


'''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 ,					snake_case__ ,					snake_case__=13 ,					snake_case__=3 ,					snake_case__=True ,					snake_case__=True ,					snake_case__=0.1 ,					snake_case__=0.1 ,					snake_case__=224 ,					snake_case__=1000 ,					snake_case__=[3, 3, 6, 4] ,					snake_case__=[48, 56, 112, 220] ,					):
		lowerCAmelCase					:    List[Any]      							= parent
		lowerCAmelCase					:    Optional[int]      							= batch_size
		lowerCAmelCase					:    Optional[Any]      							= num_channels
		lowerCAmelCase					:    List[str]      							= is_training
		lowerCAmelCase					:    Union[str, Any]      							= use_labels
		lowerCAmelCase					:    List[str]      							= hidden_dropout_prob
		lowerCAmelCase					:    List[Any]      							= attention_probs_dropout_prob
		lowerCAmelCase					:    Tuple      							= num_labels
		lowerCAmelCase					:    Optional[Any]      							= image_size
		lowerCAmelCase					:    List[Any]      							= layer_depths
		lowerCAmelCase					:    List[str]      							= embed_dims



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    List[str]      							= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

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

		lowerCAmelCase					:    Dict      							= self.get_config()

		return config, pixel_values, labels



	def 				lowercase     (					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=UpperCamelCase__ ,					layer_scale_init_value=1e-5 ,					)



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



	def 				lowercase     (					self ,					snake_case__ ,					snake_case__ ,					snake_case__    ):
		lowerCAmelCase					:    Any      							= self.num_labels
		lowerCAmelCase					:    str      							= SwiftFormerForImageClassification(UpperCamelCase__    )
		model.to(UpperCamelCase__    )
		model.eval()
		lowerCAmelCase					:    List[str]      							= model(UpperCamelCase__ ,					labels=UpperCamelCase__    )
		self.parent.assertEqual(result.logits.shape ,					(self.batch_size, self.num_labels)    )

		lowerCAmelCase					:    Any      							= SwiftFormerForImageClassification(UpperCamelCase__    )
		model.to(UpperCamelCase__    )
		model.eval()

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



	def 				lowercase     (					self    ):
		((lowerCAmelCase)							,   (lowerCAmelCase)							,   (lowerCAmelCase))					:    str      							= self.prepare_config_and_inputs()
		lowerCAmelCase					:    str      							= {'pixel_values': pixel_values}
		return config, inputs_dict



@require_torch
class  lowerCAmelCase				(				lowercase_      ,				lowercase_      ,				unittest.TestCase							):
	_lowerCamelCase   :					Dict			      = (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
	_lowerCamelCase   :					Any			      = (
	    {'''feature-extraction''': SwiftFormerModel, '''image-classification''': SwiftFormerForImageClassification}
	    if is_torch_available()
	    else {}
	)

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



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    List[str]      							= SwiftFormerModelTester(self    )
		lowerCAmelCase					:    Tuple      							= ConfigTester(
		    self ,					config_class=UpperCamelCase__ ,					has_text_modality=UpperCamelCase__ ,					hidden_size=37 ,					num_attention_heads=12 ,					num_hidden_layers=12 ,					)



	def 				lowercase     (					self    ):
		self.config_tester.run_common_tests()



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



	def 				lowercase     (					self    ):
		lowerCAmelCase							,   lowerCAmelCase					:    Any      							= self.model_tester.prepare_config_and_inputs_for_common()

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



	def 				lowercase     (					self    ):
		lowerCAmelCase							,   lowerCAmelCase					:    Dict      							= self.model_tester.prepare_config_and_inputs_for_common()

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

			lowerCAmelCase					:    Optional[Any]      							= ['pixel_values']
			self.assertListEqual(arg_names[:1] ,					UpperCamelCase__    )



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Union[str, Any]      							= self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_model(*UpperCamelCase__    )



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Union[str, Any]      							= self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__    )



	@slow
	def 				lowercase     (					self    ):







