import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase_						   =    logging.get_logger()



@dataclass
class        _lowerCAmelCase    :


  '''simple docstring'''
  a_							:       nn.Module
  a_							:       List[nn.Module]      					=field(default_factory=__UpperCAmelCase							)
  a_							:       list      					=field(default_factory=__UpperCAmelCase							)







  def UpperCamelCase_ (      self      :					Any      ,				UpperCamelCase      :					Any      ,				UpperCamelCase      :					List[str]      ,				UpperCamelCase      :					Tuple							):


    '''simple docstring'''





    _snake_case   :				Union[str, Any]														=	len(list(m.modules()							)							) == 1 or isinstance(UpperCAmelCase_      ,				nn.Convad							) or isinstance(UpperCAmelCase_      ,				nn.BatchNormad							)
    if has_not_submodules:
      self.traced.append(UpperCAmelCase_							)







  def __call__(      self      :					Optional[int]      ,				UpperCamelCase      :					Any							):


    '''simple docstring'''





    for m in self.module.modules():
      self.handles.append(m.register_forward_hook(self._forward_hook							)							)
    self.module(UpperCAmelCase_							)
    [x.remove() for x in self.handles]
    return self







  @property
  def UpperCamelCase_ (      self      :					str							):


    '''simple docstring'''





    return list(filter(lambda UpperCamelCase							: len(list(x.state_dict().keys()							)							) > 0      ,				self.traced							)							)




@dataclass
class        _lowerCAmelCase    :


  '''simple docstring'''
  a_							:       nn.Module
  a_							:       nn.Module
  a_							:       int      					=0
  a_							:       List      					=field(default_factory=__UpperCAmelCase							)
  a_							:       List      					=field(default_factory=__UpperCAmelCase							)







  def __call__(      self      :					Optional[Any]      ,				UpperCamelCase      :					Any							):


    '''simple docstring'''





    _snake_case   :				str														=	Tracker(self.dest							)(UpperCAmelCase_							).parametrized
    _snake_case   :				List[str]														=	Tracker(self.src							)(UpperCAmelCase_							).parametrized

    _snake_case   :				str														=	list(filter(lambda UpperCamelCase							: type(UpperCAmelCase_							) not in self.src_skip      ,				UpperCAmelCase_							)							)
    _snake_case   :				Any														=	list(filter(lambda UpperCamelCase							: type(UpperCAmelCase_							) not in self.dest_skip      ,				UpperCAmelCase_							)							)

    if len(UpperCAmelCase_							) != len(UpperCAmelCase_							):
      raise Exception(
          f"""Numbers of operations are different. Source module has {len(UpperCAmelCase_							)} operations while"""
          f""" destination module has {len(UpperCAmelCase_							)}."""							)

    for dest_m, src_m in zip(UpperCAmelCase_      ,				UpperCAmelCase_							):
      dest_m.load_state_dict(src_m.state_dict()							)
      if self.verbose == 1:
        print(f"""Transfered from={src_m} to={dest_m}"""							)




def     lowerCamelCase_	(      lowerCAmelCase:	Any    ,							lowerCAmelCase:	Any    ,							lowerCAmelCase:	str    ,							lowerCAmelCase:	Tuple = True				)->		int:
  print(F"""Converting {name}..."""				)
  with torch.no_grad():
    _snake_case   :				Dict														=	timm.create_model(_snake_case    ,							pretrained=_snake_case				).eval()
    _snake_case   :				Optional[int]														=	ResNetForImageClassification(_snake_case				).eval()
    _snake_case   :				str														=	ModuleTransfer(src=_snake_case    ,							dest=_snake_case				)
    _snake_case   :				Optional[Any]														=	torch.randn((1, 3, 2_24, 2_24)				)
    module_transfer(_snake_case				)

  assert torch.allclose(from_model(_snake_case				)    ,							our_model(_snake_case				).logits				), "The model logits don't match the original one."

