


'''simple docstring'''
import collections
import inspect
import unittest

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

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


if is_torch_available():
		import torch
		from torch import nn

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

if is_vision_available():
		from PIL import Image

		from transformers import AutoImageProcessor
class    lowercase	:

		"""simple docstring"""

		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]    ,   ):

							'''simple docstring'''


							UpperCamelCase__   :Tuple												= parent
							UpperCamelCase__   :Any												= batch_size
							UpperCamelCase__   :int												= image_size
							UpperCamelCase__   :int												= patch_size
							UpperCamelCase__   :int												= num_channels
							UpperCamelCase__   :Union[str, Any]												= embed_dim
							UpperCamelCase__   :int												= hidden_sizes
							UpperCamelCase__   :Optional[int]												= depths
							UpperCamelCase__   :Optional[Any]												= num_heads
							UpperCamelCase__   :Optional[Any]												= window_size
							UpperCamelCase__   :List[Any]												= mlp_ratio
							UpperCamelCase__   :List[Any]												= qkv_bias
							UpperCamelCase__   :List[Any]												= hidden_dropout_prob
							UpperCamelCase__   :List[str]												= attention_probs_dropout_prob
							UpperCamelCase__   :List[Any]												= drop_path_rate
							UpperCamelCase__   :int												= hidden_act
							UpperCamelCase__   :List[str]												= use_absolute_embeddings
							UpperCamelCase__   :Optional[int]												= patch_norm
							UpperCamelCase__   :int												= layer_norm_eps
							UpperCamelCase__   :str												= initializer_range
							UpperCamelCase__   :Any												= is_training
							UpperCamelCase__   :Optional[int]												= scope
							UpperCamelCase__   :Dict												= use_labels
							UpperCamelCase__   :List[str]												= type_sequence_label_size
							UpperCamelCase__   :Union[str, Any]												= encoder_stride
							UpperCamelCase__   :List[Any]												= out_features
							UpperCamelCase__   :int												= out_indices




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :List[str]												= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

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

							UpperCamelCase__   :Union[str, Any]												= self.get_config()

							return config, pixel_values, labels




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							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     lowerCAmelCase__				(		self    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__   :Tuple												= FocalNetModel(config=UpperCamelCase_							)
							model.to(UpperCamelCase_							)
							model.eval()
							UpperCamelCase__   :int												= model(UpperCamelCase_							)

							UpperCamelCase__   :int												= ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths							) - 1))
							UpperCamelCase__   :List[Any]												= 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     lowerCAmelCase__				(		self    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__   :Tuple												= FocalNetBackbone(config=UpperCamelCase_							)
							model.to(UpperCamelCase_							)
							model.eval()
							UpperCamelCase__   :List[Any]												= model(UpperCamelCase_							)

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

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

							# verify backbone works with out_features=None
							UpperCamelCase__   :Optional[Any]												= None
							UpperCamelCase__   :Union[str, Any]												= FocalNetBackbone(config=UpperCamelCase_							)
							model.to(UpperCamelCase_							)
							model.eval()
							UpperCamelCase__   :Optional[int]												= 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     lowerCAmelCase__				(		self    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__   :int												= FocalNetForMaskedImageModeling(config=UpperCamelCase_							)
							model.to(UpperCamelCase_							)
							model.eval()
							UpperCamelCase__   :List[Any]												= model(UpperCamelCase_							)
							self.parent.assertEqual(
							    result.reconstruction.shape    ,   (self.batch_size, self.num_channels, self.image_size, self.image_size)							)

							# test greyscale images
							UpperCamelCase__   :Dict												= 1
							UpperCamelCase__   :List[Any]												= FocalNetForMaskedImageModeling(UpperCamelCase_							)
							model.to(UpperCamelCase_							)
							model.eval()

							UpperCamelCase__   :Optional[int]												= floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
							UpperCamelCase__   :Optional[int]												= model(UpperCamelCase_							)
							self.parent.assertEqual(result.reconstruction.shape    ,   (self.batch_size, 1, self.image_size, self.image_size)							)




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__   :Optional[Any]												= self.type_sequence_label_size
							UpperCamelCase__   :List[Any]												= FocalNetForImageClassification(UpperCamelCase_							)
							model.to(UpperCamelCase_							)
							model.eval()
							UpperCamelCase__   :Tuple												= model(UpperCamelCase_    ,   labels=UpperCamelCase_							)
							self.parent.assertEqual(result.logits.shape    ,   (self.batch_size, self.type_sequence_label_size)							)

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

							UpperCamelCase__   :Optional[int]												= floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
							UpperCamelCase__   :Dict												= model(UpperCamelCase_							)
							self.parent.assertEqual(result.logits.shape    ,   (self.batch_size, self.type_sequence_label_size)							)




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :str												= self.prepare_config_and_inputs()

							UpperCamelCase__					,				UpperCamelCase__					,				UpperCamelCase__   :Any												= config_and_inputs
							UpperCamelCase__   :str												= {'''pixel_values''': pixel_values}
							return config, inputs_dict



@require_torch
class    lowercase	(							A__	,		A__	,		unittest.TestCase					):

		"""simple docstring"""

		_a     =				(
		    (
		        FocalNetModel,
		        FocalNetForImageClassification,
		        FocalNetForMaskedImageModeling,
		        FocalNetBackbone,
		    )
		    if is_torch_available()
		    else ()
		)
		_a     =				(
		    {'feature-extraction': FocalNetModel, 'image-classification': FocalNetForImageClassification}
		    if is_torch_available()
		    else {}
		)
		_a     =				False

