
"""simple docstring"""

import inspect
import unittest

from transformers import ResNetConfig
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 ResNetBackbone, ResNetForImageClassification, ResNetModel
					from transformers.models.resnet.modeling_resnet import RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import AutoImageProcessor






class        _lowercase   :




	def __init__( self		, UpperCAmelCase_		, UpperCAmelCase_=3		, UpperCAmelCase_=32		, UpperCAmelCase_=3		, UpperCAmelCase_=10		, UpperCAmelCase_=[10, 20, 30, 40]		, UpperCAmelCase_=[1, 1, 2, 1]		, UpperCAmelCase_=True		, UpperCAmelCase_=True		, UpperCAmelCase_="relu"		, UpperCAmelCase_=3		, UpperCAmelCase_=None		, UpperCAmelCase_=["stage2", "stage3", "stage4"]		, UpperCAmelCase_=[2, 3, 4]		, )		->							Dict:
			lowerCamelCase     :			Any		=    parent
			lowerCamelCase     :			List[str]		=    batch_size
			lowerCamelCase     :			List[Any]		=    image_size
			lowerCamelCase     :			str		=    num_channels
			lowerCamelCase     :			str		=    embeddings_size
			lowerCamelCase     :			Optional[int]		=    hidden_sizes
			lowerCamelCase     :			Any		=    depths
			lowerCamelCase     :			Dict		=    is_training
			lowerCamelCase     :			Tuple		=    use_labels
			lowerCamelCase     :			Optional[Any]		=    hidden_act
			lowerCamelCase     :			str		=    num_labels
			lowerCamelCase     :			List[Any]		=    scope
			lowerCamelCase     :			Optional[Any]		=    len(UpperCAmelCase_					)
			lowerCamelCase     :			List[Any]		=    out_features
			lowerCamelCase     :			str		=    out_indices




	def 			_UpperCamelCase    ( self					)		->							Dict:
			lowerCamelCase     :			Optional[Any]		=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

			lowerCamelCase     :			Union[str, Any]		=    None
			if self.use_labels:
					lowerCamelCase     :			int		=    ids_tensor([self.batch_size]		, self.num_labels					)

			lowerCamelCase     :			List[Any]		=    self.get_config()

			return config, pixel_values, labels




	def 			_UpperCamelCase    ( self					)		->							str:
			return ResNetConfig(
			    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		, )




	def 			_UpperCamelCase    ( self		, UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_					)		->							List[Any]:
			lowerCamelCase     :			List[str]		=    ResNetModel(config=UpperCAmelCase_					)
			model.to(UpperCAmelCase_					)
			model.eval()
			lowerCamelCase     :			List[Any]		=    model(UpperCAmelCase_					)
			# expected last hidden states: B, C, H // 32, W // 32
			self.parent.assertEqual(
			    result.last_hidden_state.shape		, (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)		, )




	def 			_UpperCamelCase    ( self		, UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_					)		->							Dict:
			lowerCamelCase     :			Optional[int]		=    self.num_labels
			lowerCamelCase     :			List[Any]		=    ResNetForImageClassification(UpperCAmelCase_					)
			model.to(UpperCAmelCase_					)
			model.eval()
			lowerCamelCase     :			str		=    model(UpperCAmelCase_		, labels=UpperCAmelCase_					)
			self.parent.assertEqual(result.logits.shape		, (self.batch_size, self.num_labels)					)




	def 			_UpperCamelCase    ( self		, UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_					)		->							Dict:
			lowerCamelCase     :			Union[str, Any]		=    ResNetBackbone(config=UpperCAmelCase_					)
			model.to(UpperCAmelCase_					)
			model.eval()
			lowerCamelCase     :			Union[str, 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.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     :			Any		=    None
			lowerCamelCase     :			str		=    ResNetBackbone(config=UpperCAmelCase_					)
			model.to(UpperCAmelCase_					)
			model.eval()
			lowerCamelCase     :			Dict		=    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.hidden_sizes[-1], 1, 1]					)

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




	def 			_UpperCamelCase    ( self					)		->							str:
			lowerCamelCase     :			Optional[Any]		=    self.prepare_config_and_inputs()
			lowerCamelCase	,    lowerCamelCase	,    lowerCamelCase     :			Tuple		=    config_and_inputs
			lowerCamelCase     :			Dict		=    {'pixel_values': pixel_values}
			return config, inputs_dict






@require_torch
class        _lowercase   (				__UpperCAmelCase				,       __UpperCAmelCase				,       unittest.TestCase		):
	lowercase_        =	(
	    (
	        ResNetModel,
	        ResNetForImageClassification,
	        ResNetBackbone,
	    )
	    if is_torch_available()
	    else ()
	)
	lowercase_        =	(
	    {'feature-extraction': ResNetModel, 'image-classification': ResNetForImageClassification}
	    if is_torch_available()
	    else {}
	)

	lowercase_        =	True
	lowercase_        =	False
	lowercase_        =	False
	lowercase_        =	False
	lowercase_        =	False




	def 			_UpperCamelCase    ( self					)		->							str:
			lowerCamelCase     :			Dict		=    ResNetModelTester(self					)
			lowerCamelCase     :			str		=    ConfigTester(self		, config_class=UpperCAmelCase_		, has_text_modality=UpperCAmelCase_					)




	def 			_UpperCamelCase    ( self					)		->							List[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 			_UpperCamelCase    ( self					)		->							str:
			return




	@unittest.skip(reason='ResNet does not use inputs_embeds'					)
	def 			_UpperCamelCase    ( self					)		->							Tuple:
			pass




