


'''simple docstring'''





import json
import pathlib
import unittest

import numpy as np

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import DetaImageProcessor


class 				snake_case (unittest.TestCase   ):



       def __init__(				self     ,UpperCAmelCase_     ,UpperCAmelCase_=7     ,UpperCAmelCase_=3     ,UpperCAmelCase_=30     ,UpperCAmelCase_=400     ,UpperCAmelCase_=True     ,UpperCAmelCase_=None     ,UpperCAmelCase_=True     ,UpperCAmelCase_=[0.5, 0.5, 0.5]     ,UpperCAmelCase_=[0.5, 0.5, 0.5]     ,UpperCAmelCase_=True     ,UpperCAmelCase_=1 / 255     ,UpperCAmelCase_=True     ,)  ->							Optional[int]:
           # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
           lowercase__		       =						size if size is not None else {"shortest_edge": 18, "longest_edge": 1_333}
           lowercase__		       =						parent
           lowercase__		       =						batch_size
           lowercase__		       =						num_channels
           lowercase__		       =						min_resolution
           lowercase__		       =						max_resolution
           lowercase__		       =						do_resize
           lowercase__		       =						size
           lowercase__		       =						do_normalize
           lowercase__		       =						image_mean
           lowercase__		       =						image_std
           lowercase__		       =						do_rescale
           lowercase__		       =						rescale_factor
           lowercase__		       =						do_pad



       def 							_a      (				self      )  ->							List[str]:
           return {
               "do_resize": self.do_resize,
               "size": self.size,
               "do_normalize": self.do_normalize,
               "image_mean": self.image_mean,
               "image_std": self.image_std,
               "do_rescale": self.do_rescale,
               "rescale_factor": self.rescale_factor,
               "do_pad": self.do_pad,
           }







       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_=False      )  ->							List[str]:
           if not batched:
               lowercase__		       =						image_inputs[0]
               if isinstance(UpperCAmelCase_     ,Image.Image      ):
                   lowercase__       ,       lowercase__		       =						image.size
               else:
                   lowercase__       ,       lowercase__		       =						image.shape[1], image.shape[2]
               if w < h:
                   lowercase__		       =						int(self.size["shortest_edge"] * h / w      )
                   lowercase__		       =						self.size["shortest_edge"]
               elif w > h:
                   lowercase__		       =						self.size["shortest_edge"]
                   lowercase__		       =						int(self.size["shortest_edge"] * w / h      )
               else:
                   lowercase__		       =						self.size["shortest_edge"]
                   lowercase__		       =						self.size["shortest_edge"]

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

           return expected_height, expected_width





@require_torch
@require_vision
class 				snake_case (UpperCamelCase		,      unittest.TestCase   ):
       lowerCAmelCase__  :List[Any]						   =					DetaImageProcessor if is_vision_available() else None



       def 							_a      (				self      )  ->							Optional[int]:
           lowercase__		       =						DetaImageProcessingTester(self      )



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



       def 							_a      (				self      )  ->							str:
           lowercase__		       =						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 							_a      (				self      )  ->							Dict:
           lowercase__		       =						self.image_processing_class.from_dict(self.image_processor_dict      )
           self.assertEqual(image_processor.size     ,{"shortest_edge": 18, "longest_edge": 1_333}      )
           self.assertEqual(image_processor.do_pad     ,UpperCAmelCase_      )



       def 							_a      (				self      )  ->							Optional[Any]:
           pass



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

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

           lowercase__       ,       lowercase__		       =						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
           lowercase__       ,       lowercase__		       =						self.image_processor_tester.get_expected_values(UpperCAmelCase_     ,batched=UpperCAmelCase_      )

           lowercase__		       =						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 							_a      (				self      )  ->							Union[str, Any]:
           # Initialize image_processing
           lowercase__		       =						self.image_processing_class(**self.image_processor_dict      )
           # create random numpy tensors
           lowercase__		       =						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
           lowercase__		       =						image_processing(image_inputs[0]     ,return_tensors="pt"      ).pixel_values

           lowercase__       ,       lowercase__		       =						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
           lowercase__		       =						image_processing(UpperCAmelCase_     ,return_tensors="pt"      ).pixel_values

           lowercase__       ,       lowercase__		       =						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 							_a      (				self      )  ->							str:
           # Initialize image_processing
           lowercase__		       =						self.image_processing_class(**self.image_processor_dict      )
           # create random PyTorch tensors
           lowercase__		       =						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
           lowercase__		       =						image_processing(image_inputs[0]     ,return_tensors="pt"      ).pixel_values

