

import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler






class 							A_				(	unittest.TestCase      ):
     # TODO adapt with class SchedulerCommonTest (scheduler needs Numpy Integration)
     _lowercase    :  Tuple          =			(ScoreSdeVeScheduler,)
     _lowercase    :  Tuple          =			()


     @property
     def 						UpperCAmelCase						(							self    :	List[Any]      )							->			Tuple:
      __lowerCAmelCase:     int     			=   4
      __lowerCAmelCase:     str     			=   3
      __lowerCAmelCase:     Optional[Any]     			=   8
      __lowerCAmelCase:     Any     			=   8

      __lowerCAmelCase:     Dict     			=   torch.rand((batch_size, num_channels, height, width)      )

      return sample


     @property
     def 						UpperCAmelCase						(							self    :	Dict      )							->			Optional[Any]:
      __lowerCAmelCase:     str     			=   4
      __lowerCAmelCase:     Any     			=   3
      __lowerCAmelCase:     Tuple     			=   8
      __lowerCAmelCase:     Dict     			=   8

      __lowerCAmelCase:     Optional[int]     			=   batch_size * num_channels * height * width
      __lowerCAmelCase:     str     			=   torch.arange(UpperCAmelCase      )
      __lowerCAmelCase:     Dict     			=   sample.reshape(UpperCAmelCase    ,						UpperCAmelCase    ,						UpperCAmelCase    ,						UpperCAmelCase      )
      __lowerCAmelCase:     str     			=   sample / num_elems
      __lowerCAmelCase:     List[str]     			=   sample.permute(3    ,						0    ,						1    ,						2      )

      return sample


     def 						UpperCAmelCase						(							self    :	Union[str, Any]      )							->			int:
      def model(UpperCAmelCase    :	int    ,						UpperCAmelCase    :	List[Any]    ,						*UpperCAmelCase    :	List[Any]      ):
       return sample * t / (t + 1)

      return model


     def 						UpperCAmelCase						(							self    :	int    ,						**UpperCAmelCase    :	Union[str, Any]      )							->			Dict:
      __lowerCAmelCase:     Tuple     			=   {
          'num_train_timesteps': 2_0_0_0,
          'snr': 0.15,
          'sigma_min': 0.01,
          'sigma_max': 1_3_4_8,
          'sampling_eps': 1E-5,
      }

      config.update(**UpperCAmelCase      )
      return config


     def 						UpperCAmelCase						(							self    :	Any    ,						UpperCAmelCase    :	Union[str, Any]=0    ,						**UpperCAmelCase    :	Optional[int]      )							->			List[Any]:
      __lowerCAmelCase:     int     			=   dict(self.forward_default_kwargs      )

      for scheduler_class in self.scheduler_classes:
       __lowerCAmelCase:     List[Any]     			=   self.dummy_sample
       __lowerCAmelCase:     List[Any]     			=   0.1 * sample

       __lowerCAmelCase:     Optional[int]     			=   self.get_scheduler_config(**UpperCAmelCase      )
       __lowerCAmelCase:     Optional[Any]     			=   scheduler_class(**UpperCAmelCase      )

       with tempfile.TemporaryDirectory() as tmpdirname:
        scheduler.save_config(UpperCAmelCase      )
        __lowerCAmelCase:     str     			=   scheduler_class.from_pretrained(UpperCAmelCase      )

       __lowerCAmelCase:     str     			=   scheduler.step_pred(
           UpperCAmelCase    ,						UpperCAmelCase    ,						UpperCAmelCase    ,						generator=torch.manual_seed(0      )    ,						**UpperCAmelCase      ).prev_sample
       __lowerCAmelCase:     Optional[Any]     			=   new_scheduler.step_pred(
           UpperCAmelCase    ,						UpperCAmelCase    ,						UpperCAmelCase    ,						generator=torch.manual_seed(0      )    ,						**UpperCAmelCase      ).prev_sample

       assert torch.sum(torch.abs(output - new_output      )      ) < 1E-5, "Scheduler outputs are not identical"

