



"""simple docstring"""




import copy
import unittest

from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import (
     MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
     MODEL_FOR_QUESTION_ANSWERING_MAPPING,
     MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
     MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
     LayoutLMvaConfig,
     LayoutLMvaForQuestionAnswering,
     LayoutLMvaForSequenceClassification,
     LayoutLMvaForTokenClassification,
     LayoutLMvaModel,
 )
 from transformers.models.layoutlmva.modeling_layoutlmva import LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
 from PIL import Image

 from transformers import LayoutLMvaImageProcessor






class       UpperCAmelCase_    :



      def __init__(	self	:      Any	,      A	:      int	,      A	:      List[Any]=2	,      A	:      Dict=3	,      A	:      Any=4	,      A	:      Any=2	,      A	:      Optional[Any]=7	,      A	:      Dict=True	,      A	:      List[Any]=True	,      A	:      Optional[int]=True	,      A	:      List[Any]=True	,      A	:      int=9_9	,      A	:      Optional[int]=3_6	,      A	:      Union[str, Any]=3	,      A	:      str=4	,      A	:      Optional[Any]=3_7	,      A	:      Optional[Any]="gelu"	,      A	:      Optional[Any]=0.1	,      A	:      List[Any]=0.1	,      A	:      Union[str, Any]=5_1_2	,      A	:      Any=1_6	,      A	:      str=2	,      A	:      List[str]=0.02	,      A	:      Optional[int]=6	,      A	:      Union[str, Any]=6	,      A	:      Dict=3	,      A	:      List[Any]=4	,      A	:      List[Any]=None	,      A	:      Dict=1_0_0_0	,      ):
           _UpperCAmelCase     :      int														=       parent
           _UpperCAmelCase     :      Union[str, Any]														=       batch_size
           _UpperCAmelCase     :      Tuple														=       num_channels
           _UpperCAmelCase     :      Optional[int]														=       image_size
           _UpperCAmelCase     :      Optional[int]														=       patch_size
           _UpperCAmelCase     :      int														=       text_seq_length
           _UpperCAmelCase     :      List[Any]														=       is_training
           _UpperCAmelCase     :      Optional[int]														=       use_input_mask
           _UpperCAmelCase     :      Dict														=       use_token_type_ids
           _UpperCAmelCase     :      Union[str, Any]														=       use_labels
           _UpperCAmelCase     :      Any														=       vocab_size
           _UpperCAmelCase     :      List[Any]														=       hidden_size
           _UpperCAmelCase     :      Tuple														=       num_hidden_layers
           _UpperCAmelCase     :      List[str]														=       num_attention_heads
           _UpperCAmelCase     :      Dict														=       intermediate_size
           _UpperCAmelCase     :      Any														=       hidden_act
           _UpperCAmelCase     :      str														=       hidden_dropout_prob
           _UpperCAmelCase     :      str														=       attention_probs_dropout_prob
           _UpperCAmelCase     :      Union[str, Any]														=       max_position_embeddings
           _UpperCAmelCase     :      Dict														=       type_vocab_size
           _UpperCAmelCase     :      List[Any]														=       type_sequence_label_size
           _UpperCAmelCase     :      Union[str, Any]														=       initializer_range
           _UpperCAmelCase     :      Union[str, Any]														=       coordinate_size
           _UpperCAmelCase     :      Dict														=       shape_size
           _UpperCAmelCase     :      Any														=       num_labels
           _UpperCAmelCase     :      str														=       num_choices
           _UpperCAmelCase     :      Dict														=       scope
           _UpperCAmelCase     :      str														=       range_bbox

           # LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
           _UpperCAmelCase     :      Tuple														=       text_seq_length
           _UpperCAmelCase     :      str														=       (image_size // patch_size) ** 2 + 1
           _UpperCAmelCase     :      List[Any]														=       self.text_seq_length + self.image_seq_length



      def 		snake_case_   (	self	:      Optional[int]		):
           _UpperCAmelCase     :      Dict														=       ids_tensor([self.batch_size, self.text_seq_length]	,      self.vocab_size		)

