






import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
class       _UpperCAmelCase					(       A__		,A__		,A__		,unittest.TestCase	):

				"""simple docstring"""




				lowercase__											=       StableDiffusionInstructPixaPixPipeline
				lowercase__											=       TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width""", """cross_attention_kwargs"""}
				lowercase__											=       TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
				lowercase__											=       IMAGE_TO_IMAGE_IMAGE_PARAMS
				lowercase__											=       IMAGE_TO_IMAGE_IMAGE_PARAMS




				def 				lowercase__   (     self						:    Tuple  ):

								'''simple docstring'''
								torch.manual_seed(0  )
								lowercase__    =			UNetaDConditionModel(
								    block_out_channels=(32, 64),  layers_per_block=2,  sample_size=32,  in_channels=8,  out_channels=4,  down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D'''),  up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D'''),  cross_attention_dim=32,  )
								lowercase__    =			PNDMScheduler(skip_prk_steps=lowerCamelCase  )
								torch.manual_seed(0  )
								lowercase__    =			AutoencoderKL(
								    block_out_channels=[32, 64],  in_channels=3,  out_channels=3,  down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],  up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],  latent_channels=4,  )
								torch.manual_seed(0  )
								lowercase__    =			CLIPTextConfig(
								    bos_token_id=0,  eos_token_id=2,  hidden_size=32,  intermediate_size=37,  layer_norm_eps=1E-05,  num_attention_heads=4,  num_hidden_layers=5,  pad_token_id=1,  vocab_size=1_000,  )
								lowercase__    =			CLIPTextModel(lowerCamelCase  )
								lowercase__    =			CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''  )

								lowercase__    =			{
								    '''unet''': unet,
								    '''scheduler''': scheduler,
								    '''vae''': vae,
								    '''text_encoder''': text_encoder,
								    '''tokenizer''': tokenizer,
								    '''safety_checker''': None,
								    '''feature_extractor''': None,
								}
								return components




				def 				lowercase__   (     self						:    Tuple,  lowerCamelCase						:    int,  lowerCamelCase						:    Optional[int]=0  ):

								'''simple docstring'''
								lowercase__    =			floats_tensor((1, 3, 32, 32),  rng=random.Random(lowerCamelCase  )  ).to(lowerCamelCase  )
								lowercase__    =			image.cpu().permute(0,  2,  3,  1  )[0]
								lowercase__    =			Image.fromarray(np.uinta(lowerCamelCase  )  ).convert('''RGB'''  )
								if str(lowerCamelCase  ).startswith('''mps'''  ):
												lowercase__    =			torch.manual_seed(lowerCamelCase  )
								else:
												lowercase__    =			torch.Generator(device=lowerCamelCase  ).manual_seed(lowerCamelCase  )
								lowercase__    =			{
								    '''prompt''': '''A painting of a squirrel eating a burger''',
								    '''image''': image,
								    '''generator''': generator,
								    '''num_inference_steps''': 2,
								    '''guidance_scale''': 6.0,
								    '''image_guidance_scale''': 1,
								    '''output_type''': '''numpy''',
								}
								return inputs




				def 				lowercase__   (     self						:    Optional[Any]  ):

								'''simple docstring'''
								lowercase__    =			'''cpu'''  # ensure determinism for the device-dependent torch.Generator
								lowercase__    =			self.get_dummy_components()
								lowercase__    =			StableDiffusionInstructPixaPixPipeline(**lowerCamelCase  )
								lowercase__    =			sd_pipe.to(lowerCamelCase  )
								sd_pipe.set_progress_bar_config(disable=lowerCamelCase  )

								lowercase__    =			self.get_dummy_inputs(lowerCamelCase  )
								lowercase__    =			sd_pipe(**lowerCamelCase  ).images
								lowercase__    =			image[0, -3:, -3:, -1]
								assert image.shape == (1, 32, 32, 3)
								lowercase__    =			np.array([0.7526, 0.3750, 0.4547, 0.6117, 0.5866, 0.5016, 0.4327, 0.5642, 0.4815]  )

								assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-3




				def 				lowercase__   (     self						:    Dict  ):

