"""simple docstring"""






from typing import Optional

import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...modeling_outputs import (
    BackboneOutput,
    BaseModelOutputWithNoAttention,
    BaseModelOutputWithPoolingAndNoAttention,
    ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import (
    add_code_sample_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    logging,
    replace_return_docstrings,
)
from ...utils.backbone_utils import BackboneMixin
from .configuration_resnet import ResNetConfig


__A			   = logging.get_logger(__name__)

# General docstring
__A			   = '''ResNetConfig'''

# Base docstring
__A			   = '''microsoft/resnet-50'''
__A			   = [1, 2_0_4_8, 7, 7]

# Image classification docstring
__A			   = '''microsoft/resnet-50'''
__A			   = '''tiger cat'''

__A			   = [
    '''microsoft/resnet-50''',
    # See all resnet models at https://huggingface.co/models?filter=resnet
]


class     UpperCAmelCase						(nn.Module  ):



    """simple docstring"""

    def __init__(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase = 3    ,			_UpperCAmelCase = 1    ,			_UpperCAmelCase = "relu"							):
       super().__init__()
       lowercase__:   List[Any]					=      nn.Convad(
           _UpperCAmelCase    ,			_UpperCAmelCase    ,			kernel_size=_UpperCAmelCase    ,			stride=_UpperCAmelCase    ,			padding=kernel_size // 2    ,			bias=_UpperCAmelCase							)
       lowercase__:   Tuple					=      nn.BatchNormad(_UpperCAmelCase							)
       lowercase__:   int					=      ACTaFN[activation] if activation is not None else nn.Identity()






    def _snake_case			(   self    ,			_UpperCAmelCase							):
       lowercase__:   Optional[int]					=      self.convolution(_UpperCAmelCase							)
       lowercase__:   Any					=      self.normalization(_UpperCAmelCase							)
       lowercase__:   Union[str, Any]					=      self.activation(_UpperCAmelCase							)
       return hidden_state



class     UpperCAmelCase						(nn.Module  ):



    """simple docstring"""

    def __init__(   self    ,			_UpperCAmelCase							):
       super().__init__()
       lowercase__:   Union[str, Any]					=      ResNetConvLayer(
           config.num_channels    ,			config.embedding_size    ,			kernel_size=7    ,			stride=2    ,			activation=config.hidden_act							)
       lowercase__:   List[Any]					=      nn.MaxPoolad(kernel_size=3    ,			stride=2    ,			padding=1							)
       lowercase__:   int					=      config.num_channels






    def _snake_case			(   self    ,			_UpperCAmelCase							):
       lowercase__:   int					=      pixel_values.shape[1]
       if num_channels != self.num_channels:
          raise ValueError(
              '''Make sure that the channel dimension of the pixel values match with the one set in the configuration.'''							)
       lowercase__:   Union[str, Any]					=      self.embedder(_UpperCAmelCase							)
       lowercase__:   Union[str, Any]					=      self.pooler(_UpperCAmelCase							)
       return embedding



class     UpperCAmelCase						(nn.Module  ):



    """simple docstring"""

    def __init__(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase = 2							):
       super().__init__()
       lowercase__:   Optional[int]					=      nn.Convad(_UpperCAmelCase    ,			_UpperCAmelCase    ,			kernel_size=1    ,			stride=_UpperCAmelCase    ,			bias=_UpperCAmelCase							)
       lowercase__:   List[str]					=      nn.BatchNormad(_UpperCAmelCase							)






    def _snake_case			(   self    ,			_UpperCAmelCase							):
       lowercase__:   List[str]					=      self.convolution(_UpperCAmelCase							)
       lowercase__:   Dict					=      self.normalization(_UpperCAmelCase							)
       return hidden_state



class     UpperCAmelCase						(nn.Module  ):



