
"""simple docstring"""





from argparse import ArgumentParser

from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand


_a														=		logging.get_logger(__name__)  # pylint: disable=invalid-name



def       lowerCamelCase__		(			__snake_case	)							->		Optional[int]:


					"""simple docstring"""





					if not path:
										return "pipe"

					for ext in PipelineDataFormat.SUPPORTED_FORMATS:
										if path.endswith(__snake_case	):
															return ext

					raise Exception(
					    F'''Unable to determine file format from file extension {path}. '''
					    F'''Please provide the format through --format {PipelineDataFormat.SUPPORTED_FORMATS}'''	)



def       lowerCamelCase__		(			__snake_case	)							->		str:


					"""simple docstring"""





					_UpperCamelCase							 =  pipeline(
					    task=args.task,     model=args.model if args.model else None,     config=args.config,     tokenizer=args.tokenizer,     device=args.device,     )
					_UpperCamelCase							 =  try_infer_format_from_ext(args.input	) if args.format == '''infer''' else args.format
					_UpperCamelCase							 =  PipelineDataFormat.from_str(
					    format=__snake_case,     output_path=args.output,     input_path=args.input,     column=args.column if args.column else nlp.default_input_names,     overwrite=args.overwrite,     )
					return RunCommand(__snake_case,     __snake_case	)






class     _UpperCAmelCase(    lowerCamelCase	):



		def __init__(					self				,		__a				,		__a)       ->       List[str]:






							'''simple docstring'''
							_UpperCamelCase							 =  nlp
							_UpperCamelCase							 =  reader



		@staticmethod
		def 						UpperCAmelCase							(					__a)       ->       Dict:






							'''simple docstring'''
							_UpperCamelCase							 =  parser.add_parser('''run'''				,		help='''Run a pipeline through the CLI''')
							run_parser.add_argument('''--task'''				,		choices=get_supported_tasks()				,		help='''Task to run''')
							run_parser.add_argument('''--input'''				,		type=__a				,		help='''Path to the file to use for inference''')
							run_parser.add_argument('''--output'''				,		type=__a				,		help='''Path to the file that will be used post to write results.''')
							run_parser.add_argument('''--model'''				,		type=__a				,		help='''Name or path to the model to instantiate.''')
							run_parser.add_argument('''--config'''				,		type=__a				,		help='''Name or path to the model\'s config to instantiate.''')
							run_parser.add_argument(
							    '''--tokenizer'''				,		type=__a				,		help='''Name of the tokenizer to use. (default: same as the model name)''')
							run_parser.add_argument(
							    '''--column'''				,		type=__a				,		help='''Name of the column to use as input. (For multi columns input as QA use column1,columns2)'''				,		)
							run_parser.add_argument(
							    '''--format'''				,		type=__a				,		default='''infer'''				,		choices=PipelineDataFormat.SUPPORTED_FORMATS				,		help='''Input format to read from'''				,		)
							run_parser.add_argument(
							    '''--device'''				,		type=__a				,		default=-1				,		help='''Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)'''				,		)
							run_parser.add_argument('''--overwrite'''				,		action='''store_true'''				,		help='''Allow overwriting the output file.''')
							run_parser.set_defaults(func=__a)




		def 						UpperCAmelCase							(					self)       ->       Dict:






							'''simple docstring'''
							_UpperCamelCase ,  _UpperCamelCase							 =  self._nlp, []

							for entry in self._reader:
												_UpperCamelCase							 =  nlp(**__a) if self._reader.is_multi_columns else nlp(__a)
												if isinstance(__a				,		__a):
																	outputs.append(__a)
												else:
																	outputs += output

        # Saving data
							if self._nlp.binary_output:
												_UpperCamelCase							 =  self._reader.save_binary(__a)
												logger.warning(F'''Current pipeline requires output to be in binary format, saving at {binary_path}''')
							else:
												self._reader.save(__a)








"""simple docstring"""





import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer



def       lowerCamelCase__		(			__snake_case,     __snake_case,     __snake_case,     __snake_case=5	)							->		Union[str, Any]:


					"""simple docstring"""





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


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

_a														=		"""Le camembert est <mask> :)"""
print(fill_mask(masked_input, model, tokenizer, topk=3))






