



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 						snake_case  (		SCREAMING_SNAKE_CASE_					):



   '''simple docstring'''






   snake_case_   :	PyTorchBenchmarkArguments
   snake_case_   :	PretrainedConfig
   snake_case_   :	str       =						"PyTorch"

   @property
   def        UpperCamelCase_					(   self							:     Optional[int])			->			str:

    """simple docstring"""







    return torch.__version__

   def        UpperCamelCase_					(   self							:     Optional[int]			,						lowerCAmelCase							:     str			,						lowerCAmelCase							:     int			,						lowerCAmelCase							:     int)			->			float:

    """simple docstring"""







    _snake_case      :					List[str]					=   self._prepare_inference_func(lowerCAmelCase			,						lowerCAmelCase			,						lowerCAmelCase)
    return self._measure_speed(_inference)

   def        UpperCamelCase_					(   self							:     Tuple			,						lowerCAmelCase							:     str			,						lowerCAmelCase							:     int			,						lowerCAmelCase							:     int)			->			[Memory, Optional[MemorySummary]]:

    """simple docstring"""







    _snake_case      :					List[str]					=   self._prepare_inference_func(lowerCAmelCase			,						lowerCAmelCase			,						lowerCAmelCase)
    return self._measure_memory(_inference)

   def        UpperCamelCase_					(   self							:     List[str]			,						lowerCAmelCase							:     str			,						lowerCAmelCase							:     int			,						lowerCAmelCase							:     int)			->			float:

    """simple docstring"""







    _snake_case      :					str					=   self._prepare_train_func(lowerCAmelCase			,						lowerCAmelCase			,						lowerCAmelCase)
    return self._measure_speed(_train)

   def        UpperCamelCase_					(   self							:     List[Any]			,						lowerCAmelCase							:     str			,						lowerCAmelCase							:     int			,						lowerCAmelCase							:     int)			->			[Memory, Optional[MemorySummary]]:

    """simple docstring"""







    _snake_case      :					Dict					=   self._prepare_train_func(lowerCAmelCase			,						lowerCAmelCase			,						lowerCAmelCase)
    return self._measure_memory(_train)

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

    """simple docstring"""







    _snake_case      :					int					=   self.config_dict[model_name]

    if self.args.torchscript:
     _snake_case      :					Union[str, Any]					=   True

    _snake_case      :					Dict					=   (
        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:
      _snake_case      :					Optional[Any]					=   config.architectures[0]
      _snake_case      :					Dict					=   __import__("""transformers"""			,						fromlist=[model_class])
      _snake_case      :					str					=   getattr(lowerCAmelCase			,						lowerCAmelCase)
      _snake_case      :					Union[str, Any]					=   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:
     _snake_case      :					int					=   MODEL_MAPPING[config.__class__](lowerCAmelCase)

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

    # encoder-decoder has vocab size saved differently
    _snake_case      :					List[Any]					=   config.vocab_size if hasattr(lowerCAmelCase			,						"""vocab_size""") else config.encoder.vocab_size
    _snake_case      :					Dict					=   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():
      _snake_case      :					int					=   torch.jit.trace(lowerCAmelCase			,						lowerCAmelCase)
    else:
     _snake_case      :					Any					=   model

    def encoder_decoder_forward():
     with torch.no_grad():
      _snake_case      :					List[str]					=   inference_model(lowerCAmelCase			,						decoder_input_ids=lowerCAmelCase)
     return outputs

    def encoder_forward():
     with torch.no_grad():
      _snake_case      :					List[str]					=   inference_model(lowerCAmelCase)
     return outputs

    _snake_case      :					Optional[int]					=   encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
    return _forward

   def        UpperCamelCase_					(   self							:     Tuple			,						lowerCAmelCase							:     str			,						lowerCAmelCase							:     int			,						lowerCAmelCase							:     int)			->			Callable[[], None]:

    """simple docstring"""







    _snake_case      :					Union[str, Any]					=   self.config_dict[model_name]

