code: 
  '''simple docstring'''
  from typing import Callable, List, Optional, Union
  
  import torch
  
  from ...models import UNetaDModel
  from ...schedulers import CMStochasticIterativeScheduler
  from ...utils import (
      is_accelerate_available,
      is_accelerate_version,
      logging,
      randn_tensor,
      replace_example_docstring,
  )
  from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
  
  
  lowercase__						=			logging.get_logger(__name__)  # pylint: disable=invalid-name
  
  
  lowercase__						=			"\n    Examples:\n        ```py\n        >>> import torch\n\n        >>> from diffusers import ConsistencyModelPipeline\n\n        >>> device = \"cuda\"\n        >>> # Load the cd_imagenet64_l2 checkpoint.\n        >>> model_id_or_path = \"openai/diffusers-cd_imagenet64_l2\"\n        >>> pipe = ConsistencyModelPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)\n        >>> pipe.to(device)\n\n        >>> # Onestep Sampling\n        >>> image = pipe(num_inference_steps=1).images[0]\n        >>> image.save(\"cd_imagenet64_l2_onestep_sample.png\")\n\n        >>> # Onestep sampling, class-conditional image generation\n        >>> # ImageNet-64 class label 145 corresponds to king penguins\n        >>> image = pipe(num_inference_steps=1, class_labels=145).images[0]\n        >>> image.save(\"cd_imagenet64_l2_onestep_sample_penguin.png\")\n\n        >>> # Multistep sampling, class-conditional image generation\n        >>> # Timesteps can be explicitly specified; the particular timesteps below are from the original Github repo:\n        >>> # https://github.com/openai/consistency_models/blob/main/scripts/launch.sh#L77\n        >>> image = pipe(num_inference_steps=None, timesteps=[22, 0], class_labels=145).images[0]\n        >>> image.save(\"cd_imagenet64_l2_multistep_sample_penguin.png\")\n        ```\n"
  
  
  
  
  
  
  class   snake_case__					(			__SCREAMING_SNAKE_CASE				):
  
  
  
  
  
  
  
  		"""simple docstring"""
  
  		def __init__(			self							: List[Any]     ,  UpperCamelCase__							: UNetaDModel     ,  UpperCamelCase__							: CMStochasticIterativeScheduler			)	-> None:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				super().__init__()
  
  				self.register_modules(
  				    unet=UpperCamelCase__     ,  scheduler=UpperCamelCase__     ,  )
  
  				snake_case     :					str     =	None
  
  
  
  		def 			lowerCAmelCase							(			self							: Tuple     ,  UpperCamelCase__							: str=0			)	-> int:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				if is_accelerate_available() and is_accelerate_version('''>='''     ,  '''0.14.0'''			):
  						from accelerate import cpu_offload
  				else:
  						raise ImportError('''`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher'''			)
  
  				snake_case     :					Optional[Any]     =	torch.device(f'cuda:{gpu_id}'			)
  
  				if self.device.type != "cpu":
  						self.to('''cpu'''     ,  silence_dtype_warnings=UpperCamelCase__			)
  						torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)
  
  				for cpu_offloaded_model in [self.unet]:
  						cpu_offload(UpperCamelCase__     ,  UpperCamelCase__			)
  
  				if self.safety_checker is not None:
  						cpu_offload(self.safety_checker     ,  execution_device=UpperCamelCase__     ,  offload_buffers=UpperCamelCase__			)
  
  
  
  		def 			lowerCAmelCase							(			self							: Optional[int]     ,  UpperCamelCase__							: int=0			)	-> Dict:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				if is_accelerate_available() and is_accelerate_version('''>='''     ,  '''0.17.0.dev0'''			):
  						from accelerate import cpu_offload_with_hook
  				else:
  						raise ImportError('''`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'''			)
  
  				snake_case     :					Union[str, Any]     =	torch.device(f'cuda:{gpu_id}'			)
  
  				if self.device.type != "cpu":
  						self.to('''cpu'''     ,  silence_dtype_warnings=UpperCamelCase__			)
  						torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)
  
  				snake_case     :					List[Any]     =	None
  				for cpu_offloaded_model in [self.unet]:
  						snake_case ,snake_case     :					Union[str, Any]     =	cpu_offload_with_hook(UpperCamelCase__     ,  UpperCamelCase__     ,  prev_module_hook=UpperCamelCase__			)
  
  				if self.safety_checker is not None:
  						snake_case ,snake_case     :					Optional[Any]     =	cpu_offload_with_hook(self.safety_checker     ,  UpperCamelCase__     ,  prev_module_hook=UpperCamelCase__			)
  
  				# We'll offload the last model manually.
  				snake_case     :					Optional[Any]     =	hook
  
  
  
