





import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


lowerCAmelCase							:       str					 =	logging.get_logger(__name__)

lowerCAmelCase							:       Optional[int]					 =	{
    """facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/config.json""",
    # See all BART models at https://huggingface.co/models?filter=bart
}





class 				a			(      __lowercase			):
				SCREAMING_SNAKE_CASE__     :   Any		      =     '''bart'''
				SCREAMING_SNAKE_CASE__     :   List[str]		      =     ['''past_key_values''']
				SCREAMING_SNAKE_CASE__     :   List[Any]		      =     {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}






				def __init__(  self			, _lowerCAmelCase=50265			, _lowerCAmelCase=1024			, _lowerCAmelCase=12			, _lowerCAmelCase=4096			, _lowerCAmelCase=16			, _lowerCAmelCase=12			, _lowerCAmelCase=4096			, _lowerCAmelCase=16			, _lowerCAmelCase=0.0			, _lowerCAmelCase=0.0			, _lowerCAmelCase="gelu"			, _lowerCAmelCase=1024			, _lowerCAmelCase=0.1			, _lowerCAmelCase=0.0			, _lowerCAmelCase=0.0			, _lowerCAmelCase=0.02			, _lowerCAmelCase=0.0			, _lowerCAmelCase=False			, _lowerCAmelCase=True			, _lowerCAmelCase=3			, _lowerCAmelCase=1			, _lowerCAmelCase=0			, _lowerCAmelCase=2			, _lowerCAmelCase=True			, _lowerCAmelCase=2			, _lowerCAmelCase=2			, **_lowerCAmelCase			, ):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Optional[int]           =   vocab_size
										__SCREAMING_SNAKE_CASE:				Dict           =   max_position_embeddings
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   d_model
										__SCREAMING_SNAKE_CASE:				Any           =   encoder_ffn_dim
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   encoder_layers
										__SCREAMING_SNAKE_CASE:				int           =   encoder_attention_heads
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   decoder_ffn_dim
										__SCREAMING_SNAKE_CASE:				List[str]           =   decoder_layers
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   decoder_attention_heads
										__SCREAMING_SNAKE_CASE:				int           =   dropout
										__SCREAMING_SNAKE_CASE:				Any           =   attention_dropout
										__SCREAMING_SNAKE_CASE:				str           =   activation_dropout
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   activation_function
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   init_std
										__SCREAMING_SNAKE_CASE:				List[Any]           =   encoder_layerdrop
										__SCREAMING_SNAKE_CASE:				Dict           =   decoder_layerdrop
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   classifier_dropout
										__SCREAMING_SNAKE_CASE:				str           =   use_cache
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   encoder_layers
										__SCREAMING_SNAKE_CASE:				Tuple           =   scale_embedding  # scale factor will be sqrt(d_model) if True

										super().__init__(
										    num_labels=_lowerCAmelCase			, pad_token_id=_lowerCAmelCase			, bos_token_id=_lowerCAmelCase			, eos_token_id=_lowerCAmelCase			, is_encoder_decoder=_lowerCAmelCase			, decoder_start_token_id=_lowerCAmelCase			, forced_eos_token_id=_lowerCAmelCase			, **_lowerCAmelCase			, )

										# ensure backward compatibility for BART CNN models
										if self.forced_bos_token_id is None and kwargs.get('''force_bos_token_to_be_generated'''			, _lowerCAmelCase					):
																__SCREAMING_SNAKE_CASE:				List[Any]           =   self.bos_token_id
																warnings.warn(
																    f"""Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. """
																    '''The config can simply be saved and uploaded again to be fixed.'''					)





class 				a			(      __lowercase			):






				@property
				def 			snake_case_    (  self					):

