





import colorsys
import io

import cva
import matplotlib as mpl
import matplotlib.colors as mplc
import matplotlib.figure as mplfigure
import numpy as np
import torch
from matplotlib.backends.backend_agg import FigureCanvasAgg

from utils import img_tensorize


UpperCamelCase__          =1000





class 	lowerCAmelCase__:






       '''simple docstring'''


       def __init__(					self			,  __lowerCamelCase			,  __lowerCamelCase=1.2			,  __lowerCamelCase="g"			,  __lowerCamelCase=0.5			,  __lowerCamelCase="-"			,  __lowerCamelCase="test_out.jpg"			,  __lowerCamelCase=True			,  __lowerCamelCase=False			,  __lowerCamelCase=None			,  __lowerCamelCase=None			,  __lowerCamelCase=0.7			,  )	->   Optional[int]:
            if isinstance(__lowerCamelCase			,  torch.Tensor							):
                 _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							img.numpy().astype("np.uint8"							)
            if isinstance(__lowerCamelCase			,  __lowerCamelCase							):
                 _SCREAMING_SNAKE_CASE				:		int  			=							img_tensorize(__lowerCamelCase							)
            assert isinstance(__lowerCamelCase			,  np.ndarray							)

            _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE				:		Dict  			=							img.shape[1], img.shape[0]
            _SCREAMING_SNAKE_CASE				:		Tuple  			=							mplfigure.Figure(frameon=__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							fig.get_dpi()
            _SCREAMING_SNAKE_CASE				:		str  			=							(width * scale + 1E-2) / dpi
            _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							(height * scale + 1E-2) / dpi
            fig.set_size_inches(__lowerCamelCase			,  __lowerCamelCase							)
            _SCREAMING_SNAKE_CASE				:		Any  			=							fig.add_axes([0.0, 0.0, 1.0, 1.0]							)
            ax.axis("off"							)
            ax.set_xlim(0.0			,  __lowerCamelCase							)
            ax.set_ylim(__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE				:		Any  			=							saveas
            _SCREAMING_SNAKE_CASE				:		Dict  			=							rgb
            _SCREAMING_SNAKE_CASE				:		Tuple  			=							pynb
            _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							img
            _SCREAMING_SNAKE_CASE				:		Dict  			=							edgecolor
            _SCREAMING_SNAKE_CASE				:		int  			=							0.5
            _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							linestyle
            _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							int(np.sqrt(min(__lowerCamelCase			,  __lowerCamelCase							)							) * scale // 3							)
            _SCREAMING_SNAKE_CASE				:		str  			=							width
            _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							height
            _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							scale
            _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							fig
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							ax
            _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							pad
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							idaobj
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							idaattr
            _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							FigureCanvasAgg(__lowerCamelCase							)




       def 			UpperCamelCase_	(					self			,  __lowerCamelCase			,  __lowerCamelCase=None							)	->   Optional[int]:
            if color is None:
                 _SCREAMING_SNAKE_CASE				:		Tuple  			=							self.edgecolor
            ((_SCREAMING_SNAKE_CASE)			,	(_SCREAMING_SNAKE_CASE)			,	(_SCREAMING_SNAKE_CASE)			,	(_SCREAMING_SNAKE_CASE))				:		Optional[Any]  			=							box
            _SCREAMING_SNAKE_CASE				:		int  			=							xa - xa
            _SCREAMING_SNAKE_CASE				:		Tuple  			=							ya - ya
            self.ax.add_patch(
                mpl.patches.Rectangle(
                    (xa, ya)			,  __lowerCamelCase			,  __lowerCamelCase			,  fill=__lowerCamelCase			,  edgecolor=__lowerCamelCase			,  linewidth=self.font_size // 3			,  alpha=self.alpha			,  linestyle=self.linestyle			,  )							)




