



"""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 					A_:







			"""simple docstring"""



			def __init__(	self				,   A				,   A=13				,   A=3				,   A=True				,   A=True				,   A=0.1				,   A=0.1				,   A=224				,   A=1000				,   A=[3, 3, 6, 4]				,   A=[48, 56, 112, 220]				,   ):
						_lowerCamelCase					:					Dict	       =    parent
						_lowerCamelCase					:					List[str]	       =    batch_size
						_lowerCamelCase					:					Union[str, Any]	       =    num_channels
						_lowerCamelCase					:					Dict	       =    is_training
						_lowerCamelCase					:					Any	       =    use_labels
						_lowerCamelCase					:					Dict	       =    hidden_dropout_prob
						_lowerCamelCase					:					Union[str, Any]	       =    attention_probs_dropout_prob
						_lowerCamelCase					:					Optional[Any]	       =    num_labels
						_lowerCamelCase					:					Optional[Any]	       =    image_size
						_lowerCamelCase					:					Optional[int]	       =    layer_depths
						_lowerCamelCase					:					Optional[int]	       =    embed_dims
			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					Dict	       =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

						_lowerCamelCase					:					Optional[Any]	       =    None
						if self.use_labels:
									_lowerCamelCase					:					str	       =    ids_tensor([self.batch_size]				,   self.num_labels							)

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

						return config, pixel_values, labels
			def    _lowerCAmelCase		(	self							):
						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=A				,   layer_scale_init_value=1E-5				,   )
			def    _lowerCAmelCase		(	self				,   A				,   A				,   A							):
						_lowerCamelCase					:					Optional[int]	       =    SwiftFormerModel(config=A							)
						model.to(A							)
						model.eval()
						_lowerCamelCase					:					Union[str, Any]	       =    model(A							)
						self.parent.assertEqual(result.last_hidden_state.shape				,   (self.batch_size, self.embed_dims[-1], 7, 7)							)
			def    _lowerCAmelCase		(	self				,   A				,   A				,   A							):
						_lowerCamelCase					:					Optional[int]	       =    self.num_labels
						_lowerCamelCase					:					List[str]	       =    SwiftFormerForImageClassification(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)							)

						_lowerCamelCase					:					Union[str, Any]	       =    SwiftFormerForImageClassification(A							)
						model.to(A							)
						model.eval()

						_lowerCamelCase					:					Tuple	       =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)
						_lowerCamelCase					:					Union[str, Any]	       =    model(A							)
						self.parent.assertEqual(result.logits.shape				,   (self.batch_size, self.num_labels)							)

			def    _lowerCAmelCase		(	self							):
						(_lowerCamelCase)					:					Optional[Any]	       =    self.prepare_config_and_inputs()
						_lowerCamelCase					:					Any	       =    {"""pixel_values""": pixel_values}
						return config, inputs_dict







@require_torch
class 					A_(__snake_case   ,   __snake_case   ,   unittest.TestCase      ):







			"""simple docstring"""



			a_       :			List[str]												=			(SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
			a_       :			Any												=			(
			    {"""feature-extraction""": SwiftFormerModel, """image-classification""": SwiftFormerForImageClassification}
			    if is_torch_available()
			    else {}
			)

			a_       :			Optional[Any]												=			False
			a_       :			Optional[Any]												=			False
			a_       :			List[str]												=			False
			a_       :			Tuple												=			False
			a_       :			List[Any]												=			False
			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					Optional[Any]	       =    SwiftFormerModelTester(self							)
						_lowerCamelCase					:					List[str]	       =    ConfigTester(
						    self				,   config_class=A				,   has_text_modality=A				,   hidden_size=37				,   num_attention_heads=12				,   num_hidden_layers=12				,   )
			def    _lowerCAmelCase		(	self							):
						self.config_tester.run_common_tests()
			@unittest.skip(reason='SwiftFormer does not use inputs_embeds'							)
			def    _lowerCAmelCase		(	self							):
						pass
			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					Optional[Any]	       =    self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									_lowerCamelCase					:					Dict	       =    model_class(A							)
									_lowerCamelCase					:					int	       =    model.get_output_embeddings()
									self.assertTrue(x is None or isinstance(A				,   nn.Linear							)							)
			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					int	       =    self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									_lowerCamelCase					:					Union[str, Any]	       =    model_class(A							)
									_lowerCamelCase					:					Tuple	       =    inspect.signature(model.forward							)
									# signature.parameters is an OrderedDict => so arg_names order is deterministic
									_lowerCamelCase					:					str	       =    [*signature.parameters.keys()]

