



import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
     import os

     # The slow tests are often failing with OOM error on GPU
     # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
     # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
     SCREAMING_SNAKE_CASE_:Tuple             =						"""platform"""

     import jax
     import jax.numpy as jnp

     from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
         FlaxBlenderbotSmallForConditionalGeneration,
         FlaxBlenderbotSmallModel,
         shift_tokens_right,
     )




def 	__UpperCamelCase	(  _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase=None      ,     _lowerCAmelCase=None      ,     _lowerCAmelCase=None      ,     _lowerCAmelCase=None      ,     _lowerCAmelCase=None      ,     _lowerCAmelCase=None      ,     )       ->  List[Any]:



    """simple docstring"""






    if attention_mask is None:
        A :       List[Any]          =   np.where(input_ids != config.pad_token_id      ,     1      ,     0		)
    if decoder_attention_mask is None:
        A :       Tuple          =   np.where(decoder_input_ids != config.pad_token_id      ,     1      ,     0		)
    if head_mask is None:
        A :       str          =   np.ones((config.encoder_layers, config.encoder_attention_heads)		)
    if decoder_head_mask is None:
        A :       str          =   np.ones((config.decoder_layers, config.decoder_attention_heads)		)
    if cross_attn_head_mask is None:
        A :       List[Any]          =   np.ones((config.decoder_layers, config.decoder_attention_heads)		)
    return {
        "input_ids": input_ids,
        "decoder_input_ids": decoder_input_ids,
        "attention_mask": attention_mask,
        "decoder_attention_mask": attention_mask,
    }






class SCREAMING_SNAKE_CASE__     :




    '''simple docstring'''






    def __init__(       self,		lowerCamelCase__,		lowerCamelCase__=13,		lowerCamelCase__=7,		lowerCamelCase__=True,		lowerCamelCase__=False,		lowerCamelCase__=99,		lowerCamelCase__=16,		lowerCamelCase__=2,		lowerCamelCase__=4,		lowerCamelCase__=4,		lowerCamelCase__="gelu",		lowerCamelCase__=0.1,		lowerCamelCase__=0.1,		lowerCamelCase__=32,		lowerCamelCase__=2,		lowerCamelCase__=1,		lowerCamelCase__=0,		lowerCamelCase__=0.02,		):
        A :       Union[str, Any]          =   parent
        A :       Optional[Any]          =   batch_size
        A :       str          =   seq_length
        A :       Any          =   is_training
        A :       Optional[int]          =   use_labels
        A :       Optional[Any]          =   vocab_size
        A :       Any          =   hidden_size
        A :       str          =   num_hidden_layers
        A :       Tuple          =   num_attention_heads
        A :       Optional[int]          =   intermediate_size
        A :       Optional[int]          =   hidden_act
        A :       Union[str, Any]          =   hidden_dropout_prob
        A :       List[str]          =   attention_probs_dropout_prob
        A :       int          =   max_position_embeddings
        A :       int          =   eos_token_id
        A :       Optional[Any]          =   pad_token_id
        A :       Any          =   bos_token_id
        A :       List[Any]          =   initializer_range






    def 			_lowerCAmelCase					(       self		):
        A :       int          =   np.clip(ids_tensor([self.batch_size, self.seq_length - 1],		self.vocab_size		),		3,		self.vocab_size		)
        A :       Optional[Any]          =   np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1),		dtype=np.intaa		)),		-1		)

        A :       Optional[int]          =   shift_tokens_right(lowerCamelCase__,		1,		2		)

        A :       Optional[Any]          =   BlenderbotSmallConfig(
            vocab_size=self.vocab_size,		d_model=self.hidden_size,		encoder_layers=self.num_hidden_layers,		decoder_layers=self.num_hidden_layers,		encoder_attention_heads=self.num_attention_heads,		decoder_attention_heads=self.num_attention_heads,		encoder_ffn_dim=self.intermediate_size,		decoder_ffn_dim=self.intermediate_size,		dropout=self.hidden_dropout_prob,		attention_dropout=self.attention_probs_dropout_prob,		max_position_embeddings=self.max_position_embeddings,		eos_token_id=self.eos_token_id,		bos_token_id=self.bos_token_id,		pad_token_id=self.pad_token_id,		initializer_range=self.initializer_range,		use_cache=lowerCamelCase__,		)
        A :       Any          =   prepare_blenderbot_inputs_dict(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		)
        return config, inputs_dict






    def 			_lowerCAmelCase					(       self		):
        A     ,							A :       List[Any]          =   self.prepare_config_and_inputs()
        return config, inputs_dict






