





"""simple docstring"""


import inspect
import os
import unittest

import torch

import accelerate
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_multi_gpu
from accelerate.utils import patch_environment



class   __SCREAMING_SNAKE_CASE     (    unittest.TestCase ):







  '''simple docstring'''


  def  __SCREAMING_SNAKE_CASE					(		self :							Any						)		->					List[Any]:
        _UpperCamelCase   :						Dict    =							inspect.getfile(accelerate.test_utils						)
        _UpperCamelCase   :						Any    =							os.path.sep.join(mod_file.split(os.path.sep						)[:-1] + ["scripts", "test_script.py"]						)
        _UpperCamelCase   :						Union[str, Any]    =							os.path.sep.join(
            mod_file.split(os.path.sep						)[:-1] + ["scripts", "test_distributed_data_loop.py"]						)
        _UpperCamelCase   :						Union[str, Any]    =							os.path.sep.join(mod_file.split(os.path.sep						)[:-1] + ["scripts", "test_ops.py"]						)

  @require_multi_gpu
  def  __SCREAMING_SNAKE_CASE					(		self :							str						)		->					int:
        print(F'''Found {torch.cuda.device_count()} devices.'''						)
        _UpperCamelCase   :						Dict    =							["torchrun", F'''--nproc_per_node={torch.cuda.device_count()}''', self.test_file_path]
        with patch_environment(omp_num_threads=1						):
              execute_subprocess_async(__a       , env=os.environ.copy()						)

  @require_multi_gpu
  def  __SCREAMING_SNAKE_CASE					(		self :							Dict						)		->					List[str]:
        print(F'''Found {torch.cuda.device_count()} devices.'''						)
        _UpperCamelCase   :						str    =							["torchrun", F'''--nproc_per_node={torch.cuda.device_count()}''', self.operation_file_path]
        print(F'''Command: {cmd}'''						)
        with patch_environment(omp_num_threads=1						):
              execute_subprocess_async(__a       , env=os.environ.copy()						)

  @require_multi_gpu
  def  __SCREAMING_SNAKE_CASE					(		self :							Any						)		->					int:
        _UpperCamelCase   :						Tuple    =							["torchrun", F'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__						)]
        with patch_environment(omp_num_threads=1						):
              execute_subprocess_async(__a       , env=os.environ.copy()						)


  @require_multi_gpu
  def  __SCREAMING_SNAKE_CASE					(		self :							Tuple						)		->					int:
        print(F'''Found {torch.cuda.device_count()} devices, using 2 devices only'''						)
        _UpperCamelCase   :						int    =							["torchrun", F'''--nproc_per_node={torch.cuda.device_count()}''', self.data_loop_file_path]
        with patch_environment(omp_num_threads=1       , cuda_visible_devices="0,1"						):
              execute_subprocess_async(__a       , env=os.environ.copy()						)


if __name__ == "__main__":
  lowerCamelCase__						= Accelerator()
  lowerCamelCase__						= (accelerator.state.process_index + 2, 10)
  lowerCamelCase__						= torch.randint(0, 10, shape).to(accelerator.device)

  lowerCamelCase__						= ""

  lowerCamelCase__						= accelerator.pad_across_processes(tensor)
  if tensora.shape[0] != accelerator.state.num_processes + 1:
    error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
  if not torch.equal(tensora[: accelerator.state.process_index + 2], tensor):
    error_msg += "Tensors have different values."
  if not torch.all(tensora[accelerator.state.process_index + 2 :] == 0):
    error_msg += "Padding was not done with the right value (0)."

  lowerCamelCase__						= accelerator.pad_across_processes(tensor, pad_first=True)
  if tensora.shape[0] != accelerator.state.num_processes + 1:
    error_msg += f"Found shape {tensora.shape} but should have {accelerator.state.num_processes + 1} at dim 0."
  lowerCamelCase__						= accelerator.state.num_processes - accelerator.state.process_index - 1
  if not torch.equal(tensora[index:], tensor):
    error_msg += "Tensors have different values."
  if not torch.all(tensora[:index] == 0):
    error_msg += "Padding was not done with the right value (0)."

  # Raise error at the end to make sure we don't stop at the first failure.
  if len(error_msg) > 0:
    raise ValueError(error_msg)







"""simple docstring"""


import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer




def        lowercase__     (       lowercase_		,lowercase_		,lowercase_		,lowercase_=5      )       ->		List[Any]:




      """simple docstring"""

      assert masked_input.count("<mask>"      ) == 1
      _UpperCamelCase   :						str    =							torch.tensor(tokenizer.encode(lowercase_		,add_special_tokens=lowercase_      )      ).unsqueeze(0      )  # Batch size 1
      _UpperCamelCase   :						Union[str, Any]    =							model(lowercase_      )[0]  # The last hidden-state is the first element of the output tuple
      _UpperCamelCase   :						List[Any]    =							(input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
      _UpperCamelCase   :						Tuple    =							logits[0, masked_index, :]
      _UpperCamelCase   :						List[str]    =							logits.softmax(dim=0      )
      _UpperCamelCase,	_UpperCamelCase   :						str    =							prob.topk(k=lowercase_		,dim=0      )
      _UpperCamelCase   :						str    =							" ".join(
          [tokenizer.convert_ids_to_tokens(indices[i].item()      ) for i in range(len(lowercase_      )      )]      )
      _UpperCamelCase   :						Any    =							tokenizer.mask_token
      _UpperCamelCase   :						List[Any]    =							[]
      for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(" "      )      ):
            _UpperCamelCase   :						Optional[int]    =							predicted_token_bpe.replace("\u2581"		," "      )
            if " {0}".format(lowercase_      ) in masked_input:
                  topk_filled_outputs.append(
                      (
                          masked_input.replace(" {0}".format(lowercase_      )		,lowercase_      ),
                          values[index].item(),
                          predicted_token,
                      )      )
            else:
                  topk_filled_outputs.append(
                      (
                          masked_input.replace(lowercase_		,lowercase_      ),
                          values[index].item(),
                          predicted_token,
                      )      )
      return topk_filled_outputs


lowerCamelCase__						= CamembertTokenizer.from_pretrained("camembert-base")
lowerCamelCase__						= CamembertForMaskedLM.from_pretrained("camembert-base")
model.eval()

lowerCamelCase__						= "Le camembert est <mask> :)"

print(fill_mask(masked_input, model, tokenizer, topk=3))
