






'''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       __lowercase					:

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

	def      __UpperCamelCase							(      self   :						Tuple):
			UpperCamelCase__				:					List[Any]					=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

			UpperCamelCase__				:					str					=					None
			if self.use_labels:
					UpperCamelCase__				:					Tuple					=					ids_tensor([self.batch_size]  ,					self.num_labels)

			UpperCamelCase__				:					Optional[int]					=					self.get_config()

			return config, pixel_values, labels

	def      __UpperCamelCase							(      self   :						List[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=UpperCAmelCase_  ,					layer_scale_init_value=1e-5  ,					)

	def      __UpperCamelCase							(      self   :						int  ,					UpperCAmelCase_   :						Optional[int]  ,					UpperCAmelCase_   :						Any  ,					UpperCAmelCase_   :						Union[str, Any]):
			UpperCamelCase__				:					Dict					=					SwiftFormerModel(config=UpperCAmelCase_)
			model.to(UpperCAmelCase_)
			model.eval()
			UpperCamelCase__				:					Dict					=					model(UpperCAmelCase_)
			self.parent.assertEqual(result.last_hidden_state.shape  ,					(self.batch_size, self.embed_dims[-1], 7, 7))

	def      __UpperCamelCase							(      self   :						List[str]  ,					UpperCAmelCase_   :						Optional[int]  ,					UpperCAmelCase_   :						int  ,					UpperCAmelCase_   :						Optional[Any]):
			UpperCamelCase__				:					Dict					=					self.num_labels
			UpperCamelCase__				:					Union[str, Any]					=					SwiftFormerForImageClassification(UpperCAmelCase_)
			model.to(UpperCAmelCase_)
			model.eval()
			UpperCamelCase__				:					str					=					model(UpperCAmelCase_  ,					labels=UpperCAmelCase_)
			self.parent.assertEqual(result.logits.shape  ,					(self.batch_size, self.num_labels))

			UpperCamelCase__				:					Union[str, Any]					=					SwiftFormerForImageClassification(UpperCAmelCase_)
			model.to(UpperCAmelCase_)
			model.eval()

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





	def      __UpperCamelCase							(      self   :						List[str]):
			((UpperCamelCase__),	(UpperCamelCase__),	(UpperCamelCase__))				:					Tuple					=					self.prepare_config_and_inputs()
			UpperCamelCase__				:					str					=					{'pixel_values': pixel_values}
			return config, inputs_dict







@require_torch
class       __lowercase					(__lowerCamelCase   ,				__lowerCamelCase   ,				unittest.TestCase    ):
	_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      __UpperCamelCase							(      self   :						Any):
			UpperCamelCase__				:					Union[str, Any]					=					SwiftFormerModelTester(self)
			UpperCamelCase__				:					Optional[int]					=					ConfigTester(
			    self  ,					config_class=UpperCAmelCase_  ,					has_text_modality=UpperCAmelCase_  ,					hidden_size=37  ,					num_attention_heads=12  ,					num_hidden_layers=12  ,					)

	def      __UpperCamelCase							(      self   :						Optional[int]):
			self.config_tester.run_common_tests()

	@unittest.skip(reason='SwiftFormer does not use inputs_embeds')
	def      __UpperCamelCase							(      self   :						int):
			pass

	def      __UpperCamelCase							(      self   :						Optional[Any]):
			UpperCamelCase__,	UpperCamelCase__				:					int					=					self.model_tester.prepare_config_and_inputs_for_common()

			for model_class in self.all_model_classes:
					UpperCamelCase__				:					Dict					=					model_class(UpperCAmelCase_)
					UpperCamelCase__				:					Union[str, Any]					=					model.get_output_embeddings()
					self.assertTrue(x is None or isinstance(UpperCAmelCase_  ,					nn.Linear))

	def      __UpperCamelCase							(      self   :						Tuple):
			UpperCamelCase__,	UpperCamelCase__				:					List[str]					=					self.model_tester.prepare_config_and_inputs_for_common()

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

					UpperCamelCase__				:					int					=					['pixel_values']
					self.assertListEqual(arg_names[:1]  ,					UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						Union[str, Any]):
			UpperCamelCase__				:					int					=					self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model(*UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						Tuple):
			UpperCamelCase__				:					List[str]					=					self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase_)

