code: 
  
  
  '''simple docstring'''
  
  import gc
  import unittest
  
  import numpy as np
  import torch
  from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
  
  from diffusers import PriorTransformer, UnCLIPPipeline, UnCLIPScheduler, UNetaDConditionModel, UNetaDModel
  from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
  from diffusers.utils import load_numpy, nightly, slow, torch_device
  from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
  
  from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
  from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
  
  
  enable_full_determinism()
  
  
  
  
  
  
  
  class  UpperCAmelCase (       A__		,			unittest.TestCase):
  	_lowerCamelCase :					Optional[Any]    					=					UnCLIPPipeline
  	_lowerCamelCase :					List[str]    					=					TEXT_TO_IMAGE_PARAMS - {
  	    'negative_prompt',
  	    'height',
  	    'width',
  	    'negative_prompt_embeds',
  	    'guidance_scale',
  	    'prompt_embeds',
  	    'cross_attention_kwargs',
  	}
  	_lowerCamelCase :					Dict    					=					TEXT_TO_IMAGE_BATCH_PARAMS
  	_lowerCamelCase :					Dict    					=					[
  	    'generator',
  	    'return_dict',
  	    'prior_num_inference_steps',
  	    'decoder_num_inference_steps',
  	    'super_res_num_inference_steps',
  	]
  	_lowerCamelCase :					str    					=					False
  
  	@property
  	def 		lowercase_						( self :	str					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				return 32
  
  	@property
  	def 		lowercase_						( self :	List[Any]					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				return 32
  
  	@property
  	def 		lowercase_						( self :	Any					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				return self.time_input_dim
  
  	@property
  	def 		lowercase_						( self :	Union[str, Any]					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				return self.time_input_dim * 4
  
  	@property
  	def 		lowercase_						( self :	Any					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				return 100
  
  	@property
  	def 		lowercase_						( self :	int					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				UpperCamelCase__ 						=						CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"					)
  				return tokenizer
  
  	@property
  	def 		lowercase_						( self :	int					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				torch.manual_seed(0					)
  				UpperCamelCase__ 						=						CLIPTextConfig(
  				    bos_token_id=0,					eos_token_id=2,					hidden_size=self.text_embedder_hidden_size,					projection_dim=self.text_embedder_hidden_size,					intermediate_size=37,					layer_norm_eps=1e-0_5,					num_attention_heads=4,					num_hidden_layers=5,					pad_token_id=1,					vocab_size=1000,					)
  				return CLIPTextModelWithProjection(_lowerCAmelCase					)
  
  	@property
  	def 		lowercase_						( self :	List[str]					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				torch.manual_seed(0					)
  
  				UpperCamelCase__ 						=						{
  				    "num_attention_heads": 2,
  				    "attention_head_dim": 12,
  				    "embedding_dim": self.text_embedder_hidden_size,
  				    "num_layers": 1,
  				}
  
  				UpperCamelCase__ 						=						PriorTransformer(**_lowerCAmelCase					)
  				return model
  
  	@property
  	def 		lowercase_						( self :	List[Any]					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				torch.manual_seed(0					)
  
  				UpperCamelCase__ 						=						{
  				    "clip_embeddings_dim": self.text_embedder_hidden_size,
  				    "time_embed_dim": self.time_embed_dim,
  				    "cross_attention_dim": self.cross_attention_dim,
  				}
  
  				UpperCamelCase__ 						=						UnCLIPTextProjModel(**_lowerCAmelCase					)
  				return model
  
  	@property
  	def 		lowercase_						( self :	List[str]					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				torch.manual_seed(0					)
  
  				UpperCamelCase__ 						=						{
  				    "sample_size": 32,
  				    # RGB in channels
  				    "in_channels": 3,
  				    # Out channels is double in channels because predicts mean and variance
  				    "out_channels": 6,
  				    "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
  				    "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
  				    "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
  				    "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
  				    "layers_per_block": 1,
  				    "cross_attention_dim": self.cross_attention_dim,
  				    "attention_head_dim": 4,
  				    "resnet_time_scale_shift": "scale_shift",
  				    "class_embed_type": "identity",
  				}
  