		_a     =				False
		_a     =				False
		_a     =				False
		_a     =				False




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Dict												= FocalNetModelTester(self							)
							UpperCamelCase__   :Optional[Any]												= ConfigTester(self    ,   config_class=UpperCamelCase_    ,   embed_dim=37    ,   has_text_modality=UpperCamelCase_							)




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							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     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							return




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Dict												= self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*UpperCamelCase_							)




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :str												= self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_backbone(*UpperCamelCase_							)




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :str												= self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_image_modeling(*UpperCamelCase_							)




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Any												= 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     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							pass




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

							'''simple docstring'''


							pass




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__					,				UpperCamelCase__   :Dict												= self.model_tester.prepare_config_and_inputs_for_common()

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




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__					,				UpperCamelCase__   :Any												= self.model_tester.prepare_config_and_inputs_for_common()

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

												UpperCamelCase__   :Union[str, Any]												= ['''pixel_values''']
												self.assertListEqual(arg_names[:1]    ,   UpperCamelCase_							)




		def     lowerCAmelCase__				(		self    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__   :Optional[Any]												= model_class(UpperCamelCase_							)
							model.to(UpperCamelCase_							)
							model.eval()

							with torch.no_grad():
												UpperCamelCase__   :Tuple												= model(**self._prepare_for_class(UpperCamelCase_    ,   UpperCamelCase_							)							)

							UpperCamelCase__   :Optional[int]												= outputs.hidden_states

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

							UpperCamelCase__   :int												= (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__   :Optional[int]												= outputs.reshaped_hidden_states
							self.assertEqual(len(UpperCamelCase_							)    ,   UpperCamelCase_							)

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




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__					,				UpperCamelCase__   :Dict												= self.model_tester.prepare_config_and_inputs_for_common()

							UpperCamelCase__   :Any												= (
							    self.model_tester.image_size
							    if isinstance(self.model_tester.image_size    ,   collections.abc.Iterable							)
							    else (self.model_tester.image_size, self.model_tester.image_size)
							)

							for model_class in self.all_model_classes[:-1]:
												UpperCamelCase__   :Tuple												= 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"]
												UpperCamelCase__   :str												= True

												self.check_hidden_states_output(UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_							)




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__					,				UpperCamelCase__   :Optional[int]												= self.model_tester.prepare_config_and_inputs_for_common()
							UpperCamelCase__   :Union[str, Any]												= 3

							UpperCamelCase__   :Dict												= (
							    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__   :Any												= (
							    config.patch_size
							    if isinstance(config.patch_size    ,   collections.abc.Iterable							)
							    else (config.patch_size, config.patch_size)
							)

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

							for model_class in self.all_model_classes[:-1]:
												UpperCamelCase__   :List[Any]												= 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"]
												UpperCamelCase__   :str												= True
												self.check_hidden_states_output(UpperCamelCase_    ,   UpperCamelCase_    ,   UpperCamelCase_    ,   (padded_height, padded_width)							)




		@slow
		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												UpperCamelCase__   :int												= FocalNetModel.from_pretrained(UpperCamelCase_							)
												self.assertIsNotNone(UpperCamelCase_							)




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__					,				UpperCamelCase__   :Union[str, Any]												= self.model_tester.prepare_config_and_inputs_for_common()

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



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

		"""simple docstring"""

		@cached_property
		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							return AutoImageProcessor.from_pretrained('''microsoft/focalnet-tiny'''							) if is_vision_available() else None




		@slow
		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Dict												= FocalNetForImageClassification.from_pretrained('''microsoft/focalnet-tiny'''							).to(UpperCamelCase_							)
							UpperCamelCase__   :str												= self.default_image_processor

							UpperCamelCase__   :Dict												= Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
							UpperCamelCase__   :int												= image_processor(images=UpperCamelCase_    ,   return_tensors='''pt'''							).to(UpperCamelCase_							)

							# forward pass
							with torch.no_grad():
												UpperCamelCase__   :Optional[int]												= model(**UpperCamelCase_							)

							# verify the logits
							UpperCamelCase__   :List[Any]												= torch.Size((1, 1000)							)
							self.assertEqual(outputs.logits.shape    ,   UpperCamelCase_							)
							UpperCamelCase__   :Optional[int]												= torch.tensor([0.2166, -0.4368, 0.2191]							).to(UpperCamelCase_							)
							self.assertTrue(torch.allclose(outputs.logits[0, :3]    ,   UpperCamelCase_    ,   atol=1e-4							)							)
							self.assertTrue(outputs.logits.argmax(dim=-1							).item()    ,   281							)



@require_torch
class    lowercase	(							A__	,		unittest.TestCase					):

		"""simple docstring"""

		_a     =				(FocalNetBackbone,) if is_torch_available() else ()
		_a     =				FocalNetConfig

		_a     =				False




		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							UpperCamelCase__   :Optional[Any]												= FocalNetModelTester(self							)





from __future__ import annotations





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase    )      ->       float:
	a										=							sorted(numsa + numsa    )
	a				, a										=							divmod(len(__lowerCamelCase    )							,			2    )
	if mod == 1:
		return all_numbers[div]
	else:
		return (all_numbers[div] + all_numbers[div - 1]) / 2


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		__UpperCamelCase					:       Tuple    					=      [float(x) for x in input("Enter the elements of first array: ").split()]
		__UpperCamelCase					:       List[Any]    					=      [float(x) for x in input("Enter the elements of second array: ").split()]
		print(F'The median of two arrays is: {median_of_two_arrays(array_a, array_a)}')

