'''simple docstring'''




import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    Pipeline,
    ZeroShotClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
__SCREAMING_SNAKE_CASE       :     Optional[int]     =  {"""LayoutLMv2Config""", """LayoutLMv3Config"""}

@is_pipeline_test
class 	lowerCamelCase_	(unittest.TestCase    ):
      '''simple docstring'''

      __UpperCamelCase:  str						=						MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
      __UpperCamelCase:  Any						=						TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

      if model_mapping is not None:
            __UpperCamelCase:  int						=						{config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
      if tf_model_mapping is not None:
            __UpperCamelCase:  List[Any]						=						{
                config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
            }




      def      _A	(       self						:				Union[str, Any]						,					A						:				int						,					A						:				Tuple						,					A						:				Optional[Any]       ):
             _UpperCAmelCase     :  Dict							      =    ZeroShotClassificationPipeline(
                 model=A						,					tokenizer=A						,					candidate_labels=["polics", "health"]       )
             return classifier, ["Who are you voting for in 2020?", "My stomach hurts."]




      def      _A	(       self						:				Any						,					A						:				Optional[int]						,					A						:				Union[str, Any]       ):
             _UpperCAmelCase     :  List[Any]							      =    classifier("Who are you voting for in 2020?"						,					candidate_labels="politics"       )
             self.assertEqual(A						,					{"sequence": ANY(A       ), "labels": [ANY(A       )], "scores": [ANY(A       )]}       )

             # No kwarg
             _UpperCAmelCase     :  int							      =    classifier("Who are you voting for in 2020?"						,					["politics"]       )
             self.assertEqual(A						,					{"sequence": ANY(A       ), "labels": [ANY(A       )], "scores": [ANY(A       )]}       )

             _UpperCAmelCase     :  Dict							      =    classifier("Who are you voting for in 2020?"						,					candidate_labels=["politics"]       )
             self.assertEqual(A						,					{"sequence": ANY(A       ), "labels": [ANY(A       )], "scores": [ANY(A       )]}       )

             _UpperCAmelCase     :  Tuple							      =    classifier("Who are you voting for in 2020?"						,					candidate_labels="politics, public health"       )
             self.assertEqual(
                 A						,					{"sequence": ANY(A       ), "labels": [ANY(A       ), ANY(A       )], "scores": [ANY(A       ), ANY(A       )]}       )
             self.assertAlmostEqual(sum(nested_simplify(outputs["scores"]       )       )						,					1.0       )

             _UpperCAmelCase     :  Any							      =    classifier("Who are you voting for in 2020?"						,					candidate_labels=["politics", "public health"]       )
             self.assertEqual(
                 A						,					{"sequence": ANY(A       ), "labels": [ANY(A       ), ANY(A       )], "scores": [ANY(A       ), ANY(A       )]}       )
             self.assertAlmostEqual(sum(nested_simplify(outputs["scores"]       )       )						,					1.0       )

             _UpperCAmelCase     :  Tuple							      =    classifier(
                 "Who are you voting for in 2020?"						,					candidate_labels="politics"						,					hypothesis_template="This text is about {}"       )
             self.assertEqual(A						,					{"sequence": ANY(A       ), "labels": [ANY(A       )], "scores": [ANY(A       )]}       )

             # https://github.com/huggingface/transformers/issues/13846
             _UpperCAmelCase     :  Union[str, Any]							      =    classifier(["I am happy"]						,					["positive", "negative"]       )
             self.assertEqual(
                 A						,					[
                     {"sequence": ANY(A       ), "labels": [ANY(A       ), ANY(A       )], "scores": [ANY(A       ), ANY(A       )]}
                     for i in range(1       )
                 ]						,					)
             _UpperCAmelCase     :  Dict							      =    classifier(["I am happy", "I am sad"]						,					["positive", "negative"]       )
             self.assertEqual(
                 A						,					[
                     {"sequence": ANY(A       ), "labels": [ANY(A       ), ANY(A       )], "scores": [ANY(A       ), ANY(A       )]}
                     for i in range(2       )
                 ]						,					)

