
'''simple docstring'''
from typing import Optional, Tuple

import jax
import jax.numpy as jnp
from flax import linen as nn
from flax.core.frozen_dict import FrozenDict
from transformers import CLIPConfig, FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPVisionModule






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE=1E-12				):
							_snake_case       =  jnp.divide(emb_a.T							, jnp.clip(jnp.linalg.norm(_SCREAMING_SNAKE_CASE							, axis=1				)							, a_min=_SCREAMING_SNAKE_CASE				)				).T
							_snake_case       =  jnp.divide(emb_a.T							, jnp.clip(jnp.linalg.norm(_SCREAMING_SNAKE_CASE							, axis=1				)							, a_min=_SCREAMING_SNAKE_CASE				)				).T
							return jnp.matmul(_SCREAMING_SNAKE_CASE							, norm_emb_a.T				)


class       _lowerCAmelCase   (   nn.Module				):







							'''simple docstring'''

							lowerCAmelCase_ 						=						42
							lowerCAmelCase_ 						=						jnp.floataa

							def 	lowercase							(self  )					->       str:
														_snake_case       =  FlaxCLIPVisionModule(self.config.vision_config  )
														_snake_case       =  nn.Dense(self.config.projection_dim  ,  use_bias=UpperCAmelCase  ,  dtype=self.dtype  )

														_snake_case       =  self.param("""concept_embeds"""  ,  jax.nn.initializers.ones  ,  (17, self.config.projection_dim)  )
														_snake_case       =  self.param(
														    """special_care_embeds"""  ,  jax.nn.initializers.ones  ,  (3, self.config.projection_dim)  )

														_snake_case       =  self.param("""concept_embeds_weights"""  ,  jax.nn.initializers.ones  ,  (17,)  )
														_snake_case       =  self.param("""special_care_embeds_weights"""  ,  jax.nn.initializers.ones  ,  (3,)  )





							def __call__(self  ,  UpperCAmelCase  )					->       Optional[Any]:
														_snake_case       =  self.vision_model(UpperCAmelCase  )[1]
														_snake_case       =  self.visual_projection(UpperCAmelCase  )

														_snake_case       =  jax_cosine_distance(UpperCAmelCase  ,  self.special_care_embeds  )
														_snake_case       =  jax_cosine_distance(UpperCAmelCase  ,  self.concept_embeds  )

														# increase this value to create a stronger `nfsw` filter
														# at the cost of increasing the possibility of filtering benign image inputs
														_snake_case       =  0.0

														_snake_case       =  special_cos_dist - self.special_care_embeds_weights[None, :] + adjustment
														_snake_case       =  jnp.round(UpperCAmelCase  ,  3  )
														_snake_case       =  jnp.any(special_scores > 0  ,  axis=1  ,  keepdims=UpperCAmelCase  )
														# Use a lower threshold if an image has any special care concept
														_snake_case       =  is_special_care * 0.01

														_snake_case       =  cos_dist - self.concept_embeds_weights[None, :] + special_adjustment
														_snake_case       =  jnp.round(UpperCAmelCase  ,  3  )
														_snake_case       =  jnp.any(concept_scores > 0  ,  axis=1  )

														return has_nsfw_concepts


class       _lowerCAmelCase   (   __snake_case				):







							'''simple docstring'''

							lowerCAmelCase_ 						=						CLIPConfig
							lowerCAmelCase_ 						=						"clip_input"
							lowerCAmelCase_ 						=						FlaxStableDiffusionSafetyCheckerModule

							def __init__(self  ,  UpperCAmelCase  ,  UpperCAmelCase = None  ,  UpperCAmelCase = 0  ,  UpperCAmelCase = jnp.floataa  ,  UpperCAmelCase = True  ,  **UpperCAmelCase  ,  )					->       Union[str, Any]:
														if input_shape is None:
																					_snake_case       =  (1, 224, 224, 3)
														_snake_case       =  self.module_class(config=UpperCAmelCase  ,  dtype=UpperCAmelCase  ,  **UpperCAmelCase  )
														super().__init__(UpperCAmelCase  ,  UpperCAmelCase  ,  input_shape=UpperCAmelCase  ,  seed=UpperCAmelCase  ,  dtype=UpperCAmelCase  ,  _do_init=_do_init  )

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase  ,  UpperCAmelCase = None  )					->       FrozenDict:
														# init input tensor
														_snake_case       =  jax.random.normal(UpperCAmelCase  ,  UpperCAmelCase  )

														_snake_case,							_snake_case       =  jax.random.split(UpperCAmelCase  )
														_snake_case       =  {"""params""": params_rng, """dropout""": dropout_rng}