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		):
        A :       int          =   20
        A :       List[str]          =   model_class_name(lowerCamelCase__		)

        A :       List[str]          =   model.encode(inputs_dict["""input_ids"""]		)

        A     ,							A :       Optional[int]          =   (
            inputs_dict["""decoder_input_ids"""],
            inputs_dict["""decoder_attention_mask"""],
        )

        A :       Dict          =   model.init_cache(decoder_input_ids.shape[0],		lowerCamelCase__,		lowerCamelCase__		)
        A :       int          =   jnp.ones((decoder_input_ids.shape[0], max_decoder_length),		dtype="""i4"""		)

        A :       str          =   jnp.broadcast_to(
            jnp.arange(decoder_input_ids.shape[-1] - 1		)[None, :],		(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1),		)
        A :       Tuple          =   model.decode(
            decoder_input_ids[:, :-1],		lowerCamelCase__,		decoder_attention_mask=lowerCamelCase__,		past_key_values=lowerCamelCase__,		decoder_position_ids=lowerCamelCase__,		)

        A :       Tuple          =   jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]],		dtype="""i4"""		)
        A :       Any          =   model.decode(
            decoder_input_ids[:, -1:],		lowerCamelCase__,		decoder_attention_mask=lowerCamelCase__,		past_key_values=outputs_cache.past_key_values,		decoder_position_ids=lowerCamelCase__,		)

        A :       int          =   model.decode(lowerCamelCase__,		lowerCamelCase__		)

        A :       Optional[int]          =   np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])		)		)
        self.parent.assertTrue(diff < 1e-3,		msg=f'''Max diff is {diff}'''		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		):
        A :       Tuple          =   20
        A :       Union[str, Any]          =   model_class_name(lowerCamelCase__		)

        A :       Tuple          =   model.encode(inputs_dict["""input_ids"""]		)

        A     ,							A :       Optional[Any]          =   (
            inputs_dict["""decoder_input_ids"""],
            inputs_dict["""decoder_attention_mask"""],
        )

        A :       Optional[int]          =   jnp.concatenate(
            [
                decoder_attention_mask,
                jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])		),
            ],		axis=-1,		)

        A :       Union[str, Any]          =   model.init_cache(decoder_input_ids.shape[0],		lowerCamelCase__,		lowerCamelCase__		)
        A :       str          =   jnp.broadcast_to(
            jnp.arange(decoder_input_ids.shape[-1] - 1		)[None, :],		(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1),		)

        A :       Union[str, Any]          =   model.decode(
            decoder_input_ids[:, :-1],		lowerCamelCase__,		decoder_attention_mask=lowerCamelCase__,		past_key_values=lowerCamelCase__,		decoder_position_ids=lowerCamelCase__,		)
        A :       int          =   jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]],		dtype="""i4"""		)
        A :       Dict          =   model.decode(
            decoder_input_ids[:, -1:],		lowerCamelCase__,		past_key_values=outputs_cache.past_key_values,		decoder_attention_mask=lowerCamelCase__,		decoder_position_ids=lowerCamelCase__,		)

        A :       List[Any]          =   model.decode(lowerCamelCase__,		lowerCamelCase__,		decoder_attention_mask=lowerCamelCase__		)

        A :       str          =   np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])		)		)
        self.parent.assertTrue(diff < 1e-3,		msg=f'''Max diff is {diff}'''		)






@require_flax
class SCREAMING_SNAKE_CASE__     (		unittest.TestCase    ):




    '''simple docstring'''






