"""simple docstring"""






import copy
import inspect
import unittest

from transformers import PretrainedConfig, SwiftFormerConfig
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_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 SwiftFormerForImageClassification, SwiftFormerModel
						from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import ViTImageProcessor
class 				snake_case       :




					"""simple docstring"""

					def __init__(				self   :	List[Any]    ,lowerCamelCase__   :	Optional[int]    ,lowerCamelCase__   :	Optional[Any]=13    ,lowerCamelCase__   :	Optional[Any]=3    ,lowerCamelCase__   :	int=True    ,lowerCamelCase__   :	Union[str, Any]=True    ,lowerCamelCase__   :	Union[str, Any]=0.1    ,lowerCamelCase__   :	List[Any]=0.1    ,lowerCamelCase__   :	Dict=224    ,lowerCamelCase__   :	List[str]=1_000    ,lowerCamelCase__   :	List[Any]=[3, 3, 6, 4]    ,lowerCamelCase__   :	int=[48, 56, 112, 220]    ,):
									UpperCAmelCase__             =							parent
									UpperCAmelCase__             =							batch_size
									UpperCAmelCase__             =							num_channels
									UpperCAmelCase__             =							is_training
									UpperCAmelCase__             =							use_labels
									UpperCAmelCase__             =							hidden_dropout_prob
									UpperCAmelCase__             =							attention_probs_dropout_prob
									UpperCAmelCase__             =							num_labels
									UpperCAmelCase__             =							image_size
									UpperCAmelCase__             =							layer_depths
									UpperCAmelCase__             =							embed_dims







					def 		__lowerCAmelCase						(				self   :	Optional[Any]			):
									UpperCAmelCase__             =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

									UpperCAmelCase__             =							None
									if self.use_labels:
													UpperCAmelCase__             =							ids_tensor([self.batch_size]    ,self.num_labels			)

									UpperCAmelCase__             =							self.get_config()

									return config, pixel_values, labels







					def 		__lowerCAmelCase						(				self   :	Optional[Any]			):
									return SwiftFormerConfig(
									    depths=self.layer_depths    ,embed_dims=self.embed_dims    ,mlp_ratio=4    ,downsamples=[True, True, True, True]    ,hidden_act='gelu'    ,num_labels=self.num_labels    ,down_patch_size=3    ,down_stride=2    ,down_pad=1    ,drop_rate=0.0    ,drop_path_rate=0.0    ,use_layer_scale=lowerCamelCase__    ,layer_scale_init_value=1e-5    ,)







					def 		__lowerCAmelCase						(				self   :	Optional[int]    ,lowerCamelCase__   :	Optional[int]    ,lowerCamelCase__   :	List[Any]    ,lowerCamelCase__   :	Optional[Any]			):
									UpperCAmelCase__             =							SwiftFormerModel(config=lowerCamelCase__			)
									model.to(lowerCamelCase__			)
									model.eval()
									UpperCAmelCase__             =							model(lowerCamelCase__			)
									self.parent.assertEqual(result.last_hidden_state.shape    ,(self.batch_size, self.embed_dims[-1], 7, 7)			)







					def 		__lowerCAmelCase						(				self   :	Optional[int]    ,lowerCamelCase__   :	Optional[Any]    ,lowerCamelCase__   :	str    ,lowerCamelCase__   :	Dict			):
									UpperCAmelCase__             =							self.num_labels
									UpperCAmelCase__             =							SwiftFormerForImageClassification(lowerCamelCase__			)
									model.to(lowerCamelCase__			)
									model.eval()
									UpperCAmelCase__             =							model(lowerCamelCase__    ,labels=lowerCamelCase__			)
									self.parent.assertEqual(result.logits.shape    ,(self.batch_size, self.num_labels)			)

									UpperCAmelCase__             =							SwiftFormerForImageClassification(lowerCamelCase__			)
									model.to(lowerCamelCase__			)
									model.eval()

									UpperCAmelCase__             =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)
									UpperCAmelCase__             =							model(lowerCamelCase__			)
									self.parent.assertEqual(result.logits.shape    ,(self.batch_size, self.num_labels)			)







					def 		__lowerCAmelCase						(				self   :	Union[str, Any]			):
									((UpperCAmelCase__)      ,	(UpperCAmelCase__)      ,	(UpperCAmelCase__))             =							self.prepare_config_and_inputs()
									UpperCAmelCase__             =							{'pixel_values': pixel_values}
									return config, inputs_dict






