

"""simple docstring"""



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

SCREAMING_SNAKE_CASE_    :		Any							     =  logging.get_logger(__name__)





def        _snake_case   (			UpperCAmelCase_     :    Optional[Any]    ,      UpperCAmelCase_     :    Dict	):
     def run_func(UpperCAmelCase_     :    Any	):
          @wraps(SCREAMING_SNAKE_CASE_	)
          def run_in_eager_mode(*UpperCAmelCase_     :    List[Any]    ,      **UpperCAmelCase_     :    int	):
               return func(*SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_	)

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

          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        _snake_case   (			UpperCAmelCase_     :    Tuple    ,      UpperCAmelCase_     :    Union[str, Any]    ,      UpperCAmelCase_     :    Optional[int]	):
     A__								=     random.Random()
     A__								=     [rng.randint(0    ,      vocab_size - 1	) for i in range(batch_size * sequence_length	)]
     return tf.constant(SCREAMING_SNAKE_CASE_    ,      shape=(batch_size, sequence_length)    ,      dtype=tf.intaa	)



class     a  (				_lowerCamelCase ):



    """simple docstring"""



    UpperCAmelCase         =    4_2
    UpperCAmelCase         =    4_2
    UpperCAmelCase         =    "TensorFlow"

    @property
    def  UpperCamelCase		(   self:       Any				):






         """simple docstring"""


         return tf.__version__

    def  UpperCamelCase		(   self:       List[Any]			,  UpperCamelCase:       str			,  UpperCamelCase:       int			,  UpperCamelCase:       int				):






         """simple docstring"""


         A__								=     self.args.strategy
         if strategy is None:
              raise ValueError("""A device strategy has to be initialized before using TensorFlow."""				)
         A__								=     self._prepare_inference_func(UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase				)
         return self._measure_speed(_inference				)

    def  UpperCamelCase		(   self:       Any			,  UpperCamelCase:       str			,  UpperCamelCase:       int			,  UpperCamelCase:       int				):






         """simple docstring"""


         A__								=     self.args.strategy
         if strategy is None:
              raise ValueError("""A device strategy has to be initialized before using TensorFlow."""				)
         A__								=     self._prepare_train_func(UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase				)
         return self._measure_speed(_train				)

    def  UpperCamelCase		(   self:       str			,  UpperCamelCase:       str			,  UpperCamelCase:       int			,  UpperCamelCase:       int				):






         """simple docstring"""


         if self.args.is_gpu:
              tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]			,  UpperCamelCase				)
         A__								=     self.args.strategy
         if strategy is None:
              raise ValueError("""A device strategy has to be initialized before using TensorFlow."""				)
         A__								=     self._prepare_inference_func(UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase				)
         return self._measure_memory(_inference				)

    def  UpperCamelCase		(   self:       List[str]			,  UpperCamelCase:       str			,  UpperCamelCase:       int			,  UpperCamelCase:       int				):






         """simple docstring"""


         if self.args.is_gpu:
              tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]			,  UpperCamelCase				)
         A__								=     self.args.strategy
         if strategy is None:
              raise ValueError("""A device strategy has to be initialized before using TensorFlow."""				)

         A__								=     self._prepare_train_func(UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase				)
         return self._measure_memory(_train				)

    def  UpperCamelCase		(   self:       Dict			,  UpperCamelCase:       str			,  UpperCamelCase:       int			,  UpperCamelCase:       int				):






         """simple docstring"""


         A__								=     self.config_dict[model_name]

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

         A__								=     (
             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:
                   A__								=     'TF' + config.architectures[0]  # prepend 'TF' for tensorflow model
                   A__								=     __import__("""transformers"""			,  fromlist=[model_class]				)
                   A__								=     getattr(UpperCamelCase			,  UpperCamelCase				)
                   A__								=     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:
              A__								=     TF_MODEL_MAPPING[config.__class__](UpperCamelCase				)

         # encoder-decoder has vocab size saved differently
         A__								=     config.vocab_size if hasattr(UpperCamelCase			,  """vocab_size"""				) else config.encoder.vocab_size
         A__								=     random_input_ids(UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase				)

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

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

         A__								=     encoder_decoder_forward if config.is_encoder_decoder else encoder_forward

         return _inference

    def  UpperCamelCase		(   self:       Optional[int]			,  UpperCamelCase:       str			,  UpperCamelCase:       int			,  UpperCamelCase:       int				):






         """simple docstring"""


         A__								=     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."""				)

         A__								=     (
             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:
                   A__								=     'TF' + config.architectures[0]  # prepend 'TF' for tensorflow model
                   A__								=     __import__("""transformers"""			,  fromlist=[model_class]				)
                   A__								=     getattr(UpperCamelCase			,  UpperCamelCase				)
                   A__								=     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:
              A__								=     TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](UpperCamelCase				)

