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 , __lowercase , __lowercase=13 , __lowercase=3 , __lowercase=True , __lowercase=True , __lowercase=0.1 , __lowercase=0.1 , __lowercase=224 , __lowercase=1_000 , __lowercase=[3, 3, 6, 4] , __lowercase=[48, 56, 112, 220] , )       ->	Optional[int]:
        __UpperCamelCase     :str			  =		parent
        __UpperCamelCase     :List[Any]			  =		batch_size
        __UpperCamelCase     :List[str]			  =		num_channels
        __UpperCamelCase     :Any			  =		is_training
        __UpperCamelCase     :List[Any]			  =		use_labels
        __UpperCamelCase     :int			  =		hidden_dropout_prob
        __UpperCamelCase     :List[str]			  =		attention_probs_dropout_prob
        __UpperCamelCase     :str			  =		num_labels
        __UpperCamelCase     :List[Any]			  =		image_size
        __UpperCamelCase     :Dict			  =		layer_depths
        __UpperCamelCase     :int			  =		embed_dims







    def 			UpperCamelCase__   (	self)       ->	List[Any]:
        __UpperCamelCase     :Tuple			  =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

        __UpperCamelCase     :List[Any]			  =		None
        if self.use_labels:
            __UpperCamelCase     :str			  =		ids_tensor([self.batch_size] , self.num_labels)

        __UpperCamelCase     :Optional[Any]			  =		self.get_config()

        return config, pixel_values, labels







    def 			UpperCamelCase__   (	self)       ->	Tuple:
        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 			UpperCamelCase__   (	self , __lowercase , __lowercase , __lowercase)       ->	str:
        __UpperCamelCase     :Union[str, Any]			  =		SwiftFormerModel(config=_SCREAMING_SNAKE_CASE)
        model.to(_SCREAMING_SNAKE_CASE)
        model.eval()
        __UpperCamelCase     :List[Any]			  =		model(_SCREAMING_SNAKE_CASE)
        self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.embed_dims[-1], 7, 7))







    def 			UpperCamelCase__   (	self , __lowercase , __lowercase , __lowercase)       ->	List[str]:
        __UpperCamelCase     :Optional[Any]			  =		self.num_labels
        __UpperCamelCase     :Tuple			  =		SwiftFormerForImageClassification(_SCREAMING_SNAKE_CASE)
        model.to(_SCREAMING_SNAKE_CASE)
        model.eval()
        __UpperCamelCase     :Optional[Any]			  =		model(_SCREAMING_SNAKE_CASE , labels=_SCREAMING_SNAKE_CASE)
        self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels))

        __UpperCamelCase     :str			  =		SwiftFormerForImageClassification(_SCREAMING_SNAKE_CASE)
        model.to(_SCREAMING_SNAKE_CASE)
        model.eval()

        __UpperCamelCase     :int			  =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
        __UpperCamelCase     :Dict			  =		model(_SCREAMING_SNAKE_CASE)
        self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels))







    def 			UpperCamelCase__   (	self)       ->	Any:
        (__UpperCamelCase)     :Optional[int]			  =		self.prepare_config_and_inputs()
        __UpperCamelCase     :str			  =		{'''pixel_values''': pixel_values}
        return config, inputs_dict


@require_torch
class 	lowerCamelCase_     (   UpperCAmelCase_						,	UpperCAmelCase_						,	unittest.TestCase ):






    '''simple docstring'''






    a__	:						Optional[Any]						=      (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
    a__	:						List[str]						=      (
        {"""feature-extraction""": SwiftFormerModel, """image-classification""": SwiftFormerForImageClassification}
        if is_torch_available()
        else {}
    )

    a__	:						Any						=      False
    a__	:						Optional[int]						=      False
    a__	:						Any						=      False
    a__	:						Union[str, Any]						=      False
    a__	:						Dict						=      False







    def 			UpperCamelCase__   (	self)       ->	Any:
        __UpperCamelCase     :Optional[int]			  =		SwiftFormerModelTester(self)
        __UpperCamelCase     :Dict			  =		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 			UpperCamelCase__   (	self)       ->	int:
        self.config_tester.run_common_tests()







    @unittest.skip(reason='''SwiftFormer does not use inputs_embeds''')
    def 			UpperCamelCase__   (	self)       ->	str:
        pass







    def 			UpperCamelCase__   (	self)       ->	Optional[int]:
        __UpperCamelCase     :int			  =		self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            __UpperCamelCase     :List[str]			  =		model_class(_SCREAMING_SNAKE_CASE)
            __UpperCamelCase     :Dict			  =		model.get_output_embeddings()
            self.assertTrue(x is None or isinstance(_SCREAMING_SNAKE_CASE , nn.Linear))







    def 			UpperCamelCase__   (	self)       ->	Any:
        __UpperCamelCase     :Any			  =		self.model_tester.prepare_config_and_inputs_for_common()

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

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







    def 			UpperCamelCase__   (	self)       ->	Optional[int]:
        __UpperCamelCase     :List[str]			  =		self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE)







    def 			UpperCamelCase__   (	self)       ->	Dict:
        __UpperCamelCase     :Dict			  =		self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE)







