



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							:					Optional[Any]					,		__SCREAMING_SNAKE_CASE							:					Dict					,		__SCREAMING_SNAKE_CASE							:					Dict=13					,		__SCREAMING_SNAKE_CASE							:					int=3					,		__SCREAMING_SNAKE_CASE							:					Dict=True					,		__SCREAMING_SNAKE_CASE							:					List[Any]=True					,		__SCREAMING_SNAKE_CASE							:					List[Any]=0.1					,		__SCREAMING_SNAKE_CASE							:					Optional[int]=0.1					,		__SCREAMING_SNAKE_CASE							:					List[str]=224					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]=1_000					,		__SCREAMING_SNAKE_CASE							:					Optional[Any]=[3, 3, 6, 4]					,		__SCREAMING_SNAKE_CASE							:					int=[48, 56, 112, 220]					,		):


           '''simple docstring'''

           __a							=    parent
           __a							=    batch_size
           __a							=    num_channels
           __a							=    is_training
           __a							=    use_labels
           __a							=    hidden_dropout_prob
           __a							=    attention_probs_dropout_prob
           __a							=    num_labels
           __a							=    image_size
           __a							=    layer_depths
           __a							=    embed_dims



       def 		_lowerCamelCase      (		self							:					Union[str, Any]):


           '''simple docstring'''

           __a							=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

           __a							=    self.get_config()

           return config, pixel_values, labels



       def 		_lowerCamelCase      (		self							:					Any):


           '''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=__SCREAMING_SNAKE_CASE					,		layer_scale_init_value=1E-5					,		)



       def 		_lowerCamelCase      (		self							:					Optional[Any]					,		__SCREAMING_SNAKE_CASE							:					Union[str, Any]					,		__SCREAMING_SNAKE_CASE							:					List[str]					,		__SCREAMING_SNAKE_CASE							:					int):


           '''simple docstring'''

           __a							=    SwiftFormerModel(config=__SCREAMING_SNAKE_CASE)
           model.to(__SCREAMING_SNAKE_CASE)
           model.eval()
           __a							=    model(__SCREAMING_SNAKE_CASE)
           self.parent.assertEqual(result.last_hidden_state.shape					,		(self.batch_size, self.embed_dims[-1], 7, 7))



       def 		_lowerCamelCase      (		self							:					str					,		__SCREAMING_SNAKE_CASE							:					int					,		__SCREAMING_SNAKE_CASE							:					List[str]					,		__SCREAMING_SNAKE_CASE							:					Tuple):


           '''simple docstring'''

           __a							=    self.num_labels
           __a							=    SwiftFormerForImageClassification(__SCREAMING_SNAKE_CASE)
           model.to(__SCREAMING_SNAKE_CASE)
           model.eval()
           __a							=    model(__SCREAMING_SNAKE_CASE					,		labels=__SCREAMING_SNAKE_CASE)
           self.parent.assertEqual(result.logits.shape					,		(self.batch_size, self.num_labels))

           __a							=    SwiftFormerForImageClassification(__SCREAMING_SNAKE_CASE)
           model.to(__SCREAMING_SNAKE_CASE)
           model.eval()

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





       def 		_lowerCamelCase      (		self							:					Any):


           '''simple docstring'''

           ((__a)	,       (__a)	,       (__a))							=    self.prepare_config_and_inputs()
           __a							=    {'''pixel_values''': pixel_values}
           return config, inputs_dict


@require_torch
class    _A	(     __UpperCAmelCase				,__UpperCAmelCase				,unittest.TestCase   ):
       UpperCamelCase__  :   List[Any]						 =						(SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
       UpperCamelCase__  :   str						 =						(
           {'''feature-extraction''': SwiftFormerModel, '''image-classification''': SwiftFormerForImageClassification}
           if is_torch_available()
           else {}
       )

       UpperCamelCase__  :   str						 =						False
       UpperCamelCase__  :   Union[str, Any]						 =						False
       UpperCamelCase__  :   int						 =						False
       UpperCamelCase__  :   Dict						 =						False
       UpperCamelCase__  :   List[Any]						 =						False



       def 		_lowerCamelCase      (		self							:					Optional[Any]):


           '''simple docstring'''

           __a							=    SwiftFormerModelTester(self)
           __a							=    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 		_lowerCamelCase      (		self							:					List[Any]):


           '''simple docstring'''

           self.config_tester.run_common_tests()



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


           '''simple docstring'''

           pass



       def 		_lowerCamelCase      (		self							:					Tuple):


           '''simple docstring'''

           __a	,       __a							=    self.model_tester.prepare_config_and_inputs_for_common()

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



       def 		_lowerCamelCase      (		self							:					Union[str, Any]):


           '''simple docstring'''

           __a	,       __a							=    self.model_tester.prepare_config_and_inputs_for_common()

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

               __a							=    ['''pixel_values''']
               self.assertListEqual(arg_names[:1]					,		__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					Optional[Any]):


