





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



class 				_UpperCAmelCase    (    unittest.TestCase    ):

  """simple docstring"""


  def __init__(				self :		List[Any]					,  lowerCAmelCase_ :		int					,  lowerCAmelCase_ :		Tuple=7					,  lowerCAmelCase_ :		List[str]=3					,  lowerCAmelCase_ :		Optional[Any]=3_0					,  lowerCAmelCase_ :		Optional[Any]=4_0_0					,  lowerCAmelCase_ :		List[str]=None					,  lowerCAmelCase_ :		Optional[int]=True					,  lowerCAmelCase_ :		List[Any]=True					,  lowerCAmelCase_ :		List[str]=[0.5, 0.5, 0.5]					,  lowerCAmelCase_ :		Dict=[0.5, 0.5, 0.5]					,  lowerCAmelCase_ :		Optional[Any]=1_0					,  lowerCAmelCase_ :		Optional[int]=False					,  lowerCAmelCase_ :		Optional[Any]=2_5_5					,  lowerCAmelCase_ :		List[str]="shi-labs/oneformer_demo"					,  lowerCAmelCase_ :		Tuple="ade20k_panoptic.json"					,  lowerCAmelCase_ :		List[Any]=1_0					,  )							->						Dict:
     __lowerCAmelCase								=	parent
     __lowerCAmelCase								=	batch_size
     __lowerCAmelCase								=	num_channels
     __lowerCAmelCase								=	min_resolution
     __lowerCAmelCase								=	max_resolution
     __lowerCAmelCase								=	do_resize
     __lowerCAmelCase								=	{'shortest_edge': 3_2, 'longest_edge': 1_3_3_3} if size is None else size
     __lowerCAmelCase								=	do_normalize
     __lowerCAmelCase								=	image_mean
     __lowerCAmelCase								=	image_std
     __lowerCAmelCase								=	class_info_file
     __lowerCAmelCase								=	prepare_metadata(lowerCAmelCase_					,  lowerCAmelCase_     )
     __lowerCAmelCase								=	num_text
     __lowerCAmelCase								=	repo_path

     # for the post_process_functions
     __lowerCAmelCase								=	2
     __lowerCAmelCase								=	1_0
     __lowerCAmelCase								=	1_0
     __lowerCAmelCase								=	3
     __lowerCAmelCase								=	4
     __lowerCAmelCase								=	num_labels
     __lowerCAmelCase								=	do_reduce_labels
     __lowerCAmelCase								=	ignore_index






  def 				lowercase		(				self :		Dict     )							->						Optional[int]:
     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 :		Optional[Any]					,  lowerCAmelCase_ :		str					,  lowerCAmelCase_ :		Optional[Any]=False     )							->						List[str]:
     if not batched:
        __lowerCAmelCase								=	image_inputs[0]
        if isinstance(lowerCAmelCase_					,  Image.Image     ):
           __lowerCAmelCase	,	__lowerCAmelCase								=	image.size
        else:
           __lowerCAmelCase	,	__lowerCAmelCase								=	image.shape[1], image.shape[2]
        if w < h:
           __lowerCAmelCase								=	int(self.size['shortest_edge'] * h / w     )
           __lowerCAmelCase								=	self.size['shortest_edge']
        elif w > h:
           __lowerCAmelCase								=	self.size['shortest_edge']
           __lowerCAmelCase								=	int(self.size['shortest_edge'] * w / h     )
        else:
           __lowerCAmelCase								=	self.size['shortest_edge']
           __lowerCAmelCase								=	self.size['shortest_edge']

     else:
        __lowerCAmelCase								=	[]
        for image in image_inputs:
           __lowerCAmelCase	,	__lowerCAmelCase								=	self.get_expected_values([image]     )
           expected_values.append((expected_height, expected_width)     )
        __lowerCAmelCase								=	max(lowerCAmelCase_					,  key=lambda lowerCAmelCase_     : item[0]     )[0]
        __lowerCAmelCase								=	max(lowerCAmelCase_					,  key=lambda lowerCAmelCase_     : item[1]     )[1]

     return expected_height, expected_width






  def 				lowercase		(				self :		Union[str, Any]     )							->						Dict:
     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 				_UpperCAmelCase    (    _UpperCamelCase		,    unittest.TestCase    ):

  """simple docstring"""


  a_        = OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
  # only for test_image_processing_common.test_image_proc_to_json_string
  a_        = image_processing_class






  def 				lowercase		(				self :		int     )							->						List[Any]:
     __lowerCAmelCase								=	OneFormerImageProcessorTester(self     )






