
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

lowercase__       =logging.get_logger(__name__)




def    __UpperCamelCase     (				lowerCAmelCase__							:				bool  ,   lowerCAmelCase__							:				bool  ):
 def run_func(lowerCAmelCase__							:				Tuple  ):
  @wraps(lowerCAmelCase__  )
  def run_in_eager_mode(*lowerCAmelCase__							:				List[str]  ,   **lowerCAmelCase__							:				Optional[Any]  ):
   return func(*lowerCAmelCase__  ,   **lowerCAmelCase__  )

  @wraps(lowerCAmelCase__  )
  @tf.function(experimental_compile=lowerCAmelCase__  )
  def run_in_graph_mode(*lowerCAmelCase__							:				Tuple  ,   **lowerCAmelCase__							:				List[Any]  ):
   return func(*lowerCAmelCase__  ,   **lowerCAmelCase__  )

  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    __UpperCamelCase     (				lowerCAmelCase__							:				int  ,   lowerCAmelCase__							:				int  ,   lowerCAmelCase__							:				int  ):
 __a :  int						=  random.Random()
 __a :  int						=  [rng.randint(0  ,   vocab_size - 1  ) for i in range(batch_size * sequence_length  )]
 return tf.constant(lowerCAmelCase__  ,   shape=(batch_size, sequence_length)  ,   dtype=tf.intaa  )



class 		UpperCamelCase__  (       __lowercase    ):
   _SCREAMING_SNAKE_CASE      :							TensorFlowBenchmarkArguments
   _SCREAMING_SNAKE_CASE      :							PretrainedConfig
   _SCREAMING_SNAKE_CASE      :							str	       =      "TensorFlow"
   @property
   def       lowerCAmelCase			(self :					Optional[Any]   ):
    return tf.__version__
   def       lowerCAmelCase			(self :					Dict					,				snake_case_ :					str					,				snake_case_ :					int					,				snake_case_ :					int   ):
    # initialize GPU on separate process
    __a :  Tuple						=  self.args.strategy
    if strategy is None:
     raise ValueError('''A device strategy has to be initialized before using TensorFlow.'''   )
    __a :  Optional[int]						=  self._prepare_inference_func(snake_case_					,				snake_case_					,				snake_case_   )
    return self._measure_speed(_inference   )
   def       lowerCAmelCase			(self :					Tuple					,				snake_case_ :					str					,				snake_case_ :					int					,				snake_case_ :					int   ):
    __a :  Any						=  self.args.strategy
    if strategy is None:
     raise ValueError('''A device strategy has to be initialized before using TensorFlow.'''   )
    __a :  Any						=  self._prepare_train_func(snake_case_					,				snake_case_					,				snake_case_   )
    return self._measure_speed(_train   )
   def       lowerCAmelCase			(self :					str					,				snake_case_ :					str					,				snake_case_ :					int					,				snake_case_ :					int   ):
    # initialize GPU on separate process
    if self.args.is_gpu:
     tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]					,				snake_case_   )
    __a :  Any						=  self.args.strategy
    if strategy is None:
     raise ValueError('''A device strategy has to be initialized before using TensorFlow.'''   )
    __a :  Dict						=  self._prepare_inference_func(snake_case_					,				snake_case_					,				snake_case_   )
    return self._measure_memory(_inference   )
   def       lowerCAmelCase			(self :					Optional[int]					,				snake_case_ :					str					,				snake_case_ :					int					,				snake_case_ :					int   ):
    if self.args.is_gpu:
     tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx]					,				snake_case_   )
    __a :  Tuple						=  self.args.strategy
    if strategy is None:
     raise ValueError('''A device strategy has to be initialized before using TensorFlow.'''   )

    __a :  Optional[int]						=  self._prepare_train_func(snake_case_					,				snake_case_					,				snake_case_   )
    return self._measure_memory(_train   )
   def       lowerCAmelCase			(self :					Tuple					,				snake_case_ :					str					,				snake_case_ :					int					,				snake_case_ :					int   ):
    __a :  int						=  self.config_dict[model_name]

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

    __a :  Optional[int]						=  (
        hasattr(snake_case_					,				'''architectures'''   )
        and isinstance(config.architectures					,				snake_case_   )
        and len(config.architectures   ) > 0
    )
    if not self.args.only_pretrain_model and has_model_class_in_config:
     try:
      __a :  str						=  '''TF''' + config.architectures[0]  # prepend 'TF' for tensorflow model
      __a :  str						=  __import__('''transformers'''					,				fromlist=[model_class]   )
      __a :  Optional[Any]						=  getattr(snake_case_					,				snake_case_   )
      __a :  List[Any]						=  model_cls(snake_case_   )
     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 :  List[Any]						=  TF_MODEL_MAPPING[config.__class__](snake_case_   )

    # encoder-decoder has vocab size saved differently
    __a :  str						=  config.vocab_size if hasattr(snake_case_					,				'''vocab_size'''   ) else config.encoder.vocab_size
    __a :  Dict						=  random_input_ids(snake_case_					,				snake_case_					,				snake_case_   )

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

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

    __a :  Dict						=  encoder_decoder_forward if config.is_encoder_decoder else encoder_forward

    return _inference
   def       lowerCAmelCase			(self :					int					,				snake_case_ :					str					,				snake_case_ :					int					,				snake_case_ :					int   ):
    __a :  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.'''   )

