

import functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCamelCase    :       Tuple        =							logging.get_logger(__name__)

_lowerCamelCase    :       List[str]        =							{
    "microsoft/unispeech-sat-base-100h-libri-ft": (
        "https://huggingface.co/microsoft/unispeech-sat-base-100h-libri-ft/resolve/main/config.json"
    ),
    # See all UniSpeechSat models at https://huggingface.co/models?filter=unispeech_sat
}





class  __UpperCAmelCase					(	lowerCamelCase__						):
   UpperCamelCase       					=					"""unispeech-sat"""



   def __init__(							self							:					Any,		__A							:					str=3_2,		__A							:					str=7_6_8,		__A							:					Union[str, Any]=1_2,		__A							:					str=1_2,		__A							:					Tuple=3_0_7_2,		__A							:					Optional[Any]="gelu",		__A							:					int=0.1,		__A							:					List[Any]=0.1,		__A							:					Union[str, Any]=0.1,		__A							:					Tuple=0.0,		__A							:					Union[str, Any]=0.0,		__A							:					Union[str, Any]=0.1,		__A							:					Union[str, Any]=0.1,		__A							:					str=0.0_2,		__A							:					Optional[int]=1E-5,		__A							:					List[Any]="group",		__A							:					str="gelu",		__A							:					Union[str, Any]=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2),		__A							:					Optional[Any]=(5, 2, 2, 2, 2, 2, 2),		__A							:					Tuple=(1_0, 3, 3, 3, 3, 2, 2),		__A							:					List[str]=False,		__A							:					Tuple=1_2_8,		__A							:					Optional[int]=1_6,		__A							:					Any=False,		__A							:					List[Any]=True,		__A							:					Any=0.0_5,		__A							:					List[Any]=1_0,		__A							:					List[Any]=2,		__A							:					Optional[Any]=0.0,		__A							:					Optional[Any]=1_0,		__A							:					Dict=0,		__A							:					Optional[int]=3_2_0,		__A							:					Tuple=2,		__A							:					Any=0.1,		__A							:					Dict=1_0_0,		__A							:					Optional[int]=2_5_6,		__A							:					str=2_5_6,		__A							:					List[str]=0.1,		__A							:					List[str]="mean",		__A							:					Tuple=False,		__A							:					int=False,		__A							:					Dict=2_5_6,		__A							:					List[Any]=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 1_5_0_0),		__A							:					Optional[Any]=(5, 3, 3, 1, 1),		__A							:					int=(1, 2, 3, 1, 1),		__A							:					Dict=5_1_2,		__A							:					Union[str, Any]=0,		__A							:					List[str]=1,		__A							:					Optional[Any]=2,		__A							:					Optional[int]=5_0_4,		**__A							:					int,		):
     super().__init__(**__A,		pad_token_id=__A,		bos_token_id=__A,		eos_token_id=__A						)
     UpperCAmelCase	:							List[Any]						=		hidden_size
     UpperCAmelCase	:							int						=		feat_extract_norm
     UpperCAmelCase	:							Any						=		feat_extract_activation
     UpperCAmelCase	:							int						=		list(__A						)
     UpperCAmelCase	:							str						=		list(__A						)
     UpperCAmelCase	:							Tuple						=		list(__A						)
     UpperCAmelCase	:							Optional[Any]						=		conv_bias
     UpperCAmelCase	:							List[Any]						=		num_conv_pos_embeddings
     UpperCAmelCase	:							List[str]						=		num_conv_pos_embedding_groups
     UpperCAmelCase	:							List[str]						=		len(self.conv_dim						)
     UpperCAmelCase	:							Optional[Any]						=		num_hidden_layers
     UpperCAmelCase	:							Optional[int]						=		intermediate_size
     UpperCAmelCase	:							Optional[int]						=		hidden_act
     UpperCAmelCase	:							List[Any]						=		num_attention_heads
     UpperCAmelCase	:							Optional[Any]						=		hidden_dropout
     UpperCAmelCase	:							List[str]						=		attention_dropout
     UpperCAmelCase	:							str						=		activation_dropout
     UpperCAmelCase	:							int						=		feat_proj_dropout
     UpperCAmelCase	:							Dict						=		final_dropout
     UpperCAmelCase	:							Optional[Any]						=		layerdrop
     UpperCAmelCase	:							str						=		layer_norm_eps
     UpperCAmelCase	:							Optional[int]						=		initializer_range
     UpperCAmelCase	:							str						=		vocab_size
     UpperCAmelCase	:							Dict						=		num_clusters
     UpperCAmelCase	:							Union[str, Any]						=		do_stable_layer_norm
     UpperCAmelCase	:							Dict						=		use_weighted_layer_sum

