






import math
from typing import Optional

import torch
import torch.utils.checkpoint as checkpoint
from torch import nn

from ....modeling_utils import PreTrainedModel
from ....utils import add_start_docstrings, logging
from ...bert.modeling_bert import BertModel
from .configuration_retribert import RetriBertConfig


SCREAMING_SNAKE_CASE       							=  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE       							=  [
    '''yjernite/retribert-base-uncased''',
    # See all RetriBert models at https://huggingface.co/models?filter=retribert
]
class 							A_			(  a_	):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:	int       =    RetriBertConfig
 _SCREAMING_SNAKE_CASE							:	Optional[Any]       =    None
 _SCREAMING_SNAKE_CASE							:	Optional[int]       =    "retribert"


 def        snake_case__ (		self							,     _A)       ->						Union[str, Any]:

     """simple docstring"""



     if isinstance(_A							,     nn.Linear):
         module.weight.data.normal_(mean=0.0							,     std=self.config.initializer_range)
         if module.bias is not None:
             module.bias.data.zero_()
     elif isinstance(_A							,     nn.Embedding):
         module.weight.data.normal_(mean=0.0							,     std=self.config.initializer_range)
         if module.padding_idx is not None:
             module.weight.data[module.padding_idx].zero_()
     elif isinstance(_A							,     nn.LayerNorm):
         module.bias.data.zero_()
         module.weight.data.fill_(1.0)



SCREAMING_SNAKE_CASE       							=  R'''

    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
    etc.)

    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
    and behavior.

    Parameters:
        config ([`RetriBertConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
'''
@add_start_docstrings(
    "Bert Based model to embed queries or document for document retrieval."       ,      a_       ,      )
class 							A_			(  a_	):






 '''simple docstring'''




 def __init__(		self							,     _A)       ->						Optional[int]:

     """simple docstring"""



     super().__init__(_A)
     _UpperCAmelCase :					Union[str, Any]	     = config.projection_dim

     _UpperCAmelCase :					Optional[int]	     = BertModel(_A)
     _UpperCAmelCase :					List[str]	     = None if config.share_encoders else BertModel(_A)
     _UpperCAmelCase :					str	     = nn.Dropout(config.hidden_dropout_prob)
     _UpperCAmelCase :					Optional[int]	     = nn.Linear(config.hidden_size							,     config.projection_dim							,     bias=_A)
     _UpperCAmelCase :					Any	     = nn.Linear(config.hidden_size							,     config.projection_dim							,     bias=_A)

     _UpperCAmelCase :					Optional[Any]	     = nn.CrossEntropyLoss(reduction='''mean''')

     # Initialize weights and apply final processing
     self.post_init()


 def        snake_case__ (		self							,     _A							,     _A							,     _A							,     _A=-1							,     )       ->						int:

     """simple docstring"""



     if checkpoint_batch_size < 0 or input_ids.shape[0] < checkpoint_batch_size:
         return sent_encoder(_A							,     attention_mask=_A)[1]
     else:
         # prepare implicit variables
         _UpperCAmelCase :					List[str]	     = input_ids.device
         _UpperCAmelCase :					Dict	     = input_ids.size()
         _UpperCAmelCase :					Optional[int]	     = torch.zeros(_A							,     dtype=torch.long							,     device=_A)
         _UpperCAmelCase :					Optional[int]	     = [None] * sent_encoder.config.num_hidden_layers
         _UpperCAmelCase :					torch.Tensor	     = sent_encoder.get_extended_attention_mask(
             _A							,     _A)

         # define function for checkpointing
         def partial_encode(*_A):
             _UpperCAmelCase :					Tuple	     = sent_encoder.encoder(
                 inputs[0]							,     attention_mask=inputs[1]							,     head_mask=_A							,     )
             _UpperCAmelCase :					Dict	     = encoder_outputs[0]
             _UpperCAmelCase :					List[Any]	     = sent_encoder.pooler(_A)
             return pooled_output

