



'''simple docstring'''


import sys
from typing import Tuple

import numpy as np
import torch
from PIL import Image
from torch import nn

from transformers.image_utils import PILImageResampling
from utils import img_tensorize




class      __UpperCamelCase       :







	def __init__(    self ,				_lowerCAmelCase ,				_lowerCAmelCase=sys.maxsize						)    ->    Dict:
								'''simple docstring'''






								lowercase        =							"""bilinear"""
								lowercase        =							max_size
								lowercase        =							short_edge_length







	def __call__(    self ,				_lowerCAmelCase						)    ->    str:
								'''simple docstring'''






								lowercase        =							[]
								for img in imgs:
															lowercase     ,   lowercase        =							img.shape[:2]
															# later: provide list and randomly choose index for resize
															lowercase        =							np.random.randint(self.short_edge_length[0] ,				self.short_edge_length[1] + 1						)
															if size == 0:
																						return img
															lowercase        =							size * 1.0 / min(_lowerCAmelCase ,				_lowerCAmelCase						)
															if h < w:
																						lowercase     ,   lowercase        =							size, scale * w
															else:
																						lowercase     ,   lowercase        =							scale * h, size
															if max(_lowerCAmelCase ,				_lowerCAmelCase						) > self.max_size:
																						lowercase        =							self.max_size * 1.0 / max(_lowerCAmelCase ,				_lowerCAmelCase						)
																						lowercase        =							newh * scale
																						lowercase        =							neww * scale
															lowercase        =							int(neww + 0.5						)
															lowercase        =							int(newh + 0.5						)

															if img.dtype == np.uinta:
																						lowercase        =							Image.fromarray(_lowerCAmelCase						)
																						lowercase        =							pil_image.resize((neww, newh) ,				PILImageResampling.BILINEAR						)
																						lowercase        =							np.asarray(_lowerCAmelCase						)
															else:
																						lowercase        =							img.permute(2 ,				0 ,				1						).unsqueeze(0						)  # 3, 0, 1)  # hw(c) -> nchw
																						lowercase        =							nn.functional.interpolate(
																						    _lowerCAmelCase ,				(newh, neww) ,				mode=self.interp_method ,				align_corners=_lowerCAmelCase						).squeeze(0						)
															img_augs.append(_lowerCAmelCase						)

								return img_augs




class      __UpperCamelCase       :







	def __init__(    self ,				_lowerCAmelCase						)    ->    int:
								'''simple docstring'''






								lowercase        =							ResizeShortestEdge([cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST] ,				cfg.INPUT.MAX_SIZE_TEST						)
								lowercase        =							cfg.INPUT.FORMAT
								lowercase        =							cfg.SIZE_DIVISIBILITY
								lowercase        =							cfg.PAD_VALUE
								lowercase        =							cfg.INPUT.MAX_SIZE_TEST
								lowercase        =							cfg.MODEL.DEVICE
								lowercase        =							torch.tensor(cfg.MODEL.PIXEL_STD						).to(self.device						).view(len(cfg.MODEL.PIXEL_STD						) ,				1 ,				1						)
								lowercase        =							torch.tensor(cfg.MODEL.PIXEL_MEAN						).to(self.device						).view(len(cfg.MODEL.PIXEL_STD						) ,				1 ,				1						)
								lowercase        =							lambda _lowerCAmelCase						: (x - self.pixel_mean) / self.pixel_std







	def   _a   (    self ,				_lowerCAmelCase						)    ->    Tuple:
								'''simple docstring'''






								lowercase        =							tuple(max(_lowerCAmelCase						) for s in zip(*[img.shape for img in images]						)						)
								lowercase        =							[im.shape[-2:] for im in images]
								lowercase        =							[
								    nn.functional.pad(
								        _lowerCAmelCase ,				[0, max_size[-1] - size[1], 0, max_size[-2] - size[0]] ,				value=self.pad_value ,				)
								    for size, im in zip(_lowerCAmelCase ,				_lowerCAmelCase						)
								]

								return torch.stack(_lowerCAmelCase						), torch.tensor(_lowerCAmelCase						)







	def __call__(    self ,				_lowerCAmelCase ,				_lowerCAmelCase=False						)    ->    Dict:
								'''simple docstring'''