           _UpperCAmelCase     :      Union[str, Any]														=       ids_tensor([self.batch_size, self.text_seq_length, 4]	,      self.range_bbox		)
           # Ensure that bbox is legal
           for i in range(bbox.shape[0]		):
                for j in range(bbox.shape[1]		):
                     if bbox[i, j, 3] < bbox[i, j, 1]:
                          _UpperCAmelCase     :      List[Any]														=       bbox[i, j, 3]
                          _UpperCAmelCase     :      Any														=       bbox[i, j, 1]
                          _UpperCAmelCase     :      Tuple														=       t
                     if bbox[i, j, 2] < bbox[i, j, 0]:
                          _UpperCAmelCase     :      str														=       bbox[i, j, 2]
                          _UpperCAmelCase     :      Optional[Any]														=       bbox[i, j, 0]
                          _UpperCAmelCase     :      Union[str, Any]														=       t

           _UpperCAmelCase     :      Optional[int]														=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

           _UpperCAmelCase     :      List[str]														=       None
           if self.use_input_mask:
                _UpperCAmelCase     :      Dict														=       random_attention_mask([self.batch_size, self.text_seq_length]		)

           _UpperCAmelCase     :      str														=       None
           if self.use_token_type_ids:
                _UpperCAmelCase     :      Optional[Any]														=       ids_tensor([self.batch_size, self.text_seq_length]	,      self.type_vocab_size		)

           _UpperCAmelCase     :      Tuple														=       None
           _UpperCAmelCase     :      Union[str, Any]														=       None
           if self.use_labels:
                _UpperCAmelCase     :      Optional[Any]														=       ids_tensor([self.batch_size]	,      self.type_sequence_label_size		)
                _UpperCAmelCase     :      Dict														=       ids_tensor([self.batch_size, self.text_seq_length]	,      self.num_labels		)

           _UpperCAmelCase     :      List[str]														=       LayoutLMvaConfig(
               vocab_size=self.vocab_size	,      hidden_size=self.hidden_size	,      num_hidden_layers=self.num_hidden_layers	,      num_attention_heads=self.num_attention_heads	,      intermediate_size=self.intermediate_size	,      hidden_act=self.hidden_act	,      hidden_dropout_prob=self.hidden_dropout_prob	,      attention_probs_dropout_prob=self.attention_probs_dropout_prob	,      max_position_embeddings=self.max_position_embeddings	,      type_vocab_size=self.type_vocab_size	,      initializer_range=self.initializer_range	,      coordinate_size=self.coordinate_size	,      shape_size=self.shape_size	,      input_size=self.image_size	,      patch_size=self.patch_size	,      )

           return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels



      def 		snake_case_   (	self	:      Any	,      A	:      Optional[int]	,      A	:      List[str]	,      A	:      List[str]	,      A	:      Optional[Any]	,      A	:      Any	,      A	:      List[Any]	,      A	:      List[Any]	,      A	:      Any		):
           _UpperCAmelCase     :      Dict														=       LayoutLMvaModel(config=A		)
           model.to(A		)
           model.eval()

           # text + image
           _UpperCAmelCase     :      Union[str, Any]														=       model(A	,      pixel_values=A		)
           _UpperCAmelCase     :      Dict														=       model(
               A	,      bbox=A	,      pixel_values=A	,      attention_mask=A	,      token_type_ids=A		)
           _UpperCAmelCase     :      str														=       model(A	,      bbox=A	,      pixel_values=A	,      token_type_ids=A		)
           _UpperCAmelCase     :      List[str]														=       model(A	,      bbox=A	,      pixel_values=A		)

           self.parent.assertEqual(result.last_hidden_state.shape	,      (self.batch_size, self.seq_length, self.hidden_size)		)

           # text only
           _UpperCAmelCase     :      List[Any]														=       model(A		)
           self.parent.assertEqual(
               result.last_hidden_state.shape	,      (self.batch_size, self.text_seq_length, self.hidden_size)		)

           # image only
           _UpperCAmelCase     :      Tuple														=       model(pixel_values=A		)
           self.parent.assertEqual(
               result.last_hidden_state.shape	,      (self.batch_size, self.image_seq_length, self.hidden_size)		)



      def 		snake_case_   (	self	:      Union[str, Any]	,      A	:      Union[str, Any]	,      A	:      List[str]	,      A	:      Dict	,      A	:      Any	,      A	:      Union[str, Any]	,      A	:      int	,      A	:      Dict	,      A	:      Optional[Any]		):
           _UpperCAmelCase     :      Dict														=       self.num_labels
           _UpperCAmelCase     :      Union[str, Any]														=       LayoutLMvaForSequenceClassification(A		)
           model.to(A		)
           model.eval()
           _UpperCAmelCase     :      Any														=       model(
               A	,      bbox=A	,      pixel_values=A	,      attention_mask=A	,      token_type_ids=A	,      labels=A	,      )
           self.parent.assertEqual(result.logits.shape	,      (self.batch_size, self.num_labels)		)



