
'''simple docstring'''


from datasets.utils.patching import _PatchedModuleObj, patch_submodule

from . import _test_patching



def  __snake_case     (    ):
   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

   UpperCamelCase       =  '''__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  __snake_case     (    ):
   assert _test_patching.open is open

   UpperCamelCase       =  '''__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  __snake_case     (    ):
   # pandas.read_csv is not present in _test_patching
   UpperCamelCase       =  '''__test_patch_submodule_missing_mock__'''
   with patch_submodule(_test_patching,      '''pandas.read_csv''',      _UpperCAmelCase):
      pass



def  __snake_case     (    ):
   # builtin should always be mocked even if they're not in the globals
   # in case they're loaded at one point
   UpperCamelCase       =  '''__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  __snake_case     (    ):
   UpperCamelCase       =  '''__test_patch_submodule_start_and_stop_mock__'''
   UpperCamelCase       =  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  __snake_case     (    ):
   from os import rename as original_rename
   from os.path import dirname as original_dirname
   from os.path import join as original_join

   UpperCamelCase       =  '''__test_patch_submodule_successive_join__'''
   UpperCamelCase       =  '''__test_patch_submodule_successive_dirname__'''
   UpperCamelCase       =  '''__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  __snake_case     (    ):
   UpperCamelCase       =  '''__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







'''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 						lowercase__	(  tf.keras.optimizers.schedules.LearningRateSchedule   ):






   '''simple docstring'''







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

      '''simple docstring'''







      super().__init__()
      UpperCamelCase       =  initial_learning_rate
      UpperCamelCase       =  warmup_steps
      UpperCamelCase       =  power
      UpperCamelCase       =  decay_schedule_fn
      UpperCamelCase       =  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`.
         UpperCamelCase       =  tf.cast(lowerCamelCase__ ,   tf.floataa   )
         UpperCamelCase       =  tf.cast(self.warmup_steps ,   tf.floataa   )
         UpperCamelCase       =  global_step_float / warmup_steps_float
         UpperCamelCase       =  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   ):

      '''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  __snake_case     (    _UpperCAmelCase					:       float,      _UpperCAmelCase					:       int,      _UpperCAmelCase					:       int,      _UpperCAmelCase					:       float = 0.0,      _UpperCAmelCase					:       float = 0.9,      _UpperCAmelCase					:       float = 0.9_9_9,      _UpperCAmelCase					:       float = 1E-8,      _UpperCAmelCase					:       Optional[float] = None,      _UpperCAmelCase					:       Optional[float] = None,      _UpperCAmelCase					:       float = 0.0,      _UpperCAmelCase					:       float = 1.0,      _UpperCAmelCase					:       Optional[List[str]] = None,      ):
   UpperCamelCase       =  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:
      UpperCamelCase       =  WarmUp(
          initial_learning_rate=_UpperCAmelCase,      decay_schedule_fn=_UpperCAmelCase,      warmup_steps=_UpperCAmelCase,      )
   if weight_decay_rate > 0.0:
      UpperCamelCase       =  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:
      UpperCamelCase       =  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 						lowercase__	(  snake_case_   ):






   '''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__   )
      UpperCamelCase       =  weight_decay_rate
      UpperCamelCase       =  include_in_weight_decay
      UpperCamelCase       =  exclude_from_weight_decay







   @classmethod
   def       UpperCAmelCase				(			cls ,   lowerCamelCase__   ):

      '''simple docstring'''







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







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__   ):

      '''simple docstring'''







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







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__   ):

      '''simple docstring'''







      UpperCamelCase       =  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 ,   lowerCamelCase__ ,   lowerCamelCase__=None ,   **lowerCamelCase__   ):

      '''simple docstring'''







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







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__   ):

      '''simple docstring'''







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

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

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







   def       UpperCAmelCase				(			self ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__=None   ):

      '''simple docstring'''







      UpperCamelCase     , UpperCamelCase       =  self._get_lr(var.device ,   var.dtype.base_dtype ,   lowerCamelCase__   )
      UpperCamelCase       =  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 ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__ ,   lowerCamelCase__=None   ):

      '''simple docstring'''







      UpperCamelCase     , UpperCamelCase       =  self._get_lr(var.device ,   var.dtype.base_dtype ,   lowerCamelCase__   )
      UpperCamelCase       =  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   ):

      '''simple docstring'''







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







   def       UpperCAmelCase				(			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 						lowercase__	(  snake_case_   ):






   '''simple docstring'''







   def __init__(			self   ):

      '''simple docstring'''







      UpperCamelCase       =  []
      UpperCamelCase       =  None







   @property
   def       UpperCAmelCase				(			self   ):

      '''simple docstring'''







      if self._accum_steps is None:
         UpperCamelCase       =  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   ):

      '''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:
         UpperCamelCase       =  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   ):

      '''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__   )   )