         # run embedding layer on everything at once
         _UpperCAmelCase :					Union[str, Any]	     = sent_encoder.embeddings(
             input_ids=_A							,     position_ids=_A							,     token_type_ids=_A							,     inputs_embeds=_A)
         # run encoding and pooling on one mini-batch at a time
         _UpperCAmelCase :					Optional[int]	     = []
         for b in range(math.ceil(input_ids.shape[0] / checkpoint_batch_size)):
             _UpperCAmelCase :					Optional[int]	     = embedding_output[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size]
             _UpperCAmelCase :					List[Any]	     = extended_attention_mask[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size]
             _UpperCAmelCase :					Dict	     = checkpoint.checkpoint(_A							,     _A							,     _A)
             pooled_output_list.append(_A)
         return torch.cat(_A							,     dim=0)


 def        snake_case__ (		self							,     _A							,     _A=None							,     _A=-1							,     )       ->						Optional[int]:

     """simple docstring"""



     _UpperCAmelCase :					Any	     = self.embed_sentences_checkpointed(
         _A							,     _A							,     self.bert_query							,     _A							,     )
     return self.project_query(_A)


 def        snake_case__ (		self							,     _A							,     _A=None							,     _A=-1							,     )       ->						Tuple:

     """simple docstring"""



     _UpperCAmelCase :					List[Any]	     = self.embed_sentences_checkpointed(
         _A							,     _A							,     self.bert_query if self.bert_doc is None else self.bert_doc							,     _A							,     )
     return self.project_doc(_A)


 def        snake_case__ (		self							,     _A							,     _A							,     _A							,     _A							,     _A = -1							,     )       ->						Optional[int]:

     """simple docstring"""



     _UpperCAmelCase :					List[str]	     = input_ids_query.device
     _UpperCAmelCase :					Any	     = self.embed_questions(_A							,     _A							,     _A)
     _UpperCAmelCase :					Dict	     = self.embed_answers(_A							,     _A							,     _A)
     _UpperCAmelCase :					Optional[int]	     = torch.mm(_A							,     a_reps.t())
     _UpperCAmelCase :					List[Any]	     = self.ce_loss(_A							,     torch.arange(compare_scores.shape[1]).to(_A))
     _UpperCAmelCase :					Dict	     = self.ce_loss(compare_scores.t()							,     torch.arange(compare_scores.shape[0]).to(_A))
     _UpperCAmelCase :					List[Any]	     = (loss_qa + loss_aq) / 2
     return loss








'''simple docstring'''







import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MarianConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
   import os

   # The slow tests are often failing with OOM error on GPU
   # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
   # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
   A      :     Union[str, Any]										= '''platform'''

   import jax
   import jax.numpy as jnp

   from transformers import MarianTokenizer
   from transformers.models.marian.modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, shift_tokens_right






def       lowerCAmelCase__       (				lowerCamelCase     :			int  ,lowerCamelCase     :			List[Any]  ,lowerCamelCase     :			List[Any]=None  ,lowerCamelCase     :			Union[str, Any]=None  ,lowerCamelCase     :			int=None  ,lowerCamelCase     :			List[str]=None  ,lowerCamelCase     :			Optional[int]=None  ,lowerCamelCase     :			Dict=None  ,):
       if attention_mask is None:
              _A     :	str      =					np.where(input_ids != config.pad_token_id  ,1  ,0   )
       if decoder_attention_mask is None:
              _A     :	List[str]      =					np.where(decoder_input_ids != config.pad_token_id  ,1  ,0   )
       if head_mask is None:
              _A     :	List[str]      =					np.ones((config.encoder_layers, config.encoder_attention_heads)   )
       if decoder_head_mask is None:
              _A     :	Any      =					np.ones((config.decoder_layers, config.decoder_attention_heads)   )
       if cross_attn_head_mask is None:
              _A     :	Tuple      =					np.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": attention_mask,
       }


class  __lowerCamelCase :
    """simple docstring"""


