
import importlib.util
import weakref
from functools import partialmethod

from .dependency_versions_check import dep_version_check
from .utils import is_accelerate_available, is_torch_available, logging


if is_torch_available():
     import torch

__magic_name__   				=     logging.get_logger(__name__)





def 		SCREAMING_SNAKE_CASE__			(					):
    return importlib.util.find_spec("deepspeed"							) is not None


if is_accelerate_available() and is_deepspeed_available():
     from accelerate.utils.deepspeed import HfDeepSpeedConfig as DeepSpeedConfig
else:
     # Inherits from a dummy `object` if accelerate is not available, so that python succeeds to import this file.
     # Deepspeed glue code will never inherit this dummy object as it checks if accelerate is available.
     from builtins import object as DeepSpeedConfig




class     _SCREAMING_SNAKE_CASE     (					_A       ):
      def __init__(	self						,		lowerCamelCase       ):
          # set global weakref object
          set_hf_deepspeed_config(self       )
          dep_version_check("accelerate"       )
          dep_version_check("deepspeed"       )
          super().__init__(__SCREAMING_SNAKE_CASE       )





class     _SCREAMING_SNAKE_CASE     (					_A       ):
      def __init__(	self						,		lowerCamelCase       ):
          super().__init__(__SCREAMING_SNAKE_CASE       )
          snake_case__  = None
          snake_case__  = []
      def A_	(	self       ):
          if self._dtype is None:
              raise ValueError("trainer_config_process() wasn't called yet to tell dtype"       )
          return self._dtype
      def A_	(	self						,		lowerCamelCase       ):
          snake_case__  = self.get_value(__SCREAMING_SNAKE_CASE       )
          if val is None:
              return False
          else:
              return val == "auto"
      def A_	(	self						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase=None						,		lowerCamelCase=True       ):
          snake_case__  ,    snake_case__  = self.find_config_node(__SCREAMING_SNAKE_CASE       )
          if config is None:
              return

          if config.get(__SCREAMING_SNAKE_CASE       ) == "auto":
              snake_case__  = hf_val
              return

          if not must_match:
              return

          snake_case__  = config.get(__SCREAMING_SNAKE_CASE       )
          if ds_val is not None and ds_val != hf_val:
              self.mismatches.append(F"""- ds {ds_key_long}={ds_val} vs hf {hf_key}={hf_val}"""       )

      _A  :						Tuple					=   partialmethod(_A				,    must_match=_A       )
      def A_	(	self						,		lowerCamelCase       ):
          snake_case__  = args.world_size * args.per_device_train_batch_size * args.gradient_accumulation_steps
          self.fill_match(
              "train_micro_batch_size_per_gpu"						,		args.per_device_train_batch_size						,		"per_device_train_batch_size"       )
          self.fill_match("gradient_accumulation_steps"						,		args.gradient_accumulation_steps						,		"gradient_accumulation_steps"       )
          self.fill_match("train_batch_size"						,		__SCREAMING_SNAKE_CASE						,		"train_batch_size (calculated)"       )
          self.fill_match("gradient_clipping"						,		args.max_grad_norm						,		"max_grad_norm"       )

          self.fill_match("optimizer.params.lr"						,		args.learning_rate						,		"learning_rate"       )
          self.fill_match("optimizer.params.betas"						,		[args.adam_betaa, args.adam_betaa]						,		"adam_beta1+adam_beta2"       )
          self.fill_match("optimizer.params.eps"						,		args.adam_epsilon						,		"adam_epsilon"       )
          self.fill_match("optimizer.params.weight_decay"						,		args.weight_decay						,		"weight_decay"       )

          self.fill_only("scheduler.params.warmup_min_lr"						,		0       )  # not a trainer arg
          self.fill_match("scheduler.params.warmup_max_lr"						,		args.learning_rate						,		"learning_rate"       )
          # total_num_steps - will get set in trainer_config_finalize

          # fp16
          if args.fpaa or args.fpaa_full_eval:
              snake_case__  = "apex" if args.fpaa_backend == "apex" else "amp"
          else:
              snake_case__  = None

          if args.save_on_each_node:
              # deepspeed uses shared storage by default. Let's override this setting if save_on_each_node == True
              snake_case__  = self.config.get("checkpoint"						,		{}       )
              snake_case__  = args.save_on_each_node

