


_A									=       {
    '''meter''': '''m''',
    '''kilometer''': '''km''',
    '''megametre''': '''Mm''',
    '''gigametre''': '''Gm''',
    '''terametre''': '''Tm''',
    '''petametre''': '''Pm''',
    '''exametre''': '''Em''',
    '''zettametre''': '''Zm''',
    '''yottametre''': '''Ym''',
}
# Exponent of the factor(meter)
_A									=       {
    '''m''': 0,
    '''km''': 3,
    '''Mm''': 6,
    '''Gm''': 9,
    '''Tm''': 12,
    '''Pm''': 15,
    '''Em''': 18,
    '''Zm''': 21,
    '''Ym''': 24,
}



def     __lowerCAmelCase						(							snake_case	:    float						,      snake_case	:    str						,      snake_case	:    str )       ->							float:
   __lowerCamelCase:				List[Any]     =	from_type.lower().strip("""s""" )
   __lowerCamelCase:				int     =	to_type.lower().strip("""s""" )

   __lowerCamelCase:				Any     =	UNIT_SYMBOL.get(snake_case						,      snake_case )
   __lowerCamelCase:				List[str]     =	UNIT_SYMBOL.get(snake_case						,      snake_case )

   if from_sanitized not in METRIC_CONVERSION:
      __lowerCamelCase:				Optional[int]     =	(
          f'Invalid \'from_type\' value: {from_type!r}.\n'
          f'Conversion abbreviations are: {", ".join(snake_case )}'
      )
      raise ValueError(snake_case )
   if to_sanitized not in METRIC_CONVERSION:
      __lowerCamelCase:				List[str]     =	(
          f'Invalid \'to_type\' value: {to_type!r}.\n'
          f'Conversion abbreviations are: {", ".join(snake_case )}'
      )
      raise ValueError(snake_case )
   __lowerCamelCase:				int     =	METRIC_CONVERSION[from_sanitized]
   __lowerCamelCase:				Optional[Any]     =	METRIC_CONVERSION[to_sanitized]
   __lowerCamelCase:				List[Any]     =	1

   if from_exponent > to_exponent:
      __lowerCamelCase:				List[str]     =	from_exponent - to_exponent
   else:
      __lowerCamelCase:				Tuple     =	-(to_exponent - from_exponent)

   return value * pow(10						,      snake_case )


if __name__ == "__main__":
   from doctest import testmod

   testmod()






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					:





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





     def  SCREAMING_SNAKE_CASE__       (    self					:				Union[str, Any]							):
        __lowerCamelCase:				Any     =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

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

        __lowerCamelCase:				Dict     =	self.get_config()

        return config, pixel_values, labels





     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[int]							):
        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=SCREAMING_SNAKE_CASE_    ,	layer_scale_init_value=1E-5    ,	)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Dict    ,	SCREAMING_SNAKE_CASE_					:				List[Any]    ,	SCREAMING_SNAKE_CASE_					:				Any    ,	SCREAMING_SNAKE_CASE_					:				Tuple							):
        __lowerCamelCase:				Optional[Any]     =	SwiftFormerModel(config=SCREAMING_SNAKE_CASE_							)
        model.to(SCREAMING_SNAKE_CASE_							)
        model.eval()
        __lowerCamelCase:				Dict     =	model(SCREAMING_SNAKE_CASE_							)
        self.parent.assertEqual(result.last_hidden_state.shape    ,	(self.batch_size, self.embed_dims[-1], 7, 7)							)





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

        __lowerCamelCase:				Any     =	SwiftFormerForImageClassification(SCREAMING_SNAKE_CASE_							)
        model.to(SCREAMING_SNAKE_CASE_							)
        model.eval()

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







     def  SCREAMING_SNAKE_CASE__       (    self					:				str							):
        ((__lowerCamelCase)   ,     (__lowerCamelCase)   ,     (__lowerCamelCase)):				Union[str, Any]     =	self.prepare_config_and_inputs()
        __lowerCamelCase:				Optional[int]     =	{"""pixel_values""": pixel_values}
        return config, inputs_dict



@require_torch
class        a					(			_UpperCAmelCase     ,_UpperCAmelCase     ,unittest.TestCase   ):
     UpperCAmelCase__ :  List[str]						      =	(SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
     UpperCAmelCase__ :  List[str]						      =	(
         {"feature-extraction": SwiftFormerModel, "image-classification": SwiftFormerForImageClassification}
         if is_torch_available()
         else {}
     )

     UpperCAmelCase__ :  Optional[Any]						      =	False
     UpperCAmelCase__ :  List[str]						      =	False
     UpperCAmelCase__ :  int						      =	False
     UpperCAmelCase__ :  Optional[Any]						      =	False
     UpperCAmelCase__ :  Dict						      =	False





     def  SCREAMING_SNAKE_CASE__       (    self					:				Tuple							):
        __lowerCamelCase:				Optional[Any]     =	SwiftFormerModelTester(self							)
        __lowerCamelCase:				Optional[Any]     =	ConfigTester(
            self    ,	config_class=SCREAMING_SNAKE_CASE_    ,	has_text_modality=SCREAMING_SNAKE_CASE_    ,	hidden_size=37    ,	num_attention_heads=12    ,	num_hidden_layers=12    ,	)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Any							):
        self.config_tester.run_common_tests()





