"""simple docstring"""




import json
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
     import torch

     if is_vision_available():
          from transformers import OneFormerImageProcessor
          from transformers.models.oneformer.image_processing_oneformer import binary_mask_to_rle
          from transformers.models.oneformer.modeling_oneformer import OneFormerForUniversalSegmentationOutput

if is_vision_available():
     from PIL import Image





def 							SCREAMING_SNAKE_CASE     (			_lowerCamelCase						:       Dict			,_lowerCamelCase						:       List[str]="shi-labs/oneformer_demo"	)       ->      List[str]:
  with open(hf_hub_download(_lowerCamelCase			,_lowerCamelCase			,repo_type="""dataset"""	)			,"""r"""	) as f:
    _lowerCAmelCase						:       Tuple  			=     json.load(_lowerCamelCase	)
  _lowerCAmelCase						:       str  			=     {}
  _lowerCAmelCase						:       Dict  			=     []
  _lowerCAmelCase						:       Any  			=     []
  for key, info in class_info.items():
    _lowerCAmelCase						:       Tuple  			=     info["""name"""]
    class_names.append(info["""name"""]	)
    if info["isthing"]:
      thing_ids.append(int(_lowerCamelCase	)	)
  _lowerCAmelCase						:       int  			=     thing_ids
  _lowerCAmelCase						:       List[str]  			=     class_names
  return metadata
class 						__A							( unittest.TestCase		):






     def __init__(		self				,     a__				,     a__=7				,     a__=3				,     a__=30				,     a__=400				,     a__=None				,     a__=True				,     a__=True				,     a__=[0.5, 0.5, 0.5]				,     a__=[0.5, 0.5, 0.5]				,     a__=10				,     a__=False				,     a__=255				,     a__="shi-labs/oneformer_demo"				,     a__="ade20k_panoptic.json"				,     a__=10				,     ):
       _lowerCAmelCase						:       Optional[Any]  			=     parent
       _lowerCAmelCase						:       Optional[Any]  			=     batch_size
       _lowerCAmelCase						:       str  			=     num_channels
       _lowerCAmelCase						:       str  			=     min_resolution
       _lowerCAmelCase						:       int  			=     max_resolution
       _lowerCAmelCase						:       Any  			=     do_resize
       _lowerCAmelCase						:       Optional[int]  			=     {"""shortest_edge""": 32, """longest_edge""": 1333} if size is None else size
       _lowerCAmelCase						:       List[str]  			=     do_normalize
       _lowerCAmelCase						:       Dict  			=     image_mean
       _lowerCAmelCase						:       Any  			=     image_std
       _lowerCAmelCase						:       Dict  			=     class_info_file
       _lowerCAmelCase						:       Tuple  			=     prepare_metadata(a__				,     a__  )
       _lowerCAmelCase						:       Optional[int]  			=     num_text
       _lowerCAmelCase						:       int  			=     repo_path

       # for the post_process_functions
       _lowerCAmelCase						:       Any  			=     2
       _lowerCAmelCase						:       int  			=     10
       _lowerCAmelCase						:       List[str]  			=     10
       _lowerCAmelCase						:       Optional[int]  			=     3
       _lowerCAmelCase						:       Any  			=     4
       _lowerCAmelCase						:       Optional[int]  			=     num_labels
       _lowerCAmelCase						:       List[Any]  			=     do_reduce_labels
       _lowerCAmelCase						:       Dict  			=     ignore_index






     def     __A  (		self  ):
       return {
           "do_resize": self.do_resize,
           "size": self.size,
           "do_normalize": self.do_normalize,
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "num_labels": self.num_labels,
           "do_reduce_labels": self.do_reduce_labels,
           "ignore_index": self.ignore_index,
           "class_info_file": self.class_info_file,
           "metadata": self.metadata,
           "num_text": self.num_text,
       }