								with torch.no_grad():
															if not isinstance(_lowerCAmelCase ,				_lowerCAmelCase						):
																						lowercase        =							[images]
															if single_image:
																						assert len(_lowerCAmelCase						) == 1
															for i in range(len(_lowerCAmelCase						)						):
																						if isinstance(images[i] ,				torch.Tensor						):
																													images.insert(_lowerCAmelCase ,				images.pop(_lowerCAmelCase						).to(self.device						).float()						)
																						elif not isinstance(images[i] ,				torch.Tensor						):
																													images.insert(
																													    _lowerCAmelCase ,				torch.as_tensor(img_tensorize(images.pop(_lowerCAmelCase						) ,				input_format=self.input_format						)						)
																													    .to(self.device						)
																													    .float() ,				)
            # resize smallest edge
															lowercase        =							torch.tensor([im.shape[:2] for im in images]						)
															lowercase        =							self.aug(_lowerCAmelCase						)
															# transpose images and convert to torch tensors
															# images = [torch.as_tensor(i.astype("float32")).permute(2, 0, 1).to(self.device) for i in images]
															# now normalize before pad to avoid useless arithmetic
															lowercase        =							[self.normalizer(_lowerCAmelCase						) for x in images]
															# now pad them to do the following operations
															lowercase     ,   lowercase        =							self.pad(_lowerCAmelCase						)
															# Normalize

															if self.size_divisibility > 0:
																						raise NotImplementedError()
															# pad
															lowercase        =							torch.true_divide(_lowerCAmelCase ,				_lowerCAmelCase						)
															if single_image:
																						return images[0], sizes[0], scales_yx[0]
															else:
																						return images, sizes, scales_yx







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      List[str] ,  lowercase_    :      Dict						):
							boxes[:, 0::2] *= scale_yx[:, 1]
							boxes[:, 1::2] *= scale_yx[:, 0]
							return boxes







def      SCREAMING_SNAKE_CASE				(				lowercase_    :      List[str] ,  lowercase_    :      Tuple[int, int]						):
							assert torch.isfinite(lowercase_						).all(), "Box tensor contains infinite or NaN!"
							lowercase     ,   lowercase        =							box_size
							tensor[:, 0].clamp_(min=0 ,  max=lowercase_						)
							tensor[:, 1].clamp_(min=0 ,  max=lowercase_						)
							tensor[:, 2].clamp_(min=0 ,  max=lowercase_						)
							tensor[:, 3].clamp_(min=0 ,  max=lowercase_						)







'''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      __UpperCamelCase       :







	def __init__(    self ,				_lowerCAmelCase ,				_lowerCAmelCase=13 ,				_lowerCAmelCase=3 ,				_lowerCAmelCase=True ,				_lowerCAmelCase=True ,				_lowerCAmelCase=0.1 ,				_lowerCAmelCase=0.1 ,				_lowerCAmelCase=224 ,				_lowerCAmelCase=1000 ,				_lowerCAmelCase=[3, 3, 6, 4] ,				_lowerCAmelCase=[48, 56, 112, 220] ,				)    ->    List[str]:
							'''simple docstring'''






							lowercase        =							parent
							lowercase        =							batch_size
							lowercase        =							num_channels
							lowercase        =							is_training
							lowercase        =							use_labels
							lowercase        =							hidden_dropout_prob
							lowercase        =							attention_probs_dropout_prob
							lowercase        =							num_labels
							lowercase        =							image_size
							lowercase        =							layer_depths
							lowercase        =							embed_dims







	def   _a   (    self						)    ->    Tuple:
							'''simple docstring'''






							lowercase        =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

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

							lowercase        =							self.get_config()

							return config, pixel_values, labels







	def   _a   (    self						)    ->    int:
							'''simple docstring'''






							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   _a   (    self ,				_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase						)    ->    Optional[Any]:
							'''simple docstring'''






							lowercase        =							SwiftFormerModel(config=_lowerCAmelCase						)
							model.to(_lowerCAmelCase						)
							model.eval()
							lowercase        =							model(_lowerCAmelCase						)
							self.parent.assertEqual(result.last_hidden_state.shape ,				(self.batch_size, self.embed_dims[-1], 7, 7)						)







	def   _a   (    self ,				_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase						)    ->    Optional[Any]:
							'''simple docstring'''






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

							lowercase        =							SwiftFormerForImageClassification(_lowerCAmelCase						)
							model.to(_lowerCAmelCase						)
							model.eval()

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







	def   _a   (    self						)    ->    Optional[Any]:
							'''simple docstring'''






