

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__      ):
     _lowercase    :  PyTorchBenchmarkArguments
     _lowercase    :  PretrainedConfig
     _lowercase    :  str          =			"PyTorch"


     @property
     def 						UpperCAmelCase						(							self    :	List[Any]      )							->			int:
      return torch.__version__


     def 						UpperCAmelCase						(							self    :	Union[str, Any]    ,						UpperCAmelCase    :	str    ,						UpperCAmelCase    :	int    ,						UpperCAmelCase    :	int      )							->			float:
      __lowerCAmelCase:     Union[str, Any]     			=   self._prepare_inference_func(UpperCAmelCase    ,						UpperCAmelCase    ,						UpperCAmelCase      )
      return self._measure_speed(_inference      )


     def 						UpperCAmelCase						(							self    :	List[Any]    ,						UpperCAmelCase    :	str    ,						UpperCAmelCase    :	int    ,						UpperCAmelCase    :	int      )							->			[Memory, Optional[MemorySummary]]:
      __lowerCAmelCase:     Any     			=   self._prepare_inference_func(UpperCAmelCase    ,						UpperCAmelCase    ,						UpperCAmelCase      )
      return self._measure_memory(_inference      )


     def 						UpperCAmelCase						(							self    :	Optional[int]    ,						UpperCAmelCase    :	str    ,						UpperCAmelCase    :	int    ,						UpperCAmelCase    :	int      )							->			float:
      __lowerCAmelCase:     Optional[Any]     			=   self._prepare_train_func(UpperCAmelCase    ,						UpperCAmelCase    ,						UpperCAmelCase      )
      return self._measure_speed(_train      )


     def 						UpperCAmelCase						(							self    :	List[Any]    ,						UpperCAmelCase    :	str    ,						UpperCAmelCase    :	int    ,						UpperCAmelCase    :	int      )							->			[Memory, Optional[MemorySummary]]:
      __lowerCAmelCase:     Dict     			=   self._prepare_train_func(UpperCAmelCase    ,						UpperCAmelCase    ,						UpperCAmelCase      )
      return self._measure_memory(_train      )


     def 						UpperCAmelCase						(							self    :	str    ,						UpperCAmelCase    :	str    ,						UpperCAmelCase    :	int    ,						UpperCAmelCase    :	int      )							->			Callable[[], None]:
      __lowerCAmelCase:     Any     			=   self.config_dict[model_name]

      if self.args.torchscript:
       __lowerCAmelCase:     Dict     			=   True

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

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

      # encoder-decoder has vocab size saved differently
      __lowerCAmelCase:     Optional[int]     			=   config.vocab_size if hasattr(UpperCAmelCase    ,						'vocab_size'      ) else config.encoder.vocab_size
      __lowerCAmelCase:     Tuple     			=   torch.randint(UpperCAmelCase    ,						(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:     Any     			=   torch.jit.trace(UpperCAmelCase    ,						UpperCAmelCase      )
      else:
       __lowerCAmelCase:     int     			=   model

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

      def encoder_forward():
       with torch.no_grad():
        __lowerCAmelCase:     Optional[Any]     			=   inference_model(UpperCAmelCase      )
       return outputs

      __lowerCAmelCase:     Any     			=   encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
      return _forward


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

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

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

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

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

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

      __lowerCAmelCase:     str     			=   (
          compute_loss_and_backprob_encoder_decoder
          if config.is_encoder_decoder
          else compute_loss_and_backprob_encoder
      )
      return _train


     def 						UpperCAmelCase						(							self    :	Tuple    ,						UpperCAmelCase    :	List[Any]      )							->			float:
      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(
            UpperCAmelCase    ,						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:     Union[str, Any]     			=   timeit.repeat(
           UpperCAmelCase    ,						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(UpperCAmelCase      ) / 10.0
      except RuntimeError as e:
       self.print_fn(F'''Doesn\'t fit on GPU. {e}'''      )
       return "N/A"


     def 						UpperCAmelCase						(							self    :	List[str]    ,						UpperCAmelCase    :	Callable[[], None]      )							->			[Memory, MemorySummary]:
      try:
       if self.args.trace_memory_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'
            ' `--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:     Optional[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:     List[Any]     			=   nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx      )
         __lowerCAmelCase:     Any     			=   nvml.nvmlDeviceGetMemoryInfo(UpperCAmelCase      )
         __lowerCAmelCase:     Optional[int]     			=   meminfo.used
         __lowerCAmelCase:     List[Any]     			=   Memory(UpperCAmelCase      )
         # shutdown nvml
         nvml.nvmlShutdown()
       else:
        # cpu
        __lowerCAmelCase:     int     			=   measure_peak_memory_cpu(UpperCAmelCase      )
        __lowerCAmelCase:     int     			=   Memory(UpperCAmelCase      ) if isinstance(UpperCAmelCase    ,						UpperCAmelCase      ) else memory_bytes

       if self.args.trace_memory_line_by_line:
        __lowerCAmelCase:     Optional[Any]     			=   stop_memory_tracing(UpperCAmelCase      )
       else:
        __lowerCAmelCase:     Any     			=   None

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








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 							A_				(	snake_case__      ):


     def __init__(							self    :	List[Any]    ,						UpperCAmelCase    :	AutoencoderKL    ,						UpperCAmelCase    :	CLIPTextModel    ,						UpperCAmelCase    :	CLIPTokenizer    ,						UpperCAmelCase    :	UNetaDConditionModel    ,						UpperCAmelCase    :	Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler]    ,						)							->			Tuple:
      super().__init__()