     def     __A  (		self				,     a__				,     a__=False  ):
       if not batched:
         _lowerCAmelCase						:       Any  			=     image_inputs[0]
         if isinstance(a__				,     Image.Image  ):
           _lowerCAmelCase			,	_lowerCAmelCase						:       Tuple  			=     image.size
         else:
           _lowerCAmelCase			,	_lowerCAmelCase						:       List[str]  			=     image.shape[1], image.shape[2]
         if w < h:
           _lowerCAmelCase						:       str  			=     int(self.size["""shortest_edge"""] * h / w  )
           _lowerCAmelCase						:       List[str]  			=     self.size["""shortest_edge"""]
         elif w > h:
           _lowerCAmelCase						:       Optional[int]  			=     self.size["""shortest_edge"""]
           _lowerCAmelCase						:       Union[str, Any]  			=     int(self.size["""shortest_edge"""] * w / h  )
         else:
           _lowerCAmelCase						:       Dict  			=     self.size["""shortest_edge"""]
           _lowerCAmelCase						:       List[Any]  			=     self.size["""shortest_edge"""]

       else:
         _lowerCAmelCase						:       List[Any]  			=     []
         for image in image_inputs:
           _lowerCAmelCase			,	_lowerCAmelCase						:       Optional[int]  			=     self.get_expected_values([image]  )
           expected_values.append((expected_height, expected_width)  )
         _lowerCAmelCase						:       Tuple  			=     max(a__				,     key=lambda a__  : item[0]  )[0]
         _lowerCAmelCase						:       Union[str, Any]  			=     max(a__				,     key=lambda a__  : item[1]  )[1]

       return expected_height, expected_width






     def     __A  (		self  ):
       return OneFormerForUniversalSegmentationOutput(
           # +1 for null class
           class_queries_logits=torch.randn((self.batch_size, self.num_queries, self.num_classes + 1)  )				,     masks_queries_logits=torch.randn((self.batch_size, self.num_queries, self.height, self.width)  )				,     )







@require_torch
@require_vision
class 						__A							( SCREAMING_SNAKE_CASE_    ,	unittest.TestCase		):
     _UpperCamelCase							:			Dict							=		OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
     # only for test_image_processing_common.test_image_proc_to_json_string
     _UpperCamelCase							:			List[str]							=		image_processing_class






     def     __A  (		self  ):
       _lowerCAmelCase						:       int  			=     OneFormerImageProcessorTester(self  )






     @property
     def     __A  (		self  ):
       return self.image_processing_tester.prepare_image_processor_dict()






     def     __A  (		self  ):
       _lowerCAmelCase						:       List[str]  			=     self.image_processing_class(**self.image_processor_dict  )
       self.assertTrue(hasattr(a__				,     """image_mean"""  )  )
       self.assertTrue(hasattr(a__				,     """image_std"""  )  )
       self.assertTrue(hasattr(a__				,     """do_normalize"""  )  )
       self.assertTrue(hasattr(a__				,     """do_resize"""  )  )
       self.assertTrue(hasattr(a__				,     """size"""  )  )
       self.assertTrue(hasattr(a__				,     """ignore_index"""  )  )
       self.assertTrue(hasattr(a__				,     """class_info_file"""  )  )
       self.assertTrue(hasattr(a__				,     """num_text"""  )  )
       self.assertTrue(hasattr(a__				,     """repo_path"""  )  )
       self.assertTrue(hasattr(a__				,     """metadata"""  )  )
       self.assertTrue(hasattr(a__				,     """do_reduce_labels"""  )  )






     def     __A  (		self  ):
       pass






     def     __A  (		self  ):
       # Initialize image_processor
       _lowerCAmelCase						:       Union[str, Any]  			=     self.image_processing_class(**self.image_processor_dict  )
       # create random PIL images
       _lowerCAmelCase						:       Optional[int]  			=     prepare_image_inputs(self.image_processing_tester				,     equal_resolution=a__  )
       for image in image_inputs:
         self.assertIsInstance(a__				,     Image.Image  )

       # Test not batched input
       _lowerCAmelCase						:       int  			=     image_processor(image_inputs[0]				,     ["""semantic"""]				,     return_tensors="""pt"""  ).pixel_values

