





import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
    import os

    # The slow tests are often failing with OOM error on GPU
    # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
    # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
    UpperCamelCase_												=	'platform'

    import jax
    import jax.numpy as jnp

    from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
        FlaxBlenderbotSmallForConditionalGeneration,
        FlaxBlenderbotSmallModel,
        shift_tokens_right,
    )






def 				_UpperCAmelCase			(    A				,							A				,							A=None				,							A=None				,							A=None				,							A=None				,							A=None				,							A=None				,							):




   '''simple docstring'''

   if attention_mask is None:
      UpperCAmelCase__						=np.where(input_ids != config.pad_token_id				,							1				,							0     )
   if decoder_attention_mask is None:
      UpperCAmelCase__						=np.where(decoder_input_ids != config.pad_token_id				,							1				,							0     )
   if head_mask is None:
      UpperCAmelCase__						=np.ones((config.encoder_layers, config.encoder_attention_heads)     )
   if decoder_head_mask is None:
      UpperCAmelCase__						=np.ones((config.decoder_layers, config.decoder_attention_heads)     )
   if cross_attn_head_mask is None:
      UpperCAmelCase__						=np.ones((config.decoder_layers, config.decoder_attention_heads)     )
   return {
       "input_ids": input_ids,
       "decoder_input_ids": decoder_input_ids,
       "attention_mask": attention_mask,
       "decoder_attention_mask": attention_mask,
   }

class     snake_case_		:






 '''simple docstring'''





 def __init__(  self,		A_,		A_=13,		A_=7,		A_=True,		A_=False,		A_=99,		A_=16,		A_=2,		A_=4,		A_=4,		A_="gelu",		A_=0.1,		A_=0.1,		A_=32,		A_=2,		A_=1,		A_=0,		A_=0.02,		)	->       Tuple:
    UpperCAmelCase__						=parent
    UpperCAmelCase__						=batch_size
    UpperCAmelCase__						=seq_length
    UpperCAmelCase__						=is_training
    UpperCAmelCase__						=use_labels
    UpperCAmelCase__						=vocab_size
    UpperCAmelCase__						=hidden_size
    UpperCAmelCase__						=num_hidden_layers
    UpperCAmelCase__						=num_attention_heads
    UpperCAmelCase__						=intermediate_size
    UpperCAmelCase__						=hidden_act
    UpperCAmelCase__						=hidden_dropout_prob
    UpperCAmelCase__						=attention_probs_dropout_prob
    UpperCAmelCase__						=max_position_embeddings
    UpperCAmelCase__						=eos_token_id
    UpperCAmelCase__						=pad_token_id
    UpperCAmelCase__						=bos_token_id
    UpperCAmelCase__						=initializer_range

 def  __UpperCAmelCase						(  self		)	->       Optional[Any]:
    UpperCAmelCase__						=np.clip(ids_tensor([self.batch_size, self.seq_length - 1],		self.vocab_size		),		3,		self.vocab_size		)
    UpperCAmelCase__						=np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1),		dtype=np.intaa		)),		-1		)

    UpperCAmelCase__						=shift_tokens_right(A_,		1,		2		)

    UpperCAmelCase__						=BlenderbotSmallConfig(
        vocab_size=self.vocab_size,		d_model=self.hidden_size,		encoder_layers=self.num_hidden_layers,		decoder_layers=self.num_hidden_layers,		encoder_attention_heads=self.num_attention_heads,		decoder_attention_heads=self.num_attention_heads,		encoder_ffn_dim=self.intermediate_size,		decoder_ffn_dim=self.intermediate_size,		dropout=self.hidden_dropout_prob,		attention_dropout=self.attention_probs_dropout_prob,		max_position_embeddings=self.max_position_embeddings,		eos_token_id=self.eos_token_id,		bos_token_id=self.bos_token_id,		pad_token_id=self.pad_token_id,		initializer_range=self.initializer_range,		use_cache=A_,		)
    UpperCAmelCase__						=prepare_blenderbot_inputs_dict(A_,		A_,		A_		)
    return config, inputs_dict

