



import collections
import inspect
import unittest
from typing import Dict, List, Tuple

from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available

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


if is_torch_available():
   import torch
   from torch import nn

   from transformers import MaskFormerSwinBackbone
   from transformers.models.maskformer import MaskFormerSwinModel


class 	lowerCamelCase__	:



 """simple docstring"""

 def __init__(    self					:					Union[str, Any]			,						__lowerCAmelCase					:					List[str]			,						__lowerCAmelCase					:					str=13			,						__lowerCAmelCase					:					Optional[int]=32			,						__lowerCAmelCase					:					Any=2			,						__lowerCAmelCase					:					Dict=3			,						__lowerCAmelCase					:					List[Any]=16			,						__lowerCAmelCase					:					Any=[1, 2, 1]			,						__lowerCAmelCase					:					int=[2, 2, 4]			,						__lowerCAmelCase					:					Any=2			,						__lowerCAmelCase					:					Dict=2.0			,						__lowerCAmelCase					:					Dict=True			,						__lowerCAmelCase					:					Union[str, Any]=0.0			,						__lowerCAmelCase					:					List[str]=0.0			,						__lowerCAmelCase					:					Dict=0.1			,						__lowerCAmelCase					:					str="gelu"			,						__lowerCAmelCase					:					List[str]=False			,						__lowerCAmelCase					:					str=True			,						__lowerCAmelCase					:					Optional[int]=0.02			,						__lowerCAmelCase					:					Union[str, Any]=1E-5			,						__lowerCAmelCase					:					int=True			,						__lowerCAmelCase					:					Optional[int]=None			,						__lowerCAmelCase					:					int=True			,						__lowerCAmelCase					:					Optional[Any]=10			,						__lowerCAmelCase					:					Optional[Any]=8			,						__lowerCAmelCase					:					List[str]=["stage1", "stage2", "stage3"]			,						__lowerCAmelCase					:					int=[1, 2, 3]			,						)   ->	Union[str, Any]:
    _A      =       parent
    _A      =       batch_size
    _A      =       image_size
    _A      =       patch_size
    _A      =       num_channels
    _A      =       embed_dim
    _A      =       depths
    _A      =       num_heads
    _A      =       window_size
    _A      =       mlp_ratio
    _A      =       qkv_bias
    _A      =       hidden_dropout_prob
    _A      =       attention_probs_dropout_prob
    _A      =       drop_path_rate
    _A      =       hidden_act
    _A      =       use_absolute_embeddings
    _A      =       patch_norm
    _A      =       layer_norm_eps
    _A      =       initializer_range
    _A      =       is_training
    _A      =       scope
    _A      =       use_labels
    _A      =       type_sequence_label_size
    _A      =       encoder_stride
    _A      =       out_features
    _A      =       out_indices

 def     snake_case_      (    self					:					Tuple      )   ->	Dict:
    _A      =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

    _A      =       None
    if self.use_labels:
       _A      =       ids_tensor([self.batch_size]			,						self.type_sequence_label_size      )

    _A      =       self.get_config()

    return config, pixel_values, labels

 def     snake_case_      (    self					:					Optional[Any]      )   ->	Any:
    return MaskFormerSwinConfig(
        image_size=self.image_size			,						patch_size=self.patch_size			,						num_channels=self.num_channels			,						embed_dim=self.embed_dim			,						depths=self.depths			,						num_heads=self.num_heads			,						window_size=self.window_size			,						mlp_ratio=self.mlp_ratio			,						qkv_bias=self.qkv_bias			,						hidden_dropout_prob=self.hidden_dropout_prob			,						attention_probs_dropout_prob=self.attention_probs_dropout_prob			,						drop_path_rate=self.drop_path_rate			,						hidden_act=self.hidden_act			,						use_absolute_embeddings=self.use_absolute_embeddings			,						path_norm=self.patch_norm			,						layer_norm_eps=self.layer_norm_eps			,						initializer_range=self.initializer_range			,						encoder_stride=self.encoder_stride			,						out_features=self.out_features			,						out_indices=self.out_indices			,						)

 def     snake_case_      (    self					:					Optional[Any]			,						__lowerCAmelCase					:					Union[str, Any]			,						__lowerCAmelCase					:					int			,						__lowerCAmelCase					:					List[Any]      )   ->	Optional[Any]:
    _A      =       MaskFormerSwinModel(config=__lowerCAmelCase      )
    model.to(__lowerCAmelCase      )
    model.eval()
    _A      =       model(__lowerCAmelCase      )