  _snake_case   :				Tuple														=	F"""resnet{'-'.join(name.split('resnet'				)				)}"""
  print(_snake_case				)

  if push_to_hub:
    our_model.push_to_hub(
        repo_path_or_name=save_directory / checkpoint_name    ,							commit_message='Add model'    ,							use_temp_dir=_snake_case    ,							)

    # we can use the convnext one
    _snake_case   :				List[Any]														=	AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'				)
    image_processor.push_to_hub(
        repo_path_or_name=save_directory / checkpoint_name    ,							commit_message='Add image processor'    ,							use_temp_dir=_snake_case    ,							)

    print(F"""Pushed {checkpoint_name}"""				)


def     lowerCamelCase_	(      lowerCAmelCase:	int    ,							lowerCAmelCase:	List[str] = None    ,							lowerCAmelCase:	List[str] = True				)->		Dict:
  _snake_case   :				Tuple														=	'imagenet-1k-id2label.json'
  _snake_case   :				List[str]														=	10_00
  _snake_case   :				int														=	(1, num_labels)

  _snake_case   :				List[str]														=	'huggingface/label-files'
  _snake_case   :				Union[str, Any]														=	num_labels
  _snake_case   :				Optional[Any]														=	json.load(open(hf_hub_download(_snake_case    ,							_snake_case    ,							repo_type='dataset'				)    ,							'r'				)				)
  _snake_case   :				List[Any]														=	{int(_snake_case				): v for k, v in idalabel.items()}

  _snake_case   :				int														=	idalabel
  _snake_case   :				Optional[Any]														=	{v: k for k, v in idalabel.items()}

  _snake_case   :				Optional[Any]														=	partial(_snake_case    ,							num_labels=_snake_case    ,							idalabel=_snake_case    ,							labelaid=_snake_case				)

  _snake_case   :				Optional[int]														=	{
      'resnet18': ImageNetPreTrainedConfig(
          depths=[2, 2, 2, 2]    ,							hidden_sizes=[64, 1_28, 2_56, 5_12]    ,							layer_type='basic'				),
      'resnet26': ImageNetPreTrainedConfig(
          depths=[2, 2, 2, 2]    ,							hidden_sizes=[2_56, 5_12, 10_24, 20_48]    ,							layer_type='bottleneck'				),
      'resnet34': ImageNetPreTrainedConfig(
          depths=[3, 4, 6, 3]    ,							hidden_sizes=[64, 1_28, 2_56, 5_12]    ,							layer_type='basic'				),
      'resnet50': ImageNetPreTrainedConfig(
          depths=[3, 4, 6, 3]    ,							hidden_sizes=[2_56, 5_12, 10_24, 20_48]    ,							layer_type='bottleneck'				),
      'resnet101': ImageNetPreTrainedConfig(
          depths=[3, 4, 23, 3]    ,							hidden_sizes=[2_56, 5_12, 10_24, 20_48]    ,							layer_type='bottleneck'				),
      'resnet152': ImageNetPreTrainedConfig(
          depths=[3, 8, 36, 3]    ,							hidden_sizes=[2_56, 5_12, 10_24, 20_48]    ,							layer_type='bottleneck'				),
  }

  if model_name:
    convert_weight_and_push(_snake_case    ,							names_to_config[model_name]    ,							_snake_case    ,							_snake_case				)
  else:
    for model_name, config in names_to_config.items():
      convert_weight_and_push(_snake_case    ,							_snake_case    ,							_snake_case    ,							_snake_case				)
  return config, expected_shape


if __name__ == "__main__":
      lowerCAmelCase_						   =    argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--model_name""",
          default=None,
          type=str,
          help=(
              """The name of the model you wish to convert, it must be one of the supported resnet* architecture,"""
              """ currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted."""
          ),
      )
      parser.add_argument(
          """--pytorch_dump_folder_path""",
          default=None,
          type=Path,
          required=True,
          help="""Path to the output PyTorch model directory.""",
      )
      parser.add_argument(
          """--push_to_hub""",
          default=True,
          type=bool,
          required=False,
          help="""If True, push model and image processor to the hub.""",
      )

      lowerCAmelCase_						   =    parser.parse_args()
      lowerCAmelCase_						   =    args.pytorch_dump_folder_path
      pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
      convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)











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



class        _lowerCAmelCase    (			unittest.TestCase							):


  '''simple docstring'''







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


    '''simple docstring'''





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

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







  def UpperCamelCase_ (      self      :					Union[str, Any]							):


    '''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 UpperCamelCase_ (      self      :					Tuple      ,				UpperCamelCase      :					Optional[int]      ,				UpperCamelCase      :					List[Any]=False							):


    '''simple docstring'''





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

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

    return expected_height, expected_width







  def UpperCamelCase_ (      self      :					Tuple							):


