





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       __A					(					tf.keras.optimizers.schedules.LearningRateSchedule				):

			'''simple docstring'''







			def __init__(					self ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase = 1.0 ,						__lowerCAmelCase = None ,						):



				'''simple docstring'''







				super().__init__()
				lowerCamelCase__              =	initial_learning_rate
				lowerCamelCase__              =	warmup_steps
				lowerCamelCase__              =	power
				lowerCamelCase__              =	decay_schedule_fn
				lowerCamelCase__              =	name





			def __call__(					self ,						__lowerCAmelCase							):



				'''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`.
					lowerCamelCase__              =	tf.cast(__lowerCAmelCase ,						tf.floataa							)
					lowerCamelCase__              =	tf.cast(self.warmup_steps ,						tf.floataa							)
					lowerCamelCase__              =	global_step_float / warmup_steps_float
					lowerCamelCase__              =	self.initial_learning_rate * tf.math.pow(__lowerCAmelCase ,						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=__lowerCAmelCase ,						)





			def 	__lowerCamelCase       (					self							):



				'''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 lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case ,__snake_case = 0.0 ,__snake_case = 0.9 ,__snake_case = 0.9_9_9 ,__snake_case = 1E-8 ,__snake_case = None ,__snake_case = None ,__snake_case = 0.0 ,__snake_case = 1.0 ,__snake_case = None ,)       ->   Dict:







	'''simple docstring'''







	lowerCamelCase__              =	tf.keras.optimizers.schedules.PolynomialDecay(
	    initial_learning_rate=__snake_case ,decay_steps=num_train_steps - num_warmup_steps ,end_learning_rate=init_lr * min_lr_ratio ,power=__snake_case ,)
	if num_warmup_steps:
		lowerCamelCase__              =	WarmUp(
		    initial_learning_rate=__snake_case ,decay_schedule_fn=__snake_case ,warmup_steps=__snake_case ,)
	if weight_decay_rate > 0.0:
		lowerCamelCase__              =	AdamWeightDecay(
		    learning_rate=__snake_case ,weight_decay_rate=__snake_case ,beta_a=__snake_case ,beta_a=__snake_case ,epsilon=__snake_case ,clipnorm=__snake_case ,global_clipnorm=__snake_case ,exclude_from_weight_decay=['''LayerNorm''', '''layer_norm''', '''bias'''] ,include_in_weight_decay=__snake_case ,)
	else:
		lowerCamelCase__              =	tf.keras.optimizers.Adam(
		    learning_rate=__snake_case ,beta_a=__snake_case ,beta_a=__snake_case ,epsilon=__snake_case ,clipnorm=__snake_case ,global_clipnorm=__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       __A					(					lowerCAmelCase				):

			'''simple docstring'''







			def __init__(					self ,						__lowerCAmelCase = 0.001 ,						__lowerCAmelCase = 0.9 ,						__lowerCAmelCase = 0.999 ,						__lowerCAmelCase = 1E-7 ,						__lowerCAmelCase = False ,						__lowerCAmelCase = 0.0 ,						__lowerCAmelCase = None ,						__lowerCAmelCase = None ,						__lowerCAmelCase = "AdamWeightDecay" ,						**__lowerCAmelCase ,						):



				'''simple docstring'''







				super().__init__(__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						**__lowerCAmelCase							)
				lowerCamelCase__              =	weight_decay_rate
				lowerCamelCase__              =	include_in_weight_decay
				lowerCamelCase__              =	exclude_from_weight_decay





			@classmethod
			def 	__lowerCamelCase       (					cls ,						__lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__              =	{'''WarmUp''': WarmUp}
				return super(__lowerCAmelCase ,						cls							).from_config(__lowerCAmelCase ,						custom_objects=__lowerCAmelCase							)





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase							):



				'''simple docstring'''







				super(__lowerCAmelCase ,						self							)._prepare_local(__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase							)
				lowerCamelCase__              =	tf.constant(
				    self.weight_decay_rate ,						name='''adam_weight_decay_rate'''							)





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__              =	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 	__lowerCamelCase       (					self ,						__lowerCAmelCase ,						__lowerCAmelCase=None ,						**__lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__       ,			lowerCamelCase__              =	list(zip(*__lowerCAmelCase							)							)
				return super(__lowerCAmelCase ,						self							).apply_gradients(zip(__lowerCAmelCase ,						__lowerCAmelCase							) ,						name=__lowerCAmelCase ,						**__lowerCAmelCase							)





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase							):



				'''simple docstring'''







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

				lowerCamelCase__              =	apply_state or {}
				lowerCamelCase__              =	apply_state.get((var_device, var_dtype)							)
				if coefficients is None:
					lowerCamelCase__              =	self._fallback_apply_state(__lowerCAmelCase ,						__lowerCAmelCase							)
					lowerCamelCase__              =	coefficients

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





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase=None							):



				'''simple docstring'''







				lowerCamelCase__       ,			lowerCamelCase__              =	self._get_lr(var.device ,						var.dtype.base_dtype ,						__lowerCAmelCase							)
				lowerCamelCase__              =	self._decay_weights_op(__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase							)
				with tf.control_dependencies([decay]							):
					return super(__lowerCAmelCase ,						self							)._resource_apply_dense(__lowerCAmelCase ,						__lowerCAmelCase ,						**__lowerCAmelCase							)





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase=None							):



				'''simple docstring'''







				lowerCamelCase__       ,			lowerCamelCase__              =	self._get_lr(var.device ,						var.dtype.base_dtype ,						__lowerCAmelCase							)
				lowerCamelCase__              =	self._decay_weights_op(__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase							)
				with tf.control_dependencies([decay]							):
					return super(__lowerCAmelCase ,						self							)._resource_apply_sparse(__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase ,						**__lowerCAmelCase							)





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	super().get_config()
				config.update({'''weight_decay_rate''': self.weight_decay_rate}							)
				return config





			def 	__lowerCamelCase       (					self ,						__lowerCAmelCase							):



				'''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(__lowerCAmelCase ,						__lowerCAmelCase							) is not None:
							return True

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






class       __A					(					lowerCAmelCase				):

			'''simple docstring'''







			def __init__(					self							):



				'''simple docstring'''







				lowerCamelCase__              =	[]
				lowerCamelCase__              =	None





			@property
			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				if self._accum_steps is None:
					lowerCamelCase__              =	tf.Variable(
					    tf.constant(0 ,						dtype=tf.intaa							) ,						trainable=__lowerCAmelCase ,						synchronization=tf.VariableSynchronization.ON_READ ,						aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA ,						)

				return self._accum_steps.value()





			@property
			def 	__lowerCamelCase       (					self							):



				'''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 ,						__lowerCAmelCase							):



				'''simple docstring'''







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

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

				self._accum_steps.assign_add(1							)





			def 	__lowerCamelCase       (					self							):



				'''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(__lowerCAmelCase							)							)










import warnings

from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor


_a						=   logging.get_logger(__name__)






class       __A					(					lowerCAmelCase				):

			'''simple docstring'''







			def __init__(					self ,						*__lowerCAmelCase ,						**__lowerCAmelCase							):



				'''simple docstring'''







				warnings.warn(
				    '''The class OwlViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
				    ''' use OwlViTImageProcessor instead.''' ,						__lowerCAmelCase ,						)
				super().__init__(*__lowerCAmelCase ,						**__lowerCAmelCase							)