       def 			UpperCamelCase_	(					self			,  __lowerCamelCase			,  __lowerCamelCase=None			,  __lowerCamelCase=None			,  __lowerCamelCase=None			,  __lowerCamelCase=None							)	->   List[Any]:
            if len(boxes.shape							) > 2:
                 _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							boxes[0]
            if len(obj_ids.shape							) > 1:
                 _SCREAMING_SNAKE_CASE				:		Tuple  			=							obj_ids[0]
            if len(obj_scores.shape							) > 1:
                 _SCREAMING_SNAKE_CASE				:		Any  			=							obj_scores[0]
            if len(attr_ids.shape							) > 1:
                 _SCREAMING_SNAKE_CASE				:		int  			=							attr_ids[0]
            if len(attr_scores.shape							) > 1:
                 _SCREAMING_SNAKE_CASE				:		Any  			=							attr_scores[0]
            if isinstance(__lowerCamelCase			,  torch.Tensor							):
                 _SCREAMING_SNAKE_CASE				:		List[str]  			=							boxes.numpy()
            if isinstance(__lowerCamelCase			,  __lowerCamelCase							):
                 _SCREAMING_SNAKE_CASE				:		Tuple  			=							np.array(__lowerCamelCase							)
            assert isinstance(__lowerCamelCase			,  np.ndarray							)
            _SCREAMING_SNAKE_CASE				:		List[Any]  			=							np.prod(boxes[:, 2:] - boxes[:, :2]			,  axis=1							)
            _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							np.argsort(-areas							).tolist()
            _SCREAMING_SNAKE_CASE				:		Any  			=							boxes[sorted_idxs] if boxes is not None else None
            _SCREAMING_SNAKE_CASE				:		str  			=							obj_ids[sorted_idxs] if obj_ids is not None else None
            _SCREAMING_SNAKE_CASE				:		str  			=							obj_scores[sorted_idxs] if obj_scores is not None else None
            _SCREAMING_SNAKE_CASE				:		List[Any]  			=							attr_ids[sorted_idxs] if attr_ids is not None else None
            _SCREAMING_SNAKE_CASE				:		Any  			=							attr_scores[sorted_idxs] if attr_scores is not None else None

            _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							[self._random_color(maximum=1							) for _ in range(len(__lowerCamelCase							)							)]
            _SCREAMING_SNAKE_CASE				:		int  			=							[assigned_colors[idx] for idx in sorted_idxs]
            if obj_ids is not None:
                 _SCREAMING_SNAKE_CASE				:		Tuple  			=							self._create_text_labels_attr(__lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase							)
                 for i in range(len(__lowerCamelCase							)							):
                      _SCREAMING_SNAKE_CASE				:		List[Any]  			=							assigned_colors[i]
                      self.add_box(boxes[i]			,  __lowerCamelCase							)
                      self.draw_labels(labels[i]			,  boxes[i]			,  __lowerCamelCase							)




       def 			UpperCamelCase_	(					self			,  __lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase							)	->   Dict:
            _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE				:		Any  			=							box
            _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							(xa, ya)
            _SCREAMING_SNAKE_CASE				:		int  			=							(ya - ya) * (xa - xa)
            _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							_SMALL_OBJ * self.scale
            if instance_area < small or ya - ya < 4_0 * self.scale:
                 if ya >= self.height - 5:
                      _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							(xa, ya)
                 else:
                      _SCREAMING_SNAKE_CASE				:		List[Any]  			=							(xa, ya)

            _SCREAMING_SNAKE_CASE				:		Any  			=							(ya - ya) / np.sqrt(self.height * self.width							)
            _SCREAMING_SNAKE_CASE				:		str  			=							self._change_color_brightness(__lowerCamelCase			,  brightness_factor=0.7							)
            _SCREAMING_SNAKE_CASE				:		List[Any]  			=							np.clip((height_ratio - 0.02) / 0.08 + 1			,  1.2			,  2							)
            font_size *= 0.75 * self.font_size

            self.draw_text(
                text=__lowerCamelCase			,  position=__lowerCamelCase			,  color=__lowerCamelCase			,  )




       def 			UpperCamelCase_	(					self			,  __lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase="g"			,  __lowerCamelCase="left"			,  )	->   Union[str, Any]:
            _SCREAMING_SNAKE_CASE				:		Tuple  			=							0
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							self.font_size
            _SCREAMING_SNAKE_CASE				:		Dict  			=							np.maximum(list(mplc.to_rgb(__lowerCamelCase							)							)			,  0.2							)
            _SCREAMING_SNAKE_CASE				:		Tuple  			=							max(0.8			,  np.max(__lowerCamelCase							)							)
            _SCREAMING_SNAKE_CASE				:		Tuple  			=							{
                "facecolor": "black",
                "alpha": self.alpha,
                "pad": self.pad,
                "edgecolor": "none",
            }
            _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE				:		Tuple  			=							position
            self.ax.text(
                __lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase			,  size=font_size * self.scale			,  family="sans-serif"			,  bbox=__lowerCamelCase			,  verticalalignment="top"			,  horizontalalignment=__lowerCamelCase			,  color=__lowerCamelCase			,  zorder=1_0			,  rotation=__lowerCamelCase			,  )




