"""simple docstring"""






import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    Pipeline,
    ZeroShotClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
__A			   = {"LayoutLMv2Config", "LayoutLMv3Config"}


@is_pipeline_test
class     UpperCAmelCase						(unittest.TestCase  ):



    """simple docstring"""

    _UpperCAmelCase	:Union[str, Any]						    = MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
    _UpperCAmelCase	:Dict						    = TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

    if model_mapping is not None:
        _UpperCAmelCase	:Optional[Any]						    = {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
    if tf_model_mapping is not None:
        _UpperCAmelCase	:Optional[int]						    = {
            config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
        }






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase							):
       lowercase__:   List[Any]					=      ZeroShotClassificationPipeline(
           model=_UpperCAmelCase    ,			tokenizer=_UpperCAmelCase    ,			candidate_labels=['''polics''', '''health''']							)
       return classifier, ["Who are you voting for in 2020?", "My stomach hurts."]






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase							):
       lowercase__:   Dict					=      classifier('''Who are you voting for in 2020?'''    ,			candidate_labels='''politics'''							)
       self.assertEqual(_UpperCAmelCase    ,			{'''sequence''': ANY(_UpperCAmelCase							), '''labels''': [ANY(_UpperCAmelCase							)], '''scores''': [ANY(_UpperCAmelCase							)]}							)

       # No kwarg
       lowercase__:   int					=      classifier('''Who are you voting for in 2020?'''    ,			['''politics''']							)
       self.assertEqual(_UpperCAmelCase    ,			{'''sequence''': ANY(_UpperCAmelCase							), '''labels''': [ANY(_UpperCAmelCase							)], '''scores''': [ANY(_UpperCAmelCase							)]}							)

       lowercase__:   Dict					=      classifier('''Who are you voting for in 2020?'''    ,			candidate_labels=['''politics''']							)
       self.assertEqual(_UpperCAmelCase    ,			{'''sequence''': ANY(_UpperCAmelCase							), '''labels''': [ANY(_UpperCAmelCase							)], '''scores''': [ANY(_UpperCAmelCase							)]}							)

       lowercase__:   Dict					=      classifier('''Who are you voting for in 2020?'''    ,			candidate_labels='''politics, public health'''							)
       self.assertEqual(
           _UpperCAmelCase    ,			{'''sequence''': ANY(_UpperCAmelCase							), '''labels''': [ANY(_UpperCAmelCase							), ANY(_UpperCAmelCase							)], '''scores''': [ANY(_UpperCAmelCase							), ANY(_UpperCAmelCase							)]}							)
       self.assertAlmostEqual(sum(nested_simplify(outputs['''scores''']							)							)    ,			1.0							)

       lowercase__:   List[str]					=      classifier('''Who are you voting for in 2020?'''    ,			candidate_labels=['''politics''', '''public health''']							)
       self.assertEqual(
           _UpperCAmelCase    ,			{'''sequence''': ANY(_UpperCAmelCase							), '''labels''': [ANY(_UpperCAmelCase							), ANY(_UpperCAmelCase							)], '''scores''': [ANY(_UpperCAmelCase							), ANY(_UpperCAmelCase							)]}							)
       self.assertAlmostEqual(sum(nested_simplify(outputs['''scores''']							)							)    ,			1.0							)

       lowercase__:   str					=      classifier(
           '''Who are you voting for in 2020?'''    ,			candidate_labels='''politics'''    ,			hypothesis_template='''This text is about {}'''							)
       self.assertEqual(_UpperCAmelCase    ,			{'''sequence''': ANY(_UpperCAmelCase							), '''labels''': [ANY(_UpperCAmelCase							)], '''scores''': [ANY(_UpperCAmelCase							)]}							)

       # https://github.com/huggingface/transformers/issues/13846
       lowercase__:   Optional[int]					=      classifier(['''I am happy''']    ,			['''positive''', '''negative''']							)
       self.assertEqual(
           _UpperCAmelCase    ,			[
               {'''sequence''': ANY(_UpperCAmelCase							), '''labels''': [ANY(_UpperCAmelCase							), ANY(_UpperCAmelCase							)], '''scores''': [ANY(_UpperCAmelCase							), ANY(_UpperCAmelCase							)]}
               for i in range(1							)
           ]    ,			)
       lowercase__:   Tuple					=      classifier(['''I am happy''', '''I am sad''']    ,			['''positive''', '''negative''']							)
       self.assertEqual(
           _UpperCAmelCase    ,			[
               {'''sequence''': ANY(_UpperCAmelCase							), '''labels''': [ANY(_UpperCAmelCase							), ANY(_UpperCAmelCase							)], '''scores''': [ANY(_UpperCAmelCase							), ANY(_UpperCAmelCase							)]}
               for i in range(2							)
           ]    ,			)