             with self.assertRaises(A       ):
                    classifier(""						,					candidate_labels="politics"       )

             with self.assertRaises(A       ):
                    classifier(A						,					candidate_labels="politics"       )

             with self.assertRaises(A       ):
                    classifier("Who are you voting for in 2020?"						,					candidate_labels=""       )

             with self.assertRaises(A       ):
                    classifier("Who are you voting for in 2020?"						,					candidate_labels=A       )

             with self.assertRaises(A       ):
                    classifier(
                        "Who are you voting for in 2020?"						,					candidate_labels="politics"						,					hypothesis_template="Not formatting template"						,					)

             with self.assertRaises(A       ):
                    classifier(
                        "Who are you voting for in 2020?"						,					candidate_labels="politics"						,					hypothesis_template=A						,					)

             self.run_entailment_id(A       )




      def      _A	(       self						:				Tuple						,					A						:				Pipeline       ):
             _UpperCAmelCase     :  Tuple							      =    zero_shot_classifier.model.config
             _UpperCAmelCase     :  Optional[int]							      =    config.labelaid
             _UpperCAmelCase     :  Dict							      =    zero_shot_classifier.entailment_id

             _UpperCAmelCase     :  Optional[Any]							      =    {"LABEL_0": 0, "LABEL_1": 1, "LABEL_2": 2}
             self.assertEqual(zero_shot_classifier.entailment_id						,					-1       )

             _UpperCAmelCase     :  Any							      =    {"entailment": 0, "neutral": 1, "contradiction": 2}
             self.assertEqual(zero_shot_classifier.entailment_id						,					0       )

             _UpperCAmelCase     :  List[Any]							      =    {"ENTAIL": 0, "NON-ENTAIL": 1}
             self.assertEqual(zero_shot_classifier.entailment_id						,					0       )

             _UpperCAmelCase     :  List[Any]							      =    {"ENTAIL": 2, "NEUTRAL": 1, "CONTR": 0}
             self.assertEqual(zero_shot_classifier.entailment_id						,					2       )

             _UpperCAmelCase     :  Optional[int]							      =    original_labelaid
             self.assertEqual(A						,					zero_shot_classifier.entailment_id       )




      @require_torch
      def      _A	(       self						:				Tuple       ):
             _UpperCAmelCase     :  Union[str, Any]							      =    pipeline(
                 "zero-shot-classification"						,					model="sshleifer/tiny-distilbert-base-cased-distilled-squad"						,					framework="pt"						,					)
             # There was a regression in 4.10 for this
             # Adding a test so we don't make the mistake again.
             # https://github.com/huggingface/transformers/issues/13381#issuecomment-912343499
             zero_shot_classifier(
                 "Who are you voting for in 2020?" * 100						,					candidate_labels=["politics", "public health", "science"]       )




      @require_torch
      def      _A	(       self						:				Optional[int]       ):
             _UpperCAmelCase     :  Optional[Any]							      =    pipeline(
                 "zero-shot-classification"						,					model="sshleifer/tiny-distilbert-base-cased-distilled-squad"						,					framework="pt"						,					)
             _UpperCAmelCase     :  Optional[Any]							      =    zero_shot_classifier(
                 "Who are you voting for in 2020?"						,					candidate_labels=["politics", "public health", "science"]       )

             self.assertEqual(
                 nested_simplify(A       )						,					{
                     "sequence": "Who are you voting for in 2020?",
                     "labels": ["science", "public health", "politics"],
                     "scores": [0.333, 0.333, 0.333],
                 }						,					)




      @require_tf
      def      _A	(       self						:				int       ):
             _UpperCAmelCase     :  Tuple							      =    pipeline(
                 "zero-shot-classification"						,					model="sshleifer/tiny-distilbert-base-cased-distilled-squad"						,					framework="tf"						,					)
             _UpperCAmelCase     :  Union[str, Any]							      =    zero_shot_classifier(
                 "Who are you voting for in 2020?"						,					candidate_labels=["politics", "public health", "science"]       )

             self.assertEqual(
                 nested_simplify(A       )						,					{
                     "sequence": "Who are you voting for in 2020?",
                     "labels": ["science", "public health", "politics"],
                     "scores": [0.333, 0.333, 0.333],
                 }						,					)




