


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							,					A_							,					A_=3							,					A_=32							,					A_=3							,					A_=10							,					A_=[8, 16, 32, 64]							,					A_=[1, 1, 2, 1]							,					A_=True							,					A_=True							,					A_="relu"							,					A_=3							,					A_=None							,					A_=["stage2", "stage3", "stage4"]							,					A_=[2, 3, 4]							,					A_=1							,					):
       _UpperCAmelCase		:							List[str]							=     parent
       _UpperCAmelCase		:							str							=     batch_size
       _UpperCAmelCase		:							str							=     image_size
       _UpperCAmelCase		:							Tuple							=     num_channels
       _UpperCAmelCase		:							Dict							=     embeddings_size
       _UpperCAmelCase		:							Union[str, Any]							=     hidden_sizes
       _UpperCAmelCase		:							Optional[int]							=     depths
       _UpperCAmelCase		:							Any							=     is_training
       _UpperCAmelCase		:							Tuple							=     use_labels
       _UpperCAmelCase		:							Optional[Any]							=     hidden_act
       _UpperCAmelCase		:							List[Any]							=     num_labels
       _UpperCAmelCase		:							List[Any]							=     scope
       _UpperCAmelCase		:							Dict							=     len(A_    )
       _UpperCAmelCase		:							Optional[int]							=     out_features
       _UpperCAmelCase		:							List[Any]							=     out_indices
       _UpperCAmelCase		:							str							=     num_groups


     def        __snake_case(  self    ):
       _UpperCAmelCase		:							Any							=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

       _UpperCAmelCase		:							Optional[int]							=     None
       if self.use_labels:
         _UpperCAmelCase		:							Dict							=     ids_tensor([self.batch_size]							,					self.num_labels    )

       _UpperCAmelCase		:							Tuple							=     self.get_config()

       return config, pixel_values, labels


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


     def        __snake_case(  self							,					A_							,					A_							,					A_    ):
       _UpperCAmelCase		:							int							=     self.num_labels
       _UpperCAmelCase		:							Tuple							=     BitForImageClassification(A_    )
       model.to(A_    )
       model.eval()
       _UpperCAmelCase		:							Any							=     model(A_							,					labels=A_    )
       self.parent.assertEqual(result.logits.shape							,					(self.batch_size, self.num_labels)    )


     def        __snake_case(  self							,					A_							,					A_							,					A_    ):
       _UpperCAmelCase		:							List[str]							=     BitBackbone(config=A_    )
       model.to(A_    )
       model.eval()
       _UpperCAmelCase		:							Dict							=     model(A_    )

       # 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
       _UpperCAmelCase		:							Any							=     None
       _UpperCAmelCase		:							Optional[int]							=     BitBackbone(config=A_    )
       model.to(A_    )
       model.eval()
       _UpperCAmelCase		:							Optional[Any]							=     model(A_    )

       # 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        __snake_case(  self    ):
       _UpperCAmelCase		:							str							=     self.prepare_config_and_inputs()
       _UpperCAmelCase,_UpperCAmelCase,_UpperCAmelCase		:							Optional[Any]							=     config_and_inputs
       _UpperCAmelCase		:							str							=     {"""pixel_values""": pixel_values}
       return config, inputs_dict


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

     __SCREAMING_SNAKE_CASE     					=    False
     __SCREAMING_SNAKE_CASE     					=    False
     __SCREAMING_SNAKE_CASE     					=    False
     __SCREAMING_SNAKE_CASE     					=    False
     __SCREAMING_SNAKE_CASE     					=    False


     def        __snake_case(  self    ):
       _UpperCAmelCase		:							List[str]							=     BitModelTester(self    )
       _UpperCAmelCase		:							List[Any]							=     ConfigTester(self							,					config_class=A_							,					has_text_modality=A_    )


     def        __snake_case(  self    ):
       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        __snake_case(  self    ):
       return


     @unittest.skip(reason="""Bit does not output attentions"""    )
     def        __snake_case(  self    ):
       pass


     @unittest.skip(reason="""Bit does not use inputs_embeds"""    )
     def        __snake_case(  self    ):
       pass


