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   snake_case__					:
  
  
  
  
  
  
  
  		"""simple docstring"""
  
  		lowerCamelCase           =	MarianConfig
  		lowerCamelCase           =	{}
  		lowerCamelCase           =	"""gelu"""
  
  
  
  		def __init__(			self							: Optional[Any]     ,  UpperCamelCase__							: Optional[Any]     ,  UpperCamelCase__							: Any=13     ,  UpperCamelCase__							: Optional[Any]=7     ,  UpperCamelCase__							: Union[str, Any]=True     ,  UpperCamelCase__							: Tuple=False     ,  UpperCamelCase__							: Union[str, Any]=99     ,  UpperCamelCase__							: Union[str, Any]=32     ,  UpperCamelCase__							: Any=2     ,  UpperCamelCase__							: List[Any]=4     ,  UpperCamelCase__							: Any=37     ,  UpperCamelCase__							: str=0.1     ,  UpperCamelCase__							: Any=0.1     ,  UpperCamelCase__							: str=20     ,  UpperCamelCase__							: Any=2     ,  UpperCamelCase__							: Optional[int]=1     ,  UpperCamelCase__							: Optional[int]=0     ,  )	-> Union[str, Any]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					Dict     =	parent
  				snake_case     :					Tuple     =	batch_size
  				snake_case     :					List[str]     =	seq_length
  				snake_case     :					Tuple     =	is_training
  				snake_case     :					Optional[Any]     =	use_labels
  				snake_case     :					Optional[int]     =	vocab_size
  				snake_case     :					Any     =	hidden_size
  				snake_case     :					int     =	num_hidden_layers
  				snake_case     :					List[str]     =	num_attention_heads
  				snake_case     :					Tuple     =	intermediate_size
  
  				snake_case     :					int     =	hidden_dropout_prob
  				snake_case     :					Union[str, Any]     =	attention_probs_dropout_prob
  				snake_case     :					Union[str, Any]     =	max_position_embeddings
  				snake_case     :					Optional[int]     =	eos_token_id
  				snake_case     :					List[Any]     =	pad_token_id
  				snake_case     :					Optional[Any]     =	bos_token_id
  
  
  
  		def 			lowerCAmelCase							(			self							: Dict			)	-> Dict:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					Any     =	ids_tensor([self.batch_size, self.seq_length - 1]     ,  self.vocab_size			)
  				snake_case     :					Tuple     =	tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size			)     ,  1			)
  				snake_case     :					List[Any]     =	tf.concat([input_ids, eos_tensor]     ,  axis=1			)
  
  				snake_case     :					str     =	ids_tensor([self.batch_size, self.seq_length]     ,  self.vocab_size			)
  
  				snake_case     :					str     =	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     ,  )
  				snake_case     :					Dict     =	prepare_marian_inputs_dict(UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__			)
  				return config, inputs_dict
  
  
  
  		def 			lowerCAmelCase							(			self							: List[str]     ,  UpperCamelCase__							: List[str]     ,  UpperCamelCase__							: str			)	-> Optional[int]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					int     =	TFMarianModel(config=UpperCamelCase__			).get_decoder()
  				snake_case     :					int     =	inputs_dict['''input_ids''']
  
  				snake_case     :					int     =	input_ids[:1, :]
  				snake_case     :					Dict     =	inputs_dict['''attention_mask'''][:1, :]
  				snake_case     :					List[Any]     =	inputs_dict['''head_mask''']
  				snake_case     :					str     =	1
  
  				# first forward pass
  				snake_case     :					str     =	model(UpperCamelCase__     ,  attention_mask=UpperCamelCase__     ,  head_mask=UpperCamelCase__     ,  use_cache=UpperCamelCase__			)
  
  				snake_case ,snake_case     :					List[str]     =	outputs.to_tuple()
  
  				# create hypothetical next token and extent to next_input_ids
  				snake_case     :					List[str]     =	ids_tensor((self.batch_size, 3)     ,  config.vocab_size			)
  				snake_case     :					str     =	tf.cast(ids_tensor((self.batch_size, 3)     ,  2			)     ,  tf.inta			)
  
