


import collections
import inspect
import unittest

from transformers import SwinvaConfig
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_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 SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
		from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
		from PIL import Image

		from transformers import AutoImageProcessor

class 		__a				:
		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=[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      ,   )			->	Tuple:




									"""simple docstring"""





									_UpperCAmelCase					=      parent
									_UpperCAmelCase					=      batch_size
									_UpperCAmelCase					=      image_size
									_UpperCAmelCase					=      patch_size
									_UpperCAmelCase					=      num_channels
									_UpperCAmelCase					=      embed_dim
									_UpperCAmelCase					=      depths
									_UpperCAmelCase					=      num_heads
									_UpperCAmelCase					=      window_size
									_UpperCAmelCase					=      mlp_ratio
									_UpperCAmelCase					=      qkv_bias
									_UpperCAmelCase					=      hidden_dropout_prob
									_UpperCAmelCase					=      attention_probs_dropout_prob
									_UpperCAmelCase					=      drop_path_rate
									_UpperCAmelCase					=      hidden_act
									_UpperCAmelCase					=      use_absolute_embeddings
									_UpperCAmelCase					=      patch_norm
									_UpperCAmelCase					=      layer_norm_eps
									_UpperCAmelCase					=      initializer_range
									_UpperCAmelCase					=      is_training
									_UpperCAmelCase					=      scope
									_UpperCAmelCase					=      use_labels
									_UpperCAmelCase					=      type_sequence_label_size
									_UpperCAmelCase					=      encoder_stride
		def      UpperCAmelCase__    (      self						)			->	Union[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

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

									_UpperCAmelCase					=      self.get_config()

									return config, pixel_values, labels
		def      UpperCAmelCase__    (      self						)			->	Tuple:




									"""simple docstring"""





									return SwinvaConfig(
									    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      ,   )
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	Optional[Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      SwinvaModel(config=_SCREAMING_SNAKE_CASE						)
									model.to(_SCREAMING_SNAKE_CASE						)
									model.eval()
									_UpperCAmelCase					=      model(_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths						) - 1))
									_UpperCAmelCase					=      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[int]:




									"""simple docstring"""





									_UpperCAmelCase					=      SwinvaForMaskedImageModeling(config=_SCREAMING_SNAKE_CASE						)
									model.to(_SCREAMING_SNAKE_CASE						)
									model.eval()
									_UpperCAmelCase					=      model(_SCREAMING_SNAKE_CASE						)
									self.parent.assertEqual(
									    result.logits.shape      ,   (self.batch_size, self.num_channels, self.image_size, self.image_size)						)

									# test greyscale images
									_UpperCAmelCase					=      1
									_UpperCAmelCase					=      SwinvaForMaskedImageModeling(_SCREAMING_SNAKE_CASE						)
									model.to(_SCREAMING_SNAKE_CASE						)
									model.eval()

									_UpperCAmelCase					=      floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
									_UpperCAmelCase					=      model(_SCREAMING_SNAKE_CASE						)
									self.parent.assertEqual(result.logits.shape      ,   (self.batch_size, 1, self.image_size, self.image_size)						)
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	Any:




									"""simple docstring"""





									_UpperCAmelCase					=      self.type_sequence_label_size
									_UpperCAmelCase					=      SwinvaForImageClassification(_SCREAMING_SNAKE_CASE						)
									model.to(_SCREAMING_SNAKE_CASE						)
									model.eval()
									_UpperCAmelCase					=      model(_SCREAMING_SNAKE_CASE      ,   labels=_SCREAMING_SNAKE_CASE						)
									self.parent.assertEqual(result.logits.shape      ,   (self.batch_size, self.type_sequence_label_size)						)






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




									"""simple docstring"""





									_UpperCAmelCase					=      self.prepare_config_and_inputs()
									_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase					=      config_and_inputs
									_UpperCAmelCase					=      {'pixel_values': pixel_values}
									return config, inputs_dict



@require_torch
class 		__a				(    UpperCAmelCase   ,				UpperCAmelCase   ,				unittest.TestCase      ):
		_a     :  Optional[int]           =     (
		    (SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
		)
		_a     :  Tuple           =     (
		    {'feature-extraction': SwinvaModel, 'image-classification': SwinvaForImageClassification}
		    if is_torch_available()
		    else {}
		)

