

'''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	,       __snake_case	,       __snake_case=13	,       __snake_case=32	,       __snake_case=2	,       __snake_case=3	,       __snake_case=16	,       __snake_case=[32, 64, 1_28]	,       __snake_case=[1, 2, 1]	,       __snake_case=[2, 2, 4]	,       __snake_case=2	,       __snake_case=2.0	,       __snake_case=True	,       __snake_case=0.0	,       __snake_case=0.0	,       __snake_case=0.1	,       __snake_case="gelu"	,       __snake_case=False	,       __snake_case=True	,       __snake_case=0.0_2	,       __snake_case=1e-5	,       __snake_case=True	,       __snake_case=None	,       __snake_case=True	,       __snake_case=10	,       __snake_case=8	,       __snake_case=["stage1", "stage2"]	,       __snake_case=[1, 2]	,       ):
			_UpperCamelCase   :		Optional[int]			=						parent
			_UpperCamelCase   :		List[str]			=						batch_size
			_UpperCamelCase   :		Union[str, Any]			=						image_size
			_UpperCamelCase   :		List[Any]			=						patch_size
			_UpperCamelCase   :		Dict			=						num_channels
			_UpperCamelCase   :		List[Any]			=						embed_dim
			_UpperCamelCase   :		Tuple			=						hidden_sizes
			_UpperCamelCase   :		int			=						depths
			_UpperCamelCase   :		int			=						num_heads
			_UpperCamelCase   :		Optional[int]			=						window_size
			_UpperCamelCase   :		List[Any]			=						mlp_ratio
			_UpperCamelCase   :		Optional[int]			=						qkv_bias
			_UpperCamelCase   :		str			=						hidden_dropout_prob
			_UpperCamelCase   :		Any			=						attention_probs_dropout_prob
			_UpperCamelCase   :		str			=						drop_path_rate
			_UpperCamelCase   :		Union[str, Any]			=						hidden_act
			_UpperCamelCase   :		Union[str, Any]			=						use_absolute_embeddings
			_UpperCamelCase   :		Optional[Any]			=						patch_norm
			_UpperCamelCase   :		Tuple			=						layer_norm_eps
			_UpperCamelCase   :		Tuple			=						initializer_range
			_UpperCamelCase   :		str			=						is_training
			_UpperCamelCase   :		int			=						scope
			_UpperCamelCase   :		Union[str, Any]			=						use_labels
			_UpperCamelCase   :		str			=						type_sequence_label_size
			_UpperCamelCase   :		Any			=						encoder_stride
			_UpperCamelCase   :		str			=						out_features
			_UpperCamelCase   :		Optional[Any]			=						out_indices


		def 		A__					(				self):
			_UpperCamelCase   :		List[str]			=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

			_UpperCamelCase   :		List[str]			=						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 		A__					(				self):
			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	,       __snake_case	,       __snake_case	,       __snake_case):
			_UpperCamelCase   :		Optional[Any]			=						FocalNetModel(config=__snake_case)
			model.to(__snake_case)
			model.eval()
			_UpperCamelCase   :		Union[str, Any]			=						model(__snake_case)

			_UpperCamelCase   :		str			=						((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths) - 1))
			_UpperCamelCase   :		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 		A__					(				self	,       __snake_case	,       __snake_case	,       __snake_case):
			_UpperCamelCase   :		Union[str, Any]			=						FocalNetBackbone(config=__snake_case)
			model.to(__snake_case)
			model.eval()
			_UpperCamelCase   :		str			=						model(__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
			_UpperCamelCase   :		List[Any]			=						None
			_UpperCamelCase   :		Optional[Any]			=						FocalNetBackbone(config=__snake_case)
			model.to(__snake_case)
			model.eval()
			_UpperCamelCase   :		Tuple			=						model(__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 		A__					(				self	,       __snake_case	,       __snake_case	,       __snake_case):
			_UpperCamelCase   :		List[Any]			=						FocalNetForMaskedImageModeling(config=__snake_case)
			model.to(__snake_case)
			model.eval()
			_UpperCamelCase   :		Any			=						model(__snake_case)
			self.parent.assertEqual(
			    result.reconstruction.shape	,       (self.batch_size, self.num_channels, self.image_size, self.image_size))

			# test greyscale images
			_UpperCamelCase   :		Optional[int]			=						1
			_UpperCamelCase   :		Any			=						FocalNetForMaskedImageModeling(__snake_case)
			model.to(__snake_case)
			model.eval()