    _snake_case      :					Any					=   (
        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:
      _snake_case      :					Union[str, Any]					=   config.architectures[0]
      _snake_case      :					Optional[int]					=   __import__("""transformers"""			,						fromlist=[model_class])
      _snake_case      :					Optional[int]					=   getattr(lowerCAmelCase			,						lowerCAmelCase)
      _snake_case      :					str					=   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:
     _snake_case      :					int					=   MODEL_WITH_LM_HEAD_MAPPING[config.__class__](lowerCAmelCase)

    if self.args.torchscript:
     raise NotImplementedError("""Training for torchscript is currently not implemented""")
    else:
     _snake_case      :					Union[str, Any]					=   model

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

    # encoder-decoder has vocab size saved differently
    _snake_case      :					int					=   config.vocab_size if hasattr(lowerCAmelCase			,						"""vocab_size""") else config.encoder.vocab_size
    _snake_case      :					List[str]					=   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():
     _snake_case      :					Optional[int]					=   train_model(lowerCAmelCase			,						labels=lowerCAmelCase)[0]
     loss.backward()
     return loss

    def compute_loss_and_backprob_encoder_decoder():
     _snake_case      :					Dict					=   train_model(lowerCAmelCase			,						decoder_input_ids=lowerCAmelCase			,						labels=lowerCAmelCase)[0]
     loss.backward()
     return loss

    _snake_case      :					List[Any]					=   (
        compute_loss_and_backprob_encoder_decoder
        if config.is_encoder_decoder
        else compute_loss_and_backprob_encoder
    )
    return _train

   def        UpperCamelCase_					(   self							:     Optional[int]			,						lowerCAmelCase							:     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(
          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
     _snake_case      :					Dict					=   timeit.repeat(
         lowerCAmelCase			,						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(lowerCAmelCase) / 10.0
    except RuntimeError as e:
     self.print_fn(F'''Doesn\'t fit on GPU. {e}''')
     return "N/A"


   def        UpperCamelCase_					(   self							:     Optional[Any]			,						lowerCAmelCase							:     Callable[[], None])			->			[Memory, MemorySummary]:

    """simple docstring"""







    try:
     if self.args.trace_memory_line_by_line:
      _snake_case      :					Optional[int]					=   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.""")
       _snake_case      :					str					=   """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()
       _snake_case      :					Any					=   nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx)
       _snake_case      :					Union[str, Any]					=   nvml.nvmlDeviceGetMemoryInfo(lowerCAmelCase)
       _snake_case      :					List[Any]					=   meminfo.used
       _snake_case      :					List[Any]					=   Memory(lowerCAmelCase)
       # shutdown nvml
       nvml.nvmlShutdown()
     else:
      # cpu
      _snake_case      :					Optional[Any]					=   measure_peak_memory_cpu(lowerCAmelCase)
      _snake_case      :					Dict					=   Memory(lowerCAmelCase) if isinstance(lowerCAmelCase			,						lowerCAmelCase) else memory_bytes

     if self.args.trace_memory_line_by_line:
      _snake_case      :					Optional[Any]					=   stop_memory_tracing(lowerCAmelCase)
     else:
      _snake_case      :					Union[str, Any]					=   None

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





import unittest

import numpy as np
import torch

from diffusers import DDPMPipeline, DDPMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device


enable_full_determinism()







class 						snake_case  (		unittest.TestCase					):



   '''simple docstring'''






   @property
   def        UpperCamelCase_					(   self							:     Union[str, Any])			->			int:

    """simple docstring"""







    torch.manual_seed(0)
    _snake_case      :					Any					=   UNetaDModel(
        block_out_channels=(32, 64)			,						layers_per_block=2			,						sample_size=32			,						in_channels=3			,						out_channels=3			,						down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")			,						up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")			,						)
    return model

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

    """simple docstring"""







    _snake_case      :					List[str]					=   """cpu"""
    _snake_case      :					Union[str, Any]					=   self.dummy_uncond_unet
    _snake_case      :					Union[str, Any]					=   DDPMScheduler()

