
"""simple docstring"""


import unittest

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

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import (
							    OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
							    OpenAIGPTConfig,
							    OpenAIGPTDoubleHeadsModel,
							    OpenAIGPTForSequenceClassification,
							    OpenAIGPTLMHeadModel,
							    OpenAIGPTModel,
							)

class     A_	:



							"""simple docstring"""






							def __init__(  self  :Any     ,							lowerCamelCase_  :str     ,							lowerCamelCase_  :int=13     ,							lowerCamelCase_  :str=7     ,							lowerCamelCase_  :Tuple=True     ,							lowerCamelCase_  :Union[str, Any]=True     ,							lowerCamelCase_  :Dict=True     ,							lowerCamelCase_  :Dict=99     ,							lowerCamelCase_  :int=32     ,							lowerCamelCase_  :Dict=5     ,							lowerCamelCase_  :str=4     ,							lowerCamelCase_  :Union[str, Any]=37     ,							lowerCamelCase_  :str="gelu"     ,							lowerCamelCase_  :str=0.1     ,							lowerCamelCase_  :Any=0.1     ,							lowerCamelCase_  :List[str]=512     ,							lowerCamelCase_  :Optional[Any]=16     ,							lowerCamelCase_  :List[str]=2     ,							lowerCamelCase_  :Any=0.02     ,							lowerCamelCase_  :Any=3     ,							lowerCamelCase_  :int=4     ,							lowerCamelCase_  :Dict=None     ,							):





										"""simple docstring"""

										lowerCamelCase__	:     int              =parent
										lowerCamelCase__	:     Tuple              =batch_size
										lowerCamelCase__	:     Dict              =seq_length
										lowerCamelCase__	:     Dict              =is_training
										lowerCamelCase__	:     str              =use_token_type_ids
										lowerCamelCase__	:     Optional[Any]              =use_labels
										lowerCamelCase__	:     Optional[int]              =vocab_size
										lowerCamelCase__	:     Dict              =hidden_size
										lowerCamelCase__	:     List[str]              =num_hidden_layers
										lowerCamelCase__	:     Optional[int]              =num_attention_heads
										lowerCamelCase__	:     Any              =intermediate_size
										lowerCamelCase__	:     Dict              =hidden_act
										lowerCamelCase__	:     Optional[Any]              =hidden_dropout_prob
										lowerCamelCase__	:     Union[str, Any]              =attention_probs_dropout_prob
										lowerCamelCase__	:     int              =max_position_embeddings
										lowerCamelCase__	:     Any              =type_vocab_size
										lowerCamelCase__	:     str              =type_sequence_label_size
										lowerCamelCase__	:     Optional[int]              =initializer_range
										lowerCamelCase__	:     List[str]              =num_labels
										lowerCamelCase__	:     Optional[Any]              =num_choices
										lowerCamelCase__	:     List[str]              =scope
										lowerCamelCase__	:     Tuple              =self.vocab_size - 1





							def       UpperCAmelCase__							(  self  :Dict       ):





										"""simple docstring"""

										lowerCamelCase__	:     Optional[int]              =ids_tensor([self.batch_size, self.seq_length]     ,							self.vocab_size       )

										lowerCamelCase__	:     Any              =None
										if self.use_token_type_ids:
													lowerCamelCase__	:     List[Any]              =ids_tensor([self.batch_size, self.seq_length]     ,							self.type_vocab_size       )

										lowerCamelCase__	:     Any              =None
										lowerCamelCase__	:     List[str]              =None
										lowerCamelCase__	:     Any              =None
										if self.use_labels:
													lowerCamelCase__	:     Tuple              =ids_tensor([self.batch_size]     ,							self.type_sequence_label_size       )
													lowerCamelCase__	:     List[str]              =ids_tensor([self.batch_size, self.seq_length]     ,							self.num_labels       )
													lowerCamelCase__	:     Union[str, Any]              =ids_tensor([self.batch_size]     ,							self.num_choices       )

