





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


_a									=				logging.get_logger(__name__)






class  __A   (   snake_case_			):

			'''simple docstring'''







			lowerCAmelCase_												=						42
			lowerCAmelCase_												=						42
			lowerCAmelCase_												=						"PyTorch"





			@property
			def 		__lowerCamelCase    (   self							):



				'''simple docstring'''







				return torch.__version__





			def 		__lowerCamelCase    (   self	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__        =		self._prepare_inference_func(__lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase							)
				return self._measure_speed(_inference							)





			def 		__lowerCamelCase    (   self	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__        =		self._prepare_inference_func(__lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase							)
				return self._measure_memory(_inference							)





			def 		__lowerCamelCase    (   self	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__        =		self._prepare_train_func(__lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase							)
				return self._measure_speed(_train							)





			def 		__lowerCamelCase    (   self	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__        =		self._prepare_train_func(__lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase							)
				return self._measure_memory(_train							)





			def 		__lowerCamelCase    (   self	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__        =		self.config_dict[model_name]

				if self.args.torchscript:
					lowerCamelCase__        =		True

				lowerCamelCase__        =		(
				    hasattr(__lowerCAmelCase	,  '''architectures'''							)
				    and isinstance(config.architectures	,  __lowerCAmelCase							)
				    and len(config.architectures							) > 0
				)
				if not self.args.only_pretrain_model and has_model_class_in_config:
					try:
						lowerCamelCase__        =		config.architectures[0]
						lowerCamelCase__        =		__import__('''transformers'''	,  fromlist=[model_class]							)
						lowerCamelCase__        =		getattr(__lowerCAmelCase	,  __lowerCAmelCase							)
						lowerCamelCase__        =		model_cls(__lowerCAmelCase							)
					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:
					lowerCamelCase__        =		MODEL_MAPPING[config.__class__](__lowerCAmelCase							)

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

				# encoder-decoder has vocab size saved differently
				lowerCamelCase__        =		config.vocab_size if hasattr(__lowerCAmelCase	,  '''vocab_size'''							) else config.encoder.vocab_size
				lowerCamelCase__        =		torch.randint(__lowerCAmelCase	,  (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():
						lowerCamelCase__        =		torch.jit.trace(__lowerCAmelCase	,  __lowerCAmelCase							)
				else:
					lowerCamelCase__        =		model

				def encoder_decoder_forward():
					with torch.no_grad():
						lowerCamelCase__        =		inference_model(__lowerCAmelCase	,  decoder_input_ids=__lowerCAmelCase							)
					return outputs

				def encoder_forward():
					with torch.no_grad():
						lowerCamelCase__        =		inference_model(__lowerCAmelCase							)
					return outputs

				lowerCamelCase__        =		encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
				return _forward





			def 		__lowerCamelCase    (   self	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__        =		self.config_dict[model_name]

				lowerCamelCase__        =		(
				    hasattr(__lowerCAmelCase	,  '''architectures'''							)
				    and isinstance(config.architectures	,  __lowerCAmelCase							)
				    and len(config.architectures							) > 0
				)
				if not self.args.only_pretrain_model and has_model_class_in_config:
					try:
						lowerCamelCase__        =		config.architectures[0]
						lowerCamelCase__        =		__import__('''transformers'''	,  fromlist=[model_class]							)
						lowerCamelCase__        =		getattr(__lowerCAmelCase	,  __lowerCAmelCase							)
						lowerCamelCase__        =		model_cls(__lowerCAmelCase							)
					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:
					lowerCamelCase__        =		MODEL_WITH_LM_HEAD_MAPPING[config.__class__](__lowerCAmelCase							)

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

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

				# encoder-decoder has vocab size saved differently
				lowerCamelCase__        =		config.vocab_size if hasattr(__lowerCAmelCase	,  '''vocab_size'''							) else config.encoder.vocab_size
				lowerCamelCase__        =		torch.randint(__lowerCAmelCase	,  (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():
					lowerCamelCase__        =		train_model(__lowerCAmelCase	,  labels=__lowerCAmelCase							)[0]
					loss.backward()
					return loss

