






'''simple docstring'''

import argparse
import json
import pickle
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__     =       logging.get_logger(__name__)







def    lowercase__			(				__UpperCamelCase   )->			str:
      UpperCamelCase      = SwinConfig.from_pretrained(
          """microsoft/swin-tiny-patch4-window7-224"""		,						out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]   )
      UpperCamelCase      = MaskFormerConfig(backbone_config=SCREAMING_SNAKE_CASE__   )

      UpperCamelCase      = """huggingface/label-files"""
      if "ade20k-full" in model_name:
            # this should be ok
            UpperCamelCase      = 847
            UpperCamelCase      = """maskformer-ade20k-full-id2label.json"""
      elif "ade" in model_name:
            # this should be ok
            UpperCamelCase      = 150
            UpperCamelCase      = """ade20k-id2label.json"""
      elif "coco-stuff" in model_name:
            # this should be ok
            UpperCamelCase      = 171
            UpperCamelCase      = """maskformer-coco-stuff-id2label.json"""
      elif "coco" in model_name:
            # TODO
            UpperCamelCase      = 133
            UpperCamelCase      = """coco-panoptic-id2label.json"""
      elif "cityscapes" in model_name:
            # this should be ok
            UpperCamelCase      = 19
            UpperCamelCase      = """cityscapes-id2label.json"""
      elif "vistas" in model_name:
            # this should be ok
            UpperCamelCase      = 65
            UpperCamelCase      = """mapillary-vistas-id2label.json"""

      UpperCamelCase      = json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__		,						repo_type="""dataset"""   )		,						"""r"""   )   )
      UpperCamelCase      = {int(SCREAMING_SNAKE_CASE__   ): v for k, v in idalabel.items()}

      return config







def    lowercase__			(				__UpperCamelCase   )->			List[Any]:
      UpperCamelCase      = []
      # stem
      # fmt: off
      rename_keys.append(("""backbone.patch_embed.proj.weight""", """model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight""")   )
      rename_keys.append(("""backbone.patch_embed.proj.bias""", """model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias""")   )
      rename_keys.append(("""backbone.patch_embed.norm.weight""", """model.pixel_level_module.encoder.model.embeddings.norm.weight""")   )
      rename_keys.append(("""backbone.patch_embed.norm.bias""", """model.pixel_level_module.encoder.model.embeddings.norm.bias""")   )
      # stages
      for i in range(len(config.backbone_config.depths   )   ):
            for j in range(config.backbone_config.depths[i]   ):
                  rename_keys.append((F"backbone.layers.{i}.blocks.{j}.norm1.weight", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight")   )
                  rename_keys.append((F"backbone.layers.{i}.blocks.{j}.norm1.bias", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias")   )
                  rename_keys.append((F"backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table")   )
                  rename_keys.append((F"backbone.layers.{i}.blocks.{j}.attn.relative_position_index", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index")   )
                  rename_keys.append((F"backbone.layers.{i}.blocks.{j}.attn.proj.weight", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight")   )
                  rename_keys.append((F"backbone.layers.{i}.blocks.{j}.attn.proj.bias", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias")   )
                  rename_keys.append((F"backbone.layers.{i}.blocks.{j}.norm2.weight", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight")   )
                  rename_keys.append((F"backbone.layers.{i}.blocks.{j}.norm2.bias", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias")   )
                  rename_keys.append((F"backbone.layers.{i}.blocks.{j}.mlp.fc1.weight", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight")   )
                  rename_keys.append((F"backbone.layers.{i}.blocks.{j}.mlp.fc1.bias", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias")   )
                  rename_keys.append((F"backbone.layers.{i}.blocks.{j}.mlp.fc2.weight", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight")   )
                  rename_keys.append((F"backbone.layers.{i}.blocks.{j}.mlp.fc2.bias", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias")   )

            if i < 3:
                  rename_keys.append((F"backbone.layers.{i}.downsample.reduction.weight", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight")   )
                  rename_keys.append((F"backbone.layers.{i}.downsample.norm.weight", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight")   )
                  rename_keys.append((F"backbone.layers.{i}.downsample.norm.bias", F"model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias")   )
            rename_keys.append((F"backbone.norm{i}.weight", F"model.pixel_level_module.encoder.hidden_states_norms.{i}.weight")   )
            rename_keys.append((F"backbone.norm{i}.bias", F"model.pixel_level_module.encoder.hidden_states_norms.{i}.bias")   )