           '''simple docstring'''

           __a							=    self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					List[Any]):


           '''simple docstring'''

           __a							=    self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*__SCREAMING_SNAKE_CASE)



       @slow
       def 		_lowerCamelCase      (		self							:					int):


           '''simple docstring'''

           for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               __a							=    SwiftFormerModel.from_pretrained(__SCREAMING_SNAKE_CASE)
               self.assertIsNotNone(__SCREAMING_SNAKE_CASE)



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


           '''simple docstring'''

           pass



       def 		_lowerCamelCase      (		self							:					Tuple):


           '''simple docstring'''

           def check_hidden_states_output(__SCREAMING_SNAKE_CASE							:					Optional[int]					,		__SCREAMING_SNAKE_CASE							:					Tuple					,		__SCREAMING_SNAKE_CASE							:					int):
               __a							=    model_class(__SCREAMING_SNAKE_CASE)
               model.to(__SCREAMING_SNAKE_CASE)
               model.eval()

               with torch.no_grad():
                   __a							=    model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE))

               __a							=    outputs.hidden_states

               __a							=    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),
                           ])					,		)

           __a	,       __a							=    self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               __a							=    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"]
               __a							=    True

               check_hidden_states_output(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase      (		self							:					Optional[Any]):


           '''simple docstring'''

           def _config_zero_init(__SCREAMING_SNAKE_CASE							:					List[str]):
               __a							=    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-10)
                   if isinstance(getattr(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)					,		__SCREAMING_SNAKE_CASE):
                       __a							=    _config_zero_init(getattr(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE))
                       setattr(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)
               return configs_no_init

           __a	,       __a							=    self.model_tester.prepare_config_and_inputs_for_common()

           __a							=    _config_zero_init(__SCREAMING_SNAKE_CASE)
           for model_class in self.all_model_classes:
               __a							=    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 		_lowerCamelCase      (		self							:					int):


           '''simple docstring'''

           pass





def 					__snake_case     (      ):
    __a							=    Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''    )
    return image

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



       @cached_property
       def 		_lowerCamelCase      (		self							:					int):


           '''simple docstring'''

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





       @slow
       def 		_lowerCamelCase      (		self							:					List[Any]):


           '''simple docstring'''

           __a							=    SwiftFormerForImageClassification.from_pretrained('''MBZUAI/swiftformer-xs''').to(__SCREAMING_SNAKE_CASE)

           __a							=    self.default_image_processor
           __a							=    prepare_img()
           __a							=    image_processor(images=__SCREAMING_SNAKE_CASE					,		return_tensors='''pt''').to(__SCREAMING_SNAKE_CASE)

           # forward pass
           with torch.no_grad():
               __a							=    model(**__SCREAMING_SNAKE_CASE)

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

           __a							=    torch.tensor([[-2.1703E00, 2.1107E00, -2.0811E00]]).to(__SCREAMING_SNAKE_CASE)
           self.assertTrue(torch.allclose(outputs.logits[0, :3]					,		__SCREAMING_SNAKE_CASE					,		atol=1E-4))










def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase    ):

    def get_matched_characters(_UpperCAmelCase       ,			_UpperCAmelCase    ) -> str:
        __a							=    []
        __a							=    min(len(_stra    )       ,			len(_stra    )    ) // 2
        for i, l in enumerate(_stra    ):
            __a							=    int(max(0       ,			i - limit    )    )
            __a							=    int(min(i + limit + 1       ,			len(_stra    )    )    )
            if l in _stra[left:right]:
                matched.append(_UpperCAmelCase    )
                __a							=    f'{_stra[0:_stra.index(_UpperCAmelCase    )]} {_stra[_stra.index(_UpperCAmelCase    ) + 1:]}'

        return "".join(_UpperCAmelCase    )

    # matching characters
    __a							=    get_matched_characters(_UpperCAmelCase       ,			_UpperCAmelCase    )
    __a							=    get_matched_characters(_UpperCAmelCase       ,			_UpperCAmelCase    )
    __a							=    len(_UpperCAmelCase    )

    # transposition
    __a							=    (
        len([(ca, ca) for ca, ca in zip(_UpperCAmelCase       ,			_UpperCAmelCase    ) if ca != ca]    ) // 2
    )

    if not match_count:
        __a							=    0.0
    else:
        __a							=    (
            1
            / 3
            * (
                match_count / len(_UpperCAmelCase    )
                + match_count / len(_UpperCAmelCase    )
                + (match_count - transpositions) / match_count
            )
        )

    # common prefix up to 4 characters
    __a							=    0
    for ca, ca in zip(stra[:4]       ,			stra[:4]    ):
        if ca == ca:
            prefix_len += 1
        else:
            break

    return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 print(jaro_winkler('''hello''', '''world'''))





