






"""simple docstring"""
import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, 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
							_lowerCAmelCase         =							'platform'

							import jax
							import jax.numpy as jnp

							from transformers import BlenderbotTokenizer
							from transformers.models.blenderbot.modeling_flax_blenderbot import (
							    FlaxBlenderbotForConditionalGeneration,
							    FlaxBlenderbotModel,
							    shift_tokens_right,
							)






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__=None   ,   snake_case__=None   ,   snake_case__=None   ,   snake_case__=None   ,   snake_case__=None   ,   snake_case__=None   ,   ):



	if attention_mask is None:
		A_		:						Optional[Any]	    =   np.where(input_ids != config.pad_token_id   ,   1   ,   0					)
	if decoder_attention_mask is None:
		A_		:						Union[str, Any]	    =   np.where(decoder_input_ids != config.pad_token_id   ,   1   ,   0					)
	if head_mask is None:
		A_		:						Dict	    =   np.ones((config.encoder_layers, config.encoder_attention_heads)					)
	if decoder_head_mask is None:
		A_		:						List[Any]	    =   np.ones((config.decoder_layers, config.decoder_attention_heads)					)
	if cross_attn_head_mask is None:
		A_		:						Any	    =   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      SCREAMING_SNAKE_CASE  :





	"""simple docstring"""




	def __init__(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_=13     ,     lowerCAmelCase_=7     ,     lowerCAmelCase_=True     ,     lowerCAmelCase_=False     ,     lowerCAmelCase_=99     ,     lowerCAmelCase_=16     ,     lowerCAmelCase_=2     ,     lowerCAmelCase_=4     ,     lowerCAmelCase_=4     ,     lowerCAmelCase_="gelu"     ,     lowerCAmelCase_=0.1     ,     lowerCAmelCase_=0.1     ,     lowerCAmelCase_=32     ,     lowerCAmelCase_=2     ,     lowerCAmelCase_=1     ,     lowerCAmelCase_=0     ,     lowerCAmelCase_=0.02     ,     ):
		A_		:						Tuple	    =   parent
		A_		:						int	    =   batch_size
		A_		:						List[str]	    =   seq_length
		A_		:						List[str]	    =   is_training
		A_		:						Any	    =   use_labels
		A_		:						Optional[Any]	    =   vocab_size
		A_		:						Tuple	    =   hidden_size
		A_		:						Optional[int]	    =   num_hidden_layers
		A_		:						int	    =   num_attention_heads
		A_		:						Any	    =   intermediate_size
		A_		:						Any	    =   hidden_act
		A_		:						Union[str, Any]	    =   hidden_dropout_prob
		A_		:						str	    =   attention_probs_dropout_prob
		A_		:						int	    =   max_position_embeddings
		A_		:						Optional[int]	    =   eos_token_id
		A_		:						Optional[Any]	    =   pad_token_id
		A_		:						List[Any]	    =   bos_token_id
		A_		:						Any	    =   initializer_range





	def    lowerCamelCase(self							):
		A_		:						str	    =   np.clip(ids_tensor([self.batch_size, self.seq_length - 1]     ,     self.vocab_size							)     ,     3     ,     self.vocab_size							)
		A_		:						Optional[int]	    =   np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)     ,     dtype=np.intaa							))     ,     -1							)

		A_		:						Any	    =   shift_tokens_right(lowercase__     ,     1     ,     2							)

		A_		:						Any	    =   BlenderbotConfig(
		    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=lowercase__     ,     )
		A_		:						List[Any]	    =   prepare_blenderbot_inputs_dict(lowercase__     ,     lowercase__     ,     lowercase__							)
		return config, inputs_dict





	def    lowerCamelCase(self							):
		A_		:						Any	    =   self.prepare_config_and_inputs()
		return config, inputs_dict





	def    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_							):
		A_		:						Union[str, Any]	    =   20
		A_		:						int	    =   model_class_name(lowercase__							)

		A_		:						Union[str, Any]	    =   model.encode(inputs_dict["""input_ids"""]							)

		A_		:						Optional[Any]	    =   (
		    inputs_dict['''decoder_input_ids'''],
		    inputs_dict['''decoder_attention_mask'''],
		)

		A_		:						int	    =   model.init_cache(decoder_input_ids.shape[0]     ,     lowercase__     ,     lowercase__							)
		A_		:						Dict	    =   jnp.ones((decoder_input_ids.shape[0], max_decoder_length)     ,     dtype="""i4"""							)

		A_		:						Optional[int]	    =   jnp.broadcast_to(
		    jnp.arange(decoder_input_ids.shape[-1] - 1							)[None, :]     ,     (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)     ,     )
		A_		:						List[Any]	    =   model.decode(
		    decoder_input_ids[:, :-1]     ,     lowercase__     ,     decoder_attention_mask=lowercase__     ,     past_key_values=lowercase__     ,     decoder_position_ids=lowercase__     ,     )

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

		A_		:						Optional[Any]	    =   model.decode(lowercase__     ,     lowercase__							)

		A_		:						Optional[Any]	    =   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    lowerCamelCase(self     ,     lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_							):
		A_		:						Dict	    =   20
		A_		:						Any	    =   model_class_name(lowercase__							)

