


'''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      a    :


     """simple docstring"""






     def __init__(    self					:		int							,			snake_case					:		str							,			snake_case					:		Optional[Any]=13							,			snake_case					:		Union[str, Any]=3							,			snake_case					:		List[Any]=True							,			snake_case					:		Optional[Any]=True							,			snake_case					:		List[str]=0.1							,			snake_case					:		List[str]=0.1							,			snake_case					:		List[Any]=224							,			snake_case					:		int=1000							,			snake_case					:		str=[3, 3, 6, 4]							,			snake_case					:		List[str]=[48, 56, 112, 220]							,			)						->							Any:
          __UpperCAmelCase     :			Optional[int]  							=      parent
          __UpperCAmelCase     :			Dict  							=      batch_size
          __UpperCAmelCase     :			Any  							=      num_channels
          __UpperCAmelCase     :			Dict  							=      is_training
          __UpperCAmelCase     :			Tuple  							=      use_labels
          __UpperCAmelCase     :			str  							=      hidden_dropout_prob
          __UpperCAmelCase     :			Optional[Any]  							=      attention_probs_dropout_prob
          __UpperCAmelCase     :			List[str]  							=      num_labels
          __UpperCAmelCase     :			List[Any]  							=      image_size
          __UpperCAmelCase     :			List[Any]  							=      layer_depths
          __UpperCAmelCase     :			List[str]  							=      embed_dims

     def 			lowerCamelCase__			(    self					:		Union[str, Any]      )						->							List[Any]:
          __UpperCAmelCase     :			List[Any]  							=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

          __UpperCAmelCase     :			Dict  							=      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 			lowerCamelCase__			(    self					:		Any      )						->							int:
          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=snake_case							,			layer_scale_init_value=1E-5							,			)

     def 			lowerCamelCase__			(    self					:		Optional[Any]							,			snake_case					:		Tuple							,			snake_case					:		Optional[int]							,			snake_case					:		List[Any]      )						->							Any:
          __UpperCAmelCase     :			int  							=      SwiftFormerModel(config=snake_case      )
          model.to(snake_case      )
          model.eval()
          __UpperCAmelCase     :			int  							=      model(snake_case      )
          self.parent.assertEqual(result.last_hidden_state.shape							,			(self.batch_size, self.embed_dims[-1], 7, 7)      )

     def 			lowerCamelCase__			(    self					:		Any							,			snake_case					:		Any							,			snake_case					:		Any							,			snake_case					:		List[Any]      )						->							Tuple:
          __UpperCAmelCase     :			List[str]  							=      self.num_labels
          __UpperCAmelCase     :			Optional[int]  							=      SwiftFormerForImageClassification(snake_case      )
          model.to(snake_case      )
          model.eval()
          __UpperCAmelCase     :			Optional[int]  							=      model(snake_case							,			labels=snake_case      )
          self.parent.assertEqual(result.logits.shape							,			(self.batch_size, self.num_labels)      )

          __UpperCAmelCase     :			str  							=      SwiftFormerForImageClassification(snake_case      )
          model.to(snake_case      )
          model.eval()

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






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



@require_torch
class      a    (		_a     ,  _a     ,  unittest.TestCase	):


     """simple docstring"""






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

     SCREAMING_SNAKE_CASE	:			Dict							       =       False
     SCREAMING_SNAKE_CASE	:			Optional[Any]							       =       False
     SCREAMING_SNAKE_CASE	:			Union[str, Any]							       =       False
     SCREAMING_SNAKE_CASE	:			Any							       =       False
     SCREAMING_SNAKE_CASE	:			Dict							       =       False

     def 			lowerCamelCase__			(    self					:		Dict      )						->							Any:
          __UpperCAmelCase     :			Dict  							=      SwiftFormerModelTester(self      )
          __UpperCAmelCase     :			List[Any]  							=      ConfigTester(
              self							,			config_class=snake_case							,			has_text_modality=snake_case							,			hidden_size=37							,			num_attention_heads=12							,			num_hidden_layers=12							,			)

     def 			lowerCamelCase__			(    self					:		int      )						->							Optional[Any]:
          self.config_tester.run_common_tests()

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

     def 			lowerCamelCase__			(    self					:		Tuple      )						->							List[Any]:
          __UpperCAmelCase     :			Dict  							=      self.model_tester.prepare_config_and_inputs_for_common()

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

     def 			lowerCamelCase__			(    self					:		Optional[Any]      )						->							Dict:
          __UpperCAmelCase     :			List[str]  							=      self.model_tester.prepare_config_and_inputs_for_common()

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

               __UpperCAmelCase     :			Optional[int]  							=      ['''pixel_values''']
               self.assertListEqual(arg_names[:1]							,			snake_case      )

     def 			lowerCamelCase__			(    self					:		str      )						->							List[str]:
          __UpperCAmelCase     :			List[str]  							=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*snake_case      )

     def 			lowerCamelCase__			(    self					:		int      )						->							Tuple:
          __UpperCAmelCase     :			Any  							=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*snake_case      )