								'''simple docstring'''
								lowercase__    =			'''cpu'''  # ensure determinism for the device-dependent torch.Generator
								lowercase__    =			self.get_dummy_components()
								lowercase__    =			StableDiffusionInstructPixaPixPipeline(**lowerCamelCase  )
								lowercase__    =			sd_pipe.to(lowerCamelCase  )
								sd_pipe.set_progress_bar_config(disable=lowerCamelCase  )

								lowercase__    =			self.get_dummy_inputs(lowerCamelCase  )
								lowercase__    =			'''french fries'''
								lowercase__    =			sd_pipe(**lowerCamelCase,  negative_prompt=lowerCamelCase  )
								lowercase__    =			output.images
								lowercase__    =			image[0, -3:, -3:, -1]

								assert image.shape == (1, 32, 32, 3)
								lowercase__    =			np.array([0.7511, 0.3642, 0.4553, 0.6236, 0.5797, 0.5013, 0.4343, 0.5611, 0.4831]  )

								assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-3




				def 				lowercase__   (     self						:    Optional[int]  ):

								'''simple docstring'''
								lowercase__    =			'''cpu'''  # ensure determinism for the device-dependent torch.Generator
								lowercase__    =			self.get_dummy_components()
								lowercase__    =			StableDiffusionInstructPixaPixPipeline(**lowerCamelCase  )
								lowercase__    =			sd_pipe.to(lowerCamelCase  )
								sd_pipe.set_progress_bar_config(disable=lowerCamelCase  )

								lowercase__    =			self.get_dummy_inputs(lowerCamelCase  )
								lowercase__    =			[inputs['''prompt''']] * 2

								lowercase__    =			np.array(inputs['''image''']  ).astype(np.floataa  ) / 255.0
								lowercase__    =			torch.from_numpy(lowerCamelCase  ).unsqueeze(0  ).to(lowerCamelCase  )
								lowercase__    =			image / 2 + 0.5
								lowercase__    =			image.permute(0,  3,  1,  2  )
								lowercase__    =			image.repeat(2,  1,  1,  1  )

								lowercase__    =			sd_pipe(**lowerCamelCase  ).images
								lowercase__    =			image[-1, -3:, -3:, -1]

								assert image.shape == (2, 32, 32, 3)
								lowercase__    =			np.array([0.5812, 0.5748, 0.5222, 0.5908, 0.5695, 0.7174, 0.6804, 0.5523, 0.5579]  )

								assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-3




				def 				lowercase__   (     self						:    Any  ):

								'''simple docstring'''
								lowercase__    =			'''cpu'''  # ensure determinism for the device-dependent torch.Generator
								lowercase__    =			self.get_dummy_components()
								lowercase__    =			EulerAncestralDiscreteScheduler(
								    beta_start=0.00085,  beta_end=0.012,  beta_schedule='''scaled_linear'''  )
								lowercase__    =			StableDiffusionInstructPixaPixPipeline(**lowerCamelCase  )
								lowercase__    =			sd_pipe.to(lowerCamelCase  )
								sd_pipe.set_progress_bar_config(disable=lowerCamelCase  )

								lowercase__    =			self.get_dummy_inputs(lowerCamelCase  )
								lowercase__    =			sd_pipe(**lowerCamelCase  ).images
								lowercase__    =			image[0, -3:, -3:, -1]

								lowercase__    =			[round(lowerCamelCase,  4  ) for x in image_slice.flatten().tolist()]
								print(''','''.join([str(lowerCamelCase  ) for x in slice]  )  )

								assert image.shape == (1, 32, 32, 3)
								lowercase__    =			np.array([0.7417, 0.3842, 0.4732, 0.5776, 0.5891, 0.5139, 0.4052, 0.5673, 0.4986]  )

								assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-3




				def 				lowercase__   (     self						:    Union[str, Any]  ):

