





import re
from typing import Callable, List, Optional, Union

import tensorflow as tf


try:
		from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
		from tensorflow.keras.optimizers import Adam


class 						lowerCamelCase       (tf.keras.optimizers.schedules.LearningRateSchedule      ):







							"""simple docstring"""




							def __init__( self     :					List[Any],			_UpperCAmelCase     :					float,			_UpperCAmelCase     :					Callable,			_UpperCAmelCase     :					int,			_UpperCAmelCase     :					float = 1.0,			_UpperCAmelCase     :					str = None,			)				->			Optional[int]:





														"""simple docstring"""
														super().__init__()
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						initial_learning_rate
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						warmup_steps
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						power
														SCREAMING_SNAKE_CASE__       :      Any     =						decay_schedule_fn
														SCREAMING_SNAKE_CASE__       :      str     =						name






							def __call__( self     :					str,			_UpperCAmelCase     :					int						)				->			Optional[Any]:





														"""simple docstring"""
														with tf.name_scope(self.name or "WarmUp"						) as name:
																					# Implements polynomial warmup. i.e., if global_step < warmup_steps, the
																					# learning rate will be `global_step/num_warmup_steps * init_lr`.
																					SCREAMING_SNAKE_CASE__       :      int     =						tf.cast(_UpperCAmelCase,			tf.floataa						)
																					SCREAMING_SNAKE_CASE__       :      Any     =						tf.cast(self.warmup_steps,			tf.floataa						)
																					SCREAMING_SNAKE_CASE__       :      List[str]     =						global_step_float / warmup_steps_float
																					SCREAMING_SNAKE_CASE__       :      int     =						self.initial_learning_rate * tf.math.pow(_UpperCAmelCase,			self.power						)
																					return tf.cond(
																					    global_step_float < warmup_steps_float,			lambda: warmup_learning_rate,			lambda: self.decay_schedule_fn(step - self.warmup_steps						),			name=_UpperCAmelCase,			)






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





														"""simple docstring"""
														return {
														    "initial_learning_rate": self.initial_learning_rate,
														    "decay_schedule_fn": self.decay_schedule_fn,
														    "warmup_steps": self.warmup_steps,
														    "power": self.power,
														    "name": self.name,
														}




def _a (  SCREAMING_SNAKE_CASE__      :  float			,				SCREAMING_SNAKE_CASE__      :  int			,				SCREAMING_SNAKE_CASE__      :  int			,				SCREAMING_SNAKE_CASE__      :  float = 0.0			,				SCREAMING_SNAKE_CASE__      :  float = 0.9			,				SCREAMING_SNAKE_CASE__      :  float = 0.9_9_9			,				SCREAMING_SNAKE_CASE__      :  float = 1E-8			,				SCREAMING_SNAKE_CASE__      :  Optional[float] = None			,				SCREAMING_SNAKE_CASE__      :  Optional[float] = None			,				SCREAMING_SNAKE_CASE__      :  float = 0.0			,				SCREAMING_SNAKE_CASE__      :  float = 1.0			,				SCREAMING_SNAKE_CASE__      :  Optional[List[str]] = None			,				)	->    Optional[int]:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      Tuple     =						tf.keras.optimizers.schedules.PolynomialDecay(
							    initial_learning_rate=SCREAMING_SNAKE_CASE__			,				decay_steps=num_train_steps - num_warmup_steps			,				end_learning_rate=init_lr * min_lr_ratio			,				power=SCREAMING_SNAKE_CASE__			,				)
							if num_warmup_steps:
														SCREAMING_SNAKE_CASE__       :      int     =						WarmUp(
														    initial_learning_rate=SCREAMING_SNAKE_CASE__			,				decay_schedule_fn=SCREAMING_SNAKE_CASE__			,				warmup_steps=SCREAMING_SNAKE_CASE__			,				)
							if weight_decay_rate > 0.0:
														SCREAMING_SNAKE_CASE__       :      Dict     =						AdamWeightDecay(
														    learning_rate=SCREAMING_SNAKE_CASE__			,				weight_decay_rate=SCREAMING_SNAKE_CASE__			,				beta_a=SCREAMING_SNAKE_CASE__			,				beta_a=SCREAMING_SNAKE_CASE__			,				epsilon=SCREAMING_SNAKE_CASE__			,				clipnorm=SCREAMING_SNAKE_CASE__			,				global_clipnorm=SCREAMING_SNAKE_CASE__			,				exclude_from_weight_decay=["LayerNorm", "layer_norm", "bias"]			,				include_in_weight_decay=SCREAMING_SNAKE_CASE__			,				)
							else:
														SCREAMING_SNAKE_CASE__       :      Any     =						tf.keras.optimizers.Adam(
														    learning_rate=SCREAMING_SNAKE_CASE__			,				beta_a=SCREAMING_SNAKE_CASE__			,				beta_a=SCREAMING_SNAKE_CASE__			,				epsilon=SCREAMING_SNAKE_CASE__			,				clipnorm=SCREAMING_SNAKE_CASE__			,				global_clipnorm=SCREAMING_SNAKE_CASE__			,				)
							# We return the optimizer and the LR scheduler in order to better track the
							# evolution of the LR independently of the optimizer.
							return optimizer, lr_schedule


