






from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel

from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ...utils import (
    BaseOutput,
    is_accelerate_available,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .renderer import ShapERenderer


lowerCamelCase	:							Optional[int]				=logging.get_logger(__name__)  # pylint: disable=invalid-name

lowerCamelCase	:							Any				='''
    Examples:
        ```py
        >>> from PIL import Image
        >>> import torch
        >>> from diffusers import DiffusionPipeline
        >>> from diffusers.utils import export_to_gif, load_image

        >>> device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

        >>> repo = "openai/shap-e-img2img"
        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)
        >>> pipe = pipe.to(device)

        >>> guidance_scale = 3.0
        >>> image_url = "https://hf.co/datasets/diffusers/docs-images/resolve/main/shap-e/corgi.png"
        >>> image = load_image(image_url).convert("RGB")

        >>> images = pipe(
        ...     image,
        ...     guidance_scale=guidance_scale,
        ...     num_inference_steps=64,
        ...     frame_size=256,
        ... ).images

        >>> gif_path = export_to_gif(images[0], "corgi_3d.gif")
        ```
'''





@dataclass
class 			__a  (    A__       ):
 _lowerCAmelCase       :       Union[PIL.Image.Image, np.ndarray]







class 			__a  (    A__       ):







 def __init__(		self					:   List[str]							,   SCREAMING_SNAKE_CASE					:   PriorTransformer							,   SCREAMING_SNAKE_CASE					:   CLIPVisionModel							,   SCREAMING_SNAKE_CASE					:   CLIPImageProcessor							,   SCREAMING_SNAKE_CASE					:   HeunDiscreteScheduler							,   SCREAMING_SNAKE_CASE					:   ShapERenderer							,   ):


      '''simple docstring'''






      super().__init__()

      self.register_modules(
          prior=SCREAMING_SNAKE_CASE							,   image_encoder=SCREAMING_SNAKE_CASE							,   image_processor=SCREAMING_SNAKE_CASE							,   scheduler=SCREAMING_SNAKE_CASE							,   renderer=SCREAMING_SNAKE_CASE							,   )







 def 					__lowercase					(		self					:   List[str]							,   SCREAMING_SNAKE_CASE					:   int							,   SCREAMING_SNAKE_CASE					:   int							,   SCREAMING_SNAKE_CASE					:   int							,   SCREAMING_SNAKE_CASE					:   int							,   SCREAMING_SNAKE_CASE					:   Optional[Any]							,   SCREAMING_SNAKE_CASE					:   Optional[Any]						):


      '''simple docstring'''






      if latents is None:
           UpperCamelCase__			:   int       				=  randn_tensor(SCREAMING_SNAKE_CASE							,   generator=SCREAMING_SNAKE_CASE							,   device=SCREAMING_SNAKE_CASE							,   dtype=SCREAMING_SNAKE_CASE						)
      else:
           if latents.shape != shape:
                raise ValueError(F'Unexpected latents shape, got {latents.shape}, expected {shape}'						)
           UpperCamelCase__			:   Optional[Any]       				=  latents.to(SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   str       				=  latents * scheduler.init_noise_sigma
      return latents







 def 					__lowercase					(		self					:   Optional[Any]							,   SCREAMING_SNAKE_CASE					:   Any=0						):


      '''simple docstring'''






      if is_accelerate_available():
           from accelerate import cpu_offload
      else:
           raise ImportError("Please install accelerate via `pip install accelerate`"						)

      UpperCamelCase__			:   Dict       				=  torch.device(F'cuda:{gpu_id}'						)

      UpperCamelCase__			:   Union[str, Any]       				=  [self.image_encoder, self.prior]
      for cpu_offloaded_model in models:
           if cpu_offloaded_model is not None:
                cpu_offload(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)







 @property
 def 					__lowercase					(		self					:   int						):


      '''simple docstring'''






      if self.device != torch.device("meta"						) or not hasattr(self.image_encoder							,   "_hf_hook"						):
           return self.device
      for module in self.image_encoder.modules():
           if (
               hasattr(SCREAMING_SNAKE_CASE							,   "_hf_hook"						)
               and hasattr(module._hf_hook							,   "execution_device"						)
               and module._hf_hook.execution_device is not None
           ):
                return torch.device(module._hf_hook.execution_device						)
      return self.device







 def 					__lowercase					(		self					:   Any							,   SCREAMING_SNAKE_CASE					:   List[Any]							,   SCREAMING_SNAKE_CASE					:   int							,   SCREAMING_SNAKE_CASE					:   Union[str, Any]							,   SCREAMING_SNAKE_CASE					:   str							,   ):


