
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 		__magic_name__   :





		def __init__(							self    :	Tuple						,  lowerCamelCase__    :	Optional[Any]						,  lowerCamelCase__    :	Dict=3						,  lowerCamelCase__    :	Dict=3_2						,  lowerCamelCase__    :	Tuple=3						,  lowerCamelCase__    :	Tuple=1_0						,  lowerCamelCase__    :	List[Any]=[1_0, 2_0, 3_0, 4_0]						,  lowerCamelCase__    :	Dict=[1, 1, 2, 1]						,  lowerCamelCase__    :	Union[str, Any]=True						,  lowerCamelCase__    :	int=True						,  lowerCamelCase__    :	List[Any]="relu"						,  lowerCamelCase__    :	str=3						,  lowerCamelCase__    :	List[Any]=None						,  lowerCamelCase__    :	Dict=["stage2", "stage3", "stage4"]						,  lowerCamelCase__    :	List[str]=[2, 3, 4]						,  ):
				lowerCAmelCase		:					str  	=     parent
				lowerCAmelCase		:					Any  	=     batch_size
				lowerCAmelCase		:					Optional[int]  	=     image_size
				lowerCAmelCase		:					List[str]  	=     num_channels
				lowerCAmelCase		:					Optional[int]  	=     embeddings_size
				lowerCAmelCase		:					Optional[int]  	=     hidden_sizes
				lowerCAmelCase		:					List[Any]  	=     depths
				lowerCAmelCase		:					Optional[Any]  	=     is_training
				lowerCAmelCase		:					str  	=     use_labels
				lowerCAmelCase		:					int  	=     hidden_act
				lowerCAmelCase		:					int  	=     num_labels
				lowerCAmelCase		:					int  	=     scope
				lowerCAmelCase		:					str  	=     len(a_				)
				lowerCAmelCase		:					Union[str, Any]  	=     out_features
				lowerCAmelCase		:					Optional[int]  	=     out_indices





		def       _A							(							self    :	str				):
				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		:					str  	=     ids_tensor([self.batch_size]						,  self.num_labels				)

				lowerCAmelCase		:					Dict  	=     self.get_config()

				return config, pixel_values, labels





		def       _A							(							self    :	Dict				):
				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       _A							(							self    :	Optional[Any]						,  lowerCamelCase__    :	List[Any]						,  lowerCamelCase__    :	List[Any]						,  lowerCamelCase__    :	Optional[int]				):
				lowerCAmelCase		:					Optional[int]  	=     ResNetModel(config=a_				)
				model.to(a_				)
				model.eval()
				lowerCAmelCase		:					List[str]  	=     model(a_				)
				# 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 // 3_2, self.image_size // 3_2)						,  )





		def       _A							(							self    :	List[str]						,  lowerCamelCase__    :	str						,  lowerCamelCase__    :	List[str]						,  lowerCamelCase__    :	Dict				):
				lowerCAmelCase		:					Optional[Any]  	=     self.num_labels
				lowerCAmelCase		:					str  	=     ResNetForImageClassification(a_				)
				model.to(a_				)
				model.eval()
				lowerCAmelCase		:					List[Any]  	=     model(a_						,  labels=a_				)
				self.parent.assertEqual(result.logits.shape						,  (self.batch_size, self.num_labels)				)





		def       _A							(							self    :	Any						,  lowerCamelCase__    :	int						,  lowerCamelCase__    :	str						,  lowerCamelCase__    :	Union[str, Any]				):
				lowerCAmelCase		:					Optional[Any]  	=     ResNetBackbone(config=a_				)
				model.to(a_				)
				model.eval()
				lowerCAmelCase		:					int  	=     model(a_				)

				# 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		:					str  	=     None
				lowerCAmelCase		:					Dict  	=     ResNetBackbone(config=a_				)
				model.to(a_				)
				model.eval()
				lowerCAmelCase		:					Dict  	=     model(a_				)

				# 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       _A							(							self    :	Tuple				):
				lowerCAmelCase		:					Dict  	=     self.prepare_config_and_inputs()
				lowerCAmelCase	,							lowerCAmelCase	,							lowerCAmelCase		:					List[str]  	=     config_and_inputs
				lowerCAmelCase		:					str  	=     {'''pixel_values''': pixel_values}
				return config, inputs_dict



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

		_lowerCAmelCase		=	True
		_lowerCAmelCase		=	False
		_lowerCAmelCase		=	False
		_lowerCAmelCase		=	False
		_lowerCAmelCase		=	False





		def       _A							(							self    :	int				):
				lowerCAmelCase		:					str  	=     ResNetModelTester(self				)
				lowerCAmelCase		:					Dict  	=     ConfigTester(self						,  config_class=a_						,  has_text_modality=a_				)





		def       _A							(							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       _A							(							self    :	Union[str, Any]				):
				return





		@unittest.skip(reason='''ResNet does not use inputs_embeds'''				)
		def       _A							(							self    :	Dict				):
				pass