class 						lowerCamelCase       (__lowerCamelCase      ):







							"""simple docstring"""




							def __init__( self     :					List[Any],			_UpperCAmelCase     :					Union[float, tf.keras.optimizers.schedules.LearningRateSchedule] = 0.001,			_UpperCAmelCase     :					float = 0.9,			_UpperCAmelCase     :					float = 0.999,			_UpperCAmelCase     :					float = 1E-7,			_UpperCAmelCase     :					bool = False,			_UpperCAmelCase     :					float = 0.0,			_UpperCAmelCase     :					Optional[List[str]] = None,			_UpperCAmelCase     :					Optional[List[str]] = None,			_UpperCAmelCase     :					str = "AdamWeightDecay",			**_UpperCAmelCase     :					Dict,			)				->			List[Any]:





														"""simple docstring"""
														super().__init__(_UpperCAmelCase,			_UpperCAmelCase,			_UpperCAmelCase,			_UpperCAmelCase,			_UpperCAmelCase,			_UpperCAmelCase,			**_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      List[str]     =						weight_decay_rate
														SCREAMING_SNAKE_CASE__       :      Tuple     =						include_in_weight_decay
														SCREAMING_SNAKE_CASE__       :      int     =						exclude_from_weight_decay






							@classmethod
							def  A_     ( cls     :					Union[str, Any],			_UpperCAmelCase     :					List[Any]						)				->			Optional[int]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      List[str]     =						{"WarmUp": WarmUp}
														return super(_UpperCAmelCase,			cls						).from_config(_UpperCAmelCase,			custom_objects=_UpperCAmelCase						)






							def  A_     ( self     :					List[str],			_UpperCAmelCase     :					int,			_UpperCAmelCase     :					Dict,			_UpperCAmelCase     :					List[Any]						)				->			str:





														"""simple docstring"""
														super(_UpperCAmelCase,			self						)._prepare_local(_UpperCAmelCase,			_UpperCAmelCase,			_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						tf.constant(
														    self.weight_decay_rate,			name="adam_weight_decay_rate"						)






							def  A_     ( self     :					Optional[int],			_UpperCAmelCase     :					Union[str, Any],			_UpperCAmelCase     :					Optional[int],			_UpperCAmelCase     :					List[Any]						)				->			Optional[Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      List[str]     =						self._do_use_weight_decay(var.name						)
														if do_decay:
																					return var.assign_sub(
																					    learning_rate * var * apply_state[(var.device, var.dtype.base_dtype)]["weight_decay_rate"],			use_locking=self._use_locking,			)
														return tf.no_op()






							def  A_     ( self     :					Optional[int],			_UpperCAmelCase     :					List[str],			_UpperCAmelCase     :					str=None,			**_UpperCAmelCase     :					int						)				->			int:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      int     =						list(zip(*_UpperCAmelCase						)						)
														return super(_UpperCAmelCase,			self						).apply_gradients(zip(_UpperCAmelCase,			_UpperCAmelCase						),			name=_UpperCAmelCase,			**_UpperCAmelCase						)






							def  A_     ( self     :					Any,			_UpperCAmelCase     :					Optional[Any],			_UpperCAmelCase     :					int,			_UpperCAmelCase     :					List[Any]						)				->			Union[str, Any]:





