]						)				->							List[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   int					  =							psutil.Process()
				SCREAMING_SNAKE_CASE :   int					  =							False
			def 				__UpperCamelCase			(      self				:			List[str]						)				->							List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Dict					  =							-1

				while True:
					SCREAMING_SNAKE_CASE :   Tuple					  =							max(self.process.memory_info().rss   ,   self.cpu_memory_peak						)

					# can't sleep or will not catch the peak right (this comment is here on purpose)
					if not self.peak_monitoring:
						break
			def 				__UpperCamelCase			(      self				:			Dict						)				->							Optional[int]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[Any]					  =							True
				SCREAMING_SNAKE_CASE :   Tuple					  =							threading.Thread(target=self.peak_monitor						)
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							True
				self.thread.start()






			def 				__UpperCamelCase			(      self				:			List[Any]						)				->							List[str]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[str]					  =							False
				self.thread.join()
				return self.cpu_memory_peak

a_      =     PeakCPUMemory()

def 			lowerCamelCase__			(       ):
	# Time
	SCREAMING_SNAKE_CASE :   Optional[int]					  =							{"time": time.time()}

	gc.collect()
	torch.cuda.empty_cache()

	# CPU mem
	SCREAMING_SNAKE_CASE :   List[Any]					  =							psutil.Process().memory_info().rss
	cpu_peak_tracker.start()

	# GPU mem
	for i in range(torch.cuda.device_count()):
		SCREAMING_SNAKE_CASE :   Dict					  =							torch.cuda.memory_allocated(_a)
	torch.cuda.reset_peak_memory_stats()

	return measures

def 			lowerCamelCase__			(       _a):
	# Time
	SCREAMING_SNAKE_CASE :   Optional[int]					  =							{"time": time.time() - start_measures["time"]}

	gc.collect()
	torch.cuda.empty_cache()

	# CPU mem
	SCREAMING_SNAKE_CASE :   Tuple					  =							(psutil.Process().memory_info().rss - start_measures["cpu"]) / 2**20
	SCREAMING_SNAKE_CASE :   Optional[Any]					  =							(cpu_peak_tracker.stop() - start_measures["cpu"]) / 2**20

	# GPU mem
	for i in range(torch.cuda.device_count()):
		SCREAMING_SNAKE_CASE :   Tuple					  =							(torch.cuda.memory_allocated(_a) - start_measures[str(_a)]) / 2**20
		SCREAMING_SNAKE_CASE :   Optional[Any]					  =							(torch.cuda.max_memory_allocated(_a) - start_measures[str(_a)]) / 2**20

	return measures


def 			lowerCamelCase__			(       _a				,      _a):
	print(f"{description}:")
	print(f"- Time: {measures['time']:.2f}s")
	for i in range(torch.cuda.device_count()):
		print(f"- GPU {i} allocated: {measures[str(_a)]:.2f}MiB")
		SCREAMING_SNAKE_CASE :   Tuple					  =							measures[f"{i}-peak"]
		print(f"- GPU {i} peak: {peak:.2f}MiB")
	print(f"- CPU RAM allocated: {measures['cpu']:.2f}MiB")
	print(f"- CPU RAM peak: {measures['cpu-peak']:.2f}MiB")






"""simple docstring"""


import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextModel,
    CLIPTokenizer,
    WhisperForConditionalGeneration,
    WhisperProcessor,
)

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import logging


__magic_name__       :					str						=			logging.get_logger(__name__)  # pylint: disable=invalid-name






class 							lowercase__			(   __SCREAMING_SNAKE_CASE	):

			"""simple docstring"""







			def __init__(      self						,   _A						,   _A						,   _A						,   _A						,   _A						,   _A						,   _A						,   _A						,   _A						,   ):






									'''simple docstring'''







									super().__init__()

									if safety_checker is None:
															logger.warning(
															    f"""You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"""
															    """ that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"""
															    """ results in services or applications open to the public. Both the diffusers team and Hugging Face"""
															    """ strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"""
															    """ it only for use-cases that involve analyzing network behavior or auditing its results. For more"""
															    """ information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."""    )

									self.register_modules(
									    speech_model=_A						,   speech_processor=_A						,   vae=_A						,   text_encoder=_A						,   tokenizer=_A						,   unet=_A						,   scheduler=_A						,   feature_extractor=_A						,   )







			def   _a      (      self						,   _A = "auto"    ):