    _A      =       ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths      ) - 1))
    _A      =       int(config.embed_dim * 2 ** (len(config.depths      ) - 1)      )

    self.parent.assertEqual(result.last_hidden_state.shape			,						(self.batch_size, expected_seq_len, expected_dim)      )

 def     snake_case_      (    self					:					Optional[int]			,						__lowerCAmelCase					:					Optional[int]			,						__lowerCAmelCase					:					List[Any]			,						__lowerCAmelCase					:					Optional[int]      )   ->	Tuple:
    _A      =       MaskFormerSwinBackbone(config=__lowerCAmelCase      )
    model.to(__lowerCAmelCase      )
    model.eval()
    _A      =       model(__lowerCAmelCase      )

    # verify feature maps
    self.parent.assertEqual(len(result.feature_maps      )			,						len(config.out_features      )      )
    self.parent.assertListEqual(list(result.feature_maps[0].shape      )			,						[13, 16, 16, 16]      )

    # verify channels
    self.parent.assertEqual(len(model.channels      )			,						len(config.out_features      )      )
    self.parent.assertListEqual(model.channels			,						[16, 32, 64]      )

    # verify ValueError
    with self.parent.assertRaises(__lowerCAmelCase      ):
       _A      =       ['''stem''']
       _A      =       MaskFormerSwinBackbone(config=__lowerCAmelCase      )

 def     snake_case_      (    self					:					List[str]      )   ->	Union[str, Any]:
    _A      =       self.prepare_config_and_inputs()
    _A  ,    _A  ,    _A      =       config_and_inputs
    _A      =       {'''pixel_values''': pixel_values}
    return config, inputs_dict


@require_torch
class 	lowerCamelCase__	(       _A							,  _A							,  unittest.TestCase):



 """simple docstring"""

 a__					: List[Any] =  (
     (
         MaskFormerSwinModel,
         MaskFormerSwinBackbone,
     )
     if is_torch_available()
     else ()
 )
 a__					: Dict =  {"feature-extraction": MaskFormerSwinModel} if is_torch_available() else {}
 a__					: Union[str, Any] =  False
 a__					: str =  False
 a__					: str =  False
 a__					: List[Any] =  False
 a__					: Optional[Any] =  False

 def     snake_case_      (    self					:					Optional[Any]      )   ->	int:
    _A      =       MaskFormerSwinModelTester(self      )
    _A      =       ConfigTester(self			,						config_class=__lowerCAmelCase			,						embed_dim=37      )

 @require_torch_multi_gpu
 @unittest.skip(
     reason=(
         '''`MaskFormerSwinModel` outputs `hidden_states_spatial_dimensions` which doesn\'t work well with'''
         ''' `nn.DataParallel`'''
     )      )
 def     snake_case_      (    self					:					Tuple      )   ->	List[Any]:
    pass

 def     snake_case_      (    self					:					List[str]      )   ->	List[str]:
    self.create_and_test_config_common_properties()
    self.config_tester.create_and_test_config_to_json_string()
    self.config_tester.create_and_test_config_to_json_file()
    self.config_tester.create_and_test_config_from_and_save_pretrained()
    self.config_tester.create_and_test_config_with_num_labels()
    self.config_tester.check_config_can_be_init_without_params()
    self.config_tester.check_config_arguments_init()

 def     snake_case_      (    self					:					str      )   ->	Any:
    return

 def     snake_case_      (    self					:					List[Any]      )   ->	Optional[int]:
    _A      =       self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*__lowerCAmelCase      )

 def     snake_case_      (    self					:					List[Any]      )   ->	Any:
    _A      =       self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_backbone(*__lowerCAmelCase      )

 @unittest.skip('''Swin does not use inputs_embeds'''      )
 def     snake_case_      (    self					:					Tuple      )   ->	Union[str, Any]:
    pass