      def 		snake_case_   (	self	:      List[Any]	,      A	:      Union[str, Any]	,      A	:      Optional[Any]	,      A	:      Optional[int]	,      A	:      List[Any]	,      A	:      List[str]	,      A	:      List[Any]	,      A	:      Optional[Any]	,      A	:      Any		):
           _UpperCAmelCase     :      Any														=       self.num_labels
           _UpperCAmelCase     :      List[Any]														=       LayoutLMvaForTokenClassification(config=A		)
           model.to(A		)
           model.eval()
           _UpperCAmelCase     :      Optional[Any]														=       model(
               A	,      bbox=A	,      pixel_values=A	,      attention_mask=A	,      token_type_ids=A	,      labels=A	,      )
           self.parent.assertEqual(result.logits.shape	,      (self.batch_size, self.text_seq_length, self.num_labels)		)



      def 		snake_case_   (	self	:      Union[str, Any]	,      A	:      List[str]	,      A	:      Union[str, Any]	,      A	:      int	,      A	:      Optional[Any]	,      A	:      List[Any]	,      A	:      Optional[int]	,      A	:      str	,      A	:      Dict		):
           _UpperCAmelCase     :      Tuple														=       LayoutLMvaForQuestionAnswering(config=A		)
           model.to(A		)
           model.eval()
           _UpperCAmelCase     :      Union[str, Any]														=       model(
               A	,      bbox=A	,      pixel_values=A	,      attention_mask=A	,      token_type_ids=A	,      start_positions=A	,      end_positions=A	,      )
           self.parent.assertEqual(result.start_logits.shape	,      (self.batch_size, self.seq_length)		)
           self.parent.assertEqual(result.end_logits.shape	,      (self.batch_size, self.seq_length)		)



      def 		snake_case_   (	self	:      Dict		):
           _UpperCAmelCase     :      Tuple														=       self.prepare_config_and_inputs()
           (
               (
               _UpperCAmelCase
           )							,			(
               _UpperCAmelCase
           )							,			(
               _UpperCAmelCase
           )							,			(
               _UpperCAmelCase
           )							,			(
               _UpperCAmelCase
           )							,			(
               _UpperCAmelCase
           )							,			(
               _UpperCAmelCase
           )							,			(
               _UpperCAmelCase
           )							,			
           )     :      Union[str, Any]														=       config_and_inputs
           _UpperCAmelCase     :      Optional[Any]														=       {
               "input_ids": input_ids,
               "bbox": bbox,
               "pixel_values": pixel_values,
               "token_type_ids": token_type_ids,
               "attention_mask": input_mask,
           }
           return config, inputs_dict







@require_torch
class       UpperCAmelCase_    (							_UpperCamelCase			,     _UpperCamelCase			,     unittest.TestCase		):
      __SCREAMING_SNAKE_CASE		:  Tuple						=					False
      __SCREAMING_SNAKE_CASE		:  Union[str, Any]						=					False
      __SCREAMING_SNAKE_CASE		:  str						=					False

      __SCREAMING_SNAKE_CASE		:  Union[str, Any]						=					(
          (
              LayoutLMvaModel,
              LayoutLMvaForSequenceClassification,
              LayoutLMvaForTokenClassification,
              LayoutLMvaForQuestionAnswering,
          )
          if is_torch_available()
          else ()
      )
      __SCREAMING_SNAKE_CASE		:  int						=					(
          {'document-question-answering': LayoutLMvaForQuestionAnswering, 'feature-extraction': LayoutLMvaModel}
          if is_torch_available()
          else {}
      )



      def 		snake_case_   (	self	:      List[str]	,      A	:      str	,      A	:      List[str]	,      A	:      Optional[int]	,      A	:      Optional[int]	,      A	:      List[str]		):
           # `DocumentQuestionAnsweringPipeline` is expected to work with this model, but it combines the text and visual
           # embedding along the sequence dimension (dim 1), which causes an error during post-processing as `p_mask` has
           # the sequence dimension of the text embedding only.
           # (see the line `embedding_output = torch.cat([embedding_output, visual_embeddings], dim=1)`)
           return True