    """simple docstring"""

    def __init__(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase = 1    ,			_UpperCAmelCase = "relu"							):
       super().__init__()
       lowercase__:   List[Any]					=      in_channels != out_channels or stride != 1
       lowercase__:   str					=      (
           ResNetShortCut(_UpperCAmelCase    ,			_UpperCAmelCase    ,			stride=_UpperCAmelCase							) if should_apply_shortcut else nn.Identity()
       )
       lowercase__:   Any					=      nn.Sequential(
           ResNetConvLayer(_UpperCAmelCase    ,			_UpperCAmelCase    ,			stride=_UpperCAmelCase							)    ,			ResNetConvLayer(_UpperCAmelCase    ,			_UpperCAmelCase    ,			activation=_UpperCAmelCase							)    ,			)
       lowercase__:   List[str]					=      ACTaFN[activation]






    def _snake_case			(   self    ,			_UpperCAmelCase							):
       lowercase__:   Optional[int]					=      hidden_state
       lowercase__:   Tuple					=      self.layer(_UpperCAmelCase							)
       lowercase__:   Union[str, Any]					=      self.shortcut(_UpperCAmelCase							)
       hidden_state += residual
       lowercase__:   List[str]					=      self.activation(_UpperCAmelCase							)
       return hidden_state



class     UpperCAmelCase						(nn.Module  ):



    """simple docstring"""

    def __init__(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase = 1    ,			_UpperCAmelCase = "relu"    ,			_UpperCAmelCase = 4							):
       super().__init__()
       lowercase__:   Union[str, Any]					=      in_channels != out_channels or stride != 1
       lowercase__:   Union[str, Any]					=      out_channels // reduction
       lowercase__:   Union[str, Any]					=      (
           ResNetShortCut(_UpperCAmelCase    ,			_UpperCAmelCase    ,			stride=_UpperCAmelCase							) if should_apply_shortcut else nn.Identity()
       )
       lowercase__:   Optional[Any]					=      nn.Sequential(
           ResNetConvLayer(_UpperCAmelCase    ,			_UpperCAmelCase    ,			kernel_size=1							)    ,			ResNetConvLayer(_UpperCAmelCase    ,			_UpperCAmelCase    ,			stride=_UpperCAmelCase							)    ,			ResNetConvLayer(_UpperCAmelCase    ,			_UpperCAmelCase    ,			kernel_size=1    ,			activation=_UpperCAmelCase							)    ,			)
       lowercase__:   Any					=      ACTaFN[activation]






    def _snake_case			(   self    ,			_UpperCAmelCase							):
       lowercase__:   Dict					=      hidden_state
       lowercase__:   str					=      self.layer(_UpperCAmelCase							)
       lowercase__:   List[Any]					=      self.shortcut(_UpperCAmelCase							)
       hidden_state += residual
       lowercase__:   Optional[int]					=      self.activation(_UpperCAmelCase							)
       return hidden_state



class     UpperCAmelCase						(nn.Module  ):



    """simple docstring"""

    def __init__(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase = 2    ,			_UpperCAmelCase = 2    ,			):
       super().__init__()

       lowercase__:   Tuple					=      ResNetBottleNeckLayer if config.layer_type == "bottleneck" else ResNetBasicLayer

       lowercase__:   List[Any]					=      nn.Sequential(
           # downsampling is done in the first layer with stride of 2
           layer(_UpperCAmelCase    ,			_UpperCAmelCase    ,			stride=_UpperCAmelCase    ,			activation=config.hidden_act							)    ,			*[layer(_UpperCAmelCase    ,			_UpperCAmelCase    ,			activation=config.hidden_act							) for _ in range(depth - 1							)]    ,			)






    def _snake_case			(   self    ,			_UpperCAmelCase							):
       lowercase__:   str					=      input
       for layer in self.layers:
          lowercase__:   Any					=      layer(_UpperCAmelCase							)
       return hidden_state



class     UpperCAmelCase						(nn.Module  ):



    """simple docstring"""

    def __init__(   self    ,			_UpperCAmelCase							):
       super().__init__()
       lowercase__:   List[Any]					=      nn.ModuleList([]							)
       # based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
       self.stages.append(
           ResNetStage(
               _UpperCAmelCase    ,			config.embedding_size    ,			config.hidden_sizes[0]    ,			stride=2 if config.downsample_in_first_stage else 1    ,			depth=config.depths[0]    ,			)							)
       lowercase__:   int					=      zip(config.hidden_sizes    ,			config.hidden_sizes[1:]							)
       for (in_channels, out_channels), depth in zip(_UpperCAmelCase    ,			config.depths[1:]							):
          self.stages.append(ResNetStage(_UpperCAmelCase    ,			_UpperCAmelCase    ,			_UpperCAmelCase    ,			depth=_UpperCAmelCase							)							)






