code: 
  
  
  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__  ( __A       ):
  
  
  
  
  
  	def __init__(  self				,    UpperCamelCase_				,    )		->			Dict:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   Dict			=	parent
  								a_       :   Union[str, Any]			=	13
  								a_       :   List[str]			=	7
  								a_       :   Any			=	True
  								a_       :   Any			=	True
  								a_       :   List[Any]			=	True
  								a_       :   str			=	True
  								a_       :   Any			=	True
  								a_       :   Dict			=	99
  								a_       :   Union[str, Any]			=	32
  								a_       :   List[str]			=	2
  								a_       :   Any			=	4
  								a_       :   Any			=	37
  								a_       :   Union[str, Any]			=	"""gelu"""
  								a_       :   List[Any]			=	0.1
  								a_       :   List[Any]			=	0.1
  								a_       :   int			=	512
  								a_       :   Dict			=	16
  								a_       :   Dict			=	2
  								a_       :   str			=	0.02
  								a_       :   List[str]			=	3
  								a_       :   List[Any]			=	4
  								a_       :   Optional[int]			=	None
  								a_       :   str			=	self.vocab_size - 1
  
  
  
  
  
  	def     A				(  self							)		->			List[Any]:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   Dict			=	ids_tensor([self.batch_size, self.seq_length]				,    self.vocab_size							)
  
  								a_       :   Union[str, Any]			=	None
  								if self.use_input_mask:
  															a_       :   Union[str, Any]			=	random_attention_mask([self.batch_size, self.seq_length]							)
  
  								a_       :   Optional[int]			=	None
  								if self.use_token_type_ids:
  															a_       :   Dict			=	ids_tensor([self.batch_size, self.seq_length]				,    self.type_vocab_size							)
  
  								a_       :   List[str]			=	None
  								if self.use_mc_token_ids:
  															a_       :   Dict			=	ids_tensor([self.batch_size, self.num_choices]				,    self.seq_length							)
  
  								a_       :   Tuple			=	None
  								a_       :   Optional[int]			=	None
  								a_       :   str			=	None
  								if self.use_labels:
  															a_       :   int			=	ids_tensor([self.batch_size]				,    self.type_sequence_label_size							)
  															a_       :   List[str]			=	ids_tensor([self.batch_size, self.seq_length]				,    self.num_labels							)
  															a_       :   List[str]			=	ids_tensor([self.batch_size]				,    self.num_choices							)
  
  								a_       :   List[str]			=	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				,    )
  
  								a_       :   int			=	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     A				(  self				,    UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_				,    *UpperCamelCase_							)		->			Optional[Any]:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   List[Any]			=	TFCTRLModel(config=UpperCamelCase_							)
  								a_       :   int			=	{"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
  								a_       :   Tuple			=	model(UpperCamelCase_							)
  
  								a_       :   int			=	[input_ids, None, input_mask]  # None is the input for 'past'
  								a_       :   str			=	model(UpperCamelCase_							)
  
  								a_       :   Tuple			=	model(UpperCamelCase_							)
  
  								self.parent.assertEqual(result.last_hidden_state.shape				,    (self.batch_size, self.seq_length, self.hidden_size)							)
  
  
  
  
  
  	def     A				(  self				,    UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_				,    *UpperCamelCase_							)		->			Any:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   Union[str, Any]			=	TFCTRLLMHeadModel(config=UpperCamelCase_							)
  								a_       :   Optional[int]			=	{"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
  								a_       :   int			=	model(UpperCamelCase_							)
  								self.parent.assertEqual(result.logits.shape				,    (self.batch_size, self.seq_length, self.vocab_size)							)
  
  
  
  
  
  	def     A				(  self				,    UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_				,    *UpperCamelCase_							)		->			str:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   Any			=	self.num_labels
  								a_       :   Union[str, Any]			=	ids_tensor([self.batch_size]				,    self.type_sequence_label_size							)
  								a_       :   Dict			=	{
  								    """input_ids""": input_ids,
  								    """token_type_ids""": token_type_ids,
  								    """labels""": sequence_labels,
  								}
  								a_       :   Union[str, Any]			=	TFCTRLForSequenceClassification(UpperCamelCase_							)
  								a_       :   Tuple			=	model(UpperCamelCase_							)
  								self.parent.assertEqual(result.logits.shape				,    (self.batch_size, self.num_labels)							)
  
  
  
  
  
  	def     A				(  self							)		->			Optional[int]:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   Any			=	self.prepare_config_and_inputs()
  
  								(
  								    (
  								    a_
  								)		,				(
  								    a_
  								)		,				(
  								    a_
  								)		,				(
  								    a_
  								)		,				(
  								    a_
  								)		,				(
  								    a_
  								)		,				(
  								    a_
  								)		,				(
  								    a_
  								)		,				(
  								    a_
  								)		,				
  								)       :   str			=	config_and_inputs
  
  								a_       :   Dict			=	{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
  								return config, inputs_dict
  