										"""simple docstring"""




										if self.task in ["default", "seq2seq-lm"]:
																__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   OrderedDict(
																    [
																        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
																        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
																    ]					)

																if self.use_past:
																						__SCREAMING_SNAKE_CASE:				Tuple           =   {0: '''batch'''}
																						__SCREAMING_SNAKE_CASE:				Any           =   {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
																else:
																						__SCREAMING_SNAKE_CASE:				Dict           =   {0: '''batch''', 1: '''decoder_sequence'''}
																						__SCREAMING_SNAKE_CASE:				Dict           =   {0: '''batch''', 1: '''decoder_sequence'''}

																if self.use_past:
																						self.fill_with_past_key_values_(_lowerCAmelCase			, direction='''inputs'''					)
										elif self.task == "causal-lm":
																# TODO: figure this case out.
																__SCREAMING_SNAKE_CASE:				Optional[int]           =   OrderedDict(
																    [
																        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
																        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
																    ]					)
																if self.use_past:
																						__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE:				Optional[Any]           =   self.num_layers
																						for i in range(_lowerCAmelCase					):
																												__SCREAMING_SNAKE_CASE:				int           =   {0: '''batch''', 2: '''past_sequence + sequence'''}
																												__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   {0: '''batch''', 2: '''past_sequence + sequence'''}
										else:
																__SCREAMING_SNAKE_CASE:				List[str]           =   OrderedDict(
																    [
																        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
																        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
																        ('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
																        ('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
																    ]					)

										return common_inputs






				@property
				def 			snake_case_    (  self					):

										"""simple docstring"""




										if self.task in ["default", "seq2seq-lm"]:
																__SCREAMING_SNAKE_CASE:				int           =   super().outputs
										else:
																__SCREAMING_SNAKE_CASE:				Optional[int]           =   super(_lowerCAmelCase			, self					).outputs
																if self.use_past:
																						__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE:				Optional[Any]           =   self.num_layers
																						for i in range(_lowerCAmelCase					):
																												__SCREAMING_SNAKE_CASE:				Tuple           =   {0: '''batch''', 2: '''past_sequence + sequence'''}
																												__SCREAMING_SNAKE_CASE:				Optional[Any]           =   {0: '''batch''', 2: '''past_sequence + sequence'''}
										return common_outputs






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase = -1			, _lowerCAmelCase = -1			, _lowerCAmelCase = False			, _lowerCAmelCase = None			, ):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Dict           =   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
										    _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase					)

										# Generate decoder inputs
										__SCREAMING_SNAKE_CASE:				Tuple           =   seq_length if not self.use_past else 1
										__SCREAMING_SNAKE_CASE:				str           =   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
										    _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				Dict           =   {f"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   dict(**_lowerCAmelCase			, **_lowerCAmelCase					)

										if self.use_past:
																if not is_torch_available():
																						raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''					)
																else:
																						import torch
																__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   common_inputs['''input_ids'''].shape
																__SCREAMING_SNAKE_CASE:				List[Any]           =   common_inputs['''decoder_input_ids'''].shape[1]
																__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE:				Optional[int]           =   self.num_attention_heads
																__SCREAMING_SNAKE_CASE:				List[Any]           =   (
																    batch,
																    num_encoder_attention_heads,
																    encoder_seq_length,
																    self._config.hidden_size // num_encoder_attention_heads,
																)
																__SCREAMING_SNAKE_CASE:				Dict           =   decoder_seq_length + 3
																__SCREAMING_SNAKE_CASE:				int           =   (
																    batch,
																    num_decoder_attention_heads,
																    decoder_past_length,
																    self._config.hidden_size // num_decoder_attention_heads,
																)

																__SCREAMING_SNAKE_CASE:				List[Any]           =   torch.cat(
																    [common_inputs['''decoder_attention_mask'''], torch.ones(_lowerCAmelCase			, _lowerCAmelCase					)]			, dim=1					)

