


"""simple docstring"""
import collections
import inspect
import unittest

from transformers import FocalNetConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
 import torch
 from torch import nn

 from transformers import (
     FocalNetBackbone,
     FocalNetForImageClassification,
     FocalNetForMaskedImageModeling,
     FocalNetModel,
 )
 from transformers.models.focalnet.modeling_focalnet import FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor




class 	lowercase__				:





   def __init__(							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE=13      ,   SCREAMING_SNAKE_CASE=32      ,   SCREAMING_SNAKE_CASE=2      ,   SCREAMING_SNAKE_CASE=3      ,   SCREAMING_SNAKE_CASE=16      ,   SCREAMING_SNAKE_CASE=[32, 64, 128]      ,   SCREAMING_SNAKE_CASE=[1, 2, 1]      ,   SCREAMING_SNAKE_CASE=[2, 2, 4]      ,   SCREAMING_SNAKE_CASE=2      ,   SCREAMING_SNAKE_CASE=2.0      ,   SCREAMING_SNAKE_CASE=True      ,   SCREAMING_SNAKE_CASE=0.0      ,   SCREAMING_SNAKE_CASE=0.0      ,   SCREAMING_SNAKE_CASE=0.1      ,   SCREAMING_SNAKE_CASE="gelu"      ,   SCREAMING_SNAKE_CASE=False      ,   SCREAMING_SNAKE_CASE=True      ,   SCREAMING_SNAKE_CASE=0.02      ,   SCREAMING_SNAKE_CASE=1e-5      ,   SCREAMING_SNAKE_CASE=True      ,   SCREAMING_SNAKE_CASE=None      ,   SCREAMING_SNAKE_CASE=True      ,   SCREAMING_SNAKE_CASE=10      ,   SCREAMING_SNAKE_CASE=8      ,   SCREAMING_SNAKE_CASE=["stage1", "stage2"]      ,   SCREAMING_SNAKE_CASE=[1, 2]      ,   )  ->  Optional[int]:
      _lowerCamelCase	:					Optional[Any]   =		parent
      _lowerCamelCase	:					Dict   =		batch_size
      _lowerCamelCase	:					Optional[int]   =		image_size
      _lowerCamelCase	:					List[str]   =		patch_size
      _lowerCamelCase	:					List[str]   =		num_channels
      _lowerCamelCase	:					int   =		embed_dim
      _lowerCamelCase	:					Any   =		hidden_sizes
      _lowerCamelCase	:					Tuple   =		depths
      _lowerCamelCase	:					List[str]   =		num_heads
      _lowerCamelCase	:					List[Any]   =		window_size
      _lowerCamelCase	:					int   =		mlp_ratio
      _lowerCamelCase	:					str   =		qkv_bias
      _lowerCamelCase	:					List[str]   =		hidden_dropout_prob
      _lowerCamelCase	:					Union[str, Any]   =		attention_probs_dropout_prob
      _lowerCamelCase	:					Dict   =		drop_path_rate
      _lowerCamelCase	:					Any   =		hidden_act
      _lowerCamelCase	:					Optional[Any]   =		use_absolute_embeddings
      _lowerCamelCase	:					Dict   =		patch_norm
      _lowerCamelCase	:					Any   =		layer_norm_eps
      _lowerCamelCase	:					Dict   =		initializer_range
      _lowerCamelCase	:					List[Any]   =		is_training
      _lowerCamelCase	:					int   =		scope
      _lowerCamelCase	:					List[str]   =		use_labels
      _lowerCamelCase	:					List[Any]   =		type_sequence_label_size
      _lowerCamelCase	:					Any   =		encoder_stride
      _lowerCamelCase	:					Tuple   =		out_features
      _lowerCamelCase	:					Optional[Any]   =		out_indices





   def 		UpperCamelCase_  (							self)  ->  str:
      _lowerCamelCase	:					Dict   =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

      _lowerCamelCase	:					Optional[int]   =		None
      if self.use_labels:
         _lowerCamelCase	:					List[Any]   =		ids_tensor([self.batch_size]      ,   self.type_sequence_label_size)