  
  
  
  
  @require_tf
  class 					snake_case__  ( __A      ,    __A      ,    unittest.TestCase       ):
  	UpperCAmelCase   :							Any         = (TFCTRLModel, TFCTRLLMHeadModel, TFCTRLForSequenceClassification) if is_tf_available() else ()
  	UpperCAmelCase   :							str         = (TFCTRLLMHeadModel,) if is_tf_available() else ()
  	UpperCAmelCase   :							int         = (
  	    {
  	        """feature-extraction""": TFCTRLModel,
  	        """text-classification""": TFCTRLForSequenceClassification,
  	        """text-generation""": TFCTRLLMHeadModel,
  	        """zero-shot""": TFCTRLForSequenceClassification,
  	    }
  	    if is_tf_available()
  	    else {}
  	)
  	UpperCAmelCase   :							Tuple         = False
  	UpperCAmelCase   :							Union[str, Any]         = False
  
  
  
  
  
  	def     A				(  self				,    UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_				,    UpperCamelCase_							)		->			List[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     A				(  self							)		->			Optional[int]:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   Any			=	TFCTRLModelTester(self							)
  								a_       :   List[Any]			=	ConfigTester(self				,    config_class=UpperCamelCase_				,    n_embd=37							)
  
  
  
  
  
  	def     A				(  self							)		->			List[str]:
  								"""simple docstring"""
  
  
  
  
  
  
  								self.config_tester.run_common_tests()
  
  
  
  
  
  	def     A				(  self							)		->			List[Any]:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   List[Any]			=	self.model_tester.prepare_config_and_inputs()
  								self.model_tester.create_and_check_ctrl_model(*UpperCamelCase_							)
  
  
  
  
  
  	def     A				(  self							)		->			Dict:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   Optional[Any]			=	self.model_tester.prepare_config_and_inputs()
  								self.model_tester.create_and_check_ctrl_lm_head(*UpperCamelCase_							)
  
  
  
  
  
  	def     A				(  self							)		->			str:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   Dict			=	self.model_tester.prepare_config_and_inputs()
  								self.model_tester.create_and_check_ctrl_for_sequence_classification(*UpperCamelCase_							)
  
  
  
  
  
  	def     A				(  self							)		->			Tuple:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_		,				a_       :   Any			=	self.model_tester.prepare_config_and_inputs_for_common()
  								a_       :   List[Any]			=	[TFCTRLLMHeadModel]
  								a_       :   Optional[int]			=	[TFCTRLForSequenceClassification]
  
  								for model_class in self.all_model_classes:
  															a_       :   int			=	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:
  																						a_       :   Union[str, Any]			=	model.get_output_embeddings()
  																						assert isinstance(UpperCamelCase_				,    tf.keras.layers.Layer							)
  																						a_       :   Union[str, 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:
  																						a_       :   List[str]			=	model.get_output_embeddings()
  																						assert isinstance(UpperCamelCase_				,    tf.keras.layers.Layer							)
  																						a_       :   Dict			=	model.get_bias()
  																						assert name is None
  															else:
  																						a_       :   Any			=	model.get_output_embeddings()
  																						assert x is None
  																						a_       :   Dict			=	model.get_bias()
  																						assert name is None
  
  
  
  
  
  	@slow
  	def     A				(  self							)		->			Optional[Any]:
  								"""simple docstring"""
  
  
  
  
  
  
  								for model_name in TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
  															a_       :   Optional[Any]			=	TFCTRLModel.from_pretrained(UpperCamelCase_							)
  															self.assertIsNotNone(UpperCamelCase_							)
  
  
  
  
  
  @require_tf
  class 					snake_case__  ( unittest.TestCase       ):
  
  
  
  
  
  	@slow
  	def     A				(  self							)		->			int:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   Optional[int]			=	TFCTRLLMHeadModel.from_pretrained("""ctrl"""							)
  								a_       :   int			=	tf.convert_to_tensor([[11859, 0, 1611, 8]]				,    dtype=tf.intaa							)  # Legal the president is
  								a_       :   Optional[Any]			=	[
  								    11859,
  								    0,
  								    1611,
  								    8,
  								    5,
  								    150,
  								    26449,
  								    2,
  								    19,
  								    348,
  								    469,
  								    3,
  								    2595,
  								    48,
  								    20740,
  								    246533,
  								    246533,
  								    19,
  								    30,
  								    5,
  								]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a
  
  								a_       :   Tuple			=	model.generate(UpperCamelCase_				,    do_sample=UpperCamelCase_							)
  								self.assertListEqual(output_ids[0].numpy().tolist()				,    UpperCamelCase_							)
  
  
  
style_context: 
  
  
  from ...utils import is_transformers_available
  from .pipeline_latent_diffusion_superresolution import LDMSuperResolutionPipeline
  
  
  if is_transformers_available():
  	from .pipeline_latent_diffusion import LDMBertModel, LDMTextToImagePipeline
  
  
  