code: 
  
  
  '''simple docstring'''
  
  
  
  
  
  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__				:
  
  
  
  
  
  			'''simple docstring'''
  
  			A	:							Any							= MarianConfig
  			A	:							Optional[Any]							= {}
  			A	:							int							= 'gelu'
  			def __init__(     self       : Optional[Any] ,						lowerCAmelCase_       : Tuple ,						lowerCAmelCase_       : List[Any]=13 ,						lowerCAmelCase_       : int=7 ,						lowerCAmelCase_       : Tuple=True ,						lowerCAmelCase_       : List[str]=False ,						lowerCAmelCase_       : Tuple=99 ,						lowerCAmelCase_       : int=32 ,						lowerCAmelCase_       : Union[str, Any]=2 ,						lowerCAmelCase_       : Optional[int]=4 ,						lowerCAmelCase_       : Dict=37 ,						lowerCAmelCase_       : Optional[int]=0.1 ,						lowerCAmelCase_       : Dict=0.1 ,						lowerCAmelCase_       : Optional[int]=20 ,						lowerCAmelCase_       : List[str]=2 ,						lowerCAmelCase_       : Dict=1 ,						lowerCAmelCase_       : int=0 ,						)			->		int:
  						__A=  parent
  						__A=  batch_size
  						__A=  seq_length
  						__A=  is_training
  						__A=  use_labels
  						__A=  vocab_size
  						__A=  hidden_size
  						__A=  num_hidden_layers
  						__A=  num_attention_heads
  						__A=  intermediate_size
  
  						__A=  hidden_dropout_prob
  						__A=  attention_probs_dropout_prob
  						__A=  max_position_embeddings
  						__A=  eos_token_id
  						__A=  pad_token_id
  						__A=  bos_token_id
  			def 				lowerCAmelCase    (     self       : Dict  )			->		int:
  						__A=  ids_tensor([self.batch_size, self.seq_length - 1] ,						self.vocab_size  )
  						__A=  tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size  ) ,						1  )
  						__A=  tf.concat([input_ids, eos_tensor] ,						axis=1  )
  
  						__A=  ids_tensor([self.batch_size, self.seq_length] ,						self.vocab_size  )
  
  						__A=  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 ,						)
  						__A=  prepare_marian_inputs_dict(lowerCAmelCase_ ,						lowerCAmelCase_ ,						lowerCAmelCase_  )
  						return config, inputs_dict
  
  			def 				lowerCAmelCase    (     self       : List[Any] ,						lowerCAmelCase_       : List[Any] ,						lowerCAmelCase_       : Dict  )			->		Optional[Any]:
  						__A=  TFMarianModel(config=lowerCAmelCase_  ).get_decoder()
  						__A=  inputs_dict['input_ids']
  
  						__A=  input_ids[:1, :]
  						__A=  inputs_dict['attention_mask'][:1, :]
  						__A=  inputs_dict['head_mask']
  						__A=  1
  
  						# first forward pass
  						__A=  model(lowerCAmelCase_ ,						attention_mask=lowerCAmelCase_ ,						head_mask=lowerCAmelCase_ ,						use_cache=lowerCAmelCase_  )
  
  						__A=  outputs.to_tuple()
  
  						# create hypothetical next token and extent to next_input_ids
  						__A=  ids_tensor((self.batch_size, 3) ,						config.vocab_size  )
  						__A=  tf.cast(ids_tensor((self.batch_size, 3) ,						2  ) ,						tf.inta  )
  
  						# append to next input_ids and
  						__A=  tf.concat([input_ids, next_tokens] ,						axis=-1  )
  						__A=  tf.concat([attention_mask, next_attn_mask] ,						axis=-1  )
  
  						__A=  model(lowerCAmelCase_ ,						attention_mask=lowerCAmelCase_  )[0]
  						__A=  model(lowerCAmelCase_ ,						attention_mask=lowerCAmelCase_ ,						past_key_values=lowerCAmelCase_  )[0]
  
  						self.parent.assertEqual(next_tokens.shape[1] ,						output_from_past.shape[1]  )
  
  						# select random slice
  						__A=  int(ids_tensor((1,) ,						output_from_past.shape[-1]  )  )
  						__A=  output_from_no_past[:, -3:, random_slice_idx]
  						__A=  output_from_past[:, :, random_slice_idx]
  
  						# test that outputs are equal for slice
  						tf.debugging.assert_near(lowerCAmelCase_ ,						lowerCAmelCase_ ,						rtol=1E-3  )
  
  
  
  
  
  
  def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   List[str],_SCREAMING_SNAKE_CASE				:   List[Any],_SCREAMING_SNAKE_CASE				:   str,_SCREAMING_SNAKE_CASE				:   str=None,_SCREAMING_SNAKE_CASE				:   Dict=None,_SCREAMING_SNAKE_CASE				:   Optional[Any]=None,_SCREAMING_SNAKE_CASE				:   Tuple=None,_SCREAMING_SNAKE_CASE				:   str=None,):
  