								'''simple docstring'''
								super().test_inference_batch_single_identical(expected_max_diff=3E-3  )




				def 				lowercase__   (     self						:    List[str]  ):

								'''simple docstring'''
								lowercase__    =			self.get_dummy_components()
								lowercase__    =			StableDiffusionInstructPixaPixPipeline(**lowerCamelCase  )
								lowercase__    =			VaeImageProcessor(do_resize=lowerCamelCase,  do_normalize=lowerCamelCase  )
								lowercase__    =			pipe.to(lowerCamelCase  )
								pipe.set_progress_bar_config(disable=lowerCamelCase  )

								lowercase__    =			pipe(**self.get_dummy_inputs_by_type(lowerCamelCase,  input_image_type='''pt'''  )  )[0]

								lowercase__    =			components['''vae''']
								lowercase__    =			self.get_dummy_inputs_by_type(lowerCamelCase,  input_image_type='''pt'''  )

								for image_param in self.image_latents_params:
												if image_param in inputs.keys():
																lowercase__    =			vae.encode(inputs[image_param]  ).latent_dist.mode()

								lowercase__    =			pipe(**lowerCamelCase  )[0]

								lowercase__    =			np.abs(out - out_latents_inputs  ).max()
								self.assertLess(lowerCamelCase,  1E-4,  '''passing latents as image input generate different result from passing image'''  )



@slow
@require_torch_gpu
class       _UpperCAmelCase					(       unittest.TestCase	):

				"""simple docstring"""




				def 				lowercase__   (     self						:    Tuple  ):

								'''simple docstring'''
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()




				def 				lowercase__   (     self						:    Union[str, Any],  lowerCamelCase						:    List[str]=0  ):

								'''simple docstring'''
								lowercase__    =			torch.manual_seed(lowerCamelCase  )
								lowercase__    =			load_image(
								    '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg'''  )
								lowercase__    =			{
								    '''prompt''': '''turn him into a cyborg''',
								    '''image''': image,
								    '''generator''': generator,
								    '''num_inference_steps''': 3,
								    '''guidance_scale''': 7.5,
								    '''image_guidance_scale''': 1.0,
								    '''output_type''': '''numpy''',
								}
								return inputs




				def 				lowercase__   (     self						:    int  ):

								'''simple docstring'''
								lowercase__    =			StableDiffusionInstructPixaPixPipeline.from_pretrained(
								    '''timbrooks/instruct-pix2pix''',  safety_checker=lowerCamelCase  )
								pipe.to(lowerCamelCase  )
								pipe.set_progress_bar_config(disable=lowerCamelCase  )
								pipe.enable_attention_slicing()

								lowercase__    =			self.get_inputs()
								lowercase__    =			pipe(**lowerCamelCase  ).images
								lowercase__    =			image[0, -3:, -3:, -1].flatten()

								assert image.shape == (1, 512, 512, 3)
								lowercase__    =			np.array([0.5902, 0.6015, 0.6027, 0.5983, 0.6092, 0.6061, 0.5765, 0.5785, 0.5555]  )

								assert np.abs(expected_slice - image_slice  ).max() < 1E-3




				def 				lowercase__   (     self						:    Tuple  ):

								'''simple docstring'''
								lowercase__    =			StableDiffusionInstructPixaPixPipeline.from_pretrained(
								    '''timbrooks/instruct-pix2pix''',  safety_checker=lowerCamelCase  )
								lowercase__    =			LMSDiscreteScheduler.from_config(pipe.scheduler.config  )
								pipe.to(lowerCamelCase  )
								pipe.set_progress_bar_config(disable=lowerCamelCase  )
								pipe.enable_attention_slicing()

								lowercase__    =			self.get_inputs()
								lowercase__    =			pipe(**lowerCamelCase  ).images
								lowercase__    =			image[0, -3:, -3:, -1].flatten()

								assert image.shape == (1, 512, 512, 3)
								lowercase__    =			np.array([0.6578, 0.6817, 0.6972, 0.6761, 0.6856, 0.6916, 0.6428, 0.6516, 0.6301]  )

								assert np.abs(expected_slice - image_slice  ).max() < 1E-3




				def 				lowercase__   (     self						:    Tuple  ):