    __a :  Tuple						=  (
        hasattr(snake_case_					,				'''architectures'''   )
        and isinstance(config.architectures					,				snake_case_   )
        and len(config.architectures   ) > 0
    )
    if not self.args.only_pretrain_model and has_model_class_in_config:
     try:
      __a :  str						=  '''TF''' + config.architectures[0]  # prepend 'TF' for tensorflow model
      __a :  List[str]						=  __import__('''transformers'''					,				fromlist=[model_class]   )
      __a :  Union[str, Any]						=  getattr(snake_case_					,				snake_case_   )
      __a :  Any						=  model_cls(snake_case_   )
     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 :  List[str]						=  TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](snake_case_   )

    # encoder-decoder has vocab size saved differently
    __a :  List[Any]						=  config.vocab_size if hasattr(snake_case_					,				'''vocab_size'''   ) else config.encoder.vocab_size
    __a :  Any						=  random_input_ids(snake_case_					,				snake_case_					,				snake_case_   )

    @run_with_tf_optimizations(self.args.eager_mode					,				self.args.use_xla   )
    def encoder_decoder_train():
     __a :  Optional[Any]						=  model(snake_case_					,				decoder_input_ids=snake_case_					,				labels=snake_case_					,				training=snake_case_   )[0]
     __a :  List[str]						=  tf.gradients(snake_case_					,				model.trainable_variables   )
     return gradients

    @run_with_tf_optimizations(self.args.eager_mode					,				self.args.use_xla   )
    def encoder_train():
     __a :  Tuple						=  model(snake_case_					,				labels=snake_case_					,				training=snake_case_   )[0]
     __a :  List[str]						=  tf.gradients(snake_case_					,				model.trainable_variables   )
     return gradients

    __a :  Optional[Any]						=  encoder_decoder_train if config.is_encoder_decoder else encoder_train

    return _train
   def       lowerCAmelCase			(self :					Tuple					,				snake_case_ :					Optional[int]   ):
    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(snake_case_					,				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 :  Union[str, Any]						=  timeit.repeat(
          snake_case_					,				repeat=self.args.repeat					,				number=1_0					,				)

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







   def       lowerCAmelCase			(self :					List[Any]					,				snake_case_ :					Callable[[], None]   ):
    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 :  Dict						=  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 :  Tuple						=  '''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 :  Union[str, Any]						=  nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx   )
        __a :  List[str]						=  nvml.nvmlDeviceGetMemoryInfo(snake_case_   )
        __a :  Optional[Any]						=  meminfo.used
        __a :  Any						=  Memory(snake_case_   )
        # 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 :  int						=  None
       else:
        __a :  Tuple						=  measure_peak_memory_cpu(snake_case_   )
        __a :  int						=  Memory(snake_case_   ) if isinstance(snake_case_					,				snake_case_   ) else memory_bytes
      if self.args.trace_memory_line_by_line:
       __a :  Optional[int]						=  stop_memory_tracing(snake_case_   )
       if memory is None:
        __a :  Optional[int]						=  summary.total
      else:
       __a :  Optional[Any]						=  None

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


import colorsys

from PIL import Image  # type: ignore




def    __UpperCamelCase     (				lowerCAmelCase__							:				float  ,   lowerCAmelCase__							:				float  ,   lowerCAmelCase__							:				int  ):
 __a :  Any						=  x
 __a :  List[Any]						=  y
 for step in range(lowerCAmelCase__  ):  # noqa: B007
  __a :  List[Any]						=  a * a - b * b + x
  __a :  Tuple						=  2 * a * b + y
  __a :  Optional[int]						=  a_new

  # divergence happens for all complex number with an absolute value
  # greater than 4
  if a * a + b * b > 4:
   break
 return step / (max_step - 1)




def    __UpperCamelCase     (				lowerCAmelCase__							:				float  ):
 if distance == 1:
  return (0, 0, 0)
 else:
  return (2_5_5, 2_5_5, 2_5_5)




def    __UpperCamelCase     (				lowerCAmelCase__							:				float  ):
 if distance == 1:
  return (0, 0, 0)
 else:
  return tuple(round(i * 2_5_5  ) for i in colorsys.hsv_to_rgb(lowerCAmelCase__  ,   1  ,   1  )  )




def    __UpperCamelCase     (				lowerCAmelCase__							:				int = 8_0_0  ,   lowerCAmelCase__							:				int = 6_0_0  ,   lowerCAmelCase__							:				float = -0.6  ,   lowerCAmelCase__							:				float = 0  ,   lowerCAmelCase__							:				float = 3.2  ,   lowerCAmelCase__							:				int = 5_0  ,   lowerCAmelCase__							:				bool = True  ,   ):
 __a :  int						=  Image.new('''RGB'''  ,   (image_width, image_height)  )
 __a :  Dict						=  img.load()

 # loop through the image-coordinates
 for image_x in range(lowerCAmelCase__  ):
  for image_y in range(lowerCAmelCase__  ):
   # determine the figure-coordinates based on the image-coordinates
   __a :  Optional[Any]						=  figure_width / image_width * image_height
   __a :  str						=  figure_center_x + (image_x / image_width - 0.5) * figure_width
   __a :  str						=  figure_center_y + (image_y / image_height - 0.5) * figure_height

   __a :  Tuple						=  get_distance(lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  )

   # color the corresponding pixel based on the selected coloring-function
   if use_distance_color_coding:
    __a :  Optional[int]						=  get_color_coded_rgb(lowerCAmelCase__  )
   else:
    __a :  Optional[Any]						=  get_black_and_white_rgb(lowerCAmelCase__  )

 return img


if __name__ == "__main__":
      import doctest

      doctest.testmod()

      # colored version, full figure
      lowercase__       =get_image()

      # uncomment for colored version, different section, zoomed in
      # img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
      # figure_width = 0.8)

      # uncomment for black and white version, full figure
      # img = get_image(use_distance_color_coding = False)

      # uncomment to save the image
      # img.save("mandelbrot.png")

      img.show()
