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

     '''simple docstring'''

     def __init__(					self				,				A_				,				A_				,				A_				,				A_ = 1.0				,				A_ = None				,				) ->				Optional[int]:






           """simple docstring"""
           super().__init__()
           _lowerCamelCase			    =				initial_learning_rate
           _lowerCamelCase			    =				warmup_steps
           _lowerCamelCase			    =				power
           _lowerCamelCase			    =				decay_schedule_fn
           _lowerCamelCase			    =				name



     def __call__(					self				,				A_						) ->				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`.
                 _lowerCamelCase			    =				tf.cast(A_				,				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(A_				,				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=A_				,				)





     def 		UpperCamelCase_						(					self						) ->				int:






           """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       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase ,						__UpperCAmelCase = 0.0 ,						__UpperCAmelCase = 0.9 ,						__UpperCAmelCase = 0.9_9_9 ,						__UpperCAmelCase = 1E-8 ,						__UpperCAmelCase = None ,						__UpperCAmelCase = None ,						__UpperCAmelCase = 0.0 ,						__UpperCAmelCase = 1.0 ,						__UpperCAmelCase = None ,						)						->    str:



      '''simple docstring'''

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

     '''simple docstring'''

     def __init__(					self				,				A_ = 0.001				,				A_ = 0.9				,				A_ = 0.999				,				A_ = 1E-7				,				A_ = False				,				A_ = 0.0				,				A_ = None				,				A_ = None				,				A_ = "AdamWeightDecay"				,				**A_				,				) ->				Union[str, Any]:






           """simple docstring"""
           super().__init__(A_				,				A_				,				A_				,				A_				,				A_				,				A_				,				**A_						)
           _lowerCamelCase			    =				weight_decay_rate
           _lowerCamelCase			    =				include_in_weight_decay
           _lowerCamelCase			    =				exclude_from_weight_decay



     @classmethod
     def 		UpperCamelCase_						(					cls				,				A_						) ->				Optional[Any]:






           """simple docstring"""
           _lowerCamelCase			    =				{'''WarmUp''': WarmUp}
           return super(A_				,				cls						).from_config(A_				,				custom_objects=A_						)



     def 		UpperCamelCase_						(					self				,				A_				,				A_				,				A_						) ->				int:






           """simple docstring"""
           super(A_				,				self						)._prepare_local(A_				,				A_				,				A_						)
           _lowerCamelCase			    =				tf.constant(
               self.weight_decay_rate				,				name='''adam_weight_decay_rate'''						)



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






           """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 		UpperCamelCase_						(					self				,				A_				,				A_=None				,				**A_						) ->				List[Any]:






           """simple docstring"""
           _lowerCamelCase    ,					_lowerCamelCase			    =				list(zip(*A_						)						)
           return super(A_				,				self						).apply_gradients(zip(A_				,				A_						)				,				name=A_				,				**A_						)



     def 		UpperCamelCase_						(					self				,				A_				,				A_				,				A_						) ->				int:






           """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(A_				,				A_						)
                 _lowerCamelCase			    =				coefficients

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



     def 		UpperCamelCase_						(					self				,				A_				,				A_				,				A_=None						) ->				str:






           """simple docstring"""
           _lowerCamelCase    ,					_lowerCamelCase			    =				self._get_lr(var.device				,				var.dtype.base_dtype				,				A_						)
           _lowerCamelCase			    =				self._decay_weights_op(A_				,				A_				,				A_						)
           with tf.control_dependencies([decay]						):
                 return super(A_				,				self						)._resource_apply_dense(A_				,				A_				,				**A_						)



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






           """simple docstring"""
           _lowerCamelCase    ,					_lowerCamelCase			    =				self._get_lr(var.device				,				var.dtype.base_dtype				,				A_						)
           _lowerCamelCase			    =				self._decay_weights_op(A_				,				A_				,				A_						)
           with tf.control_dependencies([decay]						):
                 return super(A_				,				self						)._resource_apply_sparse(A_				,				A_				,				A_				,				**A_						)



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






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





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






           """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(A_				,				A_						) is not None:
                             return True

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





class  UpperCamelCase				( __lowercase ):

     '''simple docstring'''

     def __init__(					self						) ->				str:






           """simple docstring"""
           _lowerCamelCase			    =				[]
           _lowerCamelCase			    =				None



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






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

           return self._accum_steps.value()



     @property
     def 		UpperCamelCase_						(					self						) ->				Union[str, 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				,				A_						) ->				List[str]:






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

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

           self._accum_steps.assign_add(1						)





     def 		UpperCamelCase_						(					self						) ->				Dict:






           """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(A_						)						)
import warnings

from ...utils import logging
from .image_processing_dpt import DPTImageProcessor


snake_case__				= logging.get_logger(__name__)



class  UpperCamelCase				( __lowercase ):

     '''simple docstring'''

     def __init__(					self				,				*A_				,				**A_						) ->				None:






           """simple docstring"""
           warnings.warn(
               '''The class DPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
               ''' use DPTImageProcessor instead.'''				,				A_				,				)
           super().__init__(*A_				,				**A_						)