								'''simple docstring'''
								lowercase__    =			StableDiffusionInstructPixaPixPipeline.from_pretrained(
								    '''timbrooks/instruct-pix2pix''',  safety_checker=lowerCamelCase  )
								lowercase__    =			DDIMScheduler.from_config(pipe.scheduler.config  )
								pipe.to(lowerCamelCase  )
								pipe.set_progress_bar_config(disable=lowerCamelCase  )
								pipe.enable_attention_slicing()

								lowercase__    =			self.get_inputs()
								lowercase__    =			pipe(**lowerCamelCase  ).images
								lowercase__    =			image[0, -3:, -3:, -1].flatten()

								assert image.shape == (1, 512, 512, 3)
								lowercase__    =			np.array([0.3828, 0.3834, 0.3818, 0.3792, 0.3865, 0.3752, 0.3792, 0.3847, 0.3753]  )

								assert np.abs(expected_slice - image_slice  ).max() < 1E-3




				def 				lowercase__   (     self						:    Optional[Any]  ):

								'''simple docstring'''
								lowercase__    =			0

								def callback_fn(lowerCamelCase						:    int,  lowerCamelCase						:    int,  lowerCamelCase						:    torch.FloatTensor  ) -> None:
												lowercase__    =			True
												nonlocal number_of_steps
												number_of_steps += 1
												if step == 1:
																lowercase__    =			latents.detach().cpu().numpy()
																assert latents.shape == (1, 4, 64, 64)
																lowercase__    =			latents[0, -3:, -3:, -1]
																lowercase__    =			np.array([-0.2463, -0.4644, -0.9756, 1.5176, 1.4414, 0.7866, 0.9897, 0.8521, 0.7983]  )

																assert np.abs(latents_slice.flatten() - expected_slice  ).max() < 5E-2
												elif step == 2:
																lowercase__    =			latents.detach().cpu().numpy()
																assert latents.shape == (1, 4, 64, 64)
																lowercase__    =			latents[0, -3:, -3:, -1]
																lowercase__    =			np.array([-0.2644, -0.4626, -0.9653, 1.5176, 1.4551, 0.7686, 0.9805, 0.8452, 0.8115]  )

																assert np.abs(latents_slice.flatten() - expected_slice  ).max() < 5E-2

								lowercase__    =			False

								lowercase__    =			StableDiffusionInstructPixaPixPipeline.from_pretrained(
								    '''timbrooks/instruct-pix2pix''',  safety_checker=lowerCamelCase,  torch_dtype=torch.floataa  )
								lowercase__    =			pipe.to(lowerCamelCase  )
								pipe.set_progress_bar_config(disable=lowerCamelCase  )
								pipe.enable_attention_slicing()

								lowercase__    =			self.get_inputs()
								pipe(**lowerCamelCase,  callback=lowerCamelCase,  callback_steps=1  )
								assert callback_fn.has_been_called
								assert number_of_steps == 3




				def 				lowercase__   (     self						:    Optional[int]  ):

								'''simple docstring'''
								torch.cuda.empty_cache()
								torch.cuda.reset_max_memory_allocated()
								torch.cuda.reset_peak_memory_stats()

								lowercase__    =			StableDiffusionInstructPixaPixPipeline.from_pretrained(
								    '''timbrooks/instruct-pix2pix''',  safety_checker=lowerCamelCase,  torch_dtype=torch.floataa  )
								lowercase__    =			pipe.to(lowerCamelCase  )
								pipe.set_progress_bar_config(disable=lowerCamelCase  )
								pipe.enable_attention_slicing(1  )
								pipe.enable_sequential_cpu_offload()

								lowercase__    =			self.get_inputs()
								lowercase__    =			pipe(**lowerCamelCase  )

								lowercase__    =			torch.cuda.max_memory_allocated()
								# make sure that less than 2.2 GB is allocated
								assert mem_bytes < 2.2 * 10**9




				def 				lowercase__   (     self						:    Dict  ):