										lowerCamelCase__	:     List[str]              =OpenAIGPTConfig(
										    vocab_size=self.vocab_size     ,							n_embd=self.hidden_size     ,							n_layer=self.num_hidden_layers     ,							n_head=self.num_attention_heads     ,							n_positions=self.max_position_embeddings     ,							pad_token_id=self.pad_token_id     ,							)

										lowerCamelCase__	:     Union[str, Any]              =ids_tensor([self.num_hidden_layers, self.num_attention_heads]     ,							2       )

										return (
										    config,
										    input_ids,
										    head_mask,
										    token_type_ids,
										    sequence_labels,
										    token_labels,
										    choice_labels,
										)





							def       UpperCAmelCase__							(  self  :Dict     ,							lowerCamelCase_  :Optional[Any]     ,							lowerCamelCase_  :str     ,							lowerCamelCase_  :List[str]     ,							lowerCamelCase_  :Any     ,							*lowerCamelCase_  :Dict       ):





										"""simple docstring"""

										lowerCamelCase__	:     List[Any]              =OpenAIGPTModel(config=lowercase_       )
										model.to(lowercase_       )
										model.eval()

										lowerCamelCase__	:     Any              =model(lowercase_     ,							token_type_ids=lowercase_     ,							head_mask=lowercase_       )
										lowerCamelCase__	:     int              =model(lowercase_     ,							token_type_ids=lowercase_       )
										lowerCamelCase__	:     Optional[int]              =model(lowercase_       )

										self.parent.assertEqual(result.last_hidden_state.shape     ,							(self.batch_size, self.seq_length, self.hidden_size)       )





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





										"""simple docstring"""

										lowerCamelCase__	:     Any              =OpenAIGPTLMHeadModel(lowercase_       )
										model.to(lowercase_       )
										model.eval()

										lowerCamelCase__	:     Optional[int]              =model(lowercase_     ,							token_type_ids=lowercase_     ,							labels=lowercase_       )
										self.parent.assertEqual(result.loss.shape     ,							()       )
										self.parent.assertEqual(result.logits.shape     ,							(self.batch_size, self.seq_length, self.vocab_size)       )





							def       UpperCAmelCase__							(  self  :int     ,							lowerCamelCase_  :List[Any]     ,							lowerCamelCase_  :List[Any]     ,							lowerCamelCase_  :int     ,							lowerCamelCase_  :str     ,							*lowerCamelCase_  :Optional[int]       ):





										"""simple docstring"""

										lowerCamelCase__	:     Union[str, Any]              =OpenAIGPTDoubleHeadsModel(lowercase_       )
										model.to(lowercase_       )
										model.eval()

										lowerCamelCase__	:     Tuple              =model(lowercase_     ,							token_type_ids=lowercase_     ,							labels=lowercase_       )
										self.parent.assertEqual(result.loss.shape     ,							()       )
										self.parent.assertEqual(result.logits.shape     ,							(self.batch_size, self.seq_length, self.vocab_size)       )





							def       UpperCAmelCase__							(  self  :Any     ,							lowerCamelCase_  :str     ,							lowerCamelCase_  :Any     ,							lowerCamelCase_  :int     ,							lowerCamelCase_  :Union[str, Any]     ,							*lowerCamelCase_  :Any       ):





										"""simple docstring"""

										lowerCamelCase__	:     Any              =self.num_labels
										lowerCamelCase__	:     Optional[Any]              =OpenAIGPTForSequenceClassification(lowercase_       )
										model.to(lowercase_       )
										model.eval()

										lowerCamelCase__	:     List[str]              =ids_tensor([self.batch_size]     ,							self.type_sequence_label_size       )
										lowerCamelCase__	:     List[str]              =model(lowercase_     ,							token_type_ids=lowercase_     ,							labels=lowercase_       )
										self.parent.assertEqual(result.logits.shape     ,							(self.batch_size, self.num_labels)       )





							def       UpperCAmelCase__							(  self  :Tuple       ):





