






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  lowerCAmelCase_       :




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




							def 						__snake_case					(   self     :   Tuple	):
											lowerCAmelCase__      			=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

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

											lowerCAmelCase__      			=						self.get_config()

											return config, pixel_values, labels




							def 						__snake_case					(   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 						__snake_case					(   self     :   str ,							SCREAMING_SNAKE_CASE_     :   Any ,							SCREAMING_SNAKE_CASE_     :   List[Any] ,							SCREAMING_SNAKE_CASE_     :   int	):
											lowerCAmelCase__      			=						FocalNetModel(config=SCREAMING_SNAKE_CASE_	)
											model.to(SCREAMING_SNAKE_CASE_	)
											model.eval()
											lowerCAmelCase__      			=						model(SCREAMING_SNAKE_CASE_	)

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

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




							def 						__snake_case					(   self     :   List[Any] ,							SCREAMING_SNAKE_CASE_     :   Optional[int] ,							SCREAMING_SNAKE_CASE_     :   Dict ,							SCREAMING_SNAKE_CASE_     :   Dict	):
											lowerCAmelCase__      			=						FocalNetBackbone(config=SCREAMING_SNAKE_CASE_	)
											model.to(SCREAMING_SNAKE_CASE_	)
											model.eval()
											lowerCAmelCase__      			=						model(SCREAMING_SNAKE_CASE_	)

											# 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__      			=						None
											lowerCAmelCase__      			=						FocalNetBackbone(config=SCREAMING_SNAKE_CASE_	)
											model.to(SCREAMING_SNAKE_CASE_	)
											model.eval()
											lowerCAmelCase__      			=						model(SCREAMING_SNAKE_CASE_	)

											# 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 						__snake_case					(   self     :   Optional[int] ,							SCREAMING_SNAKE_CASE_     :   str ,							SCREAMING_SNAKE_CASE_     :   Optional[int] ,							SCREAMING_SNAKE_CASE_     :   Tuple	):
											lowerCAmelCase__      			=						FocalNetForMaskedImageModeling(config=SCREAMING_SNAKE_CASE_	)
											model.to(SCREAMING_SNAKE_CASE_	)
											model.eval()
											lowerCAmelCase__      			=						model(SCREAMING_SNAKE_CASE_	)
											self.parent.assertEqual(
											    result.reconstruction.shape ,							(self.batch_size, self.num_channels, self.image_size, self.image_size)	)

											# test greyscale images
											lowerCAmelCase__      			=						1
											lowerCAmelCase__      			=						FocalNetForMaskedImageModeling(SCREAMING_SNAKE_CASE_	)
											model.to(SCREAMING_SNAKE_CASE_	)
											model.eval()

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




							def 						__snake_case					(   self     :   List[Any] ,							SCREAMING_SNAKE_CASE_     :   Optional[Any] ,							SCREAMING_SNAKE_CASE_     :   str ,							SCREAMING_SNAKE_CASE_     :   str	):
											lowerCAmelCase__      			=						self.type_sequence_label_size
											lowerCAmelCase__      			=						FocalNetForImageClassification(SCREAMING_SNAKE_CASE_	)
											model.to(SCREAMING_SNAKE_CASE_	)
											model.eval()
											lowerCAmelCase__      			=						model(SCREAMING_SNAKE_CASE_ ,							labels=SCREAMING_SNAKE_CASE_	)
											self.parent.assertEqual(result.logits.shape ,							(self.batch_size, self.type_sequence_label_size)	)

											# test greyscale images
											lowerCAmelCase__      			=						1
											lowerCAmelCase__      			=						FocalNetForImageClassification(SCREAMING_SNAKE_CASE_	)
											model.to(SCREAMING_SNAKE_CASE_	)
											model.eval()

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




							def 						__snake_case					(   self     :   Any	):
											lowerCAmelCase__      			=						self.prepare_config_and_inputs()

											lowerCAmelCase__			,		lowerCAmelCase__			,		lowerCAmelCase__      			=						config_and_inputs
											lowerCAmelCase__      			=						{'''pixel_values''': pixel_values}
											return config, inputs_dict