      @slow
      @require_torch
      def      _A	(       self						:				Union[str, Any]       ):
             _UpperCAmelCase     :  Any							      =    pipeline("zero-shot-classification"						,					model="roberta-large-mnli"						,					framework="pt"       )
             _UpperCAmelCase     :  Any							      =    zero_shot_classifier(
                 "Who are you voting for in 2020?"						,					candidate_labels=["politics", "public health", "science"]       )

             self.assertEqual(
                 nested_simplify(A       )						,					{
                     "sequence": "Who are you voting for in 2020?",
                     "labels": ["politics", "public health", "science"],
                     "scores": [0.976, 0.015, 0.009],
                 }						,					)
             _UpperCAmelCase     :  Optional[int]							      =    zero_shot_classifier(
                 "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks"
                 " in an encoder-decoder configuration. The best performing models also connect the encoder and decoder"
                 " through an attention mechanism. We propose a new simple network architecture, the Transformer, based"
                 " solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two"
                 " machine translation tasks show these models to be superior in quality while being more parallelizable"
                 " and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014"
                 " English-to-German translation task, improving over the existing best results, including ensembles by"
                 " over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new"
                 " single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small"
                 " fraction of the training costs of the best models from the literature. We show that the Transformer"
                 " generalizes well to other tasks by applying it successfully to English constituency parsing both with"
                 " large and limited training data."						,					candidate_labels=["machine learning", "statistics", "translation", "vision"]						,					multi_label=A						,					)
             self.assertEqual(
                 nested_simplify(A       )						,					{
                     "sequence": (
                         "The dominant sequence transduction models are based on complex recurrent or convolutional neural"
                         " networks in an encoder-decoder configuration. The best performing models also connect the"
                         " encoder and decoder through an attention mechanism. We propose a new simple network"
                         " architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence"
                         " and convolutions entirely. Experiments on two machine translation tasks show these models to be"
                         " superior in quality while being more parallelizable and requiring significantly less time to"
                         " train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,"
                         " improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014"
                         " English-to-French translation task, our model establishes a new single-model state-of-the-art"
                         " BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training"
                         " costs of the best models from the literature. We show that the Transformer generalizes well to"
                         " other tasks by applying it successfully to English constituency parsing both with large and"
                         " limited training data."
                     ),
                     "labels": ["translation", "machine learning", "vision", "statistics"],
                     "scores": [0.817, 0.713, 0.018, 0.018],
                 }						,					)





      @slow
      @require_tf
      def      _A	(       self						:				str       ):
             _UpperCAmelCase     :  Tuple							      =    pipeline("zero-shot-classification"						,					model="roberta-large-mnli"						,					framework="tf"       )
             _UpperCAmelCase     :  Optional[int]							      =    zero_shot_classifier(
                 "Who are you voting for in 2020?"						,					candidate_labels=["politics", "public health", "science"]       )

