





"""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  _UpperCAmelCase  :



      '''simple docstring'''

      def __init__(     self					,      snake_case_					,      snake_case_=3					,      snake_case_=3_2					,      snake_case_=3					,      snake_case_=1_0					,      snake_case_=[8, 1_6, 3_2, 6_4]					,      snake_case_=[1, 1, 2, 1]					,      snake_case_=True					,      snake_case_=True					,      snake_case_="relu"					,      snake_case_=3					,      snake_case_=None					,      snake_case_=["stage2", "stage3", "stage4"]					,      snake_case_=[2, 3, 4]					,      snake_case_=1					,      ):

        """simple docstring"""

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

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_  :       Dict										=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

        A_  :       Dict										=  None
        if self.use_labels:
          A_  :       Union[str, Any]										=  ids_tensor([self.batch_size]					,      self.num_labels  )

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

        return config, pixel_values, labels

      def 					lowerCamelCase_			(     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 					lowerCamelCase_			(     self					,      snake_case_					,      snake_case_					,      snake_case_  ):

        """simple docstring"""

        A_  :       str										=  BitModel(config=snake_case_  )
        model.to(snake_case_  )
        model.eval()
        A_  :       Any										=  model(snake_case_  )
        self.parent.assertEqual(
            result.last_hidden_state.shape					,      (self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2)					,      )

      def 					lowerCamelCase_			(     self					,      snake_case_					,      snake_case_					,      snake_case_  ):

        """simple docstring"""

        A_  :       Dict										=  self.num_labels
        A_  :       int										=  BitForImageClassification(snake_case_  )
        model.to(snake_case_  )
        model.eval()
        A_  :       Optional[Any]										=  model(snake_case_					,      labels=snake_case_  )
        self.parent.assertEqual(result.logits.shape					,      (self.batch_size, self.num_labels)  )

      def 					lowerCamelCase_			(     self					,      snake_case_					,      snake_case_					,      snake_case_  ):

        """simple docstring"""

        A_  :       List[Any]										=  BitBackbone(config=snake_case_  )
        model.to(snake_case_  )
        model.eval()
        A_  :       str										=  model(snake_case_  )

        # 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_  :       List[str]										=  None
        A_  :       Optional[int]										=  BitBackbone(config=snake_case_  )
        model.to(snake_case_  )
        model.eval()
        A_  :       List[str]										=  model(snake_case_  )

        # 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 					lowerCamelCase_			(     self  ):

        """simple docstring"""

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


@require_torch
class  _UpperCAmelCase  (   UpperCAmelCase__			,   UpperCAmelCase__			,   unittest.TestCase		):



      '''simple docstring'''
      lowercase_      : Any	    =   (BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
      lowercase_      : Tuple	    =   (
          {"""feature-extraction""": BitModel, """image-classification""": BitForImageClassification}
          if is_torch_available()
          else {}
      )

      lowercase_      : str	    =   False
      lowercase_      : Tuple	    =   False
      lowercase_      : List[str]	    =   False
      lowercase_      : Union[str, Any]	    =   False
      lowercase_      : Any	    =   False

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_  :       str										=  BitModelTester(self  )
        A_  :       Optional[int]										=  ConfigTester(self					,      config_class=snake_case_					,      has_text_modality=snake_case_  )

      def 					lowerCamelCase_			(     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 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        return

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

        """simple docstring"""

        pass

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

        """simple docstring"""

        pass

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

        """simple docstring"""

        pass

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_						,       A_  :       Optional[Any]										=  self.model_tester.prepare_config_and_inputs_for_common()

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

          A_  :       Dict										=  ['pixel_values']
          self.assertListEqual(arg_names[:1]					,      snake_case_  )

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_  :       Any										=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*snake_case_  )

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_  :       Tuple										=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_backbone(*snake_case_  )

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_						,       A_  :       Union[str, Any]										=  self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          A_  :       Optional[Any]										=  model_class(config=snake_case_  )
          for name, module in model.named_modules():
            if isinstance(snake_case_					,      (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 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        def check_hidden_states_output(snake_case_					,      snake_case_					,      snake_case_  ):
          A_  :       Optional[int]										=  model_class(snake_case_  )
          model.to(snake_case_  )
          model.eval()

          with torch.no_grad():
            A_  :       Tuple										=  model(**self._prepare_for_class(snake_case_					,      snake_case_  )  )

          A_  :       Optional[Any]										=  outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

          A_  :       Tuple										=  self.model_tester.num_stages
          self.assertEqual(len(snake_case_  )					,      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_  :       Optional[Any]										=  self.model_tester.prepare_config_and_inputs_for_common()
        A_  :       Any										=  ['preactivation', 'bottleneck']
        for model_class in self.all_model_classes:
          for layer_type in layers_type:
            A_  :       Union[str, Any]										=  layer_type
            A_  :       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"]
            A_  :       int										=  True

            check_hidden_states_output(snake_case_					,      snake_case_					,      snake_case_  )

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

        """simple docstring"""

        pass

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_  :       Tuple										=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*snake_case_  )

      @slow
      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          A_  :       Tuple										=  BitModel.from_pretrained(snake_case_  )
          self.assertIsNotNone(snake_case_  )





def 						UpperCAmelCase__		(     ):






  """simple docstring"""





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

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



      '''simple docstring'''

      @cached_property
      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

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

      @slow
      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_  :       Tuple										=  BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]  ).to(snake_case_  )

        A_  :       int										=  self.default_image_processor
        A_  :       Tuple										=  prepare_img()
        A_  :       Any										=  image_processor(images=snake_case_					,      return_tensors='pt'  ).to(snake_case_  )

        # forward pass
        with torch.no_grad():
          A_  :       int										=  model(**snake_case_  )

        # verify the logits
        A_  :       Tuple										=  torch.Size((1, 1_0_0_0)  )
        self.assertEqual(outputs.logits.shape					,      snake_case_  )

        A_  :       Tuple										=  torch.tensor([[-0.65_26, -0.52_63, -1.43_98]]  ).to(snake_case_  )

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


@require_torch
class  _UpperCAmelCase  (   UpperCAmelCase__			,   unittest.TestCase		):



      '''simple docstring'''
      lowercase_      : Union[str, Any]	    =   (BitBackbone,) if is_torch_available() else ()
      lowercase_      : List[str]	    =   BitConfig

      lowercase_      : Optional[int]	    =   False

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_  :       Tuple										=  BitModelTester(self  )






"""simple docstring"""




from transformers import BertTokenizerFast

from .custom_tokenization import CustomTokenizer

class  _UpperCAmelCase  (   UpperCAmelCase__		):



      '''simple docstring'''
      lowercase_      : List[str]	    =   CustomTokenizer
      pass