									'''simple docstring'''







									if slice_size == "auto":
															UpperCamelCase				:		List[Any]				   =			self.unet.config.attention_head_dim // 2
									self.unet.set_attention_slice(_A    )







			def   _a      (      self    ):






									'''simple docstring'''







									self.enable_attention_slicing(_A    )







			@torch.no_grad()
			def __call__(      self						,   _A						,   _A=1_6_0_0_0						,   _A = 5_1_2						,   _A = 5_1_2						,   _A = 5_0						,   _A = 7.5						,   _A = None						,   _A = 1						,   _A = 0.0						,   _A = None						,   _A = None						,   _A = "pil"						,   _A = True						,   _A = None						,   _A = 1						,   **_A						,   ):






									'''simple docstring'''







									UpperCamelCase				:		str				   =			self.speech_processor.feature_extractor(
									    _A						,   return_tensors="""pt"""						,   sampling_rate=_A    ).input_features.to(self.device    )
									UpperCamelCase				:		List[Any]				   =			self.speech_model.generate(_A						,   max_length=4_8_0_0_0_0    )

									UpperCamelCase				:		Optional[int]				   =			self.speech_processor.tokenizer.batch_decode(_A						,   skip_special_tokens=_A						,   normalize=_A    )[
									    0
									]

									if isinstance(_A						,   _A    ):
															UpperCamelCase				:		Tuple				   =			1
									elif isinstance(_A						,   _A    ):
															UpperCamelCase				:		List[Any]				   =			len(_A    )
									else:
															raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(_A    )}"""    )

									if height % 8 != 0 or width % 8 != 0:
															raise ValueError(f"""`height` and `width` have to be divisible by 8 but are {height} and {width}."""    )

									if (callback_steps is None) or (
									    callback_steps is not None and (not isinstance(_A						,   _A    ) or callback_steps <= 0)
									):
															raise ValueError(
															    f"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
															    f""" {type(_A    )}."""    )

									# get prompt text embeddings
									UpperCamelCase				:		Dict				   =			self.tokenizer(
									    _A						,   padding="""max_length"""						,   max_length=self.tokenizer.model_max_length						,   return_tensors="""pt"""						,   )
									UpperCamelCase				:		Dict				   =			text_inputs.input_ids

									if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
															UpperCamelCase				:		Tuple				   =			self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]    )
															logger.warning(
															    """The following part of your input was truncated because CLIP can only handle sequences up to"""
															    f""" {self.tokenizer.model_max_length} tokens: {removed_text}"""    )
															UpperCamelCase				:		int				   =			text_input_ids[:, : self.tokenizer.model_max_length]
									UpperCamelCase				:		List[str]				   =			self.text_encoder(text_input_ids.to(self.device    )    )[0]

									# duplicate text embeddings for each generation per prompt, using mps friendly method
									UpperCamelCase   ,						UpperCamelCase   ,						UpperCamelCase				:		Optional[Any]				   =			text_embeddings.shape
									UpperCamelCase				:		Optional[int]				   =			text_embeddings.repeat(1						,   _A						,   1    )
									UpperCamelCase				:		Union[str, Any]				   =			text_embeddings.view(bs_embed * num_images_per_prompt						,   _A						,   -1    )

									# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
									# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
									# corresponds to doing no classifier free guidance.
									UpperCamelCase				:		Optional[Any]				   =			guidance_scale > 1.0
									# get unconditional embeddings for classifier free guidance
									if do_classifier_free_guidance:
															UpperCamelCase				:		List[str]
															if negative_prompt is None:
																					UpperCamelCase				:		str				   =			[""""""] * batch_size
															elif type(_A    ) is not type(_A    ):
																					raise TypeError(
																					    f"""`negative_prompt` should be the same type to `prompt`, but got {type(_A    )} !="""
																					    f""" {type(_A    )}."""    )
															elif isinstance(_A						,   _A    ):
																					UpperCamelCase				:		Tuple				   =			[negative_prompt]
															elif batch_size != len(_A    ):
																					raise ValueError(
																					    f"""`negative_prompt`: {negative_prompt} has batch size {len(_A    )}, but `prompt`:"""
																					    f""" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"""
																					    """ the batch size of `prompt`."""    )
															else:
																					UpperCamelCase				:		Any				   =			negative_prompt

