




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)

SCREAMING_SNAKE_CASE__       :      List[str]  =   'bert-base-cased'
SCREAMING_SNAKE_CASE__       :      List[Any]  =   'fp16'
SCREAMING_SNAKE_CASE__       :      Optional[Any]  =   'bf16'
SCREAMING_SNAKE_CASE__       :      Union[str, Any]  =   [FPaa, BFaa]




@require_fsdp
@require_cuda
class 		UpperCamelCase__	(lowerCAmelCase__     ):

    '''simple docstring'''



    def     _lowercase     (    self     )     ->      Union[str, Any]:
           super().setUp()

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







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

           for i, strategy in enumerate(UpperCamelCase__     ):
                  lowerCamelCase   :      List[Any]			 =					self.dist_env.copy()
                  lowerCamelCase   :      Optional[Any]			 =					F'''{i + 1}'''
                  lowerCamelCase   :      str			 =					strategy
                  with mockenv_context(**UpperCamelCase__     ):
                         lowerCamelCase   :      Union[str, Any]			 =					FullyShardedDataParallelPlugin()
                         self.assertEqual(fsdp_plugin.sharding_strategy     ,  ShardingStrategy(i + 1     )     )







    def     _lowercase     (    self     )     ->      int:
           from torch.distributed.fsdp.fully_sharded_data_parallel import BackwardPrefetch

           for i, prefetch_policy in enumerate(UpperCamelCase__     ):
                  lowerCamelCase   :      Optional[Any]			 =					self.dist_env.copy()
                  lowerCamelCase   :      List[str]			 =					prefetch_policy
                  with mockenv_context(**UpperCamelCase__     ):
                         lowerCamelCase   :      Optional[Any]			 =					FullyShardedDataParallelPlugin()
                         if prefetch_policy == "NO_PREFETCH":
                                self.assertIsNone(fsdp_plugin.backward_prefetch     )
                         else:
                                self.assertEqual(fsdp_plugin.backward_prefetch     ,  BackwardPrefetch(i + 1     )     )







    def     _lowercase     (    self     )     ->      Tuple:
           from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType

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

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

           lowerCamelCase   :      List[Any]			 =					self.dist_env.copy()
           lowerCamelCase   :      Any			 =					"SIZE_BASED_WRAP"
           lowerCamelCase   :      Tuple			 =					"0"
           with mockenv_context(**UpperCamelCase__     ):
                  lowerCamelCase   :      List[str]			 =					FullyShardedDataParallelPlugin()
                  fsdp_plugin.set_auto_wrap_policy(UpperCamelCase__     )
                  self.assertIsNone(fsdp_plugin.auto_wrap_policy     )







    def     _lowercase     (    self     )     ->      Tuple:
           from torch.distributed.fsdp.fully_sharded_data_parallel import MixedPrecision
           from torch.distributed.fsdp.sharded_grad_scaler import ShardedGradScaler

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







    def     _lowercase     (    self     )     ->      Tuple:
           from torch.distributed.fsdp.fully_sharded_data_parallel import CPUOffload

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




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

    '''simple docstring'''



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

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







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







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

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







    def     _lowercase     (    self     )     ->      Tuple:
           lowerCamelCase   :      int			 =					os.path.join(self.test_scripts_folder     ,  "test_peak_memory_usage.py"     )
           lowerCamelCase   :      Tuple			 =					[
               "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():
                  lowerCamelCase   :      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(UpperCamelCase__     ):
                                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(UpperCamelCase__     ,  env=os.environ.copy()     )










'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowercase	:						Union[str, Any]       =	{
    'configuration_blenderbot': [
        'BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BlenderbotConfig',
        'BlenderbotOnnxConfig',
    ],
    'tokenization_blenderbot': ['BlenderbotTokenizer'],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowercase	:						List[Any]       =	['BlenderbotTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowercase	:						List[Any]       =	[
	    'BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'BlenderbotForCausalLM',
	    'BlenderbotForConditionalGeneration',
	    'BlenderbotModel',
	    'BlenderbotPreTrainedModel',
	]


try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowercase	:						Union[str, Any]       =	[
	    'TFBlenderbotForConditionalGeneration',
	    'TFBlenderbotModel',
	    'TFBlenderbotPreTrainedModel',
	]


try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowercase	:						Dict       =	[
	    'FlaxBlenderbotForConditionalGeneration',
	    'FlaxBlenderbotModel',
	    'FlaxBlenderbotPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_blenderbot import (
	    BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    BlenderbotConfig,
	    BlenderbotOnnxConfig,
	)
	from .tokenization_blenderbot import BlenderbotTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_blenderbot import (
		    BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    BlenderbotForCausalLM,
		    BlenderbotForConditionalGeneration,
		    BlenderbotModel,
		    BlenderbotPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_blenderbot import (
		    TFBlenderbotForConditionalGeneration,
		    TFBlenderbotModel,
		    TFBlenderbotPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_blenderbot import (
		    FlaxBlenderbotForConditionalGeneration,
		    FlaxBlenderbotModel,
		    FlaxBlenderbotPreTrainedModel,
		)

else:
	import sys

	__lowercase	:						str       =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



