






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    :    int			,		UpperCamelCase__    :    List[Any]			,		UpperCamelCase__    :    List[str]=1_3			,		UpperCamelCase__    :    List[Any]=3			,		UpperCamelCase__    :    List[Any]=True			,		UpperCamelCase__    :    Optional[Any]=True			,		UpperCamelCase__    :    Any=0.1			,		UpperCamelCase__    :    List[Any]=0.1			,		UpperCamelCase__    :    Any=2_2_4			,		UpperCamelCase__    :    List[str]=1_0_0_0			,		UpperCamelCase__    :    List[Any]=[3, 3, 6, 4]			,		UpperCamelCase__    :    Union[str, Any]=[4_8, 5_6, 1_1_2, 2_2_0]			,		):






											'''simple docstring'''







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



				def 					__magic_name__							(      self    :    Tuple):






											'''simple docstring'''







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

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

											snake_case__	  =       self.get_config()

											return config, pixel_values, labels



				def 					__magic_name__							(      self    :    Tuple):






											'''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=UpperCamelCase__			,		layer_scale_init_value=1E-5			,		)



				def 					__magic_name__							(      self    :    int			,		UpperCamelCase__    :    List[Any]			,		UpperCamelCase__    :    Union[str, Any]			,		UpperCamelCase__    :    Optional[int]):






											'''simple docstring'''







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



				def 					__magic_name__							(      self    :    Tuple			,		UpperCamelCase__    :    Union[str, Any]			,		UpperCamelCase__    :    Union[str, Any]			,		UpperCamelCase__    :    Optional[Any]):






											'''simple docstring'''







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

											snake_case__	  =       SwiftFormerForImageClassification(UpperCamelCase__)
											model.to(UpperCamelCase__)
											model.eval()

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



				def 					__magic_name__							(      self    :    str):






											'''simple docstring'''







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


@require_torch
class    _lowerCAmelCase			(		lowercase_     ,      lowercase_     ,      unittest.TestCase      ):



				"""simple docstring"""



				_lowercase				:   Dict      =  (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
				_lowercase				:   Any      =  (
				    {'''feature-extraction''': SwiftFormerModel, '''image-classification''': SwiftFormerForImageClassification}
				    if is_torch_available()
				    else {}
				)

				_lowercase				:   List[str]      =  False
				_lowercase				:   str      =  False
				_lowercase				:   Optional[int]      =  False
				_lowercase				:   Optional[int]      =  False
				_lowercase				:   str      =  False



				def 					__magic_name__							(      self    :    Optional[Any]):






											'''simple docstring'''







											snake_case__	  =       SwiftFormerModelTester(self)
											snake_case__	  =       ConfigTester(
											    self			,		config_class=UpperCamelCase__			,		has_text_modality=UpperCamelCase__			,		hidden_size=3_7			,		num_attention_heads=1_2			,		num_hidden_layers=1_2			,		)



				def 					__magic_name__							(      self    :    int):






											'''simple docstring'''







											self.config_tester.run_common_tests()



				@unittest.skip(reason="""SwiftFormer does not use inputs_embeds""")
				def 					__magic_name__							(      self    :    List[str]):






											'''simple docstring'''







											pass



				def 					__magic_name__							(      self    :    Tuple):






											'''simple docstring'''







											snake_case__  , snake_case__	  =       self.model_tester.prepare_config_and_inputs_for_common()

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



				def 					__magic_name__							(      self    :    Union[str, Any]):






											'''simple docstring'''







											snake_case__  , snake_case__	  =       self.model_tester.prepare_config_and_inputs_for_common()

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

																		snake_case__	  =       ["""pixel_values"""]
																		self.assertListEqual(arg_names[:1]			,		UpperCamelCase__)



				def 					__magic_name__							(      self    :    Any):






											'''simple docstring'''







											snake_case__	  =       self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*UpperCamelCase__)



				def 					__magic_name__							(      self    :    List[str]):






											'''simple docstring'''







											snake_case__	  =       self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__)



				@slow
				def 					__magic_name__							(      self    :    Union[str, Any]):






											'''simple docstring'''







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



				@unittest.skip(reason="""SwiftFormer does not output attentions""")
				def 					__magic_name__							(      self    :    List[Any]):






											'''simple docstring'''







											pass



				def 					__magic_name__							(      self    :    int):






											'''simple docstring'''







											def check_hidden_states_output(UpperCamelCase__    :    Optional[Any]			,		UpperCamelCase__    :    List[Any]			,		UpperCamelCase__    :    int):
																		snake_case__	  =       model_class(UpperCamelCase__)
																		model.to(UpperCamelCase__)
																		model.eval()

																		with torch.no_grad():
																									snake_case__	  =       model(**self._prepare_for_class(UpperCamelCase__			,		UpperCamelCase__))

																		snake_case__	  =       outputs.hidden_states

																		snake_case__	  =       8
																		self.assertEqual(len(UpperCamelCase__)			,		UpperCamelCase__)  # 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(UpperCamelCase__)):
																									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),
																									        ])			,		)

											snake_case__  , snake_case__	  =       self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																		snake_case__	  =       True
																		check_hidden_states_output(UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__)

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

																		check_hidden_states_output(UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__)



				def 					__magic_name__							(      self    :    Dict):