           lowercase__       ,       lowercase__		       =						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
           lowercase__		       =						image_processing(UpperCAmelCase_     ,return_tensors="pt"      ).pixel_values

           lowercase__       ,       lowercase__		       =						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 							_a      (				self      )  ->							int:
           # prepare image and target
           lowercase__		       =						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"      )
           with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"     ,"r"      ) as f:
               lowercase__		       =						json.loads(f.read()      )

           lowercase__		       =						{"image_id": 39_769, "annotations": target}

           # encode them
           lowercase__		       =						DetaImageProcessor()
           lowercase__		       =						image_processing(images=UpperCAmelCase_     ,annotations=UpperCAmelCase_     ,return_tensors="pt"      )

           # verify pixel values
           lowercase__		       =						torch.Size([1, 3, 800, 1_066]      )
           self.assertEqual(encoding["pixel_values"].shape     ,UpperCAmelCase_      )

           lowercase__		       =						torch.tensor([0.27_96, 0.31_38, 0.34_81]      )
           self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]     ,UpperCAmelCase_     ,atol=1E-4      )      )

           # verify area
           lowercase__		       =						torch.tensor([5_887.9_600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438]      )
           self.assertTrue(torch.allclose(encoding["labels"][0]["area"]     ,UpperCAmelCase_      )      )
           # verify boxes
           lowercase__		       =						torch.Size([6, 4]      )
           self.assertEqual(encoding["labels"][0]["boxes"].shape     ,UpperCAmelCase_      )
           lowercase__		       =						torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]      )
           self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]     ,UpperCAmelCase_     ,atol=1E-3      )      )
           # verify image_id
           lowercase__		       =						torch.tensor([39_769]      )
           self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]     ,UpperCAmelCase_      )      )
           # verify is_crowd
           lowercase__		       =						torch.tensor([0, 0, 0, 0, 0, 0]      )
           self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]     ,UpperCAmelCase_      )      )
           # verify class_labels
           lowercase__		       =						torch.tensor([75, 75, 63, 65, 17, 17]      )
           self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]     ,UpperCAmelCase_      )      )
           # verify orig_size
           lowercase__		       =						torch.tensor([480, 640]      )
           self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]     ,UpperCAmelCase_      )      )
           # verify size
           lowercase__		       =						torch.tensor([800, 1_066]      )
           self.assertTrue(torch.allclose(encoding["labels"][0]["size"]     ,UpperCAmelCase_      )      )







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

           lowercase__		       =						{"file_name": "000000039769.png", "image_id": 39_769, "segments_info": target}

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

           # encode them
           lowercase__		       =						DetaImageProcessor(format="coco_panoptic"      )
           lowercase__		       =						image_processing(images=UpperCAmelCase_     ,annotations=UpperCAmelCase_     ,masks_path=UpperCAmelCase_     ,return_tensors="pt"      )

           # verify pixel values
           lowercase__		       =						torch.Size([1, 3, 800, 1_066]      )
           self.assertEqual(encoding["pixel_values"].shape     ,UpperCAmelCase_      )

           lowercase__		       =						torch.tensor([0.27_96, 0.31_38, 0.34_81]      )
           self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]     ,UpperCAmelCase_     ,atol=1E-4      )      )

           # verify area
           lowercase__		       =						torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147]      )
           self.assertTrue(torch.allclose(encoding["labels"][0]["area"]     ,UpperCAmelCase_      )      )
           # verify boxes
           lowercase__		       =						torch.Size([6, 4]      )
           self.assertEqual(encoding["labels"][0]["boxes"].shape     ,UpperCAmelCase_      )
           lowercase__		       =						torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25]      )
           self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]     ,UpperCAmelCase_     ,atol=1E-3      )      )
           # verify image_id
           lowercase__		       =						torch.tensor([39_769]      )
           self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]     ,UpperCAmelCase_      )      )
           # verify is_crowd
           lowercase__		       =						torch.tensor([0, 0, 0, 0, 0, 0]      )
           self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]     ,UpperCAmelCase_      )      )
           # verify class_labels
           lowercase__		       =						torch.tensor([17, 17, 63, 75, 75, 93]      )
           self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]     ,UpperCAmelCase_      )      )
           # verify masks
           lowercase__		       =						822_873
           self.assertEqual(encoding["labels"][0]["masks"].sum().item()     ,UpperCAmelCase_      )
           # verify orig_size
           lowercase__		       =						torch.tensor([480, 640]      )
           self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]     ,UpperCAmelCase_      )      )
           # verify size
           lowercase__		       =						torch.tensor([800, 1_066]      )
           self.assertTrue(torch.allclose(encoding["labels"][0]["size"]     ,UpperCAmelCase_      )      )