      # FPN
      rename_keys.append(("""sem_seg_head.layer_4.weight""", """model.pixel_level_module.decoder.fpn.stem.0.weight""")   )
      rename_keys.append(("""sem_seg_head.layer_4.norm.weight""", """model.pixel_level_module.decoder.fpn.stem.1.weight""")   )
      rename_keys.append(("""sem_seg_head.layer_4.norm.bias""", """model.pixel_level_module.decoder.fpn.stem.1.bias""")   )
      for source_index, target_index in zip(range(3		,						0		,						-1   )		,						range(0		,						3   )   ):
            rename_keys.append((F"sem_seg_head.adapter_{source_index}.weight", F"model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight")   )
            rename_keys.append((F"sem_seg_head.adapter_{source_index}.norm.weight", F"model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight")   )
            rename_keys.append((F"sem_seg_head.adapter_{source_index}.norm.bias", F"model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias")   )
            rename_keys.append((F"sem_seg_head.layer_{source_index}.weight", F"model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight")   )
            rename_keys.append((F"sem_seg_head.layer_{source_index}.norm.weight", F"model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight")   )
            rename_keys.append((F"sem_seg_head.layer_{source_index}.norm.bias", F"model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias")   )
      rename_keys.append(("""sem_seg_head.mask_features.weight""", """model.pixel_level_module.decoder.mask_projection.weight""")   )
      rename_keys.append(("""sem_seg_head.mask_features.bias""", """model.pixel_level_module.decoder.mask_projection.bias""")   )

      # Transformer decoder
      for idx in range(config.decoder_config.decoder_layers   ):
            # self-attention out projection
            rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight", F"model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight")   )
            rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias", F"model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias")   )
            # cross-attention out projection
            rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight", F"model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight")   )
            rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias", F"model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias")   )
            # MLP 1
            rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight", F"model.transformer_module.decoder.layers.{idx}.fc1.weight")   )
            rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias", F"model.transformer_module.decoder.layers.{idx}.fc1.bias")   )
            # MLP 2
            rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight", F"model.transformer_module.decoder.layers.{idx}.fc2.weight")   )
            rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias", F"model.transformer_module.decoder.layers.{idx}.fc2.bias")   )
            # layernorm 1 (self-attention layernorm)
            rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight", F"model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight")   )
            rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias", F"model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias")   )
            # layernorm 2 (cross-attention layernorm)
            rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight", F"model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight")   )
            rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias", F"model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias")   )
            # layernorm 3 (final layernorm)
            rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight", F"model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight")   )
            rename_keys.append((F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias", F"model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias")   )

      rename_keys.append(("""sem_seg_head.predictor.transformer.decoder.norm.weight""", """model.transformer_module.decoder.layernorm.weight""")   )
      rename_keys.append(("""sem_seg_head.predictor.transformer.decoder.norm.bias""", """model.transformer_module.decoder.layernorm.bias""")   )

      # heads on top
      rename_keys.append(("""sem_seg_head.predictor.query_embed.weight""", """model.transformer_module.queries_embedder.weight""")   )

      rename_keys.append(("""sem_seg_head.predictor.input_proj.weight""", """model.transformer_module.input_projection.weight""")   )
      rename_keys.append(("""sem_seg_head.predictor.input_proj.bias""", """model.transformer_module.input_projection.bias""")   )

      rename_keys.append(("""sem_seg_head.predictor.class_embed.weight""", """class_predictor.weight""")   )
      rename_keys.append(("""sem_seg_head.predictor.class_embed.bias""", """class_predictor.bias""")   )

      for i in range(3   ):
            rename_keys.append((F"sem_seg_head.predictor.mask_embed.layers.{i}.weight", F"mask_embedder.{i}.0.weight")   )
            rename_keys.append((F"sem_seg_head.predictor.mask_embed.layers.{i}.bias", F"mask_embedder.{i}.0.bias")   )
      # fmt: on

      return rename_keys







def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase   )->			Any:
      UpperCamelCase      = dct.pop(SCREAMING_SNAKE_CASE__   )
      UpperCamelCase      = val