 def  __UpperCAmelCase						(  self		)	->       Tuple:
    UpperCAmelCase__			,							UpperCAmelCase__						=self.prepare_config_and_inputs()
    return config, inputs_dict

 def  __UpperCAmelCase						(  self,		A_,		A_,		A_		)	->       Optional[int]:
    UpperCAmelCase__						=20
    UpperCAmelCase__						=model_class_name(A_		)

    UpperCAmelCase__						=model.encode(inputs_dict["input_ids"]		)

    UpperCAmelCase__			,							UpperCAmelCase__						=(
        inputs_dict["decoder_input_ids"],
        inputs_dict["decoder_attention_mask"],
    )

    UpperCAmelCase__						=model.init_cache(decoder_input_ids.shape[0],		A_,		A_		)
    UpperCAmelCase__						=jnp.ones((decoder_input_ids.shape[0], max_decoder_length),		dtype="i4"		)

    UpperCAmelCase__						=jnp.broadcast_to(
        jnp.arange(decoder_input_ids.shape[-1] - 1		)[None, :],		(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1),		)
    UpperCAmelCase__						=model.decode(
        decoder_input_ids[:, :-1],		A_,		decoder_attention_mask=A_,		past_key_values=A_,		decoder_position_ids=A_,		)

    UpperCAmelCase__						=jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]],		dtype="i4"		)
    UpperCAmelCase__						=model.decode(
        decoder_input_ids[:, -1:],		A_,		decoder_attention_mask=A_,		past_key_values=outputs_cache.past_key_values,		decoder_position_ids=A_,		)

    UpperCAmelCase__						=model.decode(A_,		A_		)

    UpperCAmelCase__						=np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])		)		)
    self.parent.assertTrue(diff < 1E-3,		msg=f"""Max diff is {diff}"""		)






 def  __UpperCAmelCase						(  self,		A_,		A_,		A_		)	->       List[Any]:
    UpperCAmelCase__						=20
    UpperCAmelCase__						=model_class_name(A_		)

    UpperCAmelCase__						=model.encode(inputs_dict["input_ids"]		)

    UpperCAmelCase__			,							UpperCAmelCase__						=(
        inputs_dict["decoder_input_ids"],
        inputs_dict["decoder_attention_mask"],
    )

    UpperCAmelCase__						=jnp.concatenate(
        [
            decoder_attention_mask,
            jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])		),
        ],		axis=-1,		)

    UpperCAmelCase__						=model.init_cache(decoder_input_ids.shape[0],		A_,		A_		)
    UpperCAmelCase__						=jnp.broadcast_to(
        jnp.arange(decoder_input_ids.shape[-1] - 1		)[None, :],		(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1),		)

    UpperCAmelCase__						=model.decode(
        decoder_input_ids[:, :-1],		A_,		decoder_attention_mask=A_,		past_key_values=A_,		decoder_position_ids=A_,		)
    UpperCAmelCase__						=jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]],		dtype="i4"		)
    UpperCAmelCase__						=model.decode(
        decoder_input_ids[:, -1:],		A_,		past_key_values=outputs_cache.past_key_values,		decoder_attention_mask=A_,		decoder_position_ids=A_,		)

    UpperCAmelCase__						=model.decode(A_,		A_,		decoder_attention_mask=A_		)

    UpperCAmelCase__						=np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])		)		)
    self.parent.assertTrue(diff < 1E-3,		msg=f"""Max diff is {diff}"""		)

@require_flax
class     snake_case_		(   unittest.TestCase							):






 '''simple docstring'''