 @unittest.skip('''Swin does not support feedforward chunking'''      )
 def     snake_case_      (    self					:					str      )   ->	Any:
    pass

 def     snake_case_      (    self					:					List[Any]      )   ->	Union[str, Any]:
    _A  ,    _A      =       self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
       _A      =       model_class(__lowerCAmelCase      )
       self.assertIsInstance(model.get_input_embeddings()			,						(nn.Module)      )
       _A      =       model.get_output_embeddings()
       self.assertTrue(x is None or isinstance(__lowerCAmelCase			,						nn.Linear      )      )

 def     snake_case_      (    self					:					List[str]      )   ->	Dict:
    _A  ,    _A      =       self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
       _A      =       model_class(__lowerCAmelCase      )
       _A      =       inspect.signature(model.forward      )
       # signature.parameters is an OrderedDict => so arg_names order is deterministic
       _A      =       [*signature.parameters.keys()]

       _A      =       ['''pixel_values''']
       self.assertListEqual(arg_names[:1]			,						__lowerCAmelCase      )

 @unittest.skip(reason='''MaskFormerSwin is only used as backbone and doesn\'t support output_attentions'''      )
 def     snake_case_      (    self					:					int      )   ->	Dict:
    pass

 @unittest.skip(reason='''MaskFormerSwin is only used as an internal backbone'''      )
 def     snake_case_      (    self					:					Optional[Any]      )   ->	int:
    pass

 def     snake_case_      (    self					:					int			,						__lowerCAmelCase					:					int			,						__lowerCAmelCase					:					Optional[int]			,						__lowerCAmelCase					:					int			,						__lowerCAmelCase					:					List[str]      )   ->	Optional[Any]:
    _A      =       model_class(__lowerCAmelCase      )
    model.to(__lowerCAmelCase      )
    model.eval()

    with torch.no_grad():
       _A      =       model(**self._prepare_for_class(__lowerCAmelCase			,						__lowerCAmelCase      )      )

    _A      =       outputs.hidden_states

    _A      =       getattr(
        self.model_tester			,						'''expected_num_hidden_layers'''			,						len(self.model_tester.depths      ) + 1      )
    self.assertEqual(len(__lowerCAmelCase      )			,						__lowerCAmelCase      )

    # Swin has a different seq_length
    _A      =       (
        config.patch_size
        if isinstance(config.patch_size			,						collections.abc.Iterable      )
        else (config.patch_size, config.patch_size)
    )

    _A      =       (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

    self.assertListEqual(
        list(hidden_states[0].shape[-2:]      )			,						[num_patches, self.model_tester.embed_dim]			,						)

 def     snake_case_      (    self					:					Any      )   ->	int:
    _A  ,    _A      =       self.model_tester.prepare_config_and_inputs_for_common()

    _A      =       (
        self.model_tester.image_size
        if isinstance(self.model_tester.image_size			,						collections.abc.Iterable      )
        else (self.model_tester.image_size, self.model_tester.image_size)
    )

    for model_class in self.all_model_classes:
       _A      =       True
       self.check_hidden_states_output(__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase      )

       # check that output_hidden_states also work using config
       del inputs_dict["output_hidden_states"]
       _A      =       True

       self.check_hidden_states_output(__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase      )

 def     snake_case_      (    self					:					Optional[Any]      )   ->	Optional[Any]:
    _A  ,    _A      =       self.model_tester.prepare_config_and_inputs_for_common()
    _A      =       3

    _A      =       (
        self.model_tester.image_size
        if isinstance(self.model_tester.image_size			,						collections.abc.Iterable      )
        else (self.model_tester.image_size, self.model_tester.image_size)
    )
    _A      =       (
        config.patch_size
        if isinstance(config.patch_size			,						collections.abc.Iterable      )
        else (config.patch_size, config.patch_size)
    )

    _A      =       image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
    _A      =       image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

    for model_class in self.all_model_classes:
       _A      =       True
       self.check_hidden_states_output(__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase			,						(padded_height, padded_width)      )

       # check that output_hidden_states also work using config
       del inputs_dict["output_hidden_states"]
       _A      =       True
       self.check_hidden_states_output(__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase			,						(padded_height, padded_width)      )

 @unittest.skip(reason='''MaskFormerSwin doesn\'t have pretrained checkpoints'''      )
 def     snake_case_      (    self					:					Union[str, Any]      )   ->	List[Any]:
    pass