    def __init__(  self			:							List[str]      ,							SCREAMING_SNAKE_CASE			:							Dict      ,							SCREAMING_SNAKE_CASE			:							Tuple=13      ,							SCREAMING_SNAKE_CASE			:							Optional[Any]=7      ,							SCREAMING_SNAKE_CASE			:							List[str]=True      ,							SCREAMING_SNAKE_CASE			:							Any=False      ,							SCREAMING_SNAKE_CASE			:							Union[str, Any]=99      ,							SCREAMING_SNAKE_CASE			:							List[str]=16      ,							SCREAMING_SNAKE_CASE			:							Dict=2      ,							SCREAMING_SNAKE_CASE			:							Tuple=4      ,							SCREAMING_SNAKE_CASE			:							str=4      ,							SCREAMING_SNAKE_CASE			:							Tuple="gelu"      ,							SCREAMING_SNAKE_CASE			:							int=0.1      ,							SCREAMING_SNAKE_CASE			:							Tuple=0.1      ,							SCREAMING_SNAKE_CASE			:							int=32      ,							SCREAMING_SNAKE_CASE			:							Union[str, Any]=2      ,							SCREAMING_SNAKE_CASE			:							Union[str, Any]=1      ,							SCREAMING_SNAKE_CASE			:							str=0      ,							SCREAMING_SNAKE_CASE			:							Any=0.02      ,							):
           _A     :	Tuple      =					parent
           _A     :	Union[str, Any]      =					batch_size
           _A     :	List[str]      =					seq_length
           _A     :	Optional[int]      =					is_training
           _A     :	Optional[int]      =					use_labels
           _A     :	List[str]      =					vocab_size
           _A     :	Tuple      =					hidden_size
           _A     :	List[str]      =					num_hidden_layers
           _A     :	int      =					num_attention_heads
           _A     :	List[Any]      =					intermediate_size
           _A     :	Optional[int]      =					hidden_act
           _A     :	str      =					hidden_dropout_prob
           _A     :	Tuple      =					attention_probs_dropout_prob
           _A     :	Optional[Any]      =					max_position_embeddings
           _A     :	str      =					eos_token_id
           _A     :	Tuple      =					pad_token_id
           _A     :	Union[str, Any]      =					bos_token_id
           _A     :	Any      =					initializer_range


    def    A    (  self			:							Union[str, Any]):
           _A     :	int      =					np.clip(ids_tensor([self.batch_size, self.seq_length - 1]      ,							self.vocab_size)      ,							3      ,							self.vocab_size)
           _A     :	Tuple      =					np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)      ,							dtype=np.intaa))      ,							-1)

           _A     :	str      =					shift_tokens_right(SCREAMING_SNAKE_CASE      ,							1      ,							2)

           _A     :	List[Any]      =					MarianConfig(
               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_id=self.eos_token_id      ,							bos_token_id=self.bos_token_id      ,							pad_token_id=self.pad_token_id      ,							initializer_range=self.initializer_range      ,							use_cache=SCREAMING_SNAKE_CASE      ,							)
           _A     :	List[Any]      =					prepare_marian_inputs_dict(SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE)
           return config, inputs_dict


    def    A    (  self			:							List[str]):
           _A  , _A     :	Tuple      =					self.prepare_config_and_inputs()
           return config, inputs_dict


    def    A    (  self			:							int      ,							SCREAMING_SNAKE_CASE			:							List[str]      ,							SCREAMING_SNAKE_CASE			:							Any      ,							SCREAMING_SNAKE_CASE			:							List[Any]):
           _A     :	Optional[Any]      =					20
           _A     :	Any      =					model_class_name(SCREAMING_SNAKE_CASE)

           _A     :	Optional[int]      =					model.encode(inputs_dict['input_ids'])

           _A  , _A     :	Optional[int]      =					(
               inputs_dict['decoder_input_ids'],
               inputs_dict['decoder_attention_mask'],
           )

           _A     :	Any      =					model.init_cache(decoder_input_ids.shape[0]      ,							SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE)
           _A     :	Tuple      =					jnp.ones((decoder_input_ids.shape[0], max_decoder_length)      ,							dtype='i4')

           _A     :	Optional[int]      =					jnp.broadcast_to(
               jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :]      ,							(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)      ,							)
           _A     :	int      =					model.decode(
               decoder_input_ids[:, :-1]      ,							SCREAMING_SNAKE_CASE      ,							decoder_attention_mask=SCREAMING_SNAKE_CASE      ,							past_key_values=SCREAMING_SNAKE_CASE      ,							decoder_position_ids=SCREAMING_SNAKE_CASE      ,							)