  				UpperCamelCase__ 						=						UNetaDConditionModel(**_lowerCAmelCase					)
  				return model
  
  	@property
  	def 		lowercase_						( self :	Union[str, Any]					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				return {
  				    "sample_size": 64,
  				    "layers_per_block": 1,
  				    "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
  				    "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
  				    "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
  				    "in_channels": 6,
  				    "out_channels": 3,
  				}
  
  	@property
  	def 		lowercase_						( self :	Optional[int]					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				torch.manual_seed(0					)
  
  				UpperCamelCase__ 						=						UNetaDModel(**self.dummy_super_res_kwargs					)
  				return model
  
  	@property
  	def 		lowercase_						( self :	str					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				torch.manual_seed(1					)
  
  				UpperCamelCase__ 						=						UNetaDModel(**self.dummy_super_res_kwargs					)
  				return model
  
  	def 		lowercase_						( self :	Optional[int]					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				UpperCamelCase__ 						=						self.dummy_prior
  				UpperCamelCase__ 						=						self.dummy_decoder
  				UpperCamelCase__ 						=						self.dummy_text_proj
  				UpperCamelCase__ 						=						self.dummy_text_encoder
  				UpperCamelCase__ 						=						self.dummy_tokenizer
  				UpperCamelCase__ 						=						self.dummy_super_res_first
  				UpperCamelCase__ 						=						self.dummy_super_res_last
  
  				UpperCamelCase__ 						=						UnCLIPScheduler(
  				    variance_type="fixed_small_log",					prediction_type="sample",					num_train_timesteps=1000,					clip_sample_range=5.0,					)
  
  				UpperCamelCase__ 						=						UnCLIPScheduler(
  				    variance_type="learned_range",					prediction_type="epsilon",					num_train_timesteps=1000,					)
  
  				UpperCamelCase__ 						=						UnCLIPScheduler(
  				    variance_type="fixed_small_log",					prediction_type="epsilon",					num_train_timesteps=1000,					)
  
  				UpperCamelCase__ 						=						{
  				    "prior": prior,
  				    "decoder": decoder,
  				    "text_proj": text_proj,
  				    "text_encoder": text_encoder,
  				    "tokenizer": tokenizer,
  				    "super_res_first": super_res_first,
  				    "super_res_last": super_res_last,
  				    "prior_scheduler": prior_scheduler,
  				    "decoder_scheduler": decoder_scheduler,
  				    "super_res_scheduler": super_res_scheduler,
  				}
  
  				return components
  
  	def 		lowercase_						( self :	Tuple,					a_ :	str,					a_ :	List[str]=0					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				if str(_lowerCAmelCase					).startswith("mps"					):
  							UpperCamelCase__ 						=						torch.manual_seed(_lowerCAmelCase					)
  				else:
  							UpperCamelCase__ 						=						torch.Generator(device=_lowerCAmelCase					).manual_seed(_lowerCAmelCase					)
  				UpperCamelCase__ 						=						{
  				    "prompt": "horse",
  				    "generator": generator,
  				    "prior_num_inference_steps": 2,
  				    "decoder_num_inference_steps": 2,
  				    "super_res_num_inference_steps": 2,
  				    "output_type": "numpy",
  				}
  				return inputs
  
  	def 		lowercase_						( self :	Any					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				UpperCamelCase__ 						=						"cpu"
  
  				UpperCamelCase__ 						=						self.get_dummy_components()
  
  				UpperCamelCase__ 						=						self.pipeline_class(**_lowerCAmelCase					)
  				UpperCamelCase__ 						=						pipe.to(_lowerCAmelCase					)
  
  				pipe.set_progress_bar_config(disable=_lowerCAmelCase					)
  
  				UpperCamelCase__ 						=						pipe(**self.get_dummy_inputs(_lowerCAmelCase					)					)
  				UpperCamelCase__ 						=						output.images
  
  				UpperCamelCase__ 						=						pipe(
  				    **self.get_dummy_inputs(_lowerCAmelCase					),					return_dict=_lowerCAmelCase,					)[0]
  