								'''simple docstring'''
								lowercase__    =			self.get_inputs()
								# resize to resolution that is divisible by 8 but not 16 or 32
								lowercase__    =			inputs['''image'''].resize((504, 504)  )

								lowercase__    =			'''timbrooks/instruct-pix2pix'''
								lowercase__    =			StableDiffusionInstructPixaPixPipeline.from_pretrained(
								    lowerCamelCase,  safety_checker=lowerCamelCase,  )
								pipe.to(lowerCamelCase  )
								pipe.set_progress_bar_config(disable=lowerCamelCase  )
								pipe.enable_attention_slicing()

								lowercase__    =			pipe(**lowerCamelCase  )
								lowercase__    =			output.images[0]

								lowercase__    =			image[255:258, 383:386, -1]

								assert image.shape == (504, 504, 3)
								lowercase__    =			np.array([0.2726, 0.2529, 0.2664, 0.2655, 0.2641, 0.2642, 0.2591, 0.2649, 0.2590]  )

								assert np.abs(image_slice.flatten() - expected_slice  ).max() < 5E-3












import gc
import unittest

from transformers import CTRLConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import (
							    CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
							    CTRLForSequenceClassification,
							    CTRLLMHeadModel,
							    CTRLModel,
							)
class       _UpperCAmelCase					:

				"""simple docstring"""




				def __init__(     self						:    Optional[Any],  lowerCamelCase						:    Optional[int],  lowerCamelCase						:    str=14,  lowerCamelCase						:    List[str]=7,  lowerCamelCase						:    Dict=True,  lowerCamelCase						:    List[str]=True,  lowerCamelCase						:    Optional[Any]=True,  lowerCamelCase						:    Optional[Any]=True,  lowerCamelCase						:    Any=True,  lowerCamelCase						:    List[str]=99,  lowerCamelCase						:    Optional[Any]=32,  lowerCamelCase						:    Optional[int]=5,  lowerCamelCase						:    List[Any]=4,  lowerCamelCase						:    List[Any]=37,  lowerCamelCase						:    List[str]="gelu",  lowerCamelCase						:    Any=0.1,  lowerCamelCase						:    Optional[Any]=0.1,  lowerCamelCase						:    List[Any]=512,  lowerCamelCase						:    List[str]=16,  lowerCamelCase						:    Dict=2,  lowerCamelCase						:    Union[str, Any]=0.02,  lowerCamelCase						:    Optional[int]=3,  lowerCamelCase						:    Dict=4,  lowerCamelCase						:    List[Any]=None,  ):

								'''simple docstring'''
								lowercase__    =			parent
								lowercase__    =			batch_size
								lowercase__    =			seq_length
								lowercase__    =			is_training
								lowercase__    =			use_token_type_ids
								lowercase__    =			use_input_mask
								lowercase__    =			use_labels
								lowercase__    =			use_mc_token_ids
								lowercase__    =			vocab_size
								lowercase__    =			hidden_size
								lowercase__    =			num_hidden_layers
								lowercase__    =			num_attention_heads
								lowercase__    =			intermediate_size
								lowercase__    =			hidden_act
								lowercase__    =			hidden_dropout_prob
								lowercase__    =			attention_probs_dropout_prob
								lowercase__    =			max_position_embeddings
								lowercase__    =			type_vocab_size
								lowercase__    =			type_sequence_label_size
								lowercase__    =			initializer_range
								lowercase__    =			num_labels
								lowercase__    =			num_choices
								lowercase__    =			scope
								lowercase__    =			self.vocab_size - 1




				def 				lowercase__   (     self						:    Optional[int]  ):

