

"""simple docstring"""






from typing import Union

import torch
from PIL import Image
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
class 	_lowerCAmelCase    (      __snake_case       ):

							def __init__( self       :    Union[str, Any] ,		a       :    AutoencoderKL ,		a       :    CLIPTextModel ,		a       :    CLIPTokenizer ,		a       :    UNetaDConditionModel ,		a       :    Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler] ,		)   ->    List[Any]:

									"""simple docstring"""
									super().__init__()

									self.register_modules(vae=a ,		text_encoder=a ,		tokenizer=a ,		unet=a ,		scheduler=a    )

							def 					_lowerCAmelCase  ( self       :    Optional[int] ,		a       :    List[str]    )   ->    List[str]:

									"""simple docstring"""
									with torch.no_grad():
											lowercase										=							self.vae.encode(tfms.ToTensor()(a    ).unsqueeze(0    ).to(self.device    ) * 2 - 1    )
											lowercase										=							0.18_215 * latent.latent_dist.sample()
									return latent

							def 					_lowerCAmelCase  ( self       :    Dict ,		a       :    int    )   ->    Union[str, Any]:

									"""simple docstring"""
									lowercase										=							(1 / 0.18_215) * latent
									with torch.no_grad():
											lowercase										=							self.vae.decode(a    ).sample
									lowercase										=							(img / 2 + 0.5).clamp(0 ,		1    )
									lowercase										=							img.detach().cpu().permute(0 ,		2 ,		3 ,		1    ).numpy()
									lowercase										=							(img * 255).round().astype('''uint8'''    )
									return Image.fromarray(img[0]    )

							def 					_lowerCAmelCase  ( self       :    Tuple ,		a       :    Dict    )   ->    Tuple:

									"""simple docstring"""
									lowercase										=							self.tokenizer(
									    a ,		padding='''max_length''' ,		max_length=self.tokenizer.model_max_length ,		truncation=a ,		return_tensors='''pt''' ,		)

									lowercase										=							self.text_encoder(text_input.input_ids.to(self.device    )    )[0]

									lowercase										=							self.tokenizer(
									    '''''' ,		padding='''max_length''' ,		max_length=self.tokenizer.model_max_length ,		truncation=a ,		return_tensors='''pt''' ,		)

									lowercase										=							self.text_encoder(uncond_input.input_ids.to(self.device    )    )[0]

									return torch.cat([uncond_embedding, text_embedding]    )

							def __call__( self       :    Any ,		a       :    Image.Image ,		a       :    str ,		a       :    float = 0.3 ,		a       :    float = 0.6 ,		a       :    float = 0.5 ,		a       :    int = 42 ,		a       :    int = 50 ,		a       :    float = 7.5 ,		)   ->    Image.Image:

									"""simple docstring"""
									lowercase										=							steps - int(kmin * steps    )
									lowercase										=							steps - int(kmax * steps    )

									lowercase										=							self.prep_text(a    )

									self.scheduler.set_timesteps(a    )

									lowercase					,  lowercase										=							img.size
									lowercase										=							self.encode(a    )

									torch.manual_seed(a    )
									lowercase										=							torch.randn(
									    (1, self.unet.config.in_channels, height // 8, width // 8) ,		).to(self.device    )

									lowercase										=							self.scheduler.add_noise(
									    a ,		a ,		timesteps=self.scheduler.timesteps[tmax] ,		)

									lowercase										=							torch.cat([latents] * 2    )

									lowercase										=							self.scheduler.scale_model_input(a ,		self.scheduler.timesteps[tmax]    )

									with torch.no_grad():
											lowercase										=							self.unet(
											    a ,		self.scheduler.timesteps[tmax] ,		encoder_hidden_states=a ,		).sample

									lowercase					,  lowercase										=							pred.chunk(2    )
									lowercase										=							pred_uncond + guidance_scale * (pred_text - pred_uncond)

									lowercase										=							self.scheduler.step(a ,		self.scheduler.timesteps[tmax] ,		a    ).prev_sample

									for i, t in enumerate(tqdm(self.scheduler.timesteps    )    ):
											if i > tmax:
													if i < tmin:  # layout generation phase
															lowercase										=							self.scheduler.add_noise(
															    a ,		a ,		timesteps=a ,		)

															lowercase										=							(mix_factor * latents) + (
															    1 - mix_factor
															) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
															lowercase										=							torch.cat([input] * 2    )

													else:  # content generation phase
															lowercase										=							torch.cat([latents] * 2    )

													lowercase										=							self.scheduler.scale_model_input(a ,		a    )

													with torch.no_grad():
															lowercase										=							self.unet(
															    a ,		a ,		encoder_hidden_states=a ,		).sample

													lowercase					,  lowercase										=							pred.chunk(2    )
													lowercase										=							pred_uncond + guidance_scale * (pred_text - pred_uncond)

													lowercase										=							self.scheduler.step(a ,		a ,		a    ).prev_sample

									return self.decode(a    )


