




"""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_  :




    def __init__(   self						:	Any     ,			snake_case_						:	Dict     ,			snake_case_						:	Tuple=3     ,			snake_case_						:	List[str]=32     ,			snake_case_						:	int=3     ,			snake_case_						:	Optional[Any]=10     ,			snake_case_						:	int=[8, 16, 32, 64]     ,			snake_case_						:	Tuple=[1, 1, 2, 1]     ,			snake_case_						:	Tuple=True     ,			snake_case_						:	List[Any]=True     ,			snake_case_						:	List[Any]="relu"     ,			snake_case_						:	Tuple=3     ,			snake_case_						:	Optional[Any]=None     ,			snake_case_						:	int=["stage2", "stage3", "stage4"]     ,			snake_case_						:	str=[2, 3, 4]     ,			snake_case_						:	str=1     ,			)				->							Any:




     '''simple docstring'''

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




    def 							__magic_name__							(   self						:	str				)				->							Optional[Any]:




     '''simple docstring'''

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

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

     A__	=    self.get_config()

     return config, pixel_values, labels




    def 							__magic_name__							(   self						:	List[str]				)				->							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 							__magic_name__							(   self						:	Optional[int]     ,			snake_case_						:	Optional[int]     ,			snake_case_						:	Optional[Any]     ,			snake_case_						:	Optional[Any]				)				->							Any:




     '''simple docstring'''

     A__	=    BitModel(config=snake_case_				)
     model.to(snake_case_				)
     model.eval()
     A__	=    model(snake_case_				)
     self.parent.assertEqual(
         result.last_hidden_state.shape     ,			(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)     ,			)




    def 							__magic_name__							(   self						:	Union[str, Any]     ,			snake_case_						:	Optional[Any]     ,			snake_case_						:	Union[str, Any]     ,			snake_case_						:	Union[str, Any]				)				->							str:




     '''simple docstring'''

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




    def 							__magic_name__							(   self						:	Tuple     ,			snake_case_						:	Union[str, Any]     ,			snake_case_						:	Tuple     ,			snake_case_						:	Tuple				)				->							Any:




     '''simple docstring'''

     A__	=    BitBackbone(config=snake_case_				)
     model.to(snake_case_				)
     model.eval()
     A__	=    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__	=    None
     A__	=    BitBackbone(config=snake_case_				)
     model.to(snake_case_				)
     model.eval()
     A__	=    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 							__magic_name__							(   self						:	Tuple				)				->							Dict:




     '''simple docstring'''

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






@require_torch
class      UpperCAmelCase_  (						A_,       A_,       unittest.TestCase							):
    lowercase__						 =				(BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
    lowercase__						 =				(
        {'''feature-extraction''': BitModel, '''image-classification''': BitForImageClassification}
        if is_torch_available()
        else {}
    )

    lowercase__						 =				False
    lowercase__						 =				False
    lowercase__						 =				False
    lowercase__						 =				False
    lowercase__						 =				False




    def 							__magic_name__							(   self						:	int				)				->							List[str]:




     '''simple docstring'''

     A__	=    BitModelTester(self				)
     A__	=    ConfigTester(self     ,			config_class=snake_case_     ,			has_text_modality=snake_case_				)




    def 							__magic_name__							(   self						:	List[str]				)				->							Optional[int]:




     '''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 							__magic_name__							(   self						:	Optional[Any]				)				->							Optional[int]:




     '''simple docstring'''

     return




    @unittest.skip(reason="Bit does not output attentions"				)
    def 							__magic_name__							(   self						:	List[str]				)				->							List[str]:




     '''simple docstring'''

     pass




    @unittest.skip(reason="Bit does not use inputs_embeds"				)
    def 							__magic_name__							(   self						:	Dict				)				->							Optional[Any]:




     '''simple docstring'''

     pass




    @unittest.skip(reason="Bit does not support input and output embeddings"				)
    def 							__magic_name__							(   self						:	List[Any]				)				->							Optional[Any]:




     '''simple docstring'''

     pass




    def 							__magic_name__							(   self						:	List[Any]				)				->							Optional[int]:




     '''simple docstring'''

     A__,				A__	=    self.model_tester.prepare_config_and_inputs_for_common()

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

      A__	=    ["pixel_values"]
      self.assertListEqual(arg_names[:1]     ,			snake_case_				)




    def 							__magic_name__							(   self						:	Any				)				->							Union[str, Any]:




     '''simple docstring'''

     A__	=    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*snake_case_				)




    def 							__magic_name__							(   self						:	Tuple				)				->							str:




     '''simple docstring'''

     A__	=    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_backbone(*snake_case_				)




    def 							__magic_name__							(   self						:	str				)				->							Any:




     '''simple docstring'''

     A__,				A__	=    self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
      A__	=    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 							__magic_name__							(   self						:	Dict				)				->							Union[str, Any]:




     '''simple docstring'''

     def check_hidden_states_output(snake_case_						:	Union[str, Any]     ,			snake_case_						:	Optional[Any]     ,			snake_case_						:	Any				):
      A__	=    model_class(snake_case_				)
      model.to(snake_case_				)
      model.eval()

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

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

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

       check_hidden_states_output(snake_case_     ,			snake_case_     ,			snake_case_				)




    @unittest.skip(reason="Bit does not use feedforward chunking"				)
    def 							__magic_name__							(   self						:	Optional[int]				)				->							int:




     '''simple docstring'''

     pass




    def 							__magic_name__							(   self						:	Dict				)				->							List[str]:




     '''simple docstring'''

     A__	=    self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*snake_case_				)




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




     '''simple docstring'''

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







def   _SCREAMING_SNAKE_CASE       (							)						->		Optional[Any]:
 A__	=    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
 return image




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




    @cached_property
    def 							__magic_name__							(   self						:	Optional[Any]				)				->							Optional[int]:




     '''simple docstring'''

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




    @slow
    def 							__magic_name__							(   self						:	Any				)				->							str:




     '''simple docstring'''

     A__	=    BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]				).to(snake_case_				)

     A__	=    self.default_image_processor
     A__	=    prepare_img()
     A__	=    image_processor(images=snake_case_     ,			return_tensors="pt"				).to(snake_case_				)

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

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

     A__	=    torch.tensor([[-0.6526, -0.5263, -1.4398]]				).to(snake_case_				)

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






@require_torch
class      UpperCAmelCase_  (						A_,       unittest.TestCase							):
    lowercase__						 =				(BitBackbone,) if is_torch_available() else ()
    lowercase__						 =				BitConfig

    lowercase__						 =				False




    def 							__magic_name__							(   self						:	Union[str, Any]				)				->							Any:




     '''simple docstring'''

     A__	=    BitModelTester(self				)











"""simple docstring"""



from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE    =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE    =	{
    "SCUT-DLVCLab/lilt-roberta-en-base": (
        "https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json"
    ),
}




class      UpperCAmelCase_  (						A_							):
    lowercase__						 =				'''lilt'''




    def __init__(   self						:	List[str]     ,			snake_case_						:	Any=30_522     ,			snake_case_						:	Optional[Any]=768     ,			snake_case_						:	Union[str, Any]=12     ,			snake_case_						:	Union[str, Any]=12     ,			snake_case_						:	Any=3_072     ,			snake_case_						:	List[str]="gelu"     ,			snake_case_						:	Union[str, Any]=0.1     ,			snake_case_						:	List[Any]=0.1     ,			snake_case_						:	Any=512     ,			snake_case_						:	Optional[Any]=2     ,			snake_case_						:	List[str]=0.02     ,			snake_case_						:	Optional[Any]=1e-12     ,			snake_case_						:	List[Any]=0     ,			snake_case_						:	Any="absolute"     ,			snake_case_						:	str=None     ,			snake_case_						:	int=4     ,			snake_case_						:	int=1_024     ,			**snake_case_						:	Tuple     ,			)				->							List[Any]:




     '''simple docstring'''

     super().__init__(pad_token_id=snake_case_     ,			**snake_case_				)

     A__	=    vocab_size
     A__	=    hidden_size
     A__	=    num_hidden_layers
     A__	=    num_attention_heads
     A__	=    hidden_act
     A__	=    intermediate_size
     A__	=    hidden_dropout_prob
     A__	=    attention_probs_dropout_prob
     A__	=    max_position_embeddings
     A__	=    type_vocab_size
     A__	=    initializer_range
     A__	=    layer_norm_eps
     A__	=    position_embedding_type
     A__	=    classifier_dropout
     A__	=    channel_shrink_ratio
     A__	=    max_ad_position_embeddings