    _snake_case      :					Dict					=   DDPMPipeline(unet=lowerCAmelCase			,						scheduler=lowerCAmelCase)
    ddpm.to(lowerCAmelCase)
    ddpm.set_progress_bar_config(disable=lowerCAmelCase)

    _snake_case      :					Optional[Any]					=   torch.Generator(device=lowerCAmelCase).manual_seed(0)
    _snake_case      :					Optional[int]					=   ddpm(generator=lowerCAmelCase			,						num_inference_steps=2			,						output_type="""numpy""").images

    _snake_case      :					Optional[int]					=   torch.Generator(device=lowerCAmelCase).manual_seed(0)
    _snake_case      :					Union[str, Any]					=   ddpm(generator=lowerCAmelCase			,						num_inference_steps=2			,						output_type="""numpy"""			,						return_dict=lowerCAmelCase)[0]

    _snake_case      :					Any					=   image[0, -3:, -3:, -1]
    _snake_case      :					str					=   image_from_tuple[0, -3:, -3:, -1]

    assert image.shape == (1, 32, 32, 3)
    _snake_case      :					Optional[int]					=   np.array(
        [9.956E-01, 5.785E-01, 4.675E-01, 9.930E-01, 0.0, 1.000, 1.199E-03, 2.648E-04, 5.101E-04])

    assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2
    assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1E-2


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

    """simple docstring"""







    _snake_case      :					int					=   self.dummy_uncond_unet
    _snake_case      :					int					=   DDPMScheduler(prediction_type="""sample""")

    _snake_case      :					Union[str, Any]					=   DDPMPipeline(unet=lowerCAmelCase			,						scheduler=lowerCAmelCase)
    ddpm.to(lowerCAmelCase)
    ddpm.set_progress_bar_config(disable=lowerCAmelCase)

    _snake_case      :					Tuple					=   torch.manual_seed(0)
    _snake_case      :					Union[str, Any]					=   ddpm(generator=lowerCAmelCase			,						num_inference_steps=2			,						output_type="""numpy""").images

    _snake_case      :					Any					=   torch.manual_seed(0)
    _snake_case      :					Dict					=   ddpm(generator=lowerCAmelCase			,						num_inference_steps=2			,						output_type="""numpy""")[0]

    _snake_case      :					str					=   image[0, -3:, -3:, -1]
    _snake_case      :					List[Any]					=   image_eps[0, -3:, -3:, -1]

    assert image.shape == (1, 32, 32, 3)
    _snake_case      :					Any					=   1E-2 if torch_device != """mps""" else 3E-2
    assert np.abs(image_slice.flatten() - image_eps_slice.flatten()).max() < tolerance







@slow
@require_torch_gpu
class 						snake_case  (		unittest.TestCase					):



   '''simple docstring'''






   def        UpperCamelCase_					(   self							:     Dict)			->			Optional[Any]:

    """simple docstring"""







    _snake_case      :					Dict					=   """google/ddpm-cifar10-32"""

    _snake_case      :					List[Any]					=   UNetaDModel.from_pretrained(lowerCAmelCase)
    _snake_case      :					Optional[Any]					=   DDPMScheduler.from_pretrained(lowerCAmelCase)

    _snake_case      :					Dict					=   DDPMPipeline(unet=lowerCAmelCase			,						scheduler=lowerCAmelCase)
    ddpm.to(lowerCAmelCase)
    ddpm.set_progress_bar_config(disable=lowerCAmelCase)

    _snake_case      :					Optional[Any]					=   torch.manual_seed(0)
    _snake_case      :					Any					=   ddpm(generator=lowerCAmelCase			,						output_type="""numpy""").images

    _snake_case      :					Optional[Any]					=   image[0, -3:, -3:, -1]

    assert image.shape == (1, 32, 32, 3)
    _snake_case      :					Union[str, Any]					=   np.array([0.4_200, 0.3_588, 0.1_939, 0.3_847, 0.3_382, 0.2_647, 0.4_155, 0.3_582, 0.3_385])
    assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2
