


import json
import os
import unittest

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin






class       _UpperCamelCase    (					__snake_case    ,		unittest.TestCase				):

				"""simple docstring"""







				lowerCAmelCase 		= XLMTokenizer
				lowerCAmelCase 		= False

				def      _UpperCAmelCase					(						self   )	->				Dict:
					super().setUp()

					# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
					A								=				[
					    """l""",
					    """o""",
					    """w""",
					    """e""",
					    """r""",
					    """s""",
					    """t""",
					    """i""",
					    """d""",
					    """n""",
					    """w</w>""",
					    """r</w>""",
					    """t</w>""",
					    """lo""",
					    """low""",
					    """er</w>""",
					    """low</w>""",
					    """lowest</w>""",
					    """newer</w>""",
					    """wider</w>""",
					    """<unk>""",
					]
					A								=				dict(zip(a__				,   range(len(a__   )   )   )   )
					A								=				["""l o 123""", """lo w 1456""", """e r</w> 1789""", """"""]

					A								=				os.path.join(self.tmpdirname				,   VOCAB_FILES_NAMES["""vocab_file"""]   )
					A								=				os.path.join(self.tmpdirname				,   VOCAB_FILES_NAMES["""merges_file"""]   )
					with open(self.vocab_file				,   """w"""   ) as fp:
						fp.write(json.dumps(a__   )   )
					with open(self.merges_file				,   """w"""   ) as fp:
						fp.write("""\n""".join(a__   )   )

				def      _UpperCAmelCase					(						self				,   a__   )	->				List[str]:
					A								=				"""lower newer"""
					A								=				"""lower newer"""
					return input_text, output_text

				def      _UpperCAmelCase					(						self   )	->				Tuple:
					A								=				XLMTokenizer(self.vocab_file				,   self.merges_file   )

					A								=				"""lower"""
					A								=				["""low""", """er</w>"""]
					A								=				tokenizer.tokenize(a__   )
					self.assertListEqual(a__				,   a__   )

					A								=				tokens + ["""<unk>"""]
					A								=				[14, 15, 20]
					self.assertListEqual(tokenizer.convert_tokens_to_ids(a__   )				,   a__   )

				@slow
				def      _UpperCAmelCase					(						self   )	->				Union[str, Any]:
					A								=				XLMTokenizer.from_pretrained("""xlm-mlm-en-2048"""   )

					A								=				tokenizer.encode("""sequence builders"""				,   add_special_tokens=a__   )
					A								=				tokenizer.encode("""multi-sequence build"""				,   add_special_tokens=a__   )

					A								=				tokenizer.build_inputs_with_special_tokens(a__   )
					A								=				tokenizer.build_inputs_with_special_tokens(a__				,   a__   )

					assert encoded_sentence == [0] + text + [1]
					assert encoded_pair == [0] + text + [1] + text_a + [1]







import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer





def   _lowerCAmelCase				(   UpperCamelCase__:     Union[str, Any]							,					UpperCamelCase__:     Optional[int]							,					UpperCamelCase__:     Tuple							,					UpperCamelCase__:     Any=5						) ->   Optional[Any]:





	"""simple docstring"""







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


_lowercase       :      Optional[int]	=	CamembertTokenizer.from_pretrained("camembert-base")
_lowercase       :      int	=	CamembertForMaskedLM.from_pretrained("camembert-base")
model.eval()

_lowercase       :      Optional[int]	=	"Le camembert est <mask> :)"
print(fill_mask(masked_input, model, tokenizer, topk=3))