@require_torch
class 				snake_case       ( __UpperCAmelCase      ,    __UpperCAmelCase      ,    unittest.TestCase				):




					"""simple docstring"""

					snake_case__				    = (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
					snake_case__				    = (
					    {"feature-extraction": SwiftFormerModel, "image-classification": SwiftFormerForImageClassification}
					    if is_torch_available()
					    else {}
					)

					snake_case__				    = False
					snake_case__				    = False
					snake_case__				    = False
					snake_case__				    = False
					snake_case__				    = False







					def 		__lowerCAmelCase						(				self   :	Tuple			):
									UpperCAmelCase__             =							SwiftFormerModelTester(self			)
									UpperCAmelCase__             =							ConfigTester(
									    self    ,config_class=lowerCamelCase__    ,has_text_modality=lowerCamelCase__    ,hidden_size=37    ,num_attention_heads=12    ,num_hidden_layers=12    ,)







					def 		__lowerCAmelCase						(				self   :	int			):
									self.config_tester.run_common_tests()







					@unittest.skip(reason='SwiftFormer does not use inputs_embeds'			)
					def 		__lowerCAmelCase						(				self   :	Optional[Any]			):
									pass







					def 		__lowerCAmelCase						(				self   :	int			):
									UpperCAmelCase__      ,	UpperCAmelCase__             =							self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
													UpperCAmelCase__             =							model_class(lowerCamelCase__			)
													UpperCAmelCase__             =							model.get_output_embeddings()
													self.assertTrue(x is None or isinstance(lowerCamelCase__    ,nn.Linear			)			)







					def 		__lowerCAmelCase						(				self   :	Union[str, Any]			):
									UpperCAmelCase__      ,	UpperCAmelCase__             =							self.model_tester.prepare_config_and_inputs_for_common()

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

													UpperCAmelCase__             =							['pixel_values']
													self.assertListEqual(arg_names[:1]    ,lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	Optional[int]			):
									UpperCAmelCase__             =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	Dict			):
									UpperCAmelCase__             =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*lowerCamelCase__			)







					@slow
					def 		__lowerCAmelCase						(				self   :	str			):
									for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													UpperCAmelCase__             =							SwiftFormerModel.from_pretrained(lowerCamelCase__			)
													self.assertIsNotNone(lowerCamelCase__			)







					@unittest.skip(reason='SwiftFormer does not output attentions'			)
					def 		__lowerCAmelCase						(				self   :	Tuple			):
									pass







					def 		__lowerCAmelCase						(				self   :	str			):
									def check_hidden_states_output(lowerCamelCase__   :	str    ,lowerCamelCase__   :	Union[str, Any]    ,lowerCamelCase__   :	str			):
													UpperCAmelCase__             =							model_class(lowerCamelCase__			)
													model.to(lowerCamelCase__			)
													model.eval()

													with torch.no_grad():
																	UpperCAmelCase__             =							model(**self._prepare_for_class(lowerCamelCase__    ,lowerCamelCase__			)			)

													UpperCAmelCase__             =							outputs.hidden_states

													UpperCAmelCase__             =							8
													self.assertEqual(len(lowerCamelCase__			)    ,lowerCamelCase__			)  # TODO

													# SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
													# with the width and height being successively divided by 2, after every 2 blocks
													for i in range(len(lowerCamelCase__			)			):
																	self.assertEqual(
																	    hidden_states[i].shape    ,torch.Size(
																	        [
																	            self.model_tester.batch_size,
																	            self.model_tester.embed_dims[i // 2],
																	            (self.model_tester.image_size // 4) // 2 ** (i // 2),
																	            (self.model_tester.image_size // 4) // 2 ** (i // 2),
																	        ]			)    ,)

									UpperCAmelCase__      ,	UpperCAmelCase__             =							self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
													UpperCAmelCase__             =							True
													check_hidden_states_output(lowerCamelCase__    ,lowerCamelCase__    ,lowerCamelCase__			)