  @property
  def 				lowercase		(				self :		Any     )							->						Union[str, Any]:
     return self.image_processing_tester.prepare_image_processor_dict()






  def 				lowercase		(				self :		Any     )							->						Dict:
     __lowerCAmelCase								=	self.image_processing_class(**self.image_processor_dict     )
     self.assertTrue(hasattr(lowerCAmelCase_					,  'image_mean'     )     )
     self.assertTrue(hasattr(lowerCAmelCase_					,  'image_std'     )     )
     self.assertTrue(hasattr(lowerCAmelCase_					,  'do_normalize'     )     )
     self.assertTrue(hasattr(lowerCAmelCase_					,  'do_resize'     )     )
     self.assertTrue(hasattr(lowerCAmelCase_					,  'size'     )     )
     self.assertTrue(hasattr(lowerCAmelCase_					,  'ignore_index'     )     )
     self.assertTrue(hasattr(lowerCAmelCase_					,  'class_info_file'     )     )
     self.assertTrue(hasattr(lowerCAmelCase_					,  'num_text'     )     )
     self.assertTrue(hasattr(lowerCAmelCase_					,  'repo_path'     )     )
     self.assertTrue(hasattr(lowerCAmelCase_					,  'metadata'     )     )
     self.assertTrue(hasattr(lowerCAmelCase_					,  'do_reduce_labels'     )     )






  def 				lowercase		(				self :		str     )							->						Tuple:
     pass






  def 				lowercase		(				self :		Dict     )							->						List[Any]:
     # Initialize image_processor
     __lowerCAmelCase								=	self.image_processing_class(**self.image_processor_dict     )
     # create random PIL images
     __lowerCAmelCase								=	prepare_image_inputs(self.image_processing_tester					,  equal_resolution=lowerCAmelCase_     )
     for image in image_inputs:
        self.assertIsInstance(lowerCAmelCase_					,  Image.Image     )

     # Test not batched input
     __lowerCAmelCase								=	image_processor(image_inputs[0]					,  ['semantic']					,  return_tensors='pt'     ).pixel_values

     __lowerCAmelCase	,	__lowerCAmelCase								=	self.image_processing_tester.get_expected_values(lowerCAmelCase_     )

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

     # Test batched
     __lowerCAmelCase	,	__lowerCAmelCase								=	self.image_processing_tester.get_expected_values(lowerCAmelCase_					,  batched=lowerCAmelCase_     )

     __lowerCAmelCase								=	image_processor(
         lowerCAmelCase_					,  ['semantic'] * len(lowerCAmelCase_     )					,  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 :		Optional[Any]     )							->						Optional[Any]:
     # Initialize image_processor
     __lowerCAmelCase								=	self.image_processing_class(**self.image_processor_dict     )
     # create random numpy tensors
     __lowerCAmelCase								=	prepare_image_inputs(self.image_processing_tester					,  equal_resolution=lowerCAmelCase_					,  numpify=lowerCAmelCase_     )
     for image in image_inputs:
        self.assertIsInstance(lowerCAmelCase_					,  np.ndarray     )

     # Test not batched input
     __lowerCAmelCase								=	image_processor(image_inputs[0]					,  ['semantic']					,  return_tensors='pt'     ).pixel_values

     __lowerCAmelCase	,	__lowerCAmelCase								=	self.image_processing_tester.get_expected_values(lowerCAmelCase_     )

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

     # Test batched
     __lowerCAmelCase	,	__lowerCAmelCase								=	self.image_processing_tester.get_expected_values(lowerCAmelCase_					,  batched=lowerCAmelCase_     )

     __lowerCAmelCase								=	image_processor(
         lowerCAmelCase_					,  ['semantic'] * len(lowerCAmelCase_     )					,  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 :		Any     )							->						Dict:
     # Initialize image_processor
     __lowerCAmelCase								=	self.image_processing_class(**self.image_processor_dict     )
     # create random PyTorch tensors
     __lowerCAmelCase								=	prepare_image_inputs(self.image_processing_tester					,  equal_resolution=lowerCAmelCase_					,  torchify=lowerCAmelCase_     )
     for image in image_inputs:
        self.assertIsInstance(lowerCAmelCase_					,  torch.Tensor     )

     # Test not batched input
     __lowerCAmelCase								=	image_processor(image_inputs[0]					,  ['semantic']					,  return_tensors='pt'     ).pixel_values

     __lowerCAmelCase	,	__lowerCAmelCase								=	self.image_processing_tester.get_expected_values(lowerCAmelCase_     )

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

     # Test batched
     __lowerCAmelCase	,	__lowerCAmelCase								=	self.image_processing_tester.get_expected_values(lowerCAmelCase_					,  batched=lowerCAmelCase_     )