		@unittest.skip(reason='''ResNet does not support input and output embeddings'''				)
		def       _A							(							self    :	Dict				):
				pass





		def       _A							(							self    :	int				):
				lowerCAmelCase	,							lowerCAmelCase		:					List[Any]  	=     self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
						lowerCAmelCase		:					Tuple  	=     model_class(a_				)
						lowerCAmelCase		:					List[str]  	=     inspect.signature(model.forward				)
						# signature.parameters is an OrderedDict => so arg_names order is deterministic
						lowerCAmelCase		:					Optional[int]  	=     [*signature.parameters.keys()]

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





		def       _A							(							self    :	Dict				):
				lowerCAmelCase		:					str  	=     self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*a_				)





		def       _A							(							self    :	str				):
				lowerCAmelCase		:					Optional[int]  	=     self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_backbone(*a_				)





		def       _A							(							self    :	List[Any]				):
				lowerCAmelCase	,							lowerCAmelCase		:					List[str]  	=     self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
						lowerCAmelCase		:					List[Any]  	=     model_class(config=a_				)
						for name, module in model.named_modules():
								if isinstance(a_						,  (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       _A							(							self    :	str				):
				def check_hidden_states_output(lowerCamelCase__    :	Optional[Any]						,  lowerCamelCase__    :	int						,  lowerCamelCase__    :	Any				):
						lowerCAmelCase		:					Dict  	=     model_class(a_				)
						model.to(a_				)
						model.eval()

						with torch.no_grad():
								lowerCAmelCase		:					Any  	=     model(**self._prepare_for_class(a_						,  a_				)				)

						lowerCAmelCase		:					List[str]  	=     outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

						lowerCAmelCase		:					Dict  	=     self.model_tester.num_stages
						self.assertEqual(len(a_				)						,  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		:					str  	=     self.model_tester.prepare_config_and_inputs_for_common()
				lowerCAmelCase		:					Any  	=     ['''basic''', '''bottleneck''']
				for model_class in self.all_model_classes:
						for layer_type in layers_type:
								lowerCAmelCase		:					Optional[Any]  	=     layer_type
								lowerCAmelCase		:					str  	=     True
								check_hidden_states_output(a_						,  a_						,  a_				)

								# check that output_hidden_states also work using config
								del inputs_dict["output_hidden_states"]
								lowerCAmelCase		:					Optional[int]  	=     True

								check_hidden_states_output(a_						,  a_						,  a_				)





		@unittest.skip(reason='''ResNet does not use feedforward chunking'''				)
		def       _A							(							self    :	Optional[Any]				):
				pass





		def       _A							(							self    :	Tuple				):
				lowerCAmelCase		:					Union[str, Any]  	=     self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*a_				)





		@slow
		def       _A							(							self    :	Tuple				):
				for model_name in RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						lowerCAmelCase		:					Tuple  	=     ResNetModel.from_pretrained(a_				)
						self.assertIsNotNone(a_				)



def     UpperCAmelCase__							(       ):


		'''simple docstring'''

		lowerCAmelCase		:					Optional[int]  	=     Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''  )
		return image


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





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





		@slow
		def       _A							(							self    :	Any				):
				lowerCAmelCase		:					Optional[Any]  	=     ResNetForImageClassification.from_pretrained(RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]				).to(a_				)

				lowerCAmelCase		:					Union[str, Any]  	=     self.default_image_processor
				lowerCAmelCase		:					int  	=     prepare_img()
				lowerCAmelCase		:					int  	=     image_processor(images=a_						,  return_tensors='''pt'''				).to(a_				)

				# forward pass
				with torch.no_grad():
						lowerCAmelCase		:					Tuple  	=     model(**a_				)

				# verify the logits
				lowerCAmelCase		:					Dict  	=     torch.Size((1, 1_0_0_0)				)
				self.assertEqual(outputs.logits.shape						,  a_				)

				lowerCAmelCase		:					List[Any]  	=     torch.tensor([-1_1.1_0_6_9, -9.7_8_7_7, -8.3_7_7_7]				).to(a_				)

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



@require_torch
class 		__magic_name__   (						snake_case,				unittest.TestCase    ):
		_lowerCAmelCase		=	(ResNetBackbone,) if is_torch_available() else ()
		_lowerCAmelCase		=	False
		_lowerCAmelCase		=	ResNetConfig





		def       _A							(							self    :	Optional[Any]				):
				lowerCAmelCase		:					Any  	=     ResNetModelTester(self				)


from typing import TYPE_CHECKING

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


__SCREAMING_SNAKE_CASE			:   str         =     {
    'configuration_deta': ['DETA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DetaConfig'],
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE			:   Optional[int]         =     ['DetaImageProcessor']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__SCREAMING_SNAKE_CASE			:   str         =     [
		    'DETA_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'DetaForObjectDetection',
		    'DetaModel',
		    'DetaPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_deta import DETA_PRETRAINED_CONFIG_ARCHIVE_MAP, DetaConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .image_processing_deta import DetaImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_deta import (
				    DETA_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DetaForObjectDetection,
				    DetaModel,
				    DetaPreTrainedModel,
				)

else:
		import sys

		__SCREAMING_SNAKE_CASE			:   Dict         =     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