 __UpperCamelCase       =      9_9

 def  __UpperCAmelCase						(  self		)	->       Dict:
    UpperCAmelCase__						=np.array(
        [
            [71, 82, 18, 33, 46, 91, 2],
            [68, 34, 26, 58, 30, 82, 2],
            [5, 97, 17, 39, 94, 40, 2],
            [76, 83, 94, 25, 70, 78, 2],
            [87, 59, 41, 35, 48, 66, 2],
            [55, 13, 16, 58, 5, 2, 1],  # note padding
            [64, 27, 31, 51, 12, 75, 2],
            [52, 64, 86, 17, 83, 39, 2],
            [48, 61, 9, 24, 71, 82, 2],
            [26, 1, 60, 48, 22, 13, 2],
            [21, 5, 62, 28, 14, 76, 2],
            [45, 98, 37, 86, 59, 48, 2],
            [70, 70, 50, 9, 28, 0, 2],
        ],		dtype=np.intaa,		)

    UpperCAmelCase__						=input_ids.shape[0]
    UpperCAmelCase__						=BlenderbotSmallConfig(
        vocab_size=self.vocab_size,		d_model=24,		encoder_layers=2,		decoder_layers=2,		encoder_attention_heads=2,		decoder_attention_heads=2,		encoder_ffn_dim=32,		decoder_ffn_dim=32,		max_position_embeddings=48,		eos_token_id=2,		pad_token_id=1,		bos_token_id=0,		)
    return config, input_ids, batch_size

 def  __UpperCAmelCase						(  self		)	->       Dict:
    UpperCAmelCase__			,							UpperCAmelCase__			,							UpperCAmelCase__						=self._get_config_and_data()
    UpperCAmelCase__						=FlaxBlenderbotSmallForConditionalGeneration(A_		)
    UpperCAmelCase__						=lm_model(input_ids=A_		)
    UpperCAmelCase__						=(batch_size, input_ids.shape[1], config.vocab_size)
    self.assertEqual(outputs["logits"].shape,		A_		)

 def  __UpperCAmelCase						(  self		)	->       List[Any]:
    UpperCAmelCase__						=BlenderbotSmallConfig(
        vocab_size=self.vocab_size,		d_model=14,		encoder_layers=2,		decoder_layers=2,		encoder_attention_heads=2,		decoder_attention_heads=2,		encoder_ffn_dim=8,		decoder_ffn_dim=8,		max_position_embeddings=48,		)
    UpperCAmelCase__						=FlaxBlenderbotSmallForConditionalGeneration(A_		)
    UpperCAmelCase__						=np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]],		dtype=np.intaa		)
    UpperCAmelCase__						=np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]],		dtype=np.intaa		)
    UpperCAmelCase__						=lm_model(input_ids=A_,		decoder_input_ids=A_		)
    UpperCAmelCase__						=(*summary.shape, config.vocab_size)
    self.assertEqual(outputs["logits"].shape,		A_		)






 def  __UpperCAmelCase						(  self		)	->       Union[str, Any]:
    UpperCAmelCase__						=np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]],		dtype=np.intaa		)
    UpperCAmelCase__						=shift_tokens_right(A_,		1,		2		)
    UpperCAmelCase__						=np.equal(A_,		1		).astype(np.floataa		).sum()
    UpperCAmelCase__						=np.equal(A_,		1		).astype(np.floataa		).sum()
    self.assertEqual(shifted.shape,		input_ids.shape		)
    self.assertEqual(A_,		n_pad_before - 1		)
    self.assertTrue(np.equal(shifted[:, 0],		2		).all()		)

@require_flax
class     snake_case_		(   a,		unittest.TestCase,		a							):






 '''simple docstring'''





