



"""simple docstring"""






import functools
import math

import flax.linen as nn
import jax
import jax.numpy as jnp






def 	snake_case__  (						__lowerCamelCase							:       Union[str, Any]				,						__lowerCamelCase							:       List[str]				,						__lowerCamelCase							:       Optional[int]				,						__lowerCamelCase							:       Union[str, Any]				,						__lowerCamelCase							:       int = 4096      ):
			"""simple docstring"""
			lowerCamelCase__      ,  lowerCamelCase__      ,  lowerCamelCase__						:						List[Any]						      =key.shape[-3:]
			lowerCamelCase__						:						Optional[Any]						      =value.shape[-1]
			lowerCamelCase__						:						Any						      =min(__lowerCamelCase				,						__lowerCamelCase      )
			lowerCamelCase__						:						Dict						      =query / jnp.sqrt(__lowerCamelCase      )

			@functools.partial(jax.checkpoint				,						prevent_cse=__lowerCamelCase      )
			def summarize_chunk(__lowerCamelCase							:       Dict				,						__lowerCamelCase							:       List[Any]				,						__lowerCamelCase							:       int      ):
						lowerCamelCase__						:						int						      =jnp.einsum('''...qhd,...khd->...qhk'''				,						__lowerCamelCase				,						__lowerCamelCase				,						precision=__lowerCamelCase      )

						lowerCamelCase__						:						Tuple						      =jnp.max(__lowerCamelCase				,						axis=-1				,						keepdims=__lowerCamelCase      )
						lowerCamelCase__						:						Any						      =jax.lax.stop_gradient(__lowerCamelCase      )
						lowerCamelCase__						:						List[Any]						      =jnp.exp(attn_weights - max_score      )

						lowerCamelCase__						:						List[Any]						      =jnp.einsum('''...vhf,...qhv->...qhf'''				,						__lowerCamelCase				,						__lowerCamelCase				,						precision=__lowerCamelCase      )
						lowerCamelCase__						:						Union[str, Any]						      =jnp.einsum('''...qhk->...qh'''				,						__lowerCamelCase      )

						return (exp_values, exp_weights.sum(axis=-1      ), max_score)

			def chunk_scanner(__lowerCamelCase							:       Union[str, Any]      ):
						# julienne key array
						lowerCamelCase__						:						int						      =jax.lax.dynamic_slice(
						    operand=__lowerCamelCase				,						start_indices=[0] * (key.ndim - 3) + [chunk_idx, 0, 0]				,						slice_sizes=list(key.shape[:-3]      ) + [key_chunk_size, num_heads, k_features]				,						)

						# julienne value array
						lowerCamelCase__						:						Union[str, Any]						      =jax.lax.dynamic_slice(
						    operand=__lowerCamelCase				,						start_indices=[0] * (value.ndim - 3) + [chunk_idx, 0, 0]				,						slice_sizes=list(value.shape[:-3]      ) + [key_chunk_size, num_heads, v_features]				,						)

						return summarize_chunk(__lowerCamelCase				,						__lowerCamelCase				,						__lowerCamelCase      )

			lowerCamelCase__      ,  lowerCamelCase__      ,  lowerCamelCase__						:						int						      =jax.lax.map(f=__lowerCamelCase				,						xs=jnp.arange(0				,						__lowerCamelCase				,						__lowerCamelCase      )      )

			lowerCamelCase__						:						List[str]						      =jnp.max(__lowerCamelCase				,						axis=0				,						keepdims=__lowerCamelCase      )
			lowerCamelCase__						:						Dict						      =jnp.exp(chunk_max - global_max      )

			chunk_values *= jnp.expand_dims(__lowerCamelCase				,						axis=-1      )
			chunk_weights *= max_diffs

			lowerCamelCase__						:						int						      =chunk_values.sum(axis=0      )
			lowerCamelCase__						:						Optional[int]						      =jnp.expand_dims(__lowerCamelCase				,						-1      ).sum(axis=0      )