      '''simple docstring'''






      if isinstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						) and isinstance(image[0]							,   torch.Tensor						):
           UpperCamelCase__			:   List[Any]       				=  torch.cat(SCREAMING_SNAKE_CASE							,   axis=0						) if image[0].ndim == 4 else torch.stack(SCREAMING_SNAKE_CASE							,   axis=0						)

      if not isinstance(SCREAMING_SNAKE_CASE							,   torch.Tensor						):
           UpperCamelCase__			:   Any       				=  self.image_processor(SCREAMING_SNAKE_CASE							,   return_tensors="pt"						).pixel_values[0].unsqueeze(0						)

      UpperCamelCase__			:   Optional[int]       				=  image.to(dtype=self.image_encoder.dtype							,   device=SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   Any       				=  self.image_encoder(SCREAMING_SNAKE_CASE						)["last_hidden_state"]
      UpperCamelCase__			:   Optional[int]       				=  image_embeds[:, 1:, :].contiguous()  # batch_size, dim, 256

      UpperCamelCase__			:   Any       				=  image_embeds.repeat_interleave(SCREAMING_SNAKE_CASE							,   dim=0						)

      if do_classifier_free_guidance:
           UpperCamelCase__			:   Tuple       				=  torch.zeros_like(SCREAMING_SNAKE_CASE						)

           # For classifier free guidance, we need to do two forward passes.
           # Here we concatenate the unconditional and text embeddings into a single batch
           # to avoid doing two forward passes
           UpperCamelCase__			:   Tuple       				=  torch.cat([negative_image_embeds, image_embeds]						)

      return image_embeds







 @torch.no_grad()
 @replace_example_docstring(SCREAMING_SNAKE_CASE						)
 def __call__(		self					:   List[str]							,   SCREAMING_SNAKE_CASE					:   Union[PIL.Image.Image, List[PIL.Image.Image]]							,   SCREAMING_SNAKE_CASE					:   int = 1							,   SCREAMING_SNAKE_CASE					:   int = 25							,   SCREAMING_SNAKE_CASE					:   Optional[Union[torch.Generator, List[torch.Generator]]] = None							,   SCREAMING_SNAKE_CASE					:   Optional[torch.FloatTensor] = None							,   SCREAMING_SNAKE_CASE					:   float = 4.0							,   SCREAMING_SNAKE_CASE					:   int = 64							,   SCREAMING_SNAKE_CASE					:   Optional[str] = "pil"							,   SCREAMING_SNAKE_CASE					:   bool = True							,   ):


      '''simple docstring'''






      if isinstance(SCREAMING_SNAKE_CASE							,   PIL.Image.Image						):
           UpperCamelCase__			:   str       				=  1
      elif isinstance(SCREAMING_SNAKE_CASE							,   torch.Tensor						):
           UpperCamelCase__			:   Tuple       				=  image.shape[0]
      elif isinstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						) and isinstance(image[0]							,   (torch.Tensor, PIL.Image.Image)						):
           UpperCamelCase__			:   Tuple       				=  len(SCREAMING_SNAKE_CASE						)
      else:
           raise ValueError(
               F'`image` has to be of type `PIL.Image.Image`, `torch.Tensor`, `List[PIL.Image.Image]` or `List[torch.Tensor]` but is {type(SCREAMING_SNAKE_CASE						)}'						)

      UpperCamelCase__			:   Tuple       				=  self._execution_device

      UpperCamelCase__			:   List[str]       				=  batch_size * num_images_per_prompt

      UpperCamelCase__			:   List[Any]       				=  guidance_scale > 1.0
      UpperCamelCase__			:   Any       				=  self._encode_image(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)

      # prior

      self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE							,   device=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Optional[Any]       				=  self.scheduler.timesteps