      _lowerCamelCase	:					Any   =		self.get_config()

      return config, pixel_values, labels





   def 		UpperCamelCase_  (							self)  ->  int:
      return FocalNetConfig(
          image_size=self.image_size      ,   patch_size=self.patch_size      ,   num_channels=self.num_channels      ,   embed_dim=self.embed_dim      ,   hidden_sizes=self.hidden_sizes      ,   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 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)  ->  List[Any]:
      _lowerCamelCase	:					Any   =		FocalNetModel(config=__UpperCamelCase)
      model.to(__UpperCamelCase)
      model.eval()
      _lowerCamelCase	:					Optional[Any]   =		model(__UpperCamelCase)

      _lowerCamelCase	:					Dict   =		((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths) - 1))
      _lowerCamelCase	:					Dict   =		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 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)  ->  Optional[Any]:
      _lowerCamelCase	:					Optional[Any]   =		FocalNetBackbone(config=__UpperCamelCase)
      model.to(__UpperCamelCase)
      model.eval()
      _lowerCamelCase	:					Any   =		model(__UpperCamelCase)

      # verify feature maps
      self.parent.assertEqual(len(result.feature_maps)      ,   len(config.out_features))
      self.parent.assertListEqual(list(result.feature_maps[0].shape)      ,   [self.batch_size, self.image_size, 8, 8])

      # verify channels
      self.parent.assertEqual(len(model.channels)      ,   len(config.out_features))
      self.parent.assertListEqual(model.channels      ,   config.hidden_sizes[:-1])

      # verify backbone works with out_features=None
      _lowerCamelCase	:					Any   =		None
      _lowerCamelCase	:					List[Any]   =		FocalNetBackbone(config=__UpperCamelCase)
      model.to(__UpperCamelCase)
      model.eval()
      _lowerCamelCase	:					Dict   =		model(__UpperCamelCase)

      # verify feature maps
      self.parent.assertEqual(len(result.feature_maps)      ,   1)
      self.parent.assertListEqual(list(result.feature_maps[0].shape)      ,   [self.batch_size, self.image_size * 2, 4, 4])

      # verify channels
      self.parent.assertEqual(len(model.channels)      ,   1)
      self.parent.assertListEqual(model.channels      ,   [config.hidden_sizes[-1]])





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)  ->  List[str]:
      _lowerCamelCase	:					List[str]   =		FocalNetForMaskedImageModeling(config=__UpperCamelCase)
      model.to(__UpperCamelCase)
      model.eval()
      _lowerCamelCase	:					int   =		model(__UpperCamelCase)
      self.parent.assertEqual(
          result.reconstruction.shape      ,   (self.batch_size, self.num_channels, self.image_size, self.image_size))

      # test greyscale images
      _lowerCamelCase	:					int   =		1
      _lowerCamelCase	:					Union[str, Any]   =		FocalNetForMaskedImageModeling(__UpperCamelCase)
      model.to(__UpperCamelCase)
      model.eval()

      _lowerCamelCase	:					List[str]   =		floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
      _lowerCamelCase	:					Any   =		model(__UpperCamelCase)
      self.parent.assertEqual(result.reconstruction.shape      ,   (self.batch_size, 1, self.image_size, self.image_size))





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)  ->  Any:
      _lowerCamelCase	:					int   =		self.type_sequence_label_size
      _lowerCamelCase	:					List[Any]   =		FocalNetForImageClassification(__UpperCamelCase)
      model.to(__UpperCamelCase)
      model.eval()
      _lowerCamelCase	:					Any   =		model(__UpperCamelCase      ,   labels=__UpperCamelCase)
      self.parent.assertEqual(result.logits.shape      ,   (self.batch_size, self.type_sequence_label_size))

      # test greyscale images
      _lowerCamelCase	:					Dict   =		1
      _lowerCamelCase	:					int   =		FocalNetForImageClassification(__UpperCamelCase)
      model.to(__UpperCamelCase)
      model.eval()

      _lowerCamelCase	:					Any   =		floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
      _lowerCamelCase	:					int   =		model(__UpperCamelCase)
      self.parent.assertEqual(result.logits.shape      ,   (self.batch_size, self.type_sequence_label_size))





   def 		UpperCamelCase_  (							self)  ->  Optional[int]:
      _lowerCamelCase	:					Optional[Any]   =		self.prepare_config_and_inputs()

      _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase	:					Any   =		config_and_inputs
      _lowerCamelCase	:					List[Any]   =		{"""pixel_values""": pixel_values}
      return config, inputs_dict







