

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)

lowerCAmelCase__	   = """bert-base-cased"""
lowerCAmelCase__	   = """fp16"""
lowerCAmelCase__	   = """bf16"""
lowerCAmelCase__	   = [FPaa, BFaa]

@require_fsdp
@require_cuda
class 					a__						(					snake_case			):
			"""simple docstring"""







			def      UpperCamelCase		(    self       )  ->						Tuple:
							'''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      UpperCamelCase		(    self       )  ->						Optional[Any]:
							'''simple docstring'''






							from torch.distributed.fsdp.fully_sharded_data_parallel import ShardingStrategy

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


			def      UpperCamelCase		(    self       )  ->						Optional[int]:
							'''simple docstring'''






							from torch.distributed.fsdp.fully_sharded_data_parallel import BackwardPrefetch

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


			def      UpperCamelCase		(    self       )  ->						List[Any]:
							'''simple docstring'''






							from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType

							for i, state_dict_type in enumerate(lowercase       ):
											A__        =			self.dist_env.copy()
											A__        =			state_dict_type
											with mockenv_context(**lowercase       ):
															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      UpperCamelCase		(    self       )  ->						Union[str, Any]:
							'''simple docstring'''






							A__        =			AutoModel.from_pretrained(lowercase       )
							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(**lowercase       ):
															A__        =			FullyShardedDataParallelPlugin()
															fsdp_plugin.set_auto_wrap_policy(lowercase       )
															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(**lowercase       ):
											A__        =			FullyShardedDataParallelPlugin()
											with self.assertRaises(lowercase       ) as cm:
															fsdp_plugin.set_auto_wrap_policy(lowercase       )
											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(**lowercase       ):
											A__        =			FullyShardedDataParallelPlugin()
											fsdp_plugin.set_auto_wrap_policy(lowercase       )
											self.assertIsNone(fsdp_plugin.auto_wrap_policy       )


			def      UpperCamelCase		(    self       )  ->						Any:
							'''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(**lowercase       ):
															A__        =			Accelerator()
															if mp_dtype == "fp16":
																			A__        =			torch.floataa
															elif mp_dtype == "bf16":
																			A__        =			torch.bfloataa
															A__        =			MixedPrecision(param_dtype=lowercase      ,      reduce_dtype=lowercase      ,      buffer_dtype=lowercase       )
															self.assertEqual(accelerator.state.fsdp_plugin.mixed_precision_policy      ,      lowercase       )
															if mp_dtype == FPaa:
																			self.assertTrue(isinstance(accelerator.scaler      ,      lowercase       )       )
															elif mp_dtype == BFaa:
																			self.assertIsNone(accelerator.scaler       )
															AcceleratorState._reset_state(lowercase       )


			def      UpperCamelCase		(    self       )  ->						Dict:
							'''simple docstring'''






							from torch.distributed.fsdp.fully_sharded_data_parallel import CPUOffload

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

@require_fsdp
@require_multi_gpu
@slow
class 					a__						(					snake_case			):
			"""simple docstring"""







			def      UpperCamelCase		(    self       )  ->						Optional[int]:
							'''simple docstring'''






							super().setUp()
							A__        =			0.82
							A__        =			[
							    "fsdp_shard_grad_op_transformer_based_wrap",
							    "fsdp_full_shard_transformer_based_wrap",
							]
							A__        =			{
							    "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
							}
							A__        =			160
							A__        =			160

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


			def      UpperCamelCase		(    self       )  ->						List[Any]:
							'''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(lowercase       ):
															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(lowercase      ,      env=os.environ.copy()       )


			def      UpperCamelCase		(    self       )  ->						str:
							'''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(lowercase       ):
											A__        =			cmd.copy()
											cmd_config.append(F'--fsdp_sharding_strategy={i+1}'       )
											if strategy != "FULL_SHARD":
															continue
											A__        =			len(lowercase       )
											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(lowercase      ,      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(lowercase      ,      env=os.environ.copy()       )


			def      UpperCamelCase		(    self       )  ->						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(lowercase       ):
																			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(lowercase      ,      env=os.environ.copy()       )









from __future__ import annotations



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							list[int]	,       SCREAMING_SNAKE_CASE_:							list[int]	,       SCREAMING_SNAKE_CASE_:							list[int]	,       SCREAMING_SNAKE_CASE_:							list[list[str]]	,       SCREAMING_SNAKE_CASE_:							int	,       )			->      None:




				'''simple docstring'''





				A__        =			len(SCREAMING_SNAKE_CASE_      )

				# If row is equal to the size of the board it means there are a queen in each row in
				# the current board (possible_board)
				if row == n:
								# We convert the variable possible_board that looks like this: [1, 3, 0, 2] to
								# this: ['. Q . . ', '. . . Q ', 'Q . . . ', '. . Q . ']
								boards.append([". " * i + "Q " + ". " * (n - 1 - i) for i in possible_board]      )
								return

				# We iterate each column in the row to find all possible results in each row
				for col in range(SCREAMING_SNAKE_CASE_      ):
								# We apply that we learned previously. First we check that in the current board
								# (possible_board) there are not other same value because if there is it means
								# that there are a collision in vertical. Then we apply the two formulas we
								# learned before:
								#
								# 45º: y - x = b or 45: row - col = b
								# 135º: y + x = b or row + col = b.
								#
								# And we verify if the results of this two formulas not exist in their variables
								# respectively.  (diagonal_right_collisions, diagonal_left_collisions)
								#
								# If any or these are True it means there is a collision so we continue to the
								# next value in the for loop.
								if (
								    col in possible_board
								    or row - col in diagonal_right_collisions
								    or row + col in diagonal_left_collisions
								):
												continue

								# If it is False we call dfs function again and we update the inputs
								depth_first_search(
								    [*possible_board, col]	,       [*diagonal_right_collisions, row - col]	,       [*diagonal_left_collisions, row + col]	,       SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_	,       )



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							int      )			->      None:




				'''simple docstring'''





				A__        =			[]
				depth_first_search([]	,       []	,       []	,       SCREAMING_SNAKE_CASE_	,       SCREAMING_SNAKE_CASE_      )

				# Print all the boards
				for board in boards:
								for column in board:
												print(SCREAMING_SNAKE_CASE_      )
								print(""      )

				print(len(SCREAMING_SNAKE_CASE_      )	,       "solutions were found."      )


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	n_queens_solution(4)