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


     def 						UpperCAmelCase						(							self    :	str    ,						UpperCAmelCase    :	Dict      )							->			Any:
      with torch.no_grad():
       __lowerCAmelCase:     Tuple     			=   self.vae.encode(tfms.ToTensor()(UpperCAmelCase      ).unsqueeze(0      ).to(self.device      ) * 2 - 1      )
       __lowerCAmelCase:     Tuple     			=   0.18215 * latent.latent_dist.sample()
      return latent


     def 						UpperCAmelCase						(							self    :	List[Any]    ,						UpperCAmelCase    :	str      )							->			int:
      __lowerCAmelCase:     Optional[Any]     			=   (1 / 0.18215) * latent
      with torch.no_grad():
       __lowerCAmelCase:     str     			=   self.vae.decode(UpperCAmelCase      ).sample
      __lowerCAmelCase:     Any     			=   (img / 2 + 0.5).clamp(0    ,						1      )
      __lowerCAmelCase:     Optional[Any]     			=   img.detach().cpu().permute(0    ,						2    ,						3    ,						1      ).numpy()
      __lowerCAmelCase:     List[str]     			=   (img * 2_5_5).round().astype('uint8'      )
      return Image.fromarray(img[0]      )


     def 						UpperCAmelCase						(							self    :	Optional[Any]    ,						UpperCAmelCase    :	Tuple      )							->			str:
      __lowerCAmelCase:     Dict     			=   self.tokenizer(
          UpperCAmelCase    ,						padding='max_length'    ,						max_length=self.tokenizer.model_max_length    ,						truncation=UpperCAmelCase    ,						return_tensors='pt'    ,						)

      __lowerCAmelCase:     Any     			=   self.text_encoder(text_input.input_ids.to(self.device      )      )[0]

      __lowerCAmelCase:     List[str]     			=   self.tokenizer(
          ''    ,						padding='max_length'    ,						max_length=self.tokenizer.model_max_length    ,						truncation=UpperCAmelCase    ,						return_tensors='pt'    ,						)

      __lowerCAmelCase:     str     			=   self.text_encoder(uncond_input.input_ids.to(self.device      )      )[0]

      return torch.cat([uncond_embedding, text_embedding]      )


     def __call__(							self    :	Optional[int]    ,						UpperCAmelCase    :	Image.Image    ,						UpperCAmelCase    :	str    ,						UpperCAmelCase    :	float = 0.3    ,						UpperCAmelCase    :	float = 0.6    ,						UpperCAmelCase    :	float = 0.5    ,						UpperCAmelCase    :	int = 4_2    ,						UpperCAmelCase    :	int = 5_0    ,						UpperCAmelCase    :	float = 7.5    ,						)							->			Image.Image:
      __lowerCAmelCase:     Optional[int]     			=   steps - int(kmin * steps      )
      __lowerCAmelCase:     Dict     			=   steps - int(kmax * steps      )

      __lowerCAmelCase:     Union[str, Any]     			=   self.prep_text(UpperCAmelCase      )

      self.scheduler.set_timesteps(UpperCAmelCase      )

      __lowerCAmelCase			,							__lowerCAmelCase:     str     			=   img.size
      __lowerCAmelCase:     List[Any]     			=   self.encode(UpperCAmelCase      )

      torch.manual_seed(UpperCAmelCase      )
      __lowerCAmelCase:     Optional[int]     			=   torch.randn(
          (1, self.unet.config.in_channels, height // 8, width // 8)    ,						).to(self.device      )

      __lowerCAmelCase:     int     			=   self.scheduler.add_noise(
          UpperCAmelCase    ,						UpperCAmelCase    ,						timesteps=self.scheduler.timesteps[tmax]    ,						)

      __lowerCAmelCase:     Tuple     			=   torch.cat([latents] * 2      )

      __lowerCAmelCase:     int     			=   self.scheduler.scale_model_input(UpperCAmelCase    ,						self.scheduler.timesteps[tmax]      )

      with torch.no_grad():
       __lowerCAmelCase:     List[str]     			=   self.unet(
           UpperCAmelCase    ,						self.scheduler.timesteps[tmax]    ,						encoder_hidden_states=UpperCAmelCase    ,						).sample

      __lowerCAmelCase			,							__lowerCAmelCase:     Optional[Any]     			=   pred.chunk(2      )
      __lowerCAmelCase:     Union[str, Any]     			=   pred_uncond + guidance_scale * (pred_text - pred_uncond)

      __lowerCAmelCase:     List[str]     			=   self.scheduler.step(UpperCAmelCase    ,						self.scheduler.timesteps[tmax]    ,						UpperCAmelCase      ).prev_sample

      for i, t in enumerate(tqdm(self.scheduler.timesteps      )      ):
       if i > tmax:
        if i < tmin:  # layout generation phase
         __lowerCAmelCase:     Optional[Any]     			=   self.scheduler.add_noise(
             UpperCAmelCase    ,						UpperCAmelCase    ,						timesteps=UpperCAmelCase    ,						)

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

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

        __lowerCAmelCase:     List[Any]     			=   self.scheduler.scale_model_input(UpperCAmelCase    ,						UpperCAmelCase      )

        with torch.no_grad():
         __lowerCAmelCase:     Any     			=   self.unet(
             UpperCAmelCase    ,						UpperCAmelCase    ,						encoder_hidden_states=UpperCAmelCase    ,						).sample

        __lowerCAmelCase			,							__lowerCAmelCase:     Optional[int]     			=   pred.chunk(2      )
        __lowerCAmelCase:     Union[str, Any]     			=   pred_uncond + guidance_scale * (pred_text - pred_uncond)

        __lowerCAmelCase:     List[str]     			=   self.scheduler.step(UpperCAmelCase    ,						UpperCAmelCase    ,						UpperCAmelCase      ).prev_sample

      return self.decode(UpperCAmelCase      )





