code: 
  '''simple docstring'''
  import os
  
  from packaging import version
  
  from .. import __version__
  from .accelerate_utils import apply_forward_hook
  from .constants import (
      CONFIG_NAME,
      DEPRECATED_REVISION_ARGS,
      DIFFUSERS_CACHE,
      DIFFUSERS_DYNAMIC_MODULE_NAME,
      FLAX_WEIGHTS_NAME,
      HF_MODULES_CACHE,
      HUGGINGFACE_CO_RESOLVE_ENDPOINT,
      ONNX_EXTERNAL_WEIGHTS_NAME,
      ONNX_WEIGHTS_NAME,
      SAFETENSORS_WEIGHTS_NAME,
      TEXT_ENCODER_ATTN_MODULE,
      WEIGHTS_NAME,
  )
  from .deprecation_utils import deprecate
  from .doc_utils import replace_example_docstring
  from .dynamic_modules_utils import get_class_from_dynamic_module
  from .hub_utils import (
      HF_HUB_OFFLINE,
      _add_variant,
      _get_model_file,
      extract_commit_hash,
      http_user_agent,
  )
  from .import_utils import (
      BACKENDS_MAPPING,
      ENV_VARS_TRUE_AND_AUTO_VALUES,
      ENV_VARS_TRUE_VALUES,
      USE_JAX,
      USE_TF,
      USE_TORCH,
      DummyObject,
      OptionalDependencyNotAvailable,
      is_accelerate_available,
      is_accelerate_version,
      is_bsa_available,
      is_flax_available,
      is_ftfy_available,
      is_inflect_available,
      is_invisible_watermark_available,
      is_k_diffusion_available,
      is_k_diffusion_version,
      is_librosa_available,
      is_note_seq_available,
      is_omegaconf_available,
      is_onnx_available,
      is_safetensors_available,
      is_scipy_available,
      is_tensorboard_available,
      is_tf_available,
      is_torch_available,
      is_torch_version,
      is_torchsde_available,
      is_transformers_available,
      is_transformers_version,
      is_unidecode_available,
      is_wandb_available,
      is_xformers_available,
      requires_backends,
  )
  from .logging import get_logger
  from .outputs import BaseOutput
  from .pil_utils import PIL_INTERPOLATION, numpy_to_pil, pt_to_pil
  from .torch_utils import is_compiled_module, randn_tensor
  
  
  if is_torch_available():
  						from .testing_utils import (
  						    floats_tensor,
  						    load_hf_numpy,
  						    load_image,
  						    load_numpy,
  						    load_pt,
  						    nightly,
  						    parse_flag_from_env,
  						    print_tensor_test,
  						    require_torch_a,
  						    require_torch_gpu,
  						    skip_mps,
  						    slow,
  						    torch_all_close,
  						    torch_device,
  						)
  						from .torch_utils import maybe_allow_in_graph
  
  from .testing_utils import export_to_gif, export_to_video
  
  
  lowercase__						=			get_logger(__name__)
  
  
  
  
  def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__ )     ->       Any:
  
  
  		'''simple docstring'''
  
  
  		if version.parse(SCREAMING_SNAKE_CASE__ ) < version.parse(SCREAMING_SNAKE_CASE__ ):
  				if "dev" in min_version:
  						snake_case     :					Dict     =	(
  						    '''This example requires a source install from HuggingFace diffusers (see '''
  						    '''`https://huggingface.co/docs/diffusers/installation#install-from-source`),'''
  						)
  				else:
  						snake_case     :					List[str]     =	F'This example requires a minimum version of {min_version},'
  				error_message += F' but the version found is {__version__}.\n'
  				raise ImportError(SCREAMING_SNAKE_CASE__ )
  
  
  
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__			)
  
  
  