





import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow

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


if is_flax_available():
	import jax
	import jax.numpy as jnp

	from transformers.modeling_flax_pytorch_utils import (
	    convert_pytorch_state_dict_to_flax,
	    load_flax_weights_in_pytorch_model,
	)
	from transformers.models.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel

if is_torch_available():
	import torch




class 	UpperCamelCase_							:


							def __init__(				self     :List[Any]				,					__A     :Dict				,					__A     :Tuple=14				,					__A     :Optional[int]=7				,					__A     :Any=True				,					__A     :Tuple=True				,					__A     :List[str]=False				,					__A     :int=True				,					__A     :Optional[Any]=99				,					__A     :Any=32				,					__A     :Optional[int]=4				,					__A     :Optional[Any]=4				,					__A     :Union[str, Any]=4				,					__A     :Tuple=37				,					__A     :Tuple="gelu"				,					__A     :Union[str, Any]=0.1				,					__A     :List[str]=0.1				,					__A     :Optional[int]=512				,					__A     :str=0.0_2				,					) -> Union[str, Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   parent
									SCREAMING_SNAKE_CASE__								=   batch_size
									SCREAMING_SNAKE_CASE__								=   seq_length
									SCREAMING_SNAKE_CASE__								=   is_training
									SCREAMING_SNAKE_CASE__								=   use_input_mask
									SCREAMING_SNAKE_CASE__								=   use_token_type_ids
									SCREAMING_SNAKE_CASE__								=   use_labels
									SCREAMING_SNAKE_CASE__								=   vocab_size
									SCREAMING_SNAKE_CASE__								=   hidden_size
									SCREAMING_SNAKE_CASE__								=   rotary_dim
									SCREAMING_SNAKE_CASE__								=   num_hidden_layers
									SCREAMING_SNAKE_CASE__								=   num_attention_heads
									SCREAMING_SNAKE_CASE__								=   intermediate_size
									SCREAMING_SNAKE_CASE__								=   hidden_act
									SCREAMING_SNAKE_CASE__								=   hidden_dropout_prob
									SCREAMING_SNAKE_CASE__								=   attention_probs_dropout_prob
									SCREAMING_SNAKE_CASE__								=   max_position_embeddings
									SCREAMING_SNAKE_CASE__								=   initializer_range
									SCREAMING_SNAKE_CASE__								=   None
									SCREAMING_SNAKE_CASE__								=   vocab_size - 1
									SCREAMING_SNAKE_CASE__								=   vocab_size - 1
									SCREAMING_SNAKE_CASE__								=   vocab_size - 1


							def   _snake_case	(				self     :str		) -> Any:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   ids_tensor([self.batch_size, self.seq_length]				,					self.vocab_size		)

									SCREAMING_SNAKE_CASE__								=   None
									if self.use_input_mask:
											SCREAMING_SNAKE_CASE__								=   random_attention_mask([self.batch_size, self.seq_length]		)

									SCREAMING_SNAKE_CASE__								=   GPTJConfig(
									    vocab_size=self.vocab_size				,					n_embd=self.hidden_size				,					n_layer=self.num_hidden_layers				,					n_head=self.num_attention_heads				,					n_positions=self.max_position_embeddings				,					use_cache=__A				,					bos_token_id=self.bos_token_id				,					eos_token_id=self.eos_token_id				,					pad_token_id=self.pad_token_id				,					rotary_dim=self.rotary_dim				,					)

									return (config, input_ids, input_mask)


							def   _snake_case	(				self     :int		) -> Dict:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   self.prepare_config_and_inputs()
									SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   config_and_inputs
									SCREAMING_SNAKE_CASE__								=   {"""input_ids""": input_ids, """attention_mask""": attention_mask}
									return config, inputs_dict


							def   _snake_case	(				self     :int				,					__A     :List[Any]				,					__A     :int				,					__A     :Optional[Any]				,					__A     :int		) -> Tuple:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   20
									SCREAMING_SNAKE_CASE__								=   model_class_name(__A		)

									SCREAMING_SNAKE_CASE__								=   model.init_cache(input_ids.shape[0]				,					__A		)
									SCREAMING_SNAKE_CASE__								=   jnp.ones((input_ids.shape[0], max_decoder_length)				,					dtype="""i4"""		)