def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase   )->			List[Any]:
      UpperCamelCase      = [int(backbone_config.embed_dim * 2**i   ) for i in range(len(backbone_config.depths   )   )]
      for i in range(len(backbone_config.depths   )   ):
            UpperCamelCase      = num_features[i]
            for j in range(backbone_config.depths[i]   ):
                  # fmt: off
                  # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
                  UpperCamelCase      = state_dict.pop(F"backbone.layers.{i}.blocks.{j}.attn.qkv.weight"   )
                  UpperCamelCase      = state_dict.pop(F"backbone.layers.{i}.blocks.{j}.attn.qkv.bias"   )
                  # next, add query, keys and values (in that order) to the state dict
                  UpperCamelCase      = in_proj_weight[:dim, :]
                  UpperCamelCase      = in_proj_bias[: dim]
                  UpperCamelCase      = in_proj_weight[
                      dim : dim * 2, :
                  ]
                  UpperCamelCase      = in_proj_bias[
                      dim : dim * 2
                  ]
                  UpperCamelCase      = in_proj_weight[
                      -dim :, :
                  ]
                  UpperCamelCase      = in_proj_bias[-dim :]
                  # fmt: on







def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase   )->			str:
      UpperCamelCase      = config.decoder_config.hidden_size
      for idx in range(config.decoder_config.decoder_layers   ):
            # read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
            UpperCamelCase      = state_dict.pop(F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight"   )
            UpperCamelCase      = state_dict.pop(F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias"   )
            # next, add query, keys and values (in that order) to the state dict
            UpperCamelCase      = in_proj_weight[: hidden_size, :]
            UpperCamelCase      = in_proj_bias[:config.hidden_size]
            UpperCamelCase      = in_proj_weight[hidden_size : hidden_size * 2, :]
            UpperCamelCase      = in_proj_bias[hidden_size : hidden_size * 2]
            UpperCamelCase      = in_proj_weight[-hidden_size :, :]
            UpperCamelCase      = in_proj_bias[-hidden_size :]
            # read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
            UpperCamelCase      = state_dict.pop(F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight"   )
            UpperCamelCase      = state_dict.pop(F"sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias"   )
            # next, add query, keys and values (in that order) to the state dict
            UpperCamelCase      = in_proj_weight[: hidden_size, :]
            UpperCamelCase      = in_proj_bias[:config.hidden_size]
            UpperCamelCase      = in_proj_weight[hidden_size : hidden_size * 2, :]
            UpperCamelCase      = in_proj_bias[hidden_size : hidden_size * 2]
            UpperCamelCase      = in_proj_weight[-hidden_size :, :]
            UpperCamelCase      = in_proj_bias[-hidden_size :]
      # fmt: on







def    lowercase__			(				)->			str:
      UpperCamelCase      = """http://images.cocodataset.org/val2017/000000039769.jpg"""
      UpperCamelCase      = Image.open(requests.get(SCREAMING_SNAKE_CASE__		,						stream=SCREAMING_SNAKE_CASE__   ).raw   )
      return im







@torch.no_grad()
def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase		,						__UpperCamelCase = False   )->			str:
      UpperCamelCase      = get_maskformer_config(SCREAMING_SNAKE_CASE__   )

      # load original state_dict
      with open(SCREAMING_SNAKE_CASE__		,						"""rb"""   ) as f:
            UpperCamelCase      = pickle.load(SCREAMING_SNAKE_CASE__   )
      UpperCamelCase      = data["""model"""]

      # for name, param in state_dict.items():
      #     print(name, param.shape)

      # rename keys
      UpperCamelCase      = create_rename_keys(SCREAMING_SNAKE_CASE__   )
      for src, dest in rename_keys:
            rename_key(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__   )
      read_in_swin_q_k_v(SCREAMING_SNAKE_CASE__		,						config.backbone_config   )
      read_in_decoder_q_k_v(SCREAMING_SNAKE_CASE__		,						SCREAMING_SNAKE_CASE__   )

      # update to torch tensors
      for key, value in state_dict.items():
            UpperCamelCase      = torch.from_numpy(SCREAMING_SNAKE_CASE__   )

      # load 🤗 model
      UpperCamelCase      = MaskFormerForInstanceSegmentation(SCREAMING_SNAKE_CASE__   )
      model.eval()

      for name, param in model.named_parameters():
            print(SCREAMING_SNAKE_CASE__		,						param.shape   )

      UpperCamelCase  ,UpperCamelCase      = model.load_state_dict(SCREAMING_SNAKE_CASE__		,						strict=SCREAMING_SNAKE_CASE__   )
      assert missing_keys == [
          "model.pixel_level_module.encoder.model.layernorm.weight",
          "model.pixel_level_module.encoder.model.layernorm.bias",
      ]
      assert len(SCREAMING_SNAKE_CASE__   ) == 0, F"Unexpected keys: {unexpected_keys}"