       __lowerCAmelCase:     Tuple     			=   scheduler.step_correct(UpperCAmelCase    ,						UpperCAmelCase    ,						generator=torch.manual_seed(0      )    ,						**UpperCAmelCase      ).prev_sample
       __lowerCAmelCase:     Tuple     			=   new_scheduler.step_correct(
           UpperCAmelCase    ,						UpperCAmelCase    ,						generator=torch.manual_seed(0      )    ,						**UpperCAmelCase      ).prev_sample

       assert torch.sum(torch.abs(output - new_output      )      ) < 1E-5, "Scheduler correction are not identical"


     def 						UpperCAmelCase						(							self    :	List[Any]    ,						UpperCAmelCase    :	Union[str, Any]=0    ,						**UpperCAmelCase    :	str      )							->			int:
      __lowerCAmelCase:     int     			=   dict(self.forward_default_kwargs      )
      kwargs.update(UpperCAmelCase      )

      for scheduler_class in self.scheduler_classes:
       __lowerCAmelCase:     Optional[Any]     			=   self.dummy_sample
       __lowerCAmelCase:     List[str]     			=   0.1 * sample

       __lowerCAmelCase:     Dict     			=   self.get_scheduler_config()
       __lowerCAmelCase:     List[str]     			=   scheduler_class(**UpperCAmelCase      )

       with tempfile.TemporaryDirectory() as tmpdirname:
        scheduler.save_config(UpperCAmelCase      )
        __lowerCAmelCase:     Any     			=   scheduler_class.from_pretrained(UpperCAmelCase      )

       __lowerCAmelCase:     Tuple     			=   scheduler.step_pred(
           UpperCAmelCase    ,						UpperCAmelCase    ,						UpperCAmelCase    ,						generator=torch.manual_seed(0      )    ,						**UpperCAmelCase      ).prev_sample
       __lowerCAmelCase:     Optional[Any]     			=   new_scheduler.step_pred(
           UpperCAmelCase    ,						UpperCAmelCase    ,						UpperCAmelCase    ,						generator=torch.manual_seed(0      )    ,						**UpperCAmelCase      ).prev_sample

       assert torch.sum(torch.abs(output - new_output      )      ) < 1E-5, "Scheduler outputs are not identical"

       __lowerCAmelCase:     Tuple     			=   scheduler.step_correct(UpperCAmelCase    ,						UpperCAmelCase    ,						generator=torch.manual_seed(0      )    ,						**UpperCAmelCase      ).prev_sample
       __lowerCAmelCase:     str     			=   new_scheduler.step_correct(
           UpperCAmelCase    ,						UpperCAmelCase    ,						generator=torch.manual_seed(0      )    ,						**UpperCAmelCase      ).prev_sample

       assert torch.sum(torch.abs(output - new_output      )      ) < 1E-5, "Scheduler correction are not identical"


     def 						UpperCAmelCase						(							self    :	Any      )							->			Optional[int]:
      for timesteps in [1_0, 1_0_0, 1_0_0_0]:
       self.check_over_configs(num_train_timesteps=UpperCAmelCase      )


     def 						UpperCAmelCase						(							self    :	Optional[int]      )							->			List[str]:
      for sigma_min, sigma_max in zip([0.0001, 0.001, 0.01]    ,						[1, 1_0_0, 1_0_0_0]      ):
       self.check_over_configs(sigma_min=UpperCAmelCase    ,						sigma_max=UpperCAmelCase      )


     def 						UpperCAmelCase						(							self    :	int      )							->			Any:
      for t in [0.1, 0.5, 0.75]:
       self.check_over_forward(time_step=UpperCAmelCase      )


     def 						UpperCAmelCase						(							self    :	int      )							->			Any:
      __lowerCAmelCase:     Union[str, Any]     			=   dict(self.forward_default_kwargs      )

      __lowerCAmelCase:     List[Any]     			=   self.scheduler_classes[0]
      __lowerCAmelCase:     Dict     			=   self.get_scheduler_config()
      __lowerCAmelCase:     Union[str, Any]     			=   scheduler_class(**UpperCAmelCase      )

