





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  A__    :




   """simple docstring"""



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







          '''simple docstring'''

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

   def 							__lowercase		(					self)			->						str:







          '''simple docstring'''

          a__						:	Optional[Any] =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

          a__						:	Optional[Any] =				None
          if self.use_labels:
                 a__						:	Tuple =				ids_tensor([self.batch_size]					,  self.num_labels)

          a__						:	Optional[int] =				self.get_config()

          return config, pixel_values, labels

   def 							__lowercase		(					self)			->						str:







          '''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 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase)			->						List[str]:







          '''simple docstring'''

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

   def 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase)			->						str:







          '''simple docstring'''

          a__						:	Union[str, Any] =				self.num_labels
          a__						:	Union[str, Any] =				BitForImageClassification(lowercase)
          model.to(lowercase)
          model.eval()
          a__						:	List[Any] =				model(lowercase					,  labels=lowercase)
          self.parent.assertEqual(result.logits.shape					,  (self.batch_size, self.num_labels))

   def 							__lowercase		(					self					,  lowercase					,  lowercase					,  lowercase)			->						Union[str, Any]:







          '''simple docstring'''

          a__						:	List[Any] =				BitBackbone(config=lowercase)
          model.to(lowercase)
          model.eval()
          a__						:	List[str] =				model(lowercase)

          # 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__						:	Union[str, Any] =				None
          a__						:	List[Any] =				BitBackbone(config=lowercase)
          model.to(lowercase)
          model.eval()
          a__						:	Optional[int] =				model(lowercase)

          # 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 							__lowercase		(					self)			->						Optional[int]:







          '''simple docstring'''

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





@require_torch
class  A__    (							__UpperCAmelCase ,  __UpperCAmelCase ,  unittest.TestCase	):




   """simple docstring"""



   __A   :				Union[str, Any]         =  (BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
   __A   :				List[Any]         =  (
       {'''feature-extraction''': BitModel, '''image-classification''': BitForImageClassification}
       if is_torch_available()
       else {}
   )

   __A   :				Optional[Any]         =  False
   __A   :				int         =  False
   __A   :				Optional[int]         =  False
   __A   :				str         =  False
   __A   :				Optional[Any]         =  False

   def 							__lowercase		(					self)			->						Optional[int]:







          '''simple docstring'''

          a__						:	List[str] =				BitModelTester(self)
          a__						:	int =				ConfigTester(self					,  config_class=lowercase					,  has_text_modality=lowercase)

   def 							__lowercase		(					self)			->						Union[str, Any]:







          '''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 							__lowercase		(					self)			->						Union[str, Any]:







          '''simple docstring'''

          return

   @unittest.skip(reason='Bit does not output attentions')
   def 							__lowercase		(					self)			->						int:







          '''simple docstring'''

          pass

   @unittest.skip(reason='Bit does not use inputs_embeds')
   def 							__lowercase		(					self)			->						List[str]:







          '''simple docstring'''

          pass

   @unittest.skip(reason='Bit does not support input and output embeddings')
   def 							__lowercase		(					self)			->						Optional[int]:







          '''simple docstring'''

          pass

   def 							__lowercase		(					self)			->						Optional[Any]:







          '''simple docstring'''

          a__ ,							a__						:	Tuple =				self.model_tester.prepare_config_and_inputs_for_common()

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

                 a__						:	Any =				['pixel_values']
                 self.assertListEqual(arg_names[:1]					,  lowercase)

   def 							__lowercase		(					self)			->						List[str]:







          '''simple docstring'''

          a__						:	List[str] =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*lowercase)

   def 							__lowercase		(					self)			->						Any:







          '''simple docstring'''

          a__						:	List[str] =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_backbone(*lowercase)

   def 							__lowercase		(					self)			->						Optional[int]:







          '''simple docstring'''

          a__ ,							a__						:	List[str] =				self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
                 a__						:	Union[str, Any] =				model_class(config=lowercase)
                 for name, module in model.named_modules():
                        if isinstance(lowercase					,  (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 							__lowercase		(					self)			->						Optional[Any]:







          '''simple docstring'''

          def check_hidden_states_output(lowercase					,  lowercase					,  lowercase):
                 a__						:	Optional[Any] =				model_class(lowercase)
                 model.to(lowercase)
                 model.eval()

                 with torch.no_grad():
                        a__						:	Union[str, Any] =				model(**self._prepare_for_class(lowercase					,  lowercase))

                 a__						:	List[str] =				outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

                 a__						:	List[str] =				self.model_tester.num_stages
                 self.assertEqual(len(lowercase)					,  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__						:	int =				layer_type
                        a__						:	List[str] =				True
                        check_hidden_states_output(lowercase					,  lowercase					,  lowercase)