  				# append to next input_ids and
  				snake_case     :					Tuple     =	tf.concat([input_ids, next_tokens]     ,  axis=-1			)
  				snake_case     :					Any     =	tf.concat([attention_mask, next_attn_mask]     ,  axis=-1			)
  
  				snake_case     :					Dict     =	model(UpperCamelCase__     ,  attention_mask=UpperCamelCase__			)[0]
  				snake_case     :					Optional[int]     =	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
  				snake_case     :					List[str]     =	int(ids_tensor((1,)     ,  output_from_past.shape[-1]			)			)
  				snake_case     :					Optional[Any]     =	output_from_no_past[:, -3:, random_slice_idx]
  				snake_case     :					str     =	output_from_past[:, :, random_slice_idx]
  
  				# test that outputs are equal for slice
  				tf.debugging.assert_near(UpperCamelCase__     ,  UpperCamelCase__     ,  rtol=1e-3			)
  
  
  
  
  
  
  
  def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__=None   ,					SCREAMING_SNAKE_CASE__=None   ,					SCREAMING_SNAKE_CASE__=None   ,					SCREAMING_SNAKE_CASE__=None   ,					SCREAMING_SNAKE_CASE__=None   ,					)     ->       Optional[Any]:
  
  
  		'''simple docstring'''
  
  
  		if attention_mask is None:
  				snake_case     :					Dict     =	tf.cast(tf.math.not_equal(SCREAMING_SNAKE_CASE__   ,					config.pad_token_id )   ,					tf.inta )
  		if decoder_attention_mask is None:
  				snake_case     :					Optional[int]     =	tf.concat(
  				    [
  				        tf.ones(decoder_input_ids[:, :1].shape   ,					dtype=tf.inta ),
  				        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]   ,					config.pad_token_id )   ,					tf.inta ),
  				    ]   ,					axis=-1   ,					)
  		if head_mask is None:
  				snake_case     :					Tuple     =	tf.ones((config.encoder_layers, config.encoder_attention_heads) )
  		if decoder_head_mask is None:
  				snake_case     :					Optional[Any]     =	tf.ones((config.decoder_layers, config.decoder_attention_heads) )
  		if cross_attn_head_mask is None:
  				snake_case     :					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   snake_case__					(			__SCREAMING_SNAKE_CASE						,				__SCREAMING_SNAKE_CASE						,				unittest.TestCase				):
  
  
  
  
  
  
  
  		"""simple docstring"""
  
  		lowerCamelCase           =	(TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
  		lowerCamelCase           =	(TFMarianMTModel,) if is_tf_available() else ()
  		lowerCamelCase           =	(
  		    {
  		        """conversational""": TFMarianMTModel,
  		        """feature-extraction""": TFMarianModel,
  		        """summarization""": TFMarianMTModel,
  		        """text2text-generation""": TFMarianMTModel,
  		        """translation""": TFMarianMTModel,
  		    }
  		    if is_tf_available()
  		    else {}
  		)
  		lowerCamelCase           =	True
  		lowerCamelCase           =	False
  		lowerCamelCase           =	False
  
  
  
  		def 			lowerCAmelCase							(			self							: List[str]			)	-> List[Any]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					int     =	TFMarianModelTester(self			)
  				snake_case     :					int     =	ConfigTester(self     ,  config_class=UpperCamelCase__			)
  
  
  
  		def 			lowerCAmelCase							(			self							: Dict			)	-> List[Any]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				self.config_tester.run_common_tests()
  
  
  
  		def 			lowerCAmelCase							(			self							: Any			)	-> Optional[int]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					Dict     =	self.model_tester.prepare_config_and_inputs_for_common()
  				self.model_tester.check_decoder_model_past_large_inputs(*UpperCamelCase__			)
  
  
  
  
  
  
  
  @require_tf
  class   snake_case__					(			unittest.TestCase				):
  
  
  
  
  
  
  
  		"""simple docstring"""
  
  		lowerCamelCase           =	1000  # show more chars for failing integration tests
  
  
  
  		@classmethod
  		def 			lowerCAmelCase							(			cls							: int			)	-> None:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					List[str]     =	f'Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'
  				return cls
  
  
  
  		@cached_property
  		def 			lowerCAmelCase							(			self							: Tuple			)	-> MarianTokenizer:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				return AutoTokenizer.from_pretrained(self.model_name			)
  
  
  
  		@property
  		def 			lowerCAmelCase							(			self							: Optional[int]			)	-> int:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				return self.tokenizer.eos_token_id
  