"""simple docstring"""






import timeit
from typing import Callable, Optional

from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_auto import MODEL_MAPPING, MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_torch_available, logging
from .benchmark_utils import (
    Benchmark,
    Memory,
    MemorySummary,
    measure_peak_memory_cpu,
    start_memory_tracing,
    stop_memory_tracing,
)


if is_torch_available():
							import torch

							from .benchmark_args import PyTorchBenchmarkArguments


if is_pyanvml_available():
							import pyanvml.pyanvml as nvml


__lowerCAmelCase				      =   logging.get_logger(__name__)
class 	_lowerCAmelCase    (      __snake_case       ):
							__lowerCAmelCase					:    PyTorchBenchmarkArguments
							__lowerCAmelCase					:    PretrainedConfig
							__lowerCAmelCase					:    str     =  "PyTorch"

							@property
							def 					_lowerCAmelCase  ( self       :    Dict    )   ->    Dict:

									"""simple docstring"""
									return torch.__version__

							def 					_lowerCAmelCase  ( self       :    List[Any] ,		a       :    str ,		a       :    int ,		a       :    int    )   ->    float:

									"""simple docstring"""
									lowercase										=							self._prepare_inference_func(a ,		a ,		a    )
									return self._measure_speed(_inference    )

							def 					_lowerCAmelCase  ( self       :    str ,		a       :    str ,		a       :    int ,		a       :    int    )   ->    [Memory, Optional[MemorySummary]]:

									"""simple docstring"""
									lowercase										=							self._prepare_inference_func(a ,		a ,		a    )
									return self._measure_memory(_inference    )

							def 					_lowerCAmelCase  ( self       :    Union[str, Any] ,		a       :    str ,		a       :    int ,		a       :    int    )   ->    float:

									"""simple docstring"""
									lowercase										=							self._prepare_train_func(a ,		a ,		a    )
									return self._measure_speed(_train    )

							def 					_lowerCAmelCase  ( self       :    int ,		a       :    str ,		a       :    int ,		a       :    int    )   ->    [Memory, Optional[MemorySummary]]:

									"""simple docstring"""
									lowercase										=							self._prepare_train_func(a ,		a ,		a    )
									return self._measure_memory(_train    )

							def 					_lowerCAmelCase  ( self       :    List[Any] ,		a       :    str ,		a       :    int ,		a       :    int    )   ->    Callable[[], None]:

									"""simple docstring"""
									lowercase										=							self.config_dict[model_name]

									if self.args.torchscript:
											lowercase										=							True

									lowercase										=							(
									    hasattr(a ,		'''architectures'''    )
									    and isinstance(config.architectures ,		a    )
									    and len(config.architectures    ) > 0
									)
									if not self.args.only_pretrain_model and has_model_class_in_config:
											try:
													lowercase										=							config.architectures[0]
													lowercase										=							__import__('''transformers''' ,		fromlist=[model_class]    )
													lowercase										=							getattr(a ,		a    )
													lowercase										=							model_cls(a    )
											except ImportError:
													raise ImportError(
													    f"""{model_class} does not exist. If you just want to test the pretrained model, you might want to"""
													    ''' set `--only_pretrain_model` or `args.only_pretrain_model=True`.'''    )
									else:
											lowercase										=							MODEL_MAPPING[config.__class__](a    )

									model.eval()
									model.to(self.args.device    )

									# encoder-decoder has vocab size saved differently
									lowercase										=							config.vocab_size if hasattr(a ,		'''vocab_size'''    ) else config.encoder.vocab_size
									lowercase										=							torch.randint(a ,		(batch_size, sequence_length) ,		dtype=torch.long ,		device=self.args.device    )

									if self.args.fpaa:
											logger.info('''Running training in Mixed Precision...'''    )
											if not self.args.is_gpu:
													raise ValueError('''Mixed precision is possible only for GPU.'''    )
											# amp seems to have memory leaks so that memory usage
											# is measured using .half() for now https://github.com/NVIDIA/apex/issues/439
											model.half()

									if self.args.torchscript:
											with torch.no_grad():
													lowercase										=							torch.jit.trace(a ,		a    )
									else:
											lowercase										=							model

									def encoder_decoder_forward():
											with torch.no_grad():
													lowercase										=							inference_model(a ,		decoder_input_ids=a    )
											return outputs

									def encoder_forward():
											with torch.no_grad():
													lowercase										=							inference_model(a    )
											return outputs

									lowercase										=							encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
									return _forward

							def 					_lowerCAmelCase  ( self       :    int ,		a       :    str ,		a       :    int ,		a       :    int    )   ->    Callable[[], None]:

									"""simple docstring"""
									lowercase										=							self.config_dict[model_name]

									lowercase										=							(
									    hasattr(a ,		'''architectures'''    )
									    and isinstance(config.architectures ,		a    )
									    and len(config.architectures    ) > 0
									)
									if not self.args.only_pretrain_model and has_model_class_in_config:
											try:
													lowercase										=							config.architectures[0]
													lowercase										=							__import__('''transformers''' ,		fromlist=[model_class]    )
													lowercase										=							getattr(a ,		a    )
													lowercase										=							model_cls(a    )
											except ImportError:
													raise ImportError(
													    f"""{model_class} does not exist. If you just want to test the pretrained model, you might want to"""
													    ''' set `--only_pretrain_model` or `args.only_pretrain_model=True`.'''    )
									else:
											lowercase										=							MODEL_WITH_LM_HEAD_MAPPING[config.__class__](a    )

									if self.args.torchscript:
											raise NotImplementedError('''Training for torchscript is currently not implemented'''    )
									else:
											lowercase										=							model

									model.train()
									model.to(self.args.device    )

									# encoder-decoder has vocab size saved differently
									lowercase										=							config.vocab_size if hasattr(a ,		'''vocab_size'''    ) else config.encoder.vocab_size
									lowercase										=							torch.randint(a ,		(batch_size, sequence_length) ,		dtype=torch.long ,		device=self.args.device    )

									if self.args.fpaa:
											logger.info('''Running training in Mixed Precision...'''    )
											if not self.args.is_gpu:
													raise ValueError('''Mixed precision is possible only for GPU.'''    )

											# amp seems to have memory leaks so that memory usage
											# is measured using .half() for now https://github.com/NVIDIA/apex/issues/439
											model.half()

									def compute_loss_and_backprob_encoder():
											lowercase										=							train_model(a ,		labels=a    )[0]
											loss.backward()
											return loss

									def compute_loss_and_backprob_encoder_decoder():
											lowercase										=							train_model(a ,		decoder_input_ids=a ,		labels=a    )[0]
											loss.backward()
											return loss

									lowercase										=							(
									    compute_loss_and_backprob_encoder_decoder
									    if config.is_encoder_decoder
									    else compute_loss_and_backprob_encoder
									)
									return _train

							def 					_lowerCAmelCase  ( self       :    int ,		a       :    Optional[Any]    )   ->    float:

									"""simple docstring"""
									try:
											if self.args.is_tpu or self.args.torchscript:
													# run additional 10 times to stabilize compilation for tpu and torchscript
													logger.info('''Do inference on TPU or torchscript. Running model 5 times to stabilize compilation'''    )
													timeit.repeat(
													    a ,		repeat=1 ,		number=5 ,		)

											# as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
											lowercase										=							timeit.repeat(
											    a ,		repeat=self.args.repeat ,		number=10 ,		)

											if self.args.is_tpu and self.args.torch_xla_tpu_print_metrics:
													import torch_xla.debug.metrics as met

													self.print_fn(met.metrics_report()    )

											return min(a    ) / 10.0
									except RuntimeError as e:
											self.print_fn(f"""Doesn't fit on GPU. {e}"""    )
											return "N/A"

							def 					_lowerCAmelCase  ( self       :    str ,		a       :    Callable[[], None]    )   ->    [Memory, MemorySummary]:

									"""simple docstring"""
									try:
											if self.args.trace_memory_line_by_line:
													lowercase										=							start_memory_tracing('''transformers'''    )

											if self.args.is_tpu:
													# tpu
													raise NotImplementedError(
													    '''Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking with'''
													    ''' `--no-memory` or `args.memory=False`'''    )
											elif self.args.is_gpu:
													if not is_pyanvml_available():
															logger.warning(
															    '''py3nvml not installed, we won\'t log GPU memory usage. '''
															    '''Install py3nvml (pip install py3nvml) to log information about GPU.'''    )
															lowercase										=							'''N/A'''
													else:
															logger.info(
															    '''Measuring total GPU usage on GPU device. Make sure to not have additional processes running'''
															    ''' on the same GPU.'''    )
															# init nvml
															nvml.nvmlInit()
															func()
															lowercase										=							nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx    )
															lowercase										=							nvml.nvmlDeviceGetMemoryInfo(a    )
															lowercase										=							meminfo.used
															lowercase										=							Memory(a    )
															# shutdown nvml
															nvml.nvmlShutdown()
											else:
													# cpu
													lowercase										=							measure_peak_memory_cpu(a    )
													lowercase										=							Memory(a    ) if isinstance(a ,		a    ) else memory_bytes

											if self.args.trace_memory_line_by_line:
													lowercase										=							stop_memory_tracing(a    )
											else:
													lowercase										=							None

											return memory, summary
									except RuntimeError as e:
											self.print_fn(f"""Doesn't fit on GPU. {e}"""    )
											return "N/A", None