  
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  			if attention_mask is None:
  						__A=  tf.cast(tf.math.not_equal(lowerCAmelCase__,config.pad_token_id   ),tf.inta   )
  			if decoder_attention_mask is None:
  						__A=  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:
  						__A=  tf.ones((config.encoder_layers, config.encoder_attention_heads)   )
  			if decoder_head_mask is None:
  						__A=  tf.ones((config.decoder_layers, config.decoder_attention_heads)   )
  			if cross_attn_head_mask is None:
  						__A=  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      ):
  
  
  
  
  
  			'''simple docstring'''
  
  			A	:							Tuple							= (TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
  			A	:							Optional[int]							= (TFMarianMTModel,) if is_tf_available() else ()
  			A	:							List[str]							= (
  			    {
  			        'conversational': TFMarianMTModel,
  			        'feature-extraction': TFMarianModel,
  			        'summarization': TFMarianMTModel,
  			        'text2text-generation': TFMarianMTModel,
  			        'translation': TFMarianMTModel,
  			    }
  			    if is_tf_available()
  			    else {}
  			)
  			A	:							str							= True
  			A	:							Any							= False
  			A	:							Optional[Any]							= False
  			def 				lowerCAmelCase    (     self       : Optional[Any]  )			->		List[Any]:
  						__A=  TFMarianModelTester(self  )
  						__A=  ConfigTester(self ,						config_class=lowerCAmelCase_  )
  			def 				lowerCAmelCase    (     self       : Tuple  )			->		int:
  						self.config_tester.run_common_tests()
  
  			def 				lowerCAmelCase    (     self       : Any  )			->		List[Any]:
  						__A=  self.model_tester.prepare_config_and_inputs_for_common()
  						self.model_tester.check_decoder_model_past_large_inputs(*lowerCAmelCase_  )
  
  
  @require_tf
  class 	a__				(				unittest.TestCase      ):
  
  
  
  
  
  			'''simple docstring'''
  
  			A	:							List[str]							= 1000  # show more chars for failing integration tests
  			@classmethod
  			def 				lowerCAmelCase    (     cls       : Dict  )			->		List[str]:
  						__A=  F"""Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"""
  						return cls
  			@cached_property
  			def 				lowerCAmelCase    (     self       : Union[str, Any]  )			->		Dict:
  						return AutoTokenizer.from_pretrained(self.model_name  )
  			@property
  			def 				lowerCAmelCase    (     self       : Optional[Any]  )			->		List[Any]:
  						return self.tokenizer.eos_token_id
  			@cached_property
  			def 				lowerCAmelCase    (     self       : Any  )			->		Union[str, Any]:
  						warnings.simplefilter('error'  )
  						__A=  TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name  )
  						assert isinstance(lowerCAmelCase_ ,						lowerCAmelCase_  )
  						__A=  model.config
  						self.assertListEqual(c.bad_words_ids ,						[[c.pad_token_id]]  )
  						self.assertEqual(c.max_length ,						512  )
  						self.assertEqual(c.decoder_start_token_id ,						c.pad_token_id  )
  						return model
  			def 				lowerCAmelCase    (     self       : int ,						**lowerCAmelCase_       : int  )			->		str:
  						__A=  self.translate_src_text(**lowerCAmelCase_  )
  						self.assertListEqual(self.expected_text ,						lowerCAmelCase_  )
  
  			def 				lowerCAmelCase    (     self       : Optional[int] ,						**lowerCAmelCase_       : int  )			->		Optional[int]:
  						__A=  self.tokenizer(self.src_text ,						**lowerCAmelCase_ ,						padding=lowerCAmelCase_ ,						return_tensors='tf'  )
  						__A=  self.model.generate(
  						    model_inputs.input_ids ,						attention_mask=model_inputs.attention_mask ,						num_beams=2 ,						max_length=128  )
  						__A=  self.tokenizer.batch_decode(generated_ids.numpy() ,						skip_special_tokens=lowerCAmelCase_  )
  						return generated_words
  
  
  @require_sentencepiece
  @require_tokenizers
  @require_tf
  class 	a__				(				snake_case__      ):
  
  
  
  
  
  			'''simple docstring'''
  
  			A	:							Dict							= 'mt'
  			A	:							Optional[Any]							= 'en'
  			A	:							int							= ['Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem.']
  			A	:							Optional[Any]							= ['Touching gently, Jesus healed a man who was affected by the sad disease of leprosy.']
  			@unittest.skip('Skipping until #12647 is resolved.'  )
  			@slow
  			def 				lowerCAmelCase    (     self       : Optional[Any]  )			->		List[Any]:
  						self._assert_generated_batch_equal_expected()
  
  
  @require_sentencepiece
  @require_tokenizers
  @require_tf
  class 	a__				(				snake_case__      ):
  
  
  
  
  
  			'''simple docstring'''
  
  			A	:							Optional[Any]							= 'en'
  			A	:							Optional[Any]							= 'zh'
  			A	:							Union[str, Any]							= ['My name is Wolfgang and I live in Berlin']
  			A	:							Dict							= ['我叫沃尔夫冈 我住在柏林']
  			@unittest.skip('Skipping until #12647 is resolved.'  )
  			@slow
  			def 				lowerCAmelCase    (     self       : Tuple  )			->		int:
  						self._assert_generated_batch_equal_expected()
  