	@unittest.skip(reason='ResNet does not support input and output embeddings'					)
	def 			_UpperCamelCase    ( self					)		->							List[str]:
			pass




	def 			_UpperCamelCase    ( self					)		->							Any:
			lowerCamelCase	,    lowerCamelCase     :			Optional[Any]		=    self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
					lowerCamelCase     :			Optional[Any]		=    model_class(UpperCAmelCase_					)
					lowerCamelCase     :			Any		=    inspect.signature(model.forward					)
					# signature.parameters is an OrderedDict => so arg_names order is deterministic
					lowerCamelCase     :			Any		=    [*signature.parameters.keys()]

					lowerCamelCase     :			Optional[int]		=    ['pixel_values']
					self.assertListEqual(arg_names[:1]		, UpperCAmelCase_					)




	def 			_UpperCamelCase    ( self					)		->							str:
			lowerCamelCase     :			Tuple		=    self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model(*UpperCAmelCase_					)




	def 			_UpperCamelCase    ( self					)		->							int:
			lowerCamelCase     :			Optional[int]		=    self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_backbone(*UpperCAmelCase_					)




	def 			_UpperCamelCase    ( self					)		->							List[str]:
			lowerCamelCase	,    lowerCamelCase     :			Any		=    self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
					lowerCamelCase     :			Optional[Any]		=    model_class(config=UpperCAmelCase_					)
					for name, module in model.named_modules():
							if isinstance(UpperCAmelCase_		, (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 			_UpperCamelCase    ( self					)		->							Optional[Any]:
			def check_hidden_states_output(UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_					):
					lowerCamelCase     :			Union[str, Any]		=    model_class(UpperCAmelCase_					)
					model.to(UpperCAmelCase_					)
					model.eval()

					with torch.no_grad():
							lowerCamelCase     :			Union[str, Any]		=    model(**self._prepare_for_class(UpperCAmelCase_		, UpperCAmelCase_					)					)

					lowerCamelCase     :			List[Any]		=    outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

					lowerCamelCase     :			Optional[int]		=    self.model_tester.num_stages
					self.assertEqual(len(UpperCAmelCase_					)		, expected_num_stages + 1					)

					# ResNet'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     :			Tuple		=    self.model_tester.prepare_config_and_inputs_for_common()
			lowerCamelCase     :			List[Any]		=    ['basic', 'bottleneck']
			for model_class in self.all_model_classes:
					for layer_type in layers_type:
							lowerCamelCase     :			Union[str, Any]		=    layer_type
							lowerCamelCase     :			Tuple		=    True
							check_hidden_states_output(UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_					)

							# check that output_hidden_states also work using config
							del inputs_dict["output_hidden_states"]
							lowerCamelCase     :			List[Any]		=    True

							check_hidden_states_output(UpperCAmelCase_		, UpperCAmelCase_		, UpperCAmelCase_					)




	@unittest.skip(reason='ResNet does not use feedforward chunking'					)
	def 			_UpperCamelCase    ( self					)		->							int:
			pass




	def 			_UpperCamelCase    ( self					)		->							Optional[Any]:
			lowerCamelCase     :			List[str]		=    self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase_					)




	@slow
	def 			_UpperCamelCase    ( self					)		->							Union[str, Any]:
			for model_name in RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					lowerCamelCase     :			Optional[int]		=    ResNetModel.from_pretrained(UpperCAmelCase_					)
					self.assertIsNotNone(UpperCAmelCase_					)


def 		UpperCAmelCase					(		):





		'''simple docstring'''


		lowerCamelCase     :			Dict		=    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
		return image






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




	@cached_property
	def 			_UpperCamelCase    ( self					)		->							str:
			return (
			    AutoImageProcessor.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]					)
			    if is_vision_available()
			    else None
			)




	@slow
	def 			_UpperCamelCase    ( self					)		->							List[Any]:
			lowerCamelCase     :			Optional[Any]		=    ResNetForImageClassification.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]					).to(UpperCAmelCase_					)

			lowerCamelCase     :			int		=    self.default_image_processor
			lowerCamelCase     :			List[str]		=    prepare_img()
			lowerCamelCase     :			Dict		=    image_processor(images=UpperCAmelCase_		, return_tensors='pt'					).to(UpperCAmelCase_					)

			# forward pass
			with torch.no_grad():
					lowerCamelCase     :			List[str]		=    model(**UpperCAmelCase_					)

			# verify the logits
			lowerCamelCase     :			List[str]		=    torch.Size((1, 1000)					)
			self.assertEqual(outputs.logits.shape		, UpperCAmelCase_					)

			lowerCamelCase     :			str		=    torch.tensor([-11.1069, -9.7877, -8.3777]					).to(UpperCAmelCase_					)

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






@require_torch
class        _lowercase   (				__UpperCAmelCase				,       unittest.TestCase		):
	lowercase_        =	(ResNetBackbone,) if is_torch_available() else ()
	lowercase_        =	False
	lowercase_        =	ResNetConfig




	def 			_UpperCamelCase    ( self					)		->							Union[str, Any]:
			lowerCamelCase     :			Any		=    ResNetModelTester(self					)








"""simple docstring"""

from __future__ import annotations
def 		UpperCAmelCase					(		a_			):





		'''simple docstring'''


		assert (
		    isinstance(a_,						a_			) and mask > 0
		), F"""mask needs to be positive integer, your input {mask}"""
		lowerCamelCase     :			Tuple		=    []
		lowerCamelCase     :			Tuple		=    mask

		while submask:
				all_submasks.append(a_			)
				lowerCamelCase     :			Optional[int]		=    (submask - 1) & mask

		return all_submasks


if __name__ == "__main__":
					import doctest

					doctest.testmod()