      # verify results
      UpperCamelCase      = prepare_img()
      if "vistas" in model_name:
            UpperCamelCase      = 65
      elif "cityscapes" in model_name:
            UpperCamelCase      = 65535
      else:
            UpperCamelCase      = 255
      UpperCamelCase      = True if """ade""" in model_name else False
      UpperCamelCase      = MaskFormerImageProcessor(ignore_index=SCREAMING_SNAKE_CASE__		,						reduce_labels=SCREAMING_SNAKE_CASE__   )

      UpperCamelCase      = image_processor(SCREAMING_SNAKE_CASE__		,						return_tensors="""pt"""   )

      UpperCamelCase      = model(**SCREAMING_SNAKE_CASE__   )

      print("""Logits:"""		,						outputs.class_queries_logits[0, :3, :3]   )

      if model_name == "maskformer-swin-tiny-ade":
            UpperCamelCase      = torch.tensor(
                [[3.6_353, -4.4_770, -2.6_065], [0.5_081, -4.2_394, -3.5_343], [2.1_909, -5.0_353, -1.9_323]]   )
      assert torch.allclose(outputs.class_queries_logits[0, :3, :3]		,						SCREAMING_SNAKE_CASE__		,						atol=1E-4   )
      print("""Looks ok!"""   )

      if pytorch_dump_folder_path is not None:
            print(F"Saving model and image processor to {pytorch_dump_folder_path}"   )
            Path(SCREAMING_SNAKE_CASE__   ).mkdir(exist_ok=SCREAMING_SNAKE_CASE__   )
            model.save_pretrained(SCREAMING_SNAKE_CASE__   )
            image_processor.save_pretrained(SCREAMING_SNAKE_CASE__   )

      if push_to_hub:
            print("""Pushing model and image processor to the hub..."""   )
            model.push_to_hub(F"nielsr/{model_name}"   )
            image_processor.push_to_hub(F"nielsr/{model_name}"   )


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE__     =       argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--model_name',
         default='maskformer-swin-tiny-ade',
         type=str,
         help=('Name of the MaskFormer model you\'d like to convert',),
     )
     parser.add_argument(
         '--checkpoint_path',
         default='/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl',
         type=str,
         help='Path to the original state dict (.pth file).',
     )
     parser.add_argument(
         '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
     )
     parser.add_argument(
         '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
     )

     SCREAMING_SNAKE_CASE__     =       parser.parse_args()
     convert_maskformer_checkpoint(
         args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
     )








'''simple docstring'''

import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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 PIL import Image

     from transformers import ConditionalDetrImageProcessor






class     a_						(      unittest.TestCase    ):

  def __init__(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE=7      ,		_SCREAMING_SNAKE_CASE=3      ,		_SCREAMING_SNAKE_CASE=30      ,		_SCREAMING_SNAKE_CASE=400      ,		_SCREAMING_SNAKE_CASE=True      ,		_SCREAMING_SNAKE_CASE=None      ,		_SCREAMING_SNAKE_CASE=True      ,		_SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]      ,		_SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]      ,		_SCREAMING_SNAKE_CASE=True      ,		_SCREAMING_SNAKE_CASE=1 / 255      ,		_SCREAMING_SNAKE_CASE=True      ,		)  ->				Any:



        """simple docstring"""






        UpperCamelCase      = size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 1333}
        UpperCamelCase      = parent
        UpperCamelCase      = batch_size
        UpperCamelCase      = num_channels
        UpperCamelCase      = min_resolution
        UpperCamelCase      = max_resolution
        UpperCamelCase      = do_resize
        UpperCamelCase      = size
        UpperCamelCase      = do_normalize
        UpperCamelCase      = image_mean
        UpperCamelCase      = image_std
        UpperCamelCase      = do_rescale
        UpperCamelCase      = rescale_factor
        UpperCamelCase      = do_pad

  def   A__						(				self				)  ->				str:



        """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,
            "do_rescale": self.do_rescale,
            "rescale_factor": self.rescale_factor,
            "do_pad": self.do_pad,
        }


  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE=False				)  ->				Optional[int]:



        """simple docstring"""






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

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

        return expected_height, expected_width






@require_torch
@require_vision
class     a_						(      lowerCamelCase     ,	unittest.TestCase    ):
  lowercase							=				ConditionalDetrImageProcessor if is_vision_available() else None

  def   A__						(				self				)  ->				Union[str, Any]:



        """simple docstring"""






        UpperCamelCase      = ConditionalDetrImageProcessingTester(self				)

  @property
  def   A__						(				self				)  ->				List[Any]:



        """simple docstring"""






        return self.image_processor_tester.prepare_image_processor_dict()

  def   A__						(				self				)  ->				str:



        """simple docstring"""






        UpperCamelCase      = 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"""				)				)

  def   A__						(				self				)  ->				Dict:



        """simple docstring"""






        UpperCamelCase      = self.image_processing_class.from_dict(self.image_processor_dict				)
        self.assertEqual(image_processor.size      ,		{"""shortest_edge""": 18, """longest_edge""": 1333}				)
        self.assertEqual(image_processor.do_pad      ,		_SCREAMING_SNAKE_CASE				)

        UpperCamelCase      = self.image_processing_class.from_dict(
            self.image_processor_dict      ,		size=42      ,		max_size=84      ,		pad_and_return_pixel_mask=_SCREAMING_SNAKE_CASE				)
        self.assertEqual(image_processor.size      ,		{"""shortest_edge""": 42, """longest_edge""": 84}				)
        self.assertEqual(image_processor.do_pad      ,		_SCREAMING_SNAKE_CASE				)

  def   A__						(				self				)  ->				Union[str, Any]:



        """simple docstring"""






        pass

  def   A__						(				self				)  ->				List[Any]:



        """simple docstring"""






        UpperCamelCase      = self.image_processing_class(**self.image_processor_dict				)
        # create random PIL images
        UpperCamelCase      = prepare_image_inputs(self.image_processor_tester      ,		equal_resolution=_SCREAMING_SNAKE_CASE				)
        for image in image_inputs:
              self.assertIsInstance(_SCREAMING_SNAKE_CASE      ,		Image.Image				)

        # Test not batched input
        UpperCamelCase      = image_processing(image_inputs[0]      ,		return_tensors="""pt"""				).pixel_values

        UpperCamelCase  ,UpperCamelCase      = self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE				)

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

        # Test batched
        UpperCamelCase  ,UpperCamelCase      = self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE      ,		batched=_SCREAMING_SNAKE_CASE				)

        UpperCamelCase      = image_processing(_SCREAMING_SNAKE_CASE      ,		return_tensors="""pt"""				).pixel_values
        self.assertEqual(
            encoded_images.shape      ,		(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )      ,		)

  def   A__						(				self				)  ->				Optional[int]:



        """simple docstring"""






        UpperCamelCase      = self.image_processing_class(**self.image_processor_dict				)
        # create random numpy tensors
        UpperCamelCase      = prepare_image_inputs(self.image_processor_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      = image_processing(image_inputs[0]      ,		return_tensors="""pt"""				).pixel_values

        UpperCamelCase  ,UpperCamelCase      = self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE				)

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

        # Test batched
        UpperCamelCase      = image_processing(_SCREAMING_SNAKE_CASE      ,		return_tensors="""pt"""				).pixel_values

        UpperCamelCase  ,UpperCamelCase      = self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE      ,		batched=_SCREAMING_SNAKE_CASE				)

        self.assertEqual(
            encoded_images.shape      ,		(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )      ,		)

  def   A__						(				self				)  ->				List[str]:



        """simple docstring"""






        UpperCamelCase      = self.image_processing_class(**self.image_processor_dict				)
        # create random PyTorch tensors
        UpperCamelCase      = prepare_image_inputs(self.image_processor_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      = image_processing(image_inputs[0]      ,		return_tensors="""pt"""				).pixel_values

        UpperCamelCase  ,UpperCamelCase      = self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE				)

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

        # Test batched
        UpperCamelCase      = image_processing(_SCREAMING_SNAKE_CASE      ,		return_tensors="""pt"""				).pixel_values

        UpperCamelCase  ,UpperCamelCase      = self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE      ,		batched=_SCREAMING_SNAKE_CASE				)

        self.assertEqual(
            encoded_images.shape      ,		(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )      ,		)

  @slow
  def   A__						(				self				)  ->				Any:



        """simple docstring"""






        UpperCamelCase      = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""				)
        with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""      ,		"""r"""				) as f:
              UpperCamelCase      = json.loads(f.read()				)