           _A     :	Union[str, Any]      =					jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]      ,							dtype='i4')
           _A     :	Dict      =					model.decode(
               decoder_input_ids[:, -1:]      ,							SCREAMING_SNAKE_CASE      ,							decoder_attention_mask=SCREAMING_SNAKE_CASE      ,							past_key_values=outputs_cache.past_key_values      ,							decoder_position_ids=SCREAMING_SNAKE_CASE      ,							)

           _A     :	List[Any]      =					model.decode(SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE)

           _A     :	Optional[Any]      =					np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
           self.parent.assertTrue(diff < 1e-3      ,							msg=F'Max diff is {diff}')


    def    A    (  self			:							List[Any]      ,							SCREAMING_SNAKE_CASE			:							Tuple      ,							SCREAMING_SNAKE_CASE			:							Optional[int]      ,							SCREAMING_SNAKE_CASE			:							Tuple):
           _A     :	List[Any]      =					20
           _A     :	str      =					model_class_name(SCREAMING_SNAKE_CASE)

           _A     :	Optional[Any]      =					model.encode(inputs_dict['input_ids'])

           _A  , _A     :	Union[str, Any]      =					(
               inputs_dict['decoder_input_ids'],
               inputs_dict['decoder_attention_mask'],
           )

           _A     :	Any      =					jnp.concatenate(
               [
                   decoder_attention_mask,
                   jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])),
               ]      ,							axis=-1      ,							)

           _A     :	Optional[int]      =					model.init_cache(decoder_input_ids.shape[0]      ,							SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE)
           _A     :	Union[str, Any]      =					jnp.broadcast_to(
               jnp.arange(decoder_input_ids.shape[-1] - 1)[None, :]      ,							(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)      ,							)

           _A     :	Dict      =					model.decode(
               decoder_input_ids[:, :-1]      ,							SCREAMING_SNAKE_CASE      ,							decoder_attention_mask=SCREAMING_SNAKE_CASE      ,							past_key_values=SCREAMING_SNAKE_CASE      ,							decoder_position_ids=SCREAMING_SNAKE_CASE      ,							)
           _A     :	Optional[int]      =					jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]      ,							dtype='i4')
           _A     :	Dict      =					model.decode(
               decoder_input_ids[:, -1:]      ,							SCREAMING_SNAKE_CASE      ,							past_key_values=outputs_cache.past_key_values      ,							decoder_attention_mask=SCREAMING_SNAKE_CASE      ,							decoder_position_ids=SCREAMING_SNAKE_CASE      ,							)

           _A     :	Tuple      =					model.decode(SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE      ,							decoder_attention_mask=SCREAMING_SNAKE_CASE)

           _A     :	int      =					np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])))
           self.parent.assertTrue(diff < 1e-3      ,							msg=F'Max diff is {diff}')




@require_flax
class  __lowerCamelCase (       a_				,					unittest.TestCase				,					a_       ):
    """simple docstring"""
    a													=    True
    a													=    (FlaxMarianModel, FlaxMarianMTModel) if is_flax_available() else ()
    a													=    (FlaxMarianMTModel,) if is_flax_available() else ()


    def    A    (  self			:							str):
           _A     :	Any      =					FlaxMarianModelTester(self)


    def    A    (  self			:							Optional[Any]):
           _A  , _A     :	int      =					self.model_tester.prepare_config_and_inputs()
           for model_class in self.all_model_classes:
                  self.model_tester.check_use_cache_forward(SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE)


    def    A    (  self			:							int):
           _A  , _A     :	Any      =					self.model_tester.prepare_config_and_inputs()
           for model_class in self.all_model_classes:
                  self.model_tester.check_use_cache_forward_with_attn_mask(SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE)


    def    A    (  self			:							Tuple):
           _A  , _A     :	Any      =					self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                  with self.subTest(model_class.__name__):
                         _A     :	int      =					self._prepare_for_class(SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE)
                         _A     :	int      =					model_class(SCREAMING_SNAKE_CASE)

                         @jax.jit
                         def encode_jitted(SCREAMING_SNAKE_CASE			:							Union[str, Any]      ,							SCREAMING_SNAKE_CASE			:							Optional[int]=None      ,							**SCREAMING_SNAKE_CASE			:							int):
                                return model.encode(input_ids=SCREAMING_SNAKE_CASE      ,							attention_mask=SCREAMING_SNAKE_CASE)