  
  
  		@cached_property
  		def 			lowerCAmelCase							(			self							: Dict			)	-> Tuple:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				warnings.simplefilter('''error'''			)
  				snake_case     :					TFMarianMTModel     =	TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name			)
  				assert isinstance(UpperCamelCase__     ,  UpperCamelCase__			)
  				snake_case     :					Optional[int]     =	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							: Union[str, Any]     ,  **UpperCamelCase__							: int			)	-> int:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					Optional[Any]     =	self.translate_src_text(**UpperCamelCase__			)
  				self.assertListEqual(self.expected_text     ,  UpperCamelCase__			)
  
  
  
  		def 			lowerCAmelCase							(			self							: Dict     ,  **UpperCamelCase__							: Tuple			)	-> Optional[int]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					Any     =	self.tokenizer(self.src_text     ,  **UpperCamelCase__     ,  padding=UpperCamelCase__     ,  return_tensors='''tf'''			)
  				snake_case     :					Optional[int]     =	self.model.generate(
  				    model_inputs.input_ids     ,  attention_mask=model_inputs.attention_mask     ,  num_beams=2     ,  max_length=128			)
  				snake_case     :					Tuple     =	self.tokenizer.batch_decode(generated_ids.numpy()     ,  skip_special_tokens=UpperCamelCase__			)
  				return generated_words
  
  
  
  
  
  
  
  @require_sentencepiece
  @require_tokenizers
  @require_tf
  class   snake_case__					(			__SCREAMING_SNAKE_CASE				):
  
  
  
  
  
  
  
  		"""simple docstring"""
  
  		lowerCamelCase           =	"""mt"""
  		lowerCamelCase           =	"""en"""
  		lowerCamelCase           =	["""Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."""]
  		lowerCamelCase           =	["""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							: str			)	-> List[str]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				self._assert_generated_batch_equal_expected()
  
  
  
  
  
  
  
  @require_sentencepiece
  @require_tokenizers
  @require_tf
  class   snake_case__					(			__SCREAMING_SNAKE_CASE				):
  
  
  
  
  
  
  
  		"""simple docstring"""
  
  		lowerCamelCase           =	"""en"""
  		lowerCamelCase           =	"""zh"""
  		lowerCamelCase           =	["""My name is Wolfgang and I live in Berlin"""]
  		lowerCamelCase           =	["""我叫沃尔夫冈 我住在柏林"""]
  
  
  
  		@unittest.skip('''Skipping until #12647 is resolved.'''			)
  		@slow
  		def 			lowerCAmelCase							(			self							: int			)	-> List[Any]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				self._assert_generated_batch_equal_expected()
  
  
  
  
  
  
  
  @require_sentencepiece
  @require_tokenizers
  @require_tf
  class   snake_case__					(			__SCREAMING_SNAKE_CASE				):
  
  
  
  
  
  
  
  		"""simple docstring"""
  
  		lowerCamelCase           =	"""en"""
  		lowerCamelCase           =	"""ROMANCE"""
  		lowerCamelCase           =	[
  		    """>>fr<< Don't spend so much time watching TV.""",
  		    """>>pt<< Your message has been sent.""",
  		    """>>es<< He's two years older than me.""",
  		]
  		lowerCamelCase           =	[
  		    """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							: Tuple			)	-> int:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				self._assert_generated_batch_equal_expected()
  
  
  
  		@unittest.skip('''Skipping until #12647 is resolved.'''			)
  		@slow
  		def 			lowerCAmelCase							(			self							: Dict			)	-> Any:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					Optional[Any]     =	TranslationPipeline(self.model     ,  self.tokenizer     ,  framework='''tf'''			)
  				snake_case     :					int     =	pipeline(self.src_text			)
  				self.assertEqual(self.expected_text     ,  [x['''translation_text'''] for x in output]			)
  
  
  
style_context: 
  '''simple docstring'''
  from __future__ import annotations
  
  import unittest
  
  from transformers import CTRLConfig, is_tf_available
  from transformers.testing_utils import require_tf, slow
  
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
  from ...test_pipeline_mixin import PipelineTesterMixin
  
  
  if is_tf_available():
  						import tensorflow as tf
  
  						from transformers.models.ctrl.modeling_tf_ctrl import (
  						    TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
  						    TFCTRLForSequenceClassification,
  						    TFCTRLLMHeadModel,
  						    TFCTRLModel,
  						)
  