      UpperCamelCase__			:   Any       				=  self.prior.config.num_embeddings
      UpperCamelCase__			:   Union[str, Any]       				=  self.prior.config.embedding_dim

      UpperCamelCase__			:   Dict       				=  self.prepare_latents(
          (batch_size, num_embeddings * embedding_dim)							,   image_embeds.dtype							,   SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE							,   self.scheduler							,   )

      # YiYi notes: for testing only to match ldm, we can directly create a latents with desired shape: batch_size, num_embeddings, embedding_dim
      UpperCamelCase__			:   Dict       				=  latents.reshape(latents.shape[0]							,   SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)

      for i, t in enumerate(self.progress_bar(SCREAMING_SNAKE_CASE						)						):
           # expand the latents if we are doing classifier free guidance
           UpperCamelCase__			:   str       				=  torch.cat([latents] * 2						) if do_classifier_free_guidance else latents
           UpperCamelCase__			:   Dict       				=  self.scheduler.scale_model_input(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)

           UpperCamelCase__			:   List[Any]       				=  self.prior(
               SCREAMING_SNAKE_CASE							,   timestep=SCREAMING_SNAKE_CASE							,   proj_embedding=SCREAMING_SNAKE_CASE							,   ).predicted_image_embedding

           # remove the variance
           UpperCamelCase__				,     UpperCamelCase__			:   Tuple       				=  noise_pred.split(
               scaled_model_input.shape[2]							,   dim=2						)  # batch_size, num_embeddings, embedding_dim

           if do_classifier_free_guidance is not None:
                UpperCamelCase__				,     UpperCamelCase__			:   str       				=  noise_pred.chunk(2						)
                UpperCamelCase__			:   List[Any]       				=  noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

           UpperCamelCase__			:   List[Any]       				=  self.scheduler.step(
               SCREAMING_SNAKE_CASE							,   timestep=SCREAMING_SNAKE_CASE							,   sample=SCREAMING_SNAKE_CASE							,   ).prev_sample

      if output_type == "latent":
           return ShapEPipelineOutput(images=SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   Any       				=  []
      for i, latent in enumerate(SCREAMING_SNAKE_CASE						):
           print()
           UpperCamelCase__			:   Any       				=  self.renderer.decode(
               latent[None, :]							,   SCREAMING_SNAKE_CASE							,   size=SCREAMING_SNAKE_CASE							,   ray_batch_size=40_96							,   n_coarse_samples=64							,   n_fine_samples=1_28							,   )

           images.append(SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   int       				=  torch.stack(SCREAMING_SNAKE_CASE						)

      if output_type not in ["np", "pil"]:
           raise ValueError(F'Only the output types `pil` and `np` are supported not output_type={output_type}'						)

      UpperCamelCase__			:   Tuple       				=  images.cpu().numpy()

      if output_type == "pil":
           UpperCamelCase__			:   Optional[Any]       				=  [self.numpy_to_pil(SCREAMING_SNAKE_CASE						) for image in images]

      # Offload last model to CPU
      if hasattr(self							,   "final_offload_hook"						) and self.final_offload_hook is not None:
           self.final_offload_hook.offload()

      if not return_dict:
           return (images,)

      return ShapEPipelineOutput(images=SCREAMING_SNAKE_CASE						)







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





class 			__a  (    unittest.TestCase       ):







 def __init__(		self					:   Tuple							,   SCREAMING_SNAKE_CASE					:   str							,   SCREAMING_SNAKE_CASE					:   List[str]=7							,   SCREAMING_SNAKE_CASE					:   Optional[int]=3							,   SCREAMING_SNAKE_CASE					:   Tuple=30							,   SCREAMING_SNAKE_CASE					:   Dict=4_00							,   SCREAMING_SNAKE_CASE					:   Optional[int]=None							,   SCREAMING_SNAKE_CASE					:   Union[str, Any]=True							,   SCREAMING_SNAKE_CASE					:   Dict=True							,   SCREAMING_SNAKE_CASE					:   Optional[int]=[0.5, 0.5, 0.5]							,   SCREAMING_SNAKE_CASE					:   List[str]=[0.5, 0.5, 0.5]							,   SCREAMING_SNAKE_CASE					:   Optional[Any]=10							,   SCREAMING_SNAKE_CASE					:   int=False							,   SCREAMING_SNAKE_CASE					:   int=2_55							,   SCREAMING_SNAKE_CASE					:   str="shi-labs/oneformer_demo"							,   SCREAMING_SNAKE_CASE					:   List[Any]="ade20k_panoptic.json"							,   SCREAMING_SNAKE_CASE					:   Tuple=10							,   ):