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

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





			def 		__lowerCamelCase    (   self	,  __lowerCAmelCase							):



				'''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(
						    __lowerCAmelCase	,  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
					lowerCamelCase__        =		timeit.repeat(
					    __lowerCAmelCase	,  repeat=self.args.repeat	,  number=1_0	,  )

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





			def 		__lowerCamelCase    (   self	,  __lowerCAmelCase							):



				'''simple docstring'''







				try:
					if self.args.trace_memory_line_by_line:
						lowerCamelCase__        =		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.'''							)
							lowerCamelCase__        =		"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()
							lowerCamelCase__        =		nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx							)
							lowerCamelCase__        =		nvml.nvmlDeviceGetMemoryInfo(__lowerCAmelCase							)
							lowerCamelCase__        =		meminfo.used
							lowerCamelCase__        =		Memory(__lowerCAmelCase							)
							# shutdown nvml
							nvml.nvmlShutdown()
					else:
						# cpu
						lowerCamelCase__        =		measure_peak_memory_cpu(__lowerCAmelCase							)
						lowerCamelCase__        =		Memory(__lowerCAmelCase							) if isinstance(__lowerCAmelCase	,  __lowerCAmelCase							) else memory_bytes

					if self.args.trace_memory_line_by_line:
						lowerCamelCase__        =		stop_memory_tracing(__lowerCAmelCase							)
					else:
						lowerCamelCase__        =		None

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










from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import SchedulerMixin


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






@dataclass
class  __A   (   lowerCAmelCase			):

			'''simple docstring'''







			lowerCAmelCase_												=						42






class  __A   (   lowerCAmelCase      ,    lowerCAmelCase			):

			'''simple docstring'''







			lowerCAmelCase_												=						1





			@register_to_config
			def __init__(   self	,  __lowerCAmelCase = 4_0	,  __lowerCAmelCase = 0.002	,  __lowerCAmelCase = 80.0	,  __lowerCAmelCase = 0.5	,  __lowerCAmelCase = 1.0	,  __lowerCAmelCase = 7.0	,  __lowerCAmelCase = True	,  ):



				'''simple docstring'''







				lowerCamelCase__        =		sigma_max

				lowerCamelCase__        =		np.linspace(0	,  1	,  __lowerCAmelCase							)
				lowerCamelCase__        =		self._convert_to_karras(__lowerCAmelCase							)
				lowerCamelCase__        =		self.sigma_to_t(__lowerCAmelCase							)

				# setable values
				lowerCamelCase__        =		None
				lowerCamelCase__        =		torch.from_numpy(__lowerCAmelCase							)
				lowerCamelCase__        =		torch.from_numpy(__lowerCAmelCase							)
				lowerCamelCase__        =		False
				lowerCamelCase__        =		False





			def 		__lowerCamelCase    (   self	,  __lowerCAmelCase	,  __lowerCAmelCase=None							):



				'''simple docstring'''







				if schedule_timesteps is None:
					lowerCamelCase__        =		self.timesteps

				lowerCamelCase__        =		(schedule_timesteps == timestep).nonzero()
				return indices.item()





			def 		__lowerCamelCase    (   self	,  __lowerCAmelCase	,  __lowerCAmelCase							):



				'''simple docstring'''







				if isinstance(__lowerCAmelCase	,  torch.Tensor							):
					lowerCamelCase__        =		timestep.to(self.timesteps.device							)
				lowerCamelCase__        =		self.index_for_timestep(__lowerCAmelCase							)
				lowerCamelCase__        =		self.sigmas[step_idx]

				lowerCamelCase__        =		sample / ((sigma**2 + self.config.sigma_data**2) ** 0.5)

				lowerCamelCase__        =		True
				return sample





			def 		__lowerCamelCase    (   self	,  __lowerCAmelCase							):



				'''simple docstring'''







				if not isinstance(__lowerCAmelCase	,  np.ndarray							):
					lowerCamelCase__        =		np.array(__lowerCAmelCase	,  dtype=np.floataa							)

				lowerCamelCase__        =		1_0_0_0 * 0.25 * np.log(sigmas + 1E-44							)

				return timesteps





			def 		__lowerCamelCase    (   self	,  __lowerCAmelCase = None	,  __lowerCAmelCase = None	,  __lowerCAmelCase = None	,  ):