     @unittest.skip(reason="""Bit does not support input and output embeddings"""    )
     def        __snake_case(  self    ):
       pass


     def        __snake_case(  self    ):
       _UpperCAmelCase,_UpperCAmelCase		:							Optional[Any]							=     self.model_tester.prepare_config_and_inputs_for_common()

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

         _UpperCAmelCase		:							Optional[int]							=     ["""pixel_values"""]
         self.assertListEqual(arg_names[:1]							,					A_    )


     def        __snake_case(  self    ):
       _UpperCAmelCase		:							Any							=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*A_    )


     def        __snake_case(  self    ):
       _UpperCAmelCase		:							int							=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_backbone(*A_    )


     def        __snake_case(  self    ):
       _UpperCAmelCase,_UpperCAmelCase		:							List[Any]							=     self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
         _UpperCAmelCase		:							Optional[int]							=     model_class(config=A_    )
         for name, module in model.named_modules():
           if isinstance(A_							,					(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        __snake_case(  self    ):
       def check_hidden_states_output(A_							,					A_							,					A_    ):
         _UpperCAmelCase		:							Union[str, Any]							=     model_class(A_    )
         model.to(A_    )
         model.eval()

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

         _UpperCAmelCase		:							Tuple							=     outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

         _UpperCAmelCase		:							str							=     self.model_tester.num_stages
         self.assertEqual(len(A_    )							,					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]							,					)

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

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

           check_hidden_states_output(A_							,					A_							,					A_    )


     @unittest.skip(reason="""Bit does not use feedforward chunking"""    )
     def        __snake_case(  self    ):
       pass


     def        __snake_case(  self    ):
       _UpperCAmelCase		:							Dict							=     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*A_    )




     @slow
     def        __snake_case(  self    ):
       for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         _UpperCAmelCase		:							str							=     BitModel.from_pretrained(A_    )
         self.assertIsNotNone(A_    )




def    a__    (		):
  _UpperCAmelCase		:							List[Any]							=     Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""     )
  return image


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


     @cached_property
     def        __snake_case(  self    ):
       return (
           BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]    ) if is_vision_available() else None
       )




     @slow
     def        __snake_case(  self    ):
       _UpperCAmelCase		:							Tuple							=     BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]    ).to(A_    )

       _UpperCAmelCase		:							List[Any]							=     self.default_image_processor
       _UpperCAmelCase		:							Dict							=     prepare_img()
       _UpperCAmelCase		:							Any							=     image_processor(images=A_							,					return_tensors="""pt"""    ).to(A_    )

       # forward pass
       with torch.no_grad():
         _UpperCAmelCase		:							Optional[int]							=     model(**A_    )

       # verify the logits
       _UpperCAmelCase		:							List[Any]							=     torch.Size((1, 10_00)    )
       self.assertEqual(outputs.logits.shape							,					A_    )

       _UpperCAmelCase		:							Any							=     torch.tensor([[-0.6_5_2_6, -0.5_2_6_3, -1.4_3_9_8]]    ).to(A_    )

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


@require_torch
class 		_SCREAMING_SNAKE_CASE     (						A							,	unittest.TestCase     ):
     __SCREAMING_SNAKE_CASE     					=    (BitBackbone,) if is_torch_available() else ()
     __SCREAMING_SNAKE_CASE     					=    BitConfig

     __SCREAMING_SNAKE_CASE     					=    False


     def        __snake_case(  self    ):
       _UpperCAmelCase		:							Optional[Any]							=     BitModelTester(self    )









def    a__    (		snake_case__					:							int						,   snake_case__					:							int     ):
  return 1 if input_a == input_a else 0




def    a__    (		):
  assert xnor_gate(0						,   0     ) == 1
  assert xnor_gate(0						,   1     ) == 0
  assert xnor_gate(1						,   0     ) == 0
  assert xnor_gate(1						,   1     ) == 1


if __name__ == "__main__":
 print(xnor_gate(0, 0))
 print(xnor_gate(0, 1))
 print(xnor_gate(1, 0))
 print(xnor_gate(1, 1))