  
  
  
  
  
  class   snake_case__					(			__SCREAMING_SNAKE_CASE				):
  
  
  
  
  
  
  
  		"""simple docstring"""
  
  		def __init__(			self							: Union[str, Any]     ,  UpperCamelCase__							: Optional[Any]     ,  )	-> List[Any]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					List[str]     =	parent
  				snake_case     :					str     =	13
  				snake_case     :					Any     =	7
  				snake_case     :					str     =	True
  				snake_case     :					int     =	True
  				snake_case     :					Optional[Any]     =	True
  				snake_case     :					Optional[Any]     =	True
  				snake_case     :					str     =	True
  				snake_case     :					Any     =	99
  				snake_case     :					int     =	32
  				snake_case     :					str     =	2
  				snake_case     :					Optional[Any]     =	4
  				snake_case     :					str     =	37
  				snake_case     :					Optional[int]     =	'''gelu'''
  				snake_case     :					List[Any]     =	0.1
  				snake_case     :					Dict     =	0.1
  				snake_case     :					str     =	512
  				snake_case     :					List[str]     =	16
  				snake_case     :					Tuple     =	2
  				snake_case     :					List[str]     =	0.02
  				snake_case     :					Optional[int]     =	3
  				snake_case     :					Optional[int]     =	4
  				snake_case     :					List[str]     =	None
  				snake_case     :					List[str]     =	self.vocab_size - 1
  
  
  
  		def 			lowerCAmelCase							(			self							: Dict			)	-> Optional[Any]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					Union[str, Any]     =	ids_tensor([self.batch_size, self.seq_length]     ,  self.vocab_size			)
  
  				snake_case     :					Optional[Any]     =	None
  				if self.use_input_mask:
  						snake_case     :					List[str]     =	random_attention_mask([self.batch_size, self.seq_length]			)
  
  				snake_case     :					str     =	None
  				if self.use_token_type_ids:
  						snake_case     :					str     =	ids_tensor([self.batch_size, self.seq_length]     ,  self.type_vocab_size			)
  
  				snake_case     :					Optional[int]     =	None
  				if self.use_mc_token_ids:
  						snake_case     :					List[str]     =	ids_tensor([self.batch_size, self.num_choices]     ,  self.seq_length			)
  
  				snake_case     :					Dict     =	None
  				snake_case     :					Any     =	None
  				snake_case     :					str     =	None
  				if self.use_labels:
  						snake_case     :					Optional[Any]     =	ids_tensor([self.batch_size]     ,  self.type_sequence_label_size			)
  						snake_case     :					Optional[int]     =	ids_tensor([self.batch_size, self.seq_length]     ,  self.num_labels			)
  						snake_case     :					List[str]     =	ids_tensor([self.batch_size]     ,  self.num_choices			)
  
  				snake_case     :					List[Any]     =	CTRLConfig(
  				    vocab_size=self.vocab_size     ,  n_embd=self.hidden_size     ,  n_layer=self.num_hidden_layers     ,  n_head=self.num_attention_heads     ,  n_positions=self.max_position_embeddings     ,  pad_token_id=self.pad_token_id     ,  )
  
  				snake_case     :					List[Any]     =	ids_tensor([self.num_hidden_layers, self.num_attention_heads]     ,  2			)
  
  				return (
  				    config,
  				    input_ids,
  				    input_mask,
  				    head_mask,
  				    token_type_ids,
  				    mc_token_ids,
  				    sequence_labels,
  				    token_labels,
  				    choice_labels,
  				)
  
  
  
  		def 			lowerCAmelCase							(			self							: Dict     ,  UpperCamelCase__							: int     ,  UpperCamelCase__							: List[str]     ,  UpperCamelCase__							: Optional[Any]     ,  UpperCamelCase__							: Optional[int]     ,  UpperCamelCase__							: int     ,  *UpperCamelCase__							: str			)	-> List[str]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					Tuple     =	TFCTRLModel(config=UpperCamelCase__			)
  				snake_case     :					Union[str, Any]     =	{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
  				snake_case     :					Optional[int]     =	model(UpperCamelCase__			)
  
  				snake_case     :					Optional[Any]     =	[input_ids, None, input_mask]  # None is the input for 'past'
  				snake_case     :					str     =	model(UpperCamelCase__			)
  
  				snake_case     :					Optional[Any]     =	model(UpperCamelCase__			)
  
  				self.parent.assertEqual(result.last_hidden_state.shape     ,  (self.batch_size, self.seq_length, self.hidden_size)			)
  
