

"""simple docstring"""




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  ,   A_		):







										'''simple docstring'''


										if isinstance(A_  ,   A_		):
														# 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
														_UpperCAmelCase    :						Union[str, Any]							=      deepcopy(A_		)
										elif os.path.exists(A_		):
														with io.open(A_  ,   "r"  ,   encoding="utf-8"		) as f:
																		_UpperCAmelCase    :						List[Any]							=      json.load(A_		)
										else:
														try:
																		_UpperCAmelCase    :						str							=      baseaa.urlsafe_baadecode(A_		).decode("utf-8"		)
																		_UpperCAmelCase    :						str							=      json.loads(A_		)
														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}'		)

										_UpperCAmelCase    :						List[str]							=      config

										self.set_stage_and_offload()


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[str]							=      self.get_value("zero_optimization.stage"  ,   -1		)

										# offload
										_UpperCAmelCase    :						Optional[Any]							=      False
										if self.is_zeroa() or self.is_zeroa():
														_UpperCAmelCase    :						Dict							=      set(["cpu", "nvme"]		)
														_UpperCAmelCase    :						str							=      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:
																		_UpperCAmelCase    :						Optional[Any]							=      True


						def     _UpperCAmelCase			(    self  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						str							=      self.config

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

										return config, ds_key


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_=None		):







										'''simple docstring'''


										_UpperCAmelCase    :						Dict							=      self.find_config_node(A_		)
										if config is None:
														return default
										return config.get(A_  ,   A_		)


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_=False		):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[Any]							=      self.config

										# find the config node of interest if it exists
										_UpperCAmelCase    :						Any							=      ds_key_long.split("."		)
										for node in nodes:
														_UpperCAmelCase    :						Tuple							=      config
														_UpperCAmelCase    :						str							=      config.get(A_		)
														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(A_		)


						def     _UpperCAmelCase			(    self  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						Dict							=      self.get_value(A_		)
										return False if value is None else bool(A_		)


						def     _UpperCAmelCase			(    self  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						str							=      self.get_value(A_		)
										return False if value is None else not bool(A_		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										return self._stage == 2


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										return self._stage == 3





						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										return self._offload







class 			a					:


						def __init__(    self  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[str]							=      engine





						def     _UpperCAmelCase			(    self  ,   A_  ,   **A_		):







										'''simple docstring'''


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

										# 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					(		UpperCAmelCase			):


						def __init__(    self  ,   A_		):







										'''simple docstring'''


										super().__init__(A_  ,   device_placement=A_  ,   scaler=A_		)
										_UpperCAmelCase    :						List[str]							=      hasattr(self.optimizer  ,   "overflow"		)


						def     _UpperCAmelCase			(    self  ,   A_=None		):







										'''simple docstring'''


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


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


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





						@property
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


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







class 			a					(		UpperCAmelCase			):


						def __init__(    self  ,   A_  ,   A_		):







										'''simple docstring'''


										super().__init__(A_  ,   A_		)





						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


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







class 			a					:


						def __init__(    self  ,   A_  ,   A_=0.0_01  ,   A_=0  ,   **A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[Any]							=      params
										_UpperCAmelCase    :						List[Any]							=      lr
										_UpperCAmelCase    :						str							=      weight_decay
										_UpperCAmelCase    :						Dict							=      kwargs







class 			a					:


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







										'''simple docstring'''


										_UpperCAmelCase    :						str							=      optimizer
										_UpperCAmelCase    :						Optional[int]							=      total_num_steps
										_UpperCAmelCase    :						Optional[Any]							=      warmup_num_steps
										_UpperCAmelCase    :						int							=      kwargs





from typing import List, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_            = logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_            = {
    'huggingface/autoformer-tourism-monthly': 'https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json',
}

class 			a					(		UpperCAmelCase			):
						_lowercase        =     "autoformer"
						_lowercase        =     {
						    "hidden_size": "d_model",
						    "num_attention_heads": "encoder_attention_heads",
						    "num_hidden_layers": "encoder_layers",
						}