				'''simple docstring'''







				if num_inference_steps is None and timesteps is None:
					raise ValueError('''Exactly one of `num_inference_steps` or `timesteps` must be supplied.'''							)

				if num_inference_steps is not None and timesteps is not None:
					raise ValueError('''Can only pass one of `num_inference_steps` or `timesteps`.'''							)

				# Follow DDPMScheduler custom timesteps logic
				if timesteps is not None:
					for i in range(1	,  len(__lowerCAmelCase							)							):
						if timesteps[i] >= timesteps[i - 1]:
							raise ValueError('''`timesteps` must be in descending order.'''							)

					if timesteps[0] >= self.config.num_train_timesteps:
						raise ValueError(
						    F'`timesteps` must start before `self.config.train_timesteps`:'
						    F' {self.config.num_train_timesteps}.'							)

					lowerCamelCase__        =		np.array(__lowerCAmelCase	,  dtype=np.intaa							)
					lowerCamelCase__        =		True
				else:
					if num_inference_steps > self.config.num_train_timesteps:
						raise ValueError(
						    F'`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'
						    F' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'
						    F' maximal {self.config.num_train_timesteps} timesteps.'							)

					lowerCamelCase__        =		num_inference_steps

					lowerCamelCase__        =		self.config.num_train_timesteps // self.num_inference_steps
					lowerCamelCase__        =		(np.arange(0	,  __lowerCAmelCase							) * step_ratio).round()[::-1].copy().astype(np.intaa							)
					lowerCamelCase__        =		False

				# Map timesteps to Karras sigmas directly for multistep sampling
				# See https://github.com/openai/consistency_models/blob/main/cm/karras_diffusion.py#L675
				lowerCamelCase__        =		self.config.num_train_timesteps
				lowerCamelCase__        =		timesteps[::-1].copy()
				lowerCamelCase__        =		ramp / (num_train_timesteps - 1)
				lowerCamelCase__        =		self._convert_to_karras(__lowerCAmelCase							)
				lowerCamelCase__        =		self.sigma_to_t(__lowerCAmelCase							)

				lowerCamelCase__        =		np.concatenate([sigmas, [self.sigma_min]]							).astype(np.floataa							)
				lowerCamelCase__        =		torch.from_numpy(__lowerCAmelCase							).to(device=__lowerCAmelCase							)

				if str(__lowerCAmelCase							).startswith('''mps'''							):
					# mps does not support float64
					lowerCamelCase__        =		torch.from_numpy(__lowerCAmelCase							).to(__lowerCAmelCase	,  dtype=torch.floataa							)
				else:
					lowerCamelCase__        =		torch.from_numpy(__lowerCAmelCase							).to(device=__lowerCAmelCase							)





			def 		__lowerCamelCase    (   self	,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__        =		self.config.sigma_min
				lowerCamelCase__        =		self.config.sigma_max

				lowerCamelCase__        =		self.config.rho
				lowerCamelCase__        =		sigma_min ** (1 / rho)
				lowerCamelCase__        =		sigma_max ** (1 / rho)
				lowerCamelCase__        =		(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
				return sigmas





			def 		__lowerCamelCase    (   self	,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__        =		self.config.sigma_data

				lowerCamelCase__        =		sigma_data**2 / (sigma**2 + sigma_data**2)
				lowerCamelCase__        =		sigma * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
				return c_skip, c_out





			def 		__lowerCamelCase    (   self	,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__        =		self.config.sigma_min
				lowerCamelCase__        =		self.config.sigma_data

				lowerCamelCase__        =		sigma_data**2 / ((sigma - sigma_min) ** 2 + sigma_data**2)
				lowerCamelCase__        =		(sigma - sigma_min) * sigma_data / (sigma**2 + sigma_data**2) ** 0.5
				return c_skip, c_out





			def 		__lowerCamelCase    (   self	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase = None	,  __lowerCAmelCase = True	,  ):