									SCREAMING_SNAKE_CASE__								=   jnp.broadcast_to(
									    jnp.arange(input_ids.shape[-1] - 1		)[None, :]				,					(input_ids.shape[0], input_ids.shape[-1] - 1)		)
									SCREAMING_SNAKE_CASE__								=   model(
									    input_ids[:, :-1]				,					attention_mask=__A				,					past_key_values=__A				,					position_ids=__A				,					)

									SCREAMING_SNAKE_CASE__								=   jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]				,					dtype="""i4"""		)
									SCREAMING_SNAKE_CASE__								=   model(
									    input_ids[:, -1:]				,					attention_mask=__A				,					past_key_values=outputs_cache.past_key_values				,					position_ids=__A				,					)

									SCREAMING_SNAKE_CASE__								=   model(__A		)

									SCREAMING_SNAKE_CASE__								=   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   _snake_case	(				self     :Any				,					__A     :str				,					__A     :int				,					__A     :Any				,					__A     :Any		) -> Optional[int]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   20
									SCREAMING_SNAKE_CASE__								=   model_class_name(__A		)

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

									SCREAMING_SNAKE_CASE__								=   model.init_cache(input_ids.shape[0]				,					__A		)
									SCREAMING_SNAKE_CASE__								=   jnp.broadcast_to(
									    jnp.arange(input_ids.shape[-1] - 1		)[None, :]				,					(input_ids.shape[0], input_ids.shape[-1] - 1)		)

									SCREAMING_SNAKE_CASE__								=   model(
									    input_ids[:, :-1]				,					attention_mask=__A				,					past_key_values=__A				,					position_ids=__A				,					)
									SCREAMING_SNAKE_CASE__								=   jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]				,					dtype="""i4"""		)
									SCREAMING_SNAKE_CASE__								=   model(
									    input_ids[:, -1:]				,					past_key_values=outputs_cache.past_key_values				,					attention_mask=__A				,					position_ids=__A				,					)

									SCREAMING_SNAKE_CASE__								=   model(__A				,					attention_mask=__A		)

									SCREAMING_SNAKE_CASE__								=   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 	UpperCamelCase_							(  UpperCamelCase__					,	UpperCamelCase__					,	unittest.TestCase     ):
							lowerCamelCase_							     =       (FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
							lowerCamelCase_							     =       (FlaxGPTJForCausalLM,) if is_flax_available() else ()


							def   _snake_case	(				self     :str		) -> Tuple:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   FlaxGPTJModelTester(self		)


							def   _snake_case	(				self     :int		) -> Optional[Any]:



									"""simple docstring"""



									for model_class_name in self.all_model_classes:
											SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   self.model_tester.prepare_config_and_inputs()
											self.model_tester.check_use_cache_forward(__A				,					__A				,					__A				,					__A		)


							def   _snake_case	(				self     :int		) -> Optional[int]:



									"""simple docstring"""



									for model_class_name in self.all_model_classes:
											SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   self.model_tester.prepare_config_and_inputs()
											self.model_tester.check_use_cache_forward_with_attn_mask(
											    __A				,					__A				,					__A				,					__A		)


							@tooslow
							def   _snake_case	(				self     :str		) -> Optional[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   GPTaTokenizer.from_pretrained("""gpt2"""				,					pad_token="""<|endoftext|>"""				,					padding_side="""left"""		)
									SCREAMING_SNAKE_CASE__								=   tokenizer(["""Hello this is a long string""", """Hey"""]				,					return_tensors="""np"""				,					padding=__A				,					truncation=__A		)

									SCREAMING_SNAKE_CASE__								=   FlaxGPTJForCausalLM.from_pretrained("""EleutherAI/gpt-j-6B"""		)
									SCREAMING_SNAKE_CASE__								=   False
									SCREAMING_SNAKE_CASE__								=   model.config.eos_token_id

									SCREAMING_SNAKE_CASE__								=   jax.jit(model.generate		)

									SCREAMING_SNAKE_CASE__								=   jit_generate(
									    inputs["""input_ids"""]				,					attention_mask=inputs["""attention_mask"""]				,					pad_token_id=tokenizer.pad_token_id		).sequences

									SCREAMING_SNAKE_CASE__								=   tokenizer.batch_decode(__A				,					skip_special_tokens=__A		)