          # amp: similar to the pytorch native amp - it has a bunch of optional params but we won't set
          # any here unless the user did the work
          self.fill_match(
              "fp16.enabled"						,		((args.fpaa or args.fpaa_full_eval) and fpaa_backend == "amp")						,		"fp16|fp16_full_eval+fp16_backend(amp)"						,		)

          # apex: delegates amp work to apex (which needs to be available), but it cannot be used with any
          # ZeRO features
          self.fill_match("amp.enabled"						,		fpaa_backend == "apex"						,		"fp16+fp16_backend(apex)"       )
          self.fill_match("amp.opt_level"						,		args.fpaa_opt_level						,		"fp16_opt_level"       )

          self.fill_match("bf16.enabled"						,		(args.bfaa or args.bfaa_full_eval)						,		"bf16|bf16_full_eval"       )

          # deepspeed's default mode is fp16 unless there is a config that says differently
          if self.is_true("bf16.enabled"       ):
              snake_case__  = torch.bfloataa
          elif self.is_false("fp16.enabled"       ):
              snake_case__  = torch.floataa
          else:
              snake_case__  = torch.floataa



      def A_	(	self						,		lowerCamelCase						,		lowerCamelCase						,		lowerCamelCase       ):
          snake_case__  = [
              "zero_optimization.reduce_bucket_size",
              "zero_optimization.stage3_prefetch_bucket_size",
              "zero_optimization.stage3_param_persistence_threshold",
          ]
          snake_case__  = [x for x in hidden_size_based_keys if self.is_auto(__SCREAMING_SNAKE_CASE       )]

          if len(__SCREAMING_SNAKE_CASE       ) > 0:
              if hasattr(model.config						,		"hidden_size"       ):
                  snake_case__  = model.config.hidden_size
              elif hasattr(model.config						,		"hidden_sizes"       ):
                  # if there are many hidden sizes pick the largest one
                  snake_case__  = max(model.config.hidden_sizes       )
              else:
                  raise ValueError(
                      "The model's config file has neither `hidden_size` nor `hidden_sizes` entry, "
                      "therefore it's not possible to automatically fill out the following `auto` entries "
                      F"""in the DeepSpeed config file: {hidden_size_auto_keys}. You can fix that by replacing """
                      "`auto` values for these keys with an integer value of your choice."       )

              self.fill_only("zero_optimization.reduce_bucket_size"						,		hidden_size * hidden_size       )
              if self.is_zeroa():
                  # automatically assign the optimal config values based on model config
                  self.fill_only("zero_optimization.stage3_prefetch_bucket_size"						,		0.9 * hidden_size * hidden_size       )
                  self.fill_only("zero_optimization.stage3_param_persistence_threshold"						,		10 * hidden_size       )

        # scheduler
          self.fill_match("scheduler.params.total_num_steps"						,		__SCREAMING_SNAKE_CASE						,		"num_training_steps (calculated)"       )
          self.fill_match("scheduler.params.warmup_num_steps"						,		args.get_warmup_steps(__SCREAMING_SNAKE_CASE       )						,		"warmup_steps"       )

          if len(self.mismatches       ) > 0:
              snake_case__  = "\n".join(self.mismatches       )
              raise ValueError(
                  "Please correct the following DeepSpeed config values that mismatch TrainingArguments"
                  F""" values:\n{mismatches}\nThe easiest method is to set these DeepSpeed config values to 'auto'."""       )





__magic_name__   				=     None





def 		SCREAMING_SNAKE_CASE__			(					__lowerCAmelCase							):
    global _hf_deepspeed_config_weak_ref
    # will go away automatically when HfDeepSpeedConfig is destroyed (when TrainingArguments is destroyed)
    snake_case__  = weakref.ref(lowercase__							)





def 		SCREAMING_SNAKE_CASE__			(					):
    global _hf_deepspeed_config_weak_ref
    snake_case__  = None





def 		SCREAMING_SNAKE_CASE__			(					):
    if _hf_deepspeed_config_weak_ref is not None and _hf_deepspeed_config_weak_ref() is not None:
        return _hf_deepspeed_config_weak_ref().is_zeroa()
    else:
        return False