		A_		:						List[Any]	    =   model.encode(inputs_dict["""input_ids"""]							)

		A_		:						List[str]	    =   (
		    inputs_dict['''decoder_input_ids'''],
		    inputs_dict['''decoder_attention_mask'''],
		)

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

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

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

		A_		:						Optional[Any]	    =   model.decode(lowercase__     ,     lowercase__     ,     decoder_attention_mask=lowercase__							)

		A_		:						int	    =   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      SCREAMING_SNAKE_CASE  (				unittest.TestCase						):





	"""simple docstring"""




	_A    =				99





	def    lowerCamelCase(self							):
		A_		:						Dict	    =   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     ,     )

		A_		:						Union[str, Any]	    =   input_ids.shape[0]
		A_		:						Union[str, Any]	    =   BlenderbotConfig(
		    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    lowerCamelCase(self							):
		A_		:						Tuple	    =   self._get_config_and_data()
		A_		:						Dict	    =   FlaxBlenderbotForConditionalGeneration(lowercase__							)
		A_		:						List[Any]	    =   lm_model(input_ids=lowercase__							)
		A_		:						Optional[Any]	    =   (batch_size, input_ids.shape[1], config.vocab_size)
		self.assertEqual(outputs["""logits"""].shape     ,     lowercase__							)





	def    lowerCamelCase(self							):
		A_		:						Any	    =   BlenderbotConfig(
		    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     ,     )
		A_		:						Optional[Any]	    =   FlaxBlenderbotForConditionalGeneration(lowercase__							)
		A_		:						Dict	    =   np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]     ,     dtype=np.intaa							)
		A_		:						Union[str, Any]	    =   np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]     ,     dtype=np.intaa							)
		A_		:						List[str]	    =   lm_model(input_ids=lowercase__     ,     decoder_input_ids=lowercase__							)
		A_		:						Any	    =   (*summary.shape, config.vocab_size)
		self.assertEqual(outputs["""logits"""].shape     ,     lowercase__							)





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






@require_flax
class      SCREAMING_SNAKE_CASE  (				a__       ,       unittest.TestCase       ,       a__						):





	"""simple docstring"""




	_A    =				True
	_A    =				(
	    (
	        FlaxBlenderbotModel,
	        FlaxBlenderbotForConditionalGeneration,
	    )
	    if is_flax_available()
	    else ()
	)
	_A    =				(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()





	def    lowerCamelCase(self							):
		A_		:						Any	    =   FlaxBlenderbotModelTester(self							)





	def    lowerCamelCase(self							):
		A_		:						Optional[int]	    =   self.model_tester.prepare_config_and_inputs()
		for model_class in self.all_model_classes:
			self.model_tester.check_use_cache_forward(lowercase__     ,     lowercase__     ,     lowercase__							)





	def    lowerCamelCase(self							):
		A_		:						Optional[int]	    =   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(lowercase__     ,     lowercase__     ,     lowercase__							)





	def    lowerCamelCase(self							):
		A_		:						int	    =   self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes:
			with self.subTest(model_class.__name__							):
				A_		:						Dict	    =   self._prepare_for_class(lowercase__     ,     lowercase__							)
				A_		:						List[str]	    =   model_class(lowercase__							)

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

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

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

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





	def    lowerCamelCase(self							):
		A_		:						Dict	    =   self.model_tester.prepare_config_and_inputs_for_common()

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

				A_		:						List[Any]	    =   {
				    '''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(lowerCAmelCase_     ,     lowerCAmelCase_     ,     lowerCAmelCase_							):
					return model.decode(
					    decoder_input_ids=lowercase__     ,     decoder_attention_mask=lowercase__     ,     encoder_outputs=lowercase__     ,     )

				with self.subTest("""JIT Enabled"""							):
					A_		:						List[str]	    =   decode_jitted(**lowercase__							).to_tuple()

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

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





	@slow
	def    lowerCamelCase(self							):





		for model_class_name in self.all_model_classes:
			A_		:						Dict	    =   model_class_name.from_pretrained("""facebook/blenderbot-400M-distill"""							)
			# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
			A_		:						List[Any]	    =   np.ones((1, 1)							) * model.config.eos_token_id
			A_		:						Union[str, Any]	    =   model(lowercase__							)
			self.assertIsNotNone(lowercase__							)





	@unittest.skipUnless(jax_device != """cpu"""     ,     """3B test too slow on CPU."""							)
	@slow
	def    lowerCamelCase(self							):
		A_		:						str	    =   {'''num_beams''': 1, '''early_stopping''': True, '''min_length''': 15, '''max_length''': 25}
		A_		:						Tuple	    =   {'''skip_special_tokens''': True, '''clean_up_tokenization_spaces''': True}

		A_		:						Optional[Any]	    =   FlaxBlenderbotForConditionalGeneration.from_pretrained("""facebook/blenderbot-3B"""     ,     from_pt=lowercase__							)
		A_		:						int	    =   BlenderbotTokenizer.from_pretrained("""facebook/blenderbot-3B"""							)

		A_		:						Any	    =   ['''Sam''']
		A_		:						List[str]	    =   tokenizer(lowercase__     ,     return_tensors="""jax"""							)

		A_		:						Union[str, Any]	    =   model.generate(**lowercase__     ,     **lowercase__							)
		A_		:						Tuple	    =   '''Sam is a great name. It means "sun" in Gaelic.'''