						def __init__(    self  ,   A_ = None  ,   A_ = None  ,   A_ = "student_t"  ,   A_ = "nll"  ,   A_ = 1  ,   A_ = [1, 2, 3, 4, 5, 6, 7]  ,   A_ = True  ,   A_ = 0  ,   A_ = 0  ,   A_ = 0  ,   A_ = 0  ,   A_ = None  ,   A_ = None  ,   A_ = 64  ,   A_ = 2  ,   A_ = 2  ,   A_ = 2  ,   A_ = 2  ,   A_ = 32  ,   A_ = 32  ,   A_ = "gelu"  ,   A_ = 0.1  ,   A_ = 0.1  ,   A_ = 0.1  ,   A_ = 0.1  ,   A_ = 0.1  ,   A_ = 100  ,   A_ = 0.02  ,   A_ = True  ,   A_=True  ,   A_ = 10  ,   A_ = 25  ,   A_ = 3  ,   **A_  ,   ):







										'''simple docstring'''


										_UpperCAmelCase    :						List[Any]							=      prediction_length
										_UpperCAmelCase    :						Dict							=      context_length if context_length is not None else prediction_length
										_UpperCAmelCase    :						Tuple							=      distribution_output
										_UpperCAmelCase    :						List[Any]							=      loss
										_UpperCAmelCase    :						Optional[Any]							=      input_size
										_UpperCAmelCase    :						int							=      num_time_features
										_UpperCAmelCase    :						str							=      lags_sequence
										_UpperCAmelCase    :						Union[str, Any]							=      scaling
										_UpperCAmelCase    :						Union[str, Any]							=      num_dynamic_real_features
										_UpperCAmelCase    :						int							=      num_static_real_features
										_UpperCAmelCase    :						int							=      num_static_categorical_features
										if cardinality is not None and num_static_categorical_features > 0:
														if len(A_		) != num_static_categorical_features:
																		raise ValueError(
																		    "The cardinality should be a list of the same length as `num_static_categorical_features`"		)
														_UpperCAmelCase    :						Union[str, Any]							=      cardinality
										else:
														_UpperCAmelCase    :						Any							=      [0]
										if embedding_dimension is not None and num_static_categorical_features > 0:
														if len(A_		) != num_static_categorical_features:
																		raise ValueError(
																		    "The embedding dimension should be a list of the same length as `num_static_categorical_features`"		)
														_UpperCAmelCase    :						int							=      embedding_dimension
										else:
														_UpperCAmelCase    :						int							=      [min(50  ,   (cat + 1) // 2		) for cat in self.cardinality]
										_UpperCAmelCase    :						Union[str, Any]							=      num_parallel_samples

										# Transformer architecture configuration
										_UpperCAmelCase    :						Union[str, Any]							=      input_size * len(self.lags_sequence		) + self._number_of_features
										_UpperCAmelCase    :						int							=      d_model
										_UpperCAmelCase    :						Any							=      encoder_attention_heads
										_UpperCAmelCase    :						str							=      decoder_attention_heads
										_UpperCAmelCase    :						Union[str, Any]							=      encoder_ffn_dim
										_UpperCAmelCase    :						Any							=      decoder_ffn_dim
										_UpperCAmelCase    :						Any							=      encoder_layers
										_UpperCAmelCase    :						Optional[int]							=      decoder_layers

										_UpperCAmelCase    :						Optional[Any]							=      dropout
										_UpperCAmelCase    :						Union[str, Any]							=      attention_dropout
										_UpperCAmelCase    :						Dict							=      activation_dropout
										_UpperCAmelCase    :						Dict							=      encoder_layerdrop
										_UpperCAmelCase    :						int							=      decoder_layerdrop

										_UpperCAmelCase    :						Union[str, Any]							=      activation_function
										_UpperCAmelCase    :						str							=      init_std

										_UpperCAmelCase    :						Tuple							=      use_cache

										# Autoformer
										_UpperCAmelCase    :						str							=      label_length
										_UpperCAmelCase    :						Any							=      moving_average
										_UpperCAmelCase    :						Optional[Any]							=      autocorrelation_factor

										super().__init__(is_encoder_decoder=A_  ,   **A_		)





						@property
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										return (
										    sum(self.embedding_dimension		)
										    + self.num_dynamic_real_features
										    + self.num_time_features
										    + self.num_static_real_features
										    + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
										)


