


"""simple docstring"""



import torch

from accelerate import PartialState
from accelerate.utils.operations import broadcast, gather, gather_object, pad_across_processes, reduce







def 						__lowerCAmelCase		(		lowercase						:						Union[str, Any]				)			->		Optional[Any]:


    """simple docstring"""





    return (torch.arange(state.num_processes				) + 1.0 + (state.num_processes * state.process_index)).to(state.device				)







def 						__lowerCAmelCase		(		lowercase						:						Dict				)			->		List[str]:


    """simple docstring"""





    snake_case					:   Union[str, Any]         =					create_tensor(lowercase				)
    snake_case					:   Optional[int]         =					gather(lowercase				)
    assert gathered_tensor.tolist() == list(range(1		,							state.num_processes**2 + 1				)				)







def 						__lowerCAmelCase		(		lowercase						:						Optional[int]				)			->		List[Any]:


    """simple docstring"""





    snake_case					:   Any         =					[state.process_index]
    snake_case					:   Tuple         =					gather_object(lowercase				)
    assert len(lowercase				) == state.num_processes, F'{gathered_obj}, {len(lowercase				)} != {state.num_processes}'
    assert gathered_obj == list(range(state.num_processes				)				), F'{gathered_obj} != {list(range(state.num_processes				)				)}'







def 						__lowerCAmelCase		(		lowercase						:						Union[str, Any]				)			->		Optional[int]:


    """simple docstring"""





    snake_case					:   Optional[Any]         =					create_tensor(lowercase				)
    snake_case					:   Any         =					broadcast(lowercase				)
    assert broadcasted_tensor.shape == torch.Size([state.num_processes]				)
    assert broadcasted_tensor.tolist() == list(range(1		,							state.num_processes + 1				)				)







def 						__lowerCAmelCase		(		lowercase						:						Optional[Any]				)			->		str:


    """simple docstring"""





    if state.is_main_process:
        snake_case					:   List[Any]         =					torch.arange(state.num_processes + 1				).to(state.device				)
    else:
        snake_case					:   Any         =					torch.arange(state.num_processes				).to(state.device				)
    snake_case					:   Dict         =					pad_across_processes(lowercase				)
    assert padded_tensor.shape == torch.Size([state.num_processes + 1]				)
    if not state.is_main_process:
        assert padded_tensor.tolist() == list(range(0		,							state.num_processes				)				) + [0]







def 						__lowerCAmelCase		(		lowercase						:						int				)			->		Union[str, Any]:


    """simple docstring"""





    if state.num_processes != 2:
        return
    snake_case					:   List[str]         =					create_tensor(lowercase				)
    snake_case					:   List[str]         =					reduce(lowercase		,							"sum"				)
    snake_case					:   Tuple         =					torch.tensor([4.0, 6]				).to(state.device				)
    assert torch.allclose(lowercase		,							lowercase				), F'{reduced_tensor} != {truth_tensor}'







def 						__lowerCAmelCase		(		lowercase						:						Optional[int]				)			->		Any:


    """simple docstring"""





    if state.num_processes != 2:
        return
    snake_case					:   Tuple         =					create_tensor(lowercase				)
    snake_case					:   Any         =					reduce(lowercase		,							"mean"				)
    snake_case					:   List[str]         =					torch.tensor([2.0, 3]				).to(state.device				)
    assert torch.allclose(lowercase		,							lowercase				), F'{reduced_tensor} != {truth_tensor}'







def 						__lowerCAmelCase		(		lowercase						:						Optional[int]				)			->		List[Any]:


    """simple docstring"""





    main()







def 						__lowerCAmelCase		(		)			->		str:


    """simple docstring"""





    snake_case					:   Tuple         =					PartialState()
    state.print(F'State: {state}'				)
    state.print("testing gather"				)
    test_gather(lowercase				)
    state.print("testing gather_object"				)
    test_gather_object(lowercase				)
    state.print("testing broadcast"				)
    test_broadcast(lowercase				)
    state.print("testing pad_across_processes"				)
    test_pad_across_processes(lowercase				)
    state.print("testing reduce_sum"				)
    test_reduce_sum(lowercase				)
    state.print("testing reduce_mean"				)
    test_reduce_mean(lowercase				)


if __name__ == "__main__":
  main()









"""simple docstring"""



from collections import OrderedDict
from typing import Mapping

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


__snake_case         =					logging.get_logger(__name__)

__snake_case         =					{
    """facebook/xmod-base""": """https://huggingface.co/facebook/xmod-base/resolve/main/config.json""",
    """facebook/xmod-large-prenorm""": """https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json""",
    """facebook/xmod-base-13-125k""": """https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json""",
    """facebook/xmod-base-30-125k""": """https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json""",
    """facebook/xmod-base-30-195k""": """https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json""",
    """facebook/xmod-base-60-125k""": """https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json""",
    """facebook/xmod-base-60-265k""": """https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json""",
    """facebook/xmod-base-75-125k""": """https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json""",
    """facebook/xmod-base-75-269k""": """https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json""",
}







class _lowerCAmelCase       (   snake_case_			):
   __UpperCAmelCase				: Union[str, Any]													=						'''xmod'''







   def __init__(			self		,			UpperCamelCase__=3_0522		,			UpperCamelCase__=768		,			UpperCamelCase__=12		,			UpperCamelCase__=12		,			UpperCamelCase__=3072		,			UpperCamelCase__="gelu"		,			UpperCamelCase__=0.1		,			UpperCamelCase__=0.1		,			UpperCamelCase__=512		,			UpperCamelCase__=2		,			UpperCamelCase__=0.02		,			UpperCamelCase__=1e-12		,			UpperCamelCase__=1		,			UpperCamelCase__=0		,			UpperCamelCase__=2		,			UpperCamelCase__="absolute"		,			UpperCamelCase__=True		,			UpperCamelCase__=None		,			UpperCamelCase__=False		,			UpperCamelCase__=2		,			UpperCamelCase__=False		,			UpperCamelCase__=True		,			UpperCamelCase__=True		,			UpperCamelCase__=("en_XX",)		,			UpperCamelCase__=None		,			**UpperCamelCase__		,			)			->	List[str]:

       '''simple docstring'''




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

       snake_case					:   Optional[int]         =					vocab_size
       snake_case					:   Optional[Any]         =					hidden_size
       snake_case					:   Union[str, Any]         =					num_hidden_layers
       snake_case					:   int         =					num_attention_heads
       snake_case					:   Dict         =					hidden_act
       snake_case					:   Union[str, Any]         =					intermediate_size
       snake_case					:   str         =					hidden_dropout_prob
       snake_case					:   List[str]         =					attention_probs_dropout_prob
       snake_case					:   Dict         =					max_position_embeddings
       snake_case					:   Union[str, Any]         =					type_vocab_size
       snake_case					:   Union[str, Any]         =					initializer_range
       snake_case					:   str         =					layer_norm_eps
       snake_case					:   Dict         =					position_embedding_type
       snake_case					:   Any         =					use_cache
       snake_case					:   str         =					classifier_dropout
       snake_case					:   Any         =					pre_norm
       snake_case					:   Union[str, Any]         =					adapter_reduction_factor
       snake_case					:   Optional[int]         =					adapter_layer_norm
       snake_case					:   int         =					adapter_reuse_layer_norm
       snake_case					:   List[Any]         =					ln_before_adapter
       snake_case					:   Tuple         =					list(UpperCamelCase__		)
       snake_case					:   Tuple         =					default_language







class _lowerCAmelCase       (   snake_case_			):







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

       '''simple docstring'''




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