									_lowerCamelCase					:					Any	       =    ["""pixel_values"""]
									self.assertListEqual(arg_names[:1]				,   A							)
			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					Union[str, Any]	       =    self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*A							)
			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					Any	       =    self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*A							)
			@slow
			def    _lowerCAmelCase		(	self							):
						for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									_lowerCamelCase					:					List[Any]	       =    SwiftFormerModel.from_pretrained(A							)
									self.assertIsNotNone(A							)
			@unittest.skip(reason='SwiftFormer does not output attentions'							)
			def    _lowerCAmelCase		(	self							):
						pass
			def    _lowerCAmelCase		(	self							):
						def check_hidden_states_output(A				,   A				,   A							):
									_lowerCamelCase					:					Optional[Any]	       =    model_class(A							)
									model.to(A							)
									model.eval()

									with torch.no_grad():
												_lowerCamelCase					:					Dict	       =    model(**self._prepare_for_class(A				,   A							)							)

									_lowerCamelCase					:					Tuple	       =    outputs.hidden_states

									_lowerCamelCase					:					List[str]	       =    8
									self.assertEqual(len(A							)				,   A							)  # 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(A							)							):
												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),
												        ]							)				,   )

						_lowerCamelCase					:					Optional[Any]	       =    self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									_lowerCamelCase					:					Optional[int]	       =    True
									check_hidden_states_output(A				,   A				,   A							)

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

									check_hidden_states_output(A				,   A				,   A							)
			def    _lowerCAmelCase		(	self							):
						def _config_zero_init(A							):
									_lowerCamelCase					:					Tuple	       =    copy.deepcopy(A							)
									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(A				,   A				,   1E-10							)
												if isinstance(getattr(A				,   A				,   A							)				,   A							):
															_lowerCamelCase					:					Any	       =    _config_zero_init(getattr(A				,   A							)							)
															setattr(A				,   A				,   A							)
									return configs_no_init

						_lowerCamelCase					:					Dict	       =    self.model_tester.prepare_config_and_inputs_for_common()

						_lowerCamelCase					:					int	       =    _config_zero_init(A							)
						for model_class in self.all_model_classes:
									_lowerCamelCase					:					Union[str, Any]	       =    model_class(config=A							)
									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							):
						pass






def     UpperCAmelCase_					(				):




			'''simple docstring'''



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







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







			"""simple docstring"""



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

			@slow
			def    _lowerCAmelCase		(	self							):
						_lowerCamelCase					:					Union[str, Any]	       =    SwiftFormerForImageClassification.from_pretrained('MBZUAI/swiftformer-xs'							).to(A							)

						_lowerCamelCase					:					Any	       =    self.default_image_processor
						_lowerCamelCase					:					str	       =    prepare_img()
						_lowerCamelCase					:					Tuple	       =    image_processor(images=A				,   return_tensors='pt'							).to(A							)

						# forward pass
						with torch.no_grad():
									_lowerCamelCase					:					Union[str, Any]	       =    model(**A							)

						# verify the logits
						_lowerCamelCase					:					Optional[int]	       =    torch.Size((1, 1000)							)
						self.assertEqual(outputs.logits.shape				,   A							)

						_lowerCamelCase					:					Optional[Any]	       =    torch.tensor([[-2.1703E00, 2.1107E00, -2.0811E00]]							).to(A							)
						self.assertTrue(torch.allclose(outputs.logits[0, :3]				,   A				,   atol=1E-4							)							)













"""simple docstring"""







from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
				import PIL

_lowerCAmelCase            =	logging.get_logger(__name__)


def      UpperCamelCase       ( _A )     ->       List[List[ImageInput]]:
				if isinstance(_A  ,				(list, tuple) ) and isinstance(videos[0]  ,				(list, tuple) ) and is_valid_image(videos[0][0] ):
								return videos

				elif isinstance(_A  ,				(list, tuple) ) and is_valid_image(videos[0] ):
								return [videos]

				elif is_valid_image(_A ):
								return [[videos]]

				raise ValueError(F"""Could not make batched video from {videos}""" )


class      UpperCamelCase		(__snake_case     ):
						_SCREAMING_SNAKE_CASE  : str												=		["""pixel_values"""]






