





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


_UpperCamelCase    =	logging.get_logger(__name__)




class 			_lowerCamelCase	(      a      ):



      """simple docstring"""






      UpperCAmelCase_  : PyTorchBenchmarkArguments
      UpperCAmelCase_  : PretrainedConfig
      UpperCAmelCase_  : str				      ="PyTorch"







      @property
      def   UpperCAmelCase						(	self				)    ->				List[str]:






        '''simple docstring'''






        return torch.__version__







      def   UpperCAmelCase						(	self			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase				)    ->				float:






        '''simple docstring'''






        __snake_case				:			List[Any]            =    self._prepare_inference_func(lowercase__			,  lowercase__			,  lowercase__				)
        return self._measure_speed(_inference				)







      def   UpperCAmelCase						(	self			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase				)    ->				[Memory, Optional[MemorySummary]]:






        '''simple docstring'''






        __snake_case				:			Any            =    self._prepare_inference_func(lowercase__			,  lowercase__			,  lowercase__				)
        return self._measure_memory(_inference				)







      def   UpperCAmelCase						(	self			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase				)    ->				float:






        '''simple docstring'''






        __snake_case				:			Union[str, Any]            =    self._prepare_train_func(lowercase__			,  lowercase__			,  lowercase__				)
        return self._measure_speed(_train				)







      def   UpperCAmelCase						(	self			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase				)    ->				[Memory, Optional[MemorySummary]]:






        '''simple docstring'''






        __snake_case				:			Optional[Any]            =    self._prepare_train_func(lowercase__			,  lowercase__			,  lowercase__				)
        return self._measure_memory(_train				)







      def   UpperCAmelCase						(	self			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase				)    ->				Callable[[], None]:






        '''simple docstring'''






        __snake_case				:			str            =    self.config_dict[model_name]

        if self.args.torchscript:
          __snake_case				:			Any            =    True

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

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

        # encoder-decoder has vocab size saved differently
        __snake_case				:			Union[str, Any]            =    config.vocab_size if hasattr(lowercase__			,  "vocab_size"				) else config.encoder.vocab_size
        __snake_case				:			str            =    torch.randint(lowercase__			,  (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(lowercase__			,  lowercase__				)
        else:
          __snake_case				:			List[str]            =    model

        def encoder_decoder_forward():
          with torch.no_grad():
            __snake_case				:			List[Any]            =    inference_model(lowercase__			,  decoder_input_ids=lowercase__				)
          return outputs

        def encoder_forward():
          with torch.no_grad():
            __snake_case				:			str            =    inference_model(lowercase__				)
          return outputs

        __snake_case				:			Optional[Any]            =    encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
        return _forward







      def   UpperCAmelCase						(	self			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase				)    ->				Callable[[], None]:






        '''simple docstring'''






        __snake_case				:			Tuple            =    self.config_dict[model_name]

        __snake_case				:			Optional[int]            =    (
            hasattr(lowercase__			,  "architectures"				)
            and isinstance(config.architectures			,  lowercase__				)
            and len(config.architectures				) > 0
        )
        if not self.args.only_pretrain_model and has_model_class_in_config:
          try:
            __snake_case				:			Tuple            =    config.architectures[0]
            __snake_case				:			int            =    __import__("transformers"			,  fromlist=[model_class]				)
            __snake_case				:			int            =    getattr(lowercase__			,  lowercase__				)
            __snake_case				:			Union[str, Any]            =    model_cls(lowercase__				)
          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				:			Tuple            =    MODEL_WITH_LM_HEAD_MAPPING[config.__class__](lowercase__				)

        if self.args.torchscript:
          raise NotImplementedError("Training for torchscript is currently not implemented"				)
        else:
          __snake_case				:			Any            =    model

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

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

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

        __snake_case				:			Union[str, Any]            =    (
            compute_loss_and_backprob_encoder_decoder
            if config.is_encoder_decoder
            else compute_loss_and_backprob_encoder
        )
        return _train







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







      def   UpperCAmelCase						(	self			,  UpperCAmelCase				)    ->				[Memory, MemorySummary]:






        '''simple docstring'''






        try:
          if self.args.trace_memory_line_by_line:
            __snake_case				:			List[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."				)
              __snake_case				:			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()
              __snake_case				:			int            =    nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx				)
              __snake_case				:			int            =    nvml.nvmlDeviceGetMemoryInfo(lowercase__				)
              __snake_case				:			Optional[int]            =    meminfo.used
              __snake_case				:			Tuple            =    Memory(lowercase__				)
              # shutdown nvml
              nvml.nvmlShutdown()
          else:
            # cpu
            __snake_case				:			int            =    measure_peak_memory_cpu(lowercase__				)
            __snake_case				:			Tuple            =    Memory(lowercase__				) if isinstance(lowercase__			,  lowercase__				) else memory_bytes

          if self.args.trace_memory_line_by_line:
            __snake_case				:			List[Any]            =    stop_memory_tracing(lowercase__				)
          else:
            __snake_case				:			Optional[Any]            =    None

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








import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey




class 			_lowerCamelCase	:



      """simple docstring"""






      def __init__(	self			,  UpperCAmelCase			,  UpperCAmelCase				)    ->				str:






        '''simple docstring'''






        __snake_case				:			int            =    0
        # max greyscale value for #FFFFFF
        __snake_case				:			int            =    int(self.get_greyscale(255			,  255			,  255				)				)

        if not self.min_threshold < threshold < self.max_threshold:
          __snake_case				:			Tuple            =    F"""Factor value should be from 0 to {self.max_threshold}"""
          raise ValueError(UpperCAmelCase				)

        __snake_case				:			List[Any]            =    input_img
        __snake_case				:			Any            =    threshold
        __snake_case      ,				__snake_case				:			str            =    self.input_img.shape[1], self.input_img.shape[0]

        # error table size (+4 columns and +1 row) greater than input image because of
        # lack of if statements
        __snake_case				:			Dict            =    [
            [0 for _ in range(self.height + 4				)] for __ in range(self.width + 1				)
        ]
        __snake_case				:			str            =    np.ones((self.width, self.height, 3)			,  np.uinta				) * 255







      @classmethod
      def   UpperCAmelCase						(	cls			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase				)    ->				float:






        '''simple docstring'''






        return 0.114 * blue + 0.587 * green + 0.2_126 * red







      def   UpperCAmelCase						(	self				)    ->				None:






        '''simple docstring'''






        for y in range(self.height				):
          for x in range(self.width				):
            __snake_case				:			Optional[int]            =    int(self.get_greyscale(*self.input_img[y][x]				)				)
            if self.threshold > greyscale + self.error_table[y][x]:
              __snake_case				:			Any            =    (0, 0, 0)
              __snake_case				:			Optional[int]            =    greyscale + self.error_table[x][y]
            else:
              __snake_case				:			str            =    (255, 255, 255)
              __snake_case				:			Dict            =    greyscale + self.error_table[x][y] - 255
            self.error_table[y][x + 1] += int(8 / 32 * current_error				)
            self.error_table[y][x + 2] += int(4 / 32 * current_error				)
            self.error_table[y + 1][x] += int(8 / 32 * current_error				)
            self.error_table[y + 1][x + 1] += int(4 / 32 * current_error				)
            self.error_table[y + 1][x + 2] += int(2 / 32 * current_error				)
            self.error_table[y + 1][x - 1] += int(4 / 32 * current_error				)
            self.error_table[y + 1][x - 2] += int(2 / 32 * current_error				)


if __name__ == "__main__":
     # create Burke's instances with original images in greyscale
     _UpperCamelCase    =	[
         Burkes(imread('''image_data/lena.jpg''', 1), threshold)
         for threshold in (1, 126, 130, 140)
     ]

     for burkes in burkes_instances:
          burkes.process()

     for burkes in burkes_instances:
          imshow(
              F'''Original image with dithering threshold: {burkes.threshold}''',
              burkes.output_img,
          )

     waitKey(0)
     destroyAllWindows()