 @unittest.skip(reason='''This will be fixed once MaskFormerSwin is replaced by native Swin'''      )
 def     snake_case_      (    self					:					Union[str, Any]      )   ->	Union[str, Any]:
    pass

 @unittest.skip(reason='''This will be fixed once MaskFormerSwin is replaced by native Swin'''      )
 def     snake_case_      (    self					:					int      )   ->	Union[str, Any]:
    pass

 def     snake_case_      (    self					:					List[Any]      )   ->	Any:
    _A  ,    _A      =       self.model_tester.prepare_config_and_inputs_for_common()

    def set_nan_tensor_to_zero(__lowerCAmelCase					:					Dict      ):
       _A      =       0
       return t

    def check_equivalence(__lowerCAmelCase					:					str			,						__lowerCAmelCase					:					Optional[int]			,						__lowerCAmelCase					:					Dict			,						__lowerCAmelCase					:					Optional[Any]={}      ):
       with torch.no_grad():
          _A      =       model(**__lowerCAmelCase			,						return_dict=__lowerCAmelCase			,						**__lowerCAmelCase      )
          _A      =       model(**__lowerCAmelCase			,						return_dict=__lowerCAmelCase			,						**__lowerCAmelCase      ).to_tuple()

          def recursive_check(__lowerCAmelCase					:					int			,						__lowerCAmelCase					:					Dict      ):
             if isinstance(__lowerCAmelCase			,						(List, Tuple)      ):
                for tuple_iterable_value, dict_iterable_value in zip(__lowerCAmelCase			,						__lowerCAmelCase      ):
                   recursive_check(__lowerCAmelCase			,						__lowerCAmelCase      )
             elif isinstance(__lowerCAmelCase			,						__lowerCAmelCase      ):
                for tuple_iterable_value, dict_iterable_value in zip(
                    tuple_object.values()			,						dict_object.values()      ):
                   recursive_check(__lowerCAmelCase			,						__lowerCAmelCase      )
             elif tuple_object is None:
                return
             else:
                self.assertTrue(
                    torch.allclose(
                        set_nan_tensor_to_zero(__lowerCAmelCase      )			,						set_nan_tensor_to_zero(__lowerCAmelCase      )			,						atol=1E-5      )			,						msg=(
                        '''Tuple and dict output are not equal. Difference:'''
                        f''' {torch.max(torch.abs(tuple_object - dict_object      )      )}. Tuple has `nan`:'''
                        f''' {torch.isnan(__lowerCAmelCase      ).any()} and `inf`: {torch.isinf(__lowerCAmelCase      )}. Dict has'''
                        f''' `nan`: {torch.isnan(__lowerCAmelCase      ).any()} and `inf`: {torch.isinf(__lowerCAmelCase      )}.'''
                    )			,						)

          recursive_check(__lowerCAmelCase			,						__lowerCAmelCase      )

    for model_class in self.all_model_classes:
       _A      =       model_class(__lowerCAmelCase      )
       model.to(__lowerCAmelCase      )
       model.eval()

       _A      =       self._prepare_for_class(__lowerCAmelCase			,						__lowerCAmelCase      )
       _A      =       self._prepare_for_class(__lowerCAmelCase			,						__lowerCAmelCase      )
       check_equivalence(__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase      )

       _A      =       self._prepare_for_class(__lowerCAmelCase			,						__lowerCAmelCase			,						return_labels=__lowerCAmelCase      )
       _A      =       self._prepare_for_class(__lowerCAmelCase			,						__lowerCAmelCase			,						return_labels=__lowerCAmelCase      )
       check_equivalence(__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase      )

       _A      =       self._prepare_for_class(__lowerCAmelCase			,						__lowerCAmelCase      )
       _A      =       self._prepare_for_class(__lowerCAmelCase			,						__lowerCAmelCase      )
       check_equivalence(__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase			,						{'''output_hidden_states''': True}      )

       _A      =       self._prepare_for_class(__lowerCAmelCase			,						__lowerCAmelCase			,						return_labels=__lowerCAmelCase      )
       _A      =       self._prepare_for_class(__lowerCAmelCase			,						__lowerCAmelCase			,						return_labels=__lowerCAmelCase      )
       check_equivalence(__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase			,						{'''output_hidden_states''': True}      )


