
"""simple docstring"""





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)

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






@require_fsdp
@require_cuda
class    _UpperCAmelCase(   lowerCamelCase     ):



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






							'''simple docstring'''
							super().setUp()

							_UpperCamelCase										=  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(__a):
												_UpperCamelCase										=  self.dist_env.copy()
												_UpperCamelCase										=  F'''{i + 1}'''
												_UpperCamelCase										=  strategy
												with mockenv_context(**__a):
																	_UpperCamelCase										=  FullyShardedDataParallelPlugin()
																	self.assertEqual(fsdp_plugin.sharding_strategy    ,		ShardingStrategy(i + 1))



		def 						UpperCAmelCase							( self)	->	Any:






							'''simple docstring'''
							from torch.distributed.fsdp.fully_sharded_data_parallel import BackwardPrefetch

							for i, prefetch_policy in enumerate(__a):
												_UpperCamelCase										=  self.dist_env.copy()
												_UpperCamelCase										=  prefetch_policy
												with mockenv_context(**__a):
																	_UpperCamelCase										=  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)	->	Optional[Any]:






							'''simple docstring'''
							from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType

							for i, state_dict_type in enumerate(__a):
												_UpperCamelCase										=  self.dist_env.copy()
												_UpperCamelCase										=  state_dict_type
												with mockenv_context(**__a):
																	_UpperCamelCase										=  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'''
							_UpperCamelCase										=  AutoModel.from_pretrained(__a)
							for policy in FSDP_AUTO_WRAP_POLICY:
												_UpperCamelCase										=  self.dist_env.copy()
												_UpperCamelCase										=  policy
												if policy == "TRANSFORMER_BASED_WRAP":
																	_UpperCamelCase										=  '''BertLayer'''
												elif policy == "SIZE_BASED_WRAP":
																	_UpperCamelCase										=  '''2000'''
												with mockenv_context(**__a):
																	_UpperCamelCase										=  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)

							_UpperCamelCase										=  self.dist_env.copy()
							_UpperCamelCase										=  '''TRANSFORMER_BASED_WRAP'''
							_UpperCamelCase										=  '''T5Layer'''
							with mockenv_context(**__a):
												_UpperCamelCase										=  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))

							_UpperCamelCase										=  self.dist_env.copy()
							_UpperCamelCase										=  '''SIZE_BASED_WRAP'''
							_UpperCamelCase										=  '''0'''
							with mockenv_context(**__a):
												_UpperCamelCase										=  FullyShardedDataParallelPlugin()
												fsdp_plugin.set_auto_wrap_policy(__a)
												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:
												_UpperCamelCase										=  self.dist_env.copy()
												_UpperCamelCase										=  mp_dtype
												with mockenv_context(**__a):
																	_UpperCamelCase										=  Accelerator()
																	if mp_dtype == "fp16":
																						_UpperCamelCase										=  torch.floataa
																	elif mp_dtype == "bf16":
																						_UpperCamelCase										=  torch.bfloataa
																	_UpperCamelCase										=  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 						UpperCAmelCase							( self)	->	Tuple:






							'''simple docstring'''
							from torch.distributed.fsdp.fully_sharded_data_parallel import CPUOffload

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






@require_fsdp
@require_multi_gpu
@slow
class    _UpperCAmelCase(   lowerCamelCase     ):



		def 						UpperCAmelCase							( self)	->	Tuple:






							'''simple docstring'''
							super().setUp()
							_UpperCamelCase										=  0.82
							_UpperCamelCase										=  [
							    '''fsdp_shard_grad_op_transformer_based_wrap''',
							    '''fsdp_full_shard_transformer_based_wrap''',
							]
							_UpperCamelCase										=  {
							    '''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
							}
							_UpperCamelCase										=  1_60
							_UpperCamelCase										=  1_60

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



		def 						UpperCAmelCase							( self)	->	Any:






							'''simple docstring'''
							_UpperCamelCase										=  os.path.join(self.test_scripts_folder    ,		'''test_performance.py''')
							_UpperCamelCase										=  ['''accelerate''', '''launch''', '''--num_processes=2''', '''--num_machines=1''', '''--machine_rank=0''', '''--use_fsdp''']
							for config in self.performance_configs:
												_UpperCamelCase										=  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 						UpperCAmelCase							( self)	->	Tuple:






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

																	_UpperCamelCase										=  cmd_config[:-1]
																	_UpperCamelCase										=  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 						UpperCAmelCase							( self)	->	List[Any]:






							'''simple docstring'''
							_UpperCamelCase										=  os.path.join(self.test_scripts_folder    ,		'''test_peak_memory_usage.py''')
							_UpperCamelCase										=  [
							    '''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():
												_UpperCamelCase										=  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())








"""simple docstring"""





import numpy






class    _UpperCAmelCase:



		def __init__( self    ,		__a    ,		__a)	->	None:






							'''simple docstring'''
							_UpperCamelCase										=  input_array

							# Random initial weights are assigned where first argument is the
							# number of nodes in previous layer and second argument is the
							# number of nodes in the next layer.