										"""simple docstring"""

										lowerCamelCase__	:     Tuple              =self.prepare_config_and_inputs()
										(
										    (
										    lowerCamelCase__
										)       ,				(
										    lowerCamelCase__
										)       ,				(
										    lowerCamelCase__
										)       ,				(
										    lowerCamelCase__
										)       ,				(
										    lowerCamelCase__
										)       ,				(
										    lowerCamelCase__
										)       ,				(
										    lowerCamelCase__
										)       ,				
										)	:     List[Any]              =config_and_inputs
										lowerCamelCase__	:     Dict              ={
										    'input_ids': input_ids,
										    'token_type_ids': token_type_ids,
										    'head_mask': head_mask,
										}

										return config, inputs_dict






@require_torch
class     A_	(     _lowerCamelCase      ,					_lowerCamelCase      ,					_lowerCamelCase      ,					unittest.TestCase   ):



							"""simple docstring"""






							SCREAMING_SNAKE_CASE_	      =   (
							    (OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
							    if is_torch_available()
							    else ()
							)
							SCREAMING_SNAKE_CASE_	      =   (
							    (OpenAIGPTLMHeadModel,) if is_torch_available() else ()
							)  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
							SCREAMING_SNAKE_CASE_	      =   (
							    {
							        """feature-extraction""": OpenAIGPTModel,
							        """text-classification""": OpenAIGPTForSequenceClassification,
							        """text-generation""": OpenAIGPTLMHeadModel,
							        """zero-shot""": OpenAIGPTForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)





							def       UpperCAmelCase__							(  self  :Any     ,							lowerCamelCase_  :List[str]     ,							lowerCamelCase_  :str     ,							lowerCamelCase_  :Any     ,							lowerCamelCase_  :Optional[Any]     ,							lowerCamelCase_  :int       ):





										"""simple docstring"""

										if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
													# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
													# `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
													# tiny config could not be created.
													return True

										return False





							def       UpperCAmelCase__							(  self  :Optional[Any]     ,							lowerCamelCase_  :Dict     ,							lowerCamelCase_  :Any     ,							lowerCamelCase_  :int=False       ):





										"""simple docstring"""

										lowerCamelCase__	:     Tuple              =super()._prepare_for_class(lowercase_     ,							lowercase_     ,							return_labels=lowercase_       )

										if return_labels:
													if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
																lowerCamelCase__	:     Any              =torch.zeros(
																    (self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length)     ,							dtype=torch.long     ,							device=lowercase_     ,							)
																lowerCamelCase__	:     List[Any]              =inputs_dict['labels']
																lowerCamelCase__	:     int              =inputs_dict['labels']
																lowerCamelCase__	:     Tuple              =torch.zeros(
																    (self.model_tester.batch_size, self.model_tester.num_choices)     ,							dtype=torch.long     ,							device=lowercase_     ,							)
																lowerCamelCase__	:     List[Any]              =torch.zeros(
																    self.model_tester.batch_size     ,							dtype=torch.long     ,							device=lowercase_       )
										return inputs_dict





							def       UpperCAmelCase__							(  self  :List[str]       ):





										"""simple docstring"""

										lowerCamelCase__	:     Optional[int]              =OpenAIGPTModelTester(self       )
										lowerCamelCase__	:     int              =ConfigTester(self     ,							config_class=lowercase_     ,							n_embd=37       )





							def       UpperCAmelCase__							(  self  :List[Any]       ):





										"""simple docstring"""

										self.config_tester.run_common_tests()





							def       UpperCAmelCase__							(  self  :str       ):





										"""simple docstring"""

										lowerCamelCase__	:     Tuple              =self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_openai_gpt_model(*lowercase_       )





							def       UpperCAmelCase__							(  self  :Dict       ):





										"""simple docstring"""

										lowerCamelCase__	:     Optional[int]              =self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_lm_head_model(*lowercase_       )





							def       UpperCAmelCase__							(  self  :Optional[Any]       ):





										"""simple docstring"""

										lowerCamelCase__	:     Optional[int]              =self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_double_lm_head_model(*lowercase_       )





							def       UpperCAmelCase__							(  self  :Tuple       ):





										"""simple docstring"""

										lowerCamelCase__	:     Union[str, Any]              =self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*lowercase_       )





							@slow
							def       UpperCAmelCase__							(  self  :Optional[int]       ):





										"""simple docstring"""

										for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													lowerCamelCase__	:     int              =OpenAIGPTModel.from_pretrained(lowercase_       )
													self.assertIsNotNone(lowercase_       )






