





'''simple docstring'''




import inspect
import unittest

from transformers import BitConfig
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_backbone_common import BackboneTesterMixin
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 BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
  from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image


class 			SCREAMING_SNAKE_CASE   :


     def __init__(						self						,     _UpperCAmelCase						,     _UpperCAmelCase=3						,     _UpperCAmelCase=32						,     _UpperCAmelCase=3						,     _UpperCAmelCase=10						,     _UpperCAmelCase=[8, 16, 32, 64]						,     _UpperCAmelCase=[1, 1, 2, 1]						,     _UpperCAmelCase=True						,     _UpperCAmelCase=True						,     _UpperCAmelCase="relu"						,     _UpperCAmelCase=3						,     _UpperCAmelCase=None						,     _UpperCAmelCase=["stage2", "stage3", "stage4"]						,     _UpperCAmelCase=[2, 3, 4]						,     _UpperCAmelCase=1						,     ):

          '''simple docstring'''






          __A						:			List[Any]								=	parent
          __A						:			str								=	batch_size
          __A						:			int								=	image_size
          __A						:			Optional[int]								=	num_channels
          __A						:			Optional[Any]								=	embeddings_size
          __A						:			Optional[Any]								=	hidden_sizes
          __A						:			int								=	depths
          __A						:			Union[str, Any]								=	is_training
          __A						:			Optional[Any]								=	use_labels
          __A						:			List[Any]								=	hidden_act
          __A						:			Dict								=	num_labels
          __A						:			Optional[Any]								=	scope
          __A						:			List[Any]								=	len(_UpperCAmelCase)
          __A						:			Any								=	out_features
          __A						:			Optional[int]								=	out_indices
          __A						:			Any								=	num_groups


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			List[str]								=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

          __A						:			List[Any]								=	self.get_config()

          return config, pixel_values, labels


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          return BitConfig(
              num_channels=self.num_channels						,     embeddings_size=self.embeddings_size						,     hidden_sizes=self.hidden_sizes						,     depths=self.depths						,     hidden_act=self.hidden_act						,     num_labels=self.num_labels						,     out_features=self.out_features						,     out_indices=self.out_indices						,     num_groups=self.num_groups						,     )


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase):

          '''simple docstring'''






          __A						:			List[str]								=	BitModel(config=_UpperCAmelCase)
          model.to(_UpperCAmelCase)
          model.eval()
          __A						:			Optional[int]								=	model(_UpperCAmelCase)
          self.parent.assertEqual(
              result.last_hidden_state.shape						,     (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)						,     )


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase):

          '''simple docstring'''






          __A						:			Optional[Any]								=	self.num_labels
          __A						:			str								=	BitForImageClassification(_UpperCAmelCase)
          model.to(_UpperCAmelCase)
          model.eval()
          __A						:			str								=	model(_UpperCAmelCase						,     labels=_UpperCAmelCase)
          self.parent.assertEqual(result.logits.shape						,     (self.batch_size, self.num_labels))


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase):

          '''simple docstring'''






          __A						:			Tuple								=	BitBackbone(config=_UpperCAmelCase)
          model.to(_UpperCAmelCase)
          model.eval()
          __A						:			Union[str, Any]								=	model(_UpperCAmelCase)

          # verify feature maps
          self.parent.assertEqual(len(result.feature_maps)						,     len(config.out_features))
          self.parent.assertListEqual(list(result.feature_maps[0].shape)						,     [self.batch_size, self.hidden_sizes[1], 4, 4])

          # verify channels
          self.parent.assertEqual(len(model.channels)						,     len(config.out_features))
          self.parent.assertListEqual(model.channels						,     config.hidden_sizes[1:])

          # verify backbone works with out_features=None
          __A						:			Dict								=	None
          __A						:			List[str]								=	BitBackbone(config=_UpperCAmelCase)
          model.to(_UpperCAmelCase)
          model.eval()
          __A						:			Union[str, Any]								=	model(_UpperCAmelCase)

          # verify feature maps
          self.parent.assertEqual(len(result.feature_maps)						,     1)
          self.parent.assertListEqual(list(result.feature_maps[0].shape)						,     [self.batch_size, self.hidden_sizes[-1], 1, 1])

          # verify channels
          self.parent.assertEqual(len(model.channels)						,     1)
          self.parent.assertListEqual(model.channels						,     [config.hidden_sizes[-1]])


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Optional[Any]								=	self.prepare_config_and_inputs()
          __A			,__A			,__A						:			Optional[int]								=	config_and_inputs
          __A						:			List[Any]								=	{'pixel_values': pixel_values}
          return config, inputs_dict




@require_torch
class 			SCREAMING_SNAKE_CASE   (a__ ,		a__ ,		unittest.TestCase							):
     lowerCAmelCase		      =	(BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
     lowerCAmelCase		      =	(
         {'''feature-extraction''': BitModel, '''image-classification''': BitForImageClassification}
         if is_torch_available()
         else {}
     )

     lowerCAmelCase		      =	False
     lowerCAmelCase		      =	False
     lowerCAmelCase		      =	False
     lowerCAmelCase		      =	False
     lowerCAmelCase		      =	False


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Optional[int]								=	BitModelTester(self)
          __A						:			Any								=	ConfigTester(self						,     config_class=_UpperCAmelCase						,     has_text_modality=_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          self.create_and_test_config_common_properties()
          self.config_tester.create_and_test_config_to_json_string()
          self.config_tester.create_and_test_config_to_json_file()
          self.config_tester.create_and_test_config_from_and_save_pretrained()
          self.config_tester.create_and_test_config_with_num_labels()
          self.config_tester.check_config_can_be_init_without_params()
          self.config_tester.check_config_arguments_init()