								'''simple docstring'''
								lowercase__    =			ids_tensor([self.batch_size, self.seq_length],  self.vocab_size  )

								lowercase__    =			None
								if self.use_input_mask:
												lowercase__    =			random_attention_mask([self.batch_size, self.seq_length]  )

								lowercase__    =			None
								if self.use_token_type_ids:
												lowercase__    =			ids_tensor([self.batch_size, self.seq_length],  self.type_vocab_size  )

								lowercase__    =			None
								if self.use_mc_token_ids:
												lowercase__    =			ids_tensor([self.batch_size, self.num_choices],  self.seq_length  )

								lowercase__    =			None
								lowercase__    =			None
								lowercase__    =			None
								if self.use_labels:
												lowercase__    =			ids_tensor([self.batch_size],  self.type_sequence_label_size  )
												lowercase__    =			ids_tensor([self.batch_size, self.seq_length],  self.num_labels  )
												lowercase__    =			ids_tensor([self.batch_size],  self.num_choices  )

								lowercase__    =			self.get_config()

								lowercase__    =			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 				lowercase__   (     self						:    Optional[int]  ):

								'''simple docstring'''
								return 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,  )




				def 				lowercase__   (     self						:    Union[str, Any],  lowerCamelCase						:    Any,  lowerCamelCase						:    Tuple,  lowerCamelCase						:    Any,  lowerCamelCase						:    List[Any],  lowerCamelCase						:    Union[str, Any],  *lowerCamelCase						:    Dict  ):

								'''simple docstring'''
								lowercase__    =			CTRLModel(config=lowerCamelCase  )
								model.to(lowerCamelCase  )
								model.eval()

								model(lowerCamelCase,  token_type_ids=lowerCamelCase,  head_mask=lowerCamelCase  )
								model(lowerCamelCase,  token_type_ids=lowerCamelCase  )
								lowercase__    =			model(lowerCamelCase  )
								self.parent.assertEqual(result.last_hidden_state.shape,  (self.batch_size, self.seq_length, self.hidden_size)  )
								self.parent.assertEqual(len(result.past_key_values  ),  config.n_layer  )




				def 				lowercase__   (     self						:    Optional[Any],  lowerCamelCase						:    Union[str, Any],  lowerCamelCase						:    Tuple,  lowerCamelCase						:    Union[str, Any],  lowerCamelCase						:    Union[str, Any],  lowerCamelCase						:    Optional[int],  *lowerCamelCase						:    Any  ):

								'''simple docstring'''
								lowercase__    =			CTRLLMHeadModel(lowerCamelCase  )
								model.to(lowerCamelCase  )
								model.eval()

								lowercase__    =			model(lowerCamelCase,  token_type_ids=lowerCamelCase,  labels=lowerCamelCase  )
								self.parent.assertEqual(result.loss.shape,  ()  )
								self.parent.assertEqual(result.logits.shape,  (self.batch_size, self.seq_length, self.vocab_size)  )




				def 				lowercase__   (     self						:    Dict  ):

								'''simple docstring'''
								lowercase__    =			self.prepare_config_and_inputs()

								(
								    (
								    lowercase__
								) ,						(
								    lowercase__
								) ,						(
								    lowercase__
								) ,						(
								    lowercase__
								) ,						(
								    lowercase__
								) ,						(
								    lowercase__
								) ,						(
								    lowercase__
								) ,						(
								    lowercase__
								) ,						(
								    lowercase__
								) ,						
								)    =			config_and_inputs

								lowercase__    =			{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''head_mask''': head_mask}

								return config, inputs_dict




				def 				lowercase__   (     self						:    List[str],  lowerCamelCase						:    Tuple,  lowerCamelCase						:    Optional[Any],  lowerCamelCase						:    int,  lowerCamelCase						:    Any,  *lowerCamelCase						:    int  ):

								'''simple docstring'''
								lowercase__    =			self.num_labels
								lowercase__    =			CTRLForSequenceClassification(lowerCamelCase  )
								model.to(lowerCamelCase  )
								model.eval()
								lowercase__    =			ids_tensor([self.batch_size],  self.type_sequence_label_size  )
								lowercase__    =			model(lowerCamelCase,  token_type_ids=lowerCamelCase,  labels=lowerCamelCase  )
								self.parent.assertEqual(result.logits.shape,  (self.batch_size, self.num_labels)  )



