





"""simple docstring"""


from __future__ import annotations

import unittest

from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
  import tensorflow as tf

  from transformers import TFAutoModelForSeqaSeqLM, TFBlenderbotForConditionalGeneration, TFBlenderbotModel



@require_tf
class   __SCREAMING_SNAKE_CASE     :







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__				:List[Any]						  =       BlenderbotConfig
  SCREAMING_SNAKE_CASE__				:Optional[Any]						  =       {}
  SCREAMING_SNAKE_CASE__				:Optional[int]						  =       "gelu"

  def __init__(		self :							Union[str, Any]       , __a :							Dict       , __a :							Any=13       , __a :							Any=7       , __a :							Optional[int]=True       , __a :							Any=False       , __a :							Dict=99       , __a :							Optional[int]=32       , __a :							Dict=2       , __a :							List[Any]=4       , __a :							Dict=37       , __a :							Optional[Any]=0.1       , __a :							Optional[Any]=0.1       , __a :							Any=20       , __a :							Any=2       , __a :							Optional[int]=1       , __a :							str=0       , )		->					Union[str, Any]:
        _UpperCamelCase   :						Optional[Any]    =							parent
        _UpperCamelCase   :						Optional[int]    =							batch_size
        _UpperCamelCase   :						Union[str, Any]    =							seq_length
        _UpperCamelCase   :						int    =							is_training
        _UpperCamelCase   :						List[Any]    =							use_labels
        _UpperCamelCase   :						Union[str, Any]    =							vocab_size
        _UpperCamelCase   :						Dict    =							hidden_size
        _UpperCamelCase   :						int    =							num_hidden_layers
        _UpperCamelCase   :						Optional[int]    =							num_attention_heads
        _UpperCamelCase   :						Dict    =							intermediate_size

        _UpperCamelCase   :						List[Any]    =							hidden_dropout_prob
        _UpperCamelCase   :						Dict    =							attention_probs_dropout_prob
        _UpperCamelCase   :						Dict    =							max_position_embeddings
        _UpperCamelCase   :						Union[str, Any]    =							eos_token_id
        _UpperCamelCase   :						List[str]    =							pad_token_id
        _UpperCamelCase   :						Tuple    =							bos_token_id

  def  __SCREAMING_SNAKE_CASE					(		self :							Optional[Any]						)		->					Union[str, Any]:
        _UpperCamelCase   :						Dict    =							ids_tensor([self.batch_size, self.seq_length - 1]       , self.vocab_size						)
        _UpperCamelCase   :						str    =							tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size						)       , 1						)
        _UpperCamelCase   :						Any    =							tf.concat([input_ids, eos_tensor]       , axis=1						)

        _UpperCamelCase   :						Any    =							ids_tensor([self.batch_size, self.seq_length]       , self.vocab_size						)

        _UpperCamelCase   :						Optional[int]    =							self.config_cls(
            vocab_size=self.vocab_size       , d_model=self.hidden_size       , encoder_layers=self.num_hidden_layers       , decoder_layers=self.num_hidden_layers       , encoder_attention_heads=self.num_attention_heads       , decoder_attention_heads=self.num_attention_heads       , encoder_ffn_dim=self.intermediate_size       , decoder_ffn_dim=self.intermediate_size       , dropout=self.hidden_dropout_prob       , attention_dropout=self.attention_probs_dropout_prob       , max_position_embeddings=self.max_position_embeddings       , eos_token_ids=[2]       , bos_token_id=self.bos_token_id       , pad_token_id=self.pad_token_id       , decoder_start_token_id=self.pad_token_id       , **self.config_updates       , )
        _UpperCamelCase   :						Any    =							prepare_blenderbot_inputs_dict(__a       , __a       , __a						)
        return config, inputs_dict


  def  __SCREAMING_SNAKE_CASE					(		self :							str       , __a :							Dict       , __a :							Optional[int]						)		->					Optional[Any]:
        _UpperCamelCase   :						Optional[int]    =							TFBlenderbotModel(config=__a						).get_decoder()
        _UpperCamelCase   :						str    =							inputs_dict["input_ids"]

        _UpperCamelCase   :						Union[str, Any]    =							input_ids[:1, :]
        _UpperCamelCase   :						Dict    =							inputs_dict["attention_mask"][:1, :]
        _UpperCamelCase   :						List[Any]    =							inputs_dict["head_mask"]
        _UpperCamelCase   :						int    =							1

        # first forward pass
        _UpperCamelCase   :						Optional[Any]    =							model(__a       , attention_mask=__a       , head_mask=__a       , use_cache=__a						)

        _UpperCamelCase,	_UpperCamelCase   :						Any    =							outputs.to_tuple()

        # create hypothetical next token and extent to next_input_ids
        _UpperCamelCase   :						Optional[Any]    =							ids_tensor((self.batch_size, 3)       , config.vocab_size						)
        _UpperCamelCase   :						int    =							tf.cast(ids_tensor((self.batch_size, 3)       , 2						)       , tf.inta						)

        # append to next input_ids and
        _UpperCamelCase   :						List[Any]    =							tf.concat([input_ids, next_tokens]       , axis=-1						)
        _UpperCamelCase   :						int    =							tf.concat([attention_mask, next_attn_mask]       , axis=-1						)