			return all_values / all_weights






def 	snake_case__  (						__lowerCamelCase							:       Tuple				,						__lowerCamelCase							:       int				,						__lowerCamelCase							:       Optional[int]				,						__lowerCamelCase							:       Optional[int]=jax.lax.Precision.HIGHEST				,						__lowerCamelCase							:       int = 1024				,						__lowerCamelCase							:       int = 4096      ):
			"""simple docstring"""
			lowerCamelCase__      ,  lowerCamelCase__      ,  lowerCamelCase__						:						Dict						      =query.shape[-3:]

			def chunk_scanner(__lowerCamelCase							:       str				,						__lowerCamelCase							:       List[str]      ):
						# julienne query array
						lowerCamelCase__						:						List[str]						      =jax.lax.dynamic_slice(
						    operand=__lowerCamelCase				,						start_indices=([0] * (query.ndim - 3)) + [chunk_idx, 0, 0]				,						slice_sizes=list(query.shape[:-3]      ) + [min(__lowerCamelCase				,						__lowerCamelCase      ), num_heads, q_features]				,						)

						return (
						    chunk_idx + query_chunk_size,  # unused ignore it
						    _query_chunk_attention(
						        query=__lowerCamelCase				,						key=__lowerCamelCase				,						value=__lowerCamelCase				,						precision=__lowerCamelCase				,						key_chunk_size=__lowerCamelCase      ),
						)

			lowerCamelCase__      ,  lowerCamelCase__						:						Optional[Any]						      =jax.lax.scan(
			    f=__lowerCamelCase				,						init=0				,						xs=__lowerCamelCase				,						length=math.ceil(num_q / query_chunk_size      )      )

			return jnp.concatenate(__lowerCamelCase				,						axis=-3      )  # fuse the chunked result back

class 			__SCREAMING_SNAKE_CASE      ( nn.Module  ):


					'''simple docstring'''





					_a       =				42
					_a       =				8
					_a       =				6_4
					_a       =				0.0
					_a       =				False
					_a       =				jnp.floataa



					def 						snake_case			(			self				:    Dict	)->   str:
								lowerCamelCase__						:						str						      =self.dim_head * self.heads
								lowerCamelCase__						:						List[Any]						      =self.dim_head**-0.5

								# Weights were exported with old names {to_q, to_k, to_v, to_out}
								lowerCamelCase__						:						Tuple						      =nn.Dense(lowerCamelCase,    use_bias=lowerCamelCase,    dtype=self.dtype,    name='''to_q'''	)
								lowerCamelCase__						:						Optional[int]						      =nn.Dense(lowerCamelCase,    use_bias=lowerCamelCase,    dtype=self.dtype,    name='''to_k'''	)
								lowerCamelCase__						:						List[Any]						      =nn.Dense(lowerCamelCase,    use_bias=lowerCamelCase,    dtype=self.dtype,    name='''to_v'''	)

								lowerCamelCase__						:						Any						      =nn.Dense(self.query_dim,    dtype=self.dtype,    name='''to_out_0'''	)
								lowerCamelCase__						:						List[Any]						      =nn.Dropout(rate=self.dropout	)



					def 						snake_case			(			self				:    Any,    lowerCamelCase				:    str	)->   Any:
								lowerCamelCase__      ,  lowerCamelCase__      ,  lowerCamelCase__						:						Union[str, Any]						      =tensor.shape
								lowerCamelCase__						:						List[Any]						      =self.heads
								lowerCamelCase__						:						str						      =tensor.reshape(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    dim // head_size	)
								lowerCamelCase__						:						Union[str, Any]						      =jnp.transpose(lowerCamelCase,    (0, 2, 1, 3)	)
								lowerCamelCase__						:						Union[str, Any]						      =tensor.reshape(batch_size * head_size,    lowerCamelCase,    dim // head_size	)
								return tensor