         # encoder-decoder has vocab size saved differently
         A__								=     config.vocab_size if hasattr(UpperCamelCase			,  """vocab_size"""				) else config.encoder.vocab_size
         A__								=     random_input_ids(UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase				)

         @run_with_tf_optimizations(self.args.eager_mode			,  self.args.use_xla				)
         def encoder_decoder_train():
              A__								=     model(UpperCamelCase			,  decoder_input_ids=UpperCamelCase			,  labels=UpperCamelCase			,  training=UpperCamelCase				)[0]
              A__								=     tf.gradients(UpperCamelCase			,  model.trainable_variables				)
              return gradients

         @run_with_tf_optimizations(self.args.eager_mode			,  self.args.use_xla				)
         def encoder_train():
              A__								=     model(UpperCamelCase			,  labels=UpperCamelCase			,  training=UpperCamelCase				)[0]
              A__								=     tf.gradients(UpperCamelCase			,  model.trainable_variables				)
              return gradients

         A__								=     encoder_decoder_train if config.is_encoder_decoder else encoder_train

         return _train

    def  UpperCamelCase		(   self:       Dict			,  UpperCamelCase:       List[Any]				):






         """simple docstring"""


         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(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
                   A__								=     timeit.repeat(
                       UpperCamelCase			,  repeat=self.args.repeat			,  number=10			,  )

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





    def  UpperCamelCase		(   self:       Tuple			,  UpperCamelCase:       Callable[[], None]				):






         """simple docstring"""


         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."""				)
                        A__								=     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."""				)
                             A__								=     '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()
                             A__								=     nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx				)
                             A__								=     nvml.nvmlDeviceGetMemoryInfo(UpperCamelCase				)
                             A__								=     meminfo.used
                             A__								=     Memory(UpperCamelCase				)
                             # 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."""				)
                             A__								=     None
                        else:
                             A__								=     measure_peak_memory_cpu(UpperCamelCase				)
                             A__								=     Memory(UpperCamelCase				) if isinstance(UpperCamelCase			,  UpperCamelCase				) else memory_bytes
                   if self.args.trace_memory_line_by_line:
                        A__								=     stop_memory_tracing(UpperCamelCase				)
                        if memory is None:
                             A__								=     summary.total
                   else:
                        A__								=     None

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








from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class      A__							(     __magic_name__	):
				lowercase			    =  42
				lowercase			    =  42






				def __init__(						self					:      Any    ,     a					:      UNetaDModel    ,     a					:      ScoreSdeVeScheduler   ):






							'''simple docstring'''







							super().__init__()
							self.register_modules(unet=a    ,     scheduler=a   )






				@torch.no_grad()
				def __call__(						self					:      List[str]    ,     a					:      int = 1    ,     a					:      int = 2_000    ,     a					:      Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,     a					:      Optional[str] = "pil"    ,     a					:      bool = True    ,     **a					:      int    ,     ):






							'''simple docstring'''







							lowerCAmelCase__      :      List[str]         =		self.unet.config.sample_size
							lowerCAmelCase__      :      Union[str, Any]         =		(batch_size, 3, img_size, img_size)

							lowerCAmelCase__      :      Tuple         =		self.unet

							lowerCAmelCase__      :      Optional[Any]         =		randn_tensor(a    ,     generator=a   ) * self.scheduler.init_noise_sigma
							lowerCAmelCase__      :      int         =		sample.to(self.device   )

							self.scheduler.set_timesteps(a   )
							self.scheduler.set_sigmas(a   )

							for i, t in enumerate(self.progress_bar(self.scheduler.timesteps   )   ):
										lowerCAmelCase__      :      Dict         =		self.scheduler.sigmas[i] * torch.ones(shape[0]    ,     device=self.device   )

										# correction step
										for _ in range(self.scheduler.config.correct_steps   ):
													lowerCAmelCase__      :      Optional[Any]         =		self.unet(a    ,     a   ).sample
													lowerCAmelCase__      :      Dict         =		self.scheduler.step_correct(a    ,     a    ,     generator=a   ).prev_sample

										# prediction step
										lowerCAmelCase__      :      Optional[int]         =		model(a    ,     a   ).sample
										lowerCAmelCase__      :      Optional[Any]         =		self.scheduler.step_pred(a    ,     a    ,     a    ,     generator=a   )

										lowerCAmelCase__      ,				lowerCAmelCase__      :      str         =		output.prev_sample, output.prev_sample_mean

							lowerCAmelCase__      :      Any         =		sample_mean.clamp(0    ,     1   )
							lowerCAmelCase__      :      List[str]         =		sample.cpu().permute(0    ,     2    ,     3    ,     1   ).numpy()
							if output_type == "pil":
										lowerCAmelCase__      :      int         =		self.numpy_to_pil(a   )

							if not return_dict:
										return (sample,)

							return ImagePipelineOutput(images=a   )