									SCREAMING_SNAKE_CASE__								=   [
									    """Hello this is a long string of text.\n\nI'm trying to get the text of the""",
									    """Hey, I'm a little late to the party. I'm going to""",
									]

									self.assertListEqual(__A				,					__A		)


							@is_pt_flax_cross_test
							def   _snake_case	(				self     :Optional[int]		) -> List[str]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											with self.subTest(model_class.__name__		):
													# prepare inputs
													SCREAMING_SNAKE_CASE__								=   self._prepare_for_class(__A				,					__A		)
													SCREAMING_SNAKE_CASE__								=   {k: torch.tensor(v.tolist()		) for k, v in prepared_inputs_dict.items()}

													# load corresponding PyTorch class
													SCREAMING_SNAKE_CASE__								=   model_class.__name__[4:]  # Skip the "Flax" at the beginning
													SCREAMING_SNAKE_CASE__								=   getattr(__A				,					__A		)

													SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   pt_inputs["""input_ids"""].shape
													SCREAMING_SNAKE_CASE__								=   np.random.randint(0				,					seq_length - 1				,					size=(batch_size,)		)
													for batch_idx, start_index in enumerate(__A		):
															SCREAMING_SNAKE_CASE__								=   0
															SCREAMING_SNAKE_CASE__								=   1
															SCREAMING_SNAKE_CASE__								=   0
															SCREAMING_SNAKE_CASE__								=   1
													SCREAMING_SNAKE_CASE__								=   pt_model_class(__A		).eval()
													SCREAMING_SNAKE_CASE__								=   model_class(__A				,					dtype=jnp.floataa		)

													SCREAMING_SNAKE_CASE__								=   convert_pytorch_state_dict_to_flax(pt_model.state_dict()				,					__A		)
													SCREAMING_SNAKE_CASE__								=   fx_state

													with torch.no_grad():
															SCREAMING_SNAKE_CASE__								=   pt_model(**__A		).to_tuple()

													SCREAMING_SNAKE_CASE__								=   fx_model(**__A		).to_tuple()
													self.assertEqual(len(__A		)				,					len(__A		)				,					"""Output lengths differ between Flax and PyTorch"""		)
													for fx_output, pt_output in zip(__A				,					__A		):
															self.assert_almost_equals(fx_output[:, -1]				,					pt_output[:, -1].numpy()				,					4E-2		)

													with tempfile.TemporaryDirectory() as tmpdirname:
															pt_model.save_pretrained(__A		)
															SCREAMING_SNAKE_CASE__								=   model_class.from_pretrained(__A				,					from_pt=__A		)

													SCREAMING_SNAKE_CASE__								=   fx_model_loaded(**__A		).to_tuple()
													self.assertEqual(
													    len(__A		)				,					len(__A		)				,					"""Output lengths differ between Flax and PyTorch"""		)
													for fx_output_loaded, pt_output in zip(__A				,					__A		):
															self.assert_almost_equals(fx_output_loaded[:, -1]				,					pt_output[:, -1].numpy()				,					4E-2		)


							@is_pt_flax_cross_test
							def   _snake_case	(				self     :Tuple		) -> Optional[int]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   self.model_tester.prepare_config_and_inputs_for_common()
									for model_class in self.all_model_classes:
											with self.subTest(model_class.__name__		):
													# prepare inputs
													SCREAMING_SNAKE_CASE__								=   self._prepare_for_class(__A				,					__A		)
													SCREAMING_SNAKE_CASE__								=   {k: torch.tensor(v.tolist()		) for k, v in prepared_inputs_dict.items()}

													# load corresponding PyTorch class
													SCREAMING_SNAKE_CASE__								=   model_class.__name__[4:]  # Skip the "Flax" at the beginning
													SCREAMING_SNAKE_CASE__								=   getattr(__A				,					__A		)

													SCREAMING_SNAKE_CASE__								=   pt_model_class(__A		).eval()
													SCREAMING_SNAKE_CASE__								=   model_class(__A				,					dtype=jnp.floataa		)