					def 						snake_case			(			self				:    Tuple,    lowerCamelCase				:    int	)->   Union[str, Any]:
								lowerCamelCase__      ,  lowerCamelCase__      ,  lowerCamelCase__						:						List[Any]						      =tensor.shape
								lowerCamelCase__						:						str						      =self.heads
								lowerCamelCase__						:						Dict						      =tensor.reshape(batch_size // head_size,    lowerCamelCase,    lowerCamelCase,    lowerCamelCase	)
								lowerCamelCase__						:						Optional[Any]						      =jnp.transpose(lowerCamelCase,    (0, 2, 1, 3)	)
								lowerCamelCase__						:						Optional[Any]						      =tensor.reshape(batch_size // head_size,    lowerCamelCase,    dim * head_size	)
								return tensor




					def __call__(			self				:    Optional[int],    lowerCamelCase				:    Union[str, Any],    lowerCamelCase				:    Tuple=None,    lowerCamelCase				:    Tuple=True	)->   Union[str, Any]:
								lowerCamelCase__						:						Optional[Any]						      =hidden_states if context is None else context

								lowerCamelCase__						:						Any						      =self.query(lowerCamelCase	)
								lowerCamelCase__						:						Any						      =self.key(lowerCamelCase	)
								lowerCamelCase__						:						Tuple						      =self.value(lowerCamelCase	)

								lowerCamelCase__						:						int						      =self.reshape_heads_to_batch_dim(lowerCamelCase	)
								lowerCamelCase__						:						Union[str, Any]						      =self.reshape_heads_to_batch_dim(lowerCamelCase	)
								lowerCamelCase__						:						int						      =self.reshape_heads_to_batch_dim(lowerCamelCase	)

								if self.use_memory_efficient_attention:
											lowerCamelCase__						:						Tuple						      =query_states.transpose(1,    0,    2	)
											lowerCamelCase__						:						Any						      =key_states.transpose(1,    0,    2	)
											lowerCamelCase__						:						Any						      =value_states.transpose(1,    0,    2	)

											# this if statement create a chunk size for each layer of the unet
											# the chunk size is equal to the query_length dimension of the deepest layer of the unet

											lowerCamelCase__						:						Optional[Any]						      =query_states.shape[-3]
											if flatten_latent_dim % 64 == 0:
														lowerCamelCase__						:						Union[str, Any]						      =int(flatten_latent_dim / 64	)
											elif flatten_latent_dim % 16 == 0:
														lowerCamelCase__						:						Tuple						      =int(flatten_latent_dim / 16	)
											elif flatten_latent_dim % 4 == 0:
														lowerCamelCase__						:						Dict						      =int(flatten_latent_dim / 4	)
											else:
														lowerCamelCase__						:						Tuple						      =int(lowerCamelCase	)

											lowerCamelCase__						:						Optional[Any]						      =jax_memory_efficient_attention(
											    lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    query_chunk_size=lowerCamelCase,    key_chunk_size=4096 * 4	)

											lowerCamelCase__						:						Dict						      =hidden_states.transpose(1,    0,    2	)
								else:
											# compute attentions
											lowerCamelCase__						:						Dict						      =jnp.einsum('''b i d, b j d->b i j''',    lowerCamelCase,    lowerCamelCase	)
											lowerCamelCase__						:						Dict						      =attention_scores * self.scale
											lowerCamelCase__						:						Optional[int]						      =nn.softmax(lowerCamelCase,    axis=2	)

											# attend to values
											lowerCamelCase__						:						int						      =jnp.einsum('''b i j, b j d -> b i d''',    lowerCamelCase,    lowerCamelCase	)

								lowerCamelCase__						:						Union[str, Any]						      =self.reshape_batch_dim_to_heads(lowerCamelCase	)
								lowerCamelCase__						:						Tuple						      =self.proj_attn(lowerCamelCase	)
								return self.dropout_layer(lowerCamelCase,    deterministic=lowerCamelCase	)





class 			__SCREAMING_SNAKE_CASE      ( nn.Module  ):


					'''simple docstring'''





