





'''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  :



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



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

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

			lowerCAmelCase     :    List[str]						=   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     :    List[str]						=   SwiftFormerModel(config=a_	)
			model.to(a_	)
			model.eval()
			lowerCAmelCase     :    str						=   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     :    Union[str, Any]						=   SwiftFormerForImageClassification(a_	)
			model.to(a_	)
			model.eval()
			lowerCAmelCase     :    Union[str, Any]						=   model(a_		,      labels=a_	)
			self.parent.assertEqual(result.logits.shape		,      (self.batch_size, self.num_labels)	)

			lowerCAmelCase     :    Optional[int]						=   SwiftFormerForImageClassification(a_	)
			model.to(a_	)
			model.eval()

			lowerCAmelCase     :    Optional[int]						=   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)
			lowerCAmelCase     :    Any						=   model(a_	)
			self.parent.assertEqual(result.logits.shape		,      (self.batch_size, self.num_labels)	)





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






@require_torch
class 		lowerCamelCase  (      _A      ,   _A      ,   unittest.TestCase       ):
	snake_case_      =   (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
	snake_case_      =   (
	    {"feature-extraction": SwiftFormerModel, "image-classification": SwiftFormerForImageClassification}
	    if is_torch_available()
	    else {}
	)

	snake_case_      =   False
	snake_case_      =   False
	snake_case_      =   False
	snake_case_      =   False
	snake_case_      =   False



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

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



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase	,		lowerCAmelCase     :    Tuple						=   self.model_tester.prepare_config_and_inputs_for_common()

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

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



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Optional[int]						=   self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model(*a_	)



	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    List[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     :    int						=   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     :    List[Any]						=   model_class(a_	)
					model.to(a_	)
					model.eval()

					with torch.no_grad():
							lowerCAmelCase     :    Optional[Any]						=   model(**self._prepare_for_class(a_		,      a_	)	)

					lowerCAmelCase     :    Tuple						=   outputs.hidden_states

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

			for model_class in self.all_model_classes:
					lowerCAmelCase     :    Dict						=   True
					check_hidden_states_output(a_		,      a_		,      a_	)

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

					check_hidden_states_output(a_		,      a_		,      a_	)



	def   _lowerCamelCase    (   self	):
			def _config_zero_init(a_	):
					lowerCAmelCase     :    str						=   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-1_0	)
							if isinstance(getattr(a_		,      a_		,      a_	)		,      a_	):
									lowerCAmelCase     :    List[str]						=   _config_zero_init(getattr(a_		,      a_	)	)
									setattr(a_		,      a_		,      a_	)
					return configs_no_init

			lowerCAmelCase	,		lowerCAmelCase     :    Tuple						=   self.model_tester.prepare_config_and_inputs_for_common()

			lowerCAmelCase     :    List[str]						=   _config_zero_init(a_	)
			for model_class in self.all_model_classes:
					lowerCAmelCase     :    str						=   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      __A						(							):
		lowerCAmelCase     :    str						=   Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"	)
		return image

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



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





	@slow
	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    List[str]						=   SwiftFormerForImageClassification.from_pretrained("MBZUAI/swiftformer-xs"	).to(a_	)

			lowerCAmelCase     :    Tuple						=   self.default_image_processor
			lowerCAmelCase     :    Dict						=   prepare_img()
			lowerCAmelCase     :    Tuple						=   image_processor(images=a_		,      return_tensors="pt"	).to(a_	)

			# forward pass
			with torch.no_grad():
					lowerCAmelCase     :    Dict						=   model(**a_	)

			# verify the logits
			lowerCAmelCase     :    Optional[int]						=   torch.Size((1, 1_000)	)
			self.assertEqual(outputs.logits.shape		,      a_	)

			lowerCAmelCase     :    Tuple						=   torch.tensor([[-2.1_7_0_3e0_0, 2.1_1_0_7e0_0, -2.0_8_1_1e0_0]]	).to(a_	)
			self.assertTrue(torch.allclose(outputs.logits[0, :3]		,      a_		,      atol=1e-4	)	)










'''simple docstring'''






from collections import namedtuple

lowerCAmelCase       		=      namedtuple("""from_to""", """from_ to""")

lowerCAmelCase       		=      {
    """cubicmeter""": from_to(1, 1),
    """litre""": from_to(0.001, 10_00),
    """kilolitre""": from_to(1, 1),
    """gallon""": from_to(0.00454, 264.172),
    """cubicyard""": from_to(0.76455, 1.30795),
    """cubicfoot""": from_to(0.028, 35.3147),
    """cup""": from_to(0.000236588, 4226.75),
}






def      __A						(							a_						:  float      ,a_						:  str      ,a_						:  str	):
		if from_type not in METRIC_CONVERSION:
				raise ValueError(
				    f'''Invalid \'from_type\' value: {from_type!r}  Supported values are:\n'''
				    + ", ".join(a_	)	)
		if to_type not in METRIC_CONVERSION:
				raise ValueError(
				    f'''Invalid \'to_type\' value: {to_type!r}.  Supported values are:\n'''
				    + ", ".join(a_	)	)
		return value * METRIC_CONVERSION[from_type].from_ * METRIC_CONVERSION[to_type].to


if __name__ == "__main__":
					import doctest

					doctest.testmod()



