

'''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  UpperCAmelCase :

		'''simple docstring'''






		def __init__(					self   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_=13   ,			SCREAMING_SNAKE_CASE_=32   ,			SCREAMING_SNAKE_CASE_=2   ,			SCREAMING_SNAKE_CASE_=3   ,			SCREAMING_SNAKE_CASE_=16   ,			SCREAMING_SNAKE_CASE_=[32, 64, 128]   ,			SCREAMING_SNAKE_CASE_=[1, 2, 1]   ,			SCREAMING_SNAKE_CASE_=[2, 2, 4]   ,			SCREAMING_SNAKE_CASE_=2   ,			SCREAMING_SNAKE_CASE_=2.0   ,			SCREAMING_SNAKE_CASE_=True   ,			SCREAMING_SNAKE_CASE_=0.0   ,			SCREAMING_SNAKE_CASE_=0.0   ,			SCREAMING_SNAKE_CASE_=0.1   ,			SCREAMING_SNAKE_CASE_="gelu"   ,			SCREAMING_SNAKE_CASE_=False   ,			SCREAMING_SNAKE_CASE_=True   ,			SCREAMING_SNAKE_CASE_=0.02   ,			SCREAMING_SNAKE_CASE_=1E-5   ,			SCREAMING_SNAKE_CASE_=True   ,			SCREAMING_SNAKE_CASE_=None   ,			SCREAMING_SNAKE_CASE_=True   ,			SCREAMING_SNAKE_CASE_=10   ,			SCREAMING_SNAKE_CASE_=8   ,			SCREAMING_SNAKE_CASE_=["stage1", "stage2"]   ,			SCREAMING_SNAKE_CASE_=[1, 2]   ,			)  ->     List[str]:






							'''simple docstring'''



							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 			UpperCamelCase(					self		)  ->     Tuple:






							'''simple docstring'''



							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 			UpperCamelCase(					self		)  ->     str:






							'''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 			UpperCamelCase(					self   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_		)  ->     Optional[int]:






							'''simple docstring'''



							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 			UpperCamelCase(					self   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_		)  ->     List[str]:






							'''simple docstring'''



							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 			UpperCamelCase(					self   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_		)  ->     str:






							'''simple docstring'''



							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 			UpperCamelCase(					self   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_		)  ->     Optional[int]:






							'''simple docstring'''



							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 			UpperCamelCase(					self		)  ->     List[Any]:






							'''simple docstring'''



							lowerCamelCase_  				=      self.prepare_config_and_inputs()

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


@require_torch
class  UpperCAmelCase (							UpperCAmelCase__  , UpperCAmelCase__  , unittest.TestCase    ):

		'''simple docstring'''






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

		SCREAMING_SNAKE_CASE_           =					False
		SCREAMING_SNAKE_CASE_           =					False
		SCREAMING_SNAKE_CASE_           =					False
		SCREAMING_SNAKE_CASE_           =					False
		def 			UpperCamelCase(					self		)  ->     List[Any]:






							'''simple docstring'''



							lowerCamelCase_  				=      FocalNetModelTester(self		)
							lowerCamelCase_  				=      ConfigTester(self   ,			config_class=SCREAMING_SNAKE_CASE_   ,			embed_dim=37   ,			has_text_modality=SCREAMING_SNAKE_CASE_		)
		def 			UpperCamelCase(					self		)  ->     Tuple:






							'''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 			UpperCamelCase(					self		)  ->     Optional[Any]:






							'''simple docstring'''



							return
		def 			UpperCamelCase(					self		)  ->     Any:






							'''simple docstring'''



							lowerCamelCase_  				=      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_		)
		def 			UpperCamelCase(					self		)  ->     Any:






							'''simple docstring'''



							lowerCamelCase_  				=      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_backbone(*SCREAMING_SNAKE_CASE_		)
		def 			UpperCamelCase(					self		)  ->     Optional[int]:






							'''simple docstring'''



							lowerCamelCase_  				=      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_image_modeling(*SCREAMING_SNAKE_CASE_		)
		def 			UpperCamelCase(					self		)  ->     List[str]:






							'''simple docstring'''



							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 			UpperCamelCase(					self		)  ->     Optional[Any]:






							'''simple docstring'''



							pass
		@unittest.skip(reason='FocalNet does not use feedforward chunking'		)
		def 			UpperCamelCase(					self		)  ->     str:






							'''simple docstring'''



							pass
		def 			UpperCamelCase(					self		)  ->     Union[str, Any]:






							'''simple docstring'''



							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 			UpperCamelCase(					self		)  ->     List[str]:






							'''simple docstring'''



							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 			UpperCamelCase(					self   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_		)  ->     Union[str, Any]:






							'''simple docstring'''



							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 			UpperCamelCase(					self		)  ->     Any:






							'''simple docstring'''



							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 			UpperCamelCase(					self		)  ->     int:






							'''simple docstring'''



							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 			UpperCamelCase(					self		)  ->     Any:






							'''simple docstring'''



							for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												lowerCamelCase_  				=      FocalNetModel.from_pretrained(SCREAMING_SNAKE_CASE_		)
												self.assertIsNotNone(SCREAMING_SNAKE_CASE_		)






		def 			UpperCamelCase(					self		)  ->     str:






							'''simple docstring'''



							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  UpperCAmelCase (							unittest.TestCase    ):

		'''simple docstring'''






		@cached_property
		def 			UpperCamelCase(					self		)  ->     Union[str, Any]:






							'''simple docstring'''



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






		@slow
		def 			UpperCamelCase(					self		)  ->     List[str]:






							'''simple docstring'''



							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, 1000)		)
							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  UpperCAmelCase (							UpperCAmelCase__  , unittest.TestCase    ):

		'''simple docstring'''






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

		SCREAMING_SNAKE_CASE_           =					False
		def 			UpperCamelCase(					self		)  ->     Tuple:






							'''simple docstring'''



							lowerCamelCase_  				=      FocalNetModelTester(self		)





"""simple docstring"""


def  lowercase						(	lowerCAmelCase__						):
			return " ".join(input_str.split()[::-1]						)


if __name__ == "__main__":
						import doctest

						doctest.testmod()