       def 			UpperCamelCase_	(					self			,  __lowerCamelCase=None							)	->   str:
            if saveas is None:
                 _SCREAMING_SNAKE_CASE				:		List[str]  			=							self.saveas
            if saveas.lower().endswith(".jpg"							) or saveas.lower().endswith(".png"							):
                 cva.imwrite(
                     __lowerCamelCase			,  self._get_buffer()[:, :, ::-1]			,  )
            else:
                 self.fig.savefig(__lowerCamelCase							)




       def 			UpperCamelCase_	(					self			,  __lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase							)	->   int:
            _SCREAMING_SNAKE_CASE				:		int  			=							[self.idaobj[i] for i in classes]
            _SCREAMING_SNAKE_CASE				:		str  			=							[self.idaattr[i] for i in attr_classes]
            _SCREAMING_SNAKE_CASE				:		Dict  			=							[
                F"""{label} {score:.2f} {attr} {attr_score:.2f}"""
                for label, score, attr, attr_score in zip(__lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase							)
            ]
            return labels




       def 			UpperCamelCase_	(					self			,  __lowerCamelCase			,  __lowerCamelCase							)	->   Optional[int]:
            _SCREAMING_SNAKE_CASE				:		int  			=							[self.idaobj[i] for i in classes]
            if scores is not None:
                 if labels is None:
                      _SCREAMING_SNAKE_CASE				:		str  			=							["{:.0f}%".format(s * 1_0_0							) for s in scores]
                 else:
                      _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							["{} {:.0f}%".format(__lowerCamelCase			,  s * 1_0_0							) for li, s in zip(__lowerCamelCase			,  __lowerCamelCase							)]
            return labels




       def 			UpperCamelCase_	(					self			,  __lowerCamelCase=2_5_5							)	->   List[Any]:
            _SCREAMING_SNAKE_CASE				:		str  			=							np.random.randint(0			,  len(_COLORS							)							)
            _SCREAMING_SNAKE_CASE				:		List[Any]  			=							_COLORS[idx] * maximum
            if not self.rgb:
                 _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							ret[::-1]
            return ret




       def 			UpperCamelCase_	(					self							)	->   Any:
            if not self.pynb:
                 _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE				:		str  			=							self.canvas.print_to_buffer()
                 if (width, height) != (self.width, self.height):
                      _SCREAMING_SNAKE_CASE				:		List[str]  			=							cva.resize(self.img			,  (width, height)							)
                 else:
                      _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							self.img
            else:
                 _SCREAMING_SNAKE_CASE				:		Dict  			=							io.BytesIO()  # works for cairo backend
                 self.canvas.print_rgba(__lowerCamelCase							)
                 _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							self.width, self.height
                 _SCREAMING_SNAKE_CASE				:		Tuple  			=							buf.getvalue()
                 _SCREAMING_SNAKE_CASE				:		List[Any]  			=							self.img

            _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							np.frombuffer(__lowerCamelCase			,  dtype="uint8"							)
            _SCREAMING_SNAKE_CASE				:		Tuple  			=							buffer.reshape(__lowerCamelCase			,  __lowerCamelCase			,  4							)
            _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE				:		Dict  			=							np.split(__lowerCamelCase			,  [3]			,  axis=2							)

            try:
                 import numexpr as ne  # fuse them with numexpr

                 _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							ne.evaluate("img * (1 - alpha / 255.0) + rgb * (alpha / 255.0)"							)
            except ImportError:
                 _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							alpha.astype("float32"							) / 255.0
                 _SCREAMING_SNAKE_CASE				:		int  			=							img * (1 - alpha) + rgb * alpha

            return visualized_image.astype("uint8"							)