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





     def  SCREAMING_SNAKE_CASE__       (    self					:				int							):
        __lowerCamelCase   ,     __lowerCamelCase:				Optional[int]     =	self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
           __lowerCamelCase:				Optional[Any]     =	model_class(SCREAMING_SNAKE_CASE_							)
           __lowerCamelCase:				Optional[Any]     =	model.get_output_embeddings()
           self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE_    ,	nn.Linear							)							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Dict							):
        __lowerCamelCase   ,     __lowerCamelCase:				Optional[int]     =	self.model_tester.prepare_config_and_inputs_for_common()

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

           __lowerCamelCase:				List[str]     =	["""pixel_values"""]
           self.assertListEqual(arg_names[:1]    ,	SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				int							):
        __lowerCamelCase:				Tuple     =	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[int]							):
        __lowerCamelCase:				int     =	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE_							)





     @slow
     def  SCREAMING_SNAKE_CASE__       (    self					:				Tuple							):
        for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           __lowerCamelCase:				Optional[Any]     =	SwiftFormerModel.from_pretrained(SCREAMING_SNAKE_CASE_							)
           self.assertIsNotNone(SCREAMING_SNAKE_CASE_							)





     @unittest.skip(reason="""SwiftFormer does not output attentions"""							)
     def  SCREAMING_SNAKE_CASE__       (    self					:				Tuple							):
        pass





     def  SCREAMING_SNAKE_CASE__       (    self					:				List[str]							):
        def check_hidden_states_output(SCREAMING_SNAKE_CASE_					:				Optional[Any]    ,	SCREAMING_SNAKE_CASE_					:				Any    ,	SCREAMING_SNAKE_CASE_					:				List[str]							):
           __lowerCamelCase:				List[str]     =	model_class(SCREAMING_SNAKE_CASE_							)
           model.to(SCREAMING_SNAKE_CASE_							)
           model.eval()

           with torch.no_grad():
              __lowerCamelCase:				Any     =	model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)							)

           __lowerCamelCase:				Dict     =	outputs.hidden_states

           __lowerCamelCase:				Tuple     =	8
           self.assertEqual(len(SCREAMING_SNAKE_CASE_							)    ,	SCREAMING_SNAKE_CASE_							)  # 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(SCREAMING_SNAKE_CASE_							)							):
              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:				Tuple     =	self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
           __lowerCamelCase:				Dict     =	True
           check_hidden_states_output(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)

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

           check_hidden_states_output(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Any							):
        def _config_zero_init(SCREAMING_SNAKE_CASE_					:				Any							):
           __lowerCamelCase:				Dict     =	copy.deepcopy(SCREAMING_SNAKE_CASE_							)
           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(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	1E-1_0							)
              if isinstance(getattr(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)    ,	SCREAMING_SNAKE_CASE_							):
                 __lowerCamelCase:				Tuple     =	_config_zero_init(getattr(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)							)
                 setattr(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)
           return configs_no_init

        __lowerCamelCase   ,     __lowerCamelCase:				Optional[int]     =	self.model_tester.prepare_config_and_inputs_for_common()

        __lowerCamelCase:				str     =	_config_zero_init(SCREAMING_SNAKE_CASE_							)
        for model_class in self.all_model_classes:
           __lowerCamelCase:				Dict     =	model_class(config=SCREAMING_SNAKE_CASE_							)
           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  SCREAMING_SNAKE_CASE__       (    self					:				str							):
        pass



def     __lowerCAmelCase						(							)       ->							List[Any]:
   __lowerCamelCase:				Optional[Any]     =	Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
   return image







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





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







     @slow
     def  SCREAMING_SNAKE_CASE__       (    self					:				Union[str, Any]							):
        __lowerCamelCase:				Any     =	SwiftFormerForImageClassification.from_pretrained("""MBZUAI/swiftformer-xs"""							).to(SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				Any     =	self.default_image_processor
        __lowerCamelCase:				Tuple     =	prepare_img()
        __lowerCamelCase:				int     =	image_processor(images=SCREAMING_SNAKE_CASE_    ,	return_tensors="""pt"""							).to(SCREAMING_SNAKE_CASE_							)

        # forward pass
        with torch.no_grad():
           __lowerCamelCase:				List[Any]     =	model(**SCREAMING_SNAKE_CASE_							)

        # verify the logits
        __lowerCamelCase:				List[Any]     =	torch.Size((1, 1000)							)
        self.assertEqual(outputs.logits.shape    ,	SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				List[str]     =	torch.tensor([[-2.1_7_0_3E0_0, 2.1_1_0_7E0_0, -2.0_8_1_1E0_0]]							).to(SCREAMING_SNAKE_CASE_							)
        self.assertTrue(torch.allclose(outputs.logits[0, :3]    ,	SCREAMING_SNAKE_CASE_    ,	atol=1E-4							)							)