  				UpperCamelCase__ 						=						image[0, -3:, -3:, -1]
  				UpperCamelCase__ 						=						image_from_tuple[0, -3:, -3:, -1]
  
  				assert image.shape == (1, 64, 64, 3)
  
  				UpperCamelCase__ 						=						np.array(
  				    [
  				        0.9_997,
  				        0.9_988,
  				        0.0_028,
  				        0.9_997,
  				        0.9_984,
  				        0.9_965,
  				        0.0_029,
  				        0.9_986,
  				        0.0_025,
  				    ]					)
  
  				assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2
  				assert np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() < 1e-2
  
  	def 		lowercase_						( self :	str					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				UpperCamelCase__ 						=						torch.device("cpu"					)
  
  
  
  
  
  
  
  				class  UpperCAmelCase :
  					_lowerCamelCase :					Optional[int]    					=					1
  
  
  
  
  
  
  
  				UpperCamelCase__ 						=						self.get_dummy_components()
  
  				UpperCamelCase__ 						=						self.pipeline_class(**_lowerCAmelCase					)
  				UpperCamelCase__ 						=						pipe.to(_lowerCAmelCase					)
  
  				UpperCamelCase__ 						=						components["prior"]
  				UpperCamelCase__ 						=						components["decoder"]
  				UpperCamelCase__ 						=						components["super_res_first"]
  				UpperCamelCase__ 						=						components["tokenizer"]
  				UpperCamelCase__ 						=						components["text_encoder"]
  
  				UpperCamelCase__ 						=						torch.Generator(device=_lowerCAmelCase					).manual_seed(0					)
  				UpperCamelCase__ 						=						prior.dtype
  				UpperCamelCase__ 						=						1
  
  				UpperCamelCase__ 						=						(batch_size, prior.config.embedding_dim)
  				UpperCamelCase__ 						=						pipe.prepare_latents(
  				    _lowerCAmelCase,					dtype=_lowerCAmelCase,					device=_lowerCAmelCase,					generator=_lowerCAmelCase,					latents=_lowerCAmelCase,					scheduler=DummyScheduler()					)
  				UpperCamelCase__ 						=						(batch_size, decoder.config.in_channels, decoder.config.sample_size, decoder.config.sample_size)
  				UpperCamelCase__ 						=						pipe.prepare_latents(
  				    _lowerCAmelCase,					dtype=_lowerCAmelCase,					device=_lowerCAmelCase,					generator=_lowerCAmelCase,					latents=_lowerCAmelCase,					scheduler=DummyScheduler()					)
  
  				UpperCamelCase__ 						=						(
  				    batch_size,
  				    super_res_first.config.in_channels // 2,
  				    super_res_first.config.sample_size,
  				    super_res_first.config.sample_size,
  				)
  				UpperCamelCase__ 						=						pipe.prepare_latents(
  				    _lowerCAmelCase,					dtype=_lowerCAmelCase,					device=_lowerCAmelCase,					generator=_lowerCAmelCase,					latents=_lowerCAmelCase,					scheduler=DummyScheduler()					)
  
  				pipe.set_progress_bar_config(disable=_lowerCAmelCase					)
  
  				UpperCamelCase__ 						=						"this is a prompt example"
  
  				UpperCamelCase__ 						=						torch.Generator(device=_lowerCAmelCase					).manual_seed(0					)
  				UpperCamelCase__ 						=						pipe(
  				    [prompt],					generator=_lowerCAmelCase,					prior_num_inference_steps=2,					decoder_num_inference_steps=2,					super_res_num_inference_steps=2,					prior_latents=_lowerCAmelCase,					decoder_latents=_lowerCAmelCase,					super_res_latents=_lowerCAmelCase,					output_type="np",					)
  				UpperCamelCase__ 						=						output.images
  
  				UpperCamelCase__ 						=						tokenizer(
  				    _lowerCAmelCase,					padding="max_length",					max_length=tokenizer.model_max_length,					return_tensors="pt",					)
  				UpperCamelCase__ 						=						text_encoder(text_inputs.input_ids					)
  				UpperCamelCase__ 						=						text_inputs.attention_mask
  
