
"""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

_A     :			str  						=			logging.get_logger(__name__)


def     __magic_name__      ( __snake_case							:			Optional[int]	,					__snake_case							:			Tuple    )					->							Union[str, Any]:


       def run_func(__snake_case							:			Any    ):
              @wraps(_lowerCamelCase    )
              def run_in_eager_mode(*__snake_case							:			Tuple	,					**__snake_case							:			int    ):
                     return func(*_lowerCamelCase	,					**_lowerCamelCase    )

              @wraps(_lowerCamelCase    )
              @tf.function(experimental_compile=_lowerCamelCase    )
              def run_in_graph_mode(*__snake_case							:			Dict	,					**__snake_case							:			str    ):
                     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     __magic_name__      ( __snake_case							:			List[Any]	,					__snake_case							:			List[str]	,					__snake_case							:			List[Any]    )					->							["tf.Tensor"]:
       lowercase       :							Dict									=				random.Random()
       lowercase       :							str									=				[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 						a__		( __lowercase		):
   __lowerCAmelCase				=							42
   __lowerCAmelCase				=							42
   __lowerCAmelCase				=							"TensorFlow"
   @property
   def      __magic_name__     (     self							):
          return tf.__version__
   def      __magic_name__     (     self      ,       _a      ,       _a      ,       _a							):
          lowercase       :							Tuple									=				self.args.strategy
          if strategy is None:
                 raise ValueError("A device strategy has to be initialized before using TensorFlow."							)
          lowercase       :							Any									=				self._prepare_inference_func(_a      ,       _a      ,       _a							)
          return self._measure_speed(_inference							)
   def      __magic_name__     (     self      ,       _a      ,       _a      ,       _a							):
          lowercase       :							Tuple									=				self.args.strategy
          if strategy is None:
                 raise ValueError("A device strategy has to be initialized before using TensorFlow."							)
          lowercase       :							List[Any]									=				self._prepare_train_func(_a      ,       _a      ,       _a							)
          return self._measure_speed(_train							)
   def      __magic_name__     (     self      ,       _a      ,       _a      ,       _a							):

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

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

          lowercase       :							List[str]									=				self._prepare_train_func(_a      ,       _a      ,       _a							)
          return self._measure_memory(_train							)
   def      __magic_name__     (     self      ,       _a      ,       _a      ,       _a							):
          lowercase       :							List[Any]									=				self.config_dict[model_name]

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

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

          # encoder-decoder has vocab size saved differently
          lowercase       :							Optional[int]									=				config.vocab_size if hasattr(_a      ,       "vocab_size"							) else config.encoder.vocab_size
          lowercase       :							str									=				random_input_ids(_a      ,       _a      ,       _a							)

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

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

          lowercase       :							Any									=				encoder_decoder_forward if config.is_encoder_decoder else encoder_forward

          return _inference
   def      __magic_name__     (     self      ,       _a      ,       _a      ,       _a							):
          lowercase       :							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."							)

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

          # encoder-decoder has vocab size saved differently
          lowercase       :							Optional[Any]									=				config.vocab_size if hasattr(_a      ,       "vocab_size"							) else config.encoder.vocab_size
          lowercase       :							List[str]									=				random_input_ids(_a      ,       _a      ,       _a							)

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

          @run_with_tf_optimizations(self.args.eager_mode      ,       self.args.use_xla							)
          def encoder_train():
                 lowercase       :							Optional[Any]									=				model(_a      ,       labels=_a      ,       training=_a							)[0]
                 lowercase       :							Optional[int]									=				tf.gradients(_a      ,       model.trainable_variables							)
                 return gradients

          lowercase       :							str									=				encoder_decoder_train if config.is_encoder_decoder else encoder_train

          return _train
   def      __magic_name__     (     self      ,       _a							):

          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(_a      ,       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
                        lowercase       :							Optional[int]									=				timeit.repeat(
                            _a      ,       repeat=self.args.repeat      ,       number=10      ,       )