    '''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        _lowerCAmelCase    (			UpperCAmelCase_							,    unittest.TestCase							):


  '''simple docstring'''
  a_							:       Tuple      					=OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
  # only for test_image_processing_common.test_image_proc_to_json_string
  a_							:       Any      					=image_processing_class







  def UpperCamelCase_ (      self      :					Optional[Any]							):


    '''simple docstring'''





    _snake_case   :				Dict														=	OneFormerImageProcessorTester(self							)







  @property
  def UpperCamelCase_ (      self      :					Tuple							):


    '''simple docstring'''





    return self.image_processing_tester.prepare_image_processor_dict()







  def UpperCamelCase_ (      self      :					Union[str, Any]							):


    '''simple docstring'''





    _snake_case   :				int														=	self.image_processing_class(**self.image_processor_dict							)
    self.assertTrue(hasattr(UpperCamelCase      ,				'image_mean'							)							)
    self.assertTrue(hasattr(UpperCamelCase      ,				'image_std'							)							)
    self.assertTrue(hasattr(UpperCamelCase      ,				'do_normalize'							)							)
    self.assertTrue(hasattr(UpperCamelCase      ,				'do_resize'							)							)
    self.assertTrue(hasattr(UpperCamelCase      ,				'size'							)							)
    self.assertTrue(hasattr(UpperCamelCase      ,				'ignore_index'							)							)
    self.assertTrue(hasattr(UpperCamelCase      ,				'class_info_file'							)							)
    self.assertTrue(hasattr(UpperCamelCase      ,				'num_text'							)							)
    self.assertTrue(hasattr(UpperCamelCase      ,				'repo_path'							)							)
    self.assertTrue(hasattr(UpperCamelCase      ,				'metadata'							)							)
    self.assertTrue(hasattr(UpperCamelCase      ,				'do_reduce_labels'							)							)







  def UpperCamelCase_ (      self      :					List[str]							):


    '''simple docstring'''





    pass







  def UpperCamelCase_ (      self      :					Any							):


    '''simple docstring'''





    _snake_case   :				List[Any]														=	self.image_processing_class(**self.image_processor_dict							)
    # create random PIL images
    _snake_case   :				int														=	prepare_image_inputs(self.image_processing_tester      ,				equal_resolution=UpperCamelCase							)
    for image in image_inputs:
      self.assertIsInstance(UpperCamelCase      ,				Image.Image							)

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

    _snake_case							,					_snake_case   :				List[Any]														=	self.image_processing_tester.get_expected_values(UpperCamelCase							)

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

    # Test batched
    _snake_case							,					_snake_case   :				Optional[int]														=	self.image_processing_tester.get_expected_values(UpperCamelCase      ,				batched=UpperCamelCase							)

    _snake_case   :				int														=	image_processor(
        UpperCamelCase      ,				['semantic'] * len(UpperCamelCase							)      ,				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 UpperCamelCase_ (      self      :					Tuple							):


    '''simple docstring'''





    _snake_case   :				Optional[int]														=	self.image_processing_class(**self.image_processor_dict							)
    # create random numpy tensors
    _snake_case   :				int														=	prepare_image_inputs(self.image_processing_tester      ,				equal_resolution=UpperCamelCase      ,				numpify=UpperCamelCase							)
    for image in image_inputs:
      self.assertIsInstance(UpperCamelCase      ,				np.ndarray							)

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

    _snake_case							,					_snake_case   :				Optional[int]														=	self.image_processing_tester.get_expected_values(UpperCamelCase							)

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

    # Test batched
    _snake_case							,					_snake_case   :				Union[str, Any]														=	self.image_processing_tester.get_expected_values(UpperCamelCase      ,				batched=UpperCamelCase							)

    _snake_case   :				Optional[int]														=	image_processor(
        UpperCamelCase      ,				['semantic'] * len(UpperCamelCase							)      ,				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 UpperCamelCase_ (      self      :					int							):


    '''simple docstring'''





    _snake_case   :				Tuple														=	self.image_processing_class(**self.image_processor_dict							)
    # create random PyTorch tensors
    _snake_case   :				Union[str, Any]														=	prepare_image_inputs(self.image_processing_tester      ,				equal_resolution=UpperCamelCase      ,				torchify=UpperCamelCase							)
    for image in image_inputs:
      self.assertIsInstance(UpperCamelCase      ,				torch.Tensor							)

