'''simple docstring'''




import unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
  import jax.numpy as jnp

  from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
      FlaxRobertaPreLayerNormForCausalLM,
      FlaxRobertaPreLayerNormForMaskedLM,
      FlaxRobertaPreLayerNormForMultipleChoice,
      FlaxRobertaPreLayerNormForQuestionAnswering,
      FlaxRobertaPreLayerNormForSequenceClassification,
      FlaxRobertaPreLayerNormForTokenClassification,
      FlaxRobertaPreLayerNormModel,
  )




class     a__      (   unittest.TestCase ):






     def __init__( self		:		Union[str, Any]  ,				a		:		Optional[int]  ,				a		:		str=13  ,				a		:		str=7  ,				a		:		Optional[Any]=True  ,				a		:		List[Any]=True  ,				a		:		str=True  ,				a		:		str=True  ,				a		:		Dict=99  ,				a		:		Dict=32  ,				a		:		Union[str, Any]=5  ,				a		:		Tuple=4  ,				a		:		List[str]=37  ,				a		:		Optional[int]="gelu"  ,				a		:		Dict=0.1  ,				a		:		List[str]=0.1  ,				a		:		Any=5_12  ,				a		:		int=16  ,				a		:		List[Any]=2  ,				a		:		List[str]=0.02  ,				a		:		Any=4  ,				):





         """simple docstring"""



         __lowerCamelCase		=  parent
         __lowerCamelCase		=  batch_size
         __lowerCamelCase		=  seq_length
         __lowerCamelCase		=  is_training
         __lowerCamelCase		=  use_attention_mask
         __lowerCamelCase		=  use_token_type_ids
         __lowerCamelCase		=  use_labels
         __lowerCamelCase		=  vocab_size
         __lowerCamelCase		=  hidden_size
         __lowerCamelCase		=  num_hidden_layers
         __lowerCamelCase		=  num_attention_heads
         __lowerCamelCase		=  intermediate_size
         __lowerCamelCase		=  hidden_act
         __lowerCamelCase		=  hidden_dropout_prob
         __lowerCamelCase		=  attention_probs_dropout_prob
         __lowerCamelCase		=  max_position_embeddings
         __lowerCamelCase		=  type_vocab_size
         __lowerCamelCase		=  type_sequence_label_size
         __lowerCamelCase		=  initializer_range
         __lowerCamelCase		=  num_choices






     def    SCREAMING_SNAKE_CASE__   ( self		:		int    ):





         """simple docstring"""



         __lowerCamelCase		=  ids_tensor([self.batch_size, self.seq_length]  ,				self.vocab_size    )

         __lowerCamelCase		=  None
         if self.use_attention_mask:
             __lowerCamelCase		=  random_attention_mask([self.batch_size, self.seq_length]    )

         __lowerCamelCase		=  None
         if self.use_token_type_ids:
             __lowerCamelCase		=  ids_tensor([self.batch_size, self.seq_length]  ,				self.type_vocab_size    )

         __lowerCamelCase		=  RobertaPreLayerNormConfig(
             vocab_size=self.vocab_size  ,				hidden_size=self.hidden_size  ,				num_hidden_layers=self.num_hidden_layers  ,				num_attention_heads=self.num_attention_heads  ,				intermediate_size=self.intermediate_size  ,				hidden_act=self.hidden_act  ,				hidden_dropout_prob=self.hidden_dropout_prob  ,				attention_probs_dropout_prob=self.attention_probs_dropout_prob  ,				max_position_embeddings=self.max_position_embeddings  ,				type_vocab_size=self.type_vocab_size  ,				is_decoder=a  ,				initializer_range=self.initializer_range  ,				)

         return config, input_ids, token_type_ids, attention_mask






     def    SCREAMING_SNAKE_CASE__   ( self		:		Optional[Any]    ):





         """simple docstring"""