       _lowerCAmelCase			,	_lowerCAmelCase						:       Optional[int]  			=     self.image_processing_tester.get_expected_values(a__  )

       self.assertEqual(
           encoded_images.shape				,     (1, self.image_processing_tester.num_channels, expected_height, expected_width)				,     )

       # Test batched
       _lowerCAmelCase			,	_lowerCAmelCase						:       str  			=     self.image_processing_tester.get_expected_values(a__				,     batched=a__  )

       _lowerCAmelCase						:       Any  			=     image_processor(
           a__				,     ["""semantic"""] * len(a__  )				,     return_tensors="""pt"""  ).pixel_values
       self.assertEqual(
           encoded_images.shape				,     (
               self.image_processing_tester.batch_size,
               self.image_processing_tester.num_channels,
               expected_height,
               expected_width,
           )				,     )






     def     __A  (		self  ):
       # Initialize image_processor
       _lowerCAmelCase						:       Union[str, Any]  			=     self.image_processing_class(**self.image_processor_dict  )
       # create random numpy tensors
       _lowerCAmelCase						:       Optional[int]  			=     prepare_image_inputs(self.image_processing_tester				,     equal_resolution=a__				,     numpify=a__  )
       for image in image_inputs:
         self.assertIsInstance(a__				,     np.ndarray  )

       # Test not batched input
       _lowerCAmelCase						:       Tuple  			=     image_processor(image_inputs[0]				,     ["""semantic"""]				,     return_tensors="""pt"""  ).pixel_values

       _lowerCAmelCase			,	_lowerCAmelCase						:       Optional[Any]  			=     self.image_processing_tester.get_expected_values(a__  )

       self.assertEqual(
           encoded_images.shape				,     (1, self.image_processing_tester.num_channels, expected_height, expected_width)				,     )

       # Test batched
       _lowerCAmelCase			,	_lowerCAmelCase						:       Optional[Any]  			=     self.image_processing_tester.get_expected_values(a__				,     batched=a__  )

       _lowerCAmelCase						:       Optional[Any]  			=     image_processor(
           a__				,     ["""semantic"""] * len(a__  )				,     return_tensors="""pt"""  ).pixel_values
       self.assertEqual(
           encoded_images.shape				,     (
               self.image_processing_tester.batch_size,
               self.image_processing_tester.num_channels,
               expected_height,
               expected_width,
           )				,     )






     def     __A  (		self  ):
       # Initialize image_processor
       _lowerCAmelCase						:       int  			=     self.image_processing_class(**self.image_processor_dict  )
       # create random PyTorch tensors
       _lowerCAmelCase						:       int  			=     prepare_image_inputs(self.image_processing_tester				,     equal_resolution=a__				,     torchify=a__  )
       for image in image_inputs:
         self.assertIsInstance(a__				,     torch.Tensor  )

       # Test not batched input
       _lowerCAmelCase						:       Union[str, Any]  			=     image_processor(image_inputs[0]				,     ["""semantic"""]				,     return_tensors="""pt"""  ).pixel_values

       _lowerCAmelCase			,	_lowerCAmelCase						:       Union[str, Any]  			=     self.image_processing_tester.get_expected_values(a__  )

       self.assertEqual(
           encoded_images.shape				,     (1, self.image_processing_tester.num_channels, expected_height, expected_width)				,     )

       # Test batched
       _lowerCAmelCase			,	_lowerCAmelCase						:       str  			=     self.image_processing_tester.get_expected_values(a__				,     batched=a__  )

