
'''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       ,  _lowercase       ,  _lowercase=3       ,  _lowercase=32       ,  _lowercase=3       ,  _lowercase=10       ,  _lowercase=[8, 16, 32, 64]       ,  _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       ,  ):

												"""simple docstring"""



												_lowerCAmelCase    		=					parent
												_lowerCAmelCase    		=					batch_size
												_lowerCAmelCase    		=					image_size
												_lowerCAmelCase    		=					num_channels
												_lowerCAmelCase    		=					embeddings_size
												_lowerCAmelCase    		=					hidden_sizes
												_lowerCAmelCase    		=					depths
												_lowerCAmelCase    		=					is_training
												_lowerCAmelCase    		=					use_labels
												_lowerCAmelCase    		=					hidden_act
												_lowerCAmelCase    		=					num_labels
												_lowerCAmelCase    		=					scope
												_lowerCAmelCase    		=					len(_lowercase )
												_lowerCAmelCase    		=					out_features
												_lowerCAmelCase    		=					out_indices
												_lowerCAmelCase    		=					num_groups




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

												_lowerCAmelCase    		=					None
												if self.use_labels:
																		_lowerCAmelCase    		=					ids_tensor([self.batch_size]       ,  self.num_labels )

												_lowerCAmelCase    		=					self.get_config()

												return config, pixel_values, labels




						def    _lowercase      (   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    _lowercase      (   self       ,  _lowercase       ,  _lowercase       ,  _lowercase ):

												"""simple docstring"""



												_lowerCAmelCase    		=					BitModel(config=_lowercase )
												model.to(_lowercase )
												model.eval()
												_lowerCAmelCase    		=					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    _lowercase      (   self       ,  _lowercase       ,  _lowercase       ,  _lowercase ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.num_labels
												_lowerCAmelCase    		=					BitForImageClassification(_lowercase )
												model.to(_lowercase )
												model.eval()
												_lowerCAmelCase    		=					model(_lowercase       ,  labels=_lowercase )
												self.parent.assertEqual(result.logits.shape       ,  (self.batch_size, self.num_labels) )




						def    _lowercase      (   self       ,  _lowercase       ,  _lowercase       ,  _lowercase ):

												"""simple docstring"""



												_lowerCAmelCase    		=					BitBackbone(config=_lowercase )
												model.to(_lowercase )
												model.eval()
												_lowerCAmelCase    		=					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
												_lowerCAmelCase    		=					None
												_lowerCAmelCase    		=					BitBackbone(config=_lowercase )
												model.to(_lowercase )
												model.eval()
												_lowerCAmelCase    		=					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    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.prepare_config_and_inputs()
												_lowerCAmelCase    		=					config_and_inputs
												_lowerCAmelCase    		=					{"pixel_values": pixel_values}
												return config, inputs_dict






@require_torch
class     UpperCAmelCase_	(						lowerCamelCase__			,				lowerCamelCase__			,				unittest.TestCase					):






						'''simple docstring'''






						_lowercase						: Union[str, Any]										=  (BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
						_lowercase						: Optional[int]										=  (
						    {'''feature-extraction''': BitModel, '''image-classification''': BitForImageClassification}
						    if is_torch_available()
						    else {}
						)

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




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					BitModelTester(self )
												_lowerCAmelCase    		=					ConfigTester(self       ,  config_class=_lowercase       ,  has_text_modality=_lowercase )




						def    _lowercase      (   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    _lowercase      (   self ):

												"""simple docstring"""



												return




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

												"""simple docstring"""



												pass




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

												"""simple docstring"""



												pass




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

												"""simple docstring"""



												pass




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.model_tester.prepare_config_and_inputs_for_common()

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

																		_lowerCAmelCase    		=					["pixel_values"]
																		self.assertListEqual(arg_names[:1]       ,  _lowercase )




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*_lowercase )




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_backbone(*_lowercase )




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																		_lowerCAmelCase    		=					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    _lowercase      (   self ):

												"""simple docstring"""



												def check_hidden_states_output(_lowercase       ,  _lowercase       ,  _lowercase ):
																		_lowerCAmelCase    		=					model_class(_lowercase )
																		model.to(_lowercase )
																		model.eval()

																		with torch.no_grad():
																								_lowerCAmelCase    		=					model(**self._prepare_for_class(_lowercase       ,  _lowercase ) )