																__SCREAMING_SNAKE_CASE:				Optional[Any]           =   []
																# If the number of encoder and decoder layers are present in the model configuration, both are considered
																__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE:				Tuple           =   self.num_layers
																__SCREAMING_SNAKE_CASE:				Dict           =   min(_lowerCAmelCase			, _lowerCAmelCase					)
																__SCREAMING_SNAKE_CASE:				Optional[int]           =   max(_lowerCAmelCase			, _lowerCAmelCase					) - min_num_layers
																__SCREAMING_SNAKE_CASE:				str           =   '''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

																for _ in range(_lowerCAmelCase					):
																						common_inputs["past_key_values"].append(
																						    (
																						        torch.zeros(_lowerCAmelCase					),
																						        torch.zeros(_lowerCAmelCase					),
																						        torch.zeros(_lowerCAmelCase					),
																						        torch.zeros(_lowerCAmelCase					),
																						    )					)
																# TODO: test this.
																__SCREAMING_SNAKE_CASE:				Optional[Any]           =   encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
																for _ in range(_lowerCAmelCase			, _lowerCAmelCase					):
																						common_inputs["past_key_values"].append((torch.zeros(_lowerCAmelCase					), torch.zeros(_lowerCAmelCase					))					)
										return common_inputs






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase = -1			, _lowerCAmelCase = -1			, _lowerCAmelCase = False			, _lowerCAmelCase = None			, ):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Tuple           =   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
										    _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase					)

										if self.use_past:
																if not is_torch_available():
																						raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''					)
																else:
																						import torch
																__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   common_inputs['''input_ids'''].shape
																# Not using the same length for past_key_values
																__SCREAMING_SNAKE_CASE:				Any           =   seqlen + 2
																__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE:				Any           =   self.num_layers
																__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE:				Optional[Any]           =   self.num_attention_heads
																__SCREAMING_SNAKE_CASE:				Any           =   (
																    batch,
																    num_encoder_attention_heads,
																    past_key_values_length,
																    self._config.hidden_size // num_encoder_attention_heads,
																)

																__SCREAMING_SNAKE_CASE:				Any           =   common_inputs['''attention_mask'''].dtype
																__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   torch.cat(
																    [common_inputs['''attention_mask'''], torch.ones(_lowerCAmelCase			, _lowerCAmelCase			, dtype=_lowerCAmelCase					)]			, dim=1					)
																__SCREAMING_SNAKE_CASE:				str           =   [
																    (torch.zeros(_lowerCAmelCase					), torch.zeros(_lowerCAmelCase					)) for _ in range(_lowerCAmelCase					)
																]
										return common_inputs






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase = -1			, _lowerCAmelCase = -1			, _lowerCAmelCase = False			, _lowerCAmelCase = None			, ):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				str           =   compute_effective_axis_dimension(
										    _lowerCAmelCase			, fixed_dimension=OnnxConfig.default_fixed_batch			, num_token_to_add=0					)

										# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
										__SCREAMING_SNAKE_CASE:				Any           =   tokenizer.num_special_tokens_to_add(_lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				str           =   compute_effective_axis_dimension(
										    _lowerCAmelCase			, fixed_dimension=OnnxConfig.default_fixed_sequence			, num_token_to_add=_lowerCAmelCase					)

										# Generate dummy inputs according to compute batch and sequence
										__SCREAMING_SNAKE_CASE:				str           =   [''' '''.join([tokenizer.unk_token]					) * seq_length] * batch_size
										__SCREAMING_SNAKE_CASE:				List[str]           =   dict(tokenizer(_lowerCAmelCase			, return_tensors=_lowerCAmelCase					)					)
										return common_inputs






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase = -1			, _lowerCAmelCase = -1			, _lowerCAmelCase = False			, _lowerCAmelCase = None			, ):

