
'''simple docstring'''



import inspect
import unittest

from transformers import BitConfig
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, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch
						from torch import nn

						from transformers import BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
						from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image




class   UpperCAmelCase_					:






						'''simple docstring'''






						def __init__( self   ,       _lowercase   ,       _lowercase=3   ,       _lowercase=32   ,       _lowercase=3   ,       _lowercase=10   ,       _lowercase=[8, 16, 32, 64]   ,       _lowercase=[1, 1, 2, 1]   ,       _lowercase=True   ,       _lowercase=True   ,       _lowercase="relu"   ,       _lowercase=3   ,       _lowercase=None   ,       _lowercase=["stage2", "stage3", "stage4"]   ,       _lowercase=[2, 3, 4]   ,       _lowercase=1   ,       ):

												"""simple docstring"""



												_lowerCAmelCase  						=			parent
												_lowerCAmelCase  						=			batch_size
												_lowerCAmelCase  						=			image_size
												_lowerCAmelCase  						=			num_channels
												_lowerCAmelCase  						=			embeddings_size
												_lowerCAmelCase  						=			hidden_sizes
												_lowerCAmelCase  						=			depths
												_lowerCAmelCase  						=			is_training
												_lowerCAmelCase  						=			use_labels
												_lowerCAmelCase  						=			hidden_act
												_lowerCAmelCase  						=			num_labels
												_lowerCAmelCase  						=			scope
												_lowerCAmelCase  						=			len(_lowercase				)
												_lowerCAmelCase  						=			out_features
												_lowerCAmelCase  						=			out_indices
												_lowerCAmelCase  						=			num_groups




						def  _lowercase     ( self				):

												"""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.num_labels				)

												_lowerCAmelCase  						=			self.get_config()

												return config, pixel_values, labels




						def  _lowercase     ( self				):

												"""simple docstring"""



												return BitConfig(
												    num_channels=self.num_channels   ,       embeddings_size=self.embeddings_size   ,       hidden_sizes=self.hidden_sizes   ,       depths=self.depths   ,       hidden_act=self.hidden_act   ,       num_labels=self.num_labels   ,       out_features=self.out_features   ,       out_indices=self.out_indices   ,       num_groups=self.num_groups   ,       )




						def  _lowercase     ( self   ,       _lowercase   ,       _lowercase   ,       _lowercase				):

												"""simple docstring"""



												_lowerCAmelCase  						=			BitModel(config=_lowercase				)
												model.to(_lowercase				)
												model.eval()
												_lowerCAmelCase  						=			model(_lowercase				)
												self.parent.assertEqual(
												    result.last_hidden_state.shape   ,       (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)   ,       )




						def  _lowercase     ( self   ,       _lowercase   ,       _lowercase   ,       _lowercase				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.num_labels
												_lowerCAmelCase  						=			BitForImageClassification(_lowercase				)
												model.to(_lowercase				)
												model.eval()
												_lowerCAmelCase  						=			model(_lowercase   ,       labels=_lowercase				)
												self.parent.assertEqual(result.logits.shape   ,       (self.batch_size, self.num_labels)				)




						def  _lowercase     ( self   ,       _lowercase   ,       _lowercase   ,       _lowercase				):

												"""simple docstring"""



												_lowerCAmelCase  						=			BitBackbone(config=_lowercase				)
												model.to(_lowercase				)
												model.eval()
												_lowerCAmelCase  						=			model(_lowercase				)

												# 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.hidden_sizes[1], 4, 4]				)

												# 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  						=			BitBackbone(config=_lowercase				)
												model.to(_lowercase				)
												model.eval()
												_lowerCAmelCase  						=			model(_lowercase				)

												# verify feature maps
												self.parent.assertEqual(len(result.feature_maps				)   ,       1				)
												self.parent.assertListEqual(list(result.feature_maps[0].shape				)   ,       [self.batch_size, self.hidden_sizes[-1], 1, 1]				)

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




						def  _lowercase     ( self				):

												"""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_					(		_SCREAMING_SNAKE_CASE	,							_SCREAMING_SNAKE_CASE	,							unittest.TestCase			):






						'''simple docstring'''






						_lowercase		:				Dict									=       (BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
						_lowercase		:				Any									=       (
						    {'''feature-extraction''': BitModel, '''image-classification''': BitForImageClassification}
						    if is_torch_available()
						    else {}
						)

						_lowercase		:				Dict									=       False
						_lowercase		:				List[Any]									=       False
						_lowercase		:				Any									=       False
						_lowercase		:				List[str]									=       False
						_lowercase		:				Optional[int]									=       False




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			BitModelTester(self				)
												_lowerCAmelCase  						=			ConfigTester(self   ,       config_class=_lowercase   ,       has_text_modality=_lowercase				)




						def  _lowercase     ( 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  _lowercase     ( self				):

												"""simple docstring"""



												return




						@unittest.skip(reason="""Bit does not output attentions"""				)
						def  _lowercase     ( self				):

												"""simple docstring"""



												pass




						@unittest.skip(reason="""Bit does not use inputs_embeds"""				)
						def  _lowercase     ( self				):