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

													check_hidden_states_output(lowerCamelCase__    ,lowerCamelCase__    ,lowerCamelCase__			)







					def 		__lowerCAmelCase						(				self   :	str			):
									def _config_zero_init(lowerCamelCase__   :	Dict			):
													UpperCAmelCase__             =							copy.deepcopy(lowerCamelCase__			)
													for key in configs_no_init.__dict__.keys():
																	if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
																					setattr(lowerCamelCase__    ,lowerCamelCase__    ,1e-10			)
																	if isinstance(getattr(lowerCamelCase__    ,lowerCamelCase__    ,lowerCamelCase__			)    ,lowerCamelCase__			):
																					UpperCAmelCase__             =							_config_zero_init(getattr(lowerCamelCase__    ,lowerCamelCase__			)			)
																					setattr(lowerCamelCase__    ,lowerCamelCase__    ,lowerCamelCase__			)
													return configs_no_init

									UpperCAmelCase__      ,	UpperCAmelCase__             =							self.model_tester.prepare_config_and_inputs_for_common()

									UpperCAmelCase__             =							_config_zero_init(lowerCamelCase__			)
									for model_class in self.all_model_classes:
													UpperCAmelCase__             =							model_class(config=lowerCamelCase__			)
													for name, param in model.named_parameters():
																	if param.requires_grad:
																					self.assertIn(
																					    ((param.data.mean() * 1e9) / 1e9).round().item()    ,[0.0, 1.0]    ,msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''    ,)







					@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'			)
					def 		__lowerCAmelCase						(				self   :	Dict			):
									pass






def   a_						(					):
				UpperCAmelCase__             =							Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'       )
				return image







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




					"""simple docstring"""

					@cached_property
					def 		__lowerCAmelCase						(				self   :	str			):
									return ViTImageProcessor.from_pretrained('MBZUAI/swiftformer-xs'			) if is_vision_available() else None







					@slow
					def 		__lowerCAmelCase						(				self   :	Optional[int]			):
									UpperCAmelCase__             =							SwiftFormerForImageClassification.from_pretrained('MBZUAI/swiftformer-xs'			).to(lowerCamelCase__			)

									UpperCAmelCase__             =							self.default_image_processor
									UpperCAmelCase__             =							prepare_img()
									UpperCAmelCase__             =							image_processor(images=lowerCamelCase__    ,return_tensors='pt'			).to(lowerCamelCase__			)

									# forward pass
									with torch.no_grad():
													UpperCAmelCase__             =							model(**lowerCamelCase__			)

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

									UpperCAmelCase__             =							torch.tensor([[-2.1703e00, 2.1107e00, -2.0811e00]]			).to(lowerCamelCase__			)
									self.assertTrue(torch.allclose(outputs.logits[0, :3]    ,lowerCamelCase__    ,atol=1e-4			)			)






from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowercase_       		=  logging.get_logger(__name__)

lowercase_       		=  {
    "microsoft/resnet-50": "https://huggingface.co/microsoft/resnet-50/blob/main/config.json",
}


class  A						(							_UpperCAmelCase    ,							_UpperCAmelCase ):







    """simple docstring"""





    lowerCamelCase			=							'resnet'
    lowerCamelCase			=							['basic', 'bottleneck']


    def __init__(      self :       Optional[Any],lowercase_ :       int=3,lowercase_ :       List[str]=6_4,lowercase_ :       int=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8],lowercase_ :       Tuple=[3, 4, 6, 3],lowercase_ :       Union[str, Any]="bottleneck",lowercase_ :       List[str]="relu",lowercase_ :       Tuple=False,lowercase_ :       List[str]=None,lowercase_ :       List[Any]=None,**lowercase_ :       str,)->    Optional[Any]:





        '''simple docstring'''







        super().__init__(**lowercase_		)
        if layer_type not in self.layer_types:
            raise ValueError(F'layer_type={layer_type} is not one of {",".join(self.layer_types		)}'		)
        A__          =   num_channels
        A__          =   embedding_size
        A__          =   hidden_sizes
        A__          =   depths
        A__          =   layer_type
        A__          =   hidden_act
        A__          =   downsample_in_first_stage
        A__          =   ['stem'] + [F'stage{idx}' for idx in range(1,len(lowercase_		) + 1		)]
        A__      ,						A__          =   get_aligned_output_features_output_indices(
            out_features=lowercase_,out_indices=lowercase_,stage_names=self.stage_names		)




class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    lowerCamelCase			=							version.parse('1.11' )


    @property
    def 						snake_case__     (      self :       List[Any]		)->    Mapping[str, Mapping[int, str]]:





        '''simple docstring'''







        return OrderedDict(
            [
                ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
            ]		)


    @property
    def 						snake_case__     (      self :       Any		)->    float:





        '''simple docstring'''







        return 1E-3