 __UpperCamelCase       =      True
 __UpperCamelCase       =      (
     (
         FlaxBlenderbotSmallModel,
         FlaxBlenderbotSmallForConditionalGeneration,
     )
     if is_flax_available()
     else ()
 )
 __UpperCamelCase       =      (FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()

 def  __UpperCAmelCase						(  self		)	->       Optional[Any]:
    UpperCAmelCase__						=FlaxBlenderbotSmallModelTester(self		)

 def  __UpperCAmelCase						(  self		)	->       int:
    UpperCAmelCase__			,							UpperCAmelCase__						=self.model_tester.prepare_config_and_inputs()
    for model_class in self.all_model_classes:
       self.model_tester.check_use_cache_forward(A_,		A_,		A_		)

 def  __UpperCAmelCase						(  self		)	->       Optional[int]:
    UpperCAmelCase__			,							UpperCAmelCase__						=self.model_tester.prepare_config_and_inputs()
    for model_class in self.all_model_classes:
       self.model_tester.check_use_cache_forward_with_attn_mask(A_,		A_,		A_		)

 def  __UpperCAmelCase						(  self		)	->       Tuple:
    UpperCAmelCase__			,							UpperCAmelCase__						=self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
       with self.subTest(model_class.__name__		):
          UpperCAmelCase__						=self._prepare_for_class(A_,		A_		)
          UpperCAmelCase__						=model_class(A_		)

          @jax.jit
          def encode_jitted(A_,		A_=None,		**A_		):
             return model.encode(input_ids=A_,		attention_mask=A_		)

          with self.subTest("JIT Enabled"		):
             UpperCAmelCase__						=encode_jitted(**A_		).to_tuple()

          with self.subTest("JIT Disabled"		):
             with jax.disable_jit():
                UpperCAmelCase__						=encode_jitted(**A_		).to_tuple()

          self.assertEqual(len(A_		),		len(A_		)		)
          for jitted_output, output in zip(A_,		A_		):
             self.assertEqual(jitted_output.shape,		output.shape		)

 def  __UpperCAmelCase						(  self		)	->       str:
    UpperCAmelCase__			,							UpperCAmelCase__						=self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
       with self.subTest(model_class.__name__		):
          UpperCAmelCase__						=model_class(A_		)
          UpperCAmelCase__						=model.encode(inputs_dict["input_ids"],		inputs_dict["attention_mask"]		)

          UpperCAmelCase__						={
              "decoder_input_ids": inputs_dict["decoder_input_ids"],
              "decoder_attention_mask": inputs_dict["decoder_attention_mask"],
              "encoder_outputs": encoder_outputs,
          }

          @jax.jit
          def decode_jitted(A_,		A_,		A_		):
             return model.decode(
                 decoder_input_ids=A_,		decoder_attention_mask=A_,		encoder_outputs=A_,		)

          with self.subTest("JIT Enabled"		):
             UpperCAmelCase__						=decode_jitted(**A_		).to_tuple()

          with self.subTest("JIT Disabled"		):
             with jax.disable_jit():
                UpperCAmelCase__						=decode_jitted(**A_		).to_tuple()

          self.assertEqual(len(A_		),		len(A_		)		)
          for jitted_output, output in zip(A_,		A_		):
             self.assertEqual(jitted_output.shape,		output.shape		)






 @slow
 def  __UpperCAmelCase						(  self		)	->       Any:
    for model_class_name in self.all_model_classes:
       UpperCAmelCase__						=model_class_name.from_pretrained("facebook/blenderbot_small-90M"		)
       # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
       UpperCAmelCase__						=np.ones((1, 1)		) * model.config.eos_token_id
       UpperCAmelCase__						=model(A_		)
       self.assertIsNotNone(A_		)










import os

import torch

from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version


if is_torch_version('>=', FSDP_PYTORCH_VERSION):
    import torch.distributed.checkpoint as dist_cp
    from torch.distributed.checkpoint.default_planner import DefaultLoadPlanner, DefaultSavePlanner
    from torch.distributed.checkpoint.optimizer import load_sharded_optimizer_state_dict
    from torch.distributed.fsdp.fully_sharded_data_parallel import FullyShardedDataParallel as FSDP
    from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType


UpperCamelCase_												=	get_logger(__name__)






def 				_UpperCAmelCase			(    A				,							A				,							A				,							A				,							A=0     ):




   '''simple docstring'''

   os.makedirs(A				,							exist_ok=A     )
   with FSDP.state_dict_type(
       A				,							fsdp_plugin.state_dict_type				,							fsdp_plugin.state_dict_config				,							fsdp_plugin.optim_state_dict_config     ):
      UpperCAmelCase__						=model.state_dict()
      if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
         UpperCAmelCase__						=F"""{MODEL_NAME}.bin""" if model_index == 0 else F"""{MODEL_NAME}_{model_index}.bin"""
         UpperCAmelCase__						=os.path.join(A				,							A     )
         if accelerator.process_index == 0:
            logger.info(F"""Saving model to {output_model_file}"""     )
            torch.save(A				,							A     )
            logger.info(F"""Model saved to {output_model_file}"""     )
      elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
         UpperCAmelCase__						=(
             F"""{MODEL_NAME}_rank{accelerator.process_index}.bin"""
             if model_index == 0
             else F"""{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin"""
         )
         UpperCAmelCase__						=os.path.join(A				,							A     )
         logger.info(F"""Saving model to {output_model_file}"""     )
         torch.save(A				,							A     )
         logger.info(F"""Model saved to {output_model_file}"""     )
      elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
         UpperCAmelCase__						=os.path.join(A				,							F"""{MODEL_NAME}_{model_index}"""     )
         os.makedirs(A				,							exist_ok=A     )
         logger.info(F"""Saving model to {ckpt_dir}"""     )
         UpperCAmelCase__						={"model": state_dict}

         dist_cp.save_state_dict(
             state_dict=A				,							storage_writer=dist_cp.FileSystemWriter(A     )				,							planner=DefaultSavePlanner()				,							)
         logger.info(F"""Model saved to {ckpt_dir}"""     )






def 				_UpperCAmelCase			(    A				,							A				,							A				,							A				,							A=0     ):




   '''simple docstring'''

   accelerator.wait_for_everyone()
   with FSDP.state_dict_type(
       A				,							fsdp_plugin.state_dict_type				,							fsdp_plugin.state_dict_config				,							fsdp_plugin.optim_state_dict_config     ):
      if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
         if type(A     ) != FSDP and accelerator.process_index != 0:
            if not fsdp_plugin.sync_module_states:
               raise ValueError(
                   "Set the `sync_module_states` flag to `True` so that model states are synced across processes when "
                   "initializing FSDP object"     )
            return
         UpperCAmelCase__						=F"""{MODEL_NAME}.bin""" if model_index == 0 else F"""{MODEL_NAME}_{model_index}.bin"""
         UpperCAmelCase__						=os.path.join(A				,							A     )
         logger.info(F"""Loading model from {input_model_file}"""     )
         UpperCAmelCase__						=torch.load(A     )
         logger.info(F"""Model loaded from {input_model_file}"""     )
      elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
         UpperCAmelCase__						=(
             F"""{MODEL_NAME}_rank{accelerator.process_index}.bin"""
             if model_index == 0
             else F"""{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin"""
         )
         UpperCAmelCase__						=os.path.join(A				,							A     )
         logger.info(F"""Loading model from {input_model_file}"""     )
         UpperCAmelCase__						=torch.load(A     )
         logger.info(F"""Model loaded from {input_model_file}"""     )
      elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
         UpperCAmelCase__						=(
             os.path.join(A				,							F"""{MODEL_NAME}_{model_index}"""     )
             if F"""{MODEL_NAME}""" not in input_dir
             else input_dir
         )
         logger.info(F"""Loading model from {ckpt_dir}"""     )
         UpperCAmelCase__						={"model": model.state_dict()}
         dist_cp.load_state_dict(
             state_dict=A				,							storage_reader=dist_cp.FileSystemReader(A     )				,							planner=DefaultLoadPlanner()				,							)
         UpperCAmelCase__						=state_dict["model"]
         logger.info(F"""Model loaded from {ckpt_dir}"""     )
      model.load_state_dict(A     )






def 				_UpperCAmelCase			(    A				,							A				,							A				,							A				,							A				,							A=0     ):




