
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						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE=3						,						__SCREAMING_SNAKE_CASE=3_2						,						__SCREAMING_SNAKE_CASE=3						,						__SCREAMING_SNAKE_CASE=1_0						,						__SCREAMING_SNAKE_CASE=[8, 1_6, 3_2, 6_4]						,						__SCREAMING_SNAKE_CASE=[1, 1, 2, 1]						,						__SCREAMING_SNAKE_CASE=True						,						__SCREAMING_SNAKE_CASE=True						,						__SCREAMING_SNAKE_CASE="relu"						,						__SCREAMING_SNAKE_CASE=3						,						__SCREAMING_SNAKE_CASE=None						,						__SCREAMING_SNAKE_CASE=["stage2", "stage3", "stage4"]						,						__SCREAMING_SNAKE_CASE=[2, 3, 4]						,						__SCREAMING_SNAKE_CASE=1						,						)  ->   List[Any]:




           """simple docstring"""







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






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




           """simple docstring"""







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

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

           UpperCamelCase__    :   Tuple					=				self.get_config()

           return config, pixel_values, labels






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   int:




           """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						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )  ->   int:




           """simple docstring"""







           UpperCamelCase__    :   Dict					=				BitModel(config=__SCREAMING_SNAKE_CASE  )
           model.to(__SCREAMING_SNAKE_CASE  )
           model.eval()
           UpperCamelCase__    :   Optional[int]					=				model(__SCREAMING_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    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )  ->   Tuple:




           """simple docstring"""







           UpperCamelCase__    :   List[Any]					=				self.num_labels
           UpperCamelCase__    :   Tuple					=				BitForImageClassification(__SCREAMING_SNAKE_CASE  )
           model.to(__SCREAMING_SNAKE_CASE  )
           model.eval()
           UpperCamelCase__    :   Optional[int]					=				model(__SCREAMING_SNAKE_CASE						,						labels=__SCREAMING_SNAKE_CASE  )
           self.parent.assertEqual(result.logits.shape						,						(self.batch_size, self.num_labels)  )






     def    __SCREAMING_SNAKE_CASE			(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )  ->   Optional[int]:




           """simple docstring"""







           UpperCamelCase__    :   Optional[int]					=				BitBackbone(config=__SCREAMING_SNAKE_CASE  )
           model.to(__SCREAMING_SNAKE_CASE  )
           model.eval()
           UpperCamelCase__    :   Dict					=				model(__SCREAMING_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
           UpperCamelCase__    :   int					=				None
           UpperCamelCase__    :   Any					=				BitBackbone(config=__SCREAMING_SNAKE_CASE  )
           model.to(__SCREAMING_SNAKE_CASE  )
           model.eval()
           UpperCamelCase__    :   Any					=				model(__SCREAMING_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    __SCREAMING_SNAKE_CASE			(							self  )  ->   Dict:




           """simple docstring"""







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




@require_torch
class 				_lowerCamelCase (      UpperCamelCase_	,							UpperCamelCase_	,							unittest.TestCase ):





     """simple docstring"""







     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    __SCREAMING_SNAKE_CASE			(							self  )  ->   Dict:




           """simple docstring"""







           UpperCamelCase__    :   Optional[int]					=				BitModelTester(self  )
           UpperCamelCase__    :   Tuple					=				ConfigTester(self						,						config_class=__SCREAMING_SNAKE_CASE						,						has_text_modality=__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Optional[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    __SCREAMING_SNAKE_CASE			(							self  )  ->   Dict:




           """simple docstring"""







           return






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




           """simple docstring"""







           pass






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




           """simple docstring"""







           pass






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




           """simple docstring"""







           pass






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   List[Any]:




           """simple docstring"""







           UpperCamelCase__				,UpperCamelCase__    :   Dict					=				self.model_tester.prepare_config_and_inputs_for_common()

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

                 UpperCamelCase__    :   Any					=				['''pixel_values''']
                 self.assertListEqual(arg_names[:1]						,						__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Any:




           """simple docstring"""







           UpperCamelCase__    :   Optional[Any]					=				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE  )






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




           """simple docstring"""







           UpperCamelCase__    :   Union[str, Any]					=				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_backbone(*__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Dict:




           """simple docstring"""