  				UpperCamelCase__ 						=						torch.Generator(device=_lowerCAmelCase					).manual_seed(0					)
  				UpperCamelCase__ 						=						pipe(
  				    generator=_lowerCAmelCase,					prior_num_inference_steps=2,					decoder_num_inference_steps=2,					super_res_num_inference_steps=2,					prior_latents=_lowerCAmelCase,					decoder_latents=_lowerCAmelCase,					super_res_latents=_lowerCAmelCase,					text_model_output=_lowerCAmelCase,					text_attention_mask=_lowerCAmelCase,					output_type="np",					)[0]
  
  				# make sure passing text embeddings manually is identical
  				assert np.abs(image - image_from_text					).max() < 1e-4
  
  	@skip_mps
  	def 		lowercase_						( self :	Any					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				UpperCamelCase__ 						=						torch_device == "cpu"
  
  				self._test_attention_slicing_forward_pass(test_max_difference=_lowerCAmelCase,					expected_max_diff=0.01					)
  
  	@skip_mps
  	def 		lowercase_						( self :	Tuple					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				UpperCamelCase__ 						=						torch_device == "cpu"
  				UpperCamelCase__ 						=						True
  				UpperCamelCase__ 						=						[
  				    "prior_num_inference_steps",
  				    "decoder_num_inference_steps",
  				    "super_res_num_inference_steps",
  				]
  
  				self._test_inference_batch_single_identical(
  				    test_max_difference=_lowerCAmelCase,					relax_max_difference=_lowerCAmelCase,					additional_params_copy_to_batched_inputs=_lowerCAmelCase,					)
  
  	def 		lowercase_						( self :	Optional[int]					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				UpperCamelCase__ 						=						[
  				    "prior_num_inference_steps",
  				    "decoder_num_inference_steps",
  				    "super_res_num_inference_steps",
  				]
  
  				if torch_device == "mps":
  							# TODO: MPS errors with larger batch sizes
  							UpperCamelCase__ 						=						[2, 3]
  							self._test_inference_batch_consistent(
  							    batch_sizes=_lowerCAmelCase,					additional_params_copy_to_batched_inputs=_lowerCAmelCase,					)
  				else:
  							self._test_inference_batch_consistent(
  							    additional_params_copy_to_batched_inputs=_lowerCAmelCase					)
  
  	@skip_mps
  	def 		lowercase_						( self :	List[str]					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				return super().test_dict_tuple_outputs_equivalent()
  
  	@skip_mps
  	def 		lowercase_						( self :	List[Any]					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				return super().test_save_load_local()
  
  	@skip_mps
  	def 		lowercase_						( self :	Union[str, Any]					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				return super().test_save_load_optional_components()
  
  
  
  
  
  
  
  @nightly
  class  UpperCAmelCase (       unittest.TestCase):
  
  	def 		lowercase_						( self :	int					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				super().tearDown()
  				gc.collect()
  				torch.cuda.empty_cache()
  
  	def 		lowercase_						( self :	Dict					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				UpperCamelCase__ 						=						load_numpy(
  				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
  				    "/unclip/karlo_v1_alpha_horse_cpu.npy"					)
  
  				UpperCamelCase__ 						=						UnCLIPPipeline.from_pretrained("kakaobrain/karlo-v1-alpha"					)
  				pipeline.set_progress_bar_config(disable=_lowerCAmelCase					)
  
  				UpperCamelCase__ 						=						torch.manual_seed(0					)
  				UpperCamelCase__ 						=						pipeline(
  				    "horse",					num_images_per_prompt=1,					generator=_lowerCAmelCase,					output_type="np",					)
  
  				UpperCamelCase__ 						=						output.images[0]
  
  				assert image.shape == (256, 256, 3)
  				assert np.abs(expected_image - image					).max() < 1e-1
  
  
  
  
  
  
  