  		@property
  		# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
  		def 			lowerCAmelCase							(			self							: int			)	-> List[str]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				if not hasattr(self.unet     ,  '''_hf_hook'''			):
  						return self.device
  				for module in self.unet.modules():
  						if (
  						    hasattr(UpperCamelCase__     ,  '''_hf_hook'''			)
  						    and hasattr(module._hf_hook     ,  '''execution_device'''			)
  						    and module._hf_hook.execution_device is not None
  						):
  								return torch.device(module._hf_hook.execution_device			)
  				return self.device
  
  
  
  		def 			lowerCAmelCase							(			self							: int     ,  UpperCamelCase__							: Optional[Any]     ,  UpperCamelCase__							: Dict     ,  UpperCamelCase__							: int     ,  UpperCamelCase__							: List[str]     ,  UpperCamelCase__							: int     ,  UpperCamelCase__							: Optional[int]     ,  UpperCamelCase__							: Any     ,  UpperCamelCase__							: Optional[int]=None			)	-> Tuple:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					Union[str, Any]     =	(batch_size, num_channels, height, width)
  				if isinstance(UpperCamelCase__     ,  UpperCamelCase__			) and len(UpperCamelCase__			) != batch_size:
  						raise ValueError(
  						    f'You have passed a list of generators of length {len(UpperCamelCase__			)}, but requested an effective batch'
  						    f' size of {batch_size}. Make sure the batch size matches the length of the generators.'			)
  
  				if latents is None:
  						snake_case     :					List[Any]     =	randn_tensor(UpperCamelCase__     ,  generator=UpperCamelCase__     ,  device=UpperCamelCase__     ,  dtype=UpperCamelCase__			)
  				else:
  						snake_case     :					Tuple     =	latents.to(device=UpperCamelCase__     ,  dtype=UpperCamelCase__			)
  
  				# scale the initial noise by the standard deviation required by the scheduler
  				snake_case     :					str     =	latents * self.scheduler.init_noise_sigma
  				return latents
  
  
  
  		def 			lowerCAmelCase							(			self							: Any     ,  UpperCamelCase__							: torch.FloatTensor     ,  UpperCamelCase__							: str = "pil"			)	-> Union[str, Any]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				if output_type not in ["pt", "np", "pil"]:
  						raise ValueError(
  						    f'output_type={output_type} is not supported. Make sure to choose one of [\'pt\', \'np\', or \'pil\']'			)
  
  				# Equivalent to diffusers.VaeImageProcessor.denormalize
  				snake_case     :					Any     =	(sample / 2 + 0.5).clamp(0     ,  1			)
  				if output_type == "pt":
  						return sample
  
  				# Equivalent to diffusers.VaeImageProcessor.pt_to_numpy
  				snake_case     :					Optional[int]     =	sample.cpu().permute(0     ,  2     ,  3     ,  1			).numpy()
  				if output_type == "np":
  						return sample
  
  				# Output_type must be 'pil'
  				snake_case     :					Tuple     =	self.numpy_to_pil(UpperCamelCase__			)
  				return sample
  
  
  
  		def 			lowerCAmelCase							(			self							: Tuple     ,  UpperCamelCase__							: List[str]     ,  UpperCamelCase__							: Dict     ,  UpperCamelCase__							: List[str]=None			)	-> List[str]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				if self.unet.config.num_class_embeds is not None:
  						if isinstance(UpperCamelCase__     ,  UpperCamelCase__			):
  								snake_case     :					str     =	torch.tensor(UpperCamelCase__     ,  dtype=torch.int			)
  						elif isinstance(UpperCamelCase__     ,  UpperCamelCase__			):
  								assert batch_size == 1, "Batch size must be 1 if classes is an int"
  								snake_case     :					Dict     =	torch.tensor([class_labels]     ,  dtype=torch.int			)
  						elif class_labels is None:
  								# Randomly generate batch_size class labels
  								# TODO: should use generator here? int analogue of randn_tensor is not exposed in ...utils
  								snake_case     :					List[Any]     =	torch.randint(0     ,  self.unet.config.num_class_embeds     ,  size=(batch_size,)			)
  						snake_case     :					Dict     =	class_labels.to(UpperCamelCase__			)
  				else:
  						snake_case     :					int     =	None
  				return class_labels
  