       def 			UpperCamelCase_	(					self			,  __lowerCamelCase			,  __lowerCamelCase							)	->   Any:
            assert brightness_factor >= -1.0 and brightness_factor <= 1.0
            _SCREAMING_SNAKE_CASE				:		Any  			=							mplc.to_rgb(__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE				:		Dict  			=							colorsys.rgb_to_hls(*mplc.to_rgb(__lowerCamelCase							)							)
            _SCREAMING_SNAKE_CASE				:		List[Any]  			=							polygon_color[1] + (brightness_factor * polygon_color[1])
            _SCREAMING_SNAKE_CASE				:		Tuple  			=							0.0 if modified_lightness < 0.0 else modified_lightness
            _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							1.0 if modified_lightness > 1.0 else modified_lightness
            _SCREAMING_SNAKE_CASE				:		str  			=							colorsys.hls_to_rgb(polygon_color[0]			,  __lowerCamelCase			,  polygon_color[2]							)
            return modified_color
UpperCamelCase__          =(
    np.array(
        [
            0.000,
            0.447,
            0.741,
            0.850,
            0.325,
            0.098,
            0.929,
            0.694,
            0.125,
            0.494,
            0.184,
            0.556,
            0.466,
            0.674,
            0.188,
            0.301,
            0.745,
            0.933,
            0.635,
            0.078,
            0.184,
            0.300,
            0.300,
            0.300,
            0.600,
            0.600,
            0.600,
            1.000,
            0.000,
            0.000,
            1.000,
            0.500,
            0.000,
            0.749,
            0.749,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            1.000,
            0.667,
            0.000,
            1.000,
            0.333,
            0.333,
            0.000,
            0.333,
            0.667,
            0.000,
            0.333,
            1.000,
            0.000,
            0.667,
            0.333,
            0.000,
            0.667,
            0.667,
            0.000,
            0.667,
            1.000,
            0.000,
            1.000,
            0.333,
            0.000,
            1.000,
            0.667,
            0.000,
            1.000,
            1.000,
            0.000,
            0.000,
            0.333,
            0.500,
            0.000,
            0.667,
            0.500,
            0.000,
            1.000,
            0.500,
            0.333,
            0.000,
            0.500,
            0.333,
            0.333,
            0.500,
            0.333,
            0.667,
            0.500,
            0.333,
            1.000,
            0.500,
            0.667,
            0.000,
            0.500,
            0.667,
            0.333,
            0.500,
            0.667,
            0.667,
            0.500,
            0.667,
            1.000,
            0.500,
            1.000,
            0.000,
            0.500,
            1.000,
            0.333,
            0.500,
            1.000,
            0.667,
            0.500,
            1.000,
            1.000,
            0.500,
            0.000,
            0.333,
            1.000,
            0.000,
            0.667,
            1.000,
            0.000,
            1.000,
            1.000,
            0.333,
            0.000,
            1.000,
            0.333,
            0.333,
            1.000,
            0.333,
            0.667,
            1.000,
            0.333,
            1.000,
            1.000,
            0.667,
            0.000,
            1.000,
            0.667,
            0.333,
            1.000,
            0.667,
            0.667,
            1.000,
            0.667,
            1.000,
            1.000,
            1.000,
            0.000,
            1.000,
            1.000,
            0.333,
            1.000,
            1.000,
            0.667,
            1.000,
            0.333,
            0.000,
            0.000,
            0.500,
            0.000,
            0.000,
            0.667,
            0.000,
            0.000,
            0.833,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            0.167,
            0.000,
            0.000,
            0.333,
            0.000,
            0.000,
            0.500,
            0.000,
            0.000,
            0.667,
            0.000,
            0.000,
            0.833,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            0.167,
            0.000,
            0.000,
            0.333,
            0.000,
            0.000,
            0.500,
            0.000,
            0.000,
            0.667,
            0.000,
            0.000,
            0.833,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            0.143,
            0.143,
            0.143,
            0.857,
            0.857,
            0.857,
            1.000,
            1.000,
            1.000,
        ]
    )
    .astype(np.floataa)
    .reshape(-1, 3)
)






import warnings
from dataclasses import dataclass, field
from typing import Optional, Tuple

from .training_args import TrainingArguments
from .utils import cached_property, is_tf_available, logging, requires_backends


UpperCamelCase__          =logging.get_logger(__name__)

if is_tf_available():
       import tensorflow as tf





@dataclass
class 	lowerCAmelCase__(       __lowercase			):






       '''simple docstring'''


       __snake_case       	=			'tf'
       __snake_case       	=			field(
           default=__lowercase     ,   metadata={'help': 'Name of TPU'}     ,   )

       __snake_case       	=			field(
           default=__lowercase     ,   metadata={'help': 'Zone of TPU'}     ,   )

       __snake_case       	=			field(
           default=__lowercase     ,   metadata={'help': 'Name of Cloud TPU-enabled project'}     ,   )

       __snake_case       	=			field(
           default=1.0     ,   metadata={'help': 'Power for the Polynomial decay LR scheduler.'}     ,   )