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          return


     @unittest.skip(reason='Bit does not output attentions')
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          pass


     @unittest.skip(reason='Bit does not use inputs_embeds')
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          pass


     @unittest.skip(reason='Bit does not support input and output embeddings')
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          pass


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A			,__A						:			str								=	self.model_tester.prepare_config_and_inputs_for_common()

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

               __A						:			Tuple								=	['pixel_values']
               self.assertListEqual(arg_names[:1]						,     _UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Union[str, Any]								=	self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Optional[Any]								=	self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_backbone(*_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A			,__A						:			List[Any]								=	self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
               __A						:			Tuple								=	model_class(config=_UpperCAmelCase)
               for name, module in model.named_modules():
                    if isinstance(_UpperCAmelCase						,     (nn.BatchNormad, nn.GroupNorm)):
                         self.assertTrue(
                             torch.all(module.weight == 1)						,     msg=F'Parameter {name} of model {model_class} seems not properly initialized'						,     )
                         self.assertTrue(
                             torch.all(module.bias == 0)						,     msg=F'Parameter {name} of model {model_class} seems not properly initialized'						,     )


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          def check_hidden_states_output(_UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase):
               __A						:			Union[str, Any]								=	model_class(_UpperCAmelCase)
               model.to(_UpperCAmelCase)
               model.eval()

               with torch.no_grad():
                    __A						:			List[str]								=	model(**self._prepare_for_class(_UpperCAmelCase						,     _UpperCAmelCase))

               __A						:			Optional[int]								=	outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

               __A						:			Any								=	self.model_tester.num_stages
               self.assertEqual(len(_UpperCAmelCase)						,     expected_num_stages + 1)

               # Bit's feature maps are of shape (batch_size, num_channels, height, width)
               self.assertListEqual(
                   list(hidden_states[0].shape[-2:])						,     [self.model_tester.image_size // 4, self.model_tester.image_size // 4]						,     )

          __A			,__A						:			Any								=	self.model_tester.prepare_config_and_inputs_for_common()
          __A						:			int								=	['preactivation', 'bottleneck']
          for model_class in self.all_model_classes:
               for layer_type in layers_type:
                    __A						:			List[str]								=	layer_type
                    __A						:			Any								=	True
                    check_hidden_states_output(_UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase)

                    # check that output_hidden_states also work using config
                    del inputs_dict["output_hidden_states"]
                    __A						:			Dict								=	True

                    check_hidden_states_output(_UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase)


     @unittest.skip(reason='Bit does not use feedforward chunking')
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          pass


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Optional[Any]								=	self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase)


     @slow
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               __A						:			Union[str, Any]								=	BitModel.from_pretrained(_UpperCAmelCase)
               self.assertIsNotNone(_UpperCAmelCase)






def 	_lowerCAmelCase      (  )					->					Any:
     __A						:			int								=	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
     return image


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


     @cached_property
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          return (
              BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]) if is_vision_available() else None
          )


     @slow
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Optional[Any]								=	BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(_UpperCAmelCase)

          __A						:			Union[str, Any]								=	self.default_image_processor
          __A						:			Optional[int]								=	prepare_img()
          __A						:			List[Any]								=	image_processor(images=_UpperCAmelCase						,     return_tensors='pt').to(_UpperCAmelCase)

          # forward pass
          with torch.no_grad():
               __A						:			Tuple								=	model(**_UpperCAmelCase)

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

          __A						:			Dict								=	torch.tensor([[-0.6526, -0.5263, -1.4398]]).to(_UpperCAmelCase)

          self.assertTrue(torch.allclose(outputs.logits[0, :3]						,     _UpperCAmelCase						,     atol=1e-4))




@require_torch
class 			SCREAMING_SNAKE_CASE   (a__ ,		unittest.TestCase							):
     lowerCAmelCase		      =	(BitBackbone,) if is_torch_available() else ()
     lowerCAmelCase		      =	BitConfig

     lowerCAmelCase		      =	False


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			str								=	BitModelTester(self)






'''simple docstring'''




import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image


if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('''9.1.0'''):
  lowercase__   :				Dict      =   {
      '''linear''': PIL.Image.Resampling.BILINEAR,
      '''bilinear''': PIL.Image.Resampling.BILINEAR,
      '''bicubic''': PIL.Image.Resampling.BICUBIC,
      '''lanczos''': PIL.Image.Resampling.LANCZOS,
      '''nearest''': PIL.Image.Resampling.NEAREST,
  }
else:
  lowercase__   :				Any      =   {
      '''linear''': PIL.Image.LINEAR,
      '''bilinear''': PIL.Image.BILINEAR,
      '''bicubic''': PIL.Image.BICUBIC,
      '''lanczos''': PIL.Image.LANCZOS,
      '''nearest''': PIL.Image.NEAREST,
  }






def 	_lowerCAmelCase      (  __snake_case  :			Any      )					->					Optional[Any]:
     __A						:			Dict								=	(images / 2 + 0.5).clamp(0			,							1      )
     __A						:			str								=	images.cpu().permute(0			,							2			,							3			,							1      ).float().numpy()
     __A						:			Dict								=	numpy_to_pil(__snake_case      )
     return images







def 	_lowerCAmelCase      (  __snake_case  :			List[Any]      )					->					Optional[Any]:
     if images.ndim == 3:
          __A						:			List[Any]								=	images[None, ...]
     __A						:			List[str]								=	(images * 2_55).round().astype('uint8'      )
     if images.shape[-1] == 1:
          # special case for grayscale (single channel) images
          __A						:			str								=	[Image.fromarray(image.squeeze()			,							mode='L'      ) for image in images]
     else:
          __A						:			str								=	[Image.fromarray(__snake_case      ) for image in images]

     return pil_images