     @slow
     def 			lowerCamelCase__			(    self					:		Optional[int]      )						->							int:
          for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               __UpperCAmelCase     :			List[Any]  							=      SwiftFormerModel.from_pretrained(snake_case      )
               self.assertIsNotNone(snake_case      )

     @unittest.skip(reason='''SwiftFormer does not output attentions'''      )
     def 			lowerCamelCase__			(    self					:		int      )						->							List[str]:
          pass

     def 			lowerCamelCase__			(    self					:		Tuple      )						->							Dict:
          def check_hidden_states_output(snake_case					:		Tuple							,			snake_case					:		Dict							,			snake_case					:		Any      ):
               __UpperCAmelCase     :			Optional[Any]  							=      model_class(snake_case      )
               model.to(snake_case      )
               model.eval()

               with torch.no_grad():
                    __UpperCAmelCase     :			Any  							=      model(**self._prepare_for_class(snake_case							,			snake_case      )      )

               __UpperCAmelCase     :			Tuple  							=      outputs.hidden_states

               __UpperCAmelCase     :			Dict  							=      8
               self.assertEqual(len(snake_case      )							,			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(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     :			Optional[Any]  							=      self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
               __UpperCAmelCase     :			Any  							=      True
               check_hidden_states_output(snake_case							,			snake_case							,			snake_case      )

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

               check_hidden_states_output(snake_case							,			snake_case							,			snake_case      )

     def 			lowerCamelCase__			(    self					:		Any      )						->							Tuple:
          def _config_zero_init(snake_case					:		Optional[int]      ):
               __UpperCAmelCase     :			Tuple  							=      copy.deepcopy(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(snake_case							,			snake_case							,			1E-10      )
                    if isinstance(getattr(snake_case							,			snake_case							,			snake_case      )							,			snake_case      ):
                         __UpperCAmelCase     :			Optional[Any]  							=      _config_zero_init(getattr(snake_case							,			snake_case      )      )
                         setattr(snake_case							,			snake_case							,			snake_case      )
               return configs_no_init

          __UpperCAmelCase     :			Dict  							=      self.model_tester.prepare_config_and_inputs_for_common()

          __UpperCAmelCase     :			str  							=      _config_zero_init(snake_case      )
          for model_class in self.all_model_classes:
               __UpperCAmelCase     :			int  							=      model_class(config=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					:		Tuple      )						->							Dict:
          pass




def      _a     (						):





     '''simple docstring'''






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



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


     """simple docstring"""






     @cached_property
     def 			lowerCamelCase__			(    self					:		Union[str, Any]      )						->							Tuple:
          return ViTImageProcessor.from_pretrained('''MBZUAI/swiftformer-xs'''      ) if is_vision_available() else None






     @slow
     def 			lowerCamelCase__			(    self					:		Dict      )						->							Optional[Any]:
          __UpperCAmelCase     :			List[str]  							=      SwiftFormerForImageClassification.from_pretrained('''MBZUAI/swiftformer-xs'''      ).to(snake_case      )

          __UpperCAmelCase     :			str  							=      self.default_image_processor
          __UpperCAmelCase     :			Dict  							=      prepare_img()
          __UpperCAmelCase     :			Tuple  							=      image_processor(images=snake_case							,			return_tensors='''pt'''      ).to(snake_case      )

          # forward pass
          with torch.no_grad():
               __UpperCAmelCase     :			List[Any]  							=      model(**snake_case      )

          # verify the logits
          __UpperCAmelCase     :			Dict  							=      torch.Size((1, 1000)      )
          self.assertEqual(outputs.logits.shape							,			snake_case      )

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



'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__UpperCAmelCase      :List[Any]			    =       {
    "configuration_lxmert": ["LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LxmertConfig"],
    "tokenization_lxmert": ["LxmertTokenizer"],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase      :List[Any]			    =       ["LxmertTokenizerFast"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase      :List[Any]			    =       [
          "LxmertEncoder",
          "LxmertForPreTraining",
          "LxmertForQuestionAnswering",
          "LxmertModel",
          "LxmertPreTrainedModel",
          "LxmertVisualFeatureEncoder",
          "LxmertXLayer",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase      :List[str]			    =       [
          "TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFLxmertForPreTraining",
          "TFLxmertMainLayer",
          "TFLxmertModel",
          "TFLxmertPreTrainedModel",
          "TFLxmertVisualFeatureEncoder",
      ]


if TYPE_CHECKING:
      from .configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig
      from .tokenization_lxmert import LxmertTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_lxmert_fast import LxmertTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_lxmert import (
                LxmertEncoder,
                LxmertForPreTraining,
                LxmertForQuestionAnswering,
                LxmertModel,
                LxmertPreTrainedModel,
                LxmertVisualFeatureEncoder,
                LxmertXLayer,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_lxmert import (
                TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFLxmertForPreTraining,
                TFLxmertMainLayer,
                TFLxmertModel,
                TFLxmertPreTrainedModel,
                TFLxmertVisualFeatureEncoder,
            )

else:
      import sys

      __UpperCAmelCase      :int			    =       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)