


from typing import Any, Dict, List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
							from PIL import Image

							from ..image_utils import load_image

if is_torch_available():
							from ..models.auto.modeling_auto import (
							    MODEL_FOR_IMAGE_SEGMENTATION_MAPPING,
							    MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING,
							    MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
							    MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING,
							)


snake_case_  :     List[str]							  =							logging.get_logger(__name__)


snake_case_  :     Any							  =							Dict[str, Any]
snake_case_  :     List[Any]							  =							List[Prediction]

@add_end_docstrings(UpperCamelCase__					)
class   A__							(						UpperCamelCase__					):




						def __init__(    self   : Optional[Any]			,      *_a   : Any			,      **_a   : List[Any]					)							->       Union[str, Any]:


										"""simple docstring"""





										super().__init__(*_a			,      **_a					)

										if self.framework == "tf":
														raise ValueError(f"The {self.__class__} is only available in PyTorch."					)

										requires_backends(self			,      '''vision'''					)
										self.check_model_type(
										    dict(
										        MODEL_FOR_IMAGE_SEGMENTATION_MAPPING.items()
										        + MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING.items()
										        + MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING.items()
										        + MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING.items()					)					)




						def 							__UpperCamelCase			(    self   : Dict			,      **_a   : Dict					)							->       int:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					={}
										_SCREAMING_SNAKE_CASE     					={}
										if "subtask" in kwargs:
														_SCREAMING_SNAKE_CASE     					=kwargs['''subtask''']
														_SCREAMING_SNAKE_CASE     					=kwargs['''subtask''']
										if "threshold" in kwargs:
														_SCREAMING_SNAKE_CASE     					=kwargs['''threshold''']
										if "mask_threshold" in kwargs:
														_SCREAMING_SNAKE_CASE     					=kwargs['''mask_threshold''']
										if "overlap_mask_area_threshold" in kwargs:
														_SCREAMING_SNAKE_CASE     					=kwargs['''overlap_mask_area_threshold''']

										return preprocess_kwargs, {}, postprocess_kwargs




						def __call__(    self   : Tuple			,      _a   : str			,      **_a   : str					)							->       Union[Predictions, List[Prediction]]:


										"""simple docstring"""





										return super().__call__(_a			,      **_a					)




						def 							__UpperCamelCase			(    self   : Union[str, Any]			,      _a   : str			,      _a   : List[Any]=None					)							->       Tuple:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=load_image(_a					)
										_SCREAMING_SNAKE_CASE     					=[(image.height, image.width)]
										if self.model.config.__class__.__name__ == "OneFormerConfig":
														if subtask is None:
																		_SCREAMING_SNAKE_CASE     					={}
														else:
																		_SCREAMING_SNAKE_CASE     					={'''task_inputs''': [subtask]}
														_SCREAMING_SNAKE_CASE     					=self.image_processor(images=[image]			,      return_tensors='''pt'''			,      **_a					)
														_SCREAMING_SNAKE_CASE     					=self.tokenizer(
														    inputs['''task_inputs''']			,      padding='''max_length'''			,      max_length=self.model.config.task_seq_len			,      return_tensors=self.framework			,      )['''input_ids''']
										else:
														_SCREAMING_SNAKE_CASE     					=self.image_processor(images=[image]			,      return_tensors='''pt'''					)
										_SCREAMING_SNAKE_CASE     					=target_size
										return inputs




						def 							__UpperCamelCase			(    self   : List[Any]			,      _a   : List[str]					)							->       int:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=model_inputs.pop('''target_size'''					)
										_SCREAMING_SNAKE_CASE     					=self.model(**_a					)
										_SCREAMING_SNAKE_CASE     					=target_size
										return model_outputs




						def 							__UpperCamelCase			(    self   : int			,      _a   : List[Any]			,      _a   : str=None			,      _a   : Optional[int]=0.9			,      _a   : List[Any]=0.5			,      _a   : Union[str, Any]=0.5					)							->       List[str]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=None
										if subtask in {"panoptic", None} and hasattr(self.image_processor			,      '''post_process_panoptic_segmentation'''					):
														_SCREAMING_SNAKE_CASE     					=self.image_processor.post_process_panoptic_segmentation
										elif subtask in {"instance", None} and hasattr(self.image_processor			,      '''post_process_instance_segmentation'''					):
														_SCREAMING_SNAKE_CASE     					=self.image_processor.post_process_instance_segmentation

										if fn is not None:
														_SCREAMING_SNAKE_CASE     					=fn(
														    _a			,      threshold=_a			,      mask_threshold=_a			,      overlap_mask_area_threshold=_a			,      target_sizes=model_outputs['''target_size''']			,      )[0]