@require_torch
class     A_	(     unittest.TestCase   ):



							"""simple docstring"""






							@slow
							def       UpperCAmelCase__							(  self  :List[str]       ):





										"""simple docstring"""

										lowerCamelCase__	:     str              =OpenAIGPTLMHeadModel.from_pretrained('openai-gpt'       )
										model.to(lowercase_       )
										lowerCamelCase__	:     List[str]              =torch.tensor([[481, 4_735, 544]]     ,							dtype=torch.long     ,							device=lowercase_       )  # the president is
										lowerCamelCase__	:     str              =[
										    481,
										    4_735,
										    544,
										    246,
										    963,
										    870,
										    762,
										    239,
										    244,
										    40_477,
										    244,
										    249,
										    719,
										    881,
										    487,
										    544,
										    240,
										    244,
										    603,
										    481,
										]  # the president is a very good man. " \n " i\'m sure he is, " said the

										lowerCamelCase__	:     str              =model.generate(lowercase_     ,							do_sample=lowercase_       )
										self.assertListEqual(output_ids[0].tolist()     ,							lowercase_       )





'''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    :					Optional[Any]       ,				lowercase_    :					float       ,				lowercase_    :					Callable       ,				lowercase_    :					int       ,				lowercase_    :					float = 1.0       ,				lowercase_    :					str = None       ,				):
									super().__init__()
									snake_case_									=				initial_learning_rate
									snake_case_									=				warmup_steps
									snake_case_									=				power
									snake_case_									=				decay_schedule_fn
									snake_case_									=				name


				def __call__(		self    :					Tuple       ,				lowercase_    :					str					):
									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`.
														snake_case_									=				tf.cast(lowercase_       ,				tf.floataa					)
														snake_case_									=				tf.cast(self.warmup_steps       ,				tf.floataa					)
														snake_case_									=				global_step_float / warmup_steps_float
														snake_case_									=				self.initial_learning_rate * tf.math.pow(lowercase_       ,				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=lowercase_       ,				)


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

					'''simple docstring'''

					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:
										snake_case_									=				WarmUp(
										    initial_learning_rate=__UpperCAmelCase,      decay_schedule_fn=__UpperCAmelCase,      warmup_steps=__UpperCAmelCase,      )
					if weight_decay_rate > 0.0:
										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:
										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				(   _lowerCamelCase     ):


				def __init__(		self    :					Dict       ,				lowercase_    :					Union[float, tf.keras.optimizers.schedules.LearningRateSchedule] = 0.001       ,				lowercase_    :					float = 0.9       ,				lowercase_    :					float = 0.999       ,				lowercase_    :					float = 1e-7       ,				lowercase_    :					bool = False       ,				lowercase_    :					float = 0.0       ,				lowercase_    :					Optional[List[str]] = None       ,				lowercase_    :					Optional[List[str]] = None       ,				lowercase_    :					str = "AdamWeightDecay"       ,				**lowercase_    :					Optional[int]       ,				):
									super().__init__(lowercase_       ,				lowercase_       ,				lowercase_       ,				lowercase_       ,				lowercase_       ,				lowercase_       ,				**lowercase_					)
									snake_case_									=				weight_decay_rate
									snake_case_									=				include_in_weight_decay
									snake_case_									=				exclude_from_weight_decay


				@classmethod
				def   A_ (		cls    :					Dict       ,				lowercase_    :					Union[str, Any]					):
									snake_case_									=				{'''WarmUp''': WarmUp}
									return super(lowercase_       ,				cls					).from_config(lowercase_       ,				custom_objects=lowercase_					)


				def   A_ (		self    :					str       ,				lowercase_    :					Optional[int]       ,				lowercase_    :					List[str]       ,				lowercase_    :					Optional[int]					):
									super(lowercase_       ,				self					)._prepare_local(lowercase_       ,				lowercase_       ,				lowercase_					)
									snake_case_									=				tf.constant(
									    self.weight_decay_rate       ,				name='''adam_weight_decay_rate'''					)