				'''simple docstring'''







				if (
				    isinstance(__lowerCAmelCase	,  __lowerCAmelCase							)
				    or isinstance(__lowerCAmelCase	,  torch.IntTensor							)
				    or isinstance(__lowerCAmelCase	,  torch.LongTensor							)
				):
					raise ValueError(
					    (
					        '''Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'''
					        F' `{self.__class__}.step()` is not supported. Make sure to pass'
					        ''' one of the `scheduler.timesteps` as a timestep.'''
					    )	,  )

				if not self.is_scale_input_called:
					logger.warning(
					    '''The `scale_model_input` function should be called before `step` to ensure correct denoising. '''
					    '''See `StableDiffusionPipeline` for a usage example.'''							)

				if isinstance(__lowerCAmelCase	,  torch.Tensor							):
					lowerCamelCase__        =		timestep.to(self.timesteps.device							)

				lowerCamelCase__        =		self.config.sigma_min
				lowerCamelCase__        =		self.config.sigma_max

				lowerCamelCase__        =		self.index_for_timestep(__lowerCAmelCase							)

				# sigma_next corresponds to next_t in original implementation
				lowerCamelCase__        =		self.sigmas[step_index]
				if step_index + 1 < self.config.num_train_timesteps:
					lowerCamelCase__        =		self.sigmas[step_index + 1]
				else:
					# Set sigma_next to sigma_min
					lowerCamelCase__        =		self.sigmas[-1]

				# Get scalings for boundary conditions
				lowerCamelCase__    ,     lowerCamelCase__        =		self.get_scalings_for_boundary_condition(__lowerCAmelCase							)

				# 1. Denoise model output using boundary conditions
				lowerCamelCase__        =		c_out * model_output + c_skip * sample
				if self.config.clip_denoised:
					lowerCamelCase__        =		denoised.clamp(-1	,  1							)

				# 2. Sample z ~ N(0, s_noise^2 * I)
				# Noise is not used for onestep sampling.
				if len(self.timesteps							) > 1:
					lowerCamelCase__        =		randn_tensor(
					    model_output.shape	,  dtype=model_output.dtype	,  device=model_output.device	,  generator=__lowerCAmelCase							)
				else:
					lowerCamelCase__        =		torch.zeros_like(__lowerCAmelCase							)
				lowerCamelCase__        =		noise * self.config.s_noise

				lowerCamelCase__        =		sigma_next.clamp(min=__lowerCAmelCase	,  max=__lowerCAmelCase							)

				# 3. Return noisy sample
				# tau = sigma_hat, eps = sigma_min
				lowerCamelCase__        =		denoised + z * (sigma_hat**2 - sigma_min**2) ** 0.5

				if not return_dict:
					return (prev_sample,)

				return CMStochasticIterativeSchedulerOutput(prev_sample=__lowerCAmelCase							)





			def 		__lowerCamelCase    (   self	,  __lowerCAmelCase	,  __lowerCAmelCase	,  __lowerCAmelCase	,  ):



				'''simple docstring'''







				lowerCamelCase__        =		self.sigmas.to(device=original_samples.device	,  dtype=original_samples.dtype							)
				if original_samples.device.type == "mps" and torch.is_floating_point(__lowerCAmelCase							):
					# mps does not support float64
					lowerCamelCase__        =		self.timesteps.to(original_samples.device	,  dtype=torch.floataa							)
					lowerCamelCase__        =		timesteps.to(original_samples.device	,  dtype=torch.floataa							)
				else:
					lowerCamelCase__        =		self.timesteps.to(original_samples.device							)
					lowerCamelCase__        =		timesteps.to(original_samples.device							)

				lowerCamelCase__        =		[(schedule_timesteps == t).nonzero().item() for t in timesteps]

				lowerCamelCase__        =		sigmas[step_indices].flatten()
				while len(sigma.shape							) < len(original_samples.shape							):
					lowerCamelCase__        =		sigma.unsqueeze(-1							)

				lowerCamelCase__        =		original_samples + noise * sigma
				return noisy_samples





			def __len__(   self							):



				'''simple docstring'''







				return self.config.num_train_timesteps