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


		def 		A__					(				self	,       __snake_case	,       __snake_case	,       __snake_case):
			_UpperCamelCase   :		int			=						self.type_sequence_label_size
			_UpperCamelCase   :		Dict			=						FocalNetForImageClassification(__snake_case)
			model.to(__snake_case)
			model.eval()
			_UpperCamelCase   :		Tuple			=						model(__snake_case	,       labels=__snake_case)
			self.parent.assertEqual(result.logits.shape	,       (self.batch_size, self.type_sequence_label_size))

			# test greyscale images
			_UpperCamelCase   :		Dict			=						1
			_UpperCamelCase   :		Optional[int]			=						FocalNetForImageClassification(__snake_case)
			model.to(__snake_case)
			model.eval()

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






		def 		A__					(				self):
			_UpperCamelCase   :		Any			=						self.prepare_config_and_inputs()

			_UpperCamelCase   :		Union[str, Any]			=						config_and_inputs
			_UpperCamelCase   :		int			=						{'pixel_values': pixel_values}
			return config, inputs_dict







@require_torch
class 	lowercase    (				_lowercase    ,							_lowercase    ,							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 		A__					(				self):
			_UpperCamelCase   :		Optional[int]			=						FocalNetModelTester(self)
			_UpperCamelCase   :		Tuple			=						ConfigTester(self	,       config_class=__snake_case	,       embed_dim=37	,       has_text_modality=__snake_case)


		def 		A__					(				self):
			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):
			return


		def 		A__					(				self):
			_UpperCamelCase   :		Any			=						self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model(*__snake_case)


		def 		A__					(				self):
			_UpperCamelCase   :		List[str]			=						self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_backbone(*__snake_case)


		def 		A__					(				self):
			_UpperCamelCase   :		int			=						self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_masked_image_modeling(*__snake_case)


		def 		A__					(				self):
			_UpperCamelCase   :		str			=						self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_image_classification(*__snake_case)


		@unittest.skip(reason='FocalNet does not use inputs_embeds')
		def 		A__					(				self):
			pass


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


		def 		A__					(				self):
			_UpperCamelCase   :		Union[str, Any]			=						self.model_tester.prepare_config_and_inputs_for_common()

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


		def 		A__					(				self):
			_UpperCamelCase   :		str			=						self.model_tester.prepare_config_and_inputs_for_common()

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

				_UpperCamelCase   :		List[Any]			=						['pixel_values']
				self.assertListEqual(arg_names[:1]	,       __snake_case)


		def 		A__					(				self	,       __snake_case	,       __snake_case	,       __snake_case	,       __snake_case):
			_UpperCamelCase   :		Union[str, Any]			=						model_class(__snake_case)
			model.to(__snake_case)
			model.eval()

			with torch.no_grad():
				_UpperCamelCase   :		Any			=						model(**self._prepare_for_class(__snake_case	,       __snake_case))

			_UpperCamelCase   :		Tuple			=						outputs.hidden_states

			_UpperCamelCase   :		Dict			=						getattr(
			    self.model_tester	,       'expected_num_hidden_layers'	,       len(self.model_tester.depths) + 1)
			self.assertEqual(len(__snake_case)	,       __snake_case)

			# 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   :		Union[str, Any]			=						(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   :		List[str]			=						outputs.reshaped_hidden_states
			self.assertEqual(len(__snake_case)	,       __snake_case)

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


		def 		A__					(				self):
			_UpperCamelCase   :		str			=						self.model_tester.prepare_config_and_inputs_for_common()

			_UpperCamelCase   :		List[str]			=						(
			    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   :		List[str]			=						True
				self.check_hidden_states_output(__snake_case	,       __snake_case	,       __snake_case	,       __snake_case)

				# check that output_hidden_states also work using config
				del inputs_dict["output_hidden_states"]
				_UpperCamelCase   :		str			=						True

				self.check_hidden_states_output(__snake_case	,       __snake_case	,       __snake_case	,       __snake_case)


		def 		A__					(				self):
			_UpperCamelCase   :		Dict			=						self.model_tester.prepare_config_and_inputs_for_common()
			_UpperCamelCase   :		str			=						3

			_UpperCamelCase   :		Optional[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)
			)
			_UpperCamelCase   :		Dict			=						(
			    config.patch_size
			    if isinstance(config.patch_size	,       collections.abc.Iterable)
			    else (config.patch_size, config.patch_size)
			)