      def 		snake_case_   (	self	:      int		):
           _UpperCAmelCase     :      Any														=       LayoutLMvaModelTester(self		)
           _UpperCAmelCase     :      str														=       ConfigTester(self	,      config_class=A	,      hidden_size=3_7		)



      def 		snake_case_   (	self	:      Union[str, Any]	,      A	:      Union[str, Any]	,      A	:      int	,      A	:      Tuple=False		):
           _UpperCAmelCase     :      Optional[Any]														=       copy.deepcopy(A		)
           if model_class in get_values(A		):
                _UpperCAmelCase     :      List[str]														=       {
                    k: v.unsqueeze(1		).expand(-1	,      self.model_tester.num_choices	,      -1		).contiguous()
                    if isinstance(A	,      torch.Tensor		) and v.ndim > 1
                    else v
                    for k, v in inputs_dict.items()
                }
           if return_labels:
                if model_class in get_values(A		):
                     _UpperCAmelCase     :      List[Any]														=       torch.ones(self.model_tester.batch_size	,      dtype=torch.long	,      device=A		)
                elif model_class in get_values(A		):
                     _UpperCAmelCase     :      List[str]														=       torch.zeros(
                         self.model_tester.batch_size	,      dtype=torch.long	,      device=A		)
                     _UpperCAmelCase     :      int														=       torch.zeros(
                         self.model_tester.batch_size	,      dtype=torch.long	,      device=A		)
                elif model_class in [
                    *get_values(A		),
                ]:
                     _UpperCAmelCase     :      Tuple														=       torch.zeros(
                         self.model_tester.batch_size	,      dtype=torch.long	,      device=A		)
                elif model_class in [
                    *get_values(A		),
                ]:
                     _UpperCAmelCase     :      int														=       torch.zeros(
                         (self.model_tester.batch_size, self.model_tester.text_seq_length)	,      dtype=torch.long	,      device=A	,      )

           return inputs_dict



      def 		snake_case_   (	self	:      str		):
           self.config_tester.run_common_tests()



      def 		snake_case_   (	self	:      Tuple		):
           _UpperCAmelCase     :      Optional[Any]														=       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*A		)



      def 		snake_case_   (	self	:      str		):
           _UpperCAmelCase     :      Union[str, Any]														=       self.model_tester.prepare_config_and_inputs()
           for type in ["absolute", "relative_key", "relative_key_query"]:
                _UpperCAmelCase     :      Tuple														=       type
                self.model_tester.create_and_check_model(*A		)



      def 		snake_case_   (	self	:      Tuple		):
           _UpperCAmelCase     :      Tuple														=       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_sequence_classification(*A		)



      def 		snake_case_   (	self	:      Optional[int]		):
           _UpperCAmelCase     :      Tuple														=       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_token_classification(*A		)



      def 		snake_case_   (	self	:      Dict		):
           _UpperCAmelCase     :      List[Any]														=       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_question_answering(*A		)



      @slow
      def 		snake_case_   (	self	:      Dict		):
           for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                _UpperCAmelCase     :      str														=       LayoutLMvaModel.from_pretrained(A		)
                self.assertIsNotNone(A		)







def 		__snake_case		( )    ->  List[Any]:





     '''simple docstring'''

     _UpperCAmelCase     :      List[str]														=       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"							)
     return image






@require_torch
class       UpperCAmelCase_    (							unittest.TestCase		):



      @cached_property
      def 		snake_case_   (	self	:      Any		):
           return LayoutLMvaImageProcessor(apply_ocr=A		) if is_vision_available() else None



      @slow
      def 		snake_case_   (	self	:      List[str]		):
           _UpperCAmelCase     :      List[str]														=       LayoutLMvaModel.from_pretrained("microsoft/layoutlmv3-base"		).to(A		)

           _UpperCAmelCase     :      Dict														=       self.default_image_processor
           _UpperCAmelCase     :      List[str]														=       prepare_img()
           _UpperCAmelCase     :      List[str]														=       image_processor(images=A	,      return_tensors="pt"		).pixel_values.to(A		)

           _UpperCAmelCase     :      Optional[Any]														=       torch.tensor([[1, 2]]		)
           _UpperCAmelCase     :      Any														=       torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]]		).unsqueeze(0		)

           # forward pass
           _UpperCAmelCase     :      Union[str, Any]														=       model(
               input_ids=input_ids.to(A		)	,      bbox=bbox.to(A		)	,      pixel_values=pixel_values.to(A		)	,      )