		A_		:						Dict	    =   tokenizer.batch_decode(lowercase__     ,     **lowercase__							)
		assert generated_txt[0].strip() == tgt_text









"""simple docstring"""
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


_lowerCAmelCase         =							get_logger(__name__)






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

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






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__=0					):
	accelerator.wait_for_everyone()
	with FSDP.state_dict_type(
	    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(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
			A_		:						Tuple	    =   F"""{MODEL_NAME}.bin""" if model_index == 0 else F"""{MODEL_NAME}_{model_index}.bin"""
			A_		:						Any	    =   os.path.join(snake_case__   ,   snake_case__					)
			logger.info(F"""Loading model from {input_model_file}"""					)
			A_		:						List[Any]	    =   torch.load(snake_case__					)
			logger.info(F"""Model loaded from {input_model_file}"""					)
		elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
			A_		:						List[str]	    =   (
			    F"""{MODEL_NAME}_rank{accelerator.process_index}.bin"""
			    if model_index == 0
			    else F"""{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin"""
			)
			A_		:						Any	    =   os.path.join(snake_case__   ,   snake_case__					)
			logger.info(F"""Loading model from {input_model_file}"""					)
			A_		:						List[str]	    =   torch.load(snake_case__					)
			logger.info(F"""Model loaded from {input_model_file}"""					)
		elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
			A_		:						Optional[Any]	    =   (
			    os.path.join(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}"""					)
			A_		:						List[str]	    =   {"""model""": model.state_dict()}
			dist_cp.load_state_dict(
			    state_dict=snake_case__   ,   storage_reader=dist_cp.FileSystemReader(snake_case__					)   ,   planner=DefaultLoadPlanner()   ,   )
			A_		:						Tuple	    =   state_dict["""model"""]
			logger.info(F"""Model loaded from {ckpt_dir}"""					)
		model.load_state_dict(snake_case__					)






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__=0					):
	os.makedirs(snake_case__   ,   exist_ok=snake_case__					)
	with FSDP.state_dict_type(
	    snake_case__   ,   fsdp_plugin.state_dict_type   ,   fsdp_plugin.state_dict_config   ,   fsdp_plugin.optim_state_dict_config					):
		A_		:						Optional[Any]	    =   FSDP.optim_state_dict(snake_case__   ,   snake_case__					)
		if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
			if accelerator.process_index == 0:
				A_		:						Any	    =   (
				    F"""{OPTIMIZER_NAME}.bin""" if optimizer_index == 0 else F"""{OPTIMIZER_NAME}_{optimizer_index}.bin"""
				)
				A_		:						Optional[int]	    =   os.path.join(snake_case__   ,   snake_case__					)
				logger.info(F"""Saving Optimizer state to {output_optimizer_file}"""					)
				torch.save(snake_case__   ,   snake_case__					)
				logger.info(F"""Optimizer state saved in {output_optimizer_file}"""					)
		else:
			A_		:						Union[str, Any]	    =   os.path.join(snake_case__   ,   F"""{OPTIMIZER_NAME}_{optimizer_index}"""					)
			os.makedirs(snake_case__   ,   exist_ok=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(snake_case__					)   ,   planner=DefaultSavePlanner()   ,   )
			logger.info(F"""Optimizer state saved in {ckpt_dir}"""					)






def 			__UpperCamelCase	(		snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__   ,   snake_case__=0					):
	accelerator.wait_for_everyone()
	with FSDP.state_dict_type(
	    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:
			A_		:						List[str]	    =   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:
			A_		:						Tuple	    =   (
			    F"""{OPTIMIZER_NAME}.bin""" if optimizer_index == 0 else F"""{OPTIMIZER_NAME}_{optimizer_index}.bin"""
			)
			A_		:						int	    =   os.path.join(snake_case__   ,   snake_case__					)
			logger.info(F"""Loading Optimizer state from {input_optimizer_file}"""					)
			A_		:						Tuple	    =   torch.load(snake_case__					)
			logger.info(F"""Optimizer state loaded from {input_optimizer_file}"""					)
		else:
			A_		:						str	    =   (
			    os.path.join(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}"""					)
			A_		:						Union[str, Any]	    =   load_sharded_optimizer_state_dict(
			    model_state_dict=model.state_dict()   ,   optimizer_key="""optimizer"""   ,   storage_reader=dist_cp.FileSystemReader(snake_case__					)   ,   )
			A_		:						Tuple	    =   optim_state["""optimizer"""]
			logger.info(F"""Optimizer loaded from {ckpt_dir}"""					)
		A_		:						List[Any]	    =   FSDP.optim_state_dict_to_load(snake_case__   ,   snake_case__   ,   snake_case__					)
		optimizer.load_state_dict(snake_case__					)