    __lowerCamelCase			: List[str]					=      99






    def 			_lowerCAmelCase					(       self		):
        A :       Union[str, Any]          =   np.array(
            [
                [71, 82, 18, 33, 46, 91, 2],
                [68, 34, 26, 58, 30, 82, 2],
                [5, 97, 17, 39, 94, 40, 2],
                [76, 83, 94, 25, 70, 78, 2],
                [87, 59, 41, 35, 48, 66, 2],
                [55, 13, 16, 58, 5, 2, 1],  # note padding
                [64, 27, 31, 51, 12, 75, 2],
                [52, 64, 86, 17, 83, 39, 2],
                [48, 61, 9, 24, 71, 82, 2],
                [26, 1, 60, 48, 22, 13, 2],
                [21, 5, 62, 28, 14, 76, 2],
                [45, 98, 37, 86, 59, 48, 2],
                [70, 70, 50, 9, 28, 0, 2],
            ],		dtype=np.intaa,		)

        A :       Optional[int]          =   input_ids.shape[0]
        A :       Tuple          =   BlenderbotSmallConfig(
            vocab_size=self.vocab_size,		d_model=24,		encoder_layers=2,		decoder_layers=2,		encoder_attention_heads=2,		decoder_attention_heads=2,		encoder_ffn_dim=32,		decoder_ffn_dim=32,		max_position_embeddings=48,		eos_token_id=2,		pad_token_id=1,		bos_token_id=0,		)
        return config, input_ids, batch_size






    def 			_lowerCAmelCase					(       self		):
        A     ,							A     ,							A :       List[Any]          =   self._get_config_and_data()
        A :       Dict          =   FlaxBlenderbotSmallForConditionalGeneration(lowerCamelCase__		)
        A :       Optional[int]          =   lm_model(input_ids=lowerCamelCase__		)
        A :       Any          =   (batch_size, input_ids.shape[1], config.vocab_size)
        self.assertEqual(outputs["""logits"""].shape,		lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self		):
        A :       Dict          =   BlenderbotSmallConfig(
            vocab_size=self.vocab_size,		d_model=14,		encoder_layers=2,		decoder_layers=2,		encoder_attention_heads=2,		decoder_attention_heads=2,		encoder_ffn_dim=8,		decoder_ffn_dim=8,		max_position_embeddings=48,		)
        A :       Union[str, Any]          =   FlaxBlenderbotSmallForConditionalGeneration(lowerCamelCase__		)
        A :       List[Any]          =   np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]],		dtype=np.intaa		)
        A :       str          =   np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]],		dtype=np.intaa		)
        A :       Tuple          =   lm_model(input_ids=lowerCamelCase__,		decoder_input_ids=lowerCamelCase__		)
        A :       Optional[Any]          =   (*summary.shape, config.vocab_size)
        self.assertEqual(outputs["""logits"""].shape,		lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self		):
        A :       Union[str, Any]          =   np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]],		dtype=np.intaa		)
        A :       str          =   shift_tokens_right(lowerCamelCase__,		1,		2		)
        A :       Dict          =   np.equal(lowerCamelCase__,		1		).astype(np.floataa		).sum()
        A :       Tuple          =   np.equal(lowerCamelCase__,		1		).astype(np.floataa		).sum()
        self.assertEqual(shifted.shape,		input_ids.shape		)
        self.assertEqual(lowerCamelCase__,		n_pad_before - 1		)
        self.assertTrue(np.equal(shifted[:, 0],		2		).all()		)






@require_flax
class SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__ ,						unittest.TestCase ,						SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    __lowerCamelCase			: Optional[int]					=      True
    __lowerCamelCase			: List[Any]					=      (
        (
            FlaxBlenderbotSmallModel,
            FlaxBlenderbotSmallForConditionalGeneration,
        )
        if is_flax_available()
        else ()
    )
    __lowerCamelCase			: List[Any]					=      (FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()






    def 			_lowerCAmelCase					(       self		):
        A :       Union[str, Any]          =   FlaxBlenderbotSmallModelTester(self		)






    def 			_lowerCAmelCase					(       self		):
        A     ,							A :       Union[str, Any]          =   self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
            self.model_tester.check_use_cache_forward(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self		):
        A     ,							A :       List[str]          =   self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
            self.model_tester.check_use_cache_forward_with_attn_mask(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self		):
        A     ,							A :       str          =   self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            with self.subTest(model_class.__name__		):
                A :       Optional[Any]          =   self._prepare_for_class(lowerCamelCase__,		lowerCamelCase__		)
                A :       List[str]          =   model_class(lowerCamelCase__		)