										"""simple docstring"""




										if self.task in ["default", "seq2seq-lm"]:
																__SCREAMING_SNAKE_CASE:				int           =   self._generate_dummy_inputs_for_default_and_seqaseq_lm(
																    _lowerCAmelCase			, batch_size=_lowerCAmelCase			, seq_length=_lowerCAmelCase			, is_pair=_lowerCAmelCase			, framework=_lowerCAmelCase					)

										elif self.task == "causal-lm":
																__SCREAMING_SNAKE_CASE:				Optional[int]           =   self._generate_dummy_inputs_for_causal_lm(
																    _lowerCAmelCase			, batch_size=_lowerCAmelCase			, seq_length=_lowerCAmelCase			, is_pair=_lowerCAmelCase			, framework=_lowerCAmelCase					)
										else:
																__SCREAMING_SNAKE_CASE:				Any           =   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
																    _lowerCAmelCase			, batch_size=_lowerCAmelCase			, seq_length=_lowerCAmelCase			, is_pair=_lowerCAmelCase			, framework=_lowerCAmelCase					)

										return common_inputs






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase					):

										"""simple docstring"""




										if self.task in ["default", "seq2seq-lm"]:
																__SCREAMING_SNAKE_CASE:				Optional[Any]           =   super()._flatten_past_key_values_(_lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase					)
										else:
																__SCREAMING_SNAKE_CASE:				Optional[int]           =   super(_lowerCAmelCase			, self					)._flatten_past_key_values_(
																    _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase			, _lowerCAmelCase					)










import baseaa
import io
import json
import os
from copy import deepcopy

from ..optimizer import AcceleratedOptimizer
from ..scheduler import AcceleratedScheduler





class 				a			:






				def __init__(  self			, _lowerCAmelCase					):

										"""simple docstring"""




										if isinstance(_lowerCAmelCase			, _lowerCAmelCase					):
																# Don't modify user's data should they want to reuse it (e.g. in tests), because once we
																# modified it, it will not be accepted here again, since `auto` values would have been overridden
																__SCREAMING_SNAKE_CASE:				Optional[int]           =   deepcopy(_lowerCAmelCase					)
										elif os.path.exists(_lowerCAmelCase					):
																with io.open(_lowerCAmelCase			, '''r'''			, encoding='''utf-8'''					) as f:
																						__SCREAMING_SNAKE_CASE:				int           =   json.load(_lowerCAmelCase					)
										else:
																try:
																						__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   baseaa.urlsafe_baadecode(_lowerCAmelCase					).decode('''utf-8'''					)
																						__SCREAMING_SNAKE_CASE:				List[Any]           =   json.loads(_lowerCAmelCase					)
																except (UnicodeDecodeError, AttributeError, ValueError):
																						raise ValueError(
																						    f"""Expected a string path to an existing deepspeed config, or a dictionary, or a base64 encoded string. Received: {config_file_or_dict}"""					)

										__SCREAMING_SNAKE_CASE:				str           =   config

										self.set_stage_and_offload()






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				str           =   self.get_value('''zero_optimization.stage'''			, -1					)

										# offload
										__SCREAMING_SNAKE_CASE:				Dict           =   False
										if self.is_zeroa() or self.is_zeroa():
																__SCREAMING_SNAKE_CASE:				str           =   set(['''cpu''', '''nvme''']					)
																__SCREAMING_SNAKE_CASE:				Tuple           =   set(
																    [
																        self.get_value('''zero_optimization.offload_optimizer.device'''					),
																        self.get_value('''zero_optimization.offload_param.device'''					),
																    ]					)
																if len(offload_devices & offload_devices_valid					) > 0:
																						__SCREAMING_SNAKE_CASE:				Dict           =   True






				def 			snake_case_    (  self			, _lowerCAmelCase					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				int           =   self.config

										# find the config node of interest if it exists
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   ds_key_long.split('''.'''					)
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   nodes.pop()
										for node in nodes:
																__SCREAMING_SNAKE_CASE:				int           =   config.get(_lowerCAmelCase					)
																if config is None:
																						return None, ds_key

										return config, ds_key






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase=None					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE:				List[Any]           =   self.find_config_node(_lowerCAmelCase					)
										if config is None:
																return default
										return config.get(_lowerCAmelCase			, _lowerCAmelCase					)






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase=False					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				str           =   self.config

