
"""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(4_2)

a					:	Dict							=		'''bert-base-cased'''
a					:	List[str]							=		'''fp16'''
a					:	Any							=		'''bf16'''
a					:	Optional[int]							=		[FPaa, BFaa]




@require_fsdp
@require_cuda
class 		a_						( lowercase_	):







    def      _snake_case					(			self			:       List[str]     )		->Dict:
        super().setUp()

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







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

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







    def      _snake_case					(			self			:       str     )		->Dict:
        from torch.distributed.fsdp.fully_sharded_data_parallel import BackwardPrefetch

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







    def      _snake_case					(			self			:       Optional[Any]     )		->Tuple:
        from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType

        for i, state_dict_type in enumerate(UpperCamelCase__     ):
            _UpperCAmelCase          =   self.dist_env.copy()
            _UpperCAmelCase          =   state_dict_type
            with mockenv_context(**UpperCamelCase__     ):
                _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      _snake_case					(			self			:       List[str]     )		->Optional[Any]:
        _UpperCAmelCase          =   AutoModel.from_pretrained(UpperCamelCase__     )
        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(**UpperCamelCase__     ):
                _UpperCAmelCase          =   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     )

        _UpperCAmelCase          =   self.dist_env.copy()
        _UpperCAmelCase          =   """TRANSFORMER_BASED_WRAP"""
        _UpperCAmelCase          =   """T5Layer"""
        with mockenv_context(**UpperCamelCase__     ):
            _UpperCAmelCase          =   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     )     )

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







    def      _snake_case					(			self			:       Optional[Any]     )		->str:
        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(**UpperCamelCase__     ):
                _UpperCAmelCase          =   Accelerator()
                if mp_dtype == "fp16":
                    _UpperCAmelCase          =   torch.floataa
                elif mp_dtype == "bf16":
                    _UpperCAmelCase          =   torch.bfloataa
                _UpperCAmelCase          =   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      _snake_case					(			self			:       List[str]     )		->Dict:
        from torch.distributed.fsdp.fully_sharded_data_parallel import CPUOffload

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







@require_fsdp
@require_multi_gpu
@slow
class 		a_						( lowercase_	):







    def      _snake_case					(			self			:       List[Any]     )		->List[Any]:
        super().setUp()
        _UpperCAmelCase          =   0.8_2
        _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      _snake_case					(			self			:       Tuple     )		->Dict:
        _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(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      _snake_case					(			self			:       Optional[Any]     )		->Union[str, Any]:
        _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(UpperCamelCase__     ):
            _UpperCAmelCase          =   cmd.copy()
            cmd_config.append(f"""--fsdp_sharding_strategy={i+1}"""     )
            if strategy != "FULL_SHARD":
                continue
            _UpperCAmelCase          =   len(UpperCamelCase__     )
            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(UpperCamelCase__     ,					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(UpperCamelCase__     ,					env=os.environ.copy()     )







    def      _snake_case					(			self			:       Any     )		->Dict:
        _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(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"""



import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel

from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()




class 		a_						( unittest.TestCase	):







    @property
    def      _snake_case					(			self			:       Dict     )		->int:






        '''simple docstring'''






        torch.manual_seed(0     )
        _UpperCAmelCase          =   UNetaDModel(
            block_out_channels=(32, 64)     ,					layers_per_block=2     ,					sample_size=32     ,					in_channels=3     ,					out_channels=3     ,					down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")     ,					up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")     ,					)
        return model







    @property
    def      _snake_case					(			self			:       Optional[Any]     )		->str:






        '''simple docstring'''






        torch.manual_seed(0     )
        _UpperCAmelCase          =   VQModel(
            block_out_channels=[32, 64]     ,					in_channels=3     ,					out_channels=3     ,					down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]     ,					up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]     ,					latent_channels=3     ,					)
        return model







    @property
    def      _snake_case					(			self			:       List[Any]     )		->Optional[int]:






        '''simple docstring'''






        torch.manual_seed(0     )
        _UpperCAmelCase          =   CLIPTextConfig(
            bos_token_id=0     ,					eos_token_id=2     ,					hidden_size=32     ,					intermediate_size=37     ,					layer_norm_eps=1e-05     ,					num_attention_heads=4     ,					num_hidden_layers=5     ,					pad_token_id=1     ,					vocab_size=10_00     ,					)
        return CLIPTextModel(__UpperCamelCase     )







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






        '''simple docstring'''






        _UpperCAmelCase          =   self.dummy_uncond_unet
        _UpperCAmelCase          =   DDIMScheduler()
        _UpperCAmelCase          =   self.dummy_vq_model

        _UpperCAmelCase          =   LDMPipeline(unet=__UpperCamelCase     ,					vqvae=__UpperCamelCase     ,					scheduler=__UpperCamelCase     )
        ldm.to(__UpperCamelCase     )
        ldm.set_progress_bar_config(disable=__UpperCamelCase     )

        _UpperCAmelCase          =   torch.manual_seed(0     )
        _UpperCAmelCase          =   ldm(generator=__UpperCamelCase     ,					num_inference_steps=2     ,					output_type="""numpy"""     ).images

        _UpperCAmelCase          =   torch.manual_seed(0     )
        _UpperCAmelCase          =   ldm(generator=__UpperCamelCase     ,					num_inference_steps=2     ,					output_type="""numpy"""     ,					return_dict=__UpperCamelCase     )[0]

        _UpperCAmelCase          =   image[0, -3:, -3:, -1]
        _UpperCAmelCase          =   image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)
        _UpperCAmelCase          =   np.array([0.8_5_1_2, 0.8_1_8, 0.6_4_1_1, 0.6_8_0_8, 0.4_4_6_5, 0.5_6_1_8, 0.4_6, 0.6_2_3_1, 0.5_1_7_2]     )
        _UpperCAmelCase          =   1e-2 if torch_device != """mps""" else 3e-2

        assert np.abs(image_slice.flatten() - expected_slice     ).max() < tolerance
        assert np.abs(image_from_tuple_slice.flatten() - expected_slice     ).max() < tolerance







@slow
@require_torch
class 		a_						( unittest.TestCase	):







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






        '''simple docstring'''






        _UpperCAmelCase          =   LDMPipeline.from_pretrained("""CompVis/ldm-celebahq-256"""     )
        ldm.to(__UpperCamelCase     )
        ldm.set_progress_bar_config(disable=__UpperCamelCase     )

        _UpperCAmelCase          =   torch.manual_seed(0     )
        _UpperCAmelCase          =   ldm(generator=__UpperCamelCase     ,					num_inference_steps=5     ,					output_type="""numpy"""     ).images

        _UpperCAmelCase          =   image[0, -3:, -3:, -1]

        assert image.shape == (1, 2_56, 2_56, 3)
        _UpperCAmelCase          =   np.array([0.4_3_9_9, 0.4_4_9_7_5, 0.4_6_8_2_5, 0.4_7_4, 0.4_3_5_9, 0.4_5_8_1, 0.4_5_0_9_5, 0.4_3_4_1, 0.4_4_4_7]     )
        _UpperCAmelCase          =   1e-2 if torch_device != """mps""" else 3e-2

        assert np.abs(image_slice.flatten() - expected_slice     ).max() < tolerance