      '''simple docstring'''






      UpperCamelCase__			:   Tuple       				=  parent
      UpperCamelCase__			:   Optional[int]       				=  batch_size
      UpperCamelCase__			:   Any       				=  num_channels
      UpperCamelCase__			:   Optional[int]       				=  min_resolution
      UpperCamelCase__			:   Union[str, Any]       				=  max_resolution
      UpperCamelCase__			:   Optional[int]       				=  do_resize
      UpperCamelCase__			:   List[Any]       				=  {"shortest_edge": 32, "longest_edge": 13_33} if size is None else size
      UpperCamelCase__			:   Dict       				=  do_normalize
      UpperCamelCase__			:   Optional[int]       				=  image_mean
      UpperCamelCase__			:   Union[str, Any]       				=  image_std
      UpperCamelCase__			:   Union[str, Any]       				=  class_info_file
      UpperCamelCase__			:   Tuple       				=  prepare_metadata(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   str       				=  num_text
      UpperCamelCase__			:   int       				=  repo_path

      # for the post_process_functions
      UpperCamelCase__			:   int       				=  2
      UpperCamelCase__			:   str       				=  10
      UpperCamelCase__			:   Any       				=  10
      UpperCamelCase__			:   Union[str, Any]       				=  3
      UpperCamelCase__			:   List[Any]       				=  4
      UpperCamelCase__			:   Optional[int]       				=  num_labels
      UpperCamelCase__			:   Tuple       				=  do_reduce_labels
      UpperCamelCase__			:   List[str]       				=  ignore_index







 def 					__lowercase					(		self					:   int						):


      '''simple docstring'''






      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 					__lowercase					(		self					:   Union[str, Any]							,   SCREAMING_SNAKE_CASE					:   Union[str, Any]							,   SCREAMING_SNAKE_CASE					:   List[str]=False						):


      '''simple docstring'''






      if not batched:
           UpperCamelCase__			:   str       				=  image_inputs[0]
           if isinstance(SCREAMING_SNAKE_CASE							,   Image.Image						):
                UpperCamelCase__				,     UpperCamelCase__			:   Union[str, Any]       				=  image.size
           else:
                UpperCamelCase__				,     UpperCamelCase__			:   int       				=  image.shape[1], image.shape[2]
           if w < h:
                UpperCamelCase__			:   Any       				=  int(self.size["shortest_edge"] * h / w						)
                UpperCamelCase__			:   Union[str, Any]       				=  self.size["shortest_edge"]
           elif w > h:
                UpperCamelCase__			:   Union[str, Any]       				=  self.size["shortest_edge"]
                UpperCamelCase__			:   int       				=  int(self.size["shortest_edge"] * w / h						)
           else:
                UpperCamelCase__			:   Optional[Any]       				=  self.size["shortest_edge"]
                UpperCamelCase__			:   str       				=  self.size["shortest_edge"]

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

      return expected_height, expected_width







 def 					__lowercase					(		self					:   Any						):


      '''simple docstring'''






      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  (    A__       ,   unittest.TestCase       ):
 _lowerCAmelCase       :       Tuple        = OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
 # only for test_image_processing_common.test_image_proc_to_json_string
 _lowerCAmelCase       :       List[str]        = image_processing_class







 def 					__lowercase					(		self					:   str						):


      '''simple docstring'''






      UpperCamelCase__			:   Union[str, Any]       				=  OneFormerImageProcessorTester(self						)







 @property
 def 					__lowercase					(		self					:   Optional[Any]						):


      '''simple docstring'''






      return self.image_processing_tester.prepare_image_processor_dict()







 def 					__lowercase					(		self					:   str						):


      '''simple docstring'''