		_a     :  Union[str, Any]           =     False
		_a     :  str           =     False
		_a     :  Tuple           =     False
		_a     :  int           =     False
		def      UpperCAmelCase__    (      self						)			->	Union[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      SwinvaModelTester(self						)
									_UpperCAmelCase					=      ConfigTester(self      ,   config_class=_SCREAMING_SNAKE_CASE      ,   embed_dim=37						)
		def      UpperCAmelCase__    (      self						)			->	List[str]:




									"""simple docstring"""





									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						)			->	Tuple:




									"""simple docstring"""





									_UpperCAmelCase					=      self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE						)
		@unittest.skip(reason='Got `CUDA error: misaligned address` with PyTorch 2.0.0.'						)
		def      UpperCAmelCase__    (      self						)			->	Optional[int]:




									"""simple docstring"""





									pass
		@unittest.skip(reason='Swinv2 does not use inputs_embeds'						)
		def      UpperCAmelCase__    (      self						)			->	Optional[int]:




									"""simple docstring"""





									pass
		def      UpperCAmelCase__    (      self						)			->	int:




									"""simple docstring"""





									_UpperCAmelCase		,	_UpperCAmelCase					=      self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
																_UpperCAmelCase					=      model_class(_SCREAMING_SNAKE_CASE						)
																self.assertIsInstance(model.get_input_embeddings()      ,   (nn.Module)						)
																_UpperCAmelCase					=      model.get_output_embeddings()
																self.assertTrue(x is None or isinstance(_SCREAMING_SNAKE_CASE      ,   nn.Linear						)						)
		def      UpperCAmelCase__    (      self						)			->	Any:




									"""simple docstring"""





									_UpperCAmelCase		,	_UpperCAmelCase					=      self.model_tester.prepare_config_and_inputs_for_common()

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

																_UpperCAmelCase					=      ['pixel_values']
																self.assertListEqual(arg_names[:1]      ,   _SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self						)			->	List[str]:




									"""simple docstring"""





									_UpperCAmelCase		,	_UpperCAmelCase					=      self.model_tester.prepare_config_and_inputs_for_common()
									_UpperCAmelCase					=      True

									for model_class in self.all_model_classes:
																_UpperCAmelCase					=      True
																_UpperCAmelCase					=      False
																_UpperCAmelCase					=      True
																_UpperCAmelCase					=      model_class(_SCREAMING_SNAKE_CASE						)
																model.to(_SCREAMING_SNAKE_CASE						)
																model.eval()
																with torch.no_grad():
																							_UpperCAmelCase					=      model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)						)
																_UpperCAmelCase					=      outputs.attentions
																_UpperCAmelCase					=      len(self.model_tester.depths						)
																self.assertEqual(len(_SCREAMING_SNAKE_CASE						)      ,   _SCREAMING_SNAKE_CASE						)

																# check that output_attentions also work using config
																del inputs_dict["output_attentions"]
																_UpperCAmelCase					=      True
																_UpperCAmelCase					=      config.window_size**2
																_UpperCAmelCase					=      model_class(_SCREAMING_SNAKE_CASE						)
																model.to(_SCREAMING_SNAKE_CASE						)
																model.eval()
																with torch.no_grad():
																							_UpperCAmelCase					=      model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)						)
																_UpperCAmelCase					=      outputs.attentions
																self.assertEqual(len(_SCREAMING_SNAKE_CASE						)      ,   _SCREAMING_SNAKE_CASE						)

																self.assertListEqual(
																    list(attentions[0].shape[-3:]						)      ,   [self.model_tester.num_heads[0], window_size_squared, window_size_squared]      ,   )
																_UpperCAmelCase					=      len(_SCREAMING_SNAKE_CASE						)

																# Check attention is always last and order is fine
																_UpperCAmelCase					=      True
																_UpperCAmelCase					=      True
																_UpperCAmelCase					=      model_class(_SCREAMING_SNAKE_CASE						)
																model.to(_SCREAMING_SNAKE_CASE						)
																model.eval()
																with torch.no_grad():
																							_UpperCAmelCase					=      model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)						)

																if hasattr(self.model_tester      ,   'num_hidden_states_types'						):
																							_UpperCAmelCase					=      self.model_tester.num_hidden_states_types
																else:
																							# also another +1 for reshaped_hidden_states
																							_UpperCAmelCase					=      2
																self.assertEqual(out_len + added_hidden_states      ,   len(_SCREAMING_SNAKE_CASE						)						)

																_UpperCAmelCase					=      outputs.attentions

																self.assertEqual(len(_SCREAMING_SNAKE_CASE						)      ,   _SCREAMING_SNAKE_CASE						)

																self.assertListEqual(
																    list(self_attentions[0].shape[-3:]						)      ,   [self.model_tester.num_heads[0], window_size_squared, window_size_squared]      ,   )
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)			->	List[str]:




									"""simple docstring"""





									_UpperCAmelCase					=      model_class(_SCREAMING_SNAKE_CASE						)
									model.to(_SCREAMING_SNAKE_CASE						)
									model.eval()

									with torch.no_grad():
																_UpperCAmelCase					=      model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)						)

									_UpperCAmelCase					=      outputs.hidden_states

									_UpperCAmelCase					=      getattr(
									    self.model_tester      ,   'expected_num_hidden_layers'      ,   len(self.model_tester.depths						) + 1						)
									self.assertEqual(len(_SCREAMING_SNAKE_CASE						)      ,   _SCREAMING_SNAKE_CASE						)

									# Swinv2 has a different seq_length
									_UpperCAmelCase					=      (
									    config.patch_size
									    if isinstance(config.patch_size      ,   collections.abc.Iterable						)
									    else (config.patch_size, config.patch_size)
									)

									_UpperCAmelCase					=      (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]      ,   )

									_UpperCAmelCase					=      outputs.reshaped_hidden_states
									self.assertEqual(len(_SCREAMING_SNAKE_CASE						)      ,   _SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase		,	_UpperCAmelCase					=      reshaped_hidden_states[0].shape
									_UpperCAmelCase					=      (
									    reshaped_hidden_states[0].view(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   height * width						).permute(0      ,   2      ,   1						)
									)
									self.assertListEqual(
									    list(reshaped_hidden_states.shape[-2:]						)      ,   [num_patches, self.model_tester.embed_dim]      ,   )
		def      UpperCAmelCase__    (      self						)			->	Dict:




									"""simple docstring"""





									_UpperCAmelCase		,	_UpperCAmelCase					=      self.model_tester.prepare_config_and_inputs_for_common()

									_UpperCAmelCase					=      (
									    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:
																_UpperCAmelCase					=      True
																self.check_hidden_states_output(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)

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

																self.check_hidden_states_output(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self						)			->	Any:




									"""simple docstring"""





									_UpperCAmelCase		,	_UpperCAmelCase					=      self.model_tester.prepare_config_and_inputs_for_common()
									_UpperCAmelCase					=      3

									_UpperCAmelCase					=      (
									    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)
									)
									_UpperCAmelCase					=      (
									    config.patch_size
									    if isinstance(config.patch_size      ,   collections.abc.Iterable						)
									    else (config.patch_size, config.patch_size)
									)

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

									for model_class in self.all_model_classes:
																_UpperCAmelCase					=      True
																self.check_hidden_states_output(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   (padded_height, padded_width)						)

																# check that output_hidden_states also work using config
																del inputs_dict["output_hidden_states"]
																_UpperCAmelCase					=      True
																self.check_hidden_states_output(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE      ,   (padded_height, padded_width)						)
		def      UpperCAmelCase__    (      self						)			->	str:




									"""simple docstring"""





									_UpperCAmelCase					=      self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_image_modeling(*_SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self						)			->	Union[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE						)
		@slow
		def      UpperCAmelCase__    (      self						)			->	Optional[Any]:




									"""simple docstring"""





									for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																_UpperCAmelCase					=      SwinvaModel.from_pretrained(_SCREAMING_SNAKE_CASE						)
																self.assertIsNotNone(_SCREAMING_SNAKE_CASE						)






		def      UpperCAmelCase__    (      self						)			->	Tuple:




									"""simple docstring"""





									_UpperCAmelCase		,	_UpperCAmelCase					=      self.model_tester.prepare_config_and_inputs_for_common()

									_UpperCAmelCase					=      _config_zero_init(_SCREAMING_SNAKE_CASE						)
									for model_class in self.all_model_classes:
																_UpperCAmelCase					=      model_class(config=_SCREAMING_SNAKE_CASE						)
																for name, param in model.named_parameters():
																							if "embeddings" not in name and "logit_scale" 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 		__a				(    unittest.TestCase      ):
		@cached_property
		def      UpperCAmelCase__    (      self						)			->	Union[str, Any]:




									"""simple docstring"""





									return (
									    AutoImageProcessor.from_pretrained('microsoft/swinv2-tiny-patch4-window8-256'						)
									    if is_vision_available()
									    else None
									)






		@slow
		def      UpperCAmelCase__    (      self						)			->	int:




									"""simple docstring"""





