
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   __lowerCAmelCase		:



      """simple docstring"""







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




      def 						snake_case_	(					self						:				Any ):
          __lowercase						:	int					=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

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

          __lowercase						:	Tuple					=				self.get_config()

          return config, pixel_values, labels




      def 						snake_case_	(					self						:				List[str] ):
          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						:				Dict ,				_snake_case						:				int ,				_snake_case						:				Optional[int] ,				_snake_case						:				Optional[Any] ):
          __lowercase						:	Any					=				BitModel(config=lowercase_ )
          model.to(lowercase_ )
          model.eval()
          __lowercase						:	Optional[Any]					=				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 						snake_case_	(					self						:				Union[str, Any] ,				_snake_case						:				List[Any] ,				_snake_case						:				Tuple ,				_snake_case						:				Tuple ):
          __lowercase						:	int					=				self.num_labels
          __lowercase						:	List[Any]					=				BitForImageClassification(lowercase_ )
          model.to(lowercase_ )
          model.eval()
          __lowercase						:	Union[str, Any]					=				model(lowercase_ ,				labels=lowercase_ )
          self.parent.assertEqual(result.logits.shape ,				(self.batch_size, self.num_labels) )




      def 						snake_case_	(					self						:				int ,				_snake_case						:				str ,				_snake_case						:				Dict ,				_snake_case						:				List[str] ):
          __lowercase						:	List[str]					=				BitBackbone(config=lowercase_ )
          model.to(lowercase_ )
          model.eval()
          __lowercase						:	Any					=				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
          __lowercase						:	Dict					=				None
          __lowercase						:	Optional[Any]					=				BitBackbone(config=lowercase_ )
          model.to(lowercase_ )
          model.eval()
          __lowercase						:	Any					=				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 						snake_case_	(					self						:				Tuple ):
          __lowercase						:	Any					=				self.prepare_config_and_inputs()
          __lowercase			,					__lowercase			,					__lowercase						:	List[str]					=				config_and_inputs
          __lowercase						:	List[Any]					=				{'''pixel_values''': pixel_values}
          return config, inputs_dict






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



      """simple docstring"""







      A__		:	str         =			(BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
      A__		:	str         =			(
          {'''feature-extraction''': BitModel, '''image-classification''': BitForImageClassification}
          if is_torch_available()
          else {}
      )

      A__		:	Optional[int]         =			False
      A__		:	List[Any]         =			False
      A__		:	str         =			False
      A__		:	Optional[Any]         =			False
      A__		:	Optional[Any]         =			False




      def 						snake_case_	(					self						:				Union[str, Any] ):
          __lowercase						:	int					=				BitModelTester(self )
          __lowercase						:	str					=				ConfigTester(self ,				config_class=lowercase_ ,				has_text_modality=lowercase_ )




      def 						snake_case_	(					self						:				Dict ):
          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						:				Union[str, Any] ):
          return




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




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




      @unittest.skip(reason='''Bit does not support input and output embeddings''' )
      def 						snake_case_	(					self						:				Optional[Any] ):
          pass




      def 						snake_case_	(					self						:				List[str] ):
          __lowercase			,					__lowercase						:	Tuple					=				self.model_tester.prepare_config_and_inputs_for_common()

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

              __lowercase						:	Dict					=				['''pixel_values''']
              self.assertListEqual(arg_names[:1] ,				lowercase_ )




      def 						snake_case_	(					self						:				List[Any] ):
          __lowercase						:	Tuple					=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*lowercase_ )




      def 						snake_case_	(					self						:				List[str] ):
          __lowercase						:	Optional[int]					=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_backbone(*lowercase_ )




      def 						snake_case_	(					self						:				Any ):
          __lowercase			,					__lowercase						:	Optional[int]					=				self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              __lowercase						:	Tuple					=				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 						snake_case_	(					self						:				Dict ):







          def check_hidden_states_output(_snake_case						:				Any ,				_snake_case						:				Union[str, Any] ,				_snake_case						:				Dict ):
              __lowercase						:	Optional[Any]					=				model_class(lowercase_ )
              model.to(lowercase_ )
              model.eval()

              with torch.no_grad():
                  __lowercase						:	Tuple					=				model(**self._prepare_for_class(lowercase_ ,				lowercase_ ) )