													SCREAMING_SNAKE_CASE__								=   load_flax_weights_in_pytorch_model(__A				,					fx_model.params		)
													SCREAMING_SNAKE_CASE__						,    SCREAMING_SNAKE_CASE__								=   pt_inputs["""input_ids"""].shape
													SCREAMING_SNAKE_CASE__								=   np.random.randint(0				,					seq_length - 1				,					size=(batch_size,)		)
													for batch_idx, start_index in enumerate(__A		):
															SCREAMING_SNAKE_CASE__								=   0
															SCREAMING_SNAKE_CASE__								=   1
															SCREAMING_SNAKE_CASE__								=   0
															SCREAMING_SNAKE_CASE__								=   1

													# make sure weights are tied in PyTorch
													pt_model.tie_weights()

													with torch.no_grad():
															SCREAMING_SNAKE_CASE__								=   pt_model(**__A		).to_tuple()

													SCREAMING_SNAKE_CASE__								=   fx_model(**__A		).to_tuple()
													self.assertEqual(len(__A		)				,					len(__A		)				,					"""Output lengths differ between Flax and PyTorch"""		)
													for fx_output, pt_output in zip(__A				,					__A		):
															self.assert_almost_equals(fx_output[:, -1]				,					pt_output[:, -1].numpy()				,					4E-2		)

													with tempfile.TemporaryDirectory() as tmpdirname:
															fx_model.save_pretrained(__A		)
															SCREAMING_SNAKE_CASE__								=   pt_model_class.from_pretrained(__A				,					from_flax=__A		)

													with torch.no_grad():
															SCREAMING_SNAKE_CASE__								=   pt_model_loaded(**__A		).to_tuple()

													self.assertEqual(
													    len(__A		)				,					len(__A		)				,					"""Output lengths differ between Flax and PyTorch"""		)
													for fx_output, pt_output in zip(__A				,					__A		):
															self.assert_almost_equals(fx_output[:, -1]				,					pt_output[:, -1].numpy()				,					4E-2		)


							@tooslow
							def   _snake_case	(				self     :Optional[Any]		) -> str:



									"""simple docstring"""



									for model_class_name in self.all_model_classes:
											SCREAMING_SNAKE_CASE__								=   model_class_name.from_pretrained("""EleutherAI/gpt-j-6B"""		)
											SCREAMING_SNAKE_CASE__								=   model(np.ones((1, 1)		)		)
											self.assertIsNotNone(__A		)






import inspect
import os

import torch
from transformers import AutoModel
from transformers.testing_utils import mockenv_context
from transformers.trainer_utils import set_seed

import accelerate
from accelerate.accelerator import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils.testing import (
    AccelerateTestCase,
    TempDirTestCase,
    execute_subprocess_async,
    require_cuda,
    require_fsdp,
    require_multi_gpu,
    slow,
)
from accelerate.utils.constants import (
    FSDP_AUTO_WRAP_POLICY,
    FSDP_BACKWARD_PREFETCH,
    FSDP_SHARDING_STRATEGY,
    FSDP_STATE_DICT_TYPE,
)
from accelerate.utils.dataclasses import FullyShardedDataParallelPlugin
from accelerate.utils.other import patch_environment


set_seed(42)

_lowerCamelCase 					=      'bert-base-cased'
_lowerCamelCase 					=      'fp16'
_lowerCamelCase 					=      'bf16'
_lowerCamelCase 					=      [FPaa, BFaa]




@require_fsdp
@require_cuda
class 	UpperCamelCase_							(  UpperCamelCase__     ):


							def   _snake_case	(				self     :Optional[Any]		) -> Optional[Any]:



									"""simple docstring"""



									super().setUp()

									SCREAMING_SNAKE_CASE__								=   dict(
									    ACCELERATE_USE_FSDP="""true"""				,					MASTER_ADDR="""localhost"""				,					MASTER_PORT="""10999"""				,					RANK="""0"""				,					LOCAL_RANK="""0"""				,					WORLD_SIZE="""1"""				,					)


							def   _snake_case	(				self     :List[Any]		) -> Tuple:



									"""simple docstring"""



									from torch.distributed.fsdp.fully_sharded_data_parallel import ShardingStrategy

									for i, strategy in enumerate(__A		):
											SCREAMING_SNAKE_CASE__								=   self.dist_env.copy()
											SCREAMING_SNAKE_CASE__								=   f'''{i + 1}'''
											SCREAMING_SNAKE_CASE__								=   strategy
											with mockenv_context(**__A		):
													SCREAMING_SNAKE_CASE__								=   FullyShardedDataParallelPlugin()
													self.assertEqual(fsdp_plugin.sharding_strategy				,					ShardingStrategy(i + 1		)		)


							def   _snake_case	(				self     :int		) -> List[str]:



									"""simple docstring"""



									from torch.distributed.fsdp.fully_sharded_data_parallel import BackwardPrefetch

									for i, prefetch_policy in enumerate(__A		):
											SCREAMING_SNAKE_CASE__								=   self.dist_env.copy()
											SCREAMING_SNAKE_CASE__								=   prefetch_policy
											with mockenv_context(**__A		):
													SCREAMING_SNAKE_CASE__								=   FullyShardedDataParallelPlugin()
													if prefetch_policy == "NO_PREFETCH":
															self.assertIsNone(fsdp_plugin.backward_prefetch		)
													else:
															self.assertEqual(fsdp_plugin.backward_prefetch				,					BackwardPrefetch(i + 1		)		)


							def   _snake_case	(				self     :List[str]		) -> List[str]:



									"""simple docstring"""



									from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType

									for i, state_dict_type in enumerate(__A		):
											SCREAMING_SNAKE_CASE__								=   self.dist_env.copy()
											SCREAMING_SNAKE_CASE__								=   state_dict_type
											with mockenv_context(**__A		):
													SCREAMING_SNAKE_CASE__								=   FullyShardedDataParallelPlugin()
													self.assertEqual(fsdp_plugin.state_dict_type				,					StateDictType(i + 1		)		)
													if state_dict_type == "FULL_STATE_DICT":
															self.assertTrue(fsdp_plugin.state_dict_config.offload_to_cpu		)
															self.assertTrue(fsdp_plugin.state_dict_config.ranka_only		)


							def   _snake_case	(				self     :str		) -> str:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   AutoModel.from_pretrained(__A		)
									for policy in FSDP_AUTO_WRAP_POLICY:
											SCREAMING_SNAKE_CASE__								=   self.dist_env.copy()
											SCREAMING_SNAKE_CASE__								=   policy
											if policy == "TRANSFORMER_BASED_WRAP":
													SCREAMING_SNAKE_CASE__								=   """BertLayer"""
											elif policy == "SIZE_BASED_WRAP":
													SCREAMING_SNAKE_CASE__								=   """2000"""
											with mockenv_context(**__A		):
													SCREAMING_SNAKE_CASE__								=   FullyShardedDataParallelPlugin()
													fsdp_plugin.set_auto_wrap_policy(__A		)
													if policy == "NO_WRAP":
															self.assertIsNone(fsdp_plugin.auto_wrap_policy		)
													else:
															self.assertIsNotNone(fsdp_plugin.auto_wrap_policy		)

									SCREAMING_SNAKE_CASE__								=   self.dist_env.copy()
									SCREAMING_SNAKE_CASE__								=   """TRANSFORMER_BASED_WRAP"""
									SCREAMING_SNAKE_CASE__								=   """T5Layer"""
									with mockenv_context(**__A		):
											SCREAMING_SNAKE_CASE__								=   FullyShardedDataParallelPlugin()
											with self.assertRaises(__A		) as cm:
													fsdp_plugin.set_auto_wrap_policy(__A		)
											self.assertTrue("""Could not find the transformer layer class to wrap in the model.""" in str(cm.exception		)		)

									SCREAMING_SNAKE_CASE__								=   self.dist_env.copy()
									SCREAMING_SNAKE_CASE__								=   """SIZE_BASED_WRAP"""
									SCREAMING_SNAKE_CASE__								=   """0"""
									with mockenv_context(**__A		):
											SCREAMING_SNAKE_CASE__								=   FullyShardedDataParallelPlugin()
											fsdp_plugin.set_auto_wrap_policy(__A		)
											self.assertIsNone(fsdp_plugin.auto_wrap_policy		)


							def   _snake_case	(				self     :Optional[Any]		) -> Optional[int]:



									"""simple docstring"""



									from torch.distributed.fsdp.fully_sharded_data_parallel import MixedPrecision
									from torch.distributed.fsdp.sharded_grad_scaler import ShardedGradScaler

									for mp_dtype in dtypes:
											SCREAMING_SNAKE_CASE__								=   self.dist_env.copy()
											SCREAMING_SNAKE_CASE__								=   mp_dtype
											with mockenv_context(**__A		):
													SCREAMING_SNAKE_CASE__								=   Accelerator()
													if mp_dtype == "fp16":
															SCREAMING_SNAKE_CASE__								=   torch.floataa
													elif mp_dtype == "bf16":
															SCREAMING_SNAKE_CASE__								=   torch.bfloataa
													SCREAMING_SNAKE_CASE__								=   MixedPrecision(param_dtype=__A				,					reduce_dtype=__A				,					buffer_dtype=__A		)
													self.assertEqual(accelerator.state.fsdp_plugin.mixed_precision_policy				,					__A		)
													if mp_dtype == FPaa:
															self.assertTrue(isinstance(accelerator.scaler				,					__A		)		)
													elif mp_dtype == BFaa:
															self.assertIsNone(accelerator.scaler		)
													AcceleratorState._reset_state(__A		)


							def   _snake_case	(				self     :str		) -> str:



									"""simple docstring"""



									from torch.distributed.fsdp.fully_sharded_data_parallel import CPUOffload

									for flag in [True, False]:
											SCREAMING_SNAKE_CASE__								=   self.dist_env.copy()
											SCREAMING_SNAKE_CASE__								=   str(__A		).lower()
											with mockenv_context(**__A		):
													SCREAMING_SNAKE_CASE__								=   FullyShardedDataParallelPlugin()
													self.assertEqual(fsdp_plugin.cpu_offload				,					CPUOffload(offload_params=__A		)		)




@require_fsdp
@require_multi_gpu
@slow
class 	UpperCamelCase_							(  UpperCamelCase__     ):


							def   _snake_case	(				self     :Any		) -> Any:



									"""simple docstring"""



									super().setUp()
									SCREAMING_SNAKE_CASE__								=   0.8_2
									SCREAMING_SNAKE_CASE__								=   [
									    """fsdp_shard_grad_op_transformer_based_wrap""",
									    """fsdp_full_shard_transformer_based_wrap""",
									]
									SCREAMING_SNAKE_CASE__								=   {
									    """multi_gpu_fp16""": 3200,
									    """fsdp_shard_grad_op_transformer_based_wrap_fp16""": 2000,
									    """fsdp_full_shard_transformer_based_wrap_fp16""": 1900,
									    # Disabling below test as it overwhelms the RAM memory usage
									    # on CI self-hosted runner leading to tests getting killed.
									    # "fsdp_full_shard_cpu_offload_transformer_based_wrap_fp32": 1500,  # fp16 was leading to indefinite hang
									}
									SCREAMING_SNAKE_CASE__								=   160
									SCREAMING_SNAKE_CASE__								=   160

									SCREAMING_SNAKE_CASE__								=   inspect.getfile(accelerate.test_utils		)
									SCREAMING_SNAKE_CASE__								=   os.path.sep.join(mod_file.split(os.path.sep		)[:-1] + ["""scripts""", """external_deps"""]		)


							def   _snake_case	(				self     :Union[str, Any]		) -> int:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   os.path.join(self.test_scripts_folder				,					"""test_performance.py"""		)
									SCREAMING_SNAKE_CASE__								=   ["""accelerate""", """launch""", """--num_processes=2""", """--num_machines=1""", """--machine_rank=0""", """--use_fsdp"""]
									for config in self.performance_configs:
											SCREAMING_SNAKE_CASE__								=   cmd.copy()
											for i, strategy in enumerate(__A		):
													if strategy.lower() in config:
															cmd_config.append(f'''--fsdp_sharding_strategy={i+1}'''		)
															break

											if "fp32" in config:
													cmd_config.append("""--mixed_precision=no"""		)
											else:
													cmd_config.append("""--mixed_precision=fp16"""		)

											if "cpu_offload" in config:
													cmd_config.append("""--fsdp_offload_params=True"""		)

											for policy in FSDP_AUTO_WRAP_POLICY:
													if policy.lower() in config:
															cmd_config.append(f'''--fsdp_auto_wrap_policy={policy}'''		)
															break

											if policy == "TRANSFORMER_BASED_WRAP":
													cmd_config.append("""--fsdp_transformer_layer_cls_to_wrap=BertLayer"""		)
											elif policy == "SIZE_BASED_WRAP":
													cmd_config.append("""--fsdp_min_num_params=2000"""		)