                         with self.subTest('JIT Enabled'):
                                _A     :	str      =					encode_jitted(**SCREAMING_SNAKE_CASE).to_tuple()

                         with self.subTest('JIT Disabled'):
                                with jax.disable_jit():
                                       _A     :	List[str]      =					encode_jitted(**SCREAMING_SNAKE_CASE).to_tuple()

                         self.assertEqual(len(SCREAMING_SNAKE_CASE)      ,							len(SCREAMING_SNAKE_CASE))
                         for jitted_output, output in zip(SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE):
                                self.assertEqual(jitted_output.shape      ,							output.shape)


    def    A    (  self			:							Tuple):
           _A  , _A     :	Optional[int]      =					self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                  with self.subTest(model_class.__name__):
                         _A     :	List[Any]      =					model_class(SCREAMING_SNAKE_CASE)
                         _A     :	Tuple      =					model.encode(inputs_dict['input_ids']      ,							inputs_dict['attention_mask'])

                         _A     :	int      =					{
                             'decoder_input_ids': inputs_dict['decoder_input_ids'],
                             'decoder_attention_mask': inputs_dict['decoder_attention_mask'],
                             'encoder_outputs': encoder_outputs,
                         }

                         @jax.jit
                         def decode_jitted(SCREAMING_SNAKE_CASE			:							Optional[Any]      ,							SCREAMING_SNAKE_CASE			:							Any      ,							SCREAMING_SNAKE_CASE			:							List[Any]):
                                return model.decode(
                                    decoder_input_ids=SCREAMING_SNAKE_CASE      ,							decoder_attention_mask=SCREAMING_SNAKE_CASE      ,							encoder_outputs=SCREAMING_SNAKE_CASE      ,							)

                         with self.subTest('JIT Enabled'):
                                _A     :	Optional[int]      =					decode_jitted(**SCREAMING_SNAKE_CASE).to_tuple()

                         with self.subTest('JIT Disabled'):
                                with jax.disable_jit():
                                       _A     :	List[str]      =					decode_jitted(**SCREAMING_SNAKE_CASE).to_tuple()

                         self.assertEqual(len(SCREAMING_SNAKE_CASE)      ,							len(SCREAMING_SNAKE_CASE))
                         for jitted_output, output in zip(SCREAMING_SNAKE_CASE      ,							SCREAMING_SNAKE_CASE):
                                self.assertEqual(jitted_output.shape      ,							output.shape)


    @slow
    def    A    (  self			:							Optional[Any]):
           for model_class_name in self.all_model_classes:
                  _A     :	str      =					model_class_name.from_pretrained('Helsinki-NLP/opus-mt-en-de')
                  # FlaxMarianForSequenceClassification expects eos token in input_ids
                  _A     :	Optional[int]      =					np.ones((1, 1)) * model.config.eos_token_id
                  _A     :	str      =					model(SCREAMING_SNAKE_CASE)
                  self.assertIsNotNone(SCREAMING_SNAKE_CASE)




@require_flax
@require_sentencepiece
@require_tokenizers
class  __lowerCamelCase (       unittest.TestCase       ):
    """simple docstring"""
    a													=    None
    a													=    None


    @classmethod
    def    A    (  cls			:							List[Any]):
           _A     :	Optional[int]      =					F'Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'
           return cls


    @cached_property
    def    A    (  self			:							Optional[Any]):
           return MarianTokenizer.from_pretrained(self.model_name)


    @property
    def    A    (  self			:							Dict):
           return self.tokenizer.eos_token_id