      __lowerCAmelCase:     List[str]     			=   3

      __lowerCAmelCase:     Union[str, Any]     			=   self.dummy_model()
      __lowerCAmelCase:     str     			=   self.dummy_sample_deter

      scheduler.set_sigmas(UpperCAmelCase      )
      scheduler.set_timesteps(UpperCAmelCase      )
      __lowerCAmelCase:     List[str]     			=   torch.manual_seed(0      )

      for i, t in enumerate(scheduler.timesteps      ):
       __lowerCAmelCase:     Optional[Any]     			=   scheduler.sigmas[i]

       for _ in range(scheduler.config.correct_steps      ):
        with torch.no_grad():
         __lowerCAmelCase:     int     			=   model(UpperCAmelCase    ,						UpperCAmelCase      )
        __lowerCAmelCase:     int     			=   scheduler.step_correct(UpperCAmelCase    ,						UpperCAmelCase    ,						generator=UpperCAmelCase    ,						**UpperCAmelCase      ).prev_sample

       with torch.no_grad():
        __lowerCAmelCase:     int     			=   model(UpperCAmelCase    ,						UpperCAmelCase      )

       __lowerCAmelCase:     Dict     			=   scheduler.step_pred(UpperCAmelCase    ,						UpperCAmelCase    ,						UpperCAmelCase    ,						generator=UpperCAmelCase    ,						**UpperCAmelCase      )
       __lowerCAmelCase:     List[Any]     			=   output.prev_sample, output.prev_sample_mean

      __lowerCAmelCase:     Optional[int]     			=   torch.sum(torch.abs(UpperCAmelCase      )      )
      __lowerCAmelCase:     Optional[Any]     			=   torch.mean(torch.abs(UpperCAmelCase      )      )

      assert np.isclose(result_sum.item()    ,						14372758528.0      )
      assert np.isclose(result_mean.item()    ,						18714530.0      )


     def 						UpperCAmelCase						(							self    :	str      )							->			Tuple:
      __lowerCAmelCase:     Union[str, Any]     			=   dict(self.forward_default_kwargs      )

      __lowerCAmelCase:     List[Any]     			=   kwargs.pop('num_inference_steps'    ,						UpperCAmelCase      )

      for scheduler_class in self.scheduler_classes:
       __lowerCAmelCase:     Optional[Any]     			=   self.get_scheduler_config()
       __lowerCAmelCase:     Union[str, Any]     			=   scheduler_class(**UpperCAmelCase      )

       __lowerCAmelCase:     Any     			=   self.dummy_sample
       __lowerCAmelCase:     Any     			=   0.1 * sample

       if num_inference_steps is not None and hasattr(UpperCAmelCase    ,						'set_timesteps'      ):
        scheduler.set_timesteps(UpperCAmelCase      )
       elif num_inference_steps is not None and not hasattr(UpperCAmelCase    ,						'set_timesteps'      ):
        __lowerCAmelCase:     List[Any]     			=   num_inference_steps

       __lowerCAmelCase:     Tuple     			=   scheduler.step_pred(UpperCAmelCase    ,						0    ,						UpperCAmelCase    ,						generator=torch.manual_seed(0      )    ,						**UpperCAmelCase      ).prev_sample
       __lowerCAmelCase:     Optional[int]     			=   scheduler.step_pred(UpperCAmelCase    ,						1    ,						UpperCAmelCase    ,						generator=torch.manual_seed(0      )    ,						**UpperCAmelCase      ).prev_sample

       self.assertEqual(output_a.shape    ,						sample.shape      )
       self.assertEqual(output_a.shape    ,						output_a.shape      )








from __future__ import annotations

import unittest
import warnings

from transformers import AutoTokenizer, MarianConfig, MarianTokenizer, TranslationPipeline, 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, TFMarianModel, TFMarianMTModel