     __lowerCAmelCase								=	image_processor(
         lowerCAmelCase_					,  ['semantic'] * len(lowerCAmelCase_     )					,  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 :		List[Any]					,  lowerCAmelCase_ :		Union[str, Any]=False					,  lowerCAmelCase_ :		int=False					,  lowerCAmelCase_ :		str="np"     )							->						List[str]:
     __lowerCAmelCase								=	self.image_processing_class(**self.image_processor_dict     )
     # prepare image and target
     __lowerCAmelCase								=	self.image_processing_tester.num_labels
     __lowerCAmelCase								=	None
     __lowerCAmelCase								=	None
     __lowerCAmelCase								=	prepare_image_inputs(self.image_processing_tester					,  equal_resolution=lowerCAmelCase_     )
     if with_segmentation_maps:
        __lowerCAmelCase								=	num_labels
        if is_instance_map:
           __lowerCAmelCase								=	list(range(lowerCAmelCase_     )     ) * 2
           __lowerCAmelCase								=	dict(enumerate(lowerCAmelCase_     )     )
        __lowerCAmelCase								=	[
            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								=	[Image.fromarray(lowerCAmelCase_     ) for annotation in annotations]

     __lowerCAmelCase								=	image_processor(
         lowerCAmelCase_					,  ['semantic'] * len(lowerCAmelCase_     )					,  lowerCAmelCase_					,  return_tensors='pt'					,  instance_id_to_semantic_id=lowerCAmelCase_					,  pad_and_return_pixel_mask=lowerCAmelCase_					,  )

     return inputs






  def 				lowercase		(				self :		str     )							->						Tuple:
     pass






  def 				lowercase		(				self :		Optional[int]     )							->						Any:
     def common(lowerCAmelCase_ :		List[Any]=False					,  lowerCAmelCase_ :		Union[str, Any]=None     ):
        __lowerCAmelCase								=	self.comm_get_image_processor_inputs(
            with_segmentation_maps=lowerCAmelCase_					,  is_instance_map=lowerCAmelCase_					,  segmentation_type=lowerCAmelCase_     )

        __lowerCAmelCase								=	inputs['mask_labels']
        __lowerCAmelCase								=	inputs['class_labels']
        __lowerCAmelCase								=	inputs['pixel_values']
        __lowerCAmelCase								=	inputs['text_inputs']

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

     common()
     common(is_instance_map=lowerCAmelCase_     )
     common(is_instance_map=lowerCAmelCase_					,  segmentation_type='pil'     )
     common(is_instance_map=lowerCAmelCase_					,  segmentation_type='pil'     )






  def 				lowercase		(				self :		List[str]     )							->						Any:
     __lowerCAmelCase								=	np.zeros((2_0, 5_0)     )
     __lowerCAmelCase								=	1
     __lowerCAmelCase								=	1
     __lowerCAmelCase								=	1

     __lowerCAmelCase								=	binary_mask_to_rle(lowerCAmelCase_     )
     self.assertEqual(len(lowerCAmelCase_     )					,  4     )
     self.assertEqual(rle[0]					,  2_1     )
     self.assertEqual(rle[1]					,  4_5     )






  def 				lowercase		(				self :		Any     )							->						str:
     __lowerCAmelCase								=	self.image_processing_class(
         num_labels=self.image_processing_tester.num_classes					,  max_seq_length=7_7					,  task_seq_length=7_7					,  class_info_file='ade20k_panoptic.json'					,  num_text=self.image_processing_tester.num_text					,  repo_path='shi-labs/oneformer_demo'					,  )
     __lowerCAmelCase								=	self.image_processing_tester.get_fake_oneformer_outputs()

     __lowerCAmelCase								=	fature_extractor.post_process_semantic_segmentation(lowerCAmelCase_     )

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

     __lowerCAmelCase								=	[(1, 4) for i in range(self.image_processing_tester.batch_size     )]
     __lowerCAmelCase								=	fature_extractor.post_process_semantic_segmentation(lowerCAmelCase_					,  target_sizes=lowerCAmelCase_     )

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






  def 				lowercase		(				self :		Dict     )							->						Union[str, Any]:
     __lowerCAmelCase								=	self.image_processing_class(
         num_labels=self.image_processing_tester.num_classes					,  max_seq_length=7_7					,  task_seq_length=7_7					,  class_info_file='ade20k_panoptic.json'					,  num_text=self.image_processing_tester.num_text					,  repo_path='shi-labs/oneformer_demo'					,  )
     __lowerCAmelCase								=	self.image_processing_tester.get_fake_oneformer_outputs()
     __lowerCAmelCase								=	image_processor.post_process_instance_segmentation(lowerCAmelCase_					,  threshold=0     )