  
  
  		def 			lowerCAmelCase							(			self							: List[Any]     ,  UpperCamelCase__							: int     ,  UpperCamelCase__							: Any     ,  UpperCamelCase__							: List[Any]     ,  UpperCamelCase__							: List[Any]     ,  UpperCamelCase__							: List[str]     ,  UpperCamelCase__							: Union[str, Any]			)	-> Union[str, Any]:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				if num_inference_steps is None and timesteps is None:
  						raise ValueError('''Exactly one of `num_inference_steps` or `timesteps` must be supplied.'''			)
  
  				if num_inference_steps is not None and timesteps is not None:
  						logger.warning(
  						    f'Both `num_inference_steps`: {num_inference_steps} and `timesteps`: {timesteps} are supplied;'
  						    ''' `timesteps` will be used over `num_inference_steps`.'''			)
  
  				if latents is not None:
  						snake_case     :					List[Any]     =	(batch_size, 3, img_size, img_size)
  						if latents.shape != expected_shape:
  								raise ValueError(f'The shape of latents is {latents.shape} but is expected to be {expected_shape}.'			)
  
  				if (callback_steps is None) or (
  				    callback_steps is not None and (not isinstance(UpperCamelCase__     ,  UpperCamelCase__			) or callback_steps <= 0)
  				):
  						raise ValueError(
  						    f'`callback_steps` has to be a positive integer but is {callback_steps} of type'
  						    f' {type(UpperCamelCase__			)}.'			)
  
  
  
  		@torch.no_grad()
  		@replace_example_docstring(UpperCamelCase__			)
  		def __call__(			self							: Optional[int]     ,  UpperCamelCase__							: int = 1     ,  UpperCamelCase__							: Optional[Union[torch.Tensor, List[int], int]] = None     ,  UpperCamelCase__							: int = 1     ,  UpperCamelCase__							: List[int] = None     ,  UpperCamelCase__							: Optional[Union[torch.Generator, List[torch.Generator]]] = None     ,  UpperCamelCase__							: Optional[torch.FloatTensor] = None     ,  UpperCamelCase__							: Optional[str] = "pil"     ,  UpperCamelCase__							: bool = True     ,  UpperCamelCase__							: Optional[Callable[[int, int, torch.FloatTensor], None]] = None     ,  UpperCamelCase__							: int = 1     ,  )	-> str:
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  
  				snake_case     :					Optional[int]     =	self.unet.config.sample_size
  				snake_case     :					List[Any]     =	self._execution_device
  
  				# 1. Check inputs
  				self.check_inputs(UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__			)
  
  				# 2. Prepare image latents
  				# Sample image latents x_0 ~ N(0, sigma_0^2 * I)
  				snake_case     :					List[str]     =	self.prepare_latents(
  				    batch_size=UpperCamelCase__     ,  num_channels=self.unet.config.in_channels     ,  height=UpperCamelCase__     ,  width=UpperCamelCase__     ,  dtype=self.unet.dtype     ,  device=UpperCamelCase__     ,  generator=UpperCamelCase__     ,  latents=UpperCamelCase__     ,  )
  
  				# 3. Handle class_labels for class-conditional models
  				snake_case     :					List[str]     =	self.prepare_class_labels(UpperCamelCase__     ,  UpperCamelCase__     ,  class_labels=UpperCamelCase__			)
  
  				# 4. Prepare timesteps
  				if timesteps is not None:
  						self.scheduler.set_timesteps(timesteps=UpperCamelCase__     ,  device=UpperCamelCase__			)
  						snake_case     :					Union[str, Any]     =	self.scheduler.timesteps
  						snake_case     :					Optional[int]     =	len(UpperCamelCase__			)
  				else:
  						self.scheduler.set_timesteps(UpperCamelCase__			)
  						snake_case     :					List[str]     =	self.scheduler.timesteps
  
  				# 5. Denoising loop
  				# Multistep sampling: implements Algorithm 1 in the paper
  				with self.progress_bar(total=UpperCamelCase__			) as progress_bar:
  						for i, t in enumerate(UpperCamelCase__			):
  								snake_case     :					Any     =	self.scheduler.scale_model_input(UpperCamelCase__     ,  UpperCamelCase__			)
  								snake_case     :					int     =	self.unet(UpperCamelCase__     ,  UpperCamelCase__     ,  class_labels=UpperCamelCase__     ,  return_dict=UpperCamelCase__			)[0]
  
  								snake_case     :					Dict     =	self.scheduler.step(UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__     ,  generator=UpperCamelCase__			)[0]
  
  								# call the callback, if provided
  								progress_bar.update()
  								if callback is not None and i % callback_steps == 0:
  										callback(UpperCamelCase__     ,  UpperCamelCase__     ,  UpperCamelCase__			)
  
          # 6. Post-process image sample
  				snake_case     :					Any     =	self.postprocess_image(UpperCamelCase__     ,  output_type=UpperCamelCase__			)
  
  				# Offload last model to CPU
  				if hasattr(self     ,  '''final_offload_hook'''			) and self.final_offload_hook is not None:
  						self.final_offload_hook.offload()
  
  				if not return_dict:
  						return (image,)
  
  				return ImagePipelineOutput(images=UpperCamelCase__			)
  
  
  
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__			)
  
  
  