       _lowerCAmelCase						:       Optional[Any]  			=     image_processor(
           a__				,     ["""semantic"""] * len(a__  )				,     return_tensors="""pt"""  ).pixel_values
       self.assertEqual(
           encoded_images.shape				,     (
               self.image_processing_tester.batch_size,
               self.image_processing_tester.num_channels,
               expected_height,
               expected_width,
           )				,     )






     def     __A  (		self				,     a__=False				,     a__=False				,     a__="np"  ):
       _lowerCAmelCase						:       Optional[Any]  			=     self.image_processing_class(**self.image_processor_dict  )
       # prepare image and target
       _lowerCAmelCase						:       Any  			=     self.image_processing_tester.num_labels
       _lowerCAmelCase						:       List[str]  			=     None
       _lowerCAmelCase						:       List[str]  			=     None
       _lowerCAmelCase						:       List[str]  			=     prepare_image_inputs(self.image_processing_tester				,     equal_resolution=a__  )
       if with_segmentation_maps:
         _lowerCAmelCase						:       Tuple  			=     num_labels
         if is_instance_map:
           _lowerCAmelCase						:       Optional[int]  			=     list(range(a__  )  ) * 2
           _lowerCAmelCase						:       Optional[int]  			=     dict(enumerate(a__  )  )
         _lowerCAmelCase						:       int  			=     [
             np.random.randint(0				,     high * 2				,     (img.size[1], img.size[0])  ).astype(np.uinta  ) for img in image_inputs
         ]
         if segmentation_type == "pil":
           _lowerCAmelCase						:       Optional[int]  			=     [Image.fromarray(a__  ) for annotation in annotations]

       _lowerCAmelCase						:       Union[str, Any]  			=     image_processor(
           a__				,     ["""semantic"""] * len(a__  )				,     a__				,     return_tensors="""pt"""				,     instance_id_to_semantic_id=a__				,     pad_and_return_pixel_mask=a__				,     )

       return inputs






     def     __A  (		self  ):
       pass






     def     __A  (		self  ):
       def common(a__=False				,     a__=None  ):
         _lowerCAmelCase						:       str  			=     self.comm_get_image_processor_inputs(
             with_segmentation_maps=a__				,     is_instance_map=a__				,     segmentation_type=a__  )

         _lowerCAmelCase						:       List[Any]  			=     inputs["""mask_labels"""]
         _lowerCAmelCase						:       Any  			=     inputs["""class_labels"""]
         _lowerCAmelCase						:       Tuple  			=     inputs["""pixel_values"""]
         _lowerCAmelCase						:       Any  			=     inputs["""text_inputs"""]

         # check the batch_size
         for mask_label, class_label, text_input in zip(a__				,     a__				,     a__  ):
           self.assertEqual(mask_label.shape[0]				,     class_label.shape[0]  )
           # this ensure padding has happened
           self.assertEqual(mask_label.shape[1:]				,     pixel_values.shape[2:]  )
           self.assertEqual(len(a__  )				,     self.image_processing_tester.num_text  )

       common()
       common(is_instance_map=a__  )
       common(is_instance_map=a__				,     segmentation_type="""pil"""  )
       common(is_instance_map=a__				,     segmentation_type="""pil"""  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       Any  			=     np.zeros((20, 50)  )
       _lowerCAmelCase						:       Any  			=     1
       _lowerCAmelCase						:       Dict  			=     1
       _lowerCAmelCase						:       Union[str, Any]  			=     1

       _lowerCAmelCase						:       Optional[int]  			=     binary_mask_to_rle(a__  )
       self.assertEqual(len(a__  )				,     4  )
       self.assertEqual(rle[0]				,     21  )
       self.assertEqual(rle[1]				,     45  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       Optional[int]  			=     self.image_processing_class(
           num_labels=self.image_processing_tester.num_classes				,     max_seq_length=77				,     task_seq_length=77				,     class_info_file="""ade20k_panoptic.json"""				,     num_text=self.image_processing_tester.num_text				,     repo_path="""shi-labs/oneformer_demo"""				,     )
       _lowerCAmelCase						:       Any  			=     self.image_processing_tester.get_fake_oneformer_outputs()

       _lowerCAmelCase						:       Tuple  			=     fature_extractor.post_process_semantic_segmentation(a__  )

       self.assertEqual(len(a__  )				,     self.image_processing_tester.batch_size  )
       self.assertEqual(
           segmentation[0].shape				,     (
               self.image_processing_tester.height,
               self.image_processing_tester.width,
           )				,     )