      UpperCamelCase__			:   Tuple       				=  self.image_processing_class(**self.image_processor_dict						)
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE							,   "image_mean"						)						)
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE							,   "image_std"						)						)
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE							,   "do_normalize"						)						)
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE							,   "do_resize"						)						)
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE							,   "size"						)						)
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE							,   "ignore_index"						)						)
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE							,   "class_info_file"						)						)
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE							,   "num_text"						)						)
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE							,   "repo_path"						)						)
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE							,   "metadata"						)						)
      self.assertTrue(hasattr(SCREAMING_SNAKE_CASE							,   "do_reduce_labels"						)						)







 def 					__lowercase					(		self					:   List[str]						):


      '''simple docstring'''






      pass







 def 					__lowercase					(		self					:   Tuple						):


      '''simple docstring'''






      UpperCamelCase__			:   Tuple       				=  self.image_processing_class(**self.image_processor_dict						)
      # create random PIL images
      UpperCamelCase__			:   Optional[int]       				=  prepare_image_inputs(self.image_processing_tester							,   equal_resolution=SCREAMING_SNAKE_CASE						)
      for image in image_inputs:
           self.assertIsInstance(SCREAMING_SNAKE_CASE							,   Image.Image						)

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

      UpperCamelCase__				,     UpperCamelCase__			:   Optional[Any]       				=  self.image_processing_tester.get_expected_values(SCREAMING_SNAKE_CASE						)

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

      # Test batched
      UpperCamelCase__				,     UpperCamelCase__			:   Dict       				=  self.image_processing_tester.get_expected_values(SCREAMING_SNAKE_CASE							,   batched=SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   Dict       				=  image_processor(
          SCREAMING_SNAKE_CASE							,   ["semantic"] * len(SCREAMING_SNAKE_CASE						)							,   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 					__lowercase					(		self					:   int						):


      '''simple docstring'''






      UpperCamelCase__			:   Optional[Any]       				=  self.image_processing_class(**self.image_processor_dict						)
      # create random numpy tensors
      UpperCamelCase__			:   Union[str, Any]       				=  prepare_image_inputs(self.image_processing_tester							,   equal_resolution=SCREAMING_SNAKE_CASE							,   numpify=SCREAMING_SNAKE_CASE						)
      for image in image_inputs:
           self.assertIsInstance(SCREAMING_SNAKE_CASE							,   np.ndarray						)

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

      UpperCamelCase__				,     UpperCamelCase__			:   Union[str, Any]       				=  self.image_processing_tester.get_expected_values(SCREAMING_SNAKE_CASE						)

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

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

      UpperCamelCase__			:   Union[str, Any]       				=  image_processor(
          SCREAMING_SNAKE_CASE							,   ["semantic"] * len(SCREAMING_SNAKE_CASE						)							,   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 					__lowercase					(		self					:   Dict						):


      '''simple docstring'''






      UpperCamelCase__			:   Tuple       				=  self.image_processing_class(**self.image_processor_dict						)
      # create random PyTorch tensors
      UpperCamelCase__			:   Optional[int]       				=  prepare_image_inputs(self.image_processing_tester							,   equal_resolution=SCREAMING_SNAKE_CASE							,   torchify=SCREAMING_SNAKE_CASE						)
      for image in image_inputs:
           self.assertIsInstance(SCREAMING_SNAKE_CASE							,   torch.Tensor						)

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

      UpperCamelCase__				,     UpperCamelCase__			:   Tuple       				=  self.image_processing_tester.get_expected_values(SCREAMING_SNAKE_CASE						)

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

      # Test batched
      UpperCamelCase__				,     UpperCamelCase__			:   List[Any]       				=  self.image_processing_tester.get_expected_values(SCREAMING_SNAKE_CASE							,   batched=SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   Any       				=  image_processor(
          SCREAMING_SNAKE_CASE							,   ["semantic"] * len(SCREAMING_SNAKE_CASE						)							,   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 					__lowercase					(		self					:   int							,   SCREAMING_SNAKE_CASE					:   Optional[int]=False							,   SCREAMING_SNAKE_CASE					:   Optional[int]=False							,   SCREAMING_SNAKE_CASE					:   Any="np"						):


      '''simple docstring'''