@require_torch
class 	lowercase__				(					__snake_case ,__snake_case ,unittest.TestCase				):
   __UpperCAmelCase							       =					(
       (
           FocalNetModel,
           FocalNetForImageClassification,
           FocalNetForMaskedImageModeling,
           FocalNetBackbone,
       )
       if is_torch_available()
       else ()
   )
   __UpperCAmelCase							       =					(
       {'''feature-extraction''': FocalNetModel, '''image-classification''': FocalNetForImageClassification}
       if is_torch_available()
       else {}
   )
   __UpperCAmelCase							       =					False

   __UpperCAmelCase							       =					False
   __UpperCAmelCase							       =					False
   __UpperCAmelCase							       =					False
   __UpperCAmelCase							       =					False





   def 		UpperCamelCase_  (							self)  ->  int:
      _lowerCamelCase	:					str   =		FocalNetModelTester(self)
      _lowerCamelCase	:					Union[str, Any]   =		ConfigTester(self      ,   config_class=__UpperCamelCase      ,   embed_dim=37      ,   has_text_modality=__UpperCamelCase)





   def 		UpperCamelCase_  (							self)  ->  List[Any]:
      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 		UpperCamelCase_  (							self)  ->  Union[str, Any]:
      return





   def 		UpperCamelCase_  (							self)  ->  int:
      _lowerCamelCase	:					List[Any]   =		self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__UpperCamelCase)





   def 		UpperCamelCase_  (							self)  ->  int:
      _lowerCamelCase	:					Any   =		self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_backbone(*__UpperCamelCase)





   def 		UpperCamelCase_  (							self)  ->  List[str]:
      _lowerCamelCase	:					List[str]   =		self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_image_modeling(*__UpperCamelCase)





   def 		UpperCamelCase_  (							self)  ->  Union[str, Any]:
      _lowerCamelCase	:					List[str]   =		self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase)





   @unittest.skip(reason="""FocalNet does not use inputs_embeds""")
   def 		UpperCamelCase_  (							self)  ->  Union[str, Any]:
      pass





   @unittest.skip(reason="""FocalNet does not use feedforward chunking""")
   def 		UpperCamelCase_  (							self)  ->  Union[str, Any]:
      pass





   def 		UpperCamelCase_  (							self)  ->  Dict:
      _lowerCamelCase   ,    _lowerCamelCase	:					List[Any]   =		self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes[:-1]:
         _lowerCamelCase	:					Tuple   =		model_class(__UpperCamelCase)
         self.assertIsInstance(model.get_input_embeddings()      ,   (nn.Module))
         _lowerCamelCase	:					Tuple   =		model.get_output_embeddings()
         self.assertTrue(x is None or isinstance(__UpperCamelCase      ,   nn.Linear))





   def 		UpperCamelCase_  (							self)  ->  int:
      _lowerCamelCase   ,    _lowerCamelCase	:					Tuple   =		self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes[:-1]:
         _lowerCamelCase	:					int   =		model_class(__UpperCamelCase)
         _lowerCamelCase	:					Union[str, Any]   =		inspect.signature(model.forward)
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         _lowerCamelCase	:					Optional[int]   =		[*signature.parameters.keys()]

         _lowerCamelCase	:					int   =		["""pixel_values"""]
         self.assertListEqual(arg_names[:1]      ,   __UpperCamelCase)





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)  ->  Any:
      _lowerCamelCase	:					Optional[int]   =		model_class(__UpperCamelCase)
      model.to(__UpperCamelCase)
      model.eval()

      with torch.no_grad():
         _lowerCamelCase	:					Optional[int]   =		model(**self._prepare_for_class(__UpperCamelCase      ,   __UpperCamelCase))

      _lowerCamelCase	:					Tuple   =		outputs.hidden_states

      _lowerCamelCase	:					Any   =		getattr(
          self.model_tester      ,   """expected_num_hidden_layers"""      ,   len(self.model_tester.depths) + 1)
      self.assertEqual(len(__UpperCamelCase)      ,   __UpperCamelCase)

      # FocalNet has a different seq_length
      _lowerCamelCase	:					Any   =		(
          config.patch_size
          if isinstance(config.patch_size      ,   collections.abc.Iterable)
          else (config.patch_size, config.patch_size)
      )

      _lowerCamelCase	:					str   =		(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]      ,   )

      _lowerCamelCase	:					Union[str, Any]   =		outputs.reshaped_hidden_states
      self.assertEqual(len(__UpperCamelCase)      ,   __UpperCamelCase)