					_a       =				42
					_a       =				42
					_a       =				42
					_a       =				0.0
					_a       =				False
					_a       =				jnp.floataa
					_a       =				False



					def 						snake_case			(			self				:    Union[str, Any]	)->   int:
								# self attention (or cross_attention if only_cross_attention is True)
								lowerCamelCase__						:						Union[str, Any]						      =FlaxAttention(
								    self.dim,    self.n_heads,    self.d_head,    self.dropout,    self.use_memory_efficient_attention,    dtype=self.dtype	)
								# cross attention
								lowerCamelCase__						:						Union[str, Any]						      =FlaxAttention(
								    self.dim,    self.n_heads,    self.d_head,    self.dropout,    self.use_memory_efficient_attention,    dtype=self.dtype	)
								lowerCamelCase__						:						Optional[Any]						      =FlaxFeedForward(dim=self.dim,    dropout=self.dropout,    dtype=self.dtype	)
								lowerCamelCase__						:						Tuple						      =nn.LayerNorm(epsilon=1E-5,    dtype=self.dtype	)
								lowerCamelCase__						:						Optional[Any]						      =nn.LayerNorm(epsilon=1E-5,    dtype=self.dtype	)
								lowerCamelCase__						:						Optional[int]						      =nn.LayerNorm(epsilon=1E-5,    dtype=self.dtype	)
								lowerCamelCase__						:						Union[str, Any]						      =nn.Dropout(rate=self.dropout	)




					def __call__(			self				:    str,    lowerCamelCase				:    int,    lowerCamelCase				:    Tuple,    lowerCamelCase				:    Optional[int]=True	)->   Any:
								# self attention
								lowerCamelCase__						:						Optional[Any]						      =hidden_states
								if self.only_cross_attention:
											lowerCamelCase__						:						List[str]						      =self.attna(self.norma(lowerCamelCase	),    lowerCamelCase,    deterministic=lowerCamelCase	)
								else:
											lowerCamelCase__						:						str						      =self.attna(self.norma(lowerCamelCase	),    deterministic=lowerCamelCase	)
								lowerCamelCase__						:						Tuple						      =hidden_states + residual

								# cross attention
								lowerCamelCase__						:						Any						      =hidden_states
								lowerCamelCase__						:						Dict						      =self.attna(self.norma(lowerCamelCase	),    lowerCamelCase,    deterministic=lowerCamelCase	)
								lowerCamelCase__						:						Dict						      =hidden_states + residual

								# feed forward
								lowerCamelCase__						:						Optional[int]						      =hidden_states
								lowerCamelCase__						:						str						      =self.ff(self.norma(lowerCamelCase	),    deterministic=lowerCamelCase	)
								lowerCamelCase__						:						Union[str, Any]						      =hidden_states + residual

								return self.dropout_layer(lowerCamelCase,    deterministic=lowerCamelCase	)





class 			__SCREAMING_SNAKE_CASE      ( nn.Module  ):


					'''simple docstring'''





					_a       =				42
					_a       =				42
					_a       =				42
					_a       =				1
					_a       =				0.0
					_a       =				False
					_a       =				False
					_a       =				jnp.floataa
					_a       =				False



					def 						snake_case			(			self				:    str	)->   Optional[int]:
								lowerCamelCase__						:						Optional[int]						      =nn.GroupNorm(num_groups=32,    epsilon=1E-5	)

								lowerCamelCase__						:						Optional[Any]						      =self.n_heads * self.d_head
								if self.use_linear_projection:
											lowerCamelCase__						:						int						      =nn.Dense(lowerCamelCase,    dtype=self.dtype	)
								else:
											lowerCamelCase__						:						Union[str, Any]						      =nn.Conv(
											    lowerCamelCase,    kernel_size=(1, 1),    strides=(1, 1),    padding='''VALID''',    dtype=self.dtype,    )

								lowerCamelCase__						:						Optional[Any]						      =[
								    FlaxBasicTransformerBlock(
								        lowerCamelCase,    self.n_heads,    self.d_head,    dropout=self.dropout,    only_cross_attention=self.only_cross_attention,    dtype=self.dtype,    use_memory_efficient_attention=self.use_memory_efficient_attention,    )
								    for _ in range(self.depth	)
								]