															UpperCamelCase				:		Optional[int]				   =			text_input_ids.shape[-1]
															UpperCamelCase				:		List[str]				   =			self.tokenizer(
															    _A						,   padding="""max_length"""						,   max_length=_A						,   truncation=_A						,   return_tensors="""pt"""						,   )
															UpperCamelCase				:		Optional[int]				   =			self.text_encoder(uncond_input.input_ids.to(self.device    )    )[0]

															# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
															UpperCamelCase				:		List[Any]				   =			uncond_embeddings.shape[1]
															UpperCamelCase				:		Dict				   =			uncond_embeddings.repeat(1						,   _A						,   1    )
															UpperCamelCase				:		Dict				   =			uncond_embeddings.view(batch_size * num_images_per_prompt						,   _A						,   -1    )

															# For classifier free guidance, we need to do two forward passes.
															# Here we concatenate the unconditional and text embeddings into a single batch
															# to avoid doing two forward passes
															UpperCamelCase				:		str				   =			torch.cat([uncond_embeddings, text_embeddings]    )

									# get the initial random noise unless the user supplied it

									# Unlike in other pipelines, latents need to be generated in the target device
									# for 1-to-1 results reproducibility with the CompVis implementation.
									# However this currently doesn't work in `mps`.
									UpperCamelCase				:		Any				   =			(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
									UpperCamelCase				:		int				   =			text_embeddings.dtype
									if latents is None:
															if self.device.type == "mps":
																					# randn does not exist on mps
																					UpperCamelCase				:		Tuple				   =			torch.randn(_A						,   generator=_A						,   device="""cpu"""						,   dtype=_A    ).to(
																					    self.device    )
															else:
																					UpperCamelCase				:		Any				   =			torch.randn(_A						,   generator=_A						,   device=self.device						,   dtype=_A    )
									else:
															if latents.shape != latents_shape:
																					raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""    )
															UpperCamelCase				:		Optional[Any]				   =			latents.to(self.device    )

									# set timesteps
									self.scheduler.set_timesteps(_A    )

									# Some schedulers like PNDM have timesteps as arrays
									# It's more optimized to move all timesteps to correct device beforehand
									UpperCamelCase				:		Tuple				   =			self.scheduler.timesteps.to(self.device    )

									# scale the initial noise by the standard deviation required by the scheduler
									UpperCamelCase				:		Any				   =			latents * self.scheduler.init_noise_sigma

									# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
									# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
									# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
									# and should be between [0, 1]
									UpperCamelCase				:		List[Any]				   =			"""eta""" in set(inspect.signature(self.scheduler.step    ).parameters.keys()    )
									UpperCamelCase				:		str				   =			{}
									if accepts_eta:
															UpperCamelCase				:		Union[str, Any]				   =			eta

									for i, t in enumerate(self.progress_bar(_A    )    ):
															# expand the latents if we are doing classifier free guidance
															UpperCamelCase				:		Union[str, Any]				   =			torch.cat([latents] * 2    ) if do_classifier_free_guidance else latents
															UpperCamelCase				:		str				   =			self.scheduler.scale_model_input(_A						,   _A    )

															# predict the noise residual
															UpperCamelCase				:		Optional[Any]				   =			self.unet(_A						,   _A						,   encoder_hidden_states=_A    ).sample

															# perform guidance
															if do_classifier_free_guidance:
																					UpperCamelCase   ,						UpperCamelCase				:		str				   =			noise_pred.chunk(2    )
																					UpperCamelCase				:		List[str]				   =			noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

															# compute the previous noisy sample x_t -> x_t-1
															UpperCamelCase				:		Any				   =			self.scheduler.step(_A						,   _A						,   _A						,   **_A    ).prev_sample

															# call the callback, if provided
															if callback is not None and i % callback_steps == 0:
																					callback(_A						,   _A						,   _A    )

									UpperCamelCase				:		Optional[Any]				   =			1 / 0.1_82_15 * latents
									UpperCamelCase				:		Union[str, Any]				   =			self.vae.decode(_A    ).sample

									UpperCamelCase				:		List[Any]				   =			(image / 2 + 0.5).clamp(0						,   1    )

									# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
									UpperCamelCase				:		Dict				   =			image.cpu().permute(0						,   2						,   3						,   1    ).float().numpy()

									if output_type == "pil":
															UpperCamelCase				:		Optional[Any]				   =			self.numpy_to_pil(_A    )

									if not return_dict:
															return image

									return StableDiffusionPipelineOutput(images=_A						,   nsfw_content_detected=_A    )