    @slow
    def 			UpperCamelCase__   (	self)       ->	int:





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







    @unittest.skip(reason='''SwiftFormer does not output attentions''')
    def 			UpperCamelCase__   (	self)       ->	Union[str, Any]:
        pass







    def 			UpperCamelCase__   (	self)       ->	Any:





        def check_hidden_states_output(__lowercase , __lowercase , __lowercase):
            __UpperCamelCase     :Tuple			  =		model_class(_SCREAMING_SNAKE_CASE)
            model.to(_SCREAMING_SNAKE_CASE)
            model.eval()

            with torch.no_grad():
                __UpperCamelCase     :Dict			  =		model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE))

            __UpperCamelCase     :List[str]			  =		outputs.hidden_states

            __UpperCamelCase     :Optional[int]			  =		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),
                        ]) , )

        __UpperCamelCase     :Tuple			  =		self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            __UpperCamelCase     :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"]
            __UpperCamelCase     :Optional[int]			  =		True

            check_hidden_states_output(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE)







    def 			UpperCamelCase__   (	self)       ->	List[Any]:





        def _config_zero_init(__lowercase):
            __UpperCamelCase     :Optional[Any]			  =		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):
                    __UpperCamelCase     :str			  =		_config_zero_init(getattr(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE))
                    setattr(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE)
            return configs_no_init

        __UpperCamelCase     :Tuple			  =		self.model_tester.prepare_config_and_inputs_for_common()

        __UpperCamelCase     :int			  =		_config_zero_init(_SCREAMING_SNAKE_CASE)
        for model_class in self.all_model_classes:
            __UpperCamelCase     :Optional[Any]			  =		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 			UpperCamelCase__   (	self)       ->	str:
        pass



def lowerCamelCase  (   ):
    '''simple docstring'''







    __UpperCamelCase     :Optional[Any]			  =		Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''	)
    return image


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






    '''simple docstring'''






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







    @slow
    def 			UpperCamelCase__   (	self)       ->	Optional[Any]:
        __UpperCamelCase     :Tuple			  =		SwiftFormerForImageClassification.from_pretrained('''MBZUAI/swiftformer-xs''').to(_SCREAMING_SNAKE_CASE)

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

        # forward pass
        with torch.no_grad():
            __UpperCamelCase     :int			  =		model(**_SCREAMING_SNAKE_CASE)

        # verify the logits
        __UpperCamelCase     :Dict			  =		torch.Size((1, 1_000))
        self.assertEqual(outputs.logits.shape , _SCREAMING_SNAKE_CASE)

        __UpperCamelCase     :Dict			  =		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))









import inspect
import warnings
from typing import Any, Dict, Optional, Union

from packaging import version


def   _SCREAMING_SNAKE_CASE	( *SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE = None ,			SCREAMING_SNAKE_CASE=True ,			SCREAMING_SNAKE_CASE=2       ):
  from .. import __version__

  A_      :				Union[str, Any]		     =					take_from
  A_      :				Optional[Any]		     =					()
  if not isinstance(args[0] ,			SCREAMING_SNAKE_CASE       ):
    A_      :				List[str]		     =					(args,)

  for attribute, version_name, message in args:
    if version.parse(version.parse(SCREAMING_SNAKE_CASE       ).base_version       ) >= version.parse(SCREAMING_SNAKE_CASE       ):
      raise ValueError(
          f'''The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers\''''
          f''' version {__version__} is >= {version_name}'''       )

    A_      :				List[Any]		     =					None
    if isinstance(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ) and attribute in deprecated_kwargs:
      values += (deprecated_kwargs.pop(SCREAMING_SNAKE_CASE       ),)
      A_      :				Optional[Any]		     =					f'''The `{attribute}` argument is deprecated and will be removed in version {version_name}.'''
    elif hasattr(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ):
      values += (getattr(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ),)
      A_      :				int		     =					f'''The `{attribute}` attribute is deprecated and will be removed in version {version_name}.'''
    elif deprecated_kwargs is None:
      A_      :				List[Any]		     =					f'''`{attribute}` is deprecated and will be removed in version {version_name}.'''

    if warning is not None:
      A_      :				Union[str, Any]		     =					warning + ''' ''' if standard_warn else ''''''
      warnings.warn(warning + message ,			SCREAMING_SNAKE_CASE ,			stacklevel=SCREAMING_SNAKE_CASE       )

  if isinstance(SCREAMING_SNAKE_CASE ,			SCREAMING_SNAKE_CASE       ) and len(SCREAMING_SNAKE_CASE       ) > 0:
    A_      :				Dict		     =					inspect.getouterframes(inspect.currentframe()       )[1]
    A_      :				Optional[int]		     =					call_frame.filename
    A_      :				Optional[int]		     =					call_frame.lineno
    A_      :				str		     =					call_frame.function
    A_     ,       A_      :				List[str]		     =					next(iter(deprecated_kwargs.items()       )       )
    raise TypeError(f'''{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`'''       )

  if len(SCREAMING_SNAKE_CASE       ) == 0:
    return
  elif len(SCREAMING_SNAKE_CASE       ) == 1:
    return values[0]
  return values