      _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase   ,    _lowerCamelCase	:					Optional[Any]   =		reshaped_hidden_states[0].shape
      _lowerCamelCase	:					Union[str, Any]   =		(
          reshaped_hidden_states[0].view(__UpperCamelCase      ,   __UpperCamelCase      ,   height * width).permute(0      ,   2      ,   1)
      )
      self.assertListEqual(
          list(reshaped_hidden_states.shape[-2:])      ,   [num_patches, self.model_tester.embed_dim]      ,   )





   def 		UpperCamelCase_  (							self)  ->  Any:
      _lowerCamelCase   ,    _lowerCamelCase	:					Optional[Any]   =		self.model_tester.prepare_config_and_inputs_for_common()

      _lowerCamelCase	:					Tuple   =		(
          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[:-1]:
         _lowerCamelCase	:					Any   =		True
         self.check_hidden_states_output(__UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase)

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         _lowerCamelCase	:					Optional[Any]   =		True

         self.check_hidden_states_output(__UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase)





   def 		UpperCamelCase_  (							self)  ->  Optional[Any]:
      _lowerCamelCase   ,    _lowerCamelCase	:					Optional[Any]   =		self.model_tester.prepare_config_and_inputs_for_common()
      _lowerCamelCase	:					Optional[int]   =		3

      _lowerCamelCase	:					Any   =		(
          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)
      )
      _lowerCamelCase	:					Optional[Any]   =		(
          config.patch_size
          if isinstance(config.patch_size      ,   collections.abc.Iterable)
          else (config.patch_size, config.patch_size)
      )

      _lowerCamelCase	:					Optional[Any]   =		image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
      _lowerCamelCase	:					Optional[Any]   =		image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

      for model_class in self.all_model_classes[:-1]:
         _lowerCamelCase	:					str   =		True
         self.check_hidden_states_output(__UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase      ,   (padded_height, padded_width))

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         _lowerCamelCase	:					Tuple   =		True
         self.check_hidden_states_output(__UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase      ,   (padded_height, padded_width))





   @slow
   def 		UpperCamelCase_  (							self)  ->  Tuple:


      for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         _lowerCamelCase	:					Any   =		FocalNetModel.from_pretrained(__UpperCamelCase)
         self.assertIsNotNone(__UpperCamelCase)





   def 		UpperCamelCase_  (							self)  ->  Optional[Any]:
      _lowerCamelCase   ,    _lowerCamelCase	:					Optional[int]   =		self.model_tester.prepare_config_and_inputs_for_common()

      _lowerCamelCase	:					Optional[int]   =		_config_zero_init(__UpperCamelCase)
      for model_class in self.all_model_classes:
         _lowerCamelCase	:					List[str]   =		model_class(config=__UpperCamelCase)
         for name, param in model.named_parameters():
            if "embeddings" not in name and param.requires_grad:
               self.assertIn(
                   ((param.data.mean() * 1e9).round() / 1e9).item()      ,   [0.0, 1.0]      ,   msg=F'Parameter {name} of model {model_class} seems not properly initialized'      ,   )







@require_vision
@require_torch
class 	lowercase__				(					unittest.TestCase				):





   @cached_property
   def 		UpperCamelCase_  (							self)  ->  Dict:
      return AutoImageProcessor.from_pretrained("""microsoft/focalnet-tiny""") if is_vision_available() else None





   @slow
   def 		UpperCamelCase_  (							self)  ->  Optional[Any]:
      _lowerCamelCase	:					Union[str, Any]   =		FocalNetForImageClassification.from_pretrained("""microsoft/focalnet-tiny""").to(__UpperCamelCase)
      _lowerCamelCase	:					Any   =		self.default_image_processor

      _lowerCamelCase	:					str   =		Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""")
      _lowerCamelCase	:					Any   =		image_processor(images=__UpperCamelCase      ,   return_tensors="""pt""").to(__UpperCamelCase)

      # forward pass
      with torch.no_grad():
         _lowerCamelCase	:					Any   =		model(**__UpperCamelCase)

      # verify the logits
      _lowerCamelCase	:					Any   =		torch.Size((1, 1000))
      self.assertEqual(outputs.logits.shape      ,   __UpperCamelCase)
      _lowerCamelCase	:					List[str]   =		torch.tensor([0.21_66, -0.43_68, 0.21_91]).to(__UpperCamelCase)
      self.assertTrue(torch.allclose(outputs.logits[0, :3]      ,   __UpperCamelCase      ,   atol=1e-4))
      self.assertTrue(outputs.logits.argmax(dim=-1).item()      ,   281)