@require_tf
class 							A_				:
     _lowercase    :  str          =			MarianConfig
     _lowercase    :  Optional[int]          =			{}
     _lowercase    :  Optional[Any]          =			'gelu'


     def __init__(							self    :	Dict    ,						UpperCAmelCase    :	int    ,						UpperCAmelCase    :	List[Any]=1_3    ,						UpperCAmelCase    :	Optional[int]=7    ,						UpperCAmelCase    :	List[str]=True    ,						UpperCAmelCase    :	Dict=False    ,						UpperCAmelCase    :	Union[str, Any]=9_9    ,						UpperCAmelCase    :	Tuple=3_2    ,						UpperCAmelCase    :	List[str]=2    ,						UpperCAmelCase    :	Dict=4    ,						UpperCAmelCase    :	Tuple=3_7    ,						UpperCAmelCase    :	Union[str, Any]=0.1    ,						UpperCAmelCase    :	Dict=0.1    ,						UpperCAmelCase    :	Any=2_0    ,						UpperCAmelCase    :	Union[str, Any]=2    ,						UpperCAmelCase    :	Any=1    ,						UpperCAmelCase    :	str=0    ,						)							->			str:
      __lowerCAmelCase:     Tuple     			=   parent
      __lowerCAmelCase:     Any     			=   batch_size
      __lowerCAmelCase:     List[Any]     			=   seq_length
      __lowerCAmelCase:     Dict     			=   is_training
      __lowerCAmelCase:     List[Any]     			=   use_labels
      __lowerCAmelCase:     List[str]     			=   vocab_size
      __lowerCAmelCase:     Optional[Any]     			=   hidden_size
      __lowerCAmelCase:     Any     			=   num_hidden_layers
      __lowerCAmelCase:     Optional[Any]     			=   num_attention_heads
      __lowerCAmelCase:     int     			=   intermediate_size

      __lowerCAmelCase:     List[Any]     			=   hidden_dropout_prob
      __lowerCAmelCase:     List[str]     			=   attention_probs_dropout_prob
      __lowerCAmelCase:     Tuple     			=   max_position_embeddings
      __lowerCAmelCase:     Tuple     			=   eos_token_id
      __lowerCAmelCase:     List[Any]     			=   pad_token_id
      __lowerCAmelCase:     str     			=   bos_token_id


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

      __lowerCAmelCase:     str     			=   ids_tensor([self.batch_size, self.seq_length]    ,						self.vocab_size      )

      __lowerCAmelCase:     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    ,						)
      __lowerCAmelCase:     Any     			=   prepare_marian_inputs_dict(UpperCAmelCase    ,						UpperCAmelCase    ,						UpperCAmelCase      )
      return config, inputs_dict


     def 						UpperCAmelCase						(							self    :	int    ,						UpperCAmelCase    :	Any    ,						UpperCAmelCase    :	int      )							->			Union[str, Any]:
      __lowerCAmelCase:     List[Any]     			=   TFMarianModel(config=UpperCAmelCase      ).get_decoder()
      __lowerCAmelCase:     str     			=   inputs_dict['input_ids']

      __lowerCAmelCase:     Tuple     			=   input_ids[:1, :]
      __lowerCAmelCase:     Optional[Any]     			=   inputs_dict['attention_mask'][:1, :]
      __lowerCAmelCase:     Dict     			=   inputs_dict['head_mask']
      __lowerCAmelCase:     List[Any]     			=   1

      # first forward pass
      __lowerCAmelCase:     Union[str, Any]     			=   model(UpperCAmelCase    ,						attention_mask=UpperCAmelCase    ,						head_mask=UpperCAmelCase    ,						use_cache=UpperCAmelCase      )

      __lowerCAmelCase			,							__lowerCAmelCase:     int     			=   outputs.to_tuple()

      # create hypothetical next token and extent to next_input_ids
      __lowerCAmelCase:     int     			=   ids_tensor((self.batch_size, 3)    ,						config.vocab_size      )
      __lowerCAmelCase:     Tuple     			=   tf.cast(ids_tensor((self.batch_size, 3)    ,						2      )    ,						tf.inta      )

