






"""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  lowerCamelCase__ (	tf.keras.optimizers.schedules.LearningRateSchedule			):






    def __init__(			self     ,A     ,A     ,A     ,A = 1.0     ,A = None     ,):
          super().__init__()
          UpperCAmelCase          =	initial_learning_rate
          UpperCAmelCase          =	warmup_steps
          UpperCAmelCase          =	power
          UpperCAmelCase          =	decay_schedule_fn
          UpperCAmelCase          =	name






    def __call__(			self     ,A ):
          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(A     ,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(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 ):
          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 _a			(		_snake_case				,	_snake_case				,	_snake_case				,	_snake_case = 0.0				,	_snake_case = 0.9				,	_snake_case = 0.999				,	_snake_case = 1E-8				,	_snake_case = None				,	_snake_case = None				,	_snake_case = 0.0				,	_snake_case = 1.0				,	_snake_case = None				,	):







      """simple docstring"""






      UpperCAmelCase          =	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:
            UpperCAmelCase          =	WarmUp(
                initial_learning_rate=_snake_case				,	decay_schedule_fn=_snake_case				,	warmup_steps=_snake_case				,	)
      if weight_decay_rate > 0.0:
            UpperCAmelCase          =	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:
            UpperCAmelCase          =	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  lowerCamelCase__ (	snake_case			):






    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     ,):
          super().__init__(A     ,A     ,A     ,A     ,A     ,A     ,**A )
          UpperCAmelCase          =	weight_decay_rate
          UpperCAmelCase          =	include_in_weight_decay
          UpperCAmelCase          =	exclude_from_weight_decay






    @classmethod
    def     _UpperCamelCase							(			cls     ,A ):
          UpperCAmelCase          =	{"""WarmUp""": WarmUp}
          return super(A     ,cls ).from_config(A     ,custom_objects=A )






    def     _UpperCamelCase							(			self     ,A     ,A     ,A ):
          super(A     ,self )._prepare_local(A     ,A     ,A )
          UpperCAmelCase          =	tf.constant(
              self.weight_decay_rate     ,name="""adam_weight_decay_rate""" )






    def     _UpperCamelCase							(			self     ,A     ,A     ,A ):
          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     ,A     ,A=None     ,**A ):
          UpperCAmelCase      , UpperCAmelCase          =	list(zip(*A ) )
          return super(A     ,self ).apply_gradients(zip(A     ,A )     ,name=A     ,**A )






    def     _UpperCamelCase							(			self     ,A     ,A     ,A ):
          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(A     ,A )
                UpperCAmelCase          =	coefficients

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






    def     _UpperCamelCase							(			self     ,A     ,A     ,A=None ):
          UpperCAmelCase      , UpperCAmelCase          =	self._get_lr(var.device     ,var.dtype.base_dtype     ,A )
          UpperCAmelCase          =	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 ):
          UpperCAmelCase      , UpperCAmelCase          =	self._get_lr(var.device     ,var.dtype.base_dtype     ,A )
          UpperCAmelCase          =	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 ):
          UpperCAmelCase          =	super().get_config()
          config.update({"""weight_decay_rate""": self.weight_decay_rate} )
          return config






    def     _UpperCamelCase							(			self     ,A ):
          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  lowerCamelCase__ (	snake_case			):






    def __init__(			self ):
          UpperCAmelCase          =	[]
          UpperCAmelCase          =	None






    @property
    def     _UpperCamelCase							(			self ):
          if self._accum_steps is None:
                UpperCAmelCase          =	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 ):
          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 ):
          if not self._gradients:
                UpperCAmelCase          =	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 ):
          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 io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


_UpperCamelCase					      =  get_tests_dir() + """/test_data/fsmt/fsmt_val_data.json"""
with io.open(filename, """r""", encoding="""utf-8""") as f:
  _UpperCamelCase					      =  json.load(f)







@require_torch
class  lowerCamelCase__ (	unittest.TestCase			):






    def     _UpperCamelCase							(			self     ,A ):
          return FSMTTokenizer.from_pretrained(A )






    def     _UpperCamelCase							(			self     ,A ):
          UpperCAmelCase          =	FSMTForConditionalGeneration.from_pretrained(A ).to(A )
          if torch_device == "cuda":
                model.half()
          return model






    @parameterized.expand(
        [
            ["""en-ru""", 26.0],
            ["""ru-en""", 22.0],
            ["""en-de""", 22.0],
            ["""de-en""", 29.0],
        ] )
    @slow
    def     _UpperCamelCase							(			self     ,A     ,A ):
          # note: this test is not testing the best performance since it only evals a small batch
          # but it should be enough to detect a regression in the output quality
          UpperCAmelCase          =	F'''facebook/wmt19-{pair}'''
          UpperCAmelCase          =	self.get_tokenizer(A )
          UpperCAmelCase          =	self.get_model(A )

          UpperCAmelCase          =	bleu_data[pair]["""src"""]
          UpperCAmelCase          =	bleu_data[pair]["""tgt"""]

          UpperCAmelCase          =	tokenizer(A     ,return_tensors="""pt"""     ,truncation=A     ,padding="""longest""" ).to(A )
          UpperCAmelCase          =	model.generate(
              input_ids=batch.input_ids     ,num_beams=8     ,)
          UpperCAmelCase          =	tokenizer.batch_decode(
              A     ,skip_special_tokens=A     ,clean_up_tokenization_spaces=A )
          UpperCAmelCase          =	calculate_bleu(A     ,A )
          print(A )
          self.assertGreaterEqual(scores["""bleu"""]     ,A )