        UpperCamelCase      = {"""image_id""": 39769, """annotations""": target}

        # encode them
        UpperCamelCase      = ConditionalDetrImageProcessor.from_pretrained("""microsoft/conditional-detr-resnet-50"""				)
        UpperCamelCase      = image_processing(images=_SCREAMING_SNAKE_CASE      ,		annotations=_SCREAMING_SNAKE_CASE      ,		return_tensors="""pt"""				)

        # verify pixel values
        UpperCamelCase      = torch.Size([1, 3, 800, 1066]				)
        self.assertEqual(encoding["""pixel_values"""].shape      ,		_SCREAMING_SNAKE_CASE				)

        UpperCamelCase      = torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]				)
        self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]      ,		_SCREAMING_SNAKE_CASE      ,		atol=1e-4				)				)

        # verify area
        UpperCamelCase      = torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8]				)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]      ,		_SCREAMING_SNAKE_CASE				)				)
        # verify boxes
        UpperCamelCase      = torch.Size([6, 4]				)
        self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape      ,		_SCREAMING_SNAKE_CASE				)
        UpperCamelCase      = torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]				)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]      ,		_SCREAMING_SNAKE_CASE      ,		atol=1e-3				)				)
        # verify image_id
        UpperCamelCase      = torch.tensor([39769]				)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]      ,		_SCREAMING_SNAKE_CASE				)				)
        # verify is_crowd
        UpperCamelCase      = torch.tensor([0, 0, 0, 0, 0, 0]				)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]      ,		_SCREAMING_SNAKE_CASE				)				)
        # verify class_labels
        UpperCamelCase      = torch.tensor([75, 75, 63, 65, 17, 17]				)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]      ,		_SCREAMING_SNAKE_CASE				)				)
        # verify orig_size
        UpperCamelCase      = torch.tensor([480, 640]				)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]      ,		_SCREAMING_SNAKE_CASE				)				)
        # verify size
        UpperCamelCase      = torch.tensor([800, 1066]				)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]      ,		_SCREAMING_SNAKE_CASE				)				)


  @slow
  def   A__						(				self				)  ->				List[str]:



        """simple docstring"""






        UpperCamelCase      = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""				)
        with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"""      ,		"""r"""				) as f:
              UpperCamelCase      = json.loads(f.read()				)

        UpperCamelCase      = {"""file_name""": """000000039769.png""", """image_id""": 39769, """segments_info""": target}

        UpperCamelCase      = pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""				)

        # encode them
        UpperCamelCase      = ConditionalDetrImageProcessor(format="""coco_panoptic"""				)
        UpperCamelCase      = image_processing(images=_SCREAMING_SNAKE_CASE      ,		annotations=_SCREAMING_SNAKE_CASE      ,		masks_path=_SCREAMING_SNAKE_CASE      ,		return_tensors="""pt"""				)

        # verify pixel values
        UpperCamelCase      = torch.Size([1, 3, 800, 1066]				)
        self.assertEqual(encoding["""pixel_values"""].shape      ,		_SCREAMING_SNAKE_CASE				)

        UpperCamelCase      = torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]				)
        self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]      ,		_SCREAMING_SNAKE_CASE      ,		atol=1e-4				)				)

        # verify area
        UpperCamelCase      = torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]				)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]      ,		_SCREAMING_SNAKE_CASE				)				)
        # verify boxes
        UpperCamelCase      = torch.Size([6, 4]				)
        self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape      ,		_SCREAMING_SNAKE_CASE				)
        UpperCamelCase      = torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]				)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]      ,		_SCREAMING_SNAKE_CASE      ,		atol=1e-3				)				)
        # verify image_id
        UpperCamelCase      = torch.tensor([39769]				)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]      ,		_SCREAMING_SNAKE_CASE				)				)
        # verify is_crowd
        UpperCamelCase      = torch.tensor([0, 0, 0, 0, 0, 0]				)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]      ,		_SCREAMING_SNAKE_CASE				)				)
        # verify class_labels
        UpperCamelCase      = torch.tensor([17, 17, 63, 75, 75, 93]				)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]      ,		_SCREAMING_SNAKE_CASE				)				)
        # verify masks
        UpperCamelCase      = 822873
        self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()      ,		_SCREAMING_SNAKE_CASE				)
        # verify orig_size
        UpperCamelCase      = torch.tensor([480, 640]				)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]      ,		_SCREAMING_SNAKE_CASE				)				)
        # verify size
        UpperCamelCase      = torch.tensor([800, 1066]				)
        self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]      ,		_SCREAMING_SNAKE_CASE				)				)