	@slow
	def      __UpperCamelCase							(      self   :						List[Any]):
			for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
					UpperCamelCase__				:					Dict					=					SwiftFormerModel.from_pretrained(UpperCAmelCase_)
					self.assertIsNotNone(UpperCAmelCase_)

	@unittest.skip(reason='SwiftFormer does not output attentions')
	def      __UpperCamelCase							(      self   :						Optional[Any]):
			pass

	def      __UpperCamelCase							(      self   :						Union[str, Any]):
			def check_hidden_states_output(UpperCAmelCase_   :						Optional[Any]  ,					UpperCAmelCase_   :						Union[str, Any]  ,					UpperCAmelCase_   :						Dict):
					UpperCamelCase__				:					Optional[int]					=					model_class(UpperCAmelCase_)
					model.to(UpperCAmelCase_)
					model.eval()

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

					UpperCamelCase__				:					List[Any]					=					outputs.hidden_states

					UpperCamelCase__				:					Any					=					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),
							        ])  ,					)

			UpperCamelCase__,	UpperCamelCase__				:					str					=					self.model_tester.prepare_config_and_inputs_for_common()

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

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

					check_hidden_states_output(UpperCAmelCase_  ,					UpperCAmelCase_  ,					UpperCAmelCase_)

	def      __UpperCamelCase							(      self   :						Union[str, Any]):
			def _config_zero_init(UpperCAmelCase_   :						Any):
					UpperCamelCase__				:					Optional[Any]					=					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_):
									UpperCamelCase__				:					Optional[int]					=					_config_zero_init(getattr(UpperCAmelCase_  ,					UpperCAmelCase_))
									setattr(UpperCAmelCase_  ,					UpperCAmelCase_  ,					UpperCAmelCase_)
					return configs_no_init

			UpperCamelCase__,	UpperCamelCase__				:					List[str]					=					self.model_tester.prepare_config_and_inputs_for_common()

			UpperCamelCase__				:					Dict					=					_config_zero_init(UpperCAmelCase_)
			for model_class in self.all_model_classes:
					UpperCamelCase__				:					List[str]					=					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      __UpperCamelCase							(      self   :						List[str]):
			pass







def       __UpperCAmelCase						(  )							->     Dict:
		UpperCamelCase__				:					str					=					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
		return image







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

	@cached_property
	def      __UpperCamelCase							(      self   :						Optional[Any]):
			return ViTImageProcessor.from_pretrained('MBZUAI/swiftformer-xs') if is_vision_available() else None





	@slow
	def      __UpperCamelCase							(      self   :						List[str]):
			UpperCamelCase__				:					Union[str, Any]					=					SwiftFormerForImageClassification.from_pretrained('MBZUAI/swiftformer-xs').to(UpperCAmelCase_)

			UpperCamelCase__				:					Any					=					self.default_image_processor
			UpperCamelCase__				:					Tuple					=					prepare_img()
			UpperCamelCase__				:					Dict					=					image_processor(images=UpperCAmelCase_  ,					return_tensors='pt').to(UpperCAmelCase_)

			# forward pass
			with torch.no_grad():
					UpperCamelCase__				:					Optional[Any]					=					model(**UpperCAmelCase_)

			# verify the logits
			UpperCamelCase__				:					Dict					=					torch.Size((1, 1_000))
			self.assertEqual(outputs.logits.shape  ,					UpperCAmelCase_)

			UpperCamelCase__				:					Tuple					=					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))












'''simple docstring'''




from typing import Union

import fire
import torch
from tqdm import tqdm







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_ = "cpu"     ,      lowerCamelCase_ = None)							->     None:
		UpperCamelCase__				:					List[Any]					=					torch.load(lowerCamelCase_     ,      map_location=lowerCamelCase_)
		for k, v in tqdm(state_dict.items()):
				if not isinstance(lowerCamelCase_     ,      torch.Tensor):
						raise TypeError('FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin')
				UpperCamelCase__				:					int					=					v.half()
		if save_path is None:  # overwrite src_path
				UpperCamelCase__				:					List[Any]					=					src_path
		torch.save(lowerCamelCase_     ,      lowerCamelCase_)


if __name__ == "__main__":
		fire.Fire(convert)