           # verify the logits
           _UpperCAmelCase     :      str														=       torch.Size((1, 1_9_9, 7_6_8)		)
           self.assertEqual(outputs.last_hidden_state.shape	,      A		)

           _UpperCAmelCase     :      Dict														=       torch.tensor(
               [[-0.0_529, 0.3_618, 0.1_632], [-0.1_587, -0.1_667, -0.0_400], [-0.1_557, -0.1_671, -0.0_505]]		).to(A		)

           self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]	,      A	,      atol=1e-4		)		)





"""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 		__snake_case		( SCREAMING_SNAKE_CASE__      :		int		,    SCREAMING_SNAKE_CASE__      :		List[Any]="shi-labs/oneformer_demo"							)    ->  Tuple:





     '''simple docstring'''

     with open(hf_hub_download(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__		,    repo_type="dataset"							)		,    "r"							) as f:
          _UpperCAmelCase     :      Optional[int]														=       json.load(SCREAMING_SNAKE_CASE__							)
     _UpperCAmelCase     :      int														=       {}
     _UpperCAmelCase     :      Union[str, Any]														=       []
     _UpperCAmelCase     :      List[Any]														=       []
     for key, info in class_info.items():
          _UpperCAmelCase     :      List[str]														=       info["name"]
          class_names.append(info["name"]							)
          if info["isthing"]:
               thing_ids.append(int(SCREAMING_SNAKE_CASE__							)							)
     _UpperCAmelCase     :      Tuple														=       thing_ids
     _UpperCAmelCase     :      str														=       class_names
     return metadata






class       UpperCAmelCase_    (							unittest.TestCase		):



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

           # for the post_process_functions
           _UpperCAmelCase     :      str														=       2
           _UpperCAmelCase     :      Any														=       1_0
           _UpperCAmelCase     :      Optional[int]														=       1_0
           _UpperCAmelCase     :      Tuple														=       3
           _UpperCAmelCase     :      List[str]														=       4
           _UpperCAmelCase     :      int														=       num_labels
           _UpperCAmelCase     :      Optional[Any]														=       do_reduce_labels
           _UpperCAmelCase     :      Any														=       ignore_index



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

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

           return expected_height, expected_width



      def 		snake_case_   (	self	:      List[str]		):
           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		):
      __SCREAMING_SNAKE_CASE		:  str						=					OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
      # only for test_image_processing_common.test_image_proc_to_json_string
      __SCREAMING_SNAKE_CASE		:  Tuple						=					image_processing_class



      def 		snake_case_   (	self	:      Tuple		):
           _UpperCAmelCase     :      Optional[Any]														=       OneFormerImageProcessorTester(self		)



      @property
      def 		snake_case_   (	self	:      Optional[Any]		):
           return self.image_processing_tester.prepare_image_processor_dict()



      def 		snake_case_   (	self	:      Union[str, Any]		):
           _UpperCAmelCase     :      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 		snake_case_   (	self	:      List[Any]		):
           pass



      def 		snake_case_   (	self	:      Union[str, Any]		):
           # Initialize image_processor
           _UpperCAmelCase     :      List[str]														=       self.image_processing_class(**self.image_processor_dict		)
           # create random PIL images
           _UpperCAmelCase     :      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
           _UpperCAmelCase     :      Optional[Any]														=       image_processor(image_inputs[0]	,      ["semantic"]	,      return_tensors="pt"		).pixel_values

           _UpperCAmelCase							,			_UpperCAmelCase     :      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
           _UpperCAmelCase							,			_UpperCAmelCase     :      Dict														=       self.image_processing_tester.get_expected_values(A	,      batched=A		)

           _UpperCAmelCase     :      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 		snake_case_   (	self	:      str		):
           # Initialize image_processor
           _UpperCAmelCase     :      Optional[Any]														=       self.image_processing_class(**self.image_processor_dict		)
           # create random numpy tensors
           _UpperCAmelCase     :      Any														=       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
           _UpperCAmelCase     :      int														=       image_processor(image_inputs[0]	,      ["semantic"]	,      return_tensors="pt"		).pixel_values

           _UpperCAmelCase							,			_UpperCAmelCase     :      List[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
           _UpperCAmelCase							,			_UpperCAmelCase     :      int														=       self.image_processing_tester.get_expected_values(A	,      batched=A		)