@require_torch
class 	lowerCamelCase__	(       unittest.TestCase							,  _A):



 """simple docstring"""

 a__					: List[str] =  (MaskFormerSwinBackbone,) if is_torch_available() else ()
 a__					: Any =  MaskFormerSwinConfig

 def     snake_case_      (    self					:					Any      )   ->	Any:
    _A      =       MaskFormerSwinModelTester(self      )

 def     snake_case_      (    self					:					List[Any]      )   ->	Tuple:
    _A  ,    _A      =       self.model_tester.prepare_config_and_inputs_for_common()
    _A      =       inputs_dict['''pixel_values'''].shape[0]

    for backbone_class in self.all_model_classes:
       _A      =       backbone_class(__lowerCAmelCase      )
       backbone.to(__lowerCAmelCase      )
       backbone.eval()

       _A      =       backbone(**__lowerCAmelCase      )

       # Test default outputs and verify feature maps
       self.assertIsInstance(outputs.feature_maps			,						__lowerCAmelCase      )
       self.assertTrue(len(outputs.feature_maps      ) == len(backbone.channels      )      )
       for feature_map, n_channels in zip(outputs.feature_maps			,						backbone.channels      ):
          self.assertTrue(feature_map.shape[:2]			,						(batch_size, n_channels)      )
       self.assertIsNone(outputs.hidden_states      )
       self.assertIsNone(outputs.attentions      )

       # Test output_hidden_states=True
       _A      =       backbone(**__lowerCAmelCase			,						output_hidden_states=__lowerCAmelCase      )
       self.assertIsNotNone(outputs.hidden_states      )
       self.assertTrue(len(outputs.hidden_states      )			,						len(backbone.stage_names      )      )
       # We skip the stem layer
       for hidden_states, n_channels in zip(outputs.hidden_states[1:]			,						backbone.channels      ):
          for hidden_state in hidden_states:
             # Hidden states are in the format (batch_size, (height * width), n_channels)
             _A  ,    _A  ,    _A      =       hidden_state.shape
             self.assertTrue((h_batch_size, h_n_channels)			,						(batch_size, n_channels)      )

            # Test output_attentions=True
       if self.has_attentions:
          _A      =       backbone(**__lowerCAmelCase			,						output_attentions=__lowerCAmelCase      )
          self.assertIsNotNone(outputs.attentions      )








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 DeformableDetrImageProcessor


class 	lowerCamelCase__	(       unittest.TestCase):



 """simple docstring"""

 def __init__(    self					:					Optional[int]			,						__lowerCAmelCase					:					List[Any]			,						__lowerCAmelCase					:					Dict=7			,						__lowerCAmelCase					:					Tuple=3			,						__lowerCAmelCase					:					int=30			,						__lowerCAmelCase					:					Dict=4_00			,						__lowerCAmelCase					:					Optional[Any]=True			,						__lowerCAmelCase					:					List[str]=None			,						__lowerCAmelCase					:					Dict=True			,						__lowerCAmelCase					:					Optional[Any]=[0.5, 0.5, 0.5]			,						__lowerCAmelCase					:					Dict=[0.5, 0.5, 0.5]			,						__lowerCAmelCase					:					List[str]=True			,						__lowerCAmelCase					:					List[str]=1 / 2_55			,						__lowerCAmelCase					:					int=True			,						)   ->	List[str]:
    # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
    _A      =       size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 13_33}
    _A      =       parent
    _A      =       batch_size
    _A      =       num_channels
    _A      =       min_resolution
    _A      =       max_resolution
    _A      =       do_resize
    _A      =       size
    _A      =       do_normalize
    _A      =       image_mean
    _A      =       image_std
    _A      =       do_rescale
    _A      =       rescale_factor
    _A      =       do_pad

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

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

    return expected_height, expected_width


@require_torch
@require_vision
class 	lowerCamelCase__	(       _A							,  unittest.TestCase):



 """simple docstring"""

 a__					: Any =  DeformableDetrImageProcessor if is_vision_available() else None

 def     snake_case_      (    self					:					Optional[int]      )   ->	Any:
    _A      =       DeformableDetrImageProcessingTester(self      )