														"""simple docstring"""
														if apply_state is None:
																					return self._decayed_lr_t[var_dtype], {}

														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						apply_state or {}
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						apply_state.get((var_device, var_dtype)						)
														if coefficients is None:
																					SCREAMING_SNAKE_CASE__       :      Tuple     =						self._fallback_apply_state(_UpperCAmelCase,			_UpperCAmelCase						)
																					SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						coefficients

														return coefficients["lr_t"], {"apply_state": apply_state}






							def  A_     ( self     :					List[str],			_UpperCAmelCase     :					Dict,			_UpperCAmelCase     :					str,			_UpperCAmelCase     :					Union[str, Any]=None						)				->			Tuple:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Optional[int]     =						self._get_lr(var.device,			var.dtype.base_dtype,			_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      Dict     =						self._decay_weights_op(_UpperCAmelCase,			_UpperCAmelCase,			_UpperCAmelCase						)
														with tf.control_dependencies([decay]						):
																					return super(_UpperCAmelCase,			self						)._resource_apply_dense(_UpperCAmelCase,			_UpperCAmelCase,			**_UpperCAmelCase						)






							def  A_     ( self     :					Optional[Any],			_UpperCAmelCase     :					Union[str, Any],			_UpperCAmelCase     :					Optional[Any],			_UpperCAmelCase     :					List[str],			_UpperCAmelCase     :					str=None						)				->			Optional[Any]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						self._get_lr(var.device,			var.dtype.base_dtype,			_UpperCAmelCase						)
														SCREAMING_SNAKE_CASE__       :      Tuple     =						self._decay_weights_op(_UpperCAmelCase,			_UpperCAmelCase,			_UpperCAmelCase						)
														with tf.control_dependencies([decay]						):
																					return super(_UpperCAmelCase,			self						)._resource_apply_sparse(_UpperCAmelCase,			_UpperCAmelCase,			_UpperCAmelCase,			**_UpperCAmelCase						)






							def  A_     ( self     :					Tuple						)				->			Optional[int]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      str     =						super().get_config()
														config.update({"weight_decay_rate": self.weight_decay_rate}						)
														return config






							def  A_     ( self     :					Optional[Any],			_UpperCAmelCase     :					str						)				->			str:





														"""simple docstring"""
														if self.weight_decay_rate == 0:
																					return False

														if self._include_in_weight_decay:
																					for r in self._include_in_weight_decay:
																												if re.search(_UpperCAmelCase,			_UpperCAmelCase						) is not None:
																																			return True

														if self._exclude_from_weight_decay:
																					for r in self._exclude_from_weight_decay:
																												if re.search(_UpperCAmelCase,			_UpperCAmelCase						) is not None:
																																			return False
														return True


class 						lowerCamelCase       (__lowerCamelCase      ):







							"""simple docstring"""




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





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      str     =						[]
														SCREAMING_SNAKE_CASE__       :      Any     =						None






							@property
							def  A_     ( self     :					Any						)				->			List[Any]:





														"""simple docstring"""
														if self._accum_steps is None:
																					SCREAMING_SNAKE_CASE__       :      Dict     =						tf.Variable(
																					    tf.constant(0,			dtype=tf.intaa						),			trainable=_UpperCAmelCase,			synchronization=tf.VariableSynchronization.ON_READ,			aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA,			)

														return self._accum_steps.value()






							@property
							def  A_     ( self     :					Any						)				->			Any:





														"""simple docstring"""
														if not self._gradients:
																					raise ValueError("The accumulator should be called first to initialize the gradients"						)
														return [gradient.value() if gradient is not None else gradient for gradient in self._gradients]






							def __call__( self     :					List[Any],			_UpperCAmelCase     :					Optional[int]						)				->			Optional[Any]:





														"""simple docstring"""
														if not self._gradients:
																					SCREAMING_SNAKE_CASE__       :      List[Any]     =						self.step  # Create the step variable.
																					self._gradients.extend(
																					    [
																					        tf.Variable(
																					            tf.zeros_like(_UpperCAmelCase						),			trainable=_UpperCAmelCase,			synchronization=tf.VariableSynchronization.ON_READ,			aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA,			)
																					        if gradient is not None
																					        else gradient
																					        for gradient in gradients
																					    ]						)
														if len(_UpperCAmelCase						) != len(self._gradients						):
																					raise ValueError(F'''Expected {len(self._gradients						)} gradients, but got {len(_UpperCAmelCase						)}'''						)

														for accum_gradient, gradient in zip(self._gradients,			_UpperCAmelCase						):
																					if accum_gradient is not None and gradient is not None:
																												accum_gradient.assign_add(_UpperCAmelCase						)

														self._accum_steps.assign_add(1						)






							def  A_     ( self     :					Dict						)				->			List[Any]:





														"""simple docstring"""
														if not self._gradients:
																					return
														self._accum_steps.assign(0						)
														for gradient in self._gradients:
																					if gradient is not None:
																												gradient.assign(tf.zeros_like(_UpperCAmelCase						)						)













import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize


class 						lowerCamelCase       :







							"""simple docstring"""




							def __init__( self     :					Dict,			_UpperCAmelCase     :					Any,			_UpperCAmelCase     :					List[Any]=sys.maxsize						)				->			Optional[int]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						"bilinear"
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						max_size
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						short_edge_length






							def __call__( self     :					Optional[int],			_UpperCAmelCase     :					Union[str, Any]						)				->			str:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						[]
														for img in imgs:
																					SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Tuple     =						img.shape[:2]
																					# later: provide list and randomly choose index for resize
																					SCREAMING_SNAKE_CASE__       :      List[str]     =						np.random.randint(self.short_edge_length[0],			self.short_edge_length[1] + 1						)
																					if size == 0:
																												return img
																					SCREAMING_SNAKE_CASE__       :      int     =						size * 1.0 / min(_UpperCAmelCase,			_UpperCAmelCase						)
																					if h < w:
																												SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Optional[int]     =						size, scale * w
																					else:
																												SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      List[str]     =						scale * h, size
																					if max(_UpperCAmelCase,			_UpperCAmelCase						) > self.max_size:
																												SCREAMING_SNAKE_CASE__       :      str     =						self.max_size * 1.0 / max(_UpperCAmelCase,			_UpperCAmelCase						)
																												SCREAMING_SNAKE_CASE__       :      Any     =						newh * scale
																												SCREAMING_SNAKE_CASE__       :      List[str]     =						neww * scale
																					SCREAMING_SNAKE_CASE__       :      Any     =						int(neww + 0.5						)
																					SCREAMING_SNAKE_CASE__       :      List[Any]     =						int(newh + 0.5						)

																					if img.dtype == np.uinta:
																												SCREAMING_SNAKE_CASE__       :      List[Any]     =						Image.fromarray(_UpperCAmelCase						)
																												SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						pil_image.resize((neww, newh),			PILImageResampling.BILINEAR						)
																												SCREAMING_SNAKE_CASE__       :      List[str]     =						np.asarray(_UpperCAmelCase						)
																					else:
																												SCREAMING_SNAKE_CASE__       :      str     =						img.permute(2,			0,			1						).unsqueeze(0						)  # 3, 0, 1)  # hw(c) -> nchw
																												SCREAMING_SNAKE_CASE__       :      Tuple     =						nn.functional.interpolate(
																												    _UpperCAmelCase,			(newh, neww),			mode=self.interp_method,			align_corners=_UpperCAmelCase						).squeeze(0						)
																					img_augs.append(_UpperCAmelCase						)

														return img_augs


class 						lowerCamelCase       :







							"""simple docstring"""




							def __init__( self     :					Dict,			_UpperCAmelCase     :					Optional[Any]						)				->			Optional[int]:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Any     =						ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST],			cfg.INPUT.MAX_SIZE_TEST						)
														SCREAMING_SNAKE_CASE__       :      Any     =						cfg.INPUT.FORMAT
														SCREAMING_SNAKE_CASE__       :      List[str]     =						cfg.SIZE_DIVISIBILITY
														SCREAMING_SNAKE_CASE__       :      List[Any]     =						cfg.PAD_VALUE
														SCREAMING_SNAKE_CASE__       :      Dict     =						cfg.INPUT.MAX_SIZE_TEST
														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						cfg.MODEL.DEVICE
														SCREAMING_SNAKE_CASE__       :      int     =						torch.tensor(cfg.MODEL.PIXEL_STD						).to(self.device						).view(len(cfg.MODEL.PIXEL_STD						),			1,			1						)
														SCREAMING_SNAKE_CASE__       :      str     =						torch.tensor(cfg.MODEL.PIXEL_MEAN						).to(self.device						).view(len(cfg.MODEL.PIXEL_STD						),			1,			1						)
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						lambda _UpperCAmelCase						: (x - self.pixel_mean) / self.pixel_std