       __snake_case       	=			field(default=__lowercase     ,   metadata={'help': 'Whether to activate the XLA compilation or not'}			)




       @cached_property
       def 			UpperCamelCase_	(					self							)	->   Tuple["tf.distribute.Strategy", int]:
            requires_backends(self			,  ["tf"]							)
            logger.info("Tensorflow: setting up strategy"							)

            _SCREAMING_SNAKE_CASE				:		List[Any]  			=							tf.config.list_physical_devices("GPU"							)

            # Set to float16 at first
            if self.fpaa:
                 tf.keras.mixed_precision.set_global_policy("mixed_float16"							)

            if self.no_cuda:
                 _SCREAMING_SNAKE_CASE				:		List[Any]  			=							tf.distribute.OneDeviceStrategy(device="/cpu:0"							)
            else:
                 try:
                      if self.tpu_name:
                           _SCREAMING_SNAKE_CASE				:		Any  			=							tf.distribute.cluster_resolver.TPUClusterResolver(
                               self.tpu_name			,  zone=self.tpu_zone			,  project=self.gcp_project							)
                      else:
                           _SCREAMING_SNAKE_CASE				:		str  			=							tf.distribute.cluster_resolver.TPUClusterResolver()
                 except ValueError:
                      if self.tpu_name:
                           raise RuntimeError(F"""Couldn't connect to TPU {self.tpu_name}!"""							)
                      else:
                           _SCREAMING_SNAKE_CASE				:		Any  			=							None

                 if tpu:
                      # Set to bfloat16 in case of TPU
                      if self.fpaa:
                           tf.keras.mixed_precision.set_global_policy("mixed_bfloat16"							)

                      tf.config.experimental_connect_to_cluster(__lowerCamelCase							)
                      tf.tpu.experimental.initialize_tpu_system(__lowerCamelCase							)

                      _SCREAMING_SNAKE_CASE				:		List[str]  			=							tf.distribute.TPUStrategy(__lowerCamelCase							)

                 elif len(__lowerCamelCase							) == 0:
                      _SCREAMING_SNAKE_CASE				:		Dict  			=							tf.distribute.OneDeviceStrategy(device="/cpu:0"							)
                 elif len(__lowerCamelCase							) == 1:
                      _SCREAMING_SNAKE_CASE				:		Dict  			=							tf.distribute.OneDeviceStrategy(device="/gpu:0"							)
                 elif len(__lowerCamelCase							) > 1:
                      # If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
                      _SCREAMING_SNAKE_CASE				:		Any  			=							tf.distribute.MirroredStrategy()
                 else:
                      raise ValueError("Cannot find the proper strategy, please check your environment properties."							)

            return strategy




       @property
       def 			UpperCamelCase_	(					self							)	->   "tf.distribute.Strategy":
            requires_backends(self			,  ["tf"]							)
            return self._setup_strategy




       @property
       def 			UpperCamelCase_	(					self							)	->   int:
            requires_backends(self			,  ["tf"]							)
            return self._setup_strategy.num_replicas_in_sync




       @property
       def 			UpperCamelCase_	(					self							)	->   List[Any]:
            return False  # TF Logging is handled by Keras not the Trainer




       @property
       def 			UpperCamelCase_	(					self							)	->   int:
            if self.per_gpu_train_batch_size:
                 logger.warning(
                     "Using deprecated `--per_gpu_train_batch_size` argument which will be removed in a future "
                     "version. Using `--per_device_train_batch_size` is preferred."							)
            _SCREAMING_SNAKE_CASE				:		Tuple  			=							self.per_gpu_train_batch_size or self.per_device_train_batch_size
            return per_device_batch_size * self.n_replicas




       @property
       def 			UpperCamelCase_	(					self							)	->   int:
            if self.per_gpu_eval_batch_size:
                 logger.warning(
                     "Using deprecated `--per_gpu_eval_batch_size` argument which will be removed in a future "
                     "version. Using `--per_device_eval_batch_size` is preferred."							)
            _SCREAMING_SNAKE_CASE				:		int  			=							self.per_gpu_eval_batch_size or self.per_device_eval_batch_size
            return per_device_batch_size * self.n_replicas







       @property
       def 			UpperCamelCase_	(					self							)	->   int:
            requires_backends(self			,  ["tf"]							)
            warnings.warn(
                "The n_gpu argument is deprecated and will be removed in a future version, use n_replicas instead."			,  __lowerCamelCase			,  )
            return self._setup_strategy.num_replicas_in_sync