			_UpperCamelCase   :		Optional[int]			=						image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
			_UpperCamelCase   :		Union[str, Any]			=						image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

			for model_class in self.all_model_classes[:-1]:
				_UpperCamelCase   :		List[str]			=						True
				self.check_hidden_states_output(__snake_case	,       __snake_case	,       __snake_case	,       (padded_height, padded_width))

				# check that output_hidden_states also work using config
				del inputs_dict["output_hidden_states"]
				_UpperCamelCase   :		List[Any]			=						True
				self.check_hidden_states_output(__snake_case	,       __snake_case	,       __snake_case	,       (padded_height, padded_width))


		@slow
		def 		A__					(				self):
			for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
				_UpperCamelCase   :		int			=						FocalNetModel.from_pretrained(__snake_case)
				self.assertIsNotNone(__snake_case)






		def 		A__					(				self):
			_UpperCamelCase   :		Optional[Any]			=						self.model_tester.prepare_config_and_inputs_for_common()

			_UpperCamelCase   :		List[str]			=						_config_zero_init(__snake_case)
			for model_class in self.all_model_classes:
				_UpperCamelCase   :		Dict			=						model_class(config=__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 	lowercase    (				unittest.TestCase							):


		"""simple docstring"""




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






		@slow
		def 		A__					(				self):
			_UpperCamelCase   :		int			=						FocalNetForImageClassification.from_pretrained('microsoft/focalnet-tiny').to(__snake_case)
			_UpperCamelCase   :		List[str]			=						self.default_image_processor

			_UpperCamelCase   :		int			=						Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
			_UpperCamelCase   :		Union[str, Any]			=						image_processor(images=__snake_case	,       return_tensors='pt').to(__snake_case)

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

			# verify the logits
			_UpperCamelCase   :		Union[str, Any]			=						torch.Size((1, 10_00))
			self.assertEqual(outputs.logits.shape	,       __snake_case)
			_UpperCamelCase   :		Tuple			=						torch.tensor([0.2_1_6_6, -0.4_3_6_8, 0.2_1_9_1]).to(__snake_case)
			self.assertTrue(torch.allclose(outputs.logits[0, :3]	,       __snake_case	,       atol=1e-4))
			self.assertTrue(outputs.logits.argmax(dim=-1).item()	,       2_81)







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


		"""simple docstring"""




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

		a__					       =  False


		def 		A__					(				self):
			_UpperCamelCase   :		List[Any]			=						FocalNetModelTester(self)







import logging

from transformers.configuration_utils import PretrainedConfig


lowerCAmelCase__								=		logging.getLogger(__name__)







class 	lowercase    (				_lowercase							):


		"""simple docstring"""




		a__					       =  "masked_bert"


		def __init__(				self	,       __snake_case=3_05_22	,       __snake_case=7_68	,       __snake_case=12	,       __snake_case=12	,       __snake_case=30_72	,       __snake_case="gelu"	,       __snake_case=0.1	,       __snake_case=0.1	,       __snake_case=5_12	,       __snake_case=2	,       __snake_case=0.0_2	,       __snake_case=1e-12	,       __snake_case=0	,       __snake_case="topK"	,       __snake_case="constant"	,       __snake_case=0.0	,       **__snake_case	,       ):
			super().__init__(pad_token_id=__snake_case	,       **__snake_case)

			_UpperCamelCase   :		List[Any]			=						vocab_size
			_UpperCamelCase   :		Union[str, Any]			=						hidden_size
			_UpperCamelCase   :		Optional[int]			=						num_hidden_layers
			_UpperCamelCase   :		Any			=						num_attention_heads
			_UpperCamelCase   :		int			=						hidden_act
			_UpperCamelCase   :		str			=						intermediate_size
			_UpperCamelCase   :		str			=						hidden_dropout_prob
			_UpperCamelCase   :		Any			=						attention_probs_dropout_prob
			_UpperCamelCase   :		Tuple			=						max_position_embeddings
			_UpperCamelCase   :		Dict			=						type_vocab_size
			_UpperCamelCase   :		str			=						initializer_range
			_UpperCamelCase   :		List[Any]			=						layer_norm_eps
			_UpperCamelCase   :		Tuple			=						pruning_method
			_UpperCamelCase   :		Tuple			=						mask_init
			_UpperCamelCase   :		Dict			=						mask_scale




