





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

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging


_A :   Optional[Any]  			=  logging.get_logger(__name__)  # pylint: disable=invalid-name


class        __SCREAMING_SNAKE_CASE      (			lowerCAmelCase_ ):






       """simple docstring"""




       def __init__(				self					:     Optional[Any]		, A					:     AutoencoderKL		, A					:     CLIPTextModel		, A					:     CLIPTokenizer		, A					:     UNetaDConditionModel		, A					:     Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]		, A					:     StableDiffusionSafetyChecker		, A					:     CLIPImageProcessor		, )       ->List[Any]:
          super().__init__()
          self.register_modules(
              vae=A		, text_encoder=A		, tokenizer=A		, unet=A		, scheduler=A		, safety_checker=A		, feature_extractor=A		, )







       def 						__lowerCamelCase   (				self					:     Any		, A					:     Optional[Union[str, int]] = "auto"      )       ->Tuple:
          if slice_size == "auto":
             # half the attention head size is usually a good trade-off between
             # speed and memory
             lowerCamelCase__       :						List[Any]        =							self.unet.config.attention_head_dim // 2
          self.unet.set_attention_slice(A      )







       def 						__lowerCamelCase   (				self					:     List[Any]      )       ->List[Any]:
          self.enable_attention_slicing(A      )







       @torch.no_grad()
       def __call__(				self					:     Union[str, Any]		, A					:     Union[str, List[str]]		, A					:     int = 5_1_2		, A					:     int = 5_1_2		, A					:     int = 5_0		, A					:     float = 7.5		, A					:     Optional[Union[str, List[str]]] = None		, A					:     Optional[int] = 1		, A					:     float = 0.0		, A					:     Optional[torch.Generator] = None		, A					:     Optional[torch.FloatTensor] = None		, A					:     Optional[str] = "pil"		, A					:     bool = True		, A					:     Optional[Callable[[int, int, torch.FloatTensor], None]] = None		, A					:     int = 1		, A					:     Optional[torch.FloatTensor] = None		, **A					:     int		, )       ->Tuple:

          if isinstance(A		, A      ):
             lowerCamelCase__       :						str        =							1
          elif isinstance(A		, A      ):
             lowerCamelCase__       :						Dict        =							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
          lowerCamelCase__       :						Optional[Any]        =							self.tokenizer(
              A		, padding='''max_length'''		, max_length=self.tokenizer.model_max_length		, return_tensors='''pt'''		, )
          lowerCamelCase__       :						Dict        =							text_inputs.input_ids

          if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
             lowerCamelCase__       :						Optional[Any]        =							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}"      )
             lowerCamelCase__       :						Any        =							text_input_ids[:, : self.tokenizer.model_max_length]

          if text_embeddings is None:
             lowerCamelCase__       :						str        =							self.text_encoder(text_input_ids.to(self.device      )      )[0]

          # duplicate text embeddings for each generation per prompt, using mps friendly method
          lowerCamelCase__       :						int        =							text_embeddings.shape
          lowerCamelCase__       :						int        =							text_embeddings.repeat(1		, A		, 1      )
          lowerCamelCase__       :						str        =							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.
          lowerCamelCase__       :						Optional[Any]        =							guidance_scale > 1.0
          # get unconditional embeddings for classifier free guidance
          if do_classifier_free_guidance:
             lowerCamelCase__       :						List[str]
             if negative_prompt is None:
                lowerCamelCase__       :						Optional[int]        =							['''''']
             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      ):
                lowerCamelCase__       :						Optional[int]        =							[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:
                lowerCamelCase__       :						List[Any]        =							negative_prompt

             lowerCamelCase__       :						int        =							text_input_ids.shape[-1]
             lowerCamelCase__       :						Optional[int]        =							self.tokenizer(
                 A		, padding='''max_length'''		, max_length=A		, truncation=A		, return_tensors='''pt'''		, )
             lowerCamelCase__       :						Optional[Any]        =							self.text_encoder(uncond_input.input_ids.to(self.device      )      )[0]

             # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
             lowerCamelCase__       :						Any        =							uncond_embeddings.shape[1]
             lowerCamelCase__       :						Union[str, Any]        =							uncond_embeddings.repeat(A		, A		, 1      )
             lowerCamelCase__       :						str        =							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
             lowerCamelCase__       :						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`.
          lowerCamelCase__       :						Dict        =							(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
          lowerCamelCase__       :						List[Any]        =							(batch_size * num_images_per_prompt, self.unet.config.in_channels, 6_4, 6_4)
          lowerCamelCase__       :						List[Any]        =							text_embeddings.dtype
          if latents is None:
             if self.device.type == "mps":
                # randn does not exist on mps
                lowerCamelCase__       :						Dict        =							torch.randn(
                    A		, generator=A		, device='''cpu'''		, dtype=A      ).to(self.device      )
                lowerCamelCase__       :						Dict        =							torch.randn(A		, generator=A		, device='''cpu'''		, dtype=A      ).to(
                    self.device      )
             else:
                lowerCamelCase__       :						Optional[Any]        =							torch.randn(
                    A		, generator=A		, device=self.device		, dtype=A      )
                lowerCamelCase__       :						Union[str, Any]        =							torch.randn(A		, generator=A		, device=self.device		, dtype=A      )
          else:
             if latents_reference.shape != latents_shape:
                raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {latents_shape}"      )
             lowerCamelCase__       :						Optional[Any]        =							latents_reference.to(self.device      )
             lowerCamelCase__       :						Optional[int]        =							latents.to(self.device      )

          # This is the key part of the pipeline where we
          # try to ensure that the generated images w/ the same seed
          # but different sizes actually result in similar images
          lowerCamelCase__       :						List[str]        =							(latents_shape[3] - latents_shape_reference[3]) // 2
          lowerCamelCase__       :						List[Any]        =							(latents_shape[2] - latents_shape_reference[2]) // 2
          lowerCamelCase__       :						Dict        =							latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
          lowerCamelCase__       :						str        =							latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
          lowerCamelCase__       :						int        =							0 if dx < 0 else dx
          lowerCamelCase__       :						Optional[int]        =							0 if dy < 0 else dy
          lowerCamelCase__       :						Dict        =							max(-dx		, 0      )
          lowerCamelCase__       :						int        =							max(-dy		, 0      )
          # import pdb
          # pdb.set_trace()
          lowerCamelCase__       :						str        =							latents_reference[:, :, dy : dy + h, dx : dx + w]

          # 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
          lowerCamelCase__       :						Union[str, Any]        =							self.scheduler.timesteps.to(self.device      )

          # scale the initial noise by the standard deviation required by the scheduler
          lowerCamelCase__       :						List[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]
          lowerCamelCase__       :						Union[str, Any]        =							'''eta''' in set(inspect.signature(self.scheduler.step      ).parameters.keys()      )
          lowerCamelCase__       :						List[Any]        =							{}
          if accepts_eta:
             lowerCamelCase__       :						Any        =							eta

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

             # predict the noise residual
             lowerCamelCase__       :						Union[str, Any]        =							self.unet(A		, A		, encoder_hidden_states=A      ).sample

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

             # compute the previous noisy sample x_t -> x_t-1
             lowerCamelCase__       :						Dict        =							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      )

          lowerCamelCase__       :						Optional[Any]        =							1 / 0.1_82_15 * latents
          lowerCamelCase__       :						int        =							self.vae.decode(A      ).sample

          lowerCamelCase__       :						Dict        =							(image / 2 + 0.5).clamp(0		, 1      )

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

          if self.safety_checker is not None:
             lowerCamelCase__       :						Tuple        =							self.feature_extractor(self.numpy_to_pil(A      )		, return_tensors='''pt'''      ).to(
                 self.device      )
             lowerCamelCase__       :						int        =							self.safety_checker(
                 images=A		, clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype      )      )
          else:
             lowerCamelCase__       :						List[Any]        =							None

          if output_type == "pil":
             lowerCamelCase__       :						Optional[Any]        =							self.numpy_to_pil(A      )

          if not return_dict:
             return (image, has_nsfw_concept)

          return StableDiffusionPipelineOutput(images=A		, nsfw_content_detected=A      )










import random
import timeit
from functools import wraps
from typing import Callable, Optional

from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging
from .benchmark_utils import (
    Benchmark,
    Memory,
    MemorySummary,
    measure_peak_memory_cpu,
    start_memory_tracing,
    stop_memory_tracing,
)


if is_tf_available():
       import tensorflow as tf
       from tensorflow.python.framework.errors_impl import ResourceExhaustedError

       from .benchmark_args_tf import TensorFlowBenchmarkArguments

if is_pyanvml_available():
       import pyanvml.pyanvml as nvml

_A :   Any  			=  logging.get_logger(__name__)



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase					)    ->    List[str]:







   """simple docstring"""
   def run_func(UpperCAmelCase					):
      @wraps(UpperCAmelCase					)
      def run_in_eager_mode(*UpperCAmelCase      ,  **UpperCAmelCase					):
         return func(*UpperCAmelCase      ,  **UpperCAmelCase					)

