





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(4_2)

A   : str			=				'''bert-base-cased'''
A   : Union[str, Any]			=				'''fp16'''
A   : Dict			=				'''bf16'''
A   : Dict			=				[FPaa, BFaa]


@require_fsdp
@require_cuda
class   A      (SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''




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







					"""simple docstring"""






					super().setUp()

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






			def        a_	(      self       :   Dict       )     ->     List[str]:







					"""simple docstring"""






					from torch.distributed.fsdp.fully_sharded_data_parallel import ShardingStrategy

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






			def        a_	(      self       :   Dict       )     ->     List[Any]:







					"""simple docstring"""






					from torch.distributed.fsdp.fully_sharded_data_parallel import BackwardPrefetch

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






			def        a_	(      self       :   Optional[Any]       )     ->     List[Any]:







					"""simple docstring"""






					from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType

					for i, state_dict_type in enumerate(__lowerCAmelCase       ):
							A__  				=       self.dist_env.copy()
							A__  				=       state_dict_type
							with mockenv_context(**__lowerCAmelCase       ):
									A__  				=       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        a_	(      self       :   List[Any]       )     ->     str:







					"""simple docstring"""






					A__  				=       AutoModel.from_pretrained(__lowerCAmelCase       )
					for policy in FSDP_AUTO_WRAP_POLICY:
							A__  				=       self.dist_env.copy()
							A__  				=       policy
							if policy == "TRANSFORMER_BASED_WRAP":
									A__  				=       """BertLayer"""
							elif policy == "SIZE_BASED_WRAP":
									A__  				=       """2000"""
							with mockenv_context(**__lowerCAmelCase       ):
									A__  				=       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       )

					A__  				=       self.dist_env.copy()
					A__  				=       """TRANSFORMER_BASED_WRAP"""
					A__  				=       """T5Layer"""
					with mockenv_context(**__lowerCAmelCase       ):
							A__  				=       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       )       )

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






			def        a_	(      self       :   List[Any]       )     ->     str:







					"""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:
							A__  				=       self.dist_env.copy()
							A__  				=       mp_dtype
							with mockenv_context(**__lowerCAmelCase       ):
									A__  				=       Accelerator()
									if mp_dtype == "fp16":
											A__  				=       torch.floataa
									elif mp_dtype == "bf16":
											A__  				=       torch.bfloataa
									A__  				=       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        a_	(      self       :   Any       )     ->     Union[str, Any]:







					"""simple docstring"""






					from torch.distributed.fsdp.fully_sharded_data_parallel import CPUOffload

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


@require_fsdp
@require_multi_gpu
@slow
class   A      (SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''




			def        a_	(      self       :   Tuple       )     ->     str:







					"""simple docstring"""






					super().setUp()
					A__  				=       0.8_2
					A__  				=       [
					    """fsdp_shard_grad_op_transformer_based_wrap""",
					    """fsdp_full_shard_transformer_based_wrap""",
					]
					A__  				=       {
					    """multi_gpu_fp16""": 32_00,
					    """fsdp_shard_grad_op_transformer_based_wrap_fp16""": 20_00,
					    """fsdp_full_shard_transformer_based_wrap_fp16""": 19_00,
					    # 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
					}
					A__  				=       1_60
					A__  				=       1_60

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






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







					"""simple docstring"""






					A__  				=       os.path.join(self.test_scripts_folder							,     """test_performance.py"""       )
					A__  				=       ["""accelerate""", """launch""", """--num_processes=2""", """--num_machines=1""", """--machine_rank=0""", """--use_fsdp"""]
					for config in self.performance_configs:
							A__  				=       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        a_	(      self       :   Dict       )     ->     Optional[int]:







					"""simple docstring"""






					A__  				=       os.path.join(self.test_scripts_folder							,     """test_checkpointing.py"""       )
					A__  				=       [
					    """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       ):
							A__  				=       cmd.copy()
							cmd_config.append(f'--fsdp_sharding_strategy={i+1}'       )
							if strategy != "FULL_SHARD":
									continue
							A__  				=       len(__lowerCAmelCase       )
							for state_dict_type in FSDP_STATE_DICT_TYPE:
									A__  				=       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()       )

									A__  				=       cmd_config[:-1]
									A__  				=       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        a_	(      self       :   Union[str, Any]       )     ->     Optional[int]:







					"""simple docstring"""






					A__  				=       os.path.join(self.test_scripts_folder							,     """test_peak_memory_usage.py"""       )
					A__  				=       [
					    """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():
							A__  				=       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()       )










def        __lowerCamelCase (							__a       :int = 3   ,					__a       :int = 7   ,					__a       :int = 1_0_0_0_0_0_0   )		->    int:






		"""simple docstring"""

		A__  				=       0
		A__  				=       1

		for current_denominator in range(1   ,					limit + 1   ):
				A__  				=       current_denominator * numerator // denominator
				if current_denominator % denominator == 0:
						current_numerator -= 1
				if current_numerator * max_denominator > current_denominator * max_numerator:
						A__  				=       current_numerator
						A__  				=       current_denominator
		return max_numerator


if __name__ == "__main__":
					print(solution(numerator=3, denominator=7, limit=1_0_0_0_0_0_0))