       _lowerCAmelCase						:       Tuple  			=     [(1, 4) for i in range(self.image_processing_tester.batch_size  )]
       _lowerCAmelCase						:       Union[str, Any]  			=     fature_extractor.post_process_semantic_segmentation(a__				,     target_sizes=a__  )

       self.assertEqual(segmentation[0].shape				,     target_sizes[0]  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       Any  			=     self.image_processing_class(
           num_labels=self.image_processing_tester.num_classes				,     max_seq_length=77				,     task_seq_length=77				,     class_info_file="""ade20k_panoptic.json"""				,     num_text=self.image_processing_tester.num_text				,     repo_path="""shi-labs/oneformer_demo"""				,     )
       _lowerCAmelCase						:       List[Any]  			=     self.image_processing_tester.get_fake_oneformer_outputs()
       _lowerCAmelCase						:       Union[str, Any]  			=     image_processor.post_process_instance_segmentation(a__				,     threshold=0  )

       self.assertTrue(len(a__  ) == self.image_processing_tester.batch_size  )
       for el in segmentation:
         self.assertTrue("""segmentation""" in el  )
         self.assertTrue("""segments_info""" in el  )
         self.assertEqual(type(el["""segments_info"""]  )				,     a__  )
         self.assertEqual(
             el["""segmentation"""].shape				,     (self.image_processing_tester.height, self.image_processing_tester.width)  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       List[str]  			=     self.image_processing_class(
           num_labels=self.image_processing_tester.num_classes				,     max_seq_length=77				,     task_seq_length=77				,     class_info_file="""ade20k_panoptic.json"""				,     num_text=self.image_processing_tester.num_text				,     repo_path="""shi-labs/oneformer_demo"""				,     )
       _lowerCAmelCase						:       Dict  			=     self.image_processing_tester.get_fake_oneformer_outputs()
       _lowerCAmelCase						:       Any  			=     image_processor.post_process_panoptic_segmentation(a__				,     threshold=0  )

       self.assertTrue(len(a__  ) == self.image_processing_tester.batch_size  )
       for el in segmentation:
         self.assertTrue("""segmentation""" in el  )
         self.assertTrue("""segments_info""" in el  )
         self.assertEqual(type(el["""segments_info"""]  )				,     a__  )
         self.assertEqual(
             el["""segmentation"""].shape				,     (self.image_processing_tester.height, self.image_processing_tester.width)  )











"""simple docstring"""




from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class        __UpperCamelCase				(						a__						):






							def __init__(   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__ = None      ,					)	-> int:
								super().__init__()
								self.register_modules(transformer=lowerCAmelCase__      ,					vae=lowerCAmelCase__      ,					scheduler=lowerCAmelCase__      )

								# create a imagenet -> id dictionary for easier use
								a      :		str	=				{}
								if idalabel is not None:
									for key, value in idalabel.items():
										for label in value.split(","      ):
											a      :		int	=				int(lowerCAmelCase__      )
									a      :		Any	=				dict(sorted(self.labels.items()      )      )






							def     __a   (   self      ,					lowerCAmelCase__      )	-> List[int]:

								if not isinstance(lowerCAmelCase__      ,					lowerCAmelCase__      ):
									a      :		Optional[Any]	=				list(lowerCAmelCase__      )

								for l in label:
									if l not in self.labels:
										raise ValueError(
										    f"""{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}."""      )

								return [self.labels[l] for l in label]






							@torch.no_grad()
							def __call__(   self      ,					lowerCAmelCase__      ,					lowerCAmelCase__ = 4.0      ,					lowerCAmelCase__ = None      ,					lowerCAmelCase__ = 50      ,					lowerCAmelCase__ = "pil"      ,					lowerCAmelCase__ = True      ,					)	-> Union[ImagePipelineOutput, Tuple]:
								a      :		Dict	=				len(lowerCAmelCase__      )
								a      :		Tuple	=				self.transformer.config.sample_size
								a      :		Tuple	=				self.transformer.config.in_channels

								a      :		Optional[int]	=				randn_tensor(
								    shape=(batch_size, latent_channels, latent_size, latent_size)      ,					generator=lowerCAmelCase__      ,					device=self.device      ,					dtype=self.transformer.dtype      ,					)
								a      :		Tuple	=				torch.cat([latents] * 2      ) if guidance_scale > 1 else latents

								a      :		List[str]	=				torch.tensor(lowerCAmelCase__      ,					device=self.device      ).reshape(-1      )
								a      :		str	=				torch.tensor([1000] * batch_size      ,					device=self.device      )
								a      :		Dict	=				torch.cat([class_labels, class_null]      ,					0      ) if guidance_scale > 1 else class_labels