      @wraps(UpperCAmelCase					)
      @tf.function(experimental_compile=UpperCAmelCase					)
      def run_in_graph_mode(*UpperCAmelCase      ,  **UpperCAmelCase					):
         return func(*UpperCAmelCase      ,  **UpperCAmelCase					)

      if do_eager_mode is True:
         if use_xla is not False:
            raise ValueError(
                '''Cannot run model in XLA, if `args.eager_mode` is set to `True`. Please set `args.eager_mode=False`.'''					)
         return run_in_eager_mode
      else:
         return run_in_graph_mode

   return run_func



def 	_a						(   UpperCAmelCase      ,  UpperCAmelCase      ,  UpperCAmelCase					)    ->    ["tf.Tensor"]:







   """simple docstring"""
   lowerCamelCase__       :						List[Any]        =							random.Random()
   lowerCamelCase__       :						str        =							[rng.randint(0      ,  vocab_size - 1					) for i in range(batch_size * sequence_length					)]
   return tf.constant(UpperCAmelCase      ,  shape=(batch_size, sequence_length)      ,  dtype=tf.intaa					)




class        __SCREAMING_SNAKE_CASE      (			lowerCAmelCase_ ):
       _UpperCAmelCase				:  TensorFlowBenchmarkArguments
       _UpperCAmelCase				:  PretrainedConfig
       _UpperCAmelCase				:  str			   =   "TensorFlow"







       @property
       def 						__lowerCamelCase   (				self					:     int      )       ->Optional[int]:
          return tf.__version__







       def 						__lowerCamelCase   (				self					:     Optional[int]		, A					:     str		, A					:     int		, A					:     int      )       ->float:
          # initialize GPU on separate process
          lowerCamelCase__       :						Dict        =							self.args.strategy
          if strategy is None:
             raise ValueError('''A device strategy has to be initialized before using TensorFlow.'''      )
          lowerCamelCase__       :						int        =							self._prepare_inference_func(A		, A		, A      )
          return self._measure_speed(_inference      )







       def 						__lowerCamelCase   (				self					:     str		, A					:     str		, A					:     int		, A					:     int      )       ->float:
          lowerCamelCase__       :						Optional[int]        =							self.args.strategy
          if strategy is None:
             raise ValueError('''A device strategy has to be initialized before using TensorFlow.'''      )
          lowerCamelCase__       :						List[Any]        =							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]]:
          # initialize GPU on separate process
          if self.args.is_gpu:
             tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]		, A      )
          lowerCamelCase__       :						int        =							self.args.strategy
          if strategy is None:
             raise ValueError('''A device strategy has to be initialized before using TensorFlow.'''      )
          lowerCamelCase__       :						str        =							self._prepare_inference_func(A		, A		, A      )
          return self._measure_memory(_inference      )







       def 						__lowerCamelCase   (				self					:     List[str]		, A					:     str		, A					:     int		, A					:     int      )       ->[Memory, Optional[MemorySummary]]:
          if self.args.is_gpu:
             tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]		, A      )
          lowerCamelCase__       :						List[Any]        =							self.args.strategy
          if strategy is None:
             raise ValueError('''A device strategy has to be initialized before using TensorFlow.'''      )

          lowerCamelCase__       :						str        =							self._prepare_train_func(A		, A		, A      )
          return self._measure_memory(_train      )







       def 						__lowerCamelCase   (				self					:     Dict		, A					:     str		, A					:     int		, A					:     int      )       ->Callable[[], None]:
          lowerCamelCase__       :						Tuple        =							self.config_dict[model_name]

          if self.args.fpaa:
             raise NotImplementedError('''Mixed precision is currently not supported.'''      )

          lowerCamelCase__       :						Tuple        =							(
              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:
                lowerCamelCase__       :						Any        =							'''TF''' + config.architectures[0]  # prepend 'TF' for tensorflow model
                lowerCamelCase__       :						List[Any]        =							__import__('''transformers'''		, fromlist=[model_class]      )
                lowerCamelCase__       :						int        =							getattr(A		, A      )
                lowerCamelCase__       :						int        =							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:
             lowerCamelCase__       :						Union[str, Any]        =							TF_MODEL_MAPPING[config.__class__](A      )

          # encoder-decoder has vocab size saved differently
          lowerCamelCase__       :						Tuple        =							config.vocab_size if hasattr(A		, '''vocab_size'''      ) else config.encoder.vocab_size
          lowerCamelCase__       :						Optional[Any]        =							random_input_ids(A		, A		, A      )

          @run_with_tf_optimizations(self.args.eager_mode		, self.args.use_xla      )
          def encoder_decoder_forward():
             return model(A		, decoder_input_ids=A		, training=A      )