'''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 				snake_case :



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



       def 							_a      (				self      )  ->							int:
           lowercase__		       =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

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

           lowercase__		       =						self.get_config()

           return config, pixel_values, labels



       def 							_a      (				self      )  ->							Any:
           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 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_      )  ->							str:
           lowercase__		       =						FocalNetModel(config=UpperCAmelCase_      )
           model.to(UpperCAmelCase_      )
           model.eval()
           lowercase__		       =						model(UpperCAmelCase_      )

           lowercase__		       =						((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths      ) - 1))
           lowercase__		       =						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 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_      )  ->							Dict:
           lowercase__		       =						FocalNetBackbone(config=UpperCAmelCase_      )
           model.to(UpperCAmelCase_      )
           model.eval()
           lowercase__		       =						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
           lowercase__		       =						None
           lowercase__		       =						FocalNetBackbone(config=UpperCAmelCase_      )
           model.to(UpperCAmelCase_      )
           model.eval()
           lowercase__		       =						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 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_      )  ->							Dict:
           lowercase__		       =						FocalNetForMaskedImageModeling(config=UpperCAmelCase_      )
           model.to(UpperCAmelCase_      )
           model.eval()
           lowercase__		       =						model(UpperCAmelCase_      )
           self.parent.assertEqual(
               result.reconstruction.shape     ,(self.batch_size, self.num_channels, self.image_size, self.image_size)      )

           # test greyscale images
           lowercase__		       =						1
           lowercase__		       =						FocalNetForMaskedImageModeling(UpperCAmelCase_      )
           model.to(UpperCAmelCase_      )
           model.eval()

           lowercase__		       =						floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
           lowercase__		       =						model(UpperCAmelCase_      )
           self.parent.assertEqual(result.reconstruction.shape     ,(self.batch_size, 1, self.image_size, self.image_size)      )



       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_      )  ->							Optional[Any]:
           lowercase__		       =						self.type_sequence_label_size
           lowercase__		       =						FocalNetForImageClassification(UpperCAmelCase_      )
           model.to(UpperCAmelCase_      )
           model.eval()
           lowercase__		       =						model(UpperCAmelCase_     ,labels=UpperCAmelCase_      )
           self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.type_sequence_label_size)      )

           # test greyscale images
           lowercase__		       =						1
           lowercase__		       =						FocalNetForImageClassification(UpperCAmelCase_      )
           model.to(UpperCAmelCase_      )
           model.eval()

           lowercase__		       =						floats_tensor([self.batch_size, 1, self.image_size, self.image_size]      )
           lowercase__		       =						model(UpperCAmelCase_      )
           self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.type_sequence_label_size)      )







       def 							_a      (				self      )  ->							List[str]:
           lowercase__		       =						self.prepare_config_and_inputs()

           lowercase__       ,       lowercase__       ,       lowercase__		       =						config_and_inputs
           lowercase__		       =						{"pixel_values": pixel_values}
           return config, inputs_dict





@require_torch
class 				snake_case (UpperCamelCase		,      UpperCamelCase		,      unittest.TestCase   ):
       lowerCAmelCase__  :List[str]						   =					(
           (
               FocalNetModel,
               FocalNetForImageClassification,
               FocalNetForMaskedImageModeling,
               FocalNetBackbone,
           )
           if is_torch_available()
           else ()
       )
       lowerCAmelCase__  :str						   =					(
           {"feature-extraction": FocalNetModel, "image-classification": FocalNetForImageClassification}
           if is_torch_available()
           else {}
       )
       lowerCAmelCase__  :str						   =					False

       lowerCAmelCase__  :List[Any]						   =					False
       lowerCAmelCase__  :Dict						   =					False
       lowerCAmelCase__  :List[Any]						   =					False
       lowerCAmelCase__  :Union[str, Any]						   =					False



       def 							_a      (				self      )  ->							Any:
           lowercase__		       =						FocalNetModelTester(self      )
           lowercase__		       =						ConfigTester(self     ,config_class=UpperCAmelCase_     ,embed_dim=37     ,has_text_modality=UpperCAmelCase_      )



       def 							_a      (				self      )  ->							Optional[int]:
           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 							_a      (				self      )  ->							List[Any]:
           return



       def 							_a      (				self      )  ->							Tuple:
           lowercase__		       =						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*UpperCAmelCase_      )



       def 							_a      (				self      )  ->							Union[str, Any]:
           lowercase__		       =						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_backbone(*UpperCAmelCase_      )



       def 							_a      (				self      )  ->							str:
           lowercase__		       =						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_masked_image_modeling(*UpperCAmelCase_      )



       def 							_a      (				self      )  ->							Dict:
           lowercase__		       =						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 							_a      (				self      )  ->							str:
           pass