														_SCREAMING_SNAKE_CASE     					=[]
														_SCREAMING_SNAKE_CASE     					=outputs['''segmentation''']

														for segment in outputs["segments_info"]:
																		_SCREAMING_SNAKE_CASE     					=(segmentation == segment['''id''']) * 255
																		_SCREAMING_SNAKE_CASE     					=Image.fromarray(mask.numpy().astype(np.uinta					)			,      mode='''L'''					)
																		_SCREAMING_SNAKE_CASE     					=self.model.config.idalabel[segment['''label_id''']]
																		_SCREAMING_SNAKE_CASE     					=segment['''score''']
																		annotation.append({'''score''': score, '''label''': label, '''mask''': mask}					)

										elif subtask in {"semantic", None} and hasattr(self.image_processor			,      '''post_process_semantic_segmentation'''					):
														_SCREAMING_SNAKE_CASE     					=self.image_processor.post_process_semantic_segmentation(
														    _a			,      target_sizes=model_outputs['''target_size''']					)[0]

														_SCREAMING_SNAKE_CASE     					=[]
														_SCREAMING_SNAKE_CASE     					=outputs.numpy()
														_SCREAMING_SNAKE_CASE     					=np.unique(_a					)

														for label in labels:
																		_SCREAMING_SNAKE_CASE     					=(segmentation == label) * 255
																		_SCREAMING_SNAKE_CASE     					=Image.fromarray(mask.astype(np.uinta					)			,      mode='''L'''					)
																		_SCREAMING_SNAKE_CASE     					=self.model.config.idalabel[label]
																		annotation.append({'''score''': None, '''label''': label, '''mask''': mask}					)
										else:
														raise ValueError(f"Subtask {subtask} is not supported for model {type(self.model					)}"					)
										return annotation



import unittest

import numpy as np
import torch
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()

class   A__							(						UpperCamelCase__				,       unittest.TestCase					):
						UpperCAmelCase     =  KandinskyPriorPipeline
						UpperCAmelCase     =  ["prompt"]
						UpperCAmelCase     =  ["prompt", "negative_prompt"]
						UpperCAmelCase     =  [
						    "num_images_per_prompt",
						    "generator",
						    "num_inference_steps",
						    "latents",
						    "negative_prompt",
						    "guidance_scale",
						    "output_type",
						    "return_dict",
						]
						UpperCAmelCase     =  False




						@property
						def 							__UpperCamelCase			(    self   : Tuple					)							->       Tuple:


										"""simple docstring"""





										return 32




						@property
						def 							__UpperCamelCase			(    self   : Optional[int]					)							->       Tuple:


										"""simple docstring"""





										return 32




						@property
						def 							__UpperCamelCase			(    self   : Tuple					)							->       int:


										"""simple docstring"""





										return self.time_input_dim




						@property
						def 							__UpperCamelCase			(    self   : Optional[Any]					)							->       Any:


										"""simple docstring"""





										return self.time_input_dim * 4




						@property
						def 							__UpperCamelCase			(    self   : List[str]					)							->       str:


										"""simple docstring"""





										return 100




						@property
						def 							__UpperCamelCase			(    self   : List[Any]					)							->       List[str]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''					)
										return tokenizer




						@property
						def 							__UpperCamelCase			(    self   : Any					)							->       Dict:


										"""simple docstring"""





										torch.manual_seed(0					)
										_SCREAMING_SNAKE_CASE     					=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-05			,      num_attention_heads=4			,      num_hidden_layers=5			,      pad_token_id=1			,      vocab_size=1000			,      )
										return CLIPTextModelWithProjection(_a					)




						@property
						def 							__UpperCamelCase			(    self   : int					)							->       Any:


										"""simple docstring"""





										torch.manual_seed(0					)

										_SCREAMING_SNAKE_CASE     					={
										    '''num_attention_heads''': 2,
										    '''attention_head_dim''': 12,
										    '''embedding_dim''': self.text_embedder_hidden_size,
										    '''num_layers''': 1,
										}

										_SCREAMING_SNAKE_CASE     					=PriorTransformer(**_a					)
										# clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
										_SCREAMING_SNAKE_CASE     					=nn.Parameter(torch.ones(model.clip_std.shape					)					)
										return model




						@property
						def 							__UpperCamelCase			(    self   : List[str]					)							->       Optional[Any]:


										"""simple docstring"""





										torch.manual_seed(0					)
										_SCREAMING_SNAKE_CASE     					=CLIPVisionConfig(
										    hidden_size=self.text_embedder_hidden_size			,      image_size=224			,      projection_dim=self.text_embedder_hidden_size			,      intermediate_size=37			,      num_attention_heads=4			,      num_channels=3			,      num_hidden_layers=5			,      patch_size=14			,      )

										_SCREAMING_SNAKE_CASE     					=CLIPVisionModelWithProjection(_a					)
										return model




						@property
						def 							__UpperCamelCase			(    self   : Any					)							->       int:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=CLIPImageProcessor(
										    crop_size=224			,      do_center_crop=_a			,      do_normalize=_a			,      do_resize=_a			,      image_mean=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]			,      image_std=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]			,      resample=3			,      size=224			,      )

										return image_processor




						def 							__UpperCamelCase			(    self   : int					)							->       Optional[int]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=self.dummy_prior
										_SCREAMING_SNAKE_CASE     					=self.dummy_image_encoder
										_SCREAMING_SNAKE_CASE     					=self.dummy_text_encoder
										_SCREAMING_SNAKE_CASE     					=self.dummy_tokenizer
										_SCREAMING_SNAKE_CASE     					=self.dummy_image_processor

										_SCREAMING_SNAKE_CASE     					=UnCLIPScheduler(
										    variance_type='''fixed_small_log'''			,      prediction_type='''sample'''			,      num_train_timesteps=1000			,      clip_sample=_a			,      clip_sample_range=10.0			,      )

										_SCREAMING_SNAKE_CASE     					={
										    '''prior''': prior,
										    '''image_encoder''': image_encoder,
										    '''text_encoder''': text_encoder,
										    '''tokenizer''': tokenizer,
										    '''scheduler''': scheduler,
										    '''image_processor''': image_processor,
										}

										return components




						def 							__UpperCamelCase			(    self   : Optional[Any]			,      _a   : Tuple			,      _a   : str=0					)							->       Any:


										"""simple docstring"""





										if str(_a					).startswith('''mps'''					):
														_SCREAMING_SNAKE_CASE     					=torch.manual_seed(_a					)
										else:
														_SCREAMING_SNAKE_CASE     					=torch.Generator(device=_a					).manual_seed(_a					)
										_SCREAMING_SNAKE_CASE     					={
										    '''prompt''': '''horse''',
										    '''generator''': generator,
										    '''guidance_scale''': 4.0,
										    '''num_inference_steps''': 2,
										    '''output_type''': '''np''',
										}
										return inputs




						def 							__UpperCamelCase			(    self   : Optional[int]					)							->       Tuple:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					='''cpu'''

										_SCREAMING_SNAKE_CASE     					=self.get_dummy_components()

										_SCREAMING_SNAKE_CASE     					=self.pipeline_class(**_a					)
										_SCREAMING_SNAKE_CASE     					=pipe.to(_a					)

										pipe.set_progress_bar_config(disable=_a					)

										_SCREAMING_SNAKE_CASE     					=pipe(**self.get_dummy_inputs(_a					)					)
										_SCREAMING_SNAKE_CASE     					=output.image_embeds

										_SCREAMING_SNAKE_CASE     					=pipe(
										    **self.get_dummy_inputs(_a					)			,      return_dict=_a			,      )[0]

										_SCREAMING_SNAKE_CASE     					=image[0, -10:]
										_SCREAMING_SNAKE_CASE     					=image_from_tuple[0, -10:]

										assert image.shape == (1, 32)

										_SCREAMING_SNAKE_CASE     					=np.array(
										    [-0.05_32, 1.71_20, 0.36_56, -1.08_52, -0.89_46, -1.17_56, 0.43_48, 0.24_82, 0.51_46, -0.11_56]					)

										assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-2
										assert np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() < 1E-2




						@skip_mps
						def 							__UpperCamelCase			(    self   : List[str]					)							->       List[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=torch_device == '''cpu'''
										_SCREAMING_SNAKE_CASE     					=True
										_SCREAMING_SNAKE_CASE     					=False

										self._test_inference_batch_single_identical(
										    test_max_difference=_a			,      relax_max_difference=_a			,      test_mean_pixel_difference=_a			,      )




						@skip_mps
						def 							__UpperCamelCase			(    self   : List[Any]					)							->       str:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE     					=torch_device == '''cpu'''
										_SCREAMING_SNAKE_CASE     					=False

										self._test_attention_slicing_forward_pass(
										    test_max_difference=_a			,      test_mean_pixel_difference=_a			,      )