





'''simple docstring'''

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      ):





   def __init__(       self					:      List[str]					,lowerCamelCase					:      float					,lowerCamelCase					:      Callable					,lowerCamelCase					:      int					,lowerCamelCase					:      float = 1.0					,lowerCamelCase					:      str = None					,):

      '''simple docstring'''

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





   def __call__(       self					:      List[Any]					,lowerCamelCase					:      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   				=							tf.cast(lowerCamelCase					,tf.floataa     )
         __SCREAMING_SNAKE_CASE   				=							tf.cast(self.warmup_steps					,tf.floataa     )
         __SCREAMING_SNAKE_CASE   				=							global_step_float / warmup_steps_float
         __SCREAMING_SNAKE_CASE   				=							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   UpperCAmelCase__  (       self					:      Optional[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     __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				,						)    ->       Any:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							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:
      __SCREAMING_SNAKE_CASE   				=							WarmUp(
          initial_learning_rate=__UpperCAmelCase				,						decay_schedule_fn=__UpperCAmelCase				,						warmup_steps=__UpperCAmelCase				,						)
   if weight_decay_rate > 0.0:
      __SCREAMING_SNAKE_CASE   				=							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:
      __SCREAMING_SNAKE_CASE   				=							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 __a			( _snake_case      ):





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

      '''simple docstring'''

      super().__init__(lowerCamelCase					,lowerCamelCase					,lowerCamelCase					,lowerCamelCase					,lowerCamelCase					,lowerCamelCase					,**lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							weight_decay_rate
      __SCREAMING_SNAKE_CASE   				=							include_in_weight_decay
      __SCREAMING_SNAKE_CASE   				=							exclude_from_weight_decay





   @classmethod
   def   UpperCAmelCase__  (       cls					:      List[Any]					,lowerCamelCase					:      Optional[Any]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							{"""WarmUp""": WarmUp}
      return super(lowerCamelCase					,cls     ).from_config(lowerCamelCase					,custom_objects=lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      Any					,lowerCamelCase					:      Union[str, Any]					,lowerCamelCase					:      Union[str, Any]					,lowerCamelCase					:      List[Any]     ):

      '''simple docstring'''

      super(lowerCamelCase					,self     )._prepare_local(lowerCamelCase					,lowerCamelCase					,lowerCamelCase     )
      __SCREAMING_SNAKE_CASE   				=							tf.constant(
          self.weight_decay_rate					,name="""adam_weight_decay_rate"""     )





   def   UpperCAmelCase__  (       self					:      List[str]					,lowerCamelCase					:      Union[str, Any]					,lowerCamelCase					:      Dict					,lowerCamelCase					:      Union[str, Any]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							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					:      Optional[int]					,lowerCamelCase					:      List[Any]					,lowerCamelCase					:      int=None					,**lowerCamelCase					:      Optional[int]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							list(zip(*lowerCamelCase     )     )
      return super(lowerCamelCase					,self     ).apply_gradients(zip(lowerCamelCase					,lowerCamelCase     )					,name=lowerCamelCase					,**lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      Optional[Any]					,lowerCamelCase					:      Dict					,lowerCamelCase					:      Union[str, Any]					,lowerCamelCase					:      Any     ):

      '''simple docstring'''

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

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

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





   def   UpperCAmelCase__  (       self					:      Optional[int]					,lowerCamelCase					:      Optional[Any]					,lowerCamelCase					:      int					,lowerCamelCase					:      Union[str, Any]=None     ):

      '''simple docstring'''

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





   def   UpperCAmelCase__  (       self					:      Union[str, Any]					,lowerCamelCase					:      Dict					,lowerCamelCase					:      Optional[Any]					,lowerCamelCase					:      List[str]					,lowerCamelCase					:      Tuple=None     ):

      '''simple docstring'''

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





   def   UpperCAmelCase__  (       self					:      Optional[int]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							super().get_config()
      config.update({"""weight_decay_rate""": self.weight_decay_rate}     )
      return config





   def   UpperCAmelCase__  (       self					:      int					,lowerCamelCase					:      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(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			( _snake_case      ):





   def __init__(       self					:      Optional[int]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							[]
      __SCREAMING_SNAKE_CASE   				=							None





   @property
   def   UpperCAmelCase__  (       self					:      str     ):

      '''simple docstring'''

      if self._accum_steps is None:
         __SCREAMING_SNAKE_CASE   				=							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   UpperCAmelCase__  (       self					:      Optional[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					:      Union[str, Any]					,lowerCamelCase					:      List[str]     ):

      '''simple docstring'''

      if not self._gradients:
         __SCREAMING_SNAKE_CASE   				=							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   UpperCAmelCase__  (       self					:      str     ):

      '''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     )     )












'''simple docstring'''

from datasets.utils.patching import _PatchedModuleObj, patch_submodule

from . import _test_patching

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

   '''simple docstring'''







   import os as original_os
   from os import path as original_path
   from os import rename as original_rename
   from os.path import dirname as original_dirname
   from os.path import join as original_join

   assert _test_patching.os is original_os
   assert _test_patching.path is original_path
   assert _test_patching.join is original_join

   assert _test_patching.renamed_os is original_os
   assert _test_patching.renamed_path is original_path
   assert _test_patching.renamed_join is original_join

   __SCREAMING_SNAKE_CASE   				=							"""__test_patch_submodule_mock__"""
   with patch_submodule(_test_patching				,						"""os.path.join"""				,						__UpperCAmelCase   ):
      # Every way to access os.path.join must be patched, and the rest must stay untouched