                @jax.jit
                def encode_jitted(lowerCamelCase__,		lowerCamelCase__=None,		**lowerCamelCase__		):
                    return model.encode(input_ids=lowerCamelCase__,		attention_mask=lowerCamelCase__		)

                with self.subTest("""JIT Enabled"""		):
                    A :       Any          =   encode_jitted(**lowerCamelCase__		).to_tuple()

                with self.subTest("""JIT Disabled"""		):
                    with jax.disable_jit():
                        A :       Union[str, Any]          =   encode_jitted(**lowerCamelCase__		).to_tuple()

                self.assertEqual(len(lowerCamelCase__		),		len(lowerCamelCase__		)		)
                for jitted_output, output in zip(lowerCamelCase__,		lowerCamelCase__		):
                    self.assertEqual(jitted_output.shape,		output.shape		)






    def 			_lowerCAmelCase					(       self		):
        A     ,							A :       Union[str, Any]          =   self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            with self.subTest(model_class.__name__		):
                A :       List[str]          =   model_class(lowerCamelCase__		)
                A :       Union[str, Any]          =   model.encode(inputs_dict["""input_ids"""],		inputs_dict["""attention_mask"""]		)

                A :       Union[str, Any]          =   {
                    """decoder_input_ids""": inputs_dict["""decoder_input_ids"""],
                    """decoder_attention_mask""": inputs_dict["""decoder_attention_mask"""],
                    """encoder_outputs""": encoder_outputs,
                }

                @jax.jit
                def decode_jitted(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		):
                    return model.decode(
                        decoder_input_ids=lowerCamelCase__,		decoder_attention_mask=lowerCamelCase__,		encoder_outputs=lowerCamelCase__,		)

                with self.subTest("""JIT Enabled"""		):
                    A :       Optional[int]          =   decode_jitted(**lowerCamelCase__		).to_tuple()

                with self.subTest("""JIT Disabled"""		):
                    with jax.disable_jit():
                        A :       int          =   decode_jitted(**lowerCamelCase__		).to_tuple()

                self.assertEqual(len(lowerCamelCase__		),		len(lowerCamelCase__		)		)
                for jitted_output, output in zip(lowerCamelCase__,		lowerCamelCase__		):
                    self.assertEqual(jitted_output.shape,		output.shape		)






    @slow
    def 			_lowerCAmelCase					(       self		):
        for model_class_name in self.all_model_classes:
            A :       Optional[Any]          =   model_class_name.from_pretrained("""facebook/blenderbot_small-90M"""		)
            # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
            A :       List[Any]          =   np.ones((1, 1)		) * model.config.eos_token_id
            A :       Union[str, Any]          =   model(lowerCamelCase__		)
            self.assertIsNotNone(lowerCamelCase__		)







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




    '''simple docstring'''






    def __init__(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__ = 1.0,		lowerCamelCase__ = None,		):
        super().__init__()
        A :       Union[str, Any]          =   initial_learning_rate
        A :       List[Any]          =   warmup_steps
        A :       int          =   power
        A :       Optional[int]          =   decay_schedule_fn
        A :       int          =   name






    def __call__(       self,		lowerCamelCase__		):
        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`.
            A :       str          =   tf.cast(lowerCamelCase__,		tf.floataa		)
            A :       List[Any]          =   tf.cast(self.warmup_steps,		tf.floataa		)
            A :       Dict          =   global_step_float / warmup_steps_float
            A :       Union[str, Any]          =   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 			_lowerCAmelCase					(       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 	__UpperCamelCase	(  _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase = 0.0      ,     _lowerCAmelCase = 0.9      ,     _lowerCAmelCase = 0.999      ,     _lowerCAmelCase = 1e-8      ,     _lowerCAmelCase = None      ,     _lowerCAmelCase = None      ,     _lowerCAmelCase = 0.0      ,     _lowerCAmelCase = 1.0      ,     _lowerCAmelCase = None      ,     )       ->  Union[str, Any]:



    """simple docstring"""






