


import os
import sys
import unittest


__UpperCAmelCase       =							os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, '''utils'''))

import get_test_info  # noqa: E402
from get_test_info import (  # noqa: E402
    get_model_to_test_mapping,
    get_model_to_tester_mapping,
    get_test_to_tester_mapping,
)


__UpperCAmelCase       =							os.path.join('''tests''', '''models''', '''bert''', '''test_modeling_bert.py''')
__UpperCAmelCase       =							os.path.join('''tests''', '''models''', '''blip''', '''test_modeling_blip.py''')




class 		a__    (							unittest.TestCase  ):





							'''simple docstring'''






							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Optional[Any]:
														lowerCAmelCase__	       =     get_test_to_tester_mapping(snake_case__       )
														lowerCAmelCase__	       =     get_test_to_tester_mapping(snake_case__       )

														lowerCAmelCase__	       =     {'BertModelTest': 'BertModelTester'}

														lowerCAmelCase__	       =     {
														    'BlipModelTest': 'BlipModelTester',
														    'BlipTextImageModelTest': 'BlipTextImageModelsModelTester',
														    'BlipTextModelTest': 'BlipTextModelTester',
														    'BlipTextRetrievalModelTest': 'BlipTextRetrievalModelTester',
														    'BlipVQAModelTest': 'BlipVQAModelTester',
														    'BlipVisionModelTest': 'BlipVisionModelTester',
														}

														self.assertEqual(get_test_info.to_json(snake_case__       ) ,   snake_case__       )
														self.assertEqual(get_test_info.to_json(snake_case__       ) ,   snake_case__       )







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							int:
														lowerCAmelCase__	       =     get_model_to_test_mapping(snake_case__       )
														lowerCAmelCase__	       =     get_model_to_test_mapping(snake_case__       )

														lowerCAmelCase__	       =     {
														    'BertForMaskedLM': ['BertModelTest'],
														    'BertForMultipleChoice': ['BertModelTest'],
														    'BertForNextSentencePrediction': ['BertModelTest'],
														    'BertForPreTraining': ['BertModelTest'],
														    'BertForQuestionAnswering': ['BertModelTest'],
														    'BertForSequenceClassification': ['BertModelTest'],
														    'BertForTokenClassification': ['BertModelTest'],
														    'BertLMHeadModel': ['BertModelTest'],
														    'BertModel': ['BertModelTest'],
														}

														lowerCAmelCase__	       =     {
														    'BlipForConditionalGeneration': ['BlipTextImageModelTest'],
														    'BlipForImageTextRetrieval': ['BlipTextRetrievalModelTest'],
														    'BlipForQuestionAnswering': ['BlipVQAModelTest'],
														    'BlipModel': ['BlipModelTest'],
														    'BlipTextModel': ['BlipTextModelTest'],
														    'BlipVisionModel': ['BlipVisionModelTest'],
														}

														self.assertEqual(get_test_info.to_json(snake_case__       ) ,   snake_case__       )
														self.assertEqual(get_test_info.to_json(snake_case__       ) ,   snake_case__       )







							def   __SCREAMING_SNAKE_CASE							(		self       )	->							int:
														lowerCAmelCase__	       =     get_model_to_tester_mapping(snake_case__       )
														lowerCAmelCase__	       =     get_model_to_tester_mapping(snake_case__       )

														lowerCAmelCase__	       =     {
														    'BertForMaskedLM': ['BertModelTester'],
														    'BertForMultipleChoice': ['BertModelTester'],
														    'BertForNextSentencePrediction': ['BertModelTester'],
														    'BertForPreTraining': ['BertModelTester'],
														    'BertForQuestionAnswering': ['BertModelTester'],
														    'BertForSequenceClassification': ['BertModelTester'],
														    'BertForTokenClassification': ['BertModelTester'],
														    'BertLMHeadModel': ['BertModelTester'],
														    'BertModel': ['BertModelTester'],
														}

														lowerCAmelCase__	       =     {
														    'BlipForConditionalGeneration': ['BlipTextImageModelsModelTester'],
														    'BlipForImageTextRetrieval': ['BlipTextRetrievalModelTester'],
														    'BlipForQuestionAnswering': ['BlipVQAModelTester'],
														    'BlipModel': ['BlipModelTester'],
														    'BlipTextModel': ['BlipTextModelTester'],
														    'BlipVisionModel': ['BlipVisionModelTester'],
														}

														self.assertEqual(get_test_info.to_json(snake_case__       ) ,   snake_case__       )
														self.assertEqual(get_test_info.to_json(snake_case__       ) ,   snake_case__       )



'''simple docstring'''






import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer


def  _snake_case       (					A			,   A			,   A			,   A=5	)						->   List[str]:
							# Adapted from https://github.com/pytorch/fairseq/blob/master/fairseq/models/roberta/hub_interface.py
							assert masked_input.count('''<mask>'''	) == 1
							lowerCAmelCase__	       =     torch.tensor(tokenizer.encode(A			,   add_special_tokens=A	)	).unsqueeze(0	)  # Batch size 1
							lowerCAmelCase__	       =     model(A	)[0]  # The last hidden-state is the first element of the output tuple
							lowerCAmelCase__	       =     (input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
							lowerCAmelCase__	       =     logits[0, masked_index, :]
							lowerCAmelCase__	       =     logits.softmax(dim=0	)
							lowerCAmelCase__       , lowerCAmelCase__	       =     prob.topk(k=A			,   dim=0	)
							lowerCAmelCase__	       =     ''' '''.join(
							    [tokenizer.convert_ids_to_tokens(indices[i].item()	) for i in range(len(A	)	)]	)
							lowerCAmelCase__	       =     tokenizer.mask_token
							lowerCAmelCase__	       =     []
							for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(''' '''	)	):
														lowerCAmelCase__	       =     predicted_token_bpe.replace('''\u2581'''			,   ''' '''	)
														if " {0}".format(A	) in masked_input:
																					topk_filled_outputs.append(
																					    (
																					        masked_input.replace(''' {0}'''.format(A	)			,   A	),
																					        values[index].item(),
																					        predicted_token,
																					    )	)
														else:
																					topk_filled_outputs.append(
																					    (
																					        masked_input.replace(A			,   A	),
																					        values[index].item(),
																					        predicted_token,
																					    )	)
							return topk_filled_outputs


__UpperCAmelCase       =							CamembertTokenizer.from_pretrained('''camembert-base''')
__UpperCAmelCase       =							CamembertForMaskedLM.from_pretrained('''camembert-base''')
model.eval()

__UpperCAmelCase       =							'''Le camembert est <mask> :)'''






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