@require_torch
class  lowerCAmelCase_       (				snake_case__     ,    snake_case__     ,    unittest.TestCase					):
							UpperCamelCase_		:List[str]		      =    (
							    (
							        FocalNetModel,
							        FocalNetForImageClassification,
							        FocalNetForMaskedImageModeling,
							        FocalNetBackbone,
							    )
							    if is_torch_available()
							    else ()
							)
							UpperCamelCase_		:Tuple		      =    (
							    {'feature-extraction': FocalNetModel, 'image-classification': FocalNetForImageClassification}
							    if is_torch_available()
							    else {}
							)
							UpperCamelCase_		:str		      =    False

							UpperCamelCase_		:int		      =    False
							UpperCamelCase_		:Union[str, Any]		      =    False
							UpperCamelCase_		:List[str]		      =    False
							UpperCamelCase_		:List[Any]		      =    False




							def 						__snake_case					(   self     :   Optional[Any]	):
											lowerCAmelCase__      			=						FocalNetModelTester(self	)
											lowerCAmelCase__      			=						ConfigTester(self ,							config_class=SCREAMING_SNAKE_CASE_ ,							embed_dim=37 ,							has_text_modality=SCREAMING_SNAKE_CASE_	)




							def 						__snake_case					(   self     :   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 						__snake_case					(   self     :   Dict	):
											return




							def 						__snake_case					(   self     :   int	):
											lowerCAmelCase__      			=						self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_	)




							def 						__snake_case					(   self     :   int	):
											lowerCAmelCase__      			=						self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_backbone(*SCREAMING_SNAKE_CASE_	)




							def 						__snake_case					(   self     :   Any	):
											lowerCAmelCase__      			=						self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_masked_image_modeling(*SCREAMING_SNAKE_CASE_	)




							def 						__snake_case					(   self     :   str	):
											lowerCAmelCase__      			=						self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE_	)




							@unittest.skip(reason='''FocalNet does not use inputs_embeds'''	)
							def 						__snake_case					(   self     :   List[Any]	):
											pass




							@unittest.skip(reason='''FocalNet does not use feedforward chunking'''	)
							def 						__snake_case					(   self     :   Tuple	):
											pass




							def 						__snake_case					(   self     :   List[Any]	):
											lowerCAmelCase__			,		lowerCAmelCase__      			=						self.model_tester.prepare_config_and_inputs_for_common()

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




							def 						__snake_case					(   self     :   List[Any]	):
											lowerCAmelCase__			,		lowerCAmelCase__      			=						self.model_tester.prepare_config_and_inputs_for_common()

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

															lowerCAmelCase__      			=						['''pixel_values''']
															self.assertListEqual(arg_names[:1] ,							SCREAMING_SNAKE_CASE_	)




							def 						__snake_case					(   self     :   Union[str, Any] ,							SCREAMING_SNAKE_CASE_     :   Union[str, Any] ,							SCREAMING_SNAKE_CASE_     :   Tuple ,							SCREAMING_SNAKE_CASE_     :   Optional[Any] ,							SCREAMING_SNAKE_CASE_     :   List[str]	):
											lowerCAmelCase__      			=						model_class(SCREAMING_SNAKE_CASE_	)
											model.to(SCREAMING_SNAKE_CASE_	)
											model.eval()

											with torch.no_grad():
															lowerCAmelCase__      			=						model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_	)	)

											lowerCAmelCase__      			=						outputs.hidden_states

											lowerCAmelCase__      			=						getattr(
											    self.model_tester ,							'''expected_num_hidden_layers''' ,							len(self.model_tester.depths	) + 1	)
											self.assertEqual(len(SCREAMING_SNAKE_CASE_	) ,							SCREAMING_SNAKE_CASE_	)