def 		SCREAMING_SNAKE_CASE__			(					):
    if _hf_deepspeed_config_weak_ref is not None and _hf_deepspeed_config_weak_ref() is not None:
        return _hf_deepspeed_config_weak_ref().config
    else:
        return None





def 		SCREAMING_SNAKE_CASE__			(					__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase							):
    from accelerate.utils import DummyOptim, DummyScheduler

    snake_case__  = hf_deepspeed_config.config

    # Optimizer + Scheduler
    # Currently supported combos:
    # 1. DS scheduler + DS optimizer: Yes
    # 2. HF scheduler + HF optimizer: Yes
    # 3. DS scheduler + HF optimizer: Yes
    # 4. HF scheduler + DS optimizer: No
    #
    # Unless Offload is enabled in which case it's:
    # 1. DS scheduler + DS optimizer: Yes
    # 2. HF scheduler + HF optimizer: Mostly*
    # 3. DS scheduler + HF optimizer: Mostly*
    # 4. HF scheduler + DS optimizer: No
    #
    # Mostly*: All non-native DeepSpeed optimizers that have both CPU and GPU implementation should work (except LAMB)

    snake_case__  = None
    if "optimizer" in config:
        if args.adafactor:
            raise ValueError(
                "--adafactor was passed, but also found `optimizer` configured in the DeepSpeed config. "
                "Only one optimizer can be configured."							)
        snake_case__  = DummyOptim(params=lowercase__							)
    else:
        if hf_deepspeed_config.is_offload():
            logger.info(
                "Detected ZeRO Offload and non-DeepSpeed optimizers: This combination should work as long as the"
                " custom optimizer has both CPU and GPU implementation (except LAMB)"							)

        # ds supports Adam, OneBitAdam, and Lamb optimizers and can import other optimizers from torch.
        # But trainer uses AdamW by default.
        snake_case__  = trainer.create_optimizer()
        # To use other optimizers requires voiding warranty with: `zero_allow_untested_optimizer`
        snake_case__  = True

    snake_case__  = None
    if "scheduler" in config:
        snake_case__  = DummyScheduler(lowercase__							)
    else:
        if isinstance(lowercase__			,						lowercase__							):
            raise ValueError(
                "Found `optimizer` configured in the DeepSpeed config, but no `scheduler`. "
                "Please configure a scheduler in the DeepSpeed config."							)
        snake_case__  = trainer.create_scheduler(num_training_steps=lowercase__			,						optimizer=lowercase__							)

    return optimizer, lr_scheduler





def 		SCREAMING_SNAKE_CASE__			(					__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase=False							):
    from deepspeed.utils import logger as ds_logger

    snake_case__  = trainer.model
    snake_case__  = trainer.args

    snake_case__  = trainer.accelerator.state.deepspeed_plugin.hf_ds_config

    # resume config update - some bits like `model` and `num_training_steps` only become available during train
    hf_deepspeed_config.trainer_config_finalize(lowercase__			,						lowercase__			,						lowercase__							)

    # set the Deepspeed log level consistent with the Trainer
    ds_logger.setLevel(args.get_process_log_level()							)

    if inference:
        # only Z3 makes sense for the inference
        if not hf_deepspeed_config.is_zeroa():
            raise ValueError("ZeRO inference only makes sense with ZeRO Stage 3 - please adjust your config"							)

        # in case the training config is re-used for inference
        hf_deepspeed_config.del_config_sub_tree("optimizer"							)
        hf_deepspeed_config.del_config_sub_tree("lr_scheduler"							)
        snake_case__  ,    snake_case__  = None, None
        snake_case__  = None
    else:
        snake_case__  = None  # important for when deepspeed_init is used as re-init
        snake_case__  = list(filter(lambda __lowerCAmelCase							: p.requires_grad			,						model.parameters()							)							)
        snake_case__  ,    snake_case__  = deepspeed_optim_sched(
            lowercase__			,						lowercase__			,						lowercase__			,						lowercase__			,						lowercase__							)