    A :       Optional[int]          =   tf.keras.optimizers.schedules.PolynomialDecay(
        initial_learning_rate=_lowerCAmelCase      ,     decay_steps=num_train_steps - num_warmup_steps      ,     end_learning_rate=init_lr * min_lr_ratio      ,     power=_lowerCAmelCase      ,     )
    if num_warmup_steps:
        A :       Dict          =   WarmUp(
            initial_learning_rate=_lowerCAmelCase      ,     decay_schedule_fn=_lowerCAmelCase      ,     warmup_steps=_lowerCAmelCase      ,     )
    if weight_decay_rate > 0.0:
        A :       str          =   AdamWeightDecay(
            learning_rate=_lowerCAmelCase      ,     weight_decay_rate=_lowerCAmelCase      ,     beta_a=_lowerCAmelCase      ,     beta_a=_lowerCAmelCase      ,     epsilon=_lowerCAmelCase      ,     clipnorm=_lowerCAmelCase      ,     global_clipnorm=_lowerCAmelCase      ,     exclude_from_weight_decay=["""LayerNorm""", """layer_norm""", """bias"""]      ,     include_in_weight_decay=_lowerCAmelCase      ,     )
    else:
        A :       Optional[int]          =   tf.keras.optimizers.Adam(
            learning_rate=_lowerCAmelCase      ,     beta_a=_lowerCAmelCase      ,     beta_a=_lowerCAmelCase      ,     epsilon=_lowerCAmelCase      ,     clipnorm=_lowerCAmelCase      ,     global_clipnorm=_lowerCAmelCase      ,     )
    # 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 SCREAMING_SNAKE_CASE__     (		SCREAMING_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__,		):
        super().__init__(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		**lowerCamelCase__		)
        A :       int          =   weight_decay_rate
        A :       Any          =   include_in_weight_decay
        A :       Dict          =   exclude_from_weight_decay






    @classmethod
    def 			_lowerCAmelCase					(       cls,		lowerCamelCase__		):
        A :       Tuple          =   {"""WarmUp""": WarmUp}
        return super(lowerCamelCase__,		cls		).from_config(lowerCamelCase__,		custom_objects=lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		):
        super(lowerCamelCase__,		self		)._prepare_local(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		)
        A :       List[str]          =   tf.constant(
            self.weight_decay_rate,		name="""adam_weight_decay_rate"""		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		):
        A :       Any          =   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 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__=None,		**lowerCamelCase__		):
        A     ,							A :       Dict          =   list(zip(*lowerCamelCase__		)		)
        return super(lowerCamelCase__,		self		).apply_gradients(zip(lowerCamelCase__,		lowerCamelCase__		),		name=lowerCamelCase__,		**lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		):
        if apply_state is None:
            return self._decayed_lr_t[var_dtype], {}

        A :       Union[str, Any]          =   apply_state or {}
        A :       Optional[int]          =   apply_state.get((var_device, var_dtype)		)
        if coefficients is None:
            A :       Dict          =   self._fallback_apply_state(lowerCamelCase__,		lowerCamelCase__		)
            A :       List[Any]          =   coefficients

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






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__=None		):
        A     ,							A :       str          =   self._get_lr(var.device,		var.dtype.base_dtype,		lowerCamelCase__		)
        A :       Any          =   self._decay_weights_op(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		)
        with tf.control_dependencies([decay]		):
            return super(lowerCamelCase__,		self		)._resource_apply_dense(lowerCamelCase__,		lowerCamelCase__,		**lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__=None		):
        A     ,							A :       Tuple          =   self._get_lr(var.device,		var.dtype.base_dtype,		lowerCamelCase__		)
        A :       Optional[Any]          =   self._decay_weights_op(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		)
        with tf.control_dependencies([decay]		):
            return super(lowerCamelCase__,		self		)._resource_apply_sparse(lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__,		**lowerCamelCase__		)






    def 			_lowerCAmelCase					(       self		):
        A :       Dict          =   super().get_config()
        config.update({"""weight_decay_rate""": self.weight_decay_rate}		)
        return config






    def 			_lowerCAmelCase					(       self,		lowerCamelCase__		):
        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 SCREAMING_SNAKE_CASE__     (		SCREAMING_SNAKE_CASE__    ):




    '''simple docstring'''






    def __init__(       self		):
        A :       List[str]          =   []
        A :       List[str]          =   None






    @property
    def 			_lowerCAmelCase					(       self		):
        if self._accum_steps is None:
            A :       str          =   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 			_lowerCAmelCase					(       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,		lowerCamelCase__		):
        if not self._gradients:
            A :       int          =   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 			_lowerCAmelCase					(       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(lowerCamelCase__		)		)


