
"""simple docstring"""




import torch

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


def   __magic_name__	(      __snake_case				:       Tuple  ) ->				Dict:
       return (torch.arange(state.num_processes  ) + 1.0 + (state.num_processes * state.process_index)).to(state.device  )


def   __magic_name__	(      __snake_case				:       Tuple  ) ->				Dict:
       lowercase    :				Tuple       		=							create_tensor(_SCREAMING_SNAKE_CASE  )
       lowercase    :				List[str]       		=							gather(_SCREAMING_SNAKE_CASE  )
       assert gathered_tensor.tolist() == list(range(1     , state.num_processes**2 + 1  )  )


def   __magic_name__	(      __snake_case				:       Tuple  ) ->				List[str]:
       lowercase    :				int       		=							[state.process_index]
       lowercase    :				Tuple       		=							gather_object(_SCREAMING_SNAKE_CASE  )
       assert len(_SCREAMING_SNAKE_CASE  ) == state.num_processes, f"""{gathered_obj}, {len(_SCREAMING_SNAKE_CASE  )} != {state.num_processes}"""
       assert gathered_obj == list(range(state.num_processes  )  ), f"""{gathered_obj} != {list(range(state.num_processes  )  )}"""


def   __magic_name__	(      __snake_case				:       Dict  ) ->				Dict:
       lowercase    :				List[str]       		=							create_tensor(_SCREAMING_SNAKE_CASE  )
       lowercase    :				str       		=							broadcast(_SCREAMING_SNAKE_CASE  )
       assert broadcasted_tensor.shape == torch.Size([state.num_processes]  )
       assert broadcasted_tensor.tolist() == list(range(1     , state.num_processes + 1  )  )


def   __magic_name__	(      __snake_case				:       str  ) ->				List[Any]:
       # We need to pad the tensor with one more element if we are the main process
       # to ensure that we can pad
       if state.is_main_process:
              lowercase    :				Tuple       		=							torch.arange(state.num_processes + 1  ).to(state.device  )
       else:
              lowercase    :				List[str]       		=							torch.arange(state.num_processes  ).to(state.device  )
       lowercase    :				int       		=							pad_across_processes(_SCREAMING_SNAKE_CASE  )
       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   __magic_name__	(      __snake_case				:       int  ) ->				Tuple:
       # For now runs on only two processes
       if state.num_processes != 2:
              return
       lowercase    :				List[Any]       		=							create_tensor(_SCREAMING_SNAKE_CASE  )
       lowercase    :				int       		=							reduce(_SCREAMING_SNAKE_CASE     , "sum"  )
       lowercase    :				int       		=							torch.tensor([4.0, 6]  ).to(state.device  )
       assert torch.allclose(_SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE  ), f"""{reduced_tensor} != {truth_tensor}"""


def   __magic_name__	(      __snake_case				:       int  ) ->				Tuple:
       # For now runs on only two processes
       if state.num_processes != 2:
              return
       lowercase    :				Union[str, Any]       		=							create_tensor(_SCREAMING_SNAKE_CASE  )
       lowercase    :				Dict       		=							reduce(_SCREAMING_SNAKE_CASE     , "mean"  )
       lowercase    :				int       		=							torch.tensor([2.0, 3]  ).to(state.device  )
       assert torch.allclose(_SCREAMING_SNAKE_CASE     , _SCREAMING_SNAKE_CASE  ), f"""{reduced_tensor} != {truth_tensor}"""


def   __magic_name__	(      __snake_case				:       Any  ) ->				int:
       # For xla_spawn (TPUs)
       main()


def   __magic_name__	(      ) ->				Dict:
       lowercase    :				Any       		=							PartialState()
       state.print(f"""State: {state}"""  )
       state.print("testing gather"  )
       test_gather(_SCREAMING_SNAKE_CASE  )
       state.print("testing gather_object"  )
       test_gather_object(_SCREAMING_SNAKE_CASE  )
       state.print("testing broadcast"  )
       test_broadcast(_SCREAMING_SNAKE_CASE  )
       state.print("testing pad_across_processes"  )
       test_pad_across_processes(_SCREAMING_SNAKE_CASE  )
       state.print("testing reduce_sum"  )
       test_reduce_sum(_SCREAMING_SNAKE_CASE  )
       state.print("testing reduce_mean"  )
       test_reduce_mean(_SCREAMING_SNAKE_CASE  )


if __name__ == "__main__":
   main()






'''simple docstring'''



import os
import sys

__lowercase	:						List[Any]       =	os.path.join(os.path.dirname(__file__), 'src')
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


__lowercase	:						int       =	[
    'torch',
    'numpy',
    'tokenizers',
    'filelock',
    'requests',
    'tqdm',
    'regex',
    'sentencepiece',
    'sacremoses',
    'importlib_metadata',
    'huggingface_hub',
]






@add_start_docstrings(AutoConfig.__doc__   )
def   lowerCamelCase							(*_SCREAMING_SNAKE_CASE      :   List[str]						,      **_SCREAMING_SNAKE_CASE      :   List[Any]   ):
					return AutoConfig.from_pretrained(*_SCREAMING_SNAKE_CASE						,      **_SCREAMING_SNAKE_CASE   )






@add_start_docstrings(AutoTokenizer.__doc__   )
def   lowerCamelCase							(*_SCREAMING_SNAKE_CASE      :   str						,      **_SCREAMING_SNAKE_CASE      :   Any   ):
					return AutoTokenizer.from_pretrained(*_SCREAMING_SNAKE_CASE						,      **_SCREAMING_SNAKE_CASE   )






@add_start_docstrings(AutoModel.__doc__   )
def   lowerCamelCase							(*_SCREAMING_SNAKE_CASE      :   int						,      **_SCREAMING_SNAKE_CASE      :   Union[str, Any]   ):
					return AutoModel.from_pretrained(*_SCREAMING_SNAKE_CASE						,      **_SCREAMING_SNAKE_CASE   )






@add_start_docstrings(AutoModelForCausalLM.__doc__   )
def   lowerCamelCase							(*_SCREAMING_SNAKE_CASE      :   List[Any]						,      **_SCREAMING_SNAKE_CASE      :   Optional[int]   ):
					return AutoModelForCausalLM.from_pretrained(*_SCREAMING_SNAKE_CASE						,      **_SCREAMING_SNAKE_CASE   )






@add_start_docstrings(AutoModelForMaskedLM.__doc__   )
def   lowerCamelCase							(*_SCREAMING_SNAKE_CASE      :   Union[str, Any]						,      **_SCREAMING_SNAKE_CASE      :   List[Any]   ):
					return AutoModelForMaskedLM.from_pretrained(*_SCREAMING_SNAKE_CASE						,      **_SCREAMING_SNAKE_CASE   )






@add_start_docstrings(AutoModelForSequenceClassification.__doc__   )
def   lowerCamelCase							(*_SCREAMING_SNAKE_CASE      :   Optional[Any]						,      **_SCREAMING_SNAKE_CASE      :   Any   ):
					return AutoModelForSequenceClassification.from_pretrained(*_SCREAMING_SNAKE_CASE						,      **_SCREAMING_SNAKE_CASE   )






@add_start_docstrings(AutoModelForQuestionAnswering.__doc__   )
def   lowerCamelCase							(*_SCREAMING_SNAKE_CASE      :   Any						,      **_SCREAMING_SNAKE_CASE      :   List[str]   ):
					return AutoModelForQuestionAnswering.from_pretrained(*_SCREAMING_SNAKE_CASE						,      **_SCREAMING_SNAKE_CASE   )