    @cached_property
    def    A    (  self			:							int):
           _A     :	FlaxMarianMTModel      =					FlaxMarianMTModel.from_pretrained(self.model_name)
           self.assertEqual(model.config.decoder_start_token_id      ,							model.config.pad_token_id)
           return model


    def    A    (  self			:							Union[str, Any]      ,							**SCREAMING_SNAKE_CASE			:							int):
           _A     :	str      =					self.translate_src_text(**SCREAMING_SNAKE_CASE)
           self.assertListEqual(self.expected_text      ,							SCREAMING_SNAKE_CASE)


    def    A    (  self			:							str      ,							**SCREAMING_SNAKE_CASE			:							List[Any]):
           _A     :	int      =					self.tokenizer(self.src_text      ,							padding=SCREAMING_SNAKE_CASE      ,							return_tensors='np'      ,							**SCREAMING_SNAKE_CASE)
           _A     :	Union[str, Any]      =					self.model.generate(
               model_inputs.input_ids      ,							attention_mask=model_inputs.attention_mask      ,							num_beams=2      ,							max_length=128      ,							).sequences
           _A     :	int      =					self.tokenizer.batch_decode(SCREAMING_SNAKE_CASE      ,							skip_special_tokens=SCREAMING_SNAKE_CASE)
           return generated_words




@require_flax
@require_sentencepiece
@require_tokenizers
class  __lowerCamelCase (       a_       ):
    """simple docstring"""
    a													=    "en"
    a													=    "fr"
    a													=    [
        "I am a small frog.",
        "Now I can forget the 100 words of german that I know.",
    ]
    a													=    [
        "Je suis une petite grenouille.",
        "Maintenant, je peux oublier les 100 mots d'allemand que je connais.",
    ]


    @slow
    def    A    (  self			:							Any):
           self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class  __lowerCamelCase (       a_       ):
    """simple docstring"""
    a													=    "fr"
    a													=    "en"
    a													=    [
        "Donnez moi le micro.",
        "Tom et Mary étaient assis à une table.",  # Accents
    ]
    a													=    [
        "Give me the microphone.",
        "Tom and Mary were sitting at a table.",
    ]


    @slow
    def    A    (  self			:							Any):
           self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class  __lowerCamelCase (       a_       ):
    """simple docstring"""
    a													=    "mt"
    a													=    "en"
    a													=    ["Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."]
    a													=    ["Touching gently, Jesus healed a man who was affected by the sad disease of leprosy."]


    @slow
    def    A    (  self			:							List[Any]):
           self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class  __lowerCamelCase (       a_       ):
    """simple docstring"""
    a													=    "en"
    a													=    "de"
    a													=    [
        "I am a small frog.",
        "Now I can forget the 100 words of german that I know.",
        "Tom asked his teacher for advice.",
        "That's how I would do it.",
        "Tom really admired Mary's courage.",
        "Turn around and close your eyes.",
    ]
    a													=    [
        "Ich bin ein kleiner Frosch.",
        "Jetzt kann ich die 100 Wörter des Deutschen vergessen, die ich kenne.",
        "Tom bat seinen Lehrer um Rat.",
        "So würde ich das machen.",
        "Tom bewunderte Marias Mut wirklich.",
        "Drehen Sie sich um und schließen Sie die Augen.",
    ]


    @slow
    def    A    (  self			:							Optional[Any]):
           self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class  __lowerCamelCase (       a_       ):
    """simple docstring"""
    a													=    "en"
    a													=    "zh"
    a													=    ["My name is Wolfgang and I live in Berlin"]
    a													=    ["我叫沃尔夫冈 我住在柏林"]


    @slow
    def    A    (  self			:							Optional[Any]):
           self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class  __lowerCamelCase (       a_       ):
    """simple docstring"""
    a													=    "ru"
    a													=    "fr"
    a													=    ["Он показал мне рукопись своей новой пьесы."]
    a													=    ["Il m'a montré le manuscrit de sa nouvelle pièce."]


    @slow
    def    A    (  self			:							Optional[int]):
           self._assert_generated_batch_equal_expected()




@require_flax
@require_sentencepiece
@require_tokenizers
class  __lowerCamelCase (       a_       ):
    """simple docstring"""
    a													=    "en"
    a													=    "ROMANCE"
    a													=    [
        ">>fr<< Don't spend so much time watching TV.",
        ">>pt<< Your message has been sent.",
        ">>es<< He's two years older than me.",
    ]
    a													=    [
        "Ne passez pas autant de temps à regarder la télé.",
        "A sua mensagem foi enviada.",
        "Es dos años más viejo que yo.",
    ]


    @slow
    def    A    (  self			:							Any):
           self._assert_generated_batch_equal_expected()