  @slow
  @require_torch_gpu
  class  UpperCAmelCase (       unittest.TestCase):
  
  	def 		lowercase_						( self :	Tuple					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				super().tearDown()
  				gc.collect()
  				torch.cuda.empty_cache()
  
  	def 		lowercase_						( self :	int					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				UpperCamelCase__ 						=						load_numpy(
  				    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
  				    "/unclip/karlo_v1_alpha_horse_fp16.npy"					)
  
  				UpperCamelCase__ 						=						UnCLIPPipeline.from_pretrained("kakaobrain/karlo-v1-alpha",					torch_dtype=torch.floataa					)
  				UpperCamelCase__ 						=						pipeline.to(_lowerCAmelCase					)
  				pipeline.set_progress_bar_config(disable=_lowerCAmelCase					)
  
  				UpperCamelCase__ 						=						torch.Generator(device="cpu"					).manual_seed(0					)
  				UpperCamelCase__ 						=						pipeline(
  				    "horse",					generator=_lowerCAmelCase,					output_type="np",					)
  
  				UpperCamelCase__ 						=						output.images[0]
  
  				assert image.shape == (256, 256, 3)
  
  				assert_mean_pixel_difference(_lowerCAmelCase,					_lowerCAmelCase					)
  
  	def 		lowercase_						( self :	List[Any]					):
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  				torch.cuda.empty_cache()
  				torch.cuda.reset_max_memory_allocated()
  				torch.cuda.reset_peak_memory_stats()
  
  				UpperCamelCase__ 						=						UnCLIPPipeline.from_pretrained("kakaobrain/karlo-v1-alpha",					torch_dtype=torch.floataa					)
  				UpperCamelCase__ 						=						pipe.to(_lowerCAmelCase					)
  				pipe.set_progress_bar_config(disable=_lowerCAmelCase					)
  				pipe.enable_attention_slicing()
  				pipe.enable_sequential_cpu_offload()
  
  				UpperCamelCase__ 						=						pipe(
  				    "horse",					num_images_per_prompt=1,					prior_num_inference_steps=2,					decoder_num_inference_steps=2,					super_res_num_inference_steps=2,					output_type="np",					)
  
  				UpperCamelCase__ 						=						torch.cuda.max_memory_allocated()
  				# make sure that less than 7 GB is allocated
  				assert mem_bytes < 7 * 10**9
style_context: 
  
  
  '''simple docstring'''
  
  from typing import List
  
  from ...processing_utils import ProcessorMixin
  from ...utils import is_torch_available
  
  
  if is_torch_available():
         import torch
  
  
  
  
  
  
  
  class  UpperCAmelCase (       SCREAMING_SNAKE_CASE__):
   _lowerCamelCase :					Tuple    					=					['image_processor', 'tokenizer']
   _lowerCamelCase :					Tuple    					=					'OneFormerImageProcessor'
   _lowerCamelCase :					List[Any]    					=					('CLIPTokenizer', 'CLIPTokenizerFast')
  
   def __init__( self :	Any,					a_ :	Dict=None,					a_ :	int=None,					a_ :	int = 77,					a_ :	int = 77,					**a_ :	int					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      if image_processor is None:
         raise ValueError("You need to specify an `image_processor`."					)
      if tokenizer is None:
         raise ValueError("You need to specify a `tokenizer`."					)
  
      UpperCamelCase__ 						=						max_seq_length
      UpperCamelCase__ 						=						task_seq_length
  
      super().__init__(a_,					a_					)
  
   def 		lowercase_						( self :	Any,					a_ :	Optional[Any]=None,					a_ :	Optional[int]=77					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      if text_list is None:
         raise ValueError("tokens cannot be None."					)
  
      UpperCamelCase__ 						=						self.tokenizer(a_,					padding="max_length",					max_length=a_,					truncation=a_					)
  
      UpperCamelCase__						,		UpperCamelCase__ 						=						tokens["attention_mask"], tokens["input_ids"]
  
      UpperCamelCase__ 						=						[]
      for attn_mask, input_id in zip(a_,					a_					):
         UpperCamelCase__ 						=						torch.tensor(a_					) * torch.tensor(a_					)
         token_inputs.append(token.unsqueeze(0					)					)
  
