





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 					__A    (    lowerCAmelCase ):

			'''simple docstring'''







			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				super().setUp()

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





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				from torch.distributed.fsdp.fully_sharded_data_parallel import ShardingStrategy

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





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				from torch.distributed.fsdp.fully_sharded_data_parallel import BackwardPrefetch

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





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType

				for i, state_dict_type in enumerate(__lowerCAmelCase							):
					lowerCamelCase__								=		self.dist_env.copy()
					lowerCamelCase__								=		state_dict_type
					with mockenv_context(**__lowerCAmelCase							):
						lowerCamelCase__								=		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 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		AutoModel.from_pretrained(__lowerCAmelCase							)
				for policy in FSDP_AUTO_WRAP_POLICY:
					lowerCamelCase__								=		self.dist_env.copy()
					lowerCamelCase__								=		policy
					if policy == "TRANSFORMER_BASED_WRAP":
						lowerCamelCase__								=		'''BertLayer'''
					elif policy == "SIZE_BASED_WRAP":
						lowerCamelCase__								=		'''2000'''
					with mockenv_context(**__lowerCAmelCase							):
						lowerCamelCase__								=		FullyShardedDataParallelPlugin()
						fsdp_plugin.set_auto_wrap_policy(__lowerCAmelCase							)
						if policy == "NO_WRAP":
							self.assertIsNone(fsdp_plugin.auto_wrap_policy							)
						else:
							self.assertIsNotNone(fsdp_plugin.auto_wrap_policy							)

				lowerCamelCase__								=		self.dist_env.copy()
				lowerCamelCase__								=		'''TRANSFORMER_BASED_WRAP'''
				lowerCamelCase__								=		'''T5Layer'''
				with mockenv_context(**__lowerCAmelCase							):
					lowerCamelCase__								=		FullyShardedDataParallelPlugin()
					with self.assertRaises(__lowerCAmelCase							) as cm:
						fsdp_plugin.set_auto_wrap_policy(__lowerCAmelCase							)
					self.assertTrue('''Could not find the transformer layer class to wrap in the model.''' in str(cm.exception							)							)

				lowerCamelCase__								=		self.dist_env.copy()
				lowerCamelCase__								=		'''SIZE_BASED_WRAP'''
				lowerCamelCase__								=		'''0'''
				with mockenv_context(**__lowerCAmelCase							):
					lowerCamelCase__								=		FullyShardedDataParallelPlugin()
					fsdp_plugin.set_auto_wrap_policy(__lowerCAmelCase							)
					self.assertIsNone(fsdp_plugin.auto_wrap_policy							)





			def 		__lowerCamelCase				(    self							):



				'''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:
					lowerCamelCase__								=		self.dist_env.copy()
					lowerCamelCase__								=		mp_dtype
					with mockenv_context(**__lowerCAmelCase							):
						lowerCamelCase__								=		Accelerator()
						if mp_dtype == "fp16":
							lowerCamelCase__								=		torch.floataa
						elif mp_dtype == "bf16":
							lowerCamelCase__								=		torch.bfloataa
						lowerCamelCase__								=		MixedPrecision(param_dtype=__lowerCAmelCase       ,  reduce_dtype=__lowerCAmelCase       ,  buffer_dtype=__lowerCAmelCase							)
						self.assertEqual(accelerator.state.fsdp_plugin.mixed_precision_policy       ,  __lowerCAmelCase							)
						if mp_dtype == FPaa:
							self.assertTrue(isinstance(accelerator.scaler       ,  __lowerCAmelCase							)							)
						elif mp_dtype == BFaa:
							self.assertIsNone(accelerator.scaler							)
						AcceleratorState._reset_state(__lowerCAmelCase							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				from torch.distributed.fsdp.fully_sharded_data_parallel import CPUOffload

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






@require_fsdp
@require_multi_gpu
@slow
class 					__A    (    lowerCAmelCase ):

			'''simple docstring'''