								if self.use_linear_projection:
											lowerCamelCase__						:						Optional[int]						      =nn.Dense(lowerCamelCase,    dtype=self.dtype	)
								else:
											lowerCamelCase__						:						Union[str, Any]						      =nn.Conv(
											    lowerCamelCase,    kernel_size=(1, 1),    strides=(1, 1),    padding='''VALID''',    dtype=self.dtype,    )

								lowerCamelCase__						:						int						      =nn.Dropout(rate=self.dropout	)




					def __call__(			self				:    Any,    lowerCamelCase				:    Tuple,    lowerCamelCase				:    str,    lowerCamelCase				:    str=True	)->   Any:
								lowerCamelCase__      ,  lowerCamelCase__      ,  lowerCamelCase__      ,  lowerCamelCase__						:						str						      =hidden_states.shape
								lowerCamelCase__						:						List[str]						      =hidden_states
								lowerCamelCase__						:						int						      =self.norm(lowerCamelCase	)
								if self.use_linear_projection:
											lowerCamelCase__						:						Union[str, Any]						      =hidden_states.reshape(lowerCamelCase,    height * width,    lowerCamelCase	)
											lowerCamelCase__						:						Dict						      =self.proj_in(lowerCamelCase	)
								else:
											lowerCamelCase__						:						Union[str, Any]						      =self.proj_in(lowerCamelCase	)
											lowerCamelCase__						:						Any						      =hidden_states.reshape(lowerCamelCase,    height * width,    lowerCamelCase	)

								for transformer_block in self.transformer_blocks:
											lowerCamelCase__						:						Optional[Any]						      =transformer_block(lowerCamelCase,    lowerCamelCase,    deterministic=lowerCamelCase	)

								if self.use_linear_projection:
											lowerCamelCase__						:						Dict						      =self.proj_out(lowerCamelCase	)
											lowerCamelCase__						:						Optional[int]						      =hidden_states.reshape(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    lowerCamelCase	)
								else:
											lowerCamelCase__						:						Optional[Any]						      =hidden_states.reshape(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    lowerCamelCase	)
											lowerCamelCase__						:						str						      =self.proj_out(lowerCamelCase	)

								lowerCamelCase__						:						List[Any]						      =hidden_states + residual
								return self.dropout_layer(lowerCamelCase,    deterministic=lowerCamelCase	)





class 			__SCREAMING_SNAKE_CASE      ( nn.Module  ):


					'''simple docstring'''





					_a       =				42
					_a       =				0.0
					_a       =				jnp.floataa



					def 						snake_case			(			self				:    Tuple	)->   Optional[Any]:
								# The second linear layer needs to be called
								# net_2 for now to match the index of the Sequential layer
								lowerCamelCase__						:						List[str]						      =FlaxGEGLU(self.dim,    self.dropout,    self.dtype	)
								lowerCamelCase__						:						Union[str, Any]						      =nn.Dense(self.dim,    dtype=self.dtype	)




					def __call__(			self				:    List[Any],    lowerCamelCase				:    Union[str, Any],    lowerCamelCase				:    Optional[int]=True	)->   Optional[int]:
								lowerCamelCase__						:						Dict						      =self.net_a(lowerCamelCase,    deterministic=lowerCamelCase	)
								lowerCamelCase__						:						str						      =self.net_a(lowerCamelCase	)
								return hidden_states





class 			__SCREAMING_SNAKE_CASE      ( nn.Module  ):


					'''simple docstring'''