       with self.assertRaises(_UpperCAmelCase							):
          classifier(''''''    ,			candidate_labels='''politics'''							)

       with self.assertRaises(_UpperCAmelCase							):
          classifier(_UpperCAmelCase    ,			candidate_labels='''politics'''							)

       with self.assertRaises(_UpperCAmelCase							):
          classifier('''Who are you voting for in 2020?'''    ,			candidate_labels=''''''							)

       with self.assertRaises(_UpperCAmelCase							):
          classifier('''Who are you voting for in 2020?'''    ,			candidate_labels=_UpperCAmelCase							)

       with self.assertRaises(_UpperCAmelCase							):
          classifier(
              '''Who are you voting for in 2020?'''    ,			candidate_labels='''politics'''    ,			hypothesis_template='''Not formatting template'''    ,			)

       with self.assertRaises(_UpperCAmelCase							):
          classifier(
              '''Who are you voting for in 2020?'''    ,			candidate_labels='''politics'''    ,			hypothesis_template=_UpperCAmelCase    ,			)

       self.run_entailment_id(_UpperCAmelCase							)






    def _snake_case			(   self    ,			_UpperCAmelCase							):
       lowercase__:   Dict					=      zero_shot_classifier.model.config
       lowercase__:   Tuple					=      config.labelaid
       lowercase__:   Union[str, Any]					=      zero_shot_classifier.entailment_id

       lowercase__:   Optional[Any]					=      {'''LABEL_0''': 0, '''LABEL_1''': 1, '''LABEL_2''': 2}
       self.assertEqual(zero_shot_classifier.entailment_id    ,			-1							)

       lowercase__:   Any					=      {'''entailment''': 0, '''neutral''': 1, '''contradiction''': 2}
       self.assertEqual(zero_shot_classifier.entailment_id    ,			0							)

       lowercase__:   Any					=      {'''ENTAIL''': 0, '''NON-ENTAIL''': 1}
       self.assertEqual(zero_shot_classifier.entailment_id    ,			0							)

       lowercase__:   Union[str, Any]					=      {'''ENTAIL''': 2, '''NEUTRAL''': 1, '''CONTR''': 0}
       self.assertEqual(zero_shot_classifier.entailment_id    ,			2							)

       lowercase__:   Any					=      original_labelaid
       self.assertEqual(_UpperCAmelCase    ,			zero_shot_classifier.entailment_id							)






    @require_torch
    def _snake_case			(   self							):
       lowercase__:   int					=      pipeline(
           '''zero-shot-classification'''    ,			model='''sshleifer/tiny-distilbert-base-cased-distilled-squad'''    ,			framework='''pt'''    ,			)
       # There was a regression in 4.10 for this
       # Adding a test so we don't make the mistake again.
       # https://github.com/huggingface/transformers/issues/13381#issuecomment-912343499
       zero_shot_classifier(
           '''Who are you voting for in 2020?''' * 100    ,			candidate_labels=['''politics''', '''public health''', '''science''']							)






    @require_torch
    def _snake_case			(   self							):
       lowercase__:   Optional[int]					=      pipeline(
           '''zero-shot-classification'''    ,			model='''sshleifer/tiny-distilbert-base-cased-distilled-squad'''    ,			framework='''pt'''    ,			)
       lowercase__:   Dict					=      zero_shot_classifier(
           '''Who are you voting for in 2020?'''    ,			candidate_labels=['''politics''', '''public health''', '''science''']							)

       self.assertEqual(
           nested_simplify(_UpperCAmelCase							)    ,			{
               '''sequence''': '''Who are you voting for in 2020?''',
               '''labels''': ['''science''', '''public health''', '''politics'''],
               '''scores''': [0.333, 0.333, 0.333],
           }    ,			)