  
  
  		def 			lowerCAmelCase							(			self							: Any     ,  UpperCamelCase__							: int     ,  UpperCamelCase__							: Union[str, Any]     ,  UpperCamelCase__							: str     ,  UpperCamelCase__							: Any     ,  UpperCamelCase__							: int     ,  *UpperCamelCase__							: List[str]			)	-> List[Any]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					Union[str, Any]     =	TFCTRLLMHeadModel(config=UpperCamelCase__			)
  				snake_case     :					int     =	{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
  				snake_case     :					Any     =	model(UpperCamelCase__			)
  				self.parent.assertEqual(result.logits.shape     ,  (self.batch_size, self.seq_length, self.vocab_size)			)
  
  
  
  		def 			lowerCAmelCase							(			self							: str     ,  UpperCamelCase__							: Tuple     ,  UpperCamelCase__							: Tuple     ,  UpperCamelCase__							: Dict     ,  UpperCamelCase__							: str     ,  UpperCamelCase__							: Tuple     ,  *UpperCamelCase__							: Tuple			)	-> Optional[Any]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					int     =	self.num_labels
  				snake_case     :					Optional[int]     =	ids_tensor([self.batch_size]     ,  self.type_sequence_label_size			)
  				snake_case     :					Any     =	{
  				    '''input_ids''': input_ids,
  				    '''token_type_ids''': token_type_ids,
  				    '''labels''': sequence_labels,
  				}
  				snake_case     :					Dict     =	TFCTRLForSequenceClassification(UpperCamelCase__			)
  				snake_case     :					int     =	model(UpperCamelCase__			)
  				self.parent.assertEqual(result.logits.shape     ,  (self.batch_size, self.num_labels)			)
  
  
  
  		def 			lowerCAmelCase							(			self							: int			)	-> str:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					Union[str, Any]     =	self.prepare_config_and_inputs()
  
  				(
  				    (
  				    snake_case
  				) ,(
  				    snake_case
  				) ,(
  				    snake_case
  				) ,(
  				    snake_case
  				) ,(
  				    snake_case
  				) ,(
  				    snake_case
  				) ,(
  				    snake_case
  				) ,(
  				    snake_case
  				) ,(
  				    snake_case
  				) ,
  				)     :					str     =	config_and_inputs
  
  				snake_case     :					Any     =	{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
  				return config, inputs_dict
  
  
  
  
  
  
  
  @require_tf
  class   snake_case__					(			__SCREAMING_SNAKE_CASE						,				__SCREAMING_SNAKE_CASE						,				unittest.TestCase				):
  
  
  
  
  
  
  
  		"""simple docstring"""
  
  		lowerCamelCase           =	(TFCTRLModel, TFCTRLLMHeadModel, TFCTRLForSequenceClassification) if is_tf_available() else ()
  		lowerCamelCase           =	(TFCTRLLMHeadModel,) if is_tf_available() else ()
  		lowerCamelCase           =	(
  		    {
  		        """feature-extraction""": TFCTRLModel,
  		        """text-classification""": TFCTRLForSequenceClassification,
  		        """text-generation""": TFCTRLLMHeadModel,
  		        """zero-shot""": TFCTRLForSequenceClassification,
  		    }
  		    if is_tf_available()
  		    else {}
  		)
  		lowerCamelCase           =	False
  		lowerCamelCase           =	False
  
  
  
  		def 			lowerCAmelCase							(			self							: Union[str, Any]     ,  UpperCamelCase__							: Union[str, Any]     ,  UpperCamelCase__							: List[str]     ,  UpperCamelCase__							: Tuple     ,  UpperCamelCase__							: Union[str, Any]     ,  UpperCamelCase__							: Dict			)	-> str:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
  						# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
  						# `CTRLConfig` was never used in pipeline tests, either because of a missing checkpoint or because a tiny
  						# config could not be created.
  						return True
  
  				return False
  
  
  
  		def 			lowerCAmelCase							(			self							: Optional[Any]			)	-> Optional[Any]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					str     =	TFCTRLModelTester(self			)
  				snake_case     :					Dict     =	ConfigTester(self     ,  config_class=UpperCamelCase__     ,  n_embd=37			)
  