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase = False    ,			_UpperCAmelCase = True							):
       lowercase__:   List[str]					=      () if output_hidden_states else None

       for stage_module in self.stages:
          if output_hidden_states:
             lowercase__:   Union[str, Any]					=      hidden_states + (hidden_state,)

          lowercase__:   int					=      stage_module(_UpperCAmelCase							)

       if output_hidden_states:
          lowercase__:   List[str]					=      hidden_states + (hidden_state,)

       if not return_dict:
          return tuple(v for v in [hidden_state, hidden_states] if v is not None							)

       return BaseModelOutputWithNoAttention(
           last_hidden_state=_UpperCAmelCase    ,			hidden_states=_UpperCAmelCase    ,			)



class     UpperCAmelCase						(A__  ):



    """simple docstring"""

    _UpperCAmelCase	:List[str]						    = ResNetConfig
    _UpperCAmelCase	:Optional[Any]						    = '''resnet'''
    _UpperCAmelCase	:Tuple						    = '''pixel_values'''
    _UpperCAmelCase	:List[str]						    = True






    def _snake_case			(   self    ,			_UpperCAmelCase							):






       if isinstance(_UpperCAmelCase    ,			nn.Convad							):
          nn.init.kaiming_normal_(module.weight    ,			mode='''fan_out'''    ,			nonlinearity='''relu'''							)
       elif isinstance(_UpperCAmelCase    ,			(nn.BatchNormad, nn.GroupNorm)							):
          nn.init.constant_(module.weight    ,			1							)
          nn.init.constant_(module.bias    ,			0							)






    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase=False							):






       if isinstance(_UpperCAmelCase    ,			_UpperCAmelCase							):
          lowercase__:   Tuple					=      value



__A			   = R'''
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.

    Parameters:
        config ([`ResNetConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
'''

__A			   = R'''
    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`ConvNextImageProcessor.__call__`] for details.

        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
'''


@add_start_docstrings(
    "The bare ResNet model outputting raw features without any specific head on top."    ,A__    ,)
class     UpperCAmelCase						(A__  ):



    """simple docstring"""

    def __init__(   self    ,			_UpperCAmelCase							):
       super().__init__(_UpperCAmelCase							)
       lowercase__:   str					=      config
       lowercase__:   List[Any]					=      ResNetEmbeddings(_UpperCAmelCase							)
       lowercase__:   Optional[Any]					=      ResNetEncoder(_UpperCAmelCase							)
       lowercase__:   Dict					=      nn.AdaptiveAvgPoolad((1, 1)							)
       # Initialize weights and apply final processing
       self.post_init()






    @add_start_docstrings_to_model_forward(_UpperCAmelCase							)
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC    ,			output_type=_UpperCAmelCase    ,			config_class=_CONFIG_FOR_DOC    ,			modality='''vision'''    ,			expected_output=_EXPECTED_OUTPUT_SHAPE    ,			)
    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase = None    ,			_UpperCAmelCase = None							):
       lowercase__:   Union[str, Any]					=      (
           output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
       )
       lowercase__:   int					=      return_dict if return_dict is not None else self.config.use_return_dict

       lowercase__:   Tuple					=      self.embedder(_UpperCAmelCase							)

       lowercase__:   Dict					=      self.encoder(
           _UpperCAmelCase    ,			output_hidden_states=_UpperCAmelCase    ,			return_dict=_UpperCAmelCase							)

       lowercase__:   Dict					=      encoder_outputs[0]

       lowercase__:   Optional[Any]					=      self.pooler(_UpperCAmelCase							)

       if not return_dict:
          return (last_hidden_state, pooled_output) + encoder_outputs[1:]

       return BaseModelOutputWithPoolingAndNoAttention(
           last_hidden_state=_UpperCAmelCase    ,			pooler_output=_UpperCAmelCase    ,			hidden_states=encoder_outputs.hidden_states    ,			)



@add_start_docstrings(
    "\n    ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    "    ,A__    ,)
class     UpperCAmelCase						(A__  ):