             self.assertEqual(
                 nested_simplify(A       )						,					{
                     "sequence": "Who are you voting for in 2020?",
                     "labels": ["politics", "public health", "science"],
                     "scores": [0.976, 0.015, 0.009],
                 }						,					)
             _UpperCAmelCase     :  Optional[int]							      =    zero_shot_classifier(
                 "The dominant sequence transduction models are based on complex recurrent or convolutional neural networks"
                 " in an encoder-decoder configuration. The best performing models also connect the encoder and decoder"
                 " through an attention mechanism. We propose a new simple network architecture, the Transformer, based"
                 " solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two"
                 " machine translation tasks show these models to be superior in quality while being more parallelizable"
                 " and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014"
                 " English-to-German translation task, improving over the existing best results, including ensembles by"
                 " over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new"
                 " single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small"
                 " fraction of the training costs of the best models from the literature. We show that the Transformer"
                 " generalizes well to other tasks by applying it successfully to English constituency parsing both with"
                 " large and limited training data."						,					candidate_labels=["machine learning", "statistics", "translation", "vision"]						,					multi_label=A						,					)
             self.assertEqual(
                 nested_simplify(A       )						,					{
                     "sequence": (
                         "The dominant sequence transduction models are based on complex recurrent or convolutional neural"
                         " networks in an encoder-decoder configuration. The best performing models also connect the"
                         " encoder and decoder through an attention mechanism. We propose a new simple network"
                         " architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence"
                         " and convolutions entirely. Experiments on two machine translation tasks show these models to be"
                         " superior in quality while being more parallelizable and requiring significantly less time to"
                         " train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,"
                         " improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014"
                         " English-to-French translation task, our model establishes a new single-model state-of-the-art"
                         " BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training"
                         " costs of the best models from the literature. We show that the Transformer generalizes well to"
                         " other tasks by applying it successfully to English constituency parsing both with large and"
                         " limited training data."
                     ),
                     "labels": ["translation", "machine learning", "vision", "statistics"],
                     "scores": [0.817, 0.713, 0.018, 0.018],
                 }						,					)







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

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







def 	_a      (						SCREAMING_SNAKE_CASE_							:				Dict      ,      SCREAMING_SNAKE_CASE_							:				int      ,      SCREAMING_SNAKE_CASE_							:				Optional[int]      ,      SCREAMING_SNAKE_CASE_							:				List[str]      ,      SCREAMING_SNAKE_CASE_							:				Any=0     ):
					accelerator.wait_for_everyone()
					with FSDP.state_dict_type(
					    SCREAMING_SNAKE_CASE_      ,      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(SCREAMING_SNAKE_CASE_     ) != 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
															__lowerCAmelCase				   =       F"""{MODEL_NAME}.bin""" if model_index == 0 else F"""{MODEL_NAME}_{model_index}.bin"""
															__lowerCAmelCase				   =       os.path.join(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     )
															logger.info(F"""Loading model from {input_model_file}"""     )
															__lowerCAmelCase				   =       torch.load(SCREAMING_SNAKE_CASE_     )
															logger.info(F"""Model loaded from {input_model_file}"""     )
										elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
															__lowerCAmelCase				   =       (
															    F"""{MODEL_NAME}_rank{accelerator.process_index}.bin"""
															    if model_index == 0
															    else F"""{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin"""
															)
															__lowerCAmelCase				   =       os.path.join(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     )
															logger.info(F"""Loading model from {input_model_file}"""     )
															__lowerCAmelCase				   =       torch.load(SCREAMING_SNAKE_CASE_     )
															logger.info(F"""Model loaded from {input_model_file}"""     )
										elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
															__lowerCAmelCase				   =       (
															    os.path.join(SCREAMING_SNAKE_CASE_      ,      F"""{MODEL_NAME}_{model_index}"""     )
															    if F"""{MODEL_NAME}""" not in input_dir
															    else input_dir
															)
															logger.info(F"""Loading model from {ckpt_dir}"""     )
															__lowerCAmelCase				   =       {"model": model.state_dict()}
															dist_cp.load_state_dict(
															    state_dict=SCREAMING_SNAKE_CASE_      ,      storage_reader=dist_cp.FileSystemReader(SCREAMING_SNAKE_CASE_     )      ,      planner=DefaultLoadPlanner()      ,      )
															__lowerCAmelCase				   =       state_dict["model"]
															logger.info(F"""Model loaded from {ckpt_dir}"""     )
										model.load_state_dict(SCREAMING_SNAKE_CASE_     )







