




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   :    str		,				A_   :    float		,				A_   :    Callable		,				A_   :    int		,				A_   :    float = 1.0		,				A_   :    str = None		,				)  ->      Any:

       """simple docstring"""






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

 def __call__(							self   :    Dict		,				A_   :    Any    )  ->      int:

       """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 							a__					(							self   :    Union[str, Any]    )  ->      Dict:

       """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      _SCREAMING_SNAKE_CASE     (				lowercase      :			float      ,				lowercase      :			int      ,				lowercase      :			int      ,				lowercase      :			float = 0.0      ,				lowercase      :			float = 0.9      ,				lowercase      :			float = 0.999      ,				lowercase      :			float = 1e-8      ,				lowercase      :			Optional[float] = None      ,				lowercase      :			Optional[float] = None      ,				lowercase      :			float = 0.0      ,				lowercase      :			float = 1.0      ,				lowercase      :			Optional[List[str]] = None      ,				):


      '''simple docstring'''



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



 '''simple docstring'''


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

       """simple docstring"""






       lowerCamelCase_							=       {'WarmUp': WarmUp}
       return super(A_		,				cls    ).from_config(A_		,				custom_objects=A_    )

 def 							a__					(							self   :    Dict		,				A_   :    List[str]		,				A_   :    Optional[int]		,				A_   :    Optional[int]    )  ->      Optional[int]:

       """simple docstring"""






       super(A_		,				self    )._prepare_local(A_		,				A_		,				A_    )
       lowerCamelCase_							=       tf.constant(
           self.weight_decay_rate		,				name='adam_weight_decay_rate'    )

 def 							a__					(							self   :    int		,				A_   :    str		,				A_   :    Any		,				A_   :    List[Any]    )  ->      Tuple:

       """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 							a__					(							self   :    int		,				A_   :    Any		,				A_   :    Optional[int]=None		,				**A_   :    Any    )  ->      Optional[Any]:

       """simple docstring"""






       lowerCamelCase_	,							lowerCamelCase_							=       list(zip(*A_    )    )
       return super(A_		,				self    ).apply_gradients(zip(A_		,				A_    )		,				name=A_		,				**A_    )

 def 							a__					(							self   :    Any		,				A_   :    Tuple		,				A_   :    List[str]		,				A_   :    List[str]    )  ->      str:

       """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 							a__					(							self   :    Dict		,				A_   :    str		,				A_   :    Dict		,				A_   :    str=None    )  ->      Dict:

       """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 							a__					(							self   :    Tuple		,				A_   :    List[Any]		,				A_   :    Union[str, Any]		,				A_   :    List[str]		,				A_   :    Tuple=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 							a__					(							self   :    List[Any]    )  ->      Any:

       """simple docstring"""






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





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



 '''simple docstring'''


 def __init__(							self   :    Any    )  ->      Tuple:

       """simple docstring"""






       lowerCamelCase_							=       []
       lowerCamelCase_							=       None

 @property
 def 							a__					(							self   :    Dict    )  ->      Optional[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 							a__					(							self   :    List[Any]    )  ->      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   :    List[Any]		,				A_   :    int    )  ->      int:

       """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 							a__					(							self   :    List[Any]    )  ->      List[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(A_    )    )





"""simple docstring"""




import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
   from transformers import AutoModelForSeqaSeqLM, AutoTokenizer

@require_torch
@require_sentencepiece
@require_tokenizers
class 						a__		( unittest.TestCase		):
   @slow
   def      __magic_name__     (     self							):
          lowercase       :							Tuple									=				AutoModelForSeqaSeqLM.from_pretrained("google/mt5-small"      ,       return_dict=_a							).to(_a							)
          lowercase       :							Union[str, Any]									=				AutoTokenizer.from_pretrained("google/mt5-small"							)

          lowercase       :							Optional[Any]									=				tokenizer("Hello there"      ,       return_tensors="pt"							).input_ids
          lowercase       :							Union[str, Any]									=				tokenizer("Hi I am"      ,       return_tensors="pt"							).input_ids

          lowercase       :							int									=				model(input_ids.to(_a							)      ,       labels=labels.to(_a							)							).loss
          lowercase       :							str									=				-(labels.shape[-1] * loss.item())

          lowercase       :							List[str]									=				-8_4.9_1_2_7
          self.assertTrue(abs(mtf_score - EXPECTED_SCORE							) < 1E-4							)