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





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Tuple     =						tuple(max(_UpperCAmelCase						) for s in zip(*[img.shape for img in images]						)						)
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						[im.shape[-2:] for im in images]
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						[
														    nn.functional.pad(
														        _UpperCAmelCase,			[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]],			value=self.pad_value,			)
														    for size, im in zip(_UpperCAmelCase,			_UpperCAmelCase						)
														]

														return torch.stack(_UpperCAmelCase						), torch.tensor(_UpperCAmelCase						)






							def __call__( self     :					Any,			_UpperCAmelCase     :					Dict,			_UpperCAmelCase     :					List[str]=False						)				->			Optional[Any]:





														"""simple docstring"""
														with torch.no_grad():
																					if not isinstance(_UpperCAmelCase,			_UpperCAmelCase						):
																												SCREAMING_SNAKE_CASE__       :      str     =						[images]
																					if single_image:
																												assert len(_UpperCAmelCase						) == 1
																					for i in range(len(_UpperCAmelCase						)						):
																												if isinstance(images[i],			torch.Tensor						):
																																			images.insert(_UpperCAmelCase,			images.pop(_UpperCAmelCase						).to(self.device						).float()						)
																												elif not isinstance(images[i],			torch.Tensor						):
																																			images.insert(
																																			    _UpperCAmelCase,			torch.as_tensor(img_tensorize(images.pop(_UpperCAmelCase						),			input_format=self.input_format						)						)
																																			    .to(self.device						)
																																			    .float(),			)
            # resize smallest edge
																					SCREAMING_SNAKE_CASE__       :      List[str]     =						torch.tensor([im.shape[:2] for im in images]						)
																					SCREAMING_SNAKE_CASE__       :      Tuple     =						self.aug(_UpperCAmelCase						)
																					# transpose images and convert to torch tensors
																					# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
																					# now normalize before pad to avoid useless arithmetic
																					SCREAMING_SNAKE_CASE__       :      List[Any]     =						[self.normalizer(_UpperCAmelCase						) for x in images]
																					# now pad them to do the following operations
																					SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      List[str]     =						self.pad(_UpperCAmelCase						)
																					# Normalize

																					if self.size_divisibility > 0:
																												raise NotImplementedError()
																					# pad
																					SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						torch.true_divide(_UpperCAmelCase,			_UpperCAmelCase						)
																					if single_image:
																												return images[0], sizes[0], scales_yx[0]
																					else:
																												return images, sizes, scales_yx




def _a (  SCREAMING_SNAKE_CASE__      :  int			,				SCREAMING_SNAKE_CASE__      :  Dict       )	->    Union[str, Any]:

							'''simple docstring'''






							boxes[:, 0::2] *= scale_yx[:, 1]
							boxes[:, 1::2] *= scale_yx[:, 0]
							return boxes







def _a (  SCREAMING_SNAKE_CASE__      :  List[Any]			,				SCREAMING_SNAKE_CASE__      :  Tuple[int, int]       )	->    List[Any]:

							'''simple docstring'''






							assert torch.isfinite(SCREAMING_SNAKE_CASE__       ).all(), "Box tensor contains infinite or NaN!"
							SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      int     =						box_size
							tensor[:, 0].clamp_(min=0			,				max=SCREAMING_SNAKE_CASE__       )
							tensor[:, 1].clamp_(min=0			,				max=SCREAMING_SNAKE_CASE__       )
							tensor[:, 2].clamp_(min=0			,				max=SCREAMING_SNAKE_CASE__       )
							tensor[:, 3].clamp_(min=0			,				max=SCREAMING_SNAKE_CASE__       )