									_UpperCAmelCase					=      SwinvaForImageClassification.from_pretrained('microsoft/swinv2-tiny-patch4-window8-256'						).to(
									    _SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      self.default_image_processor

									_UpperCAmelCase					=      Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
									_UpperCAmelCase					=      image_processor(images=_SCREAMING_SNAKE_CASE      ,   return_tensors='pt'						).to(_SCREAMING_SNAKE_CASE						)

									# forward pass
									with torch.no_grad():
																_UpperCAmelCase					=      model(**_SCREAMING_SNAKE_CASE						)

									# verify the logits
									_UpperCAmelCase					=      torch.Size((1, 1000)						)
									self.assertEqual(outputs.logits.shape      ,   _SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      torch.tensor([-0.3947, -0.4306, 0.0026]						).to(_SCREAMING_SNAKE_CASE						)
									self.assertTrue(torch.allclose(outputs.logits[0, :3]      ,   _SCREAMING_SNAKE_CASE      ,   atol=1e-4						)						)




import json
import pathlib
import unittest

import numpy as np

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import ConditionalDetrImageProcessor

class 		__a				(    unittest.TestCase      ):
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE=7      ,   _SCREAMING_SNAKE_CASE=3      ,   _SCREAMING_SNAKE_CASE=30      ,   _SCREAMING_SNAKE_CASE=400      ,   _SCREAMING_SNAKE_CASE=True      ,   _SCREAMING_SNAKE_CASE=None      ,   _SCREAMING_SNAKE_CASE=True      ,   _SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]      ,   _SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]      ,   _SCREAMING_SNAKE_CASE=True      ,   _SCREAMING_SNAKE_CASE=1 / 255      ,   _SCREAMING_SNAKE_CASE=True      ,   )			->	str:




									"""simple docstring"""





									_UpperCAmelCase					=      size if size is not None else {'shortest_edge': 18, 'longest_edge': 1333}
									_UpperCAmelCase					=      parent
									_UpperCAmelCase					=      batch_size
									_UpperCAmelCase					=      num_channels
									_UpperCAmelCase					=      min_resolution
									_UpperCAmelCase					=      max_resolution
									_UpperCAmelCase					=      do_resize
									_UpperCAmelCase					=      size
									_UpperCAmelCase					=      do_normalize
									_UpperCAmelCase					=      image_mean
									_UpperCAmelCase					=      image_std
									_UpperCAmelCase					=      do_rescale
									_UpperCAmelCase					=      rescale_factor
									_UpperCAmelCase					=      do_pad
		def      UpperCAmelCase__    (      self						)			->	Union[str, Any]:




									"""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      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE=False						)			->	Optional[Any]:




									"""simple docstring"""





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

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

									return expected_height, expected_width



@require_torch
@require_vision
class 		__a				(    UpperCAmelCase   ,				unittest.TestCase      ):
		_a     :  Tuple           =     ConditionalDetrImageProcessor if is_vision_available() else None
		def      UpperCAmelCase__    (      self						)			->	Any:




									"""simple docstring"""





									_UpperCAmelCase					=      ConditionalDetrImageProcessingTester(self						)
		@property
		def      UpperCAmelCase__    (      self						)			->	Dict:




									"""simple docstring"""





									return self.image_processor_tester.prepare_image_processor_dict()
		def      UpperCAmelCase__    (      self						)			->	List[str]:




									"""simple docstring"""





									_UpperCAmelCase					=      self.image_processing_class(**self.image_processor_dict						)
									self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,   'image_mean'						)						)
									self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,   'image_std'						)						)
									self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,   'do_normalize'						)						)
									self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,   'do_resize'						)						)
									self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE      ,   'size'						)						)
		def      UpperCAmelCase__    (      self						)			->	Optional[int]:




									"""simple docstring"""





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

									_UpperCAmelCase					=      self.image_processing_class.from_dict(
									    self.image_processor_dict      ,   size=42      ,   max_size=84      ,   pad_and_return_pixel_mask=_SCREAMING_SNAKE_CASE						)
									self.assertEqual(image_processor.size      ,   {'shortest_edge': 42, 'longest_edge': 84}						)
									self.assertEqual(image_processor.do_pad      ,   _SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self						)			->	Dict:




									"""simple docstring"""





									pass
		def      UpperCAmelCase__    (      self						)			->	List[Any]:




									"""simple docstring"""