def 	_a      (						SCREAMING_SNAKE_CASE_							:				Dict      ,      SCREAMING_SNAKE_CASE_							:				int      ,      SCREAMING_SNAKE_CASE_							:				List[str]      ,      SCREAMING_SNAKE_CASE_							:				Optional[Any]      ,      SCREAMING_SNAKE_CASE_							:				Optional[int]      ,      SCREAMING_SNAKE_CASE_							:				str=0     ):
					os.makedirs(SCREAMING_SNAKE_CASE_      ,      exist_ok=SCREAMING_SNAKE_CASE_     )
					with FSDP.state_dict_type(
					    SCREAMING_SNAKE_CASE_      ,      fsdp_plugin.state_dict_type      ,      fsdp_plugin.state_dict_config      ,      fsdp_plugin.optim_state_dict_config     ):
										__lowerCAmelCase				   =       FSDP.optim_state_dict(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     )
										if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
															if accelerator.process_index == 0:
																				__lowerCAmelCase				   =       (
																				    F"""{OPTIMIZER_NAME}.bin""" if optimizer_index == 0 else F"""{OPTIMIZER_NAME}_{optimizer_index}.bin"""
																				)
																				__lowerCAmelCase				   =       os.path.join(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     )
																				logger.info(F"""Saving Optimizer state to {output_optimizer_file}"""     )
																				torch.save(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     )
																				logger.info(F"""Optimizer state saved in {output_optimizer_file}"""     )
										else:
															__lowerCAmelCase				   =       os.path.join(SCREAMING_SNAKE_CASE_      ,      F"""{OPTIMIZER_NAME}_{optimizer_index}"""     )
															os.makedirs(SCREAMING_SNAKE_CASE_      ,      exist_ok=SCREAMING_SNAKE_CASE_     )
															logger.info(F"""Saving Optimizer state to {ckpt_dir}"""     )
															dist_cp.save_state_dict(
															    state_dict={"optimizer": optim_state}      ,      storage_writer=dist_cp.FileSystemWriter(SCREAMING_SNAKE_CASE_     )      ,      planner=DefaultSavePlanner()      ,      )
															logger.info(F"""Optimizer state saved in {ckpt_dir}"""     )







def 	_a      (						SCREAMING_SNAKE_CASE_							:				Dict      ,      SCREAMING_SNAKE_CASE_							:				Dict      ,      SCREAMING_SNAKE_CASE_							:				Tuple      ,      SCREAMING_SNAKE_CASE_							:				List[str]      ,      SCREAMING_SNAKE_CASE_							:				str      ,      SCREAMING_SNAKE_CASE_							:				Dict=0     ):
					accelerator.wait_for_everyone()
					with FSDP.state_dict_type(
					    SCREAMING_SNAKE_CASE_      ,      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:
															__lowerCAmelCase				   =       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:
															__lowerCAmelCase				   =       (
															    F"""{OPTIMIZER_NAME}.bin""" if optimizer_index == 0 else F"""{OPTIMIZER_NAME}_{optimizer_index}.bin"""
															)
															__lowerCAmelCase				   =       os.path.join(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     )
															logger.info(F"""Loading Optimizer state from {input_optimizer_file}"""     )
															__lowerCAmelCase				   =       torch.load(SCREAMING_SNAKE_CASE_     )
															logger.info(F"""Optimizer state loaded from {input_optimizer_file}"""     )
										else:
															__lowerCAmelCase				   =       (
															    os.path.join(SCREAMING_SNAKE_CASE_      ,      F"""{OPTIMIZER_NAME}_{optimizer_index}"""     )
															    if F"""{OPTIMIZER_NAME}""" not in input_dir
															    else input_dir
															)
															logger.info(F"""Loading Optimizer from {ckpt_dir}"""     )
															__lowerCAmelCase				   =       load_sharded_optimizer_state_dict(
															    model_state_dict=model.state_dict()      ,      optimizer_key="optimizer"      ,      storage_reader=dist_cp.FileSystemReader(SCREAMING_SNAKE_CASE_     )      ,      )
															__lowerCAmelCase				   =       optim_state["optimizer"]
															logger.info(F"""Optimizer loaded from {ckpt_dir}"""     )
										__lowerCAmelCase				   =       FSDP.optim_state_dict_to_load(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     )
										optimizer.load_state_dict(SCREAMING_SNAKE_CASE_     )