    """simple docstring"""

    def __init__(   self    ,			_UpperCAmelCase							):
       super().__init__(_UpperCAmelCase							)
       lowercase__:   Union[str, Any]					=      config.num_labels
       lowercase__:   Any					=      ResNetModel(_UpperCAmelCase							)
       # classification head
       lowercase__:   str					=      nn.Sequential(
           nn.Flatten()    ,			nn.Linear(config.hidden_sizes[-1]    ,			config.num_labels							) if config.num_labels > 0 else nn.Identity()    ,			)
       # initialize weights and apply final processing
       self.post_init()






    @add_start_docstrings_to_model_forward(_UpperCAmelCase							)
    @add_code_sample_docstrings(
        checkpoint=_IMAGE_CLASS_CHECKPOINT    ,			output_type=_UpperCAmelCase    ,			config_class=_CONFIG_FOR_DOC    ,			expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT    ,			)
    def _snake_case			(   self    ,			_UpperCAmelCase = None    ,			_UpperCAmelCase = None    ,			_UpperCAmelCase = None    ,			_UpperCAmelCase = None    ,			):
       lowercase__:   Union[str, Any]					=      return_dict if return_dict is not None else self.config.use_return_dict

       lowercase__:   Tuple					=      self.resnet(_UpperCAmelCase    ,			output_hidden_states=_UpperCAmelCase    ,			return_dict=_UpperCAmelCase							)

       lowercase__:   List[Any]					=      outputs.pooler_output if return_dict else outputs[1]

       lowercase__:   str					=      self.classifier(_UpperCAmelCase							)

       lowercase__:   Optional[Any]					=      None

       if labels is not None:
          if self.config.problem_type is None:
             if self.num_labels == 1:
                lowercase__:   Dict					=      "regression"
             elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
                lowercase__:   List[str]					=      "single_label_classification"
             else:
                lowercase__:   Tuple					=      "multi_label_classification"
          if self.config.problem_type == "regression":
             lowercase__:   Dict					=      MSELoss()
             if self.num_labels == 1:
                lowercase__:   Optional[Any]					=      loss_fct(logits.squeeze()    ,			labels.squeeze()							)
             else:
                lowercase__:   Any					=      loss_fct(_UpperCAmelCase    ,			_UpperCAmelCase							)
          elif self.config.problem_type == "single_label_classification":
             lowercase__:   List[str]					=      CrossEntropyLoss()
             lowercase__:   Dict					=      loss_fct(logits.view(-1    ,			self.num_labels							)    ,			labels.view(-1							)							)
          elif self.config.problem_type == "multi_label_classification":
             lowercase__:   str					=      BCEWithLogitsLoss()
             lowercase__:   str					=      loss_fct(_UpperCAmelCase    ,			_UpperCAmelCase							)

       if not return_dict:
          lowercase__:   Dict					=      (logits,) + outputs[2:]
          return (loss,) + output if loss is not None else output

       return ImageClassifierOutputWithNoAttention(loss=_UpperCAmelCase    ,			logits=_UpperCAmelCase    ,			hidden_states=outputs.hidden_states							)



@add_start_docstrings(
    "\n    ResNet backbone, to be used with frameworks like DETR and MaskFormer.\n    "    ,A__    ,)
class     UpperCAmelCase						(A__    ,A__  ):



    """simple docstring"""

    def __init__(   self    ,			_UpperCAmelCase							):
       super().__init__(_UpperCAmelCase							)
       super()._init_backbone(_UpperCAmelCase							)

       lowercase__:   Union[str, Any]					=      [config.embedding_size] + config.hidden_sizes
       lowercase__:   str					=      ResNetEmbeddings(_UpperCAmelCase							)
       lowercase__:   List[str]					=      ResNetEncoder(_UpperCAmelCase							)

       # initialize weights and apply final processing
       self.post_init()






    @add_start_docstrings_to_model_forward(_UpperCAmelCase							)
    @replace_return_docstrings(output_type=_UpperCAmelCase    ,			config_class=_CONFIG_FOR_DOC							)
    def _snake_case			(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase = None    ,			_UpperCAmelCase = None							):
       lowercase__:   Optional[Any]					=      return_dict if return_dict is not None else self.config.use_return_dict
       lowercase__:   Tuple					=      (
           output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
       )

       lowercase__:   int					=      self.embedder(_UpperCAmelCase							)

       lowercase__:   Union[str, Any]					=      self.encoder(_UpperCAmelCase    ,			output_hidden_states=_UpperCAmelCase    ,			return_dict=_UpperCAmelCase							)