      UpperCamelCase__ 						=						torch.cat(a_,					dim=0					)
      return token_inputs
  
   def __call__( self :	str,					a_ :	Dict=None,					a_ :	str=None,					a_ :	Tuple=None,					**a_ :	Any					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      if task_inputs is None:
         raise ValueError("You have to specify the task_input. Found None."					)
      elif images is None:
         raise ValueError("You have to specify the image. Found None."					)
  
      if not all(task in ["semantic", "instance", "panoptic"] for task in task_inputs					):
         raise ValueError("task_inputs must be semantic, instance, or panoptic."					)
  
      UpperCamelCase__ 						=						self.image_processor(a_,					a_,					a_,					**a_					)
  
      if isinstance(a_,					a_					):
         UpperCamelCase__ 						=						[task_inputs]
  
      if isinstance(a_,					a_					) and all(isinstance(a_,					a_					) for task_input in task_inputs					):
         UpperCamelCase__ 						=						[]
         for task in task_inputs:
            UpperCamelCase__ 						=						f'the task is {task}'
            task_token_inputs.append(a_					)
         UpperCamelCase__ 						=						self._preprocess_text(a_,					max_length=self.task_seq_length					)
      else:
         raise TypeError("Task Inputs should be a string or a list of strings."					)
  
      if hasattr(a_,					"text_inputs"					):
         UpperCamelCase__ 						=						encoded_inputs.text_inputs
  
         UpperCamelCase__ 						=						[]
         for texts in texts_list:
            UpperCamelCase__ 						=						self._preprocess_text(a_,					max_length=self.max_seq_length					)
            text_inputs.append(text_input_list.unsqueeze(0					)					)
  
         UpperCamelCase__ 						=						torch.cat(a_,					dim=0					)
  
      return encoded_inputs
  
   def 		lowercase_						( self :	Union[str, Any],					a_ :	Tuple=None,					a_ :	List[str]=None,					a_ :	Optional[Any]=None,					**a_ :	Tuple					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      if task_inputs is None:
         raise ValueError("You have to specify the task_input. Found None."					)
      elif images is None:
         raise ValueError("You have to specify the image. Found None."					)
  
      if not all(task in ["semantic", "instance", "panoptic"] for task in task_inputs					):
         raise ValueError("task_inputs must be semantic, instance, or panoptic."					)
  
      UpperCamelCase__ 						=						self.image_processor.encode_inputs(a_,					a_,					a_,					**a_					)
  
      if isinstance(a_,					a_					):
         UpperCamelCase__ 						=						[task_inputs]
  
      if isinstance(a_,					a_					) and all(isinstance(a_,					a_					) for task_input in task_inputs					):
         UpperCamelCase__ 						=						[]
         for task in task_inputs:
            UpperCamelCase__ 						=						f'the task is {task}'
            task_token_inputs.append(a_					)
         UpperCamelCase__ 						=						self._preprocess_text(a_,					max_length=self.task_seq_length					)
      else:
         raise TypeError("Task Inputs should be a string or a list of strings."					)
  
      if hasattr(a_,					"text_inputs"					):
         UpperCamelCase__ 						=						encoded_inputs.text_inputs
  
         UpperCamelCase__ 						=						[]
         for texts in texts_list:
            UpperCamelCase__ 						=						self._preprocess_text(a_,					max_length=self.max_seq_length					)
            text_inputs.append(text_input_list.unsqueeze(0					)					)
  
         UpperCamelCase__ 						=						torch.cat(a_,					dim=0					)
  
      return encoded_inputs
  
   def 		lowercase_						( self :	Any,					*a_ :	int,					**a_ :	Optional[int]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      return self.image_processor.post_process_semantic_segmentation(*a_,					**a_					)
  
   def 		lowercase_						( self :	str,					*a_ :	Tuple,					**a_ :	Optional[int]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      return self.image_processor.post_process_instance_segmentation(*a_,					**a_					)
  
   def 		lowercase_						( self :	Dict,					*a_ :	str,					**a_ :	int					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      return self.image_processor.post_process_panoptic_segmentation(*a_,					**a_					)