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






  def 				lowercase		(				self :		Optional[Any]     )							->						Tuple:
     __lowerCAmelCase								=	self.image_processing_class(
         num_labels=self.image_processing_tester.num_classes					,  max_seq_length=7_7					,  task_seq_length=7_7					,  class_info_file='ade20k_panoptic.json'					,  num_text=self.image_processing_tester.num_text					,  repo_path='shi-labs/oneformer_demo'					,  )
     __lowerCAmelCase								=	self.image_processing_tester.get_fake_oneformer_outputs()
     __lowerCAmelCase								=	image_processor.post_process_panoptic_segmentation(lowerCAmelCase_					,  threshold=0     )

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








from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_snake_case  :    List[Any]						    =  logging.get_logger(__name__)

_snake_case  :    List[Any]						    =  {
    'microsoft/beit-base-patch16-224-pt22k': (
        'https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json'
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}



class 				_UpperCAmelCase    (    _UpperCamelCase    ):

  """simple docstring"""


  a_        = """beit"""






  def __init__(				self :		List[Any]					,  lowerCAmelCase_ :		Tuple=8_1_9_2					,  lowerCAmelCase_ :		Optional[int]=7_6_8					,  lowerCAmelCase_ :		int=1_2					,  lowerCAmelCase_ :		Optional[int]=1_2					,  lowerCAmelCase_ :		Any=3_0_7_2					,  lowerCAmelCase_ :		Optional[int]="gelu"					,  lowerCAmelCase_ :		Any=0.0					,  lowerCAmelCase_ :		Any=0.0					,  lowerCAmelCase_ :		Any=0.02					,  lowerCAmelCase_ :		int=1e-12					,  lowerCAmelCase_ :		int=2_2_4					,  lowerCAmelCase_ :		str=1_6					,  lowerCAmelCase_ :		int=3					,  lowerCAmelCase_ :		Dict=False					,  lowerCAmelCase_ :		int=False					,  lowerCAmelCase_ :		List[Any]=False					,  lowerCAmelCase_ :		int=False					,  lowerCAmelCase_ :		List[str]=0.1					,  lowerCAmelCase_ :		Union[str, Any]=0.1					,  lowerCAmelCase_ :		List[str]=True					,  lowerCAmelCase_ :		List[Any]=[3, 5, 7, 1_1]					,  lowerCAmelCase_ :		Optional[Any]=[1, 2, 3, 6]					,  lowerCAmelCase_ :		Tuple=True					,  lowerCAmelCase_ :		Dict=0.4					,  lowerCAmelCase_ :		Tuple=2_5_6					,  lowerCAmelCase_ :		Any=1					,  lowerCAmelCase_ :		Any=False					,  lowerCAmelCase_ :		Optional[int]=2_5_5					,  **lowerCAmelCase_ :		Any					,  )							->						Dict:
     super().__init__(**lowerCAmelCase_     )

     __lowerCAmelCase								=	vocab_size
     __lowerCAmelCase								=	hidden_size
     __lowerCAmelCase								=	num_hidden_layers
     __lowerCAmelCase								=	num_attention_heads
     __lowerCAmelCase								=	intermediate_size
     __lowerCAmelCase								=	hidden_act
     __lowerCAmelCase								=	hidden_dropout_prob
     __lowerCAmelCase								=	attention_probs_dropout_prob
     __lowerCAmelCase								=	initializer_range
     __lowerCAmelCase								=	layer_norm_eps

     __lowerCAmelCase								=	image_size
     __lowerCAmelCase								=	patch_size
     __lowerCAmelCase								=	num_channels
     __lowerCAmelCase								=	use_mask_token
     __lowerCAmelCase								=	use_absolute_position_embeddings
     __lowerCAmelCase								=	use_relative_position_bias
     __lowerCAmelCase								=	use_shared_relative_position_bias
     __lowerCAmelCase								=	layer_scale_init_value
     __lowerCAmelCase								=	drop_path_rate
     __lowerCAmelCase								=	use_mean_pooling
     # decode head attributes (semantic segmentation)
     __lowerCAmelCase								=	out_indices
     __lowerCAmelCase								=	pool_scales
     # auxiliary head attributes (semantic segmentation)
     __lowerCAmelCase								=	use_auxiliary_head
     __lowerCAmelCase								=	auxiliary_loss_weight
     __lowerCAmelCase								=	auxiliary_channels
     __lowerCAmelCase								=	auxiliary_num_convs
     __lowerCAmelCase								=	auxiliary_concat_input
     __lowerCAmelCase								=	semantic_loss_ignore_index





class 				_UpperCAmelCase    (    _UpperCamelCase    ):

  """simple docstring"""


  a_        = version.parse("""1.11"""    )






  @property
  def 				lowercase		(				self :		Tuple     )							->						Mapping[str, Mapping[int, str]]:
     return OrderedDict(
         [
             ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
         ]     )






  @property
  def 				lowercase		(				self :		Optional[Any]     )							->						float:
     return 1e-4