										# find the config node of interest if it exists
										__SCREAMING_SNAKE_CASE:				str           =   ds_key_long.split('''.'''					)
										for node in nodes:
																__SCREAMING_SNAKE_CASE:				Tuple           =   config
																__SCREAMING_SNAKE_CASE:				List[str]           =   config.get(_lowerCAmelCase					)
																if config is None:
																						if must_exist:
																												raise ValueError(f"""Can't find {ds_key_long} entry in the config: {self.config}"""					)
																						else:
																												return

        # if found remove it
										if parent_config is not None:
																parent_config.pop(_lowerCAmelCase					)






				def 			snake_case_    (  self			, _lowerCAmelCase					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   self.get_value(_lowerCAmelCase					)
										return False if value is None else bool(_lowerCAmelCase					)






				def 			snake_case_    (  self			, _lowerCAmelCase					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Any           =   self.get_value(_lowerCAmelCase					)
										return False if value is None else not bool(_lowerCAmelCase					)






				def 			snake_case_    (  self					):

										"""simple docstring"""




										return self._stage == 2






				def 			snake_case_    (  self					):

										"""simple docstring"""




										return self._stage == 3






				def 			snake_case_    (  self					):

										"""simple docstring"""




										return self._offload





class 				a			:






				def __init__(  self			, _lowerCAmelCase					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Optional[int]           =   engine






				def 			snake_case_    (  self			, _lowerCAmelCase			, **_lowerCAmelCase					):

										"""simple docstring"""




										self.engine.backward(_lowerCAmelCase			, **_lowerCAmelCase					)

										# Deepspeed's `engine.step` performs the following operations:
										# - gradient accumulation check
										# - gradient clipping
										# - optimizer step
										# - zero grad
										# - checking overflow
										# - lr_scheduler step (only if engine.lr_scheduler is not None)
										self.engine.step()
										# and this plugin overrides the above calls with no-ops when Accelerate runs under
										# Deepspeed, but allows normal functionality for non-Deepspeed cases thus enabling a simple
										# training loop that works transparently under many training regimes.





class 				a			(      __lowercase			):






				def __init__(  self			, _lowerCAmelCase					):

										"""simple docstring"""




										super().__init__(_lowerCAmelCase			, device_placement=_lowerCAmelCase			, scaler=_lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				str           =   hasattr(self.optimizer			, '''overflow'''					)






				def 			snake_case_    (  self			, _lowerCAmelCase=None					):

										"""simple docstring"""




										pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed






				def 			snake_case_    (  self					):

										"""simple docstring"""




										pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed






				@property
				def 			snake_case_    (  self					):

										"""simple docstring"""




										if self.__has_overflow__:
																return self.optimizer.overflow
										return False





class 				a			(      __lowercase			):






				def __init__(  self			, _lowerCAmelCase			, _lowerCAmelCase					):

										"""simple docstring"""




										super().__init__(_lowerCAmelCase			, _lowerCAmelCase					)






				def 			snake_case_    (  self					):

										"""simple docstring"""




										pass  # `accelerator.backward(loss)` is doing that automatically. Therefore, its implementation is not needed





class 				a			:






				def __init__(  self			, _lowerCAmelCase			, _lowerCAmelCase=0.001			, _lowerCAmelCase=0			, **_lowerCAmelCase					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				str           =   params
										__SCREAMING_SNAKE_CASE:				int           =   lr
										__SCREAMING_SNAKE_CASE:				List[str]           =   weight_decay
										__SCREAMING_SNAKE_CASE:				List[Any]           =   kwargs





class 				a			:






				def __init__(  self			, _lowerCAmelCase			, _lowerCAmelCase=None			, _lowerCAmelCase=0			, **_lowerCAmelCase					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				int           =   optimizer
										__SCREAMING_SNAKE_CASE:				int           =   total_num_steps
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   warmup_num_steps
										__SCREAMING_SNAKE_CASE:				str           =   kwargs