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

      __lowerCAmelCase:     Optional[int]     			=   model(UpperCAmelCase    ,						attention_mask=UpperCAmelCase      )[0]
      __lowerCAmelCase:     List[str]     			=   model(UpperCAmelCase    ,						attention_mask=UpperCAmelCase    ,						past_key_values=UpperCAmelCase      )[0]

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

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

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







def _a				( SCREAMING_SNAKE_CASE	:    str					,      SCREAMING_SNAKE_CASE	:    Dict					,      SCREAMING_SNAKE_CASE	:    Dict					,      SCREAMING_SNAKE_CASE	:    Optional[int]=None					,      SCREAMING_SNAKE_CASE	:    Dict=None					,      SCREAMING_SNAKE_CASE	:    str=None					,      SCREAMING_SNAKE_CASE	:    List[str]=None					,      SCREAMING_SNAKE_CASE	:    str=None					,      )					->					Dict:

 """simple docstring"""






 if attention_mask is None:
  __lowerCAmelCase:     Dict     			=   tf.cast(tf.math.not_equal(SCREAMING_SNAKE_CASE					,      config.pad_token_id	)					,      tf.inta	)
 if decoder_attention_mask is None:
  __lowerCAmelCase:     Tuple     			=   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:
  __lowerCAmelCase:     List[str]     			=   tf.ones((config.encoder_layers, config.encoder_attention_heads)	)
 if decoder_head_mask is None:
  __lowerCAmelCase:     List[Any]     			=   tf.ones((config.decoder_layers, config.decoder_attention_heads)	)
 if cross_attn_head_mask is None:
  __lowerCAmelCase:     Dict     			=   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 							A_				(	snake_case__							,			snake_case__							,			unittest.TestCase      ):
     _lowercase    :  Optional[int]          =			(TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
     _lowercase    :  int          =			(TFMarianMTModel,) if is_tf_available() else ()
     _lowercase    :  Optional[int]          =			(
         {
             'conversational': TFMarianMTModel,
             'feature-extraction': TFMarianModel,
             'summarization': TFMarianMTModel,
             'text2text-generation': TFMarianMTModel,
             'translation': TFMarianMTModel,
         }
         if is_tf_available()
         else {}
     )
     _lowercase    :  Dict          =			True
     _lowercase    :  List[Any]          =			False
     _lowercase    :  Optional[int]          =			False


     def 						UpperCAmelCase						(							self    :	Dict      )							->			Any:
      __lowerCAmelCase:     List[Any]     			=   TFMarianModelTester(self      )
      __lowerCAmelCase:     Any     			=   ConfigTester(self    ,						config_class=UpperCAmelCase      )


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


     def 						UpperCAmelCase						(							self    :	Dict      )							->			Any:
      __lowerCAmelCase:     Tuple     			=   self.model_tester.prepare_config_and_inputs_for_common()
      self.model_tester.check_decoder_model_past_large_inputs(*UpperCAmelCase      )







@require_tf
class 							A_				(	unittest.TestCase      ):
     _lowercase    :  int          =			1_0_0_0  # show more chars for failing integration tests


     @classmethod
     def 						UpperCAmelCase						(							cls    :	Dict      )							->			None:
      __lowerCAmelCase:     int     			=   F'''Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'''
      return cls


     @cached_property
     def 						UpperCAmelCase						(							self    :	Union[str, Any]      )							->			MarianTokenizer:
      return AutoTokenizer.from_pretrained(self.model_name      )


     @property
     def 						UpperCAmelCase						(							self    :	Any      )							->			int:
      return self.tokenizer.eos_token_id