												"""simple docstring"""



												pass




						@unittest.skip(reason="""Bit does not support input and output embeddings"""				)
						def  _lowercase     ( self				):

												"""simple docstring"""



												pass




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase						,   _lowerCAmelCase  						=			self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																		_lowerCAmelCase  						=			model_class(_lowercase				)
																		_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]   ,       _lowercase				)




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*_lowercase				)




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_backbone(*_lowercase				)




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase						,   _lowerCAmelCase  						=			self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																		_lowerCAmelCase  						=			model_class(config=_lowercase				)
																		for name, module in model.named_modules():
																								if isinstance(_lowercase   ,       (nn.BatchNormad, nn.GroupNorm)				):
																														self.assertTrue(
																														    torch.all(module.weight == 1				)   ,       msg=F'Parameter {name} of model {model_class} seems not properly initialized'   ,       )
																														self.assertTrue(
																														    torch.all(module.bias == 0				)   ,       msg=F'Parameter {name} of model {model_class} seems not properly initialized'   ,       )




						def  _lowercase     ( self				):

												"""simple docstring"""



												def check_hidden_states_output(_lowercase   ,       _lowercase   ,       _lowercase				):
																		_lowerCAmelCase  						=			model_class(_lowercase				)
																		model.to(_lowercase				)
																		model.eval()

																		with torch.no_grad():
																								_lowerCAmelCase  						=			model(**self._prepare_for_class(_lowercase   ,       _lowercase				)				)

																		_lowerCAmelCase  						=			outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

																		_lowerCAmelCase  						=			self.model_tester.num_stages
																		self.assertEqual(len(_lowercase				)   ,       expected_num_stages + 1				)

																		# Bit's feature maps are of shape (batch_size, num_channels, height, width)
																		self.assertListEqual(
																		    list(hidden_states[0].shape[-2:]				)   ,       [self.model_tester.image_size // 4, self.model_tester.image_size // 4]   ,       )

												_lowerCAmelCase						,   _lowerCAmelCase  						=			self.model_tester.prepare_config_and_inputs_for_common()
												_lowerCAmelCase  						=			["""preactivation""", """bottleneck"""]
												for model_class in self.all_model_classes:
																		for layer_type in layers_type:
																								_lowerCAmelCase  						=			layer_type
																								_lowerCAmelCase  						=			True
																								check_hidden_states_output(_lowercase   ,       _lowercase   ,       _lowercase				)

																								# check that output_hidden_states also work using config
																								del inputs_dict["output_hidden_states"]
																								_lowerCAmelCase  						=			True

																								check_hidden_states_output(_lowercase   ,       _lowercase   ,       _lowercase				)




						@unittest.skip(reason="""Bit does not use feedforward chunking"""				)
						def  _lowercase     ( self				):

												"""simple docstring"""



												pass




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_classification(*_lowercase				)




						@slow
						def  _lowercase     ( self				):

												"""simple docstring"""



												for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		_lowerCAmelCase  						=			BitModel.from_pretrained(_lowercase				)
																		self.assertIsNotNone(_lowercase				)






def A				():
						_lowerCAmelCase  						=			Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""		)
						return image




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






						'''simple docstring'''






						@cached_property
						def  _lowercase     ( self				):

												"""simple docstring"""



												return (
												    BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]				) if is_vision_available() else None
												)




						@slow
						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]				).to(_lowercase				)

												_lowerCAmelCase  						=			self.default_image_processor
												_lowerCAmelCase  						=			prepare_img()
												_lowerCAmelCase  						=			image_processor(images=_lowercase   ,       return_tensors="""pt"""				).to(_lowercase				)

												# forward pass
												with torch.no_grad():
																		_lowerCAmelCase  						=			model(**_lowercase				)

												# verify the logits
												_lowerCAmelCase  						=			torch.Size((1, 1_000)				)
												self.assertEqual(outputs.logits.shape   ,       _lowercase				)

												_lowerCAmelCase  						=			torch.tensor([[-0.6526, -0.5263, -1.4398]]				).to(_lowercase				)

												self.assertTrue(torch.allclose(outputs.logits[0, :3]   ,       _lowercase   ,       atol=1e-4				)				)






@require_torch
class   UpperCAmelCase_					(		_SCREAMING_SNAKE_CASE	,							unittest.TestCase			):






						'''simple docstring'''






						_lowercase		:				List[Any]									=       (BitBackbone,) if is_torch_available() else ()
						_lowercase		:				str									=       BitConfig

						_lowercase		:				Union[str, Any]									=       False




						def  _lowercase     ( self				):

												"""simple docstring"""



												_lowerCAmelCase  						=			BitModelTester(self				)


'''simple docstring'''



from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_lowercase    =		{"""configuration_van""": ["""VAN_PRETRAINED_CONFIG_ARCHIVE_MAP""", """VanConfig"""]}


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowercase    =		[
						    """VAN_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """VanForImageClassification""",
						    """VanModel""",
						    """VanPreTrainedModel""",
						]

if TYPE_CHECKING:
						from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_van import (
												    VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
												    VanForImageClassification,
												    VanModel,
												    VanPreTrainedModel,
												)

else:
						import sys

						_lowercase    =		_LazyModule(__name__, globals()["""__file__"""], _import_structure)