							((lowercase)     ,   (lowercase)     ,   (lowercase))        =							self.prepare_config_and_inputs()
							lowercase        =							{"""pixel_values""": pixel_values}
							return config, inputs_dict




@require_torch
class      __UpperCamelCase       (_UpperCAmelCase			,   _UpperCAmelCase			,   unittest.TestCase			):
	__A			  =					(SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
	__A			  =					(
	    {'''feature-extraction''': SwiftFormerModel, '''image-classification''': SwiftFormerForImageClassification}
	    if is_torch_available()
	    else {}
	)

	__A			  =					False
	__A			  =					False
	__A			  =					False
	__A			  =					False
	__A			  =					False







	def   _a   (    self						)    ->    Dict:
							'''simple docstring'''






							lowercase        =							SwiftFormerModelTester(self						)
							lowercase        =							ConfigTester(
							    self ,				config_class=_lowerCAmelCase ,				has_text_modality=_lowerCAmelCase ,				hidden_size=37 ,				num_attention_heads=12 ,				num_hidden_layers=12 ,				)







	def   _a   (    self						)    ->    List[Any]:
							'''simple docstring'''






							self.config_tester.run_common_tests()







	@unittest.skip(reason="""SwiftFormer does not use inputs_embeds"""						)
	def   _a   (    self						)    ->    List[str]:
							'''simple docstring'''






							pass







	def   _a   (    self						)    ->    Dict:
							'''simple docstring'''






							lowercase     ,   lowercase        =							self.model_tester.prepare_config_and_inputs_for_common()

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







	def   _a   (    self						)    ->    int:
							'''simple docstring'''






							lowercase     ,   lowercase        =							self.model_tester.prepare_config_and_inputs_for_common()

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

													lowercase        =							["""pixel_values"""]
													self.assertListEqual(arg_names[:1] ,				_lowerCAmelCase						)







	def   _a   (    self						)    ->    List[str]:
							'''simple docstring'''






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







	def   _a   (    self						)    ->    Optional[Any]:
							'''simple docstring'''






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







	@slow
	def   _a   (    self						)    ->    Any:
							'''simple docstring'''






							for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													lowercase        =							SwiftFormerModel.from_pretrained(_lowerCAmelCase						)
													self.assertIsNotNone(_lowerCAmelCase						)







	@unittest.skip(reason="""SwiftFormer does not output attentions"""						)
	def   _a   (    self						)    ->    Optional[Any]:
							'''simple docstring'''






							pass







	def   _a   (    self						)    ->    Union[str, Any]:
							'''simple docstring'''






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

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

													lowercase        =							outputs.hidden_states

													lowercase        =							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),
																			        ]						) ,				)

							lowercase     ,   lowercase        =							self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
													lowercase        =							True
													check_hidden_states_output(_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase						)

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

													check_hidden_states_output(_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase						)







	def   _a   (    self						)    ->    Dict:
							'''simple docstring'''






							def _config_zero_init(_lowerCAmelCase						):
													lowercase        =							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						):
																									lowercase        =							_config_zero_init(getattr(_lowerCAmelCase ,				_lowerCAmelCase						)						)
																									setattr(_lowerCAmelCase ,				_lowerCAmelCase ,				_lowerCAmelCase						)
													return configs_no_init

							lowercase     ,   lowercase        =							self.model_tester.prepare_config_and_inputs_for_common()

							lowercase        =							_config_zero_init(_lowerCAmelCase						)
							for model_class in self.all_model_classes:
													lowercase        =							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   _a   (    self						)    ->    Any:
							'''simple docstring'''






							pass







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




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







	@cached_property
	def   _a   (    self						)    ->    List[str]:
							'''simple docstring'''






							return ViTImageProcessor.from_pretrained("""MBZUAI/swiftformer-xs"""						) if is_vision_available() else None







	@slow
	def   _a   (    self						)    ->    List[Any]:
							'''simple docstring'''






							lowercase        =							SwiftFormerForImageClassification.from_pretrained("""MBZUAI/swiftformer-xs"""						).to(_lowerCAmelCase						)

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

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

							# verify the logits
							lowercase        =							torch.Size((1, 1000)						)
							self.assertEqual(outputs.logits.shape ,				_lowerCAmelCase						)

							lowercase        =							torch.tensor([[-2.17_03E00, 2.11_07E00, -2.08_11E00]]						).to(_lowerCAmelCase						)
							self.assertTrue(torch.allclose(outputs.logits[0, :3] ,				_lowerCAmelCase ,				atol=1E-4						)						)