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

    _snake_case							,					_snake_case   :				int														=	self.image_processing_tester.get_expected_values(UpperCamelCase							)

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

    # Test batched
    _snake_case							,					_snake_case   :				int														=	self.image_processing_tester.get_expected_values(UpperCamelCase      ,				batched=UpperCamelCase							)

    _snake_case   :				List[str]														=	image_processor(
        UpperCamelCase      ,				['semantic'] * len(UpperCamelCase							)      ,				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 UpperCamelCase_ (      self      :					Optional[Any]      ,				UpperCamelCase      :					Tuple=False      ,				UpperCamelCase      :					str=False      ,				UpperCamelCase      :					Dict="np"							):


    '''simple docstring'''





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

    _snake_case   :				List[Any]														=	image_processor(
        UpperCamelCase      ,				['semantic'] * len(UpperCamelCase							)      ,				UpperCamelCase      ,				return_tensors='pt'      ,				instance_id_to_semantic_id=UpperCamelCase      ,				pad_and_return_pixel_mask=UpperCamelCase      ,				)

    return inputs







  def UpperCamelCase_ (      self      :					Optional[int]							):


    '''simple docstring'''





    pass







  def UpperCamelCase_ (      self      :					Dict							):


    '''simple docstring'''





    def common(UpperCamelCase      :					Any=False      ,				UpperCamelCase      :					int=None							):
      _snake_case   :				Any														=	self.comm_get_image_processor_inputs(
          with_segmentation_maps=UpperCamelCase      ,				is_instance_map=UpperCamelCase      ,				segmentation_type=UpperCamelCase							)

      _snake_case   :				Union[str, Any]														=	inputs['mask_labels']
      _snake_case   :				Optional[int]														=	inputs['class_labels']
      _snake_case   :				Optional[int]														=	inputs['pixel_values']
      _snake_case   :				Optional[Any]														=	inputs['text_inputs']

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

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







  def UpperCamelCase_ (      self      :					Any							):


    '''simple docstring'''





    _snake_case   :				Union[str, Any]														=	np.zeros((20, 50)							)
    _snake_case   :				int														=	1
    _snake_case   :				int														=	1
    _snake_case   :				Optional[Any]														=	1

    _snake_case   :				List[Any]														=	binary_mask_to_rle(UpperCamelCase							)
    self.assertEqual(len(UpperCamelCase							)      ,				4							)
    self.assertEqual(rle[0]      ,				21							)
    self.assertEqual(rle[1]      ,				45							)







  def UpperCamelCase_ (      self      :					Any							):


    '''simple docstring'''





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

    _snake_case   :				Any														=	fature_extractor.post_process_semantic_segmentation(UpperCamelCase							)

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

    _snake_case   :				Optional[Any]														=	[(1, 4) for i in range(self.image_processing_tester.batch_size							)]
    _snake_case   :				Union[str, Any]														=	fature_extractor.post_process_semantic_segmentation(UpperCamelCase      ,				target_sizes=UpperCamelCase							)

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







  def UpperCamelCase_ (      self      :					List[str]							):


    '''simple docstring'''





    _snake_case   :				Any														=	self.image_processing_class(
        num_labels=self.image_processing_tester.num_classes      ,				max_seq_length=77      ,				task_seq_length=77      ,				class_info_file='ade20k_panoptic.json'      ,				num_text=self.image_processing_tester.num_text      ,				repo_path='shi-labs/oneformer_demo'      ,				)
    _snake_case   :				Optional[int]														=	self.image_processing_tester.get_fake_oneformer_outputs()
    _snake_case   :				int														=	image_processor.post_process_instance_segmentation(UpperCamelCase      ,				threshold=0							)

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







  def UpperCamelCase_ (      self      :					str							):


    '''simple docstring'''





    _snake_case   :				str														=	self.image_processing_class(
        num_labels=self.image_processing_tester.num_classes      ,				max_seq_length=77      ,				task_seq_length=77      ,				class_info_file='ade20k_panoptic.json'      ,				num_text=self.image_processing_tester.num_text      ,				repo_path='shi-labs/oneformer_demo'      ,				)
    _snake_case   :				Optional[Any]														=	self.image_processing_tester.get_fake_oneformer_outputs()
    _snake_case   :				Any														=	image_processor.post_process_panoptic_segmentation(UpperCamelCase      ,				threshold=0							)

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