       lowercase__:   List[str]					=      outputs.hidden_states

       lowercase__:   int					=      ()
       for idx, stage in enumerate(self.stage_names							):
          if stage in self.out_features:
             feature_maps += (hidden_states[idx],)

       if not return_dict:
          lowercase__:   List[str]					=      (feature_maps,)
          if output_hidden_states:
             output += (outputs.hidden_states,)
          return output

       return BackboneOutput(
           feature_maps=_UpperCAmelCase    ,			hidden_states=outputs.hidden_states if output_hidden_states else None    ,			attentions=_UpperCAmelCase    ,			)








import json
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
 import torch

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

if is_vision_available():
 from PIL import Image







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





class 			__a  (    unittest.TestCase       ):







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


      '''simple docstring'''






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

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







 def 					__lowercase					(		self					:   int						):


      '''simple docstring'''






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







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


      '''simple docstring'''






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

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

      return expected_height, expected_width







 def 					__lowercase					(		self					:   Any						):


      '''simple docstring'''






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







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







 def 					__lowercase					(		self					:   str						):


      '''simple docstring'''






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







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


      '''simple docstring'''






      return self.image_processing_tester.prepare_image_processor_dict()







 def 					__lowercase					(		self					:   str						):


      '''simple docstring'''






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







 def 					__lowercase					(		self					:   List[str]						):


      '''simple docstring'''






      pass







 def 					__lowercase					(		self					:   Tuple						):


      '''simple docstring'''






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

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

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

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

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

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







 def 					__lowercase					(		self					:   int						):


      '''simple docstring'''






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

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

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

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

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

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







 def 					__lowercase					(		self					:   Dict						):


      '''simple docstring'''






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

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

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

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

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

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







 def 					__lowercase					(		self					:   int							,   SCREAMING_SNAKE_CASE					:   Optional[int]=False							,   SCREAMING_SNAKE_CASE					:   Optional[int]=False							,   SCREAMING_SNAKE_CASE					:   Any="np"						):


      '''simple docstring'''






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

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

      return inputs







 def 					__lowercase					(		self					:   int						):


      '''simple docstring'''






      pass







 def 					__lowercase					(		self					:   str						):


      '''simple docstring'''






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

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

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

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







 def 					__lowercase					(		self					:   List[str]						):


      '''simple docstring'''






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

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







 def 					__lowercase					(		self					:   Tuple						):


      '''simple docstring'''






      UpperCamelCase__			:   int       				=  self.image_processing_class(
          num_labels=self.image_processing_tester.num_classes							,   max_seq_length=77							,   task_seq_length=77							,   class_info_file="ade20k_panoptic.json"							,   num_text=self.image_processing_tester.num_text							,   repo_path="shi-labs/oneformer_demo"							,   )
      UpperCamelCase__			:   int       				=  self.image_processing_tester.get_fake_oneformer_outputs()

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

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

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

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







 def 					__lowercase					(		self					:   str						):


      '''simple docstring'''






      UpperCamelCase__			:   Tuple       				=  self.image_processing_class(
          num_labels=self.image_processing_tester.num_classes							,   max_seq_length=77							,   task_seq_length=77							,   class_info_file="ade20k_panoptic.json"							,   num_text=self.image_processing_tester.num_text							,   repo_path="shi-labs/oneformer_demo"							,   )
      UpperCamelCase__			:   List[str]       				=  self.image_processing_tester.get_fake_oneformer_outputs()
      UpperCamelCase__			:   Optional[int]       				=  image_processor.post_process_instance_segmentation(SCREAMING_SNAKE_CASE							,   threshold=0						)

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







 def 					__lowercase					(		self					:   Dict						):


      '''simple docstring'''






      UpperCamelCase__			:   int       				=  self.image_processing_class(
          num_labels=self.image_processing_tester.num_classes							,   max_seq_length=77							,   task_seq_length=77							,   class_info_file="ade20k_panoptic.json"							,   num_text=self.image_processing_tester.num_text							,   repo_path="shi-labs/oneformer_demo"							,   )
      UpperCamelCase__			:   Any       				=  self.image_processing_tester.get_fake_oneformer_outputs()
      UpperCamelCase__			:   Tuple       				=  image_processor.post_process_panoptic_segmentation(SCREAMING_SNAKE_CASE							,   threshold=0						)

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