    @require_tf
    def _snake_case			(   self							):
       lowercase__:   Optional[int]					=      pipeline(
           '''zero-shot-classification'''    ,			model='''sshleifer/tiny-distilbert-base-cased-distilled-squad'''    ,			framework='''tf'''    ,			)
       lowercase__:   List[Any]					=      zero_shot_classifier(
           '''Who are you voting for in 2020?'''    ,			candidate_labels=['''politics''', '''public health''', '''science''']							)

       self.assertEqual(
           nested_simplify(_UpperCAmelCase							)    ,			{
               '''sequence''': '''Who are you voting for in 2020?''',
               '''labels''': ['''science''', '''public health''', '''politics'''],
               '''scores''': [0.333, 0.333, 0.333],
           }    ,			)






    @slow
    @require_torch
    def _snake_case			(   self							):
       lowercase__:   Dict					=      pipeline('''zero-shot-classification'''    ,			model='''roberta-large-mnli'''    ,			framework='''pt'''							)
       lowercase__:   Union[str, Any]					=      zero_shot_classifier(
           '''Who are you voting for in 2020?'''    ,			candidate_labels=['''politics''', '''public health''', '''science''']							)

       self.assertEqual(
           nested_simplify(_UpperCAmelCase							)    ,			{
               '''sequence''': '''Who are you voting for in 2020?''',
               '''labels''': ['''politics''', '''public health''', '''science'''],
               '''scores''': [0.976, 0.015, 0.009],
           }    ,			)
       lowercase__:   Dict					=      zero_shot_classifier(
           '''The dominant sequence transduction models are based on complex recurrent or convolutional neural networks'''
           ''' in an encoder-decoder configuration. The best performing models also connect the encoder and decoder'''
           ''' through an attention mechanism. We propose a new simple network architecture, the Transformer, based'''
           ''' solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two'''
           ''' machine translation tasks show these models to be superior in quality while being more parallelizable'''
           ''' and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014'''
           ''' English-to-German translation task, improving over the existing best results, including ensembles by'''
           ''' over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new'''
           ''' single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small'''
           ''' fraction of the training costs of the best models from the literature. We show that the Transformer'''
           ''' generalizes well to other tasks by applying it successfully to English constituency parsing both with'''
           ''' large and limited training data.'''    ,			candidate_labels=['''machine learning''', '''statistics''', '''translation''', '''vision''']    ,			multi_label=_UpperCAmelCase    ,			)
       self.assertEqual(
           nested_simplify(_UpperCAmelCase							)    ,			{
               '''sequence''': (
                   '''The dominant sequence transduction models are based on complex recurrent or convolutional neural'''
                   ''' networks in an encoder-decoder configuration. The best performing models also connect the'''
                   ''' encoder and decoder through an attention mechanism. We propose a new simple network'''
                   ''' architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence'''
                   ''' and convolutions entirely. Experiments on two machine translation tasks show these models to be'''
                   ''' superior in quality while being more parallelizable and requiring significantly less time to'''
                   ''' train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,'''
                   ''' improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014'''
                   ''' English-to-French translation task, our model establishes a new single-model state-of-the-art'''
                   ''' BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training'''
                   ''' costs of the best models from the literature. We show that the Transformer generalizes well to'''
                   ''' other tasks by applying it successfully to English constituency parsing both with large and'''
                   ''' limited training data.'''
               ),
               '''labels''': ['''translation''', '''machine learning''', '''vision''', '''statistics'''],
               '''scores''': [0.817, 0.713, 0.018, 0.018],
           }    ,			)






    @slow
    @require_tf
    def _snake_case			(   self							):
       lowercase__:   Optional[int]					=      pipeline('''zero-shot-classification'''    ,			model='''roberta-large-mnli'''    ,			framework='''tf'''							)
       lowercase__:   Union[str, Any]					=      zero_shot_classifier(
           '''Who are you voting for in 2020?'''    ,			candidate_labels=['''politics''', '''public health''', '''science''']							)