														_snake_case       =  self.module.init(UpperCAmelCase  ,  UpperCAmelCase  )["""params"""]

														return random_params





							def __call__(self  ,  UpperCAmelCase  ,  UpperCAmelCase = None  ,  )					->       int:
														_snake_case       =  jnp.transpose(UpperCAmelCase  ,  (0, 2, 3, 1)  )

														return self.module.apply(
														    {"""params""": params or self.params}  ,  jnp.array(UpperCAmelCase  ,  dtype=jnp.floataa  )  ,  rngs={}  ,  )

'''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)

__lowerCAmelCase												=	'bert-base-cased'
__lowerCAmelCase												=	'fp16'
__lowerCAmelCase												=	'bf16'
__lowerCAmelCase												=	[FPaa, BFaa]


@require_fsdp
@require_cuda
class       _lowerCAmelCase   (   __snake_case				):







							'''simple docstring'''

							def 	lowercase							(self  )					->       Optional[Any]:
														super().setUp()

														_snake_case       =  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  ):
																					_snake_case       =  self.dist_env.copy()
																					_snake_case       =  f"""{i + 1}"""
																					_snake_case       =  strategy
																					with mockenv_context(**UpperCAmelCase  ):
																												_snake_case       =  FullyShardedDataParallelPlugin()
																												self.assertEqual(fsdp_plugin.sharding_strategy  ,  ShardingStrategy(i + 1  )  )

							def 	lowercase							(self  )					->       List[str]:
														from torch.distributed.fsdp.fully_sharded_data_parallel import BackwardPrefetch

														for i, prefetch_policy in enumerate(UpperCAmelCase  ):
																					_snake_case       =  self.dist_env.copy()
																					_snake_case       =  prefetch_policy
																					with mockenv_context(**UpperCAmelCase  ):
																												_snake_case       =  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  )					->       int:
														from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType

														for i, state_dict_type in enumerate(UpperCAmelCase  ):
																					_snake_case       =  self.dist_env.copy()
																					_snake_case       =  state_dict_type
																					with mockenv_context(**UpperCAmelCase  ):
																												_snake_case       =  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  )					->       Optional[Any]:
														_snake_case       =  AutoModel.from_pretrained(UpperCAmelCase  )
														for policy in FSDP_AUTO_WRAP_POLICY:
																					_snake_case       =  self.dist_env.copy()
																					_snake_case       =  policy
																					if policy == "TRANSFORMER_BASED_WRAP":
																												_snake_case       =  """BertLayer"""
																					elif policy == "SIZE_BASED_WRAP":
																												_snake_case       =  """2000"""
																					with mockenv_context(**UpperCAmelCase  ):
																												_snake_case       =  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  )

														_snake_case       =  self.dist_env.copy()
														_snake_case       =  """TRANSFORMER_BASED_WRAP"""
														_snake_case       =  """T5Layer"""
														with mockenv_context(**UpperCAmelCase  ):
																					_snake_case       =  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  )  )

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

							def 	lowercase							(self  )					->       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       =  self.dist_env.copy()
																					_snake_case       =  mp_dtype
																					with mockenv_context(**UpperCAmelCase  ):
																												_snake_case       =  Accelerator()
																												if mp_dtype == "fp16":
																																			_snake_case       =  torch.floataa
																												elif mp_dtype == "bf16":
																																			_snake_case       =  torch.bfloataa
																												_snake_case       =  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  )					->       Optional[int]:
														from torch.distributed.fsdp.fully_sharded_data_parallel import CPUOffload

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


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







							'''simple docstring'''

							def 	lowercase							(self  )					->       List[Any]:
														super().setUp()
														_snake_case       =  0.82
														_snake_case       =  [
														    """fsdp_shard_grad_op_transformer_based_wrap""",
														    """fsdp_full_shard_transformer_based_wrap""",
														]
														_snake_case       =  {
														    """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       =  160
														_snake_case       =  160

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

							def 	lowercase							(self  )					->       Any:
														_snake_case       =  os.path.join(self.test_scripts_folder  ,  """test_performance.py"""  )
														_snake_case       =  ["""accelerate""", """launch""", """--num_processes=2""", """--num_machines=1""", """--machine_rank=0""", """--use_fsdp"""]
														for config in self.performance_configs:
																					_snake_case       =  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  )					->       Union[str, Any]:
														_snake_case       =  os.path.join(self.test_scripts_folder  ,  """test_checkpointing.py"""  )
														_snake_case       =  [
														    """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  ):
																					_snake_case       =  cmd.copy()
																					cmd_config.append(f"""--fsdp_sharding_strategy={i+1}"""  )
																					if strategy != "FULL_SHARD":
																												continue
																					_snake_case       =  len(UpperCAmelCase  )
																					for state_dict_type in FSDP_STATE_DICT_TYPE:
																												_snake_case       =  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()  )

																												_snake_case       =  cmd_config[:-1]
																												_snake_case       =  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  )					->       Optional[Any]:
														_snake_case       =  os.path.join(self.test_scripts_folder  ,  """test_peak_memory_usage.py"""  )
														_snake_case       =  [
														    """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       =  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()  )