@require_torch
class 	lowercase__				(					__snake_case ,unittest.TestCase				):
   __UpperCAmelCase							       =					(FocalNetBackbone,) if is_torch_available() else ()
   __UpperCAmelCase							       =					FocalNetConfig

   __UpperCAmelCase							       =					False





   def 		UpperCamelCase_  (							self)  ->  int:
      _lowerCamelCase	:					Any   =		FocalNetModelTester(self)




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 DetaImageProcessor


class SCREAMING_SNAKE_CASE	(		unittest.TestCase				):






			"""simple docstring"""




			def __init__(     self			,					__UpperCamelCase			,					__UpperCamelCase=7			,					__UpperCamelCase=3			,					__UpperCamelCase=30			,					__UpperCamelCase=4_00			,					__UpperCamelCase=True			,					__UpperCamelCase=None			,					__UpperCamelCase=True			,					__UpperCamelCase=[0.5, 0.5, 0.5]			,					__UpperCamelCase=[0.5, 0.5, 0.5]			,					__UpperCamelCase=True			,					__UpperCamelCase=1 / 2_55			,					__UpperCamelCase=True			,					):

				"""simple docstring"""


				snake_case_   		=			size if size is not None else {'shortest_edge': 18, 'longest_edge': 13_33}
				snake_case_   		=			parent
				snake_case_   		=			batch_size
				snake_case_   		=			num_channels
				snake_case_   		=			min_resolution
				snake_case_   		=			max_resolution
				snake_case_   		=			do_resize
				snake_case_   		=			size
				snake_case_   		=			do_normalize
				snake_case_   		=			image_mean
				snake_case_   		=			image_std
				snake_case_   		=			do_rescale
				snake_case_   		=			rescale_factor
				snake_case_   		=			do_pad


			def       __lowerCAmelCase			(     self				):

				"""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       __lowerCAmelCase			(     self			,					__UpperCamelCase			,					__UpperCamelCase=False				):

				"""simple docstring"""


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

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

				return expected_height, expected_width







@require_torch
@require_vision
class SCREAMING_SNAKE_CASE	(		__snake_case				,    unittest.TestCase				):






			"""simple docstring"""




			__A    	=	DetaImageProcessor if is_vision_available() else None


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			DetaImageProcessingTester(self				)


			@property
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				return self.image_processor_tester.prepare_image_processor_dict()


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			self.image_processing_class(**self.image_processor_dict				)
				self.assertTrue(hasattr(__UpperCamelCase			,					'image_mean'				)				)
				self.assertTrue(hasattr(__UpperCamelCase			,					'image_std'				)				)
				self.assertTrue(hasattr(__UpperCamelCase			,					'do_normalize'				)				)
				self.assertTrue(hasattr(__UpperCamelCase			,					'do_resize'				)				)
				self.assertTrue(hasattr(__UpperCamelCase			,					'do_rescale'				)				)
				self.assertTrue(hasattr(__UpperCamelCase			,					'do_pad'				)				)
				self.assertTrue(hasattr(__UpperCamelCase			,					'size'				)				)


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			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			,					__UpperCamelCase				)


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				pass


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


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

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

				snake_case_		,							snake_case_   		=			self.image_processor_tester.get_expected_values(__UpperCamelCase				)

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

				# Test batched
				snake_case_		,							snake_case_   		=			self.image_processor_tester.get_expected_values(__UpperCamelCase			,					batched=__UpperCamelCase				)

				snake_case_   		=			image_processing(__UpperCamelCase			,					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       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			self.image_processing_class(**self.image_processor_dict				)
				# create random numpy tensors
				snake_case_   		=			prepare_image_inputs(self.image_processor_tester			,					equal_resolution=__UpperCamelCase			,					numpify=__UpperCamelCase				)
				for image in image_inputs:
					self.assertIsInstance(__UpperCamelCase			,					np.ndarray				)

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

				snake_case_		,							snake_case_   		=			self.image_processor_tester.get_expected_values(__UpperCamelCase				)