					_a       =				42
					_a       =				0.0
					_a       =				jnp.floataa



					def 						snake_case			(			self				:    Optional[int]	)->   Dict:
								lowerCamelCase__						:						str						      =self.dim * 4
								lowerCamelCase__						:						Union[str, Any]						      =nn.Dense(inner_dim * 2,    dtype=self.dtype	)
								lowerCamelCase__						:						Dict						      =nn.Dropout(rate=self.dropout	)




					def __call__(			self				:    Optional[int],    lowerCamelCase				:    Any,    lowerCamelCase				:    Dict=True	)->   str:
								lowerCamelCase__						:						int						      =self.proj(lowerCamelCase	)
								lowerCamelCase__      ,  lowerCamelCase__						:						Union[str, Any]						      =jnp.split(lowerCamelCase,    2,    axis=2	)
								return self.dropout_layer(hidden_linear * nn.gelu(lowerCamelCase	),    deterministic=lowerCamelCase	)





"""simple docstring"""
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

lowercase__   			=      logging.get_logger(__name__)







def 		__a  (      )      ->Dict:
  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   __snake_case			(	__lowerCAmelCase							):



      def __init__(		self       ,     lowercase)      ->     int:





        '''simple docstring'''





        set_hf_deepspeed_config(self)
        dep_version_check('accelerate')
        dep_version_check('deepspeed')
        super().__init__(lowercase)







class   __snake_case			(	__lowerCAmelCase							):



      def __init__(		self       ,     lowercase)      ->     List[Any]:





        '''simple docstring'''





        super().__init__(lowercase)
        a__:	List[Any]     				=      None
        a__:	int     				=      []



      def     lowerCamelCase_  (		self)      ->     Any:





        '''simple docstring'''





        if self._dtype is None:
          raise ValueError('trainer_config_process() wasn\'t called yet to tell dtype')
        return self._dtype



      def     lowerCamelCase_  (		self       ,     lowercase)      ->     Optional[Any]:





        '''simple docstring'''





        a__:	Optional[int]     				=      self.get_value(lowercase)
        if val is None:
          return False
        else:
          return val == "auto"



      def     lowerCamelCase_  (		self       ,     lowercase       ,     lowercase       ,     lowercase=None       ,     lowercase=True)      ->     int:





        '''simple docstring'''





        a__				,       a__:	int     				=      self.find_config_node(lowercase)
        if config is None:
          return

        if config.get(lowercase) == "auto":
          a__:	Optional[int]     				=      hf_val
          return

        if not must_match:
          return

        a__:	List[Any]     				=      config.get(lowercase)
        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__								=						partialmethod(__lowerCAmelCase			, must_match=__lowerCAmelCase							)



      def     lowerCamelCase_  (		self       ,     lowercase)      ->     Dict:





        '''simple docstring'''





        a__:	str     				=      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'       ,     lowercase       ,     '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:
          a__:	Tuple     				=      'apex' if args.fpaa_backend == 'apex' else 'amp'
        else:
          a__:	Optional[int]     				=      None

        if args.save_on_each_node:
          # deepspeed uses shared storage by default. Let's override this setting if save_on_each_node == True
          a__:	Union[str, Any]     				=      self.config.get('checkpoint'       ,     {})
          a__:	Tuple     				=      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'):
          a__:	Optional[int]     				=      torch.bfloataa
        elif self.is_false('fp16.enabled'):
          a__:	Tuple     				=      torch.floataa
        else:
          a__:	Tuple     				=      torch.floataa



      def     lowerCamelCase_  (		self       ,     lowercase       ,     lowercase       ,     lowercase)      ->     Any:





        '''simple docstring'''





        a__:	List[Any]     				=      [
            'zero_optimization.reduce_bucket_size',
            'zero_optimization.stage3_prefetch_bucket_size',
            'zero_optimization.stage3_param_persistence_threshold',
        ]
        a__:	Union[str, Any]     				=      [x for x in hidden_size_based_keys if self.is_auto(lowercase)]

        if len(lowercase) > 0:
          if hasattr(model.config       ,     'hidden_size'):
            a__:	Any     				=      model.config.hidden_size
          elif hasattr(model.config       ,     'hidden_sizes'):
            # if there are many hidden sizes pick the largest one
            a__:	List[str]     				=      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'       ,     lowercase       ,     'num_training_steps (calculated)')
        self.fill_match('scheduler.params.warmup_num_steps'       ,     args.get_warmup_steps(lowercase)       ,     'warmup_steps')

        if len(self.mismatches) > 0:
          a__:	int     				=      '\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\'.')