							# Random initial weights are assigned.
							# self.input_array.shape[1] is used to represent number of nodes in input layer.
							# First hidden layer consists of 4 nodes.
							_UpperCamelCase										=  numpy.random.rand(
							    self.input_array.shape[1]    ,		4)

							# Random initial values for the first hidden layer.
							# First hidden layer has 4 nodes.
							# Second hidden layer has 3 nodes.
							_UpperCamelCase										=  numpy.random.rand(
							    4    ,		3)

							# Random initial values for the second hidden layer.
							# Second hidden layer has 3 nodes.
							# Output layer has 1 node.
							_UpperCamelCase										=  numpy.random.rand(3    ,		1)

							# Real output values provided.
							_UpperCamelCase										=  output_array

							# Predicted output values by the neural network.
							# Predicted_output array initially consists of zeroes.
							_UpperCamelCase										=  numpy.zeros(output_array.shape)



		def 						UpperCAmelCase							( self)	->	numpy.ndarray:






							'''simple docstring'''
							_UpperCamelCase										=  sigmoid(
							    numpy.dot(self.input_array    ,		self.input_layer_and_first_hidden_layer_weights))

							# layer_between_first_hidden_layer_and_second_hidden_layer is the layer
							# connecting the first hidden set of nodes with the second hidden set of nodes.
							_UpperCamelCase										=  sigmoid(
							    numpy.dot(
							        self.layer_between_input_and_first_hidden_layer    ,		self.first_hidden_layer_and_second_hidden_layer_weights    ,		))

							# layer_between_second_hidden_layer_and_output is the layer connecting
							# second hidden layer with the output node.
							_UpperCamelCase										=  sigmoid(
							    numpy.dot(
							        self.layer_between_first_hidden_layer_and_second_hidden_layer    ,		self.second_hidden_layer_and_output_layer_weights    ,		))

							return self.layer_between_second_hidden_layer_and_output



		def 						UpperCAmelCase							( self)	->	None:






							'''simple docstring'''
							_UpperCamelCase										=  numpy.dot(
							    self.layer_between_first_hidden_layer_and_second_hidden_layer.T    ,		2
							    * (self.output_array - self.predicted_output)
							    * sigmoid_derivative(self.predicted_output)    ,		)
							_UpperCamelCase										=  numpy.dot(
							    self.layer_between_input_and_first_hidden_layer.T    ,		numpy.dot(
							        2
							        * (self.output_array - self.predicted_output)
							        * sigmoid_derivative(self.predicted_output)    ,		self.second_hidden_layer_and_output_layer_weights.T    ,		)
							    * sigmoid_derivative(
							        self.layer_between_first_hidden_layer_and_second_hidden_layer)    ,		)
							_UpperCamelCase										=  numpy.dot(
							    self.input_array.T    ,		numpy.dot(
							        numpy.dot(
							            2
							            * (self.output_array - self.predicted_output)
							            * sigmoid_derivative(self.predicted_output)    ,		self.second_hidden_layer_and_output_layer_weights.T    ,		)
							        * sigmoid_derivative(
							            self.layer_between_first_hidden_layer_and_second_hidden_layer)    ,		self.first_hidden_layer_and_second_hidden_layer_weights.T    ,		)
							    * sigmoid_derivative(self.layer_between_input_and_first_hidden_layer)    ,		)

							self.input_layer_and_first_hidden_layer_weights += (
							    updated_input_layer_and_first_hidden_layer_weights
							)
							self.first_hidden_layer_and_second_hidden_layer_weights += (
							    updated_first_hidden_layer_and_second_hidden_layer_weights
							)
							self.second_hidden_layer_and_output_layer_weights += (
							    updated_second_hidden_layer_and_output_layer_weights
							)



		def 						UpperCAmelCase							( self    ,		__a    ,		__a    ,		__a)	->	None:






							'''simple docstring'''
							for iteration in range(1    ,		iterations + 1):
												_UpperCamelCase										=  self.feedforward()
												self.back_propagation()
												if give_loss:
																	_UpperCamelCase										=  numpy.mean(numpy.square(output - self.feedforward()))
																	print(F'''Iteration {iteration} Loss: {loss}''')




		def 						UpperCAmelCase							( self    ,		__a)	->	int:






							'''simple docstring'''
							_UpperCamelCase										=  input_arr

							_UpperCamelCase										=  sigmoid(
							    numpy.dot(self.array    ,		self.input_layer_and_first_hidden_layer_weights))

							_UpperCamelCase										=  sigmoid(
							    numpy.dot(
							        self.layer_between_input_and_first_hidden_layer    ,		self.first_hidden_layer_and_second_hidden_layer_weights    ,		))

							_UpperCamelCase										=  sigmoid(
							    numpy.dot(
							        self.layer_between_first_hidden_layer_and_second_hidden_layer    ,		self.second_hidden_layer_and_output_layer_weights    ,		))

							return int(self.layer_between_second_hidden_layer_and_output > 0.6)





def 				lowerCamelCase__		(      __snake_case     )							->		numpy.ndarray:


					"""simple docstring"""





					return 1 / (1 + numpy.exp(-value     ))



def 				lowerCamelCase__		(      __snake_case     )							->		numpy.ndarray:


					"""simple docstring"""





					return (value) * (1 - (value))



def 				lowerCamelCase__		(      )							->		int:


					"""simple docstring"""





					_UpperCamelCase										=  numpy.array(
					    (
					        [0, 0, 0],
					        [0, 0, 1],
					        [0, 1, 0],
					        [0, 1, 1],
					        [1, 0, 0],
					        [1, 0, 1],
					        [1, 1, 0],
					        [1, 1, 1],
					    ),					dtype=numpy.floataa,					)

					# True output values for the given input values.
					_UpperCamelCase										=  numpy.array(([0], [1], [1], [0], [1], [0], [0], [1]),					dtype=numpy.floataa     )

					# Calling neural network class.
					_UpperCamelCase										=  TwoHiddenLayerNeuralNetwork(
					    input_array=__snake_case,					output_array=__snake_case     )

					# Calling training function.
					# Set give_loss to True if you want to see loss in every iteration.
					neural_network.train(output=__snake_case,					iterations=10,					give_loss=__snake_case     )

					return neural_network.predict(numpy.array(([1, 1, 1]),					dtype=numpy.floataa     )     )


if __name__ == "__main__":
				example()