  
  @require_sentencepiece
  @require_tokenizers
  @require_tf
  class 	a__				(				snake_case__      ):
  
  
  
  
  
  			'''simple docstring'''
  
  			A	:							str							= 'en'
  			A	:							Optional[int]							= 'ROMANCE'
  			A	:							Dict							= [
  			    '>>fr<< Don\'t spend so much time watching TV.',
  			    '>>pt<< Your message has been sent.',
  			    '>>es<< He\'s two years older than me.',
  			]
  			A	:							List[str]							= [
  			    '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 				lowerCAmelCase    (     self       : List[Any]  )			->		Optional[int]:
  						self._assert_generated_batch_equal_expected()
  
  			@unittest.skip('Skipping until #12647 is resolved.'  )
  			@slow
  			def 				lowerCAmelCase    (     self       : Any  )			->		Dict:
  						__A=  TranslationPipeline(self.model ,						self.tokenizer ,						framework='tf'  )
  						__A=  pipeline(self.src_text  )
  						self.assertEqual(self.expected_text ,						[x['translation_text'] for x in output]  )
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  
  
  from collections import OrderedDict
  from typing import Mapping
  
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfig
  from ...utils import logging
  
  
  UpperCAmelCase__      =							logging.get_logger(__name__)
  
  UpperCAmelCase__      =							{
      '''google/mobilebert-uncased''': '''https://huggingface.co/google/mobilebert-uncased/resolve/main/config.json'''
  }
  class 	a__				(				a_      ):
  
  
  
  
  
  			'''simple docstring'''
  
  			A	:							int							= MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
  			A	:							Optional[int]							= '''mobilebert'''
  			def __init__(     self       : Optional[int] ,						lowerCAmelCase_       : str=30_522 ,						lowerCAmelCase_       : List[Any]=512 ,						lowerCAmelCase_       : Optional[int]=24 ,						lowerCAmelCase_       : str=4 ,						lowerCAmelCase_       : List[str]=512 ,						lowerCAmelCase_       : List[str]="relu" ,						lowerCAmelCase_       : Dict=0.0 ,						lowerCAmelCase_       : List[Any]=0.1 ,						lowerCAmelCase_       : Optional[Any]=512 ,						lowerCAmelCase_       : str=2 ,						lowerCAmelCase_       : Dict=0.02 ,						lowerCAmelCase_       : Any=1E-12 ,						lowerCAmelCase_       : List[Any]=0 ,						lowerCAmelCase_       : Union[str, Any]=128 ,						lowerCAmelCase_       : Any=True ,						lowerCAmelCase_       : Optional[Any]=True ,						lowerCAmelCase_       : List[Any]=128 ,						lowerCAmelCase_       : Dict=False ,						lowerCAmelCase_       : Tuple=True ,						lowerCAmelCase_       : Any=4 ,						lowerCAmelCase_       : Optional[Any]="no_norm" ,						lowerCAmelCase_       : Optional[Any]=True ,						lowerCAmelCase_       : Union[str, Any]=None ,						**lowerCAmelCase_       : Union[str, Any] ,						)			->		List[Any]:
  						super().__init__(pad_token_id=lowerCAmelCase_ ,						**lowerCAmelCase_  )
  
  						__A=  vocab_size
  						__A=  hidden_size
  						__A=  num_hidden_layers
  						__A=  num_attention_heads
  						__A=  hidden_act
  						__A=  intermediate_size
  						__A=  hidden_dropout_prob
  						__A=  attention_probs_dropout_prob
  						__A=  max_position_embeddings
  						__A=  type_vocab_size
  						__A=  initializer_range
  						__A=  layer_norm_eps
  						__A=  embedding_size
  						__A=  trigram_input
  						__A=  use_bottleneck
  						__A=  intra_bottleneck_size
  						__A=  use_bottleneck_attention
  						__A=  key_query_shared_bottleneck
  						__A=  num_feedforward_networks
  						__A=  normalization_type
  						__A=  classifier_activation
  
  						if self.use_bottleneck:
  									__A=  intra_bottleneck_size
  						else:
  									__A=  hidden_size
  
  						__A=  classifier_dropout
  
  
  class 	a__				(				a_      ):
  
  
  
  
  
  			'''simple docstring'''
  
  			@property
  			def 				lowerCAmelCase    (     self       : List[str]  )			->		Mapping[str, Mapping[int, str]]:
  						if self.task == "multiple-choice":
  									__A=  {0: 'batch', 1: 'choice', 2: 'sequence'}
  						else:
  									__A=  {0: 'batch', 1: 'sequence'}
  						return OrderedDict(
  						    [
  						        ('input_ids', dynamic_axis),
  						        ('attention_mask', dynamic_axis),
  						        ('token_type_ids', dynamic_axis),
  						    ]  )
  
  
  
  