								# set step values
								self.scheduler.set_timesteps(lowerCAmelCase__      )

								for t in self.progress_bar(self.scheduler.timesteps      ):
									if guidance_scale > 1:
										a      :		Any	=				latent_model_input[: len(lowerCAmelCase__      ) // 2]
										a      :		Tuple	=				torch.cat([half, half]      ,					dim=0      )
									a      :		List[str]	=				self.scheduler.scale_model_input(lowerCAmelCase__      ,					lowerCAmelCase__      )

									a      :		Dict	=				t
									if not torch.is_tensor(lowerCAmelCase__      ):
										# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
										# This would be a good case for the `match` statement (Python 3.10+)
										a      :		Optional[int]	=				latent_model_input.device.type == "mps"
										if isinstance(lowerCAmelCase__      ,					lowerCAmelCase__      ):
											a      :		Union[str, Any]	=				torch.floataa if is_mps else torch.floataa
										else:
											a      :		str	=				torch.intaa if is_mps else torch.intaa
										a      :		List[str]	=				torch.tensor([timesteps]      ,					dtype=lowerCAmelCase__      ,					device=latent_model_input.device      )
									elif len(timesteps.shape      ) == 0:
										a      :		List[Any]	=				timesteps[None].to(latent_model_input.device      )
									# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
									a      :		Dict	=				timesteps.expand(latent_model_input.shape[0]      )
									# predict noise model_output
									a      :		Union[str, Any]	=				self.transformer(
									    lowerCAmelCase__      ,					timestep=lowerCAmelCase__      ,					class_labels=lowerCAmelCase__      ).sample

									# perform guidance
									if guidance_scale > 1:
										a,       a      :		Any	=				noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
										a,       a      :		Union[str, Any]	=				torch.split(lowerCAmelCase__      ,					len(lowerCAmelCase__      ) // 2      ,					dim=0      )

										a      :		Dict	=				uncond_eps + guidance_scale * (cond_eps - uncond_eps)
										a      :		Optional[int]	=				torch.cat([half_eps, half_eps]      ,					dim=0      )

										a      :		Optional[int]	=				torch.cat([eps, rest]      ,					dim=1      )

									# learned sigma
									if self.transformer.config.out_channels // 2 == latent_channels:
										a,       a      :		str	=				torch.split(lowerCAmelCase__      ,					lowerCAmelCase__      ,					dim=1      )
									else:
										a      :		Any	=				noise_pred

									# compute previous image: x_t -> x_t-1
									a      :		Optional[int]	=				self.scheduler.step(lowerCAmelCase__      ,					lowerCAmelCase__      ,					lowerCAmelCase__      ).prev_sample

								if guidance_scale > 1:
									a,       a      :		Tuple	=				latent_model_input.chunk(2      ,					dim=0      )
								else:
									a      :		Tuple	=				latent_model_input

								a      :		Optional[Any]	=				1 / self.vae.config.scaling_factor * latents
								a      :		Any	=				self.vae.decode(lowerCAmelCase__      ).sample

								a      :		Tuple	=				(samples / 2 + 0.5).clamp(0      ,					1      )

								# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
								a      :		Optional[Any]	=				samples.cpu().permute(0      ,					2      ,					3      ,					1      ).float().numpy()

								if output_type == "pil":
									a      :		int	=				self.numpy_to_pil(lowerCAmelCase__      )

								if not return_dict:
									return (samples,)

								return ImagePipelineOutput(images=lowerCAmelCase__      )