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

              __lowercase						:	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] ,				)

          __lowercase			,					__lowercase						:	List[Any]					=				self.model_tester.prepare_config_and_inputs_for_common()
          __lowercase						:	List[Any]					=				['''preactivation''', '''bottleneck''']
          for model_class in self.all_model_classes:
              for layer_type in layers_type:
                  __lowercase						:	Any					=				layer_type
                  __lowercase						:	Dict					=				True
                  check_hidden_states_output(lowercase_ ,				lowercase_ ,				lowercase_ )

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

                  check_hidden_states_output(lowercase_ ,				lowercase_ ,				lowercase_ )




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




      def 						snake_case_	(					self						:				Any ):
          __lowercase						:	Tuple					=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*lowercase_ )




      @slow
      def 						snake_case_	(					self						:				str ):







          for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              __lowercase						:	Any					=				BitModel.from_pretrained(lowercase_ )
              self.assertIsNotNone(lowercase_ )






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


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



      """simple docstring"""







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




      @slow
      def 						snake_case_	(					self						:				Optional[int] ):
          __lowercase						:	Optional[int]					=				BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(lowercase_ )

          __lowercase						:	Dict					=				self.default_image_processor
          __lowercase						:	int					=				prepare_img()
          __lowercase						:	Dict					=				image_processor(images=lowercase_ ,				return_tensors='''pt''' ).to(lowercase_ )

          # forward pass
          with torch.no_grad():
              __lowercase						:	Union[str, Any]					=				model(**lowercase_ )

          # verify the logits
          __lowercase						:	Optional[int]					=				torch.Size((1, 1000) )
          self.assertEqual(outputs.logits.shape ,				lowercase_ )

          __lowercase						:	List[str]					=				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   __lowerCAmelCase		(		_UpperCAmelCase       ,       unittest.TestCase					):



      """simple docstring"""







      A__		:	List[Any]         =			(BitBackbone,) if is_torch_available() else ()
      A__		:	int         =			BitConfig

      A__		:	Optional[Any]         =			False




      def 						snake_case_	(					self						:				Tuple ):
          __lowercase						:	Optional[Any]					=				BitModelTester(self )





def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	float    ,			SCREAMING_SNAKE_CASE__			:	float    ,			SCREAMING_SNAKE_CASE__			:	float    ,			SCREAMING_SNAKE_CASE__			:	float    ,			SCREAMING_SNAKE_CASE__			:	float    ,			)							->      float:





    '''simple docstring'''






    A__          =   [redshift, radiation_density, matter_density, dark_energy]
    if any(p < 0 for p in parameters      ):
        raise ValueError('All input parameters must be positive'      )

    if any(p > 1 for p in parameters[1:4]      ):
        raise ValueError('Relative densities cannot be greater than one'      )
    else:
        A__          =   1 - (matter_density + radiation_density + dark_energy)

        A__          =   (
            radiation_density * (redshift + 1) ** 4
            + matter_density * (redshift + 1) ** 3
            + curvature * (redshift + 1) ** 2
            + dark_energy
        )

        A__          =   hubble_constant * e_a ** (1 / 2)
        return hubble


if __name__ == "__main__":
     import doctest

     # run doctest
     doctest.testmod()

     # demo LCDM approximation
     lowercase_       		=  0.3

     print(
         hubble_parameter(
             hubble_constant=68.3,
             radiation_density=1e-4,
             matter_density=matter_density,
             dark_energy=1 - matter_density,
             redshift=0,
         )
     )