    # keep for quick debug:
    # from pprint import pprint; pprint(config)

    return optimizer, lr_scheduler





def 		SCREAMING_SNAKE_CASE__			(					__lowerCAmelCase			,						__lowerCAmelCase							):
    import glob

    snake_case__  = sorted(glob.glob(F"""{checkpoint_path}/global_step*"""							)							)

    if len(lowercase__							) > 0:
        logger.info(F"""Attempting to resume from {checkpoint_path}"""							)
        # this magically updates self.optimizer and self.lr_scheduler
        snake_case__  ,    snake_case__  = deepspeed_engine.load_checkpoint(
            lowercase__			,						load_optimizer_states=lowercase__			,						load_lr_scheduler_states=lowercase__							)
        if load_path is None:
            raise ValueError(F"""[deepspeed] failed to resume from checkpoint {checkpoint_path}"""							)
    else:
        raise ValueError(F"""Can't find a valid checkpoint at {checkpoint_path}"""							)






"""simple docstring"""




import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import (
    TaBlock,
    TaConfig,
    TaLayerNorm,
)

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin





class 			__UpperCamelCase (   _A     ,		_A     ,		_A						):


		@register_to_config
		def __init__(self :		Optional[Any]     ,     __SCREAMING_SNAKE_CASE :		int     ,     __SCREAMING_SNAKE_CASE :		int     ,     __SCREAMING_SNAKE_CASE :		int     ,     __SCREAMING_SNAKE_CASE :		float     ,     __SCREAMING_SNAKE_CASE :		int     ,     __SCREAMING_SNAKE_CASE :		int     ,     __SCREAMING_SNAKE_CASE :		int     ,     __SCREAMING_SNAKE_CASE :		int     ,     __SCREAMING_SNAKE_CASE :		str     ,     __SCREAMING_SNAKE_CASE :		bool = False     ,     ):
							super().__init__()

							A			=					nn.Linear(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE     ,     bias=__SCREAMING_SNAKE_CASE)

							A			=					nn.Embedding(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE)
							A			=					False

							A			=					nn.Dropout(p=__SCREAMING_SNAKE_CASE)

							A			=					TaConfig(
							    d_model=__SCREAMING_SNAKE_CASE     ,     num_heads=__SCREAMING_SNAKE_CASE     ,     d_kv=__SCREAMING_SNAKE_CASE     ,     d_ff=__SCREAMING_SNAKE_CASE     ,     feed_forward_proj=__SCREAMING_SNAKE_CASE     ,     dropout_rate=__SCREAMING_SNAKE_CASE     ,     is_decoder=__SCREAMING_SNAKE_CASE     ,     is_encoder_decoder=__SCREAMING_SNAKE_CASE     ,     )
							A			=					nn.ModuleList()
							for lyr_num in range(__SCREAMING_SNAKE_CASE):
												A			=					TaBlock(__SCREAMING_SNAKE_CASE)
												self.encoders.append(__SCREAMING_SNAKE_CASE)

							A			=					TaLayerNorm(__SCREAMING_SNAKE_CASE)
							A			=					nn.Dropout(p=__SCREAMING_SNAKE_CASE)


		def 					SCREAMING_SNAKE_CASE__   (self :		int     ,     __SCREAMING_SNAKE_CASE :		Any     ,     __SCREAMING_SNAKE_CASE :		str):
							A			=					self.input_proj(__SCREAMING_SNAKE_CASE)

							# terminal relative positional encodings
							A			=					encoder_inputs.shape[1]
							A			=					torch.arange(__SCREAMING_SNAKE_CASE     ,     device=encoder_inputs.device)

							A			=					encoder_inputs_mask.sum(-1)
							A			=					torch.roll(input_positions.unsqueeze(0)     ,     tuple(seq_lens.tolist())     ,     dims=0)
							x += self.position_encoding(__SCREAMING_SNAKE_CASE)

							A			=					self.dropout_pre(__SCREAMING_SNAKE_CASE)

							# inverted the attention mask
							A			=					encoder_inputs.size()
							A			=					self.get_extended_attention_mask(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE)

							for lyr in self.encoders:
												A			=					lyr(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE)[0]
							A			=					self.layer_norm(__SCREAMING_SNAKE_CASE)

							return self.dropout_post(__SCREAMING_SNAKE_CASE), encoder_inputs_mask