											cmd_config.extend(
											    [
											        self.test_file_path,
											        f'''--output_dir={self.tmpdir}''',
											        f'''--performance_lower_bound={self.performance_lower_bound}''',
											    ]		)
											with patch_environment(omp_num_threads=1		):
													execute_subprocess_async(__A				,					env=os.environ.copy()		)


							def   _snake_case	(				self     :Dict		) -> Union[str, Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   os.path.join(self.test_scripts_folder				,					"""test_checkpointing.py"""		)
									SCREAMING_SNAKE_CASE__								=   [
									    """accelerate""",
									    """launch""",
									    """--num_processes=2""",
									    """--num_machines=1""",
									    """--machine_rank=0""",
									    """--use_fsdp""",
									    """--mixed_precision=fp16""",
									    """--fsdp_transformer_layer_cls_to_wrap=BertLayer""",
									]

									for i, strategy in enumerate(__A		):
											SCREAMING_SNAKE_CASE__								=   cmd.copy()
											cmd_config.append(f'''--fsdp_sharding_strategy={i+1}'''		)
											if strategy != "FULL_SHARD":
													continue
											SCREAMING_SNAKE_CASE__								=   len(__A		)
											for state_dict_type in FSDP_STATE_DICT_TYPE:
													SCREAMING_SNAKE_CASE__								=   cmd_config[:state_dict_config_index]
													cmd_config.append(f'''--fsdp_state_dict_type={state_dict_type}'''		)
													cmd_config.extend(
													    [
													        self.test_file_path,
													        f'''--output_dir={self.tmpdir}''',
													        """--partial_train_epoch=1""",
													    ]		)
													with patch_environment(omp_num_threads=1		):
															execute_subprocess_async(__A				,					env=os.environ.copy()		)

													SCREAMING_SNAKE_CASE__								=   cmd_config[:-1]
													SCREAMING_SNAKE_CASE__								=   os.path.join(self.tmpdir				,					"""epoch_0"""		)
													cmd_config.extend(
													    [
													        f'''--resume_from_checkpoint={resume_from_checkpoint}''',
													    ]		)
													with patch_environment(omp_num_threads=1		):
															execute_subprocess_async(__A				,					env=os.environ.copy()		)


							def   _snake_case	(				self     :Tuple		) -> str:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=   os.path.join(self.test_scripts_folder				,					"""test_peak_memory_usage.py"""		)
									SCREAMING_SNAKE_CASE__								=   [
									    """accelerate""",
									    """launch""",
									    """--num_processes=2""",
									    """--num_machines=1""",
									    """--machine_rank=0""",
									]
									for spec, peak_mem_upper_bound in self.peak_memory_usage_upper_bound.items():
											SCREAMING_SNAKE_CASE__								=   cmd.copy()
											if "fp16" in spec:
													cmd_config.extend(["""--mixed_precision=fp16"""]		)
											else:
													cmd_config.extend(["""--mixed_precision=no"""]		)

											if "multi_gpu" in spec:
													continue
											else:
													cmd_config.extend(["""--use_fsdp"""]		)
													for i, strategy in enumerate(__A		):
															if strategy.lower() in spec:
																	cmd_config.append(f'''--fsdp_sharding_strategy={i+1}'''		)
																	break

													if "cpu_offload" in spec:
															cmd_config.append("""--fsdp_offload_params=True"""		)

													for policy in FSDP_AUTO_WRAP_POLICY:
															if policy.lower() in spec:
																	cmd_config.append(f'''--fsdp_auto_wrap_policy={policy}'''		)
																	break

													if policy == "TRANSFORMER_BASED_WRAP":
															cmd_config.append("""--fsdp_transformer_layer_cls_to_wrap=BertLayer"""		)
													elif policy == "SIZE_BASED_WRAP":
															cmd_config.append("""--fsdp_min_num_params=2000"""		)

											cmd_config.extend(
											    [
											        self.test_file_path,
											        f'''--output_dir={self.tmpdir}''',
											        f'''--peak_memory_upper_bound={peak_mem_upper_bound}''',
											        f'''--n_train={self.n_train}''',
											        f'''--n_val={self.n_val}''',
											    ]		)
											with patch_environment(omp_num_threads=1		):
													execute_subprocess_async(__A				,					env=os.environ.copy()		)