 @property
 def     snake_case_      (    self					:					Union[str, Any]      )   ->	Dict:
    return self.image_processor_tester.prepare_image_processor_dict()

 def     snake_case_      (    self					:					Optional[int]      )   ->	List[str]:
    _A      =       self.image_processing_class(**self.image_processor_dict      )
    self.assertTrue(hasattr(__lowerCAmelCase			,						'''image_mean'''      )      )
    self.assertTrue(hasattr(__lowerCAmelCase			,						'''image_std'''      )      )
    self.assertTrue(hasattr(__lowerCAmelCase			,						'''do_normalize'''      )      )
    self.assertTrue(hasattr(__lowerCAmelCase			,						'''do_resize'''      )      )
    self.assertTrue(hasattr(__lowerCAmelCase			,						'''do_rescale'''      )      )
    self.assertTrue(hasattr(__lowerCAmelCase			,						'''do_pad'''      )      )
    self.assertTrue(hasattr(__lowerCAmelCase			,						'''size'''      )      )

 def     snake_case_      (    self					:					List[str]      )   ->	int:
    _A      =       self.image_processing_class.from_dict(self.image_processor_dict      )
    self.assertEqual(image_processor.size			,						{'''shortest_edge''': 18, '''longest_edge''': 13_33}      )
    self.assertEqual(image_processor.do_pad			,						__lowerCAmelCase      )

    _A      =       self.image_processing_class.from_dict(
        self.image_processor_dict			,						size=42			,						max_size=84			,						pad_and_return_pixel_mask=__lowerCAmelCase      )
    self.assertEqual(image_processor.size			,						{'''shortest_edge''': 42, '''longest_edge''': 84}      )
    self.assertEqual(image_processor.do_pad			,						__lowerCAmelCase      )

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

 def     snake_case_      (    self					:					List[str]      )   ->	Optional[int]:
    # Initialize image_processing
    _A      =       self.image_processing_class(**self.image_processor_dict      )
    # create random PIL images
    _A      =       prepare_image_inputs(self.image_processor_tester			,						equal_resolution=__lowerCAmelCase      )
    for image in image_inputs:
       self.assertIsInstance(__lowerCAmelCase			,						Image.Image      )

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

    _A  ,    _A      =       self.image_processor_tester.get_expected_values(__lowerCAmelCase      )

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

    # Test batched
    _A  ,    _A      =       self.image_processor_tester.get_expected_values(__lowerCAmelCase			,						batched=__lowerCAmelCase      )

    _A      =       image_processing(__lowerCAmelCase			,						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     snake_case_      (    self					:					Tuple      )   ->	int:
    # Initialize image_processing
    _A      =       self.image_processing_class(**self.image_processor_dict      )
    # create random numpy tensors
    _A      =       prepare_image_inputs(self.image_processor_tester			,						equal_resolution=__lowerCAmelCase			,						numpify=__lowerCAmelCase      )
    for image in image_inputs:
       self.assertIsInstance(__lowerCAmelCase			,						np.ndarray      )

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

    _A  ,    _A      =       self.image_processor_tester.get_expected_values(__lowerCAmelCase      )

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

    # Test batched
    _A      =       image_processing(__lowerCAmelCase			,						return_tensors='''pt'''      ).pixel_values

    _A  ,    _A      =       self.image_processor_tester.get_expected_values(__lowerCAmelCase			,						batched=__lowerCAmelCase      )

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

 def     snake_case_      (    self					:					Optional[Any]      )   ->	int:
    # Initialize image_processing
    _A      =       self.image_processing_class(**self.image_processor_dict      )
    # create random PyTorch tensors
    _A      =       prepare_image_inputs(self.image_processor_tester			,						equal_resolution=__lowerCAmelCase			,						torchify=__lowerCAmelCase      )
    for image in image_inputs:
       self.assertIsInstance(__lowerCAmelCase			,						torch.Tensor      )

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

    _A  ,    _A      =       self.image_processor_tester.get_expected_values(__lowerCAmelCase      )

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

    # Test batched
    _A      =       image_processing(__lowerCAmelCase			,						return_tensors='''pt'''      ).pixel_values