           _UpperCAmelCase     :      List[str]														=       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 		snake_case_   (	self	:      Optional[int]		):
           # Initialize image_processor
           _UpperCAmelCase     :      Dict														=       self.image_processing_class(**self.image_processor_dict		)
           # create random PyTorch tensors
           _UpperCAmelCase     :      str														=       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
           _UpperCAmelCase     :      Tuple														=       image_processor(image_inputs[0]	,      ["semantic"]	,      return_tensors="pt"		).pixel_values

           _UpperCAmelCase							,			_UpperCAmelCase     :      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
           _UpperCAmelCase							,			_UpperCAmelCase     :      List[str]														=       self.image_processing_tester.get_expected_values(A	,      batched=A		)

           _UpperCAmelCase     :      Optional[int]														=       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 		snake_case_   (	self	:      Optional[int]	,      A	:      Tuple=False	,      A	:      Optional[Any]=False	,      A	:      int="np"		):
           _UpperCAmelCase     :      List[Any]														=       self.image_processing_class(**self.image_processor_dict		)
           # prepare image and target
           _UpperCAmelCase     :      List[str]														=       self.image_processing_tester.num_labels
           _UpperCAmelCase     :      Any														=       None
           _UpperCAmelCase     :      List[Any]														=       None
           _UpperCAmelCase     :      Dict														=       prepare_image_inputs(self.image_processing_tester	,      equal_resolution=A		)
           if with_segmentation_maps:
                _UpperCAmelCase     :      Union[str, Any]														=       num_labels
                if is_instance_map:
                     _UpperCAmelCase     :      Optional[int]														=       list(range(A		)		) * 2
                     _UpperCAmelCase     :      Union[str, Any]														=       dict(enumerate(A		)		)
                _UpperCAmelCase     :      Tuple														=       [
                    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     :      Optional[int]														=       [Image.fromarray(A		) for annotation in annotations]

           _UpperCAmelCase     :      int														=       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 		snake_case_   (	self	:      Any		):
           pass



      def 		snake_case_   (	self	:      Dict		):
           def common(A	:      List[Any]=False	,      A	:      List[str]=None		):
                _UpperCAmelCase     :      str														=       self.comm_get_image_processor_inputs(
                    with_segmentation_maps=A	,      is_instance_map=A	,      segmentation_type=A		)

                _UpperCAmelCase     :      Optional[int]														=       inputs["mask_labels"]
                _UpperCAmelCase     :      str														=       inputs["class_labels"]
                _UpperCAmelCase     :      List[str]														=       inputs["pixel_values"]
                _UpperCAmelCase     :      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 		snake_case_   (	self	:      int		):
           _UpperCAmelCase     :      Optional[Any]														=       np.zeros((2_0, 5_0)		)
           _UpperCAmelCase     :      Dict														=       1
           _UpperCAmelCase     :      Optional[int]														=       1
           _UpperCAmelCase     :      Optional[int]														=       1

           _UpperCAmelCase     :      List[str]														=       binary_mask_to_rle(A		)
           self.assertEqual(len(A		)	,      4		)
           self.assertEqual(rle[0]	,      2_1		)
           self.assertEqual(rle[1]	,      4_5		)



      def 		snake_case_   (	self	:      Optional[Any]		):
           _UpperCAmelCase     :      Optional[Any]														=       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"	,      )
           _UpperCAmelCase     :      int														=       self.image_processing_tester.get_fake_oneformer_outputs()

           _UpperCAmelCase     :      List[str]														=       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,
               )	,      )

           _UpperCAmelCase     :      Tuple														=       [(1, 4) for i in range(self.image_processing_tester.batch_size		)]
           _UpperCAmelCase     :      Optional[Any]														=       fature_extractor.post_process_semantic_segmentation(A	,      target_sizes=A		)

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



      def 		snake_case_   (	self	:      Dict		):
           _UpperCAmelCase     :      Tuple														=       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"	,      )
           _UpperCAmelCase     :      Union[str, Any]														=       self.image_processing_tester.get_fake_oneformer_outputs()
           _UpperCAmelCase     :      Tuple														=       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 		snake_case_   (	self	:      Any		):
           _UpperCAmelCase     :      Optional[int]														=       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"	,      )
           _UpperCAmelCase     :      Optional[Any]														=       self.image_processing_tester.get_fake_oneformer_outputs()
           _UpperCAmelCase     :      Tuple														=       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)		)