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



	@unittest.skip(reason='SwiftFormer does not output attentions'    )
	def 				lowercase     (					self    ):
		pass



	def 				lowercase     (					self    ):







		def check_hidden_states_output(snake_case__ ,					snake_case__ ,					snake_case__    ):
			lowerCAmelCase					:    int      							= model_class(UpperCamelCase__    )
			model.to(UpperCamelCase__    )
			model.eval()

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

			lowerCAmelCase					:    int      							= outputs.hidden_states

			lowerCAmelCase					:    Dict      							= 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),
				        ]    ) ,					)

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

		for model_class in self.all_model_classes:
			lowerCAmelCase					:    Optional[Any]      							= True
			check_hidden_states_output(UpperCamelCase__ ,					UpperCamelCase__ ,					UpperCamelCase__    )

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

			check_hidden_states_output(UpperCamelCase__ ,					UpperCamelCase__ ,					UpperCamelCase__    )



	def 				lowercase     (					self    ):







		def _config_zero_init(snake_case__    ):
			lowerCAmelCase					:    Dict      							= 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-1_0    )
				if isinstance(getattr(UpperCamelCase__ ,					UpperCamelCase__ ,					UpperCamelCase__    ) ,					UpperCamelCase__    ):
					lowerCAmelCase					:    Optional[int]      							= _config_zero_init(getattr(UpperCamelCase__ ,					UpperCamelCase__    )    )
					setattr(UpperCamelCase__ ,					UpperCamelCase__ ,					UpperCamelCase__    )
			return configs_no_init

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

		lowerCAmelCase					:    Union[str, Any]      							= _config_zero_init(UpperCamelCase__    )
		for model_class in self.all_model_classes:
			lowerCAmelCase					:    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 				lowercase     (					self    ):
		pass


def 			__UpperCamelCase    (    )    ->      Optional[Any]:




	"""simple docstring"""





	lowerCAmelCase					:    Union[str, Any]      							= Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
	return image






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



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



	@slow
	def 				lowercase     (					self    ):
		lowerCAmelCase					:    List[str]      							= SwiftFormerForImageClassification.from_pretrained('MBZUAI/swiftformer-xs'    ).to(UpperCamelCase__    )

		lowerCAmelCase					:    Optional[Any]      							= self.default_image_processor
		lowerCAmelCase					:    List[Any]      							= prepare_img()
		lowerCAmelCase					:    Any      							= image_processor(images=UpperCamelCase__ ,					return_tensors='pt'    ).to(UpperCamelCase__    )

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

		# verify the logits
		lowerCAmelCase					:    Union[str, Any]      							= torch.Size((1, 1000)    )
		self.assertEqual(outputs.logits.shape ,					UpperCamelCase__    )

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



'''simple docstring'''




import os

import pytest
import yaml

from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict

@pytest.mark.parametrize(
    'files'					,   [
        ['full:README.md', 'dataset_infos.json'],
        ['empty:README.md', 'dataset_infos.json'],
        ['dataset_infos.json'],
        ['full:README.md'],
    ]					,   )
def 			__UpperCamelCase    (    _A    :				str					,   _A    :				List[Any]				)    ->      Union[str, Any]:




	"""simple docstring"""





	lowerCAmelCase					:    Optional[int]      							= tmp_path_factory.mktemp('dset_infos_dir'				)
	if "full:README.md" in files:
		with open(dataset_infos_dir / 'README.md'					,   'w'				) as f:
			f.write('---\ndataset_info:\n  dataset_size: 42\n---'				)
	if "empty:README.md" in files:
		with open(dataset_infos_dir / 'README.md'					,   'w'				) as f:
			f.write(''				)
    # we want to support dataset_infos.json for backward compatibility
	if "dataset_infos.json" in files:
		with open(dataset_infos_dir / 'dataset_infos.json'					,   'w'				) as f:
			f.write('{"default": {"dataset_size": 42}}'				)
	lowerCAmelCase					:    Union[str, Any]      							= DatasetInfosDict.from_directory(_A				)
	assert dataset_infos
	assert dataset_infos["default"].dataset_size == 42

