




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			,						lowerCamelCase__			,						lowerCamelCase__=13			,						lowerCamelCase__=3			,						lowerCamelCase__=True			,						lowerCamelCase__=True			,						lowerCamelCase__=0.1			,						lowerCamelCase__=0.1			,						lowerCamelCase__=224			,						lowerCamelCase__=1_000			,						lowerCamelCase__=[3, 3, 6, 4]			,						lowerCamelCase__=[48, 56, 112, 220]			,						)    ->      Tuple:



							'''simple docstring'''
							__lowerCamelCase		       =    parent
							__lowerCamelCase		       =    batch_size
							__lowerCamelCase		       =    num_channels
							__lowerCamelCase		       =    is_training
							__lowerCamelCase		       =    use_labels
							__lowerCamelCase		       =    hidden_dropout_prob
							__lowerCamelCase		       =    attention_probs_dropout_prob
							__lowerCamelCase		       =    num_labels
							__lowerCamelCase		       =    image_size
							__lowerCamelCase		       =    layer_depths
							__lowerCamelCase		       =    embed_dims


			def       lowercase_    (      self							)    ->      Union[str, Any]:



							'''simple docstring'''
							__lowerCamelCase		       =    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

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

							__lowerCamelCase		       =    self.get_config()

							return config, pixel_values, labels


			def       lowercase_    (      self							)    ->      int:



							'''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=lowerCamelCase__			,						layer_scale_init_value=1e-5			,						)


			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__							)    ->      List[Any]:



							'''simple docstring'''
							__lowerCamelCase		       =    SwiftFormerModel(config=lowerCamelCase__							)
							model.to(lowerCamelCase__							)
							model.eval()
							__lowerCamelCase		       =    model(lowerCamelCase__							)
							self.parent.assertEqual(result.last_hidden_state.shape			,						(self.batch_size, self.embed_dims[-1], 7, 7)							)


			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__							)    ->      Dict:



							'''simple docstring'''
							__lowerCamelCase		       =    self.num_labels
							__lowerCamelCase		       =    SwiftFormerForImageClassification(lowerCamelCase__							)
							model.to(lowerCamelCase__							)
							model.eval()
							__lowerCamelCase		       =    model(lowerCamelCase__			,						labels=lowerCamelCase__							)
							self.parent.assertEqual(result.logits.shape			,						(self.batch_size, self.num_labels)							)

							__lowerCamelCase		       =    SwiftFormerForImageClassification(lowerCamelCase__							)
							model.to(lowerCamelCase__							)
							model.eval()

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






			def       lowercase_    (      self							)    ->      Dict:



							'''simple docstring'''
							((__lowerCamelCase)       ,						(__lowerCamelCase)       ,						(__lowerCamelCase))		       =    self.prepare_config_and_inputs()
							__lowerCamelCase		       =    {'pixel_values': pixel_values}
							return config, inputs_dict







@require_torch
class     __lowerCAmelCase (       __magic_name__			,				__magic_name__			,				unittest.TestCase ):

			"""simple docstring"""







			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       lowercase_    (      self							)    ->      List[Any]:



							'''simple docstring'''
							__lowerCamelCase		       =    SwiftFormerModelTester(self							)
							__lowerCamelCase		       =    ConfigTester(
							    self			,						config_class=lowerCamelCase__			,						has_text_modality=lowerCamelCase__			,						hidden_size=37			,						num_attention_heads=12			,						num_hidden_layers=12			,						)


			def       lowercase_    (      self							)    ->      Union[str, Any]:



							'''simple docstring'''
							self.config_tester.run_common_tests()


			@unittest.skip(reason='SwiftFormer does not use inputs_embeds'							)
			def       lowercase_    (      self							)    ->      List[Any]:



							'''simple docstring'''
							pass


			def       lowercase_    (      self							)    ->      int:



							'''simple docstring'''
							__lowerCamelCase       ,						__lowerCamelCase		       =    self.model_tester.prepare_config_and_inputs_for_common()

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


			def       lowercase_    (      self							)    ->      Optional[Any]:



							'''simple docstring'''
							__lowerCamelCase       ,						__lowerCamelCase		       =    self.model_tester.prepare_config_and_inputs_for_common()

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

											__lowerCamelCase		       =    ['pixel_values']
											self.assertListEqual(arg_names[:1]			,						lowerCamelCase__							)


			def       lowercase_    (      self							)    ->      Dict:



							'''simple docstring'''
							__lowerCamelCase		       =    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*lowerCamelCase__							)


			def       lowercase_    (      self							)    ->      Optional[Any]:



							'''simple docstring'''
							__lowerCamelCase		       =    self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*lowerCamelCase__							)


			@slow
			def       lowercase_    (      self							)    ->      int:



							'''simple docstring'''
							for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											__lowerCamelCase		       =    SwiftFormerModel.from_pretrained(lowerCamelCase__							)
											self.assertIsNotNone(lowerCamelCase__							)


			@unittest.skip(reason='SwiftFormer does not output attentions'							)
			def       lowercase_    (      self							)    ->      Dict:



							'''simple docstring'''
							pass


			def       lowercase_    (      self							)    ->      List[str]:



							'''simple docstring'''
							def check_hidden_states_output(lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__							):
											__lowerCamelCase		       =    model_class(lowerCamelCase__							)
											model.to(lowerCamelCase__							)
											model.eval()

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

											__lowerCamelCase		       =    outputs.hidden_states

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

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

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

											check_hidden_states_output(lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__							)


			def       lowercase_    (      self							)    ->      List[str]:



							'''simple docstring'''
							def _config_zero_init(lowerCamelCase__							):
											__lowerCamelCase		       =    copy.deepcopy(lowerCamelCase__							)
											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(lowerCamelCase__			,						lowerCamelCase__			,						1e-10							)
															if isinstance(getattr(lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__							)			,						lowerCamelCase__							):
																			__lowerCamelCase		       =    _config_zero_init(getattr(lowerCamelCase__			,						lowerCamelCase__							)							)
																			setattr(lowerCamelCase__			,						lowerCamelCase__			,						lowerCamelCase__							)
											return configs_no_init

							__lowerCamelCase       ,						__lowerCamelCase		       =    self.model_tester.prepare_config_and_inputs_for_common()

							__lowerCamelCase		       =    _config_zero_init(lowerCamelCase__							)
							for model_class in self.all_model_classes:
											__lowerCamelCase		       =    model_class(config=lowerCamelCase__							)
											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       lowercase_    (      self							)    ->      int:



							'''simple docstring'''
							pass







def 			lowerCamelCase_		(       )     ->       Tuple:





				"""simple docstring"""


				__lowerCamelCase		       =    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
				return image







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

			"""simple docstring"""







			@cached_property
			def       lowercase_    (      self							)    ->      List[str]:



							'''simple docstring'''
							return ViTImageProcessor.from_pretrained('MBZUAI/swiftformer-xs'							) if is_vision_available() else None






			@slow
			def       lowercase_    (      self							)    ->      Optional[Any]:



							'''simple docstring'''
							__lowerCamelCase		       =    SwiftFormerForImageClassification.from_pretrained('MBZUAI/swiftformer-xs'							).to(lowerCamelCase__							)

							__lowerCamelCase		       =    self.default_image_processor
							__lowerCamelCase		       =    prepare_img()
							__lowerCamelCase		       =    image_processor(images=lowerCamelCase__			,						return_tensors='pt'							).to(lowerCamelCase__							)

							# forward pass
							with torch.no_grad():
											__lowerCamelCase		       =    model(**lowerCamelCase__							)

							# verify the logits
							__lowerCamelCase		       =    torch.Size((1, 1_000)							)
							self.assertEqual(outputs.logits.shape			,						lowerCamelCase__							)

							__lowerCamelCase		       =    torch.tensor([[-2.17_03e00, 2.11_07e00, -2.08_11e00]]							).to(lowerCamelCase__							)
							self.assertTrue(torch.allclose(outputs.logits[0, :3]			,						lowerCamelCase__			,						atol=1e-4							)							)










from __future__ import annotations

import typing
from collections.abc import Iterable

import numpy as np

a		:       Dict			     = typing.Union[Iterable[float], Iterable[int], np.ndarray]  # noqa: UP007
a		:       Any			     = typing.Union[np.floataa, int, float]  # noqa: UP007






def 				lowerCamelCase__      (    __lowerCamelCase			:     Vector  ,		__lowerCamelCase			:     Vector		):
  return np.sqrt(np.sum((np.asarray(__lowerCamelCase		) - np.asarray(__lowerCamelCase		)) ** 2		)		)






def 				lowerCamelCase__      (    __lowerCamelCase			:     Vector  ,		__lowerCamelCase			:     Vector		):
  return sum((va - va) ** 2 for va, va in zip(__lowerCamelCase  ,		__lowerCamelCase		)		) ** (1 / 2)


if __name__ == "__main__":






       def 				lowerCamelCase__      (    ):
         from timeit import timeit

         print("""Without Numpy"""		)
         print(
             timeit(
                 """euclidean_distance_no_np([1, 2, 3], [4, 5, 6])"""  ,		number=10000  ,		globals=globals()  ,		)		)
         print("""With Numpy"""		)
         print(
             timeit(
                 """euclidean_distance([1, 2, 3], [4, 5, 6])"""  ,		number=10000  ,		globals=globals()  ,		)		)







       benchmark()