@require_torch
class       _UpperCAmelCase					(       A__		,A__		,A__		,unittest.TestCase	):

				"""simple docstring"""




				lowercase__											=       (CTRLModel, CTRLLMHeadModel, CTRLForSequenceClassification) if is_torch_available() else ()
				lowercase__											=       (CTRLLMHeadModel,) if is_torch_available() else ()
				lowercase__											=       (
				    {
				        """feature-extraction""": CTRLModel,
				        """text-classification""": CTRLForSequenceClassification,
				        """text-generation""": CTRLLMHeadModel,
				        """zero-shot""": CTRLForSequenceClassification,
				    }
				    if is_torch_available()
				    else {}
				)
				lowercase__											=       True
				lowercase__											=       False
				lowercase__											=       False




				def 				lowercase__   (     self						:    Dict,  lowerCamelCase						:    Optional[int],  lowerCamelCase						:    str,  lowerCamelCase						:    Any,  lowerCamelCase						:    List[str],  lowerCamelCase						:    Optional[Any]  ):

								'''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 				lowercase__   (     self						:    Any  ):

								'''simple docstring'''
								lowercase__    =			CTRLModelTester(self  )
								lowercase__    =			ConfigTester(self,  config_class=lowerCamelCase,  n_embd=37  )




				def 				lowercase__   (     self						:    Tuple  ):

								'''simple docstring'''
								super().tearDown()
								# clean-up as much as possible GPU memory occupied by PyTorch
								gc.collect()
								torch.cuda.empty_cache()




				def 				lowercase__   (     self						:    int  ):

								'''simple docstring'''
								self.config_tester.run_common_tests()




				def 				lowercase__   (     self						:    str  ):

								'''simple docstring'''
								lowercase__    =			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_ctrl_model(*lowerCamelCase  )




				def 				lowercase__   (     self						:    List[Any]  ):

								'''simple docstring'''
								lowercase__    =			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_lm_head_model(*lowerCamelCase  )




				@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''  )
				def 				lowercase__   (     self						:    Union[str, Any]  ):

								'''simple docstring'''
								pass




				@slow
				def 				lowercase__   (     self						:    Any  ):

								'''simple docstring'''
								for model_name in CTRL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												lowercase__    =			CTRLModel.from_pretrained(lowerCamelCase  )
												self.assertIsNotNone(lowerCamelCase  )




				@unittest.skip('''The model doesn\'t support left padding'''  )  # and it's not used enough to be worth fixing :)
				def 				lowercase__   (     self						:    List[Any]  ):

								'''simple docstring'''
								pass



@require_torch
class       _UpperCAmelCase					(       unittest.TestCase	):

				"""simple docstring"""




				def 				lowercase__   (     self						:    Any  ):

								'''simple docstring'''
								super().tearDown()
								# clean-up as much as possible GPU memory occupied by PyTorch
								gc.collect()
								torch.cuda.empty_cache()




				@slow
				def 				lowercase__   (     self						:    int  ):

								'''simple docstring'''
								lowercase__    =			CTRLLMHeadModel.from_pretrained('''ctrl'''  )
								model.to(lowerCamelCase  )
								lowercase__    =			torch.tensor(
								    [[11_859, 0, 1_611, 8]],  dtype=torch.long,  device=lowerCamelCase  )  # Legal the president is
								lowercase__    =			[
								    11_859,
								    0,
								    1_611,
								    8,
								    5,
								    150,
								    26_449,
								    2,
								    19,
								    348,
								    469,
								    3,
								    2_595,
								    48,
								    20_740,
								    246_533,
								    246_533,
								    19,
								    30,
								    5,
								]  # Legal the president is a good guy and I don't want to lose my job. \n \n I have a

								lowercase__    =			model.generate(lowerCamelCase,  do_sample=lowerCamelCase  )
								self.assertListEqual(output_ids[0].tolist(),  lowerCamelCase  )