       @unittest.skip(reason="FocalNet does not use feedforward chunking"      )
       def 							_a      (				self      )  ->							Optional[int]:
           pass



       def 							_a      (				self      )  ->							int:
           lowercase__       ,       lowercase__		       =						self.model_tester.prepare_config_and_inputs_for_common()

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



       def 							_a      (				self      )  ->							Union[str, Any]:
           lowercase__       ,       lowercase__		       =						self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes[:-1]:
               lowercase__		       =						model_class(UpperCAmelCase_      )
               lowercase__		       =						inspect.signature(model.forward      )
               # signature.parameters is an OrderedDict => so arg_names order is deterministic
               lowercase__		       =						[*signature.parameters.keys()]

               lowercase__		       =						["pixel_values"]
               self.assertListEqual(arg_names[:1]     ,UpperCAmelCase_      )



       def 							_a      (				self     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_      )  ->							List[Any]:
           lowercase__		       =						model_class(UpperCAmelCase_      )
           model.to(UpperCAmelCase_      )
           model.eval()

           with torch.no_grad():
               lowercase__		       =						model(**self._prepare_for_class(UpperCAmelCase_     ,UpperCAmelCase_      )      )

           lowercase__		       =						outputs.hidden_states

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

           lowercase__		       =						(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]     ,)

           lowercase__		       =						outputs.reshaped_hidden_states
           self.assertEqual(len(UpperCAmelCase_      )     ,UpperCAmelCase_      )

           lowercase__       ,       lowercase__       ,       lowercase__       ,       lowercase__		       =						reshaped_hidden_states[0].shape
           lowercase__		       =						(
               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 							_a      (				self      )  ->							str:
           lowercase__       ,       lowercase__		       =						self.model_tester.prepare_config_and_inputs_for_common()

           lowercase__		       =						(
               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]:
               lowercase__		       =						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"]
               lowercase__		       =						True

               self.check_hidden_states_output(UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_      )



       def 							_a      (				self      )  ->							int:
           lowercase__       ,       lowercase__		       =						self.model_tester.prepare_config_and_inputs_for_common()
           lowercase__		       =						3

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

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

           for model_class in self.all_model_classes[:-1]:
               lowercase__		       =						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"]
               lowercase__		       =						True
               self.check_hidden_states_output(UpperCAmelCase_     ,UpperCAmelCase_     ,UpperCAmelCase_     ,(padded_height, padded_width)      )



       @slow
       def 							_a      (				self      )  ->							Dict:
           for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               lowercase__		       =						FocalNetModel.from_pretrained(UpperCAmelCase_      )
               self.assertIsNotNone(UpperCAmelCase_      )







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

           lowercase__		       =						_config_zero_init(UpperCAmelCase_      )
           for model_class in self.all_model_classes:
               lowercase__		       =						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 				snake_case (unittest.TestCase   ):



       @cached_property
       def 							_a      (				self      )  ->							Optional[int]:
           # TODO update organization
           return AutoImageProcessor.from_pretrained("microsoft/focalnet-tiny"      ) if is_vision_available() else None







       @slow
       def 							_a      (				self      )  ->							List[str]:
           lowercase__		       =						FocalNetForImageClassification.from_pretrained("microsoft/focalnet-tiny"      ).to(UpperCAmelCase_      )
           lowercase__		       =						self.default_image_processor

           lowercase__		       =						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"      )
           lowercase__		       =						image_processor(images=UpperCAmelCase_     ,return_tensors="pt"      ).to(UpperCAmelCase_      )

           # forward pass
           with torch.no_grad():
               lowercase__		       =						model(**UpperCAmelCase_      )

           # verify the logits
           lowercase__		       =						torch.Size((1, 1_000)      )
           self.assertEqual(outputs.logits.shape     ,UpperCAmelCase_      )
           lowercase__		       =						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 				snake_case (UpperCamelCase		,      unittest.TestCase   ):
       lowerCAmelCase__  :Tuple						   =					(FocalNetBackbone,) if is_torch_available() else ()
       lowerCAmelCase__  :int						   =					FocalNetConfig

       lowerCAmelCase__  :List[Any]						   =					False



       def 							_a      (				self      )  ->							Optional[int]:
           lowercase__		       =						FocalNetModelTester(self      )