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

           for model_class in self.all_model_classes:
                 UpperCamelCase__    :   Union[str, Any]					=				model_class(config=__SCREAMING_SNAKE_CASE  )
                 for name, module in model.named_modules():
                       if isinstance(__SCREAMING_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    __SCREAMING_SNAKE_CASE			(							self  )  ->   Tuple:




           """simple docstring"""







           def check_hidden_states_output(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  ):
                 UpperCamelCase__    :   List[Any]					=				model_class(__SCREAMING_SNAKE_CASE  )
                 model.to(__SCREAMING_SNAKE_CASE  )
                 model.eval()

                 with torch.no_grad():
                       UpperCamelCase__    :   Optional[Any]					=				model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )  )

                 UpperCamelCase__    :   Union[str, Any]					=				outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

                 UpperCamelCase__    :   Tuple					=				self.model_tester.num_stages
                 self.assertEqual(len(__SCREAMING_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]						,						)

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

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

                       check_hidden_states_output(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )






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




           """simple docstring"""







           pass






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Tuple:




           """simple docstring"""







           UpperCamelCase__    :   List[Any]					=				self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*__SCREAMING_SNAKE_CASE  )






     @slow
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   int:




           """simple docstring"""







           for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 UpperCamelCase__    :   str					=				BitModel.from_pretrained(__SCREAMING_SNAKE_CASE  )
                 self.assertIsNotNone(__SCREAMING_SNAKE_CASE  )


def 							SCREAMING_SNAKE_CASE_      (			):
      UpperCamelCase__    :   List[str]					=				Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
      return image




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





     """simple docstring"""







     @cached_property
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Any:




           """simple docstring"""







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






     @slow
     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   List[str]:




           """simple docstring"""







           UpperCamelCase__    :   Tuple					=				BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]  ).to(__SCREAMING_SNAKE_CASE  )

           UpperCamelCase__    :   Union[str, Any]					=				self.default_image_processor
           UpperCamelCase__    :   List[str]					=				prepare_img()
           UpperCamelCase__    :   Any					=				image_processor(images=__SCREAMING_SNAKE_CASE						,						return_tensors='''pt'''  ).to(__SCREAMING_SNAKE_CASE  )

           # forward pass
           with torch.no_grad():
                 UpperCamelCase__    :   str					=				model(**__SCREAMING_SNAKE_CASE  )

           # verify the logits
           UpperCamelCase__    :   Dict					=				torch.Size((1, 1_0_0_0)  )
           self.assertEqual(outputs.logits.shape						,						__SCREAMING_SNAKE_CASE  )

           UpperCamelCase__    :   Optional[int]					=				torch.tensor([[-0.6526, -0.5263, -1.4398]]  ).to(__SCREAMING_SNAKE_CASE  )

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




@require_torch
class 				_lowerCamelCase (      UpperCamelCase_	,							unittest.TestCase ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		   =  (BitBackbone,) if is_torch_available() else ()
     SCREAMING_SNAKE_CASE_		   =  BitConfig

     SCREAMING_SNAKE_CASE_		   =  False






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Any:




           """simple docstring"""







           UpperCamelCase__    :   str					=				BitModelTester(self  )




from __future__ import annotations

from collections.abc import Callable


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ = 1_0_0 ,		):
      UpperCamelCase__    :   Union[str, Any]					=				x_start
      UpperCamelCase__    :   List[Any]					=				fnc(UpperCamelCase__		)
      UpperCamelCase__    :   Any					=				0.0
      for _ in range(UpperCamelCase__		):
            # Approximates small segments of curve as linear and solve
            # for trapezoidal area
            UpperCamelCase__    :   str					=				(x_end - x_start) / steps + xa
            UpperCamelCase__    :   Dict					=				fnc(UpperCamelCase__		)
            area += abs(fxa + fxa		) * (xa - xa) / 2
            # Increment step
            UpperCamelCase__    :   Tuple					=				xa
            UpperCamelCase__    :   Union[str, Any]					=				fxa
      return area


if __name__ == "__main__":


    def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__		):
          return x**3 + x**2

    print("f(x) = x^3 + x^2")
    print("The area between the curve, x = -5, x = 5 and the x axis is:")
    lowerCamelCase      =1_0
    while i <= 1_0_0_0_0_0:
        print(F'''with {i} steps: {trapezoidal_area(f, -5, 5, i)}''')
        i *= 1_0