			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				super().setUp()
				lowerCamelCase__								=		0.82
				lowerCamelCase__								=		[
				    '''fsdp_shard_grad_op_transformer_based_wrap''',
				    '''fsdp_full_shard_transformer_based_wrap''',
				]
				lowerCamelCase__								=		{
				    '''multi_gpu_fp16''': 3_2_0_0,
				    '''fsdp_shard_grad_op_transformer_based_wrap_fp16''': 2_0_0_0,
				    '''fsdp_full_shard_transformer_based_wrap_fp16''': 1_9_0_0,
				    # 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__								=		1_6_0
				lowerCamelCase__								=		1_6_0

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





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		os.path.join(self.test_scripts_folder       ,  '''test_performance.py'''							)
				lowerCamelCase__								=		['''accelerate''', '''launch''', '''--num_processes=2''', '''--num_machines=1''', '''--machine_rank=0''', '''--use_fsdp''']
				for config in self.performance_configs:
					lowerCamelCase__								=		cmd.copy()
					for i, strategy in enumerate(__lowerCAmelCase							):
						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(__lowerCAmelCase       ,  env=os.environ.copy()							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







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

						lowerCamelCase__								=		cmd_config[:-1]
						lowerCamelCase__								=		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(__lowerCAmelCase       ,  env=os.environ.copy()							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		os.path.join(self.test_scripts_folder       ,  '''test_peak_memory_usage.py'''							)
				lowerCamelCase__								=		[
				    '''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__								=		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(__lowerCAmelCase							):
							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(__lowerCAmelCase       ,  env=os.environ.copy()							)










from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_a 						=      logging.get_logger(__name__)

_a 						=      {
    "camembert-base": "https://huggingface.co/camembert-base/resolve/main/config.json",
    "umberto-commoncrawl-cased-v1": (
        "https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json"
    ),
    "umberto-wikipedia-uncased-v1": (
        "https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json"
    ),
}






class 					__A    (    lowerCAmelCase ):

			'''simple docstring'''







			lowerCAmelCase_							     =						"""camembert"""





			def __init__(    self       ,  __lowerCAmelCase=3_0_5_2_2       ,  __lowerCAmelCase=7_6_8       ,  __lowerCAmelCase=1_2       ,  __lowerCAmelCase=1_2       ,  __lowerCAmelCase=3_0_7_2       ,  __lowerCAmelCase="gelu"       ,  __lowerCAmelCase=0.1       ,  __lowerCAmelCase=0.1       ,  __lowerCAmelCase=5_1_2       ,  __lowerCAmelCase=2       ,  __lowerCAmelCase=0.02       ,  __lowerCAmelCase=1E-12       ,  __lowerCAmelCase=1       ,  __lowerCAmelCase=0       ,  __lowerCAmelCase=2       ,  __lowerCAmelCase="absolute"       ,  __lowerCAmelCase=True       ,  __lowerCAmelCase=None       ,  **__lowerCAmelCase       ,  ):



				'''simple docstring'''







				super().__init__(pad_token_id=__lowerCAmelCase       ,  bos_token_id=__lowerCAmelCase       ,  eos_token_id=__lowerCAmelCase       ,  **__lowerCAmelCase							)

				lowerCamelCase__								=		vocab_size
				lowerCamelCase__								=		hidden_size
				lowerCamelCase__								=		num_hidden_layers
				lowerCamelCase__								=		num_attention_heads
				lowerCamelCase__								=		hidden_act
				lowerCamelCase__								=		intermediate_size
				lowerCamelCase__								=		hidden_dropout_prob
				lowerCamelCase__								=		attention_probs_dropout_prob
				lowerCamelCase__								=		max_position_embeddings
				lowerCamelCase__								=		type_vocab_size
				lowerCamelCase__								=		initializer_range
				lowerCamelCase__								=		layer_norm_eps
				lowerCamelCase__								=		position_embedding_type
				lowerCamelCase__								=		use_cache
				lowerCamelCase__								=		classifier_dropout






class 					__A    (    lowerCAmelCase ):

			'''simple docstring'''







			@property
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				if self.task == "multiple-choice":
					lowerCamelCase__								=		{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
				else:
					lowerCamelCase__								=		{0: '''batch''', 1: '''sequence'''}
				return OrderedDict(
				    [
				        ('''input_ids''', dynamic_axis),
				        ('''attention_mask''', dynamic_axis),
				    ]							)



