


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)

_snake_case  :	Tuple			      =       "bert-base-cased"
_snake_case  :	List[str]			      =       "fp16"
_snake_case  :	Optional[Any]			      =       "bf16"
_snake_case  :	Dict			      =       [FPaa, BFaa]

@require_fsdp
@require_cuda
class        a	(_lowerCAmelCase   ):




							"""simple docstring"""


							def 					__snake_case      (							self :						List[str]       )			->					str:
														super().setUp()

														__snake_case	:    List[str]											=     dict(
														    ACCELERATE_USE_FSDP="true"					,			MASTER_ADDR="localhost"					,			MASTER_PORT="10999"					,			RANK="0"					,			LOCAL_RANK="0"					,			WORLD_SIZE="1"					,			)

							def 					__snake_case      (							self :						Any       )			->					List[str]:
														from torch.distributed.fsdp.fully_sharded_data_parallel import ShardingStrategy

														for i, strategy in enumerate(lowerCamelCase       ):
																					__snake_case	:    int											=     self.dist_env.copy()
																					__snake_case	:    Tuple											=     F'{i + 1}'
																					__snake_case	:    Tuple											=     strategy
																					with mockenv_context(**lowerCamelCase       ):
																												__snake_case	:    int											=     FullyShardedDataParallelPlugin()
																												self.assertEqual(fsdp_plugin.sharding_strategy					,			ShardingStrategy(i + 1       )       )

							def 					__snake_case      (							self :						List[Any]       )			->					Tuple:
														from torch.distributed.fsdp.fully_sharded_data_parallel import BackwardPrefetch

														for i, prefetch_policy in enumerate(lowerCamelCase       ):
																					__snake_case	:    Dict											=     self.dist_env.copy()
																					__snake_case	:    int											=     prefetch_policy
																					with mockenv_context(**lowerCamelCase       ):
																												__snake_case	:    Union[str, Any]											=     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 :						Dict       )			->					Optional[int]:
														from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType

														for i, state_dict_type in enumerate(lowerCamelCase       ):
																					__snake_case	:    List[str]											=     self.dist_env.copy()
																					__snake_case	:    Optional[int]											=     state_dict_type
																					with mockenv_context(**lowerCamelCase       ):
																												__snake_case	:    Optional[int]											=     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 :						Optional[int]       )			->					str:
														__snake_case	:    List[str]											=     AutoModel.from_pretrained(lowerCamelCase       )
														for policy in FSDP_AUTO_WRAP_POLICY:
																					__snake_case	:    Tuple											=     self.dist_env.copy()
																					__snake_case	:    Optional[Any]											=     policy
																					if policy == "TRANSFORMER_BASED_WRAP":
																												__snake_case	:    Dict											=     "BertLayer"
																					elif policy == "SIZE_BASED_WRAP":
																												__snake_case	:    Any											=     "2000"
																					with mockenv_context(**lowerCamelCase       ):
																												__snake_case	:    Tuple											=     FullyShardedDataParallelPlugin()
																												fsdp_plugin.set_auto_wrap_policy(lowerCamelCase       )
																												if policy == "NO_WRAP":
																																			self.assertIsNone(fsdp_plugin.auto_wrap_policy       )
																												else:
																																			self.assertIsNotNone(fsdp_plugin.auto_wrap_policy       )

														__snake_case	:    Any											=     self.dist_env.copy()
														__snake_case	:    Union[str, Any]											=     "TRANSFORMER_BASED_WRAP"
														__snake_case	:    List[Any]											=     "T5Layer"
														with mockenv_context(**lowerCamelCase       ):
																					__snake_case	:    Optional[int]											=     FullyShardedDataParallelPlugin()
																					with self.assertRaises(lowerCamelCase       ) as cm:
																												fsdp_plugin.set_auto_wrap_policy(lowerCamelCase       )
																					self.assertTrue("Could not find the transformer layer class to wrap in the model." in str(cm.exception       )       )

														__snake_case	:    Optional[Any]											=     self.dist_env.copy()
														__snake_case	:    Union[str, Any]											=     "SIZE_BASED_WRAP"
														__snake_case	:    Dict											=     "0"
														with mockenv_context(**lowerCamelCase       ):
																					__snake_case	:    str											=     FullyShardedDataParallelPlugin()
																					fsdp_plugin.set_auto_wrap_policy(lowerCamelCase       )
																					self.assertIsNone(fsdp_plugin.auto_wrap_policy       )

							def 					__snake_case      (							self :						Dict       )			->					Optional[int]:
														from torch.distributed.fsdp.fully_sharded_data_parallel import MixedPrecision
														from torch.distributed.fsdp.sharded_grad_scaler import ShardedGradScaler