          @run_with_tf_optimizations(self.args.eager_mode		, self.args.use_xla      )
          def encoder_forward():
             return model(A		, training=A      )

          lowerCamelCase__       :						int        =							encoder_decoder_forward if config.is_encoder_decoder else encoder_forward

          return _inference







       def 						__lowerCamelCase   (				self					:     List[str]		, A					:     str		, A					:     int		, A					:     int      )       ->Callable[[], None]:
          lowerCamelCase__       :						Tuple        =							self.config_dict[model_name]

          if self.args.eager_mode is not False:
             raise ValueError('''Training cannot be done in eager mode. Please make sure that `args.eager_mode = False`.'''      )

          if self.args.fpaa:
             raise NotImplementedError('''Mixed precision is currently not supported.'''      )

          lowerCamelCase__       :						Optional[int]        =							(
              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:
                lowerCamelCase__       :						Any        =							'''TF''' + config.architectures[0]  # prepend 'TF' for tensorflow model
                lowerCamelCase__       :						List[str]        =							__import__('''transformers'''		, fromlist=[model_class]      )
                lowerCamelCase__       :						Optional[int]        =							getattr(A		, A      )
                lowerCamelCase__       :						Optional[Any]        =							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:
             lowerCamelCase__       :						List[str]        =							TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](A      )

          # encoder-decoder has vocab size saved differently
          lowerCamelCase__       :						Optional[int]        =							config.vocab_size if hasattr(A		, '''vocab_size'''      ) else config.encoder.vocab_size
          lowerCamelCase__       :						Dict        =							random_input_ids(A		, A		, A      )

          @run_with_tf_optimizations(self.args.eager_mode		, self.args.use_xla      )
          def encoder_decoder_train():
             lowerCamelCase__       :						int        =							model(A		, decoder_input_ids=A		, labels=A		, training=A      )[0]
             lowerCamelCase__       :						List[Any]        =							tf.gradients(A		, model.trainable_variables      )
             return gradients

          @run_with_tf_optimizations(self.args.eager_mode		, self.args.use_xla      )
          def encoder_train():
             lowerCamelCase__       :						Optional[int]        =							model(A		, labels=A		, training=A      )[0]
             lowerCamelCase__       :						List[str]        =							tf.gradients(A		, model.trainable_variables      )
             return gradients

          lowerCamelCase__       :						Tuple        =							encoder_decoder_train if config.is_encoder_decoder else encoder_train

          return _train







       def 						__lowerCamelCase   (				self					:     Tuple		, A					:     Any      )       ->float:
          with self.args.strategy.scope():
             try:
                if self.args.is_tpu or self.args.use_xla:
                   # run additional 10 times to stabilize compilation for tpu
                   logger.info('''Do inference on TPU. 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
                lowerCamelCase__       :						Optional[Any]        =							timeit.repeat(
                    A		, repeat=self.args.repeat		, number=1_0		, )

                return min(A      ) / 10.0
             except ResourceExhaustedError as e:
                self.print_fn(F"Doesn't fit on GPU. {e}"      )







       def 						__lowerCamelCase   (				self					:     List[Any]		, A					:     Callable[[], None]      )       ->[Memory, MemorySummary]:
          logger.info(
              '''Note that TensorFlow allocates more memory than '''
              '''it might need to speed up computation. '''
              '''The memory reported here corresponds to the memory '''
              '''reported by `nvidia-smi`, which can vary depending '''
              '''on total available memory on the GPU that is used.'''      )
          with self.args.strategy.scope():
             try:
                if self.args.trace_memory_line_by_line:
                   if not self.args.eager_mode:
                      raise ValueError(
                          '''`args.eager_mode` is set to `False`. Make sure to run model in eager mode to measure memory'''
                          ''' consumption line by line.'''      )
                   lowerCamelCase__       :						Union[str, Any]        =							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 `args.memory=False`'''      )
                elif self.args.is_gpu:
                   # 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__       :						Union[str, Any]        =							'''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__       :						Any        =							nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx      )
                      lowerCamelCase__       :						Optional[int]        =							nvml.nvmlDeviceGetMemoryInfo(A      )
                      lowerCamelCase__       :						List[Any]        =							meminfo.used
                      lowerCamelCase__       :						Union[str, Any]        =							Memory(A      )
                      # shutdown nvml
                      nvml.nvmlShutdown()
                else:
                   # cpu
                   if self.args.trace_memory_line_by_line:
                      logger.info(
                          '''When enabling line by line tracing, the max peak memory for CPU is inaccurate in'''
                          ''' TensorFlow.'''      )
                      lowerCamelCase__       :						Tuple        =							None
                   else:
                      lowerCamelCase__       :						Dict        =							measure_peak_memory_cpu(A      )
                      lowerCamelCase__       :						Optional[Any]        =							Memory(A      ) if isinstance(A		, A      ) else memory_bytes
                if self.args.trace_memory_line_by_line:
                   lowerCamelCase__       :						Union[str, Any]        =							stop_memory_tracing(A      )
                   if memory is None:
                      lowerCamelCase__       :						Dict        =							summary.total
                else:
                   lowerCamelCase__       :						Optional[int]        =							None

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