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

      UpperCamelCase__			:   Optional[int]       				=  image_processor(
          SCREAMING_SNAKE_CASE							,   ["semantic"] * len(SCREAMING_SNAKE_CASE						)							,   SCREAMING_SNAKE_CASE							,   return_tensors="pt"							,   instance_id_to_semantic_id=SCREAMING_SNAKE_CASE							,   pad_and_return_pixel_mask=SCREAMING_SNAKE_CASE							,   )

      return inputs







 def 					__lowercase					(		self					:   int						):


      '''simple docstring'''






      pass







 def 					__lowercase					(		self					:   str						):


      '''simple docstring'''






      def common(SCREAMING_SNAKE_CASE					:   Optional[int]=False							,   SCREAMING_SNAKE_CASE					:   str=None						):
           UpperCamelCase__			:   Any       				=  self.comm_get_image_processor_inputs(
               with_segmentation_maps=SCREAMING_SNAKE_CASE							,   is_instance_map=SCREAMING_SNAKE_CASE							,   segmentation_type=SCREAMING_SNAKE_CASE						)

           UpperCamelCase__			:   str       				=  inputs["mask_labels"]
           UpperCamelCase__			:   Optional[Any]       				=  inputs["class_labels"]
           UpperCamelCase__			:   List[str]       				=  inputs["pixel_values"]
           UpperCamelCase__			:   int       				=  inputs["text_inputs"]

           # check the batch_size
           for mask_label, class_label, text_input in zip(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						):
                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(SCREAMING_SNAKE_CASE						)							,   self.image_processing_tester.num_text						)

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







 def 					__lowercase					(		self					:   List[str]						):


      '''simple docstring'''






      UpperCamelCase__			:   Optional[int]       				=  np.zeros((20, 50)						)
      UpperCamelCase__			:   int       				=  1
      UpperCamelCase__			:   Dict       				=  1
      UpperCamelCase__			:   Dict       				=  1

      UpperCamelCase__			:   int       				=  binary_mask_to_rle(SCREAMING_SNAKE_CASE						)
      self.assertEqual(len(SCREAMING_SNAKE_CASE						)							,   4						)
      self.assertEqual(rle[0]							,   21						)
      self.assertEqual(rle[1]							,   45						)







 def 					__lowercase					(		self					:   Tuple						):


      '''simple docstring'''






      UpperCamelCase__			:   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"							,   )
      UpperCamelCase__			:   int       				=  self.image_processing_tester.get_fake_oneformer_outputs()

      UpperCamelCase__			:   Union[str, Any]       				=  fature_extractor.post_process_semantic_segmentation(SCREAMING_SNAKE_CASE						)

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

      UpperCamelCase__			:   List[str]       				=  [(1, 4) for i in range(self.image_processing_tester.batch_size						)]
      UpperCamelCase__			:   Optional[Any]       				=  fature_extractor.post_process_semantic_segmentation(SCREAMING_SNAKE_CASE							,   target_sizes=SCREAMING_SNAKE_CASE						)

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







 def 					__lowercase					(		self					:   str						):


      '''simple docstring'''






      UpperCamelCase__			:   Tuple       				=  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"							,   )
      UpperCamelCase__			:   List[str]       				=  self.image_processing_tester.get_fake_oneformer_outputs()
      UpperCamelCase__			:   Optional[int]       				=  image_processor.post_process_instance_segmentation(SCREAMING_SNAKE_CASE							,   threshold=0						)

      self.assertTrue(len(SCREAMING_SNAKE_CASE						) == 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"]						)							,   SCREAMING_SNAKE_CASE						)
           self.assertEqual(
               el["segmentation"].shape							,   (self.image_processing_tester.height, self.image_processing_tester.width)						)







 def 					__lowercase					(		self					:   Dict						):


      '''simple docstring'''






      UpperCamelCase__			:   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"							,   )
      UpperCamelCase__			:   Any       				=  self.image_processing_tester.get_fake_oneformer_outputs()
      UpperCamelCase__			:   Tuple       				=  image_processor.post_process_panoptic_segmentation(SCREAMING_SNAKE_CASE							,   threshold=0						)

      self.assertTrue(len(SCREAMING_SNAKE_CASE						) == 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"]						)							,   SCREAMING_SNAKE_CASE						)
           self.assertEqual(
               el["segmentation"].shape							,   (self.image_processing_tester.height, self.image_processing_tester.width)						)