






from collections import OrderedDict
from typing import Mapping

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


lowerCamelCase					    =	logging.get_logger(__name__)

lowerCamelCase					    =	{
    '''uclanlp/plbart-base''': '''https://huggingface.co/uclanlp/plbart-base/resolve/main/config.json''',
    # See all PLBART models at https://huggingface.co/models?filter=plbart
}

class        snake_case_				(							lowercase_   ):







   """simple docstring"""



   __lowerCAmelCase  =    """plbart"""
   __lowerCAmelCase  =    ["""past_key_values"""]
   __lowerCAmelCase  =    {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}

   def __init__(     self:						Tuple ,							snake_case__:						List[Any]=50_005 ,							snake_case__:						Tuple=1_024 ,							snake_case__:						Tuple=6 ,							snake_case__:						Tuple=3_072 ,							snake_case__:						Any=12 ,							snake_case__:						str=6 ,							snake_case__:						str=3_072 ,							snake_case__:						Any=12 ,							snake_case__:						Any=0.0 ,							snake_case__:						List[Any]=0.0 ,							snake_case__:						Optional[int]=True ,							snake_case__:						Union[str, Any]=True ,							snake_case__:						Optional[int]="gelu" ,							snake_case__:						Tuple=768 ,							snake_case__:						str=0.1 ,							snake_case__:						Union[str, Any]=0.1 ,							snake_case__:						Optional[Any]=0.0 ,							snake_case__:						int=0.02 ,							snake_case__:						Tuple=0.0 ,							snake_case__:						str=True ,							snake_case__:						Optional[int]=1 ,							snake_case__:						Tuple=0 ,							snake_case__:						List[str]=2 ,							snake_case__:						Optional[int]=2 ,							**snake_case__:						Dict ,							)	->		Optional[int]:


       """simple docstring"""



       _lowercase     			=						vocab_size
       _lowercase     			=						max_position_embeddings
       _lowercase     			=						d_model
       _lowercase     			=						encoder_ffn_dim
       _lowercase     			=						encoder_layers
       _lowercase     			=						encoder_attention_heads
       _lowercase     			=						decoder_ffn_dim
       _lowercase     			=						decoder_layers
       _lowercase     			=						decoder_attention_heads
       _lowercase     			=						dropout
       _lowercase     			=						attention_dropout
       _lowercase     			=						activation_dropout
       _lowercase     			=						activation_function
       _lowercase     			=						init_std
       _lowercase     			=						encoder_layerdrop
       _lowercase     			=						decoder_layerdrop
       _lowercase     			=						classifier_dropout
       _lowercase     			=						use_cache
       _lowercase     			=						encoder_layers
       _lowercase     			=						scale_embedding  # scale factor will be sqrt(d_model) if True
       super().__init__(
           pad_token_id=snake_case__ ,							bos_token_id=snake_case__ ,							eos_token_id=snake_case__ ,							is_encoder_decoder=snake_case__ ,							forced_eos_token_id=snake_case__ ,							**snake_case__ ,							)






class        snake_case_				(							lowercase_   ):







   """simple docstring"""



   @property
   def       SCREAMING_SNAKE_CASE__					(     self:						str						)	->		Mapping[str, Mapping[int, str]]:


       """simple docstring"""



       return OrderedDict(
           [
               ('input_ids', {0: 'batch', 1: 'sequence'}),
               ('attention_mask', {0: 'batch', 1: 'sequence'}),
           ]						)

   @property
   def       SCREAMING_SNAKE_CASE__					(     self:						Any						)	->		Mapping[str, Mapping[int, str]]:


       """simple docstring"""



       if self.use_past:
           return OrderedDict(
               [
                   ('last_hidden_state', {0: 'batch', 1: 'sequence'}),
                   ('past_keys', {0: 'batch', 2: 'sequence'}),
                   ('encoder_last_hidden_state', {0: 'batch', 1: 'sequence'}),
               ]						)
       else:
           return OrderedDict(
               [
                   ('last_hidden_state', {0: 'batch', 1: 'sequence'}),
                   ('encoder_last_hidden_state', {0: 'batch', 1: 'sequence'}),
               ]						)








import torch

from accelerate import Accelerator, DistributedType

class        snake_case_				:







   """simple docstring"""



   def __enter__(     self:						int						)	->		Tuple:


       """simple docstring"""



       if self.enabled:
           _lowercase     			=						self.model.no_sync()
           self.model_sync_obj.__enter__()

       return self

   def __exit__(     self:						str ,							snake_case__:						Tuple ,							snake_case__:						int ,							snake_case__:						Tuple						)	->		Any:


       """simple docstring"""



       if self.enabled:
           # Average all models on exit
           self._sync_and_avg_model_params()
           self.model_sync_obj.__exit__(snake_case__ ,							snake_case__ ,							snake_case__						)

   def __init__(     self:						int ,							snake_case__:						Accelerator ,							snake_case__:						torch.nn.Module ,							snake_case__:						int ,							snake_case__:						bool = True						)	->		Optional[Any]:


       """simple docstring"""



       if accelerator.distributed_type not in [
           DistributedType.NO,
           DistributedType.MULTI_CPU,
           DistributedType.MULTI_GPU,
       ]:
           raise NotImplementedError('LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)'						)
       _lowercase     			=						enabled and accelerator.distributed_type != DistributedType.NO
       _lowercase     			=						0
       if self.enabled:
           _lowercase     			=						accelerator
           _lowercase     			=						model
           _lowercase     			=						local_sgd_steps

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


       """simple docstring"""



       self.num_steps += 1
       if not self.enabled:
           return

       if self.num_steps % self.local_sgd_steps == 0:
           self._sync_and_avg_model_params()

   def       SCREAMING_SNAKE_CASE__					(     self:						Optional[int]						)	->		Dict:


       """simple docstring"""



       self.accelerator.wait_for_everyone()
       with self.accelerator.autocast():
           for param in self.model.parameters():
               _lowercase     			=						self.accelerator.reduce(param.data ,							reduction='mean'						)