     if (
         (len(self.conv_stride						) != self.num_feat_extract_layers)
         or (len(self.conv_kernel						) != self.num_feat_extract_layers)
         or (len(self.conv_dim						) != self.num_feat_extract_layers)
     ):
       raise ValueError(
           '''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
           ''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
           F''' {len(self.conv_dim						)}`, `len(config.conv_stride) = {len(self.conv_stride						)}`,'''
           F''' `len(config.conv_kernel) = {len(self.conv_kernel						)}`.'''						)

     # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
     UpperCAmelCase	:							List[str]						=		apply_spec_augment
     UpperCAmelCase	:							Optional[int]						=		mask_time_prob
     UpperCAmelCase	:							int						=		mask_time_length
     UpperCAmelCase	:							Union[str, Any]						=		mask_time_min_masks
     UpperCAmelCase	:							Dict						=		mask_feature_prob
     UpperCAmelCase	:							List[Any]						=		mask_feature_length
     UpperCAmelCase	:							List[str]						=		mask_feature_min_masks

     # parameters for pretraining with codevector quantized representations
     UpperCAmelCase	:							List[Any]						=		num_codevectors_per_group
     UpperCAmelCase	:							Tuple						=		num_codevector_groups
     UpperCAmelCase	:							Tuple						=		contrastive_logits_temperature
     UpperCAmelCase	:							Any						=		feat_quantizer_dropout
     UpperCAmelCase	:							Optional[int]						=		num_negatives
     UpperCAmelCase	:							str						=		codevector_dim
     UpperCAmelCase	:							Dict						=		proj_codevector_dim
     UpperCAmelCase	:							Optional[Any]						=		diversity_loss_weight

     # ctc loss
     UpperCAmelCase	:							List[Any]						=		ctc_loss_reduction
     UpperCAmelCase	:							List[str]						=		ctc_zero_infinity

     # SequenceClassification-specific parameter. Feel free to ignore for other classes.
     UpperCAmelCase	:							List[str]						=		classifier_proj_size

     # XVector-specific parameters. Feel free to ignore for other classes.
     UpperCAmelCase	:							int						=		list(__A						)
     UpperCAmelCase	:							Dict						=		list(__A						)
     UpperCAmelCase	:							int						=		list(__A						)
     UpperCAmelCase	:							Union[str, Any]						=		xvector_output_dim





   @property
   def 	__magic_name__   (							self							:					str						):
     return functools.reduce(operator.mul,		self.conv_stride,		1						)








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)

_lowerCamelCase    :       List[str]        =							"bert-base-cased"
_lowerCamelCase    :       str        =							"fp16"
_lowerCamelCase    :       Optional[int]        =							"bf16"
_lowerCamelCase    :       List[str]        =							[FPaa, BFaa]





@require_fsdp
@require_cuda
class  __UpperCAmelCase					(	lowerCamelCase__						):



   def 	__magic_name__   (							self							:					Tuple						):
     super().setUp()

     UpperCAmelCase	:							Optional[Any]						=			dict(
         ACCELERATE_USE_FSDP='''true''',		MASTER_ADDR='''localhost''',		MASTER_PORT='''10999''',		RANK='''0''',		LOCAL_RANK='''0''',		WORLD_SIZE='''1''',		)



   def 	__magic_name__   (							self							:					int						):
     from torch.distributed.fsdp.fully_sharded_data_parallel import ShardingStrategy

