




"""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 lowerCAmelCase__	:






			'''simple docstring'''



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

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

							_lowerCamelCase     :			int    =    self.get_config()

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

							_lowerCamelCase     :			List[Any]    =    SwiftFormerForImageClassification(lowercase  )
							model.to(lowercase  )
							model.eval()

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






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






@require_torch
class lowerCAmelCase__	(     lowercase,	lowercase,	unittest.TestCase						):






			'''simple docstring'''



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

			lowerCamelCase__      =			False
			lowerCamelCase__      =			False
			lowerCamelCase__      =			False
			lowerCamelCase__      =			False
			lowerCamelCase__      =			False
			def 					A_    (  self  ):
							_lowerCamelCase     :			List[str]    =    SwiftFormerModelTester(self  )
							_lowerCamelCase     :			Optional[int]    =    ConfigTester(
							    self  ,      config_class=lowercase  ,      has_text_modality=lowercase  ,      hidden_size=37  ,      num_attention_heads=12  ,      num_hidden_layers=12  ,      )
			def 					A_    (  self  ):
							self.config_tester.run_common_tests()
			@unittest.skip(reason='SwiftFormer does not use inputs_embeds'  )
			def 					A_    (  self  ):
							pass
			def 					A_    (  self  ):
							_lowerCamelCase, _lowerCamelCase     :			Dict    =    self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
											_lowerCamelCase     :			Optional[Any]    =    model_class(lowercase  )
											_lowerCamelCase     :			int    =    model.get_output_embeddings()
											self.assertTrue(x is None or isinstance(lowercase  ,      nn.Linear  )  )
			def 					A_    (  self  ):
							_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(lowercase  )
											_lowerCamelCase     :			Any    =    inspect.signature(model.forward  )
											# signature.parameters is an OrderedDict => so arg_names order is deterministic
											_lowerCamelCase     :			int    =    [*signature.parameters.keys()]

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

											with torch.no_grad():
															_lowerCamelCase     :			List[str]    =    model(**self._prepare_for_class(lowercase  ,      lowercase  )  )

											_lowerCamelCase     :			Dict    =    outputs.hidden_states

											_lowerCamelCase     :			Optional[int]    =    8
											self.assertEqual(len(lowercase  )  ,      lowercase  )  # 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(lowercase  )  ):
															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, _lowerCamelCase     :			Optional[Any]    =    self.model_tester.prepare_config_and_inputs_for_common()

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

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

											check_hidden_states_output(lowercase  ,      lowercase  ,      lowercase  )
			def 					A_    (  self  ):
							def _config_zero_init(lowercase  ):
											_lowerCamelCase     :			List[str]    =    copy.deepcopy(lowercase  )
											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(lowercase  ,      lowercase  ,      1E-10  )
															if isinstance(getattr(lowercase  ,      lowercase  ,      lowercase  )  ,      lowercase  ):
																			_lowerCamelCase     :			int    =    _config_zero_init(getattr(lowercase  ,      lowercase  )  )
																			setattr(lowercase  ,      lowercase  ,      lowercase  )
											return configs_no_init

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

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







def      _snake_case			(				):
				_lowerCamelCase     :			List[Any]    =    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'		)
				return image






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






			'''simple docstring'''



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






			@slow
			def 					A_    (  self  ):
							_lowerCamelCase     :			Dict    =    SwiftFormerForImageClassification.from_pretrained('MBZUAI/swiftformer-xs'  ).to(lowercase  )

							_lowerCamelCase     :			Tuple    =    self.default_image_processor
							_lowerCamelCase     :			Optional[int]    =    prepare_img()
							_lowerCamelCase     :			Optional[int]    =    image_processor(images=lowercase  ,      return_tensors='pt'  ).to(lowercase  )

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

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

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





"""simple docstring"""






import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


lowercase__     						=						logging.get_logger(__name__)  # pylint: disable=invalid-name






class lowerCAmelCase__	(     lowercase						):






			'''simple docstring'''



			def __init__(  self  ,      lowercase  ,      lowercase=768  ):
							super().__init__(lowercase  )
							_lowerCamelCase     :			Any    =    proj_size

							_lowerCamelCase     :			Dict    =    CLIPVisionModel(lowercase  )
							_lowerCamelCase     :			List[str]    =    PaintByExampleMapper(lowercase  )
							_lowerCamelCase     :			Optional[Any]    =    nn.LayerNorm(config.hidden_size  )
							_lowerCamelCase     :			int    =    nn.Linear(config.hidden_size  ,      self.proj_size  )

							# uncondition for scaling
							_lowerCamelCase     :			str    =    nn.Parameter(torch.randn((1, 1, self.proj_size)  )  )






			def 					A_    (  self  ,      lowercase  ,      lowercase=False  ):
							_lowerCamelCase     :			Union[str, Any]    =    self.model(pixel_values=lowercase  )
							_lowerCamelCase     :			int    =    clip_output.pooler_output
							_lowerCamelCase     :			str    =    self.mapper(latent_states[:, None]  )
							_lowerCamelCase     :			List[Any]    =    self.final_layer_norm(lowercase  )
							_lowerCamelCase     :			Dict    =    self.proj_out(lowercase  )
							if return_uncond_vector:
											return latent_states, self.uncond_vector

							return latent_states






class lowerCAmelCase__	(     nn.Module						):






			'''simple docstring'''



			def __init__(  self  ,      lowercase  ):
							super().__init__()
							_lowerCamelCase     :			Tuple    =    (config.num_hidden_layers + 1) // 5
							_lowerCamelCase     :			int    =    config.hidden_size
							_lowerCamelCase     :			Optional[Any]    =    1
							_lowerCamelCase     :			str    =    nn.ModuleList(
							    [
							        BasicTransformerBlock(lowercase  ,      lowercase  ,      lowercase  ,      activation_fn='gelu'  ,      attention_bias=lowercase  )
							        for _ in range(lowercase  )
							    ]  )






			def 					A_    (  self  ,      lowercase  ):
							for block in self.blocks:
											_lowerCamelCase     :			Tuple    =    block(lowercase  )

							return hidden_states