     @cached_property
     def 						UpperCAmelCase						(							self    :	int      )							->			Optional[int]:
      warnings.simplefilter('error'      )
      __lowerCAmelCase:     TFMarianMTModel     			=   TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name      )
      assert isinstance(UpperCAmelCase    ,						UpperCAmelCase      )
      __lowerCAmelCase:     List[str]     			=   model.config
      self.assertListEqual(c.bad_words_ids    ,						[[c.pad_token_id]]      )
      self.assertEqual(c.max_length    ,						5_1_2      )
      self.assertEqual(c.decoder_start_token_id    ,						c.pad_token_id      )
      return model


     def 						UpperCAmelCase						(							self    :	List[str]    ,						**UpperCAmelCase    :	Dict      )							->			int:
      __lowerCAmelCase:     Optional[int]     			=   self.translate_src_text(**UpperCAmelCase      )
      self.assertListEqual(self.expected_text    ,						UpperCAmelCase      )


     def 						UpperCAmelCase						(							self    :	List[str]    ,						**UpperCAmelCase    :	Optional[int]      )							->			Optional[int]:
      __lowerCAmelCase:     Dict     			=   self.tokenizer(self.src_text    ,						**UpperCAmelCase    ,						padding=UpperCAmelCase    ,						return_tensors='tf'      )
      __lowerCAmelCase:     Tuple     			=   self.model.generate(
          model_inputs.input_ids    ,						attention_mask=model_inputs.attention_mask    ,						num_beams=2    ,						max_length=1_2_8      )
      __lowerCAmelCase:     Union[str, Any]     			=   self.tokenizer.batch_decode(generated_ids.numpy()    ,						skip_special_tokens=UpperCAmelCase      )
      return generated_words







@require_sentencepiece
@require_tokenizers
@require_tf
class 							A_				(	snake_case__      ):
     _lowercase    :  int          =			'mt'
     _lowercase    :  Union[str, Any]          =			'en'
     _lowercase    :  str          =			['Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem.']
     _lowercase    :  str          =			['Touching gently, Jesus healed a man who was affected by the sad disease of leprosy.']


     @unittest.skip('Skipping until #12647 is resolved.'      )
     @slow
     def 						UpperCAmelCase						(							self    :	Any      )							->			List[str]:
      self._assert_generated_batch_equal_expected()







@require_sentencepiece
@require_tokenizers
@require_tf
class 							A_				(	snake_case__      ):
     _lowercase    :  Optional[int]          =			'en'
     _lowercase    :  Optional[Any]          =			'zh'
     _lowercase    :  Tuple          =			['My name is Wolfgang and I live in Berlin']
     _lowercase    :  List[Any]          =			['我叫沃尔夫冈 我住在柏林']


     @unittest.skip('Skipping until #12647 is resolved.'      )
     @slow
     def 						UpperCAmelCase						(							self    :	List[str]      )							->			Optional[Any]:
      self._assert_generated_batch_equal_expected()







@require_sentencepiece
@require_tokenizers
@require_tf
class 							A_				(	snake_case__      ):
     _lowercase    :  Tuple          =			'en'
     _lowercase    :  Optional[int]          =			'ROMANCE'
     _lowercase    :  Any          =			[
         '>>fr<< Don\'t spend so much time watching TV.',
         '>>pt<< Your message has been sent.',
         '>>es<< He\'s two years older than me.',
     ]
     _lowercase    :  Tuple          =			[
         'Ne passez pas autant de temps à regarder la télé.',
         'A sua mensagem foi enviada.',
         'Es dos años más viejo que yo.',
     ]


     @unittest.skip('Skipping until #12647 is resolved.'      )
     @slow
     def 						UpperCAmelCase						(							self    :	Tuple      )							->			Dict:
      self._assert_generated_batch_equal_expected()


     @unittest.skip('Skipping until #12647 is resolved.'      )
     @slow
     def 						UpperCAmelCase						(							self    :	Union[str, Any]      )							->			List[str]:
      __lowerCAmelCase:     Optional[Any]     			=   TranslationPipeline(self.model    ,						self.tokenizer    ,						framework='tf'      )
      __lowerCAmelCase:     Tuple     			=   pipeline(self.src_text      )
      self.assertEqual(self.expected_text    ,						[x['translation_text'] for x in output]      )