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

				# Test batched
				snake_case_   		=			image_processing(__UpperCamelCase			,					return_tensors='pt'				).pixel_values

				snake_case_		,							snake_case_   		=			self.image_processor_tester.get_expected_values(__UpperCamelCase			,					batched=__UpperCamelCase				)

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


			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


				snake_case_   		=			self.image_processing_class(**self.image_processor_dict				)
				# create random PyTorch tensors
				snake_case_   		=			prepare_image_inputs(self.image_processor_tester			,					equal_resolution=__UpperCamelCase			,					torchify=__UpperCamelCase				)
				for image in image_inputs:
					self.assertIsInstance(__UpperCamelCase			,					torch.Tensor				)

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

				snake_case_		,							snake_case_   		=			self.image_processor_tester.get_expected_values(__UpperCamelCase				)

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

				# Test batched
				snake_case_   		=			image_processing(__UpperCamelCase			,					return_tensors='pt'				).pixel_values

				snake_case_		,							snake_case_   		=			self.image_processor_tester.get_expected_values(__UpperCamelCase			,					batched=__UpperCamelCase				)

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


			@slow
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


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

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

				# encode them
				snake_case_   		=			DetaImageProcessor()
				snake_case_   		=			image_processing(images=__UpperCamelCase			,					annotations=__UpperCamelCase			,					return_tensors='pt'				)

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

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

				# verify area
				snake_case_   		=			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']			,					__UpperCamelCase				)				)
				# verify boxes
				snake_case_   		=			torch.Size([6, 4]				)
				self.assertEqual(encoding['labels'][0]['boxes'].shape			,					__UpperCamelCase				)
				snake_case_   		=			torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]				)
				self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]			,					__UpperCamelCase			,					atol=1E-3				)				)
				# verify image_id
				snake_case_   		=			torch.tensor([3_97_69]				)
				self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']			,					__UpperCamelCase				)				)
				# verify is_crowd
				snake_case_   		=			torch.tensor([0, 0, 0, 0, 0, 0]				)
				self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']			,					__UpperCamelCase				)				)
				# verify class_labels
				snake_case_   		=			torch.tensor([75, 75, 63, 65, 17, 17]				)
				self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']			,					__UpperCamelCase				)				)
				# verify orig_size
				snake_case_   		=			torch.tensor([4_80, 6_40]				)
				self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']			,					__UpperCamelCase				)				)
				# verify size
				snake_case_   		=			torch.tensor([8_00, 10_66]				)
				self.assertTrue(torch.allclose(encoding['labels'][0]['size']			,					__UpperCamelCase				)				)







			@slow
			def       __lowerCAmelCase			(     self				):

				"""simple docstring"""


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

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

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

				# encode them
				snake_case_   		=			DetaImageProcessor(format='coco_panoptic'				)
				snake_case_   		=			image_processing(images=__UpperCamelCase			,					annotations=__UpperCamelCase			,					masks_path=__UpperCamelCase			,					return_tensors='pt'				)

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

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

				# verify area
				snake_case_   		=			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']			,					__UpperCamelCase				)				)
				# verify boxes
				snake_case_   		=			torch.Size([6, 4]				)
				self.assertEqual(encoding['labels'][0]['boxes'].shape			,					__UpperCamelCase				)
				snake_case_   		=			torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]				)
				self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]			,					__UpperCamelCase			,					atol=1E-3				)				)
				# verify image_id
				snake_case_   		=			torch.tensor([3_97_69]				)
				self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']			,					__UpperCamelCase				)				)
				# verify is_crowd
				snake_case_   		=			torch.tensor([0, 0, 0, 0, 0, 0]				)
				self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']			,					__UpperCamelCase				)				)
				# verify class_labels
				snake_case_   		=			torch.tensor([17, 17, 63, 75, 75, 93]				)
				self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']			,					__UpperCamelCase				)				)
				# verify masks
				snake_case_   		=			82_28_73
				self.assertEqual(encoding['labels'][0]['masks'].sum().item()			,					__UpperCamelCase				)
				# verify orig_size
				snake_case_   		=			torch.tensor([4_80, 6_40]				)
				self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']			,					__UpperCamelCase				)				)
				# verify size
				snake_case_   		=			torch.tensor([8_00, 10_66]				)
				self.assertTrue(torch.allclose(encoding['labels'][0]['size']			,					__UpperCamelCase				)				)