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

											lowerCAmelCase__      			=						(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__      			=						outputs.reshaped_hidden_states
											self.assertEqual(len(SCREAMING_SNAKE_CASE_	) ,							SCREAMING_SNAKE_CASE_	)

											lowerCAmelCase__			,		lowerCAmelCase__			,		lowerCAmelCase__			,		lowerCAmelCase__      			=						reshaped_hidden_states[0].shape
											lowerCAmelCase__      			=						(
											    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 						__snake_case					(   self     :   Union[str, Any]	):
											lowerCAmelCase__			,		lowerCAmelCase__      			=						self.model_tester.prepare_config_and_inputs_for_common()

											lowerCAmelCase__      			=						(
											    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__      			=						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"]
															lowerCAmelCase__      			=						True

															self.check_hidden_states_output(SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_ ,							SCREAMING_SNAKE_CASE_	)




							def 						__snake_case					(   self     :   Tuple	):
											lowerCAmelCase__			,		lowerCAmelCase__      			=						self.model_tester.prepare_config_and_inputs_for_common()
											lowerCAmelCase__      			=						3

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

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

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




							@slow
							def 						__snake_case					(   self     :   str	):
											for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															lowerCAmelCase__      			=						FocalNetModel.from_pretrained(SCREAMING_SNAKE_CASE_	)
															self.assertIsNotNone(SCREAMING_SNAKE_CASE_	)




							def 						__snake_case					(   self     :   List[str]	):
											lowerCAmelCase__			,		lowerCAmelCase__      			=						self.model_tester.prepare_config_and_inputs_for_common()

											lowerCAmelCase__      			=						_config_zero_init(SCREAMING_SNAKE_CASE_	)
											for model_class in self.all_model_classes:
															lowerCAmelCase__      			=						model_class(config=SCREAMING_SNAKE_CASE_	)
															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  lowerCAmelCase_       (				unittest.TestCase					):




							@cached_property
							def 						__snake_case					(   self     :   str	):
											# TODO update organization
											return AutoImageProcessor.from_pretrained('''microsoft/focalnet-tiny'''	) if is_vision_available() else None




							@slow
							def 						__snake_case					(   self     :   str	):
											lowerCAmelCase__      			=						FocalNetForImageClassification.from_pretrained('''microsoft/focalnet-tiny'''	).to(SCREAMING_SNAKE_CASE_	)
											lowerCAmelCase__      			=						self.default_image_processor

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

											# forward pass
											with torch.no_grad():
															lowerCAmelCase__      			=						model(**SCREAMING_SNAKE_CASE_	)

											# verify the logits
											lowerCAmelCase__      			=						torch.Size((1, 1_000)	)
											self.assertEqual(outputs.logits.shape ,							SCREAMING_SNAKE_CASE_	)
											lowerCAmelCase__      			=						torch.tensor([0.2_166, -0.4_368, 0.2_191]	).to(SCREAMING_SNAKE_CASE_	)
											self.assertTrue(torch.allclose(outputs.logits[0, :3] ,							SCREAMING_SNAKE_CASE_ ,							atol=1e-4	)	)
											self.assertTrue(outputs.logits.argmax(dim=-1	).item() ,							281	)



@require_torch
class  lowerCAmelCase_       (				snake_case__     ,    unittest.TestCase					):
							UpperCamelCase_		:Dict		      =    (FocalNetBackbone,) if is_torch_available() else ()
							UpperCamelCase_		:Any		      =    FocalNetConfig

							UpperCamelCase_		:int		      =    False




							def 						__snake_case					(   self     :   List[str]	):
											lowerCAmelCase__      			=						FocalNetModelTester(self	)













from __future__ import annotations





def 	lowerCAmelCase_		(lowercase__						:       list[int]       )		->    bool:


				'''simple docstring'''




				return len(set(lowercase__       )       ) == len(lowercase__       )


if __name__ == "__main__":
			import doctest

			doctest.testmod()





