





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       :   Optional[Any]					= logging.get_logger(__name__)

# General docstring
A       :   Optional[int]					= '''ResNetConfig'''

# Base docstring
A       :   str					= '''microsoft/resnet-50'''
A       :   Any					= [1, 2_0_4_8, 7, 7]

# Image classification docstring
A       :   Any					= '''microsoft/resnet-50'''
A       :   List[Any]					= '''tiger cat'''

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


class     A     (nn.Module       ):
			'''simple docstring'''




			def __init__(     self  :       Union[str, Any]				,      __lowerCAmelCase  :       int				,      __lowerCAmelCase  :       int				,      __lowerCAmelCase  :       int = 3				,      __lowerCAmelCase  :       int = 1				,      __lowerCAmelCase  :       str = "relu"  )      ->      Tuple:







					"""simple docstring"""






					super().__init__()
					A__     =  nn.Convad(
					    __lowerCAmelCase				,      __lowerCAmelCase				,      kernel_size=__lowerCAmelCase				,      stride=__lowerCAmelCase				,      padding=kernel_size // 2				,      bias=__lowerCAmelCase  )
					A__     =  nn.BatchNormad(__lowerCAmelCase  )
					A__     =  ACTaFN[activation] if activation is not None else nn.Identity()







			def   a_			(     self  :       Tuple				,      __lowerCAmelCase  :       Tensor  )      ->      Tensor:







					"""simple docstring"""






					A__     =  self.convolution(__lowerCAmelCase  )
					A__     =  self.normalization(__lowerCAmelCase  )
					A__     =  self.activation(__lowerCAmelCase  )
					return hidden_state


class     A     (nn.Module       ):
			'''simple docstring'''




			def __init__(     self  :       Optional[int]				,      __lowerCAmelCase  :       ResNetConfig  )      ->      List[Any]:







					"""simple docstring"""






					super().__init__()
					A__     =  ResNetConvLayer(
					    config.num_channels				,      config.embedding_size				,      kernel_size=7				,      stride=2				,      activation=config.hidden_act  )
					A__     =  nn.MaxPoolad(kernel_size=3				,      stride=2				,      padding=1  )
					A__     =  config.num_channels







			def   a_			(     self  :       List[str]				,      __lowerCAmelCase  :       Tensor  )      ->      Tensor:







					"""simple docstring"""






					A__     =  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."""  )
					A__     =  self.embedder(__lowerCAmelCase  )
					A__     =  self.pooler(__lowerCAmelCase  )
					return embedding


class     A     (nn.Module       ):
			'''simple docstring'''




			def __init__(     self  :       str				,      __lowerCAmelCase  :       int				,      __lowerCAmelCase  :       int				,      __lowerCAmelCase  :       int = 2  )      ->      List[str]:







					"""simple docstring"""






					super().__init__()
					A__     =  nn.Convad(__lowerCAmelCase				,      __lowerCAmelCase				,      kernel_size=1				,      stride=__lowerCAmelCase				,      bias=__lowerCAmelCase  )
					A__     =  nn.BatchNormad(__lowerCAmelCase  )







			def   a_			(     self  :       List[Any]				,      __lowerCAmelCase  :       Tensor  )      ->      Tensor:







					"""simple docstring"""






					A__     =  self.convolution(__lowerCAmelCase  )
					A__     =  self.normalization(__lowerCAmelCase  )
					return hidden_state


class     A     (nn.Module       ):
			'''simple docstring'''




			def __init__(     self  :       Optional[int]				,      __lowerCAmelCase  :       int				,      __lowerCAmelCase  :       int				,      __lowerCAmelCase  :       int = 1				,      __lowerCAmelCase  :       str = "relu"  )      ->      Dict:







					"""simple docstring"""






					super().__init__()
					A__     =  in_channels != out_channels or stride != 1
					A__     =  (
					    ResNetShortCut(__lowerCAmelCase				,      __lowerCAmelCase				,      stride=__lowerCAmelCase  ) if should_apply_shortcut else nn.Identity()
					)
					A__     =  nn.Sequential(
					    ResNetConvLayer(__lowerCAmelCase				,      __lowerCAmelCase				,      stride=__lowerCAmelCase  )				,      ResNetConvLayer(__lowerCAmelCase				,      __lowerCAmelCase				,      activation=__lowerCAmelCase  )				,      )
					A__     =  ACTaFN[activation]







			def   a_			(     self  :       Any				,      __lowerCAmelCase  :       Any  )      ->      List[str]:







					"""simple docstring"""






					A__     =  hidden_state
					A__     =  self.layer(__lowerCAmelCase  )
					A__     =  self.shortcut(__lowerCAmelCase  )
					hidden_state += residual
					A__     =  self.activation(__lowerCAmelCase  )
					return hidden_state


class     A     (nn.Module       ):
			'''simple docstring'''




			def __init__(     self  :       Tuple				,      __lowerCAmelCase  :       int				,      __lowerCAmelCase  :       int				,      __lowerCAmelCase  :       int = 1				,      __lowerCAmelCase  :       str = "relu"				,      __lowerCAmelCase  :       int = 4  )      ->      Optional[Any]:







					"""simple docstring"""






					super().__init__()
					A__     =  in_channels != out_channels or stride != 1
					A__     =  out_channels // reduction
					A__     =  (
					    ResNetShortCut(__lowerCAmelCase				,      __lowerCAmelCase				,      stride=__lowerCAmelCase  ) if should_apply_shortcut else nn.Identity()
					)
					A__     =  nn.Sequential(
					    ResNetConvLayer(__lowerCAmelCase				,      __lowerCAmelCase				,      kernel_size=1  )				,      ResNetConvLayer(__lowerCAmelCase				,      __lowerCAmelCase				,      stride=__lowerCAmelCase  )				,      ResNetConvLayer(__lowerCAmelCase				,      __lowerCAmelCase				,      kernel_size=1				,      activation=__lowerCAmelCase  )				,      )
					A__     =  ACTaFN[activation]







			def   a_			(     self  :       Any				,      __lowerCAmelCase  :       List[str]  )      ->      Optional[Any]:







					"""simple docstring"""






					A__     =  hidden_state
					A__     =  self.layer(__lowerCAmelCase  )
					A__     =  self.shortcut(__lowerCAmelCase  )
					hidden_state += residual
					A__     =  self.activation(__lowerCAmelCase  )
					return hidden_state


class     A     (nn.Module       ):
			'''simple docstring'''




			def __init__(     self  :       str				,      __lowerCAmelCase  :       ResNetConfig				,      __lowerCAmelCase  :       int				,      __lowerCAmelCase  :       int				,      __lowerCAmelCase  :       int = 2				,      __lowerCAmelCase  :       int = 2				,      )      ->      Tuple:







					"""simple docstring"""






					super().__init__()

					A__     =  ResNetBottleNeckLayer if config.layer_type == """bottleneck""" else ResNetBasicLayer

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







			def   a_			(     self  :       Any				,      __lowerCAmelCase  :       Tensor  )      ->      Tensor:







					"""simple docstring"""






					A__     =  input
					for layer in self.layers:
							A__     =  layer(__lowerCAmelCase  )
					return hidden_state


class     A     (nn.Module       ):
			'''simple docstring'''




			def __init__(     self  :       Dict				,      __lowerCAmelCase  :       ResNetConfig  )      ->      Optional[Any]:







					"""simple docstring"""






					super().__init__()
					A__     =  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(
					        __lowerCAmelCase				,      config.embedding_size				,      config.hidden_sizes[0]				,      stride=2 if config.downsample_in_first_stage else 1				,      depth=config.depths[0]				,      )  )
					A__     =  zip(config.hidden_sizes				,      config.hidden_sizes[1:]  )
					for (in_channels, out_channels), depth in zip(__lowerCAmelCase				,      config.depths[1:]  ):
							self.stages.append(ResNetStage(__lowerCAmelCase				,      __lowerCAmelCase				,      __lowerCAmelCase				,      depth=__lowerCAmelCase  )  )







			def   a_			(     self  :       List[Any]				,      __lowerCAmelCase  :       Tensor				,      __lowerCAmelCase  :       bool = False				,      __lowerCAmelCase  :       bool = True  )      ->      BaseModelOutputWithNoAttention:







					"""simple docstring"""






					A__     =  () if output_hidden_states else None

					for stage_module in self.stages:
							if output_hidden_states:
									A__     =  hidden_states + (hidden_state,)

							A__     =  stage_module(__lowerCAmelCase  )

					if output_hidden_states:
							A__     =  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=__lowerCAmelCase				,      hidden_states=__lowerCAmelCase				,      )


class     A     (_A       ):
			'''simple docstring'''




			__lowerCamelCase      :			Dict							     =       ResNetConfig
			__lowerCamelCase      :			Union[str, Any]							     =       "resnet"
			__lowerCamelCase      :			Dict							     =       "pixel_values"
			__lowerCamelCase      :			Dict							     =       True






			def   a_			(     self  :       List[Any]				,      __lowerCAmelCase  :       List[str]  )      ->      Optional[Any]:







					"""simple docstring"""






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







			def   a_			(     self  :       List[str]				,      __lowerCAmelCase  :       str				,      __lowerCAmelCase  :       Tuple=False  )      ->      Any:







					"""simple docstring"""






					if isinstance(__lowerCAmelCase				,      __lowerCAmelCase  ):
							A__     =  value

A       :   Optional[Any]					= 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       :   Optional[Any]					= 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     A     (_A       ):
			'''simple docstring'''




			def __init__(     self  :       str				,      __lowerCAmelCase  :       int  )      ->      Tuple:







					"""simple docstring"""






					super().__init__(__lowerCAmelCase  )
					A__     =  config
					A__     =  ResNetEmbeddings(__lowerCAmelCase  )
					A__     =  ResNetEncoder(__lowerCAmelCase  )
					A__     =  nn.AdaptiveAvgPoolad((1, 1)  )
					# Initialize weights and apply final processing
					self.post_init()







			@add_start_docstrings_to_model_forward(__lowerCAmelCase  )
			@add_code_sample_docstrings(
			    checkpoint=_CHECKPOINT_FOR_DOC				,      output_type=__lowerCAmelCase				,      config_class=_CONFIG_FOR_DOC				,      modality="""vision"""				,      expected_output=_EXPECTED_OUTPUT_SHAPE				,      )
			def   a_			(     self  :       List[str]				,      __lowerCAmelCase  :       Tensor				,      __lowerCAmelCase  :       Optional[bool] = None				,      __lowerCAmelCase  :       Optional[bool] = None  )      ->      BaseModelOutputWithPoolingAndNoAttention:







					"""simple docstring"""






					A__     =  (
					    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
					)
					A__     =  return_dict if return_dict is not None else self.config.use_return_dict

					A__     =  self.embedder(__lowerCAmelCase  )

					A__     =  self.encoder(
					    __lowerCAmelCase				,      output_hidden_states=__lowerCAmelCase				,      return_dict=__lowerCAmelCase  )

					A__     =  encoder_outputs[0]

					A__     =  self.pooler(__lowerCAmelCase  )

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

					return BaseModelOutputWithPoolingAndNoAttention(
					    last_hidden_state=__lowerCAmelCase				,      pooler_output=__lowerCAmelCase				,      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     A     (_A       ):
			'''simple docstring'''




			def __init__(     self  :       Optional[Any]				,      __lowerCAmelCase  :       Dict  )      ->      Optional[Any]:







					"""simple docstring"""






					super().__init__(__lowerCAmelCase  )
					A__     =  config.num_labels
					A__     =  ResNetModel(__lowerCAmelCase  )
					# classification head
					A__     =  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(__lowerCAmelCase  )
			@add_code_sample_docstrings(
			    checkpoint=_IMAGE_CLASS_CHECKPOINT				,      output_type=__lowerCAmelCase				,      config_class=_CONFIG_FOR_DOC				,      expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT				,      )
			def   a_			(     self  :       Union[str, Any]				,      __lowerCAmelCase  :       Optional[torch.FloatTensor] = None				,      __lowerCAmelCase  :       Optional[torch.LongTensor] = None				,      __lowerCAmelCase  :       Optional[bool] = None				,      __lowerCAmelCase  :       Optional[bool] = None				,      )      ->      ImageClassifierOutputWithNoAttention:







					"""simple docstring"""






					A__     =  return_dict if return_dict is not None else self.config.use_return_dict

					A__     =  self.resnet(__lowerCAmelCase				,      output_hidden_states=__lowerCAmelCase				,      return_dict=__lowerCAmelCase  )

					A__     =  outputs.pooler_output if return_dict else outputs[1]

					A__     =  self.classifier(__lowerCAmelCase  )

					A__     =  None

					if labels is not None:
							if self.config.problem_type is None:
									if self.num_labels == 1:
											A__     =  """regression"""
									elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
											A__     =  """single_label_classification"""
									else:
											A__     =  """multi_label_classification"""
							if self.config.problem_type == "regression":
									A__     =  MSELoss()
									if self.num_labels == 1:
											A__     =  loss_fct(logits.squeeze()				,      labels.squeeze()  )
									else:
											A__     =  loss_fct(__lowerCAmelCase				,      __lowerCAmelCase  )
							elif self.config.problem_type == "single_label_classification":
									A__     =  CrossEntropyLoss()
									A__     =  loss_fct(logits.view(-1				,      self.num_labels  )				,      labels.view(-1  )  )
							elif self.config.problem_type == "multi_label_classification":
									A__     =  BCEWithLogitsLoss()
									A__     =  loss_fct(__lowerCAmelCase				,      __lowerCAmelCase  )

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

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


@add_start_docstrings(
    '''\n    ResNet backbone, to be used with frameworks like DETR and MaskFormer.\n    '''  ,	_A  ,	)
class     A     (_A  ,	_A       ):
			'''simple docstring'''




			def __init__(     self  :       int				,      __lowerCAmelCase  :       str  )      ->      Union[str, Any]:







					"""simple docstring"""






					super().__init__(__lowerCAmelCase  )
					super()._init_backbone(__lowerCAmelCase  )

					A__     =  [config.embedding_size] + config.hidden_sizes
					A__     =  ResNetEmbeddings(__lowerCAmelCase  )
					A__     =  ResNetEncoder(__lowerCAmelCase  )

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







			@add_start_docstrings_to_model_forward(__lowerCAmelCase  )
			@replace_return_docstrings(output_type=__lowerCAmelCase				,      config_class=_CONFIG_FOR_DOC  )
			def   a_			(     self  :       Dict				,      __lowerCAmelCase  :       Tensor				,      __lowerCAmelCase  :       Optional[bool] = None				,      __lowerCAmelCase  :       Optional[bool] = None  )      ->      BackboneOutput:







					"""simple docstring"""






					A__     =  return_dict if return_dict is not None else self.config.use_return_dict
					A__     =  (
					    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
					)

					A__     =  self.embedder(__lowerCAmelCase  )

					A__     =  self.encoder(__lowerCAmelCase				,      output_hidden_states=__lowerCAmelCase				,      return_dict=__lowerCAmelCase  )

					A__     =  outputs.hidden_states

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

					if not return_dict:
							A__     =  (feature_maps,)
							if output_hidden_states:
									output += (outputs.hidden_states,)
							return output

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








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      )      )