        _UpperCamelCase   :						Tuple    =							model(__a       , attention_mask=__a						)[0]
        _UpperCamelCase   :						Dict    =							model(__a       , attention_mask=__a       , past_key_values=__a						)[0]

        self.parent.assertEqual(next_tokens.shape[1]       , output_from_past.shape[1]						)

        # select random slice
        _UpperCamelCase   :						Tuple    =							int(ids_tensor((1,)       , output_from_past.shape[-1]						)						)
        _UpperCamelCase   :						List[Any]    =							output_from_no_past[:, -3:, random_slice_idx]
        _UpperCamelCase   :						Optional[Any]    =							output_from_past[:, :, random_slice_idx]

        # test that outputs are equal for slice
        tf.debugging.assert_near(__a       , __a       , rtol=1e-3						)





def        lowercase__     (       lowercase_		,lowercase_		,lowercase_		,lowercase_=None		,lowercase_=None		,lowercase_=None		,lowercase_=None		,lowercase_=None		,)       ->		Optional[int]:




      """simple docstring"""

      if attention_mask is None:
            _UpperCamelCase   :						Optional[int]    =							tf.cast(tf.math.not_equal(lowercase_		,config.pad_token_id      )		,tf.inta      )
      if decoder_attention_mask is None:
            _UpperCamelCase   :						Optional[int]    =							tf.concat(
                [
                    tf.ones(decoder_input_ids[:, :1].shape		,dtype=tf.inta      ),
                    tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]		,config.pad_token_id      )		,tf.inta      ),
                ]		,axis=-1		,)
      if head_mask is None:
            _UpperCamelCase   :						Optional[Any]    =							tf.ones((config.encoder_layers, config.encoder_attention_heads)      )
      if decoder_head_mask is None:
            _UpperCamelCase   :						Dict    =							tf.ones((config.decoder_layers, config.decoder_attention_heads)      )
      if cross_attn_head_mask is None:
            _UpperCamelCase   :						Optional[int]    =							tf.ones((config.decoder_layers, config.decoder_attention_heads)      )
      return {
          "input_ids": input_ids,
          "decoder_input_ids": decoder_input_ids,
          "attention_mask": attention_mask,
          "decoder_attention_mask": decoder_attention_mask,
          "head_mask": head_mask,
          "decoder_head_mask": decoder_head_mask,
          "cross_attn_head_mask": cross_attn_head_mask,
      }



@require_tf
class   __SCREAMING_SNAKE_CASE     (    _UpperCamelCase		,	_UpperCamelCase		,	unittest.TestCase ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__				:List[str]						  =       (TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
  SCREAMING_SNAKE_CASE__				:Tuple						  =       (TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
  SCREAMING_SNAKE_CASE__				:Optional[Any]						  =       (
      {
          "conversational": TFBlenderbotForConditionalGeneration,
          "feature-extraction": TFBlenderbotModel,
          "summarization": TFBlenderbotForConditionalGeneration,
          "text2text-generation": TFBlenderbotForConditionalGeneration,
          "translation": TFBlenderbotForConditionalGeneration,
      }
      if is_tf_available()
      else {}
  )
  SCREAMING_SNAKE_CASE__				:Dict						  =       True
  SCREAMING_SNAKE_CASE__				:int						  =       False
  SCREAMING_SNAKE_CASE__				:Tuple						  =       False

  def  __SCREAMING_SNAKE_CASE					(		self :							Optional[int]						)		->					Dict:
        _UpperCamelCase   :						Dict    =							TFBlenderbotModelTester(self						)
        _UpperCamelCase   :						Union[str, Any]    =							ConfigTester(self       , config_class=__a						)

  def  __SCREAMING_SNAKE_CASE					(		self :							Optional[Any]						)		->					Optional[Any]:
        self.config_tester.run_common_tests()


  def  __SCREAMING_SNAKE_CASE					(		self :							Any						)		->					Tuple:
        _UpperCamelCase   :						Any    =							self.model_tester.prepare_config_and_inputs_for_common()
        self.model_tester.check_decoder_model_past_large_inputs(*__a						)





@require_tokenizers
@require_tf
class   __SCREAMING_SNAKE_CASE     (    unittest.TestCase ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__				:Optional[Any]						  =       ["My friends are cool but they eat too many carbs."]
  SCREAMING_SNAKE_CASE__				:List[Any]						  =       "facebook/blenderbot-400M-distill"

  @cached_property
  def  __SCREAMING_SNAKE_CASE					(		self :							Union[str, Any]						)		->					Optional[int]:
        return BlenderbotTokenizer.from_pretrained(self.model_name						)

  @cached_property
  def  __SCREAMING_SNAKE_CASE					(		self :							str						)		->					List[str]:
        _UpperCamelCase   :						Any    =							TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name						)
        return model


  @slow
  def  __SCREAMING_SNAKE_CASE					(		self :							List[str]						)		->					Tuple:
        _UpperCamelCase   :						Union[str, Any]    =							self.tokenizer(self.src_text       , return_tensors="tf"						)
        _UpperCamelCase   :						Union[str, Any]    =							self.model.generate(
            model_inputs.input_ids       , )
        _UpperCamelCase   :						Optional[Any]    =							self.tokenizer.batch_decode(generated_ids.numpy()       , skip_special_tokens=__a						)[0]
        assert (
            generated_words
            == " That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?"
        )







"""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))