						def __init__(		self  :str			, __magic_name__  :bool = True			, __magic_name__  :Dict[str, int] = None			, __magic_name__  :PILImageResampling = PILImageResampling.BILINEAR			, __magic_name__  :bool = True			, __magic_name__  :Dict[str, int] = None			, __magic_name__  :bool = True			, __magic_name__  :Union[int, float] = 1 / 255			, __magic_name__  :bool = True			, __magic_name__  :bool = True			, __magic_name__  :Optional[Union[float, List[float]]] = None			, __magic_name__  :Optional[Union[float, List[float]]] = None			, **__magic_name__  :List[str]			, )      ->None:
										super().__init__(**__magic_name__				)
										lowercase    :      Union[str, Any]       = size if size is not None else {"""shortest_edge""": 256}
										lowercase    :      Tuple       = get_size_dict(__magic_name__			, default_to_square=__magic_name__				)
										lowercase    :      List[Any]       = crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
										lowercase    :      List[str]       = get_size_dict(__magic_name__			, param_name="""crop_size"""				)

										lowercase    :      Tuple       = do_resize
										lowercase    :      Optional[Any]       = size
										lowercase    :      Union[str, Any]       = do_center_crop
										lowercase    :      List[Any]       = crop_size
										lowercase    :      Union[str, Any]       = resample
										lowercase    :      Optional[Any]       = do_rescale
										lowercase    :      int       = rescale_factor
										lowercase    :      Dict       = offset
										lowercase    :      Optional[int]       = do_normalize
										lowercase    :      Optional[Any]       = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
										lowercase    :      Tuple       = image_std if image_std is not None else IMAGENET_STANDARD_STD






						def      __snake_case      (		self  :Any			, __magic_name__  :np.ndarray			, __magic_name__  :Dict[str, int]			, __magic_name__  :PILImageResampling = PILImageResampling.BILINEAR			, __magic_name__  :Optional[Union[str, ChannelDimension]] = None			, **__magic_name__  :Optional[Any]			, )      ->np.ndarray:
										lowercase    :      Optional[int]       = get_size_dict(__magic_name__			, default_to_square=__magic_name__				)
										if "shortest_edge" in size:
														lowercase    :      int       = get_resize_output_image_size(__magic_name__			, size["""shortest_edge"""]			, default_to_square=__magic_name__				)
										elif "height" in size and "width" in size:
														lowercase    :      Tuple       = (size["""height"""], size["""width"""])
										else:
														raise ValueError(f"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"""				)
										return resize(__magic_name__			, size=__magic_name__			, resample=__magic_name__			, data_format=__magic_name__			, **__magic_name__				)






						def      __snake_case      (		self  :Optional[Any]			, __magic_name__  :np.ndarray			, __magic_name__  :Dict[str, int]			, __magic_name__  :Optional[Union[str, ChannelDimension]] = None			, **__magic_name__  :str			, )      ->np.ndarray:
										lowercase    :      Any       = get_size_dict(__magic_name__				)
										if "height" not in size or "width" not in size:
														raise ValueError(f"""Size must have 'height' and 'width' as keys. Got {size.keys()}"""				)
										return center_crop(__magic_name__			, size=(size["""height"""], size["""width"""])			, data_format=__magic_name__			, **__magic_name__				)






						def      __snake_case      (		self  :Tuple			, __magic_name__  :np.ndarray			, __magic_name__  :Union[int, float]			, __magic_name__  :bool = True			, __magic_name__  :Optional[Union[str, ChannelDimension]] = None			, **__magic_name__  :Optional[Any]			, )      ->Any:
										lowercase    :      Optional[int]       = image.astype(np.floataa				)
										if offset:
														lowercase    :      Optional[int]       = image - (scale / 2)
										return rescale(__magic_name__			, scale=__magic_name__			, data_format=__magic_name__			, **__magic_name__				)






						def      __snake_case      (		self  :int			, __magic_name__  :np.ndarray			, __magic_name__  :Union[float, List[float]]			, __magic_name__  :Union[float, List[float]]			, __magic_name__  :Optional[Union[str, ChannelDimension]] = None			, **__magic_name__  :Union[str, Any]			, )      ->np.ndarray:
										return normalize(__magic_name__			, mean=__magic_name__			, std=__magic_name__			, data_format=__magic_name__			, **__magic_name__				)






						def      __snake_case      (		self  :List[Any]			, __magic_name__  :ImageInput			, __magic_name__  :bool = None			, __magic_name__  :Dict[str, int] = None			, __magic_name__  :PILImageResampling = None			, __magic_name__  :bool = None			, __magic_name__  :Dict[str, int] = None			, __magic_name__  :bool = None			, __magic_name__  :float = None			, __magic_name__  :bool = None			, __magic_name__  :bool = None			, __magic_name__  :Optional[Union[float, List[float]]] = None			, __magic_name__  :Optional[Union[float, List[float]]] = None			, __magic_name__  :Optional[ChannelDimension] = ChannelDimension.FIRST			, )      ->np.ndarray:
										if do_resize and size is None or resample is None:
														raise ValueError("""Size and resample must be specified if do_resize is True."""				)