       self.assertEqual(
           nested_simplify(_UpperCAmelCase							)    ,			{
               '''sequence''': '''Who are you voting for in 2020?''',
               '''labels''': ['''politics''', '''public health''', '''science'''],
               '''scores''': [0.976, 0.015, 0.009],
           }    ,			)
       lowercase__:   List[Any]					=      zero_shot_classifier(
           '''The dominant sequence transduction models are based on complex recurrent or convolutional neural networks'''
           ''' in an encoder-decoder configuration. The best performing models also connect the encoder and decoder'''
           ''' through an attention mechanism. We propose a new simple network architecture, the Transformer, based'''
           ''' solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two'''
           ''' machine translation tasks show these models to be superior in quality while being more parallelizable'''
           ''' and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014'''
           ''' English-to-German translation task, improving over the existing best results, including ensembles by'''
           ''' over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new'''
           ''' single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small'''
           ''' fraction of the training costs of the best models from the literature. We show that the Transformer'''
           ''' generalizes well to other tasks by applying it successfully to English constituency parsing both with'''
           ''' large and limited training data.'''    ,			candidate_labels=['''machine learning''', '''statistics''', '''translation''', '''vision''']    ,			multi_label=_UpperCAmelCase    ,			)
       self.assertEqual(
           nested_simplify(_UpperCAmelCase							)    ,			{
               '''sequence''': (
                   '''The dominant sequence transduction models are based on complex recurrent or convolutional neural'''
                   ''' networks in an encoder-decoder configuration. The best performing models also connect the'''
                   ''' encoder and decoder through an attention mechanism. We propose a new simple network'''
                   ''' architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence'''
                   ''' and convolutions entirely. Experiments on two machine translation tasks show these models to be'''
                   ''' superior in quality while being more parallelizable and requiring significantly less time to'''
                   ''' train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,'''
                   ''' improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014'''
                   ''' English-to-French translation task, our model establishes a new single-model state-of-the-art'''
                   ''' BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training'''
                   ''' costs of the best models from the literature. We show that the Transformer generalizes well to'''
                   ''' other tasks by applying it successfully to English constituency parsing both with large and'''
                   ''' limited training data.'''
               ),
               '''labels''': ['''translation''', '''machine learning''', '''vision''', '''statistics'''],
               '''scores''': [0.817, 0.713, 0.018, 0.018],
           }    ,			)

"""simple docstring"""






from __future__ import annotations

import unittest

from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, 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, TFMBartForConditionalGeneration, TFMBartModel


@require_tf
class     UpperCAmelCase						:



    """simple docstring"""

    _UpperCAmelCase	:Union[str, Any]						    = MBartConfig
    _UpperCAmelCase	:str						    = {}
    _UpperCAmelCase	:Union[str, Any]						    = "gelu"






    def __init__(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase=13    ,			_UpperCAmelCase=7    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase=False    ,			_UpperCAmelCase=99    ,			_UpperCAmelCase=32    ,			_UpperCAmelCase=2    ,			_UpperCAmelCase=4    ,			_UpperCAmelCase=37    ,			_UpperCAmelCase=0.1    ,			_UpperCAmelCase=0.1    ,			_UpperCAmelCase=20    ,			_UpperCAmelCase=2    ,			_UpperCAmelCase=1    ,			_UpperCAmelCase=0    ,			):
       lowercase__:   List[str]					=      parent
       lowercase__:   List[Any]					=      batch_size
       lowercase__:   List[Any]					=      seq_length
       lowercase__:   str					=      is_training
       lowercase__:   List[str]					=      use_labels
       lowercase__:   Optional[int]					=      vocab_size
       lowercase__:   int					=      hidden_size
       lowercase__:   Optional[int]					=      num_hidden_layers
       lowercase__:   int					=      num_attention_heads
       lowercase__:   Tuple					=      intermediate_size
       lowercase__:   Optional[Any]					=      hidden_dropout_prob
       lowercase__:   Optional[int]					=      attention_probs_dropout_prob
       lowercase__:   str					=      max_position_embeddings
       lowercase__:   Union[str, Any]					=      eos_token_id
       lowercase__:   int					=      pad_token_id
       lowercase__:   List[str]					=      bos_token_id






    def _snake_case			(   self							):
       lowercase__:   int					=      ids_tensor([self.batch_size, self.seq_length - 1]    ,			self.vocab_size							)
       lowercase__:   str					=      tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size							)    ,			1							)
       lowercase__:   List[Any]					=      tf.concat([input_ids, eos_tensor]    ,			axis=1							)

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

       lowercase__:   Optional[Any]					=      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    ,			)
       lowercase__:   Optional[int]					=      prepare_mbart_inputs_dict(_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase							)
       return config, inputs_dict






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase							):
       lowercase__:   List[str]					=      TFMBartModel(config=_UpperCAmelCase							).get_decoder()
       lowercase__:   Tuple					=      inputs_dict['''input_ids''']

       lowercase__:   Optional[Any]					=      input_ids[:1, :]
       lowercase__:   Optional[int]					=      inputs_dict['''attention_mask'''][:1, :]
       lowercase__:   List[str]					=      inputs_dict['''head_mask''']
       lowercase__:   Optional[int]					=      1