				def   A_ (		self    :					str       ,				lowercase_    :					Union[str, Any]       ,				lowercase_    :					Dict       ,				lowercase_    :					Any					):
									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   A_ (		self    :					Union[str, Any]       ,				lowercase_    :					int       ,				lowercase_    :					str=None       ,				**lowercase_    :					List[str]					):
									snake_case_			,snake_case_									=				list(zip(*lowercase_					)					)
									return super(lowercase_       ,				self					).apply_gradients(zip(lowercase_       ,				lowercase_					)       ,				name=lowercase_       ,				**lowercase_					)


				def   A_ (		self    :					List[Any]       ,				lowercase_    :					str       ,				lowercase_    :					str       ,				lowercase_    :					Any					):
									if apply_state is None:
														return self._decayed_lr_t[var_dtype], {}

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

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


				def   A_ (		self    :					Union[str, Any]       ,				lowercase_    :					int       ,				lowercase_    :					Union[str, Any]       ,				lowercase_    :					Optional[int]=None					):
									snake_case_			,snake_case_									=				self._get_lr(var.device       ,				var.dtype.base_dtype       ,				lowercase_					)
									snake_case_									=				self._decay_weights_op(lowercase_       ,				lowercase_       ,				lowercase_					)
									with tf.control_dependencies([decay]					):
														return super(lowercase_       ,				self					)._resource_apply_dense(lowercase_       ,				lowercase_       ,				**lowercase_					)


				def   A_ (		self    :					Optional[Any]       ,				lowercase_    :					Optional[Any]       ,				lowercase_    :					int       ,				lowercase_    :					str       ,				lowercase_    :					List[Any]=None					):
									snake_case_			,snake_case_									=				self._get_lr(var.device       ,				var.dtype.base_dtype       ,				lowercase_					)
									snake_case_									=				self._decay_weights_op(lowercase_       ,				lowercase_       ,				lowercase_					)
									with tf.control_dependencies([decay]					):
														return super(lowercase_       ,				self					)._resource_apply_sparse(lowercase_       ,				lowercase_       ,				lowercase_       ,				**lowercase_					)


				def   A_ (		self    :					Union[str, Any]					):
									snake_case_									=				super().get_config()
									config.update({'''weight_decay_rate''': self.weight_decay_rate}					)
									return config


				def   A_ (		self    :					Optional[int]       ,				lowercase_    :					int					):
									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(lowercase_       ,				lowercase_					) is not None:
																								return True

									if self._exclude_from_weight_decay:
														for r in self._exclude_from_weight_decay:
																			if re.search(lowercase_       ,				lowercase_					) is not None:
																								return False
									return True







class 						a				(   _lowerCamelCase     ):


				def __init__(		self    :					List[Any]					):
									snake_case_									=				[]
									snake_case_									=				None


				@property
				def   A_ (		self    :					Union[str, Any]					):
									if self._accum_steps is None:
														snake_case_									=				tf.Variable(
														    tf.constant(0       ,				dtype=tf.intaa					)       ,				trainable=lowercase_       ,				synchronization=tf.VariableSynchronization.ON_READ       ,				aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA       ,				)

									return self._accum_steps.value()


				@property
				def   A_ (		self    :					Dict					):
									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    :					Any       ,				lowercase_    :					int					):
									if not self._gradients:
														snake_case_									=				self.step  # Create the step variable.
														self._gradients.extend(
														    [
														        tf.Variable(
														            tf.zeros_like(lowercase_					)       ,				trainable=lowercase_       ,				synchronization=tf.VariableSynchronization.ON_READ       ,				aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA       ,				)
														        if gradient is not None
														        else gradient
														        for gradient in gradients
														    ]					)
									if len(lowercase_					) != len(self._gradients					):
														raise ValueError(F"Expected {len(self._gradients					)} gradients, but got {len(lowercase_					)}"					)

									for accum_gradient, gradient in zip(self._gradients       ,				lowercase_					):
														if accum_gradient is not None and gradient is not None:
																			accum_gradient.assign_add(lowercase_					)

									self._accum_steps.assign_add(1					)


				def   A_ (		self    :					Optional[int]					):
									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(lowercase_					)					)