   '''simple docstring'''

   os.makedirs(A				,							exist_ok=A     )
   with FSDP.state_dict_type(
       A				,							fsdp_plugin.state_dict_type				,							fsdp_plugin.state_dict_config				,							fsdp_plugin.optim_state_dict_config     ):
      UpperCAmelCase__						=FSDP.optim_state_dict(A				,							A     )
      if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
         if accelerator.process_index == 0:
            UpperCAmelCase__						=(
                F"""{OPTIMIZER_NAME}.bin""" if optimizer_index == 0 else F"""{OPTIMIZER_NAME}_{optimizer_index}.bin"""
            )
            UpperCAmelCase__						=os.path.join(A				,							A     )
            logger.info(F"""Saving Optimizer state to {output_optimizer_file}"""     )
            torch.save(A				,							A     )
            logger.info(F"""Optimizer state saved in {output_optimizer_file}"""     )
      else:
         UpperCAmelCase__						=os.path.join(A				,							F"""{OPTIMIZER_NAME}_{optimizer_index}"""     )
         os.makedirs(A				,							exist_ok=A     )
         logger.info(F"""Saving Optimizer state to {ckpt_dir}"""     )
         dist_cp.save_state_dict(
             state_dict={"optimizer": optim_state}				,							storage_writer=dist_cp.FileSystemWriter(A     )				,							planner=DefaultSavePlanner()				,							)
         logger.info(F"""Optimizer state saved in {ckpt_dir}"""     )






def 				_UpperCAmelCase			(    A				,							A				,							A				,							A				,							A				,							A=0     ):




   '''simple docstring'''

   accelerator.wait_for_everyone()
   with FSDP.state_dict_type(
       A				,							fsdp_plugin.state_dict_type				,							fsdp_plugin.state_dict_config				,							fsdp_plugin.optim_state_dict_config     ):
      if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
         UpperCAmelCase__						=None
         # below check should work but currently it isn't working (mostly opytorch issue),
         # in the meantime disabling it at the cost of excess memory usage
         # if accelerator.process_index == 0 or not fsdp_plugin.optim_state_dict_config.rank0_only:
         UpperCAmelCase__						=(
             F"""{OPTIMIZER_NAME}.bin""" if optimizer_index == 0 else F"""{OPTIMIZER_NAME}_{optimizer_index}.bin"""
         )
         UpperCAmelCase__						=os.path.join(A				,							A     )
         logger.info(F"""Loading Optimizer state from {input_optimizer_file}"""     )
         UpperCAmelCase__						=torch.load(A     )
         logger.info(F"""Optimizer state loaded from {input_optimizer_file}"""     )
      else:
         UpperCAmelCase__						=(
             os.path.join(A				,							F"""{OPTIMIZER_NAME}_{optimizer_index}"""     )
             if F"""{OPTIMIZER_NAME}""" not in input_dir
             else input_dir
         )
         logger.info(F"""Loading Optimizer from {ckpt_dir}"""     )
         UpperCAmelCase__						=load_sharded_optimizer_state_dict(
             model_state_dict=model.state_dict()				,							optimizer_key="optimizer"				,							storage_reader=dist_cp.FileSystemReader(A     )				,							)
         UpperCAmelCase__						=optim_state["optimizer"]
         logger.info(F"""Optimizer loaded from {ckpt_dir}"""     )
      UpperCAmelCase__						=FSDP.optim_state_dict_to_load(A				,							A				,							A     )
      optimizer.load_state_dict(A     )