                        return min(_a							) / 1_0.0
                 except ResourceExhaustedError as e:
                        self.print_fn(f"""Doesn\'t fit on GPU. {e}"""							)

   def      __magic_name__     (     self      ,       _a							):
          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."							)
                               lowercase       :							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."							)
                                      lowercase       :							int									=				"""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()
                                      lowercase       :							Any									=				nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx							)
                                      lowercase       :							Union[str, Any]									=				nvml.nvmlDeviceGetMemoryInfo(_a							)
                                      lowercase       :							Union[str, Any]									=				meminfo.used
                                      lowercase       :							List[Any]									=				Memory(_a							)
                                      # 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."							)
                                      lowercase       :							int									=				None
                               else:
                                      lowercase       :							Dict									=				measure_peak_memory_cpu(_a							)
                                      lowercase       :							Union[str, Any]									=				Memory(_a							) if isinstance(_a      ,       _a							) else memory_bytes
                        if self.args.trace_memory_line_by_line:
                               lowercase       :							Tuple									=				stop_memory_tracing(_a							)
                               if memory is None:
                                      lowercase       :							Any									=				summary.total
                        else:
                               lowercase       :							Any									=				None

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









'''simple docstring'''






import cva
import numpy as np




class  _A :

		def __init__(   self					:   Any  ,      __magic_name__					:   float  ,      __magic_name__					:   int			)					->    Optional[int]:


					"""simple docstring"""

					if k in (0.04, 0.06):
								__snake_case					:      List[str]							      =     k
								__snake_case					:      int							      =     window_size
					else:
								raise ValueError("""invalid k value"""			)

		def __str__(   self					:   Union[str, Any]			)					->    str:


					"""simple docstring"""

					return str(self.k			)

		def 	lowercase__     (   self					:   Dict  ,      __magic_name__					:   str			)					->    tuple[cva.Mat, list[list[int]]]:


					"""simple docstring"""

					__snake_case					:      Dict							      =     cva.imread(__magic_name__  ,      0			)
					__snake_case      ,     __snake_case					:      List[str]							      =     img.shape
					__snake_case					:      list[list[int]]							      =     []
					__snake_case					:      str							      =     img.copy()
					__snake_case					:      Tuple							      =     cva.cvtColor(__magic_name__  ,      cva.COLOR_GRAY2RGB			)
					__snake_case      ,     __snake_case					:      List[Any]							      =     np.gradient(__magic_name__			)
					__snake_case					:      Optional[Any]							      =     dx**2
					__snake_case					:      Tuple							      =     dy**2
					__snake_case					:      List[Any]							      =     dx * dy
					__snake_case					:      List[Any]							      =     0.04
					__snake_case					:      Tuple							      =     self.window_size // 2
					for y in range(__magic_name__  ,      h - offset			):
								for x in range(__magic_name__  ,      w - offset			):
											__snake_case					:      Dict							      =     ixx[
											    y - offset : y + offset + 1, x - offset : x + offset + 1
											].sum()
											__snake_case					:      Optional[int]							      =     iyy[
											    y - offset : y + offset + 1, x - offset : x + offset + 1
											].sum()
											__snake_case					:      str							      =     ixy[
											    y - offset : y + offset + 1, x - offset : x + offset + 1
											].sum()

											__snake_case					:      List[str]							      =     (wxx * wyy) - (wxy**2)
											__snake_case					:      Dict							      =     wxx + wyy
											__snake_case					:      List[str]							      =     det - k * (trace**2)
											# Can change the value
											if r > 0.5:
														corner_list.append([x, y, r]			)
														color_img.itemset((y, x, 0)  ,      0			)
														color_img.itemset((y, x, 1)  ,      0			)
														color_img.itemset((y, x, 2)  ,      2_55			)
					return color_img, corner_list


if __name__ == "__main__":
	__UpperCamelCase					    =	HarrisCorner(0.04, 3)
	__UpperCamelCase    ,		__UpperCamelCase					    =	edge_detect.detect("path_to_image")
	cva.imwrite("detect.png", color_img)