  
  
  		def 			lowerCAmelCase							(			self							: int			)	-> Optional[int]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				self.config_tester.run_common_tests()
  
  
  
  		def 			lowerCAmelCase							(			self							: Dict			)	-> Tuple:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					Optional[int]     =	self.model_tester.prepare_config_and_inputs()
  				self.model_tester.create_and_check_ctrl_model(*UpperCamelCase__			)
  
  
  
  		def 			lowerCAmelCase							(			self							: Dict			)	-> List[str]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					Union[str, Any]     =	self.model_tester.prepare_config_and_inputs()
  				self.model_tester.create_and_check_ctrl_lm_head(*UpperCamelCase__			)
  
  
  
  		def 			lowerCAmelCase							(			self							: List[str]			)	-> str:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					Dict     =	self.model_tester.prepare_config_and_inputs()
  				self.model_tester.create_and_check_ctrl_for_sequence_classification(*UpperCamelCase__			)
  
  
  
  		def 			lowerCAmelCase							(			self							: Tuple			)	-> List[Any]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case ,snake_case     :					Union[str, Any]     =	self.model_tester.prepare_config_and_inputs_for_common()
  				snake_case     :					int     =	[TFCTRLLMHeadModel]
  				snake_case     :					Tuple     =	[TFCTRLForSequenceClassification]
  
  				for model_class in self.all_model_classes:
  						snake_case     :					List[Any]     =	model_class(UpperCamelCase__			)
  						model.build()  # may be needed for the get_bias() call below
  						assert isinstance(model.get_input_embeddings()     ,  tf.keras.layers.Layer			)
  
  						if model_class in list_lm_models:
  								snake_case     :					Optional[Any]     =	model.get_output_embeddings()
  								assert isinstance(UpperCamelCase__     ,  tf.keras.layers.Layer			)
  								snake_case     :					Any     =	model.get_bias()
  								assert isinstance(UpperCamelCase__     ,  UpperCamelCase__			)
  								for k, v in name.items():
  										assert isinstance(UpperCamelCase__     ,  tf.Variable			)
  						elif model_class in list_other_models_with_output_ebd:
  								snake_case     :					List[Any]     =	model.get_output_embeddings()
  								assert isinstance(UpperCamelCase__     ,  tf.keras.layers.Layer			)
  								snake_case     :					int     =	model.get_bias()
  								assert name is None
  						else:
  								snake_case     :					Tuple     =	model.get_output_embeddings()
  								assert x is None
  								snake_case     :					Dict     =	model.get_bias()
  								assert name is None
  
  
  
  		@slow
  		def 			lowerCAmelCase							(			self							: Optional[Any]			)	-> Optional[int]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				for model_name in TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
  						snake_case     :					str     =	TFCTRLModel.from_pretrained(UpperCamelCase__			)
  						self.assertIsNotNone(UpperCamelCase__			)
  
  
  
  
  
  
  
  @require_tf
  class   snake_case__					(			unittest.TestCase				):
  
  
  
  
  
  
  
  		"""simple docstring"""
  
  		@slow
  		def 			lowerCAmelCase							(			self							: Optional[int]			)	-> Union[str, Any]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					Optional[Any]     =	TFCTRLLMHeadModel.from_pretrained('''ctrl'''			)
  				snake_case     :					Optional[Any]     =	tf.convert_to_tensor([[1_1859, 0, 1611, 8]]     ,  dtype=tf.intaa			)  # Legal the president is
  				snake_case     :					Dict     =	[
  				    1_1859,
  				    0,
  				    1611,
  				    8,
  				    5,
  				    150,
  				    2_6449,
  				    2,
  				    19,
  				    348,
  				    469,
  				    3,
  				    2595,
  				    48,
  				    2_0740,
  				    24_6533,
  				    24_6533,
  				    19,
  				    30,
  				    5,
  				]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a
  
  				snake_case     :					int     =	model.generate(UpperCamelCase__     ,  do_sample=UpperCamelCase__			)
  				self.assertListEqual(output_ids[0].numpy().tolist()     ,  UpperCamelCase__			)
  
  
  