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



   def 	__magic_name__   (							self							:					Dict						):
     from torch.distributed.fsdp.fully_sharded_data_parallel import BackwardPrefetch

     for i, prefetch_policy in enumerate(__A						):
       UpperCAmelCase	:							int						=			self.dist_env.copy()
       UpperCAmelCase	:							Dict						=			prefetch_policy
       with mockenv_context(**__A						):
         UpperCAmelCase	:							Optional[int]						=			FullyShardedDataParallelPlugin()
         if prefetch_policy == "NO_PREFETCH":
           self.assertIsNone(fsdp_plugin.backward_prefetch						)
         else:
           self.assertEqual(fsdp_plugin.backward_prefetch,		BackwardPrefetch(i + 1						)						)



   def 	__magic_name__   (							self							:					Any						):
     from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType

     for i, state_dict_type in enumerate(__A						):
       UpperCAmelCase	:							Any						=			self.dist_env.copy()
       UpperCAmelCase	:							int						=			state_dict_type
       with mockenv_context(**__A						):
         UpperCAmelCase	:							str						=			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 	__magic_name__   (							self							:					int						):
     UpperCAmelCase	:							Optional[int]						=			AutoModel.from_pretrained(__A						)
     for policy in FSDP_AUTO_WRAP_POLICY:
       UpperCAmelCase	:							Any						=			self.dist_env.copy()
       UpperCAmelCase	:							List[Any]						=			policy
       if policy == "TRANSFORMER_BASED_WRAP":
         UpperCAmelCase	:							Tuple						=			'''BertLayer'''
       elif policy == "SIZE_BASED_WRAP":
         UpperCAmelCase	:							List[str]						=			'''2000'''
       with mockenv_context(**__A						):
         UpperCAmelCase	:							Optional[Any]						=			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	:							List[str]						=			self.dist_env.copy()
     UpperCAmelCase	:							Tuple						=			'''TRANSFORMER_BASED_WRAP'''
     UpperCAmelCase	:							Optional[Any]						=			'''T5Layer'''
     with mockenv_context(**__A						):
       UpperCAmelCase	:							int						=			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	:							List[Any]						=			self.dist_env.copy()
     UpperCAmelCase	:							str						=			'''SIZE_BASED_WRAP'''
     UpperCAmelCase	:							str						=			'''0'''
     with mockenv_context(**__A						):
       UpperCAmelCase	:							Optional[int]						=			FullyShardedDataParallelPlugin()
       fsdp_plugin.set_auto_wrap_policy(__A						)
       self.assertIsNone(fsdp_plugin.auto_wrap_policy						)



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

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






@require_fsdp
@require_multi_gpu
@slow
class  __UpperCAmelCase					(	lowerCamelCase__						):



   def 	__magic_name__   (							self							:					List[Any]						):
     super().setUp()
     UpperCAmelCase	:							int						=			0.8_2
     UpperCAmelCase	:							List[str]						=			[
         '''fsdp_shard_grad_op_transformer_based_wrap''',
         '''fsdp_full_shard_transformer_based_wrap''',
     ]
     UpperCAmelCase	:							int						=			{
         '''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
     }
     UpperCAmelCase	:							List[str]						=			1_6_0
     UpperCAmelCase	:							Optional[int]						=			1_6_0

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



   def 	__magic_name__   (							self							:					str						):
     UpperCAmelCase	:							str						=			os.path.join(self.test_scripts_folder,		'''test_performance.py'''						)
     UpperCAmelCase	:							Any						=			['''accelerate''', '''launch''', '''--num_processes=2''', '''--num_machines=1''', '''--machine_rank=0''', '''--use_fsdp''']
     for config in self.performance_configs:
       UpperCAmelCase	:							Union[str, Any]						=			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 	__magic_name__   (							self							:					Tuple						):
     UpperCAmelCase	:							Dict						=			os.path.join(self.test_scripts_folder,		'''test_checkpointing.py'''						)
     UpperCAmelCase	:							Optional[int]						=			[
         '''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	:							Optional[Any]						=			cmd.copy()
       cmd_config.append(F'''--fsdp_sharding_strategy={i+1}'''						)
       if strategy != "FULL_SHARD":
         continue
       UpperCAmelCase	:							Any						=			len(__A						)
       for state_dict_type in FSDP_STATE_DICT_TYPE:
         UpperCAmelCase	:							List[Any]						=			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	:							Tuple						=			cmd_config[:-1]
         UpperCAmelCase	:							List[str]						=			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 	__magic_name__   (							self							:					Any						):
     UpperCAmelCase	:							Optional[Any]						=			os.path.join(self.test_scripts_folder,		'''test_peak_memory_usage.py'''						)
     UpperCAmelCase	:							str						=			[
         '''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	:							List[Any]						=			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()						)