														for mp_dtype in dtypes:
																					__snake_case	:    List[Any]											=     self.dist_env.copy()
																					__snake_case	:    Any											=     mp_dtype
																					with mockenv_context(**lowerCamelCase       ):
																												__snake_case	:    Optional[Any]											=     Accelerator()
																												if mp_dtype == "fp16":
																																			__snake_case	:    List[str]											=     torch.floataa
																												elif mp_dtype == "bf16":
																																			__snake_case	:    int											=     torch.bfloataa
																												__snake_case	:    Optional[int]											=     MixedPrecision(param_dtype=lowerCamelCase					,			reduce_dtype=lowerCamelCase					,			buffer_dtype=lowerCamelCase       )
																												self.assertEqual(accelerator.state.fsdp_plugin.mixed_precision_policy					,			lowerCamelCase       )
																												if mp_dtype == FPaa:
																																			self.assertTrue(isinstance(accelerator.scaler					,			lowerCamelCase       )       )
																												elif mp_dtype == BFaa:
																																			self.assertIsNone(accelerator.scaler       )
																												AcceleratorState._reset_state(lowerCamelCase       )






							def 					__snake_case      (							self :						Optional[int]       )			->					Optional[int]:
														from torch.distributed.fsdp.fully_sharded_data_parallel import CPUOffload

														for flag in [True, False]:
																					__snake_case	:    Optional[Any]											=     self.dist_env.copy()
																					__snake_case	:    Tuple											=     str(lowerCamelCase       ).lower()
																					with mockenv_context(**lowerCamelCase       ):
																												__snake_case	:    Dict											=     FullyShardedDataParallelPlugin()
																												self.assertEqual(fsdp_plugin.cpu_offload					,			CPUOffload(offload_params=lowerCamelCase       )       )

@require_fsdp
@require_multi_gpu
@slow
class        a	(_lowerCAmelCase   ):




							"""simple docstring"""


							def 					__snake_case      (							self :						Dict       )			->					List[str]:
														super().setUp()
														__snake_case	:    Any											=     0.82
														__snake_case	:    Dict											=     [
														    "fsdp_shard_grad_op_transformer_based_wrap",
														    "fsdp_full_shard_transformer_based_wrap",
														]
														__snake_case	:    int											=     {
														    "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
														}
														__snake_case	:    Optional[Any]											=     160
														__snake_case	:    int											=     160

														__snake_case	:    Optional[int]											=     inspect.getfile(accelerate.test_utils       )
														__snake_case	:    Union[str, Any]											=     os.path.sep.join(mod_file.split(os.path.sep       )[:-1] + ["scripts", "external_deps"]       )

							def 					__snake_case      (							self :						Tuple       )			->					Dict:
														__snake_case	:    str											=     os.path.join(self.test_scripts_folder					,			"test_performance.py"       )
														__snake_case	:    Optional[int]											=     ["accelerate", "launch", "--num_processes=2", "--num_machines=1", "--machine_rank=0", "--use_fsdp"]
														for config in self.performance_configs:
																					__snake_case	:    Dict											=     cmd.copy()
																					for i, strategy in enumerate(lowerCamelCase       ):
																												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(lowerCamelCase					,			env=os.environ.copy()       )

							def 					__snake_case      (							self :						Dict       )			->					List[Any]:
														__snake_case	:    Union[str, Any]											=     os.path.join(self.test_scripts_folder					,			"test_checkpointing.py"       )
														__snake_case	:    Tuple											=     [
														    "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(lowerCamelCase       ):
																					__snake_case	:    Optional[Any]											=     cmd.copy()
																					cmd_config.append(F'--fsdp_sharding_strategy={i+1}'       )
																					if strategy != "FULL_SHARD":
																												continue
																					__snake_case	:    Tuple											=     len(lowerCamelCase       )
																					for state_dict_type in FSDP_STATE_DICT_TYPE:
																												__snake_case	:    Union[str, Any]											=     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(lowerCamelCase					,			env=os.environ.copy()       )

																												__snake_case	:    Dict											=     cmd_config[:-1]
																												__snake_case	:    Optional[int]											=     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(lowerCamelCase					,			env=os.environ.copy()       )






							def 					__snake_case      (							self :						Any       )			->					Union[str, Any]:
														__snake_case	:    str											=     os.path.join(self.test_scripts_folder					,			"test_peak_memory_usage.py"       )
														__snake_case	:    List[Any]											=     [
														    "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():
																					__snake_case	:    str											=     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(lowerCamelCase       ):
																																			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(lowerCamelCase					,			env=os.environ.copy()       )





import math







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase			):

							if initial_intensity < 0:
														raise ValueError("The value of intensity cannot be negative"			)
														# handling of negative values of initial intensity
							if angle < 0 or angle > 3_6_0:
														raise ValueError("In Malus Law, the angle is in the range 0-360 degrees"			)
														# handling of values out of allowed range
							return initial_intensity * (math.cos(math.radians(__lowerCamelCase			)			) ** 2)


if __name__ == "__main__":
			import doctest

			doctest.testmod(name="malus_law")