lowercase__   			=      None







def 		__a  (      _SCREAMING_SNAKE_CASE  )      ->List[Any]:
  # this is a special weakref global object to allow us to get to Deepspeed config from APIs
  # that don't have an easy way to get to the Deepspeed config outside of the Trainer domain.
  global _hf_deepspeed_config_weak_ref
  # will go away automatically when HfDeepSpeedConfig is destroyed (when TrainingArguments is destroyed)
  a__:	Any     				=      weakref.ref(_SCREAMING_SNAKE_CASE  )







def 		__a  (      )      ->List[Any]:
  # useful for unit tests to ensure the global state doesn't leak - call from `tearDown` method
  global _hf_deepspeed_config_weak_ref
  a__:	Any     				=      None







def 		__a  (      )      ->Union[str, Any]:
  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 		__a  (      )      ->Tuple:
  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 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )      ->Dict:
  from accelerate.utils import DummyOptim, DummyScheduler

  a__:	Optional[Any]     				=      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)

  a__:	Dict     				=      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.'  )
    a__:	str     				=      DummyOptim(params=_SCREAMING_SNAKE_CASE  )
  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.
    a__:	Any     				=      trainer.create_optimizer()
    # To use other optimizers requires voiding warranty with: `zero_allow_untested_optimizer`
    a__:	str     				=      True

  a__:	Dict     				=      None
  if "scheduler" in config:
    a__:	List[str]     				=      DummyScheduler(_SCREAMING_SNAKE_CASE  )
  else:
    if isinstance(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  ):
      raise ValueError(
          'Found `optimizer` configured in the DeepSpeed config, but no `scheduler`. '
          'Please configure a scheduler in the DeepSpeed config.'  )
    a__:	Tuple     				=      trainer.create_scheduler(num_training_steps=_SCREAMING_SNAKE_CASE						,	optimizer=_SCREAMING_SNAKE_CASE  )

  return optimizer, lr_scheduler







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE=False  )      ->Dict:
  from deepspeed.utils import logger as ds_logger

  a__:	Optional[int]     				=      trainer.model
  a__:	List[Any]     				=      trainer.args

  a__:	Optional[int]     				=      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(_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )

  # 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'  )
    a__				,       a__:	Optional[int]     				=      None, None
    a__:	Union[str, Any]     				=      None
  else:
    a__:	List[Any]     				=      None  # important for when deepspeed_init is used as re-init
    a__:	Union[str, Any]     				=      list(filter(lambda _SCREAMING_SNAKE_CASE  : p.requires_grad						,	model.parameters()  )  )
    a__				,       a__:	Optional[int]     				=      deepspeed_optim_sched(
        _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )

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

  return optimizer, lr_scheduler







def 		__a  (      _SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE  )      ->int:
  # it's possible that the user is trying to resume from model_path, which doesn't necessarily
  # contain a deepspeed checkpoint. e.g. examples just check if the dir exists and assume it's
  # a resume from a checkpoint and not just a local pretrained weight. So we check here if the
  # path contains what looks like a deepspeed checkpoint
  import glob

  a__:	Tuple     				=      sorted(glob.glob(F'{checkpoint_path}/global_step*'  )  )

  if len(_SCREAMING_SNAKE_CASE  ) > 0:
    logger.info(F'Attempting to resume from {checkpoint_path}'  )
    # this magically updates self.optimizer and self.lr_scheduler
    a__				,       a__:	Union[str, Any]     				=      deepspeed_engine.load_checkpoint(
        _SCREAMING_SNAKE_CASE						,	load_optimizer_states=_SCREAMING_SNAKE_CASE						,	load_lr_scheduler_states=_SCREAMING_SNAKE_CASE  )
    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}'  )