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

                        check_hidden_states_output(lowercase					,  lowercase					,  lowercase)

   @unittest.skip(reason='Bit does not use feedforward chunking')
   def 							__lowercase		(					self)			->						List[Any]:







          '''simple docstring'''

          pass

   def 							__lowercase		(					self)			->						str:







          '''simple docstring'''

          a__						:	Optional[Any] =				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*lowercase)

   @slow
   def 							__lowercase		(					self)			->						Optional[int]:







          '''simple docstring'''

          for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 a__						:	Dict =				BitModel.from_pretrained(lowercase)
                 self.assertIsNotNone(lowercase)



def A_	(		)  ->		int:
       a__						:	str =				Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'				)
       return image





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




   """simple docstring"""



   @cached_property
   def 							__lowercase		(					self)			->						Dict:







          '''simple docstring'''

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

   @slow
   def 							__lowercase		(					self)			->						str:







          '''simple docstring'''

          a__						:	Any =				BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(lowercase)

          a__						:	Tuple =				self.default_image_processor
          a__						:	str =				prepare_img()
          a__						:	Tuple =				image_processor(images=lowercase					,  return_tensors='pt').to(lowercase)

          # forward pass
          with torch.no_grad():
                 a__						:	Any =				model(**lowercase)

          # verify the logits
          a__						:	Any =				torch.Size((1, 1000))
          self.assertEqual(outputs.logits.shape					,  lowercase)

          a__						:	Optional[Any] =				torch.tensor([[-0.65_26, -0.52_63, -1.43_98]]).to(lowercase)

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





@require_torch
class  A__    (							__UpperCAmelCase ,  unittest.TestCase	):




   """simple docstring"""



   __A   :				Optional[int]         =  (BitBackbone,) if is_torch_available() else ()
   __A   :				Optional[Any]         =  BitConfig

   __A   :				int         =  False

   def 							__lowercase		(					self)			->						Optional[int]:







          '''simple docstring'''

          a__						:	str =				BitModelTester(self)










from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase       :       Optional[Any]        =				logging.get_logger(__name__)

lowerCamelCase       :       List[str]        =				{
    '''google/canine-s''': '''https://huggingface.co/google/canine-s/resolve/main/config.json''',
    # See all CANINE models at https://huggingface.co/models?filter=canine
}

class 						lowerCAmelCase   (       __a	):

					'''simple docstring'''

					_A  :	List[Any]     =					'''canine'''



					def __init__(     self :		List[str]						,   __a :		Optional[int]=768						,   __a :		Any=12						,   __a :		Any=12						,   __a :		Dict=3072						,   __a :		Dict="gelu"						,   __a :		List[Any]=0.1						,   __a :		List[Any]=0.1						,   __a :		Tuple=16384						,   __a :		List[Any]=16						,   __a :		List[Any]=0.02						,   __a :		Optional[Any]=1E-12						,   __a :		Dict=0						,   __a :		List[Any]=0xe_0_0_0						,   __a :		Optional[int]=0xe_0_0_1						,   __a :		Any=4						,   __a :		Dict=4						,   __a :		Optional[int]=8						,   __a :		Any=16384						,   __a :		Optional[Any]=128						,   **__a :		List[str]						,   )	->     Union[str, Any]:


												"""simple docstring"""

												super().__init__(pad_token_id=__a						,   bos_token_id=__a						,   eos_token_id=__a						,   **__a							)

												__lowercase					:							int			=   max_position_embeddings
												__lowercase					:							List[str]			=   hidden_size
												__lowercase					:							List[Any]			=   num_hidden_layers
												__lowercase					:							Dict			=   num_attention_heads
												__lowercase					:							int			=   intermediate_size
												__lowercase					:							Dict			=   hidden_act
												__lowercase					:							Tuple			=   hidden_dropout_prob
												__lowercase					:							Union[str, Any]			=   attention_probs_dropout_prob
												__lowercase					:							Union[str, Any]			=   initializer_range
												__lowercase					:							Any			=   type_vocab_size
												__lowercase					:							int			=   layer_norm_eps

												# Character config:
												__lowercase					:							int			=   downsampling_rate
												__lowercase					:							str			=   upsampling_kernel_size
												__lowercase					:							Union[str, Any]			=   num_hash_functions
												__lowercase					:							Optional[Any]			=   num_hash_buckets
												__lowercase					:							Optional[int]			=   local_transformer_stride