



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        snake_case__    :







						"""simple docstring"""




						def __init__(       self ,       __lowercase ,       __lowercase=3 ,       __lowercase=3_2 ,       __lowercase=3 ,       __lowercase=1_0 ,       __lowercase=[8, 1_6, 3_2, 6_4] ,       __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 ,       ) ->				Union[str, Any]:
									"""simple docstring"""




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

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Tuple:
									"""simple docstring"""




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

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

									a__       :						Dict							=  self.get_config()

									return config, pixel_values, labels

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				List[Any]:
									"""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 ,       __lowercase ,       __lowercase ,       __lowercase  ) ->				Any:
									"""simple docstring"""




									a__       :						List[Any]							=  BitModel(config=__lowercase  )
									model.to(__lowercase  )
									model.eval()
									a__       :						Tuple							=  model(__lowercase  )
									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 ,       __lowercase ,       __lowercase ,       __lowercase  ) ->				List[Any]:
									"""simple docstring"""




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

						def 							SCREAMING_SNAKE_CASE__(       self ,       __lowercase ,       __lowercase ,       __lowercase  ) ->				Dict:
									"""simple docstring"""




									a__       :						Tuple							=  BitBackbone(config=__lowercase  )
									model.to(__lowercase  )
									model.eval()
									a__       :						Dict							=  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__       :						List[Any]							=  None
									a__       :						int							=  BitBackbone(config=__lowercase  )
									model.to(__lowercase  )
									model.eval()
									a__       :						Tuple							=  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 							SCREAMING_SNAKE_CASE__(       self  ) ->				Tuple:
									"""simple docstring"""




									a__       :						Union[str, Any]							=  self.prepare_config_and_inputs()
									a__	,						a__	,						a__       :						List[str]							=  config_and_inputs
									a__       :						List[str]							=  {"""pixel_values""": pixel_values}
									return config, inputs_dict




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







						"""simple docstring"""




						__lowerCAmelCase					:int      =					(BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
						__lowerCAmelCase					:Tuple      =					(
						    {"feature-extraction": BitModel, "image-classification": BitForImageClassification}
						    if is_torch_available()
						    else {}
						)

						__lowerCAmelCase					:str      =					False
						__lowerCAmelCase					:Any      =					False
						__lowerCAmelCase					:Optional[Any]      =					False
						__lowerCAmelCase					:Any      =					False
						__lowerCAmelCase					:Tuple      =					False

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				int:
									"""simple docstring"""




									a__       :						Optional[int]							=  BitModelTester(self  )
									a__       :						Union[str, Any]							=  ConfigTester(self ,       config_class=__lowercase ,       has_text_modality=__lowercase  )

						def 							SCREAMING_SNAKE_CASE__(       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 							SCREAMING_SNAKE_CASE__(       self  ) ->				Union[str, Any]:
									"""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[str]:
									"""simple docstring"""




									pass

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




									pass

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Tuple:
									"""simple docstring"""




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

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

												a__       :						Union[str, Any]							=  ["""pixel_values"""]
												self.assertListEqual(arg_names[:1] ,       __lowercase  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Any:
									"""simple docstring"""




									a__       :						List[Any]							=  self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*__lowercase  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Optional[int]:
									"""simple docstring"""




									a__       :						Optional[Any]							=  self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_backbone(*__lowercase  )

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Optional[int]:
									"""simple docstring"""




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

									for model_class in self.all_model_classes:
												a__       :						str							=  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 							SCREAMING_SNAKE_CASE__(       self  ) ->				Optional[int]:
									"""simple docstring"""




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

												with torch.no_grad():
															a__       :						Dict							=  model(**self._prepare_for_class(__lowercase ,       __lowercase  )  )

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

												a__       :						Any							=  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__       :						Optional[int]							=  ["""preactivation""", """bottleneck"""]
									for model_class in self.all_model_classes:
												for layer_type in layers_type:
															a__       :						Dict							=  layer_type
															a__       :						Dict							=  True
															check_hidden_states_output(__lowercase ,       __lowercase ,       __lowercase  )

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

															check_hidden_states_output(__lowercase ,       __lowercase ,       __lowercase  )

						@unittest.skip(reason="""Bit does not use feedforward chunking"""  )
						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				int:
									"""simple docstring"""




									pass

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				int:
									"""simple docstring"""




									a__       :						Any							=  self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*__lowercase  )

						@slow
						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Dict:
									"""simple docstring"""




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




def      lowerCAmelCase_					(  )							->	Dict:







			"""simple docstring"""
			a__       :						str							=  Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""")
			return image

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







						"""simple docstring"""




						@cached_property
						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Dict:
									"""simple docstring"""




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

						@slow
						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				Dict:
									"""simple docstring"""




									a__       :						Optional[int]							=  BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]  ).to(__lowercase  )

									a__       :						Optional[int]							=  self.default_image_processor
									a__       :						Union[str, Any]							=  prepare_img()
									a__       :						Union[str, Any]							=  image_processor(images=__lowercase ,       return_tensors="""pt"""  ).to(__lowercase  )

									# forward pass
									with torch.no_grad():
												a__       :						List[Any]							=  model(**__lowercase  )

									# verify the logits
									a__       :						Any							=  torch.Size((1, 1_0_0_0)  )
									self.assertEqual(outputs.logits.shape ,       __lowercase  )

									a__       :						Optional[Any]							=  torch.tensor([[-0.6_5_2_6, -0.5_2_6_3, -1.4_3_9_8]]  ).to(__lowercase  )

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




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







						"""simple docstring"""




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

						__lowerCAmelCase					:List[Any]      =					False

						def 							SCREAMING_SNAKE_CASE__(       self  ) ->				str:
									"""simple docstring"""




									a__       :						Union[str, Any]							=  BitModelTester(self  )









_lowercase		:				Optional[int]      		=[
    (1000, "M"),
    (900, "CM"),
    (500, "D"),
    (400, "CD"),
    (100, "C"),
    (90, "XC"),
    (50, "L"),
    (40, "XL"),
    (10, "X"),
    (9, "IX"),
    (5, "V"),
    (4, "IV"),
    (1, "I"),
]

def      lowerCAmelCase_					(  _lowercase    :       str)							->	int:







			"""simple docstring"""
			a__       :						Union[str, Any]							=  {"""I""": 1, """V""": 5, """X""": 10, """L""": 50, """C""": 100, """D""": 500, """M""": 1000}
			a__       :						List[Any]							=  0
			a__       :						Dict							=  0
			while place < len(_lowercase):
						if (place + 1 < len(_lowercase)) and (vals[roman[place]] < vals[roman[place + 1]]):
									total += vals[roman[place + 1]] - vals[roman[place]]
									place += 2
						else:
									total += vals[roman[place]]
									place += 1
			return total

def      lowerCAmelCase_					(  _lowercase    :       int)							->	str:







			"""simple docstring"""
			a__       :						Optional[Any]							=  []
			for arabic, roman in ROMAN:
						((a__)	,						(a__))       :						Optional[Any]							=  divmod(_lowercase						,       _lowercase)
						result.append(roman * factor)
						if number == 0:
									break
			return "".join(_lowercase)


if __name__ == "__main__":
		import doctest

		doctest.testmod()