         __lowerCamelCase		=  self.prepare_config_and_inputs()
         __lowerCamelCase	,  __lowerCamelCase	,  __lowerCamelCase	,  __lowerCamelCase		=  config_and_inputs
         __lowerCamelCase		=  {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
         return config, inputs_dict






     def    SCREAMING_SNAKE_CASE__   ( self		:		Optional[int]    ):





         """simple docstring"""



         __lowerCamelCase		=  self.prepare_config_and_inputs()
         __lowerCamelCase	,  __lowerCamelCase	,  __lowerCamelCase	,  __lowerCamelCase		=  config_and_inputs

         __lowerCamelCase		=  True
         __lowerCamelCase		=  floats_tensor([self.batch_size, self.seq_length, self.hidden_size]    )
         __lowerCamelCase		=  ids_tensor([self.batch_size, self.seq_length]  ,				vocab_size=2    )

         return (
             config,
             input_ids,
             token_type_ids,
             encoder_hidden_states,
             encoder_attention_mask,
         )




@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class     a__      (   UpperCAmelCase__							,   unittest.TestCase ):
     lowerCamelCase  :     Optional[Any]							=True

     lowerCamelCase  :     Dict							=(
         (
             FlaxRobertaPreLayerNormModel,
             FlaxRobertaPreLayerNormForCausalLM,
             FlaxRobertaPreLayerNormForMaskedLM,
             FlaxRobertaPreLayerNormForSequenceClassification,
             FlaxRobertaPreLayerNormForTokenClassification,
             FlaxRobertaPreLayerNormForMultipleChoice,
             FlaxRobertaPreLayerNormForQuestionAnswering,
         )
         if is_flax_available()
         else ()
     )






     def    SCREAMING_SNAKE_CASE__   ( self		:		Optional[int]    ):





         """simple docstring"""



         __lowerCamelCase		=  FlaxRobertaPreLayerNormModelTester(self    )






     @slow
     def    SCREAMING_SNAKE_CASE__   ( self		:		Dict    ):





         """simple docstring"""



         for model_class_name in self.all_model_classes:
             __lowerCamelCase		=  model_class_name.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''  ,				from_pt=a    )
             __lowerCamelCase		=  model(np.ones((1, 1)    )    )
             self.assertIsNotNone(a    )




@require_flax
class     a__      (   unittest.TestCase ):






     @slow
     def    SCREAMING_SNAKE_CASE__   ( self		:		Optional[int]    ):





         """simple docstring"""



         __lowerCamelCase		=  FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''  ,				from_pt=a    )

         __lowerCamelCase		=  np.array([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]  ,				dtype=jnp.intaa    )
         __lowerCamelCase		=  model(a    )[0]
         __lowerCamelCase		=  [1, 11, 5_02_65]
         self.assertEqual(list(output.shape    )  ,				a    )
         # compare the actual values for a slice.
         __lowerCamelCase		=  np.array(
             [[[40.48_80, 18.01_99, -5.23_67], [-1.88_77, -4.08_85, 10.70_85], [-2.26_13, -5.61_10, 7.26_65]]]  ,				dtype=np.floataa    )
         self.assertTrue(np.allclose(output[:, :3, :3]  ,				a  ,				atol=1e-4    )    )






     @slow
     def    SCREAMING_SNAKE_CASE__   ( self		:		List[Any]    ):





         """simple docstring"""



         __lowerCamelCase		=  FlaxRobertaPreLayerNormModel.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''  ,				from_pt=a    )

         __lowerCamelCase		=  np.array([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]]  ,				dtype=jnp.intaa    )
         __lowerCamelCase		=  model(a    )[0]
         # compare the actual values for a slice.
         __lowerCamelCase		=  np.array(
             [[[0.02_08, -0.03_56, 0.02_37], [-0.15_69, -0.04_11, -0.26_26], [0.18_79, 0.01_25, -0.00_89]]]  ,				dtype=np.floataa    )
         self.assertTrue(np.allclose(output[:, :3, :3]  ,				a  ,				atol=1e-4    )    )


"""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))