    _A  ,    _A      =       self.image_processor_tester.get_expected_values(__lowerCAmelCase			,						batched=__lowerCAmelCase      )

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

 @slow
 def     snake_case_      (    self					:					Optional[Any]      )   ->	Optional[int]:
    # prepare image and target
    _A      =       Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
    with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''			,						'''r'''      ) as f:
       _A      =       json.loads(f.read()      )

    _A      =       {'''image_id''': 3_97_69, '''annotations''': target}

    # encode them
    _A      =       DeformableDetrImageProcessor()
    _A      =       image_processing(images=__lowerCAmelCase			,						annotations=__lowerCAmelCase			,						return_tensors='''pt'''      )

    # verify pixel values
    _A      =       torch.Size([1, 3, 8_00, 10_66]      )
    self.assertEqual(encoding['''pixel_values'''].shape			,						__lowerCAmelCase      )

    _A      =       torch.tensor([0.2796, 0.3138, 0.3481]      )
    self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]			,						__lowerCAmelCase			,						atol=1E-4      )      )

    # verify area
    _A      =       torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]      )
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']			,						__lowerCAmelCase      )      )
    # verify boxes
    _A      =       torch.Size([6, 4]      )
    self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape			,						__lowerCAmelCase      )
    _A      =       torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]      )
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]			,						__lowerCAmelCase			,						atol=1E-3      )      )
    # verify image_id
    _A      =       torch.tensor([3_97_69]      )
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']			,						__lowerCAmelCase      )      )
    # verify is_crowd
    _A      =       torch.tensor([0, 0, 0, 0, 0, 0]      )
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']			,						__lowerCAmelCase      )      )
    # verify class_labels
    _A      =       torch.tensor([75, 75, 63, 65, 17, 17]      )
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']			,						__lowerCAmelCase      )      )
    # verify orig_size
    _A      =       torch.tensor([4_80, 6_40]      )
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']			,						__lowerCAmelCase      )      )
    # verify size
    _A      =       torch.tensor([8_00, 10_66]      )
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']			,						__lowerCAmelCase      )      )

 @slow
 def     snake_case_      (    self					:					List[str]      )   ->	List[str]:
    # prepare image, target and masks_path
    _A      =       Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''      )
    with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''			,						'''r'''      ) as f:
       _A      =       json.loads(f.read()      )

    _A      =       {'''file_name''': '''000000039769.png''', '''image_id''': 3_97_69, '''segments_info''': target}

    _A      =       pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''      )

    # encode them
    _A      =       DeformableDetrImageProcessor(format='''coco_panoptic'''      )
    _A      =       image_processing(images=__lowerCAmelCase			,						annotations=__lowerCAmelCase			,						masks_path=__lowerCAmelCase			,						return_tensors='''pt'''      )

    # verify pixel values
    _A      =       torch.Size([1, 3, 8_00, 10_66]      )
    self.assertEqual(encoding['''pixel_values'''].shape			,						__lowerCAmelCase      )

    _A      =       torch.tensor([0.2796, 0.3138, 0.3481]      )
    self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]			,						__lowerCAmelCase			,						atol=1E-4      )      )

    # verify area
    _A      =       torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147]      )
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']			,						__lowerCAmelCase      )      )
    # verify boxes
    _A      =       torch.Size([6, 4]      )
    self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape			,						__lowerCAmelCase      )
    _A      =       torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]      )
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]			,						__lowerCAmelCase			,						atol=1E-3      )      )
    # verify image_id
    _A      =       torch.tensor([3_97_69]      )
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']			,						__lowerCAmelCase      )      )
    # verify is_crowd
    _A      =       torch.tensor([0, 0, 0, 0, 0, 0]      )
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']			,						__lowerCAmelCase      )      )
    # verify class_labels
    _A      =       torch.tensor([17, 17, 63, 75, 75, 93]      )
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']			,						__lowerCAmelCase      )      )
    # verify masks
    _A      =       82_28_73
    self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()			,						__lowerCAmelCase      )
    # verify orig_size
    _A      =       torch.tensor([4_80, 6_40]      )
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']			,						__lowerCAmelCase      )      )
    # verify size
    _A      =       torch.tensor([8_00, 10_66]      )
    self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']			,						__lowerCAmelCase      )      )