																		_lowerCAmelCase    		=					outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

																		_lowerCAmelCase    		=					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]       ,  )

												_lowerCAmelCase    		=					self.model_tester.prepare_config_and_inputs_for_common()
												_lowerCAmelCase    		=					["preactivation", "bottleneck"]
												for model_class in self.all_model_classes:
																		for layer_type in layers_type:
																								_lowerCAmelCase    		=					layer_type
																								_lowerCAmelCase    		=					True
																								check_hidden_states_output(_lowercase       ,  _lowercase       ,  _lowercase )

																								# check that output_hidden_states also work using config
																								del inputs_dict["output_hidden_states"]
																								_lowerCAmelCase    		=					True

																								check_hidden_states_output(_lowercase       ,  _lowercase       ,  _lowercase )




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

												"""simple docstring"""



												pass




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_classification(*_lowercase )




						@slow
						def    _lowercase      (   self ):

												"""simple docstring"""



												for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		_lowerCAmelCase    		=					BitModel.from_pretrained(_lowercase )
																		self.assertIsNotNone(_lowercase )






def A ():
						_lowerCAmelCase    		=					Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						)
						return image




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






						'''simple docstring'''






						@cached_property
						def    _lowercase      (   self ):

												"""simple docstring"""



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




						@slow
						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(_lowercase )

												_lowerCAmelCase    		=					self.default_image_processor
												_lowerCAmelCase    		=					prepare_img()
												_lowerCAmelCase    		=					image_processor(images=_lowercase       ,  return_tensors="""pt""" ).to(_lowercase )

												# forward pass
												with torch.no_grad():
																		_lowerCAmelCase    		=					model(**_lowercase )

												# verify the logits
												_lowerCAmelCase    		=					torch.Size((1, 1_000) )
												self.assertEqual(outputs.logits.shape       ,  _lowercase )

												_lowerCAmelCase    		=					torch.tensor([[-0.6526, -0.5263, -1.4398]] ).to(_lowercase )

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






@require_torch
class     UpperCAmelCase_	(						lowerCamelCase__			,				unittest.TestCase					):






						'''simple docstring'''






						_lowercase						: Any										=  (BitBackbone,) if is_torch_available() else ()
						_lowercase						: Tuple										=  BitConfig

						_lowercase						: str										=  False




						def    _lowercase      (   self ):

												"""simple docstring"""



												_lowerCAmelCase    		=					BitModelTester(self )







import logging

from transformers import PretrainedConfig


_lowerCAmelCase				:   str						       =						logging.getLogger(__name__)


_lowerCAmelCase				:   Dict						       =						{
    '''bertabs-finetuned-cnndm''': '''https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json''',
}


class        __magic_name__	(				lowerCamelCase__				):





     """simple docstring"""



     __UpperCamelCase            =  '''bertabs'''



     def __init__(				self							:Optional[int]      ,   snake_case							:Any=30_522      ,   snake_case							:List[str]=512      ,   snake_case							:str=6      ,   snake_case							:int=512      ,   snake_case							:Optional[Any]=8      ,   snake_case							:Tuple=512      ,   snake_case							:str=0.2      ,   snake_case							:Any=6      ,   snake_case							:Optional[Any]=768      ,   snake_case							:Optional[Any]=8      ,   snake_case							:List[Any]=2_048      ,   snake_case							:Dict=0.2      ,   **snake_case							:List[str]      ,   ):





        '''simple docstring'''




        super().__init__(**snake_case	)

        A_       :					List[str]        =      vocab_size
        A_       :					int        =      max_pos

        A_       :					Tuple        =      enc_layers
        A_       :					Tuple        =      enc_hidden_size
        A_       :					str        =      enc_heads
        A_       :					Optional[Any]        =      enc_ff_size
        A_       :					Optional[Any]        =      enc_dropout

        A_       :					List[str]        =      dec_layers
        A_       :					List[Any]        =      dec_hidden_size
        A_       :					Optional[int]        =      dec_heads
        A_       :					Any        =      dec_ff_size
        A_       :					Optional[int]        =      dec_dropout