@pytest.mark.parametrize(
    'dataset_info'					,   [
        DatasetInfo(),
        DatasetInfo(
            description='foo'					,   features=Features({'a': Value('int32'				)}				)					,   builder_name='builder'					,   config_name='config'					,   version='1.0.0'					,   splits=[{'name': 'train'}]					,   download_size=42					,   ),
    ]					,   )
def 			__UpperCamelCase    (    _A    :				str					,   _A    :				DatasetInfo				)    ->      Optional[int]:




	"""simple docstring"""





	lowerCAmelCase					:    str      							= str(_A				)
	dataset_info.write_to_directory(_A				)
	lowerCAmelCase					:    List[str]      							= DatasetInfo.from_directory(_A				)
	assert dataset_info == reloaded
	assert os.path.exists(os.path.join(_A					,   'dataset_info.json'				)				)

def 			__UpperCamelCase    (    )    ->      List[str]:




	"""simple docstring"""





	lowerCAmelCase					:    Tuple      							= DatasetInfo(
	    description='foo'					,   citation='bar'					,   homepage='https://foo.bar'					,   license='CC0'					,   features=Features({'a': Value('int32'				)}				)					,   post_processed={}					,   supervised_keys=()					,   task_templates=[]					,   builder_name='builder'					,   config_name='config'					,   version='1.0.0'					,   splits=[{'name': 'train', 'num_examples': 42}]					,   download_checksums={}					,   download_size=13_37					,   post_processing_size=4_42					,   dataset_size=12_34					,   size_in_bytes=13_37 + 4_42 + 12_34					,   )
	lowerCAmelCase					:    Optional[int]      							= dataset_info._to_yaml_dict()
	assert sorted(_A				) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML				)
	for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
		assert key in dataset_info_yaml_dict
		assert isinstance(dataset_info_yaml_dict[key]					,   (list, dict, int, str)				)
	lowerCAmelCase					:    Any      							= yaml.safe_dump(_A				)
	lowerCAmelCase					:    int      							= yaml.safe_load(_A				)
	assert dataset_info_yaml_dict == reloaded

def 			__UpperCamelCase    (    )    ->      Dict:




	"""simple docstring"""





	lowerCAmelCase					:    Union[str, Any]      							= DatasetInfo()
	lowerCAmelCase					:    List[Any]      							= dataset_info._to_yaml_dict()
	assert dataset_info_yaml_dict == {}






@pytest.mark.parametrize(
    'dataset_infos_dict'					,   [
        DatasetInfosDict(),
        DatasetInfosDict({'default': DatasetInfo()}				),
        DatasetInfosDict({'my_config_name': DatasetInfo()}				),
        DatasetInfosDict(
            {
                'default': DatasetInfo(
                    description='foo'					,   features=Features({'a': Value('int32'				)}				)					,   builder_name='builder'					,   config_name='config'					,   version='1.0.0'					,   splits=[{'name': 'train'}]					,   download_size=42					,   )
            }				),
        DatasetInfosDict(
            {
                'v1': DatasetInfo(dataset_size=42				),
                'v2': DatasetInfo(dataset_size=13_37				),
            }				),
    ]					,   )
def 			__UpperCamelCase    (    _A    :				Tuple					,   _A    :				DatasetInfosDict				)    ->      List[Any]:




	"""simple docstring"""





	lowerCAmelCase					:    Tuple      							= str(_A				)
	dataset_infos_dict.write_to_directory(_A				)
	lowerCAmelCase					:    List[str]      							= DatasetInfosDict.from_directory(_A				)

	# the config_name of the dataset_infos_dict take over the attribute
	for config_name, dataset_info in dataset_infos_dict.items():
		lowerCAmelCase					:    Tuple      							= config_name
		# the yaml representation doesn't include fields like description or citation
		# so we just test that we can recover what we can from the yaml
		lowerCAmelCase					:    Optional[Any]      							= DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict()				)
	assert dataset_infos_dict == reloaded

	if dataset_infos_dict:
		assert os.path.exists(os.path.join(_A					,   'README.md'				)				)