										if do_center_crop and crop_size is None:
														raise ValueError("""Crop size must be specified if do_center_crop is True."""				)

										if do_rescale and rescale_factor is None:
														raise ValueError("""Rescale factor must be specified if do_rescale is True."""				)

										if do_normalize and (image_mean is None or image_std is None):
														raise ValueError("""Image mean and std must be specified if do_normalize is True."""				)

										if offset and not do_rescale:
														raise ValueError("""For offset, do_rescale must also be set to True."""				)

										# All transformations expect numpy arrays.
										lowercase    :      Optional[Any]       = to_numpy_array(__magic_name__				)

										if do_resize:
														lowercase    :      Any       = self.resize(image=__magic_name__			, size=__magic_name__			, resample=__magic_name__				)

										if do_center_crop:
														lowercase    :      Any       = self.center_crop(__magic_name__			, size=__magic_name__				)

										if do_rescale:
														lowercase    :      Optional[int]       = self.rescale(image=__magic_name__			, scale=__magic_name__			, offset=__magic_name__				)

										if do_normalize:
														lowercase    :      Union[str, Any]       = self.normalize(image=__magic_name__			, mean=__magic_name__			, std=__magic_name__				)

										lowercase    :      Optional[Any]       = to_channel_dimension_format(__magic_name__			, __magic_name__				)
										return image






						def      __snake_case      (		self  :Union[str, Any]			, __magic_name__  :ImageInput			, __magic_name__  :bool = None			, __magic_name__  :Dict[str, int] = None			, __magic_name__  :PILImageResampling = None			, __magic_name__  :bool = None			, __magic_name__  :Dict[str, int] = None			, __magic_name__  :bool = None			, __magic_name__  :float = None			, __magic_name__  :bool = None			, __magic_name__  :bool = None			, __magic_name__  :Optional[Union[float, List[float]]] = None			, __magic_name__  :Optional[Union[float, List[float]]] = None			, __magic_name__  :Optional[Union[str, TensorType]] = None			, __magic_name__  :ChannelDimension = ChannelDimension.FIRST			, **__magic_name__  :List[str]			, )      ->PIL.Image.Image:
										lowercase    :      List[Any]       = do_resize if do_resize is not None else self.do_resize
										lowercase    :      Dict       = resample if resample is not None else self.resample
										lowercase    :      Optional[int]       = do_center_crop if do_center_crop is not None else self.do_center_crop
										lowercase    :      List[Any]       = do_rescale if do_rescale is not None else self.do_rescale
										lowercase    :      Tuple       = rescale_factor if rescale_factor is not None else self.rescale_factor
										lowercase    :      Optional[Any]       = offset if offset is not None else self.offset
										lowercase    :      str       = do_normalize if do_normalize is not None else self.do_normalize
										lowercase    :      List[str]       = image_mean if image_mean is not None else self.image_mean
										lowercase    :      Union[str, Any]       = image_std if image_std is not None else self.image_std

										lowercase    :      Optional[int]       = size if size is not None else self.size
										lowercase    :      Optional[Any]       = get_size_dict(__magic_name__			, default_to_square=__magic_name__				)
										lowercase    :      Tuple       = crop_size if crop_size is not None else self.crop_size
										lowercase    :      List[str]       = get_size_dict(__magic_name__			, param_name="""crop_size"""				)

										if not valid_images(__magic_name__				):
														raise ValueError(
														    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
														    """torch.Tensor, tf.Tensor or jax.ndarray."""				)

										lowercase    :      Tuple       = make_batched(__magic_name__				)

										lowercase    :      List[str]       = [
										    [
										        self._preprocess_image(
										            image=__magic_name__			, do_resize=__magic_name__			, size=__magic_name__			, resample=__magic_name__			, do_center_crop=__magic_name__			, crop_size=__magic_name__			, do_rescale=__magic_name__			, rescale_factor=__magic_name__			, offset=__magic_name__			, do_normalize=__magic_name__			, image_mean=__magic_name__			, image_std=__magic_name__			, data_format=__magic_name__			, )
										        for img in video
										    ]
										    for video in videos
										]

										lowercase    :      Dict       = {"""pixel_values""": videos}
										return BatchFeature(data=__magic_name__			, tensor_type=__magic_name__				)
