
'''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__			)




'''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__			)