											'''simple docstring'''







											def _config_zero_init(UpperCamelCase__    :    int):
																		snake_case__	  =       copy.deepcopy(UpperCamelCase__)
																		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(UpperCamelCase__			,		UpperCamelCase__			,		1E-10)
																									if isinstance(getattr(UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__)			,		UpperCamelCase__):
																																snake_case__	  =       _config_zero_init(getattr(UpperCamelCase__			,		UpperCamelCase__))
																																setattr(UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__)
																		return configs_no_init

											snake_case__  , snake_case__	  =       self.model_tester.prepare_config_and_inputs_for_common()

											snake_case__	  =       _config_zero_init(UpperCamelCase__)
											for model_class in self.all_model_classes:
																		snake_case__	  =       model_class(config=UpperCamelCase__)
																		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 					__magic_name__							(      self    :    Optional[Any]):






											'''simple docstring'''







											pass


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



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



				"""simple docstring"""



				@cached_property
				def 					__magic_name__							(      self    :    List[str]):






											'''simple docstring'''







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



				@slow
				def 					__magic_name__							(      self    :    List[Any]):






											'''simple docstring'''







											snake_case__	  =       SwiftFormerForImageClassification.from_pretrained("""MBZUAI/swiftformer-xs""").to(UpperCamelCase__)

											snake_case__	  =       self.default_image_processor
											snake_case__	  =       prepare_img()
											snake_case__	  =       image_processor(images=UpperCamelCase__			,		return_tensors="""pt""").to(UpperCamelCase__)

											# forward pass
											with torch.no_grad():
																		snake_case__	  =       model(**UpperCamelCase__)

											# verify the logits
											snake_case__	  =       torch.Size((1, 1_0_0_0))
											self.assertEqual(outputs.logits.shape			,		UpperCamelCase__)

											snake_case__	  =       torch.tensor([[-2.1_703E00, 2.1_107E00, -2.0_811E00]]).to(UpperCamelCase__)
											self.assertTrue(torch.allclose(outputs.logits[0, :3]			,		UpperCamelCase__			,		atol=1E-4))








import unittest

import torch

from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()


class    _lowerCAmelCase			(		lowercase_     ,      lowercase_     ,      unittest.TestCase      ):



				"""simple docstring"""



				_lowercase				:   Optional[int]      =  VQModel
				_lowercase				:   str      =  '''sample'''



				@property
				def 					__magic_name__							(      self    :    Union[str, Any]			,		UpperCamelCase__    :    Tuple=(3_2, 3_2)):






											'''simple docstring'''







											snake_case__	  =       4
											snake_case__	  =       3

											snake_case__	  =       floats_tensor((batch_size, num_channels) + sizes).to(UpperCamelCase__)

											return {"sample": image}



				@property
				def 					__magic_name__							(      self    :    str):






											'''simple docstring'''







											return (3, 3_2, 3_2)



				@property
				def 					__magic_name__							(      self    :    List[str]):






											'''simple docstring'''







											return (3, 3_2, 3_2)



				def 					__magic_name__							(      self    :    int):






											'''simple docstring'''







											snake_case__	  =       {
											    """block_out_channels""": [3_2, 6_4],
											    """in_channels""": 3,
											    """out_channels""": 3,
											    """down_block_types""": ["""DownEncoderBlock2D""", """DownEncoderBlock2D"""],
											    """up_block_types""": ["""UpDecoderBlock2D""", """UpDecoderBlock2D"""],
											    """latent_channels""": 3,
											}
											snake_case__	  =       self.dummy_input
											return init_dict, inputs_dict



				def 					__magic_name__							(      self    :    Optional[int]):






											'''simple docstring'''







											pass



				def 					__magic_name__							(      self    :    Tuple):






											'''simple docstring'''







											pass



				def 					__magic_name__							(      self    :    str):






											'''simple docstring'''







											snake_case__  , snake_case__	  =       VQModel.from_pretrained("""fusing/vqgan-dummy"""			,		output_loading_info=UpperCamelCase__)
											self.assertIsNotNone(UpperCamelCase__)
											self.assertEqual(len(loading_info["""missing_keys"""])			,		0)

											model.to(UpperCamelCase__)
											snake_case__	  =       model(**self.dummy_input)

											assert image is not None, "Make sure output is not None"



				def 					__magic_name__							(      self    :    Dict):






											'''simple docstring'''







											snake_case__	  =       VQModel.from_pretrained("""fusing/vqgan-dummy""")
											model.to(UpperCamelCase__).eval()

											torch.manual_seed(0)
											if torch.cuda.is_available():
																		torch.cuda.manual_seed_all(0)

											snake_case__	  =       torch.randn(1			,		model.config.in_channels			,		model.config.sample_size			,		model.config.sample_size)
											snake_case__	  =       image.to(UpperCamelCase__)
											with torch.no_grad():
																		snake_case__	  =       model(UpperCamelCase__).sample

											snake_case__	  =       output[0, -1, -3:, -3:].flatten().cpu()
											# fmt: off
											snake_case__	  =       torch.tensor([-0.01_53, -0.40_44, -0.18_80, -0.51_61, -0.24_18, -0.40_72, -0.16_12, -0.06_33, -0.01_43])
											# fmt: on
											self.assertTrue(torch.allclose(UpperCamelCase__			,		UpperCamelCase__			,		atol=1E-3))