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

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

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

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

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

									_UpperCAmelCase					=      image_processing(_SCREAMING_SNAKE_CASE      ,   return_tensors='pt'						).pixel_values
									self.assertEqual(
									    encoded_images.shape      ,   (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )      ,   )
		def      UpperCAmelCase__    (      self						)			->	int:




									"""simple docstring"""





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

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

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

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

									# Test batched
									_UpperCAmelCase					=      image_processing(_SCREAMING_SNAKE_CASE      ,   return_tensors='pt'						).pixel_values

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

									self.assertEqual(
									    encoded_images.shape      ,   (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )      ,   )
		def      UpperCAmelCase__    (      self						)			->	Any:




									"""simple docstring"""





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

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

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

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

									# Test batched
									_UpperCAmelCase					=      image_processing(_SCREAMING_SNAKE_CASE      ,   return_tensors='pt'						).pixel_values

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

									self.assertEqual(
									    encoded_images.shape      ,   (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        expected_height,
									        expected_width,
									    )      ,   )
		@slow
		def      UpperCAmelCase__    (      self						)			->	Optional[Any]:




									"""simple docstring"""





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

									_UpperCAmelCase					=      {'image_id': 39769, 'annotations': target}

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

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

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

									# verify area
									_UpperCAmelCase					=      torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]						)
									self.assertTrue(torch.allclose(encoding['labels'][0]['area']      ,   _SCREAMING_SNAKE_CASE						)						)
									# verify boxes
									_UpperCAmelCase					=      torch.Size([6, 4]						)
									self.assertEqual(encoding['labels'][0]['boxes'].shape      ,   _SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]						)
									self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]      ,   _SCREAMING_SNAKE_CASE      ,   atol=1e-3						)						)
									# verify image_id
									_UpperCAmelCase					=      torch.tensor([39769]						)
									self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']      ,   _SCREAMING_SNAKE_CASE						)						)
									# verify is_crowd
									_UpperCAmelCase					=      torch.tensor([0, 0, 0, 0, 0, 0]						)
									self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']      ,   _SCREAMING_SNAKE_CASE						)						)
									# verify class_labels
									_UpperCAmelCase					=      torch.tensor([75, 75, 63, 65, 17, 17]						)
									self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']      ,   _SCREAMING_SNAKE_CASE						)						)
									# verify orig_size
									_UpperCAmelCase					=      torch.tensor([480, 640]						)
									self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']      ,   _SCREAMING_SNAKE_CASE						)						)
									# verify size
									_UpperCAmelCase					=      torch.tensor([800, 1066]						)
									self.assertTrue(torch.allclose(encoding['labels'][0]['size']      ,   _SCREAMING_SNAKE_CASE						)						)






		@slow
		def      UpperCAmelCase__    (      self						)			->	List[Any]:




									"""simple docstring"""





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

									_UpperCAmelCase					=      {'file_name': '000000039769.png', 'image_id': 39769, 'segments_info': target}

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

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

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

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

									# verify area
									_UpperCAmelCase					=      torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]						)
									self.assertTrue(torch.allclose(encoding['labels'][0]['area']      ,   _SCREAMING_SNAKE_CASE						)						)
									# verify boxes
									_UpperCAmelCase					=      torch.Size([6, 4]						)
									self.assertEqual(encoding['labels'][0]['boxes'].shape      ,   _SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]						)
									self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]      ,   _SCREAMING_SNAKE_CASE      ,   atol=1e-3						)						)
									# verify image_id
									_UpperCAmelCase					=      torch.tensor([39769]						)
									self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']      ,   _SCREAMING_SNAKE_CASE						)						)
									# verify is_crowd
									_UpperCAmelCase					=      torch.tensor([0, 0, 0, 0, 0, 0]						)
									self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']      ,   _SCREAMING_SNAKE_CASE						)						)
									# verify class_labels
									_UpperCAmelCase					=      torch.tensor([17, 17, 63, 75, 75, 93]						)
									self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']      ,   _SCREAMING_SNAKE_CASE						)						)
									# verify masks
									_UpperCAmelCase					=      822873
									self.assertEqual(encoding['labels'][0]['masks'].sum().item()      ,   _SCREAMING_SNAKE_CASE						)
									# verify orig_size
									_UpperCAmelCase					=      torch.tensor([480, 640]						)
									self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']      ,   _SCREAMING_SNAKE_CASE						)						)
									# verify size
									_UpperCAmelCase					=      torch.tensor([800, 1066]						)
									self.assertTrue(torch.allclose(encoding['labels'][0]['size']      ,   _SCREAMING_SNAKE_CASE						)						)