      # check os.path.join
      assert isinstance(_test_patching.os				,						_PatchedModuleObj   )
      assert isinstance(_test_patching.os.path				,						_PatchedModuleObj   )
      assert _test_patching.os.path.join is mock

      # check path.join
      assert isinstance(_test_patching.path				,						_PatchedModuleObj   )
      assert _test_patching.path.join is mock

      # check join
      assert _test_patching.join is mock

      # check that the other attributes are untouched
      assert _test_patching.os.rename is original_rename
      assert _test_patching.path.dirname is original_dirname
      assert _test_patching.os.path.dirname is original_dirname

      # Even renamed modules or objects must be patched

      # check renamed_os.path.join
      assert isinstance(_test_patching.renamed_os				,						_PatchedModuleObj   )
      assert isinstance(_test_patching.renamed_os.path				,						_PatchedModuleObj   )
      assert _test_patching.renamed_os.path.join is mock

      # check renamed_path.join
      assert isinstance(_test_patching.renamed_path				,						_PatchedModuleObj   )
      assert _test_patching.renamed_path.join is mock

      # check renamed_join
      assert _test_patching.renamed_join is mock

      # check that the other attributes are untouched
      assert _test_patching.renamed_os.rename is original_rename
      assert _test_patching.renamed_path.dirname is original_dirname
      assert _test_patching.renamed_os.path.dirname is original_dirname

   # check that everthing is back to normal when the patch is over

   assert _test_patching.os is original_os
   assert _test_patching.path is original_path
   assert _test_patching.join is original_join

   assert _test_patching.renamed_os is original_os
   assert _test_patching.renamed_path is original_path
   assert _test_patching.renamed_join is original_join

def     __magic_name__							(				)    ->       Tuple:

   '''simple docstring'''







   assert _test_patching.open is open

   __SCREAMING_SNAKE_CASE   				=							"""__test_patch_submodule_builtin_mock__"""
   # _test_patching has "open" in its globals
   assert _test_patching.open is open
   with patch_submodule(_test_patching				,						"""open"""				,						__UpperCAmelCase   ):
      assert _test_patching.open is mock

   # check that everthing is back to normal when the patch is over

   assert _test_patching.open is open

def     __magic_name__							(				)    ->       Any:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							"""__test_patch_submodule_missing_mock__"""
   with patch_submodule(_test_patching				,						"""pandas.read_csv"""				,						__UpperCAmelCase   ):
      pass

def     __magic_name__							(				)    ->       Dict:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							"""__test_patch_submodule_missing_builtin_mock__"""
   # _test_patching doesn't have "len" in its globals
   assert getattr(_test_patching				,						"""len"""				,						__UpperCAmelCase   ) is None
   with patch_submodule(_test_patching				,						"""len"""				,						__UpperCAmelCase   ):
      assert _test_patching.len is mock
   assert _test_patching.len is len

def     __magic_name__							(				)    ->       Tuple:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							"""__test_patch_submodule_start_and_stop_mock__"""
   __SCREAMING_SNAKE_CASE   				=							patch_submodule(_test_patching				,						"""open"""				,						__UpperCAmelCase   )
   assert _test_patching.open is open
   patch.start()
   assert _test_patching.open is mock
   patch.stop()
   assert _test_patching.open is open

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

   '''simple docstring'''







   from os import rename as original_rename
   from os.path import dirname as original_dirname
   from os.path import join as original_join

   __SCREAMING_SNAKE_CASE   				=							"""__test_patch_submodule_successive_join__"""
   __SCREAMING_SNAKE_CASE   				=							"""__test_patch_submodule_successive_dirname__"""
   __SCREAMING_SNAKE_CASE   				=							"""__test_patch_submodule_successive_rename__"""
   assert _test_patching.os.path.join is original_join
   assert _test_patching.os.path.dirname is original_dirname
   assert _test_patching.os.rename is original_rename

   with patch_submodule(_test_patching				,						"""os.path.join"""				,						__UpperCAmelCase   ):
      with patch_submodule(_test_patching				,						"""os.rename"""				,						__UpperCAmelCase   ):
         with patch_submodule(_test_patching				,						"""os.path.dirname"""				,						__UpperCAmelCase   ):
            assert _test_patching.os.path.join is mock_join
            assert _test_patching.os.path.dirname is mock_dirname
            assert _test_patching.os.rename is mock_rename

    # try another order
   with patch_submodule(_test_patching				,						"""os.rename"""				,						__UpperCAmelCase   ):
      with patch_submodule(_test_patching				,						"""os.path.join"""				,						__UpperCAmelCase   ):
         with patch_submodule(_test_patching				,						"""os.path.dirname"""				,						__UpperCAmelCase   ):
            assert _test_patching.os.path.join is mock_join
            assert _test_patching.os.path.dirname is mock_dirname
            assert _test_patching.os.rename is mock_rename

   assert _test_patching.os.path.join is original_join
   assert _test_patching.os.path.dirname is original_dirname
   assert _test_patching.os.rename is original_rename







def     __magic_name__							(				)    ->       str:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							"""__test_patch_submodule_doesnt_exist_mock__"""
   with patch_submodule(_test_patching				,						"""__module_that_doesn_exist__.__attribute_that_doesn_exist__"""				,						__UpperCAmelCase   ):
      pass
   with patch_submodule(_test_patching				,						"""os.__attribute_that_doesn_exist__"""				,						__UpperCAmelCase   ):
      pass