       # first forward pass
       lowercase__:   List[Any]					=      model(_UpperCAmelCase    ,			attention_mask=_UpperCAmelCase    ,			head_mask=_UpperCAmelCase    ,			use_cache=_UpperCAmelCase							)

       lowercase__, lowercase__:   Any					=      outputs.to_tuple()
       lowercase__:   List[str]					=      past_key_values[1]



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase=None       ,    __UpperCAmelCase=None       ,    __UpperCAmelCase=None       ,    __UpperCAmelCase=None       ,    __UpperCAmelCase=None       ,    )  ->  int:
   if attention_mask is None:
      lowercase__:   Union[str, Any]					=      tf.cast(tf.math.not_equal(__UpperCAmelCase       ,    config.pad_token_id )       ,    tf.inta )
   if decoder_attention_mask is None:
      lowercase__:   List[str]					=      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:
      lowercase__:   str					=      tf.ones((config.encoder_layers, config.encoder_attention_heads) )
   if decoder_head_mask is None:
      lowercase__:   List[str]					=      tf.ones((config.decoder_layers, config.decoder_attention_heads) )
   if cross_attn_head_mask is None:
      lowercase__:   Union[str, Any]					=      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     UpperCAmelCase						(_UpperCAmelCase    ,_UpperCAmelCase    ,unittest.TestCase  ):



    """simple docstring"""

    _UpperCAmelCase	:Dict						    = (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
    _UpperCAmelCase	:List[Any]						    = (TFMBartForConditionalGeneration,) if is_tf_available() else ()
    _UpperCAmelCase	:List[str]						    = (
        {
            "conversational": TFMBartForConditionalGeneration,
            "feature-extraction": TFMBartModel,
            "summarization": TFMBartForConditionalGeneration,
            "text2text-generation": TFMBartForConditionalGeneration,
            "translation": TFMBartForConditionalGeneration,
        }
        if is_tf_available()
        else {}
    )
    _UpperCAmelCase	:str						    = True
    _UpperCAmelCase	:List[Any]						    = False
    _UpperCAmelCase	:str						    = False






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase							):
       if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
          # Exception encountered when calling layer '...'
          return True

       return False






    def _snake_case			(   self							):
       lowercase__:   Tuple					=      TFMBartModelTester(self							)
       lowercase__:   Optional[Any]					=      ConfigTester(self    ,			config_class=_UpperCAmelCase							)






    def _snake_case			(   self							):
       self.config_tester.run_common_tests()






    def _snake_case			(   self							):
       lowercase__:   List[str]					=      self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.check_decoder_model_past_large_inputs(*_UpperCAmelCase							)



@require_sentencepiece
@require_tokenizers
@require_tf
class     UpperCAmelCase						(unittest.TestCase  ):



    """simple docstring"""

    _UpperCAmelCase	:Optional[int]						    = [
        " UN Chief Says There Is No Military Solution in Syria",
    ]
    _UpperCAmelCase	:Any						    = [
        "Şeful ONU declară că nu există o soluţie militară în Siria",
    ]
    _UpperCAmelCase	:Tuple						    = "facebook/mbart-large-en-ro"






    @cached_property
    def _snake_case			(   self							):
       return AutoTokenizer.from_pretrained(self.model_name							)






    @cached_property
    def _snake_case			(   self							):
       lowercase__:   Optional[int]					=      TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name							)
       return model






    def _snake_case			(   self    ,			**_UpperCAmelCase							):
       lowercase__:   List[Any]					=      self.translate_src_text(**_UpperCAmelCase							)
       self.assertListEqual(self.expected_text    ,			_UpperCAmelCase							)






    def _snake_case			(   self    ,			**_UpperCAmelCase							):
       lowercase__:   str					=      self.tokenizer(self.src_text    ,			**_UpperCAmelCase    ,			return_tensors='''tf'''							)
       lowercase__:   Optional[int]					=      self.model.generate(
           model_inputs.input_ids    ,			attention_mask=model_inputs.attention_mask    ,			num_beams=2							)
       lowercase__:   Tuple					=      self.tokenizer.batch_decode(_UpperCAmelCase    ,			skip_special_tokens=_UpperCAmelCase							)
       return generated_words






    @slow
    def _snake_case			(   self							):
       self._assert_generated_batch_equal_expected()
