





'''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       __SCREAMING_SNAKE_CASE	:



     '''simple docstring'''






     def __init__(					self						,   lowerCamelCase						,   lowerCamelCase=3						,   lowerCamelCase=32						,   lowerCamelCase=3						,   lowerCamelCase=10						,   lowerCamelCase=[8, 16, 32, 64]						,   lowerCamelCase=[1, 1, 2, 1]						,   lowerCamelCase=True						,   lowerCamelCase=True						,   lowerCamelCase="relu"						,   lowerCamelCase=3						,   lowerCamelCase=None						,   lowerCamelCase=["stage2", "stage3", "stage4"]						,   lowerCamelCase=[2, 3, 4]						,   lowerCamelCase=1						,   ):
       _snake_case     		=				parent
       _snake_case     		=				batch_size
       _snake_case     		=				image_size
       _snake_case     		=				num_channels
       _snake_case     		=				embeddings_size
       _snake_case     		=				hidden_sizes
       _snake_case     		=				depths
       _snake_case     		=				is_training
       _snake_case     		=				use_labels
       _snake_case     		=				hidden_act
       _snake_case     		=				num_labels
       _snake_case     		=				scope
       _snake_case     		=				len(lowerCamelCase	)
       _snake_case     		=				out_features
       _snake_case     		=				out_indices
       _snake_case     		=				num_groups

     def 	UpperCamelCase(					self	):
       _snake_case     		=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

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

       _snake_case     		=				self.get_config()

       return config, pixel_values, labels

     def 	UpperCamelCase(					self	):
       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 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	):
       _snake_case     		=				BitModel(config=lowerCamelCase	)
       model.to(lowerCamelCase	)
       model.eval()
       _snake_case     		=				model(lowerCamelCase	)
       self.parent.assertEqual(
           result.last_hidden_state.shape						,   (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)						,   )

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	):
       _snake_case     		=				self.num_labels
       _snake_case     		=				BitForImageClassification(lowerCamelCase	)
       model.to(lowerCamelCase	)
       model.eval()
       _snake_case     		=				model(lowerCamelCase						,   labels=lowerCamelCase	)
       self.parent.assertEqual(result.logits.shape						,   (self.batch_size, self.num_labels)	)

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	):
       _snake_case     		=				BitBackbone(config=lowerCamelCase	)
       model.to(lowerCamelCase	)
       model.eval()
       _snake_case     		=				model(lowerCamelCase	)

       # 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
       _snake_case     		=				None
       _snake_case     		=				BitBackbone(config=lowerCamelCase	)
       model.to(lowerCamelCase	)
       model.eval()
       _snake_case     		=				model(lowerCamelCase	)

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







@require_torch
class       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase					,    __UpperCamelCase					,    unittest.TestCase			):



     '''simple docstring'''






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

     UpperCAmelCase__       :						int      =   False
     UpperCAmelCase__       :						Union[str, Any]      =   False
     UpperCAmelCase__       :						int      =   False
     UpperCAmelCase__       :						Optional[Any]      =   False
     UpperCAmelCase__       :						Dict      =   False

     def 	UpperCamelCase(					self	):
       _snake_case     		=				BitModelTester(self	)
       _snake_case     		=				ConfigTester(self						,   config_class=lowerCamelCase						,   has_text_modality=lowerCamelCase	)

     def 	UpperCamelCase(					self	):
       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 	UpperCamelCase(					self	):
       return

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

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

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

     def 	UpperCamelCase(					self	):
       _snake_case		,		_snake_case     		=				self.model_tester.prepare_config_and_inputs_for_common()

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

         _snake_case     		=				["pixel_values"]
         self.assertListEqual(arg_names[:1]						,   lowerCamelCase	)

     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*lowerCamelCase	)

     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_backbone(*lowerCamelCase	)

     def 	UpperCamelCase(					self	):
       _snake_case		,		_snake_case     		=				self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
         _snake_case     		=				model_class(config=lowerCamelCase	)
         for name, module in model.named_modules():
           if isinstance(lowerCamelCase						,   (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 	UpperCamelCase(					self	):
       def check_hidden_states_output(lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	):
         _snake_case     		=				model_class(lowerCamelCase	)
         model.to(lowerCamelCase	)
         model.eval()

         with torch.no_grad():
           _snake_case     		=				model(**self._prepare_for_class(lowerCamelCase						,   lowerCamelCase	)	)

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

         _snake_case     		=				self.model_tester.num_stages
         self.assertEqual(len(lowerCamelCase	)						,   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]						,   )

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

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

           check_hidden_states_output(lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	)

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

     def 	UpperCamelCase(					self	):
       _snake_case     		=				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_image_classification(*lowerCamelCase	)






     @slow
     def 	UpperCamelCase(					self	):
       for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         _snake_case     		=				BitModel.from_pretrained(lowerCamelCase	)
         self.assertIsNotNone(lowerCamelCase	)







def 				snake_case_      (		):







  '''simple docstring'''



  _snake_case     		=				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"       )
  return image


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



     '''simple docstring'''






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






     @slow
     def 	UpperCamelCase(					self	):
       _snake_case     		=				BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]	).to(lowerCamelCase	)

       _snake_case     		=				self.default_image_processor
       _snake_case     		=				prepare_img()
       _snake_case     		=				image_processor(images=lowerCamelCase						,   return_tensors="pt"	).to(lowerCamelCase	)

       # forward pass
       with torch.no_grad():
         _snake_case     		=				model(**lowerCamelCase	)

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

       _snake_case     		=				torch.tensor([[-0.6526, -0.5263, -1.4398]]	).to(lowerCamelCase	)

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







@require_torch
class       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase					,    unittest.TestCase			):



     '''simple docstring'''






     UpperCAmelCase__       :						str      =   (BitBackbone,) if is_torch_available() else ()
     UpperCAmelCase__       :						Any      =   BitConfig

     UpperCAmelCase__       :						Optional[Any]      =   False

     def 	UpperCamelCase(					self	):
       _snake_case     		=				BitModelTester(self	)









'''simple docstring'''



import warnings

from ...utils import is_sklearn_available, requires_backends


if is_sklearn_available():
 from scipy.stats import pearsonr, spearmanr
 from sklearn.metrics import fa_score, matthews_corrcoef


__magic_name__   :						List[str]    =			(
    """This metric will be removed from the library soon, metrics should be handled with the 🤗 Evaluate """
    """library. You can have a look at this example script for pointers: """
    """https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py"""
)




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  warnings.warn(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )
  requires_backends(SCREAMING_SNAKE_CASE__					,	"sklearn"       )
  return (preds == labels).mean()




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  warnings.warn(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )
  requires_backends(SCREAMING_SNAKE_CASE__					,	"sklearn"       )
  _snake_case     		=				simple_accuracy(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )
  _snake_case     		=				fa_score(y_true=SCREAMING_SNAKE_CASE__					,	y_pred=SCREAMING_SNAKE_CASE__       )
  return {
      "acc": acc,
      "f1": fa,
      "acc_and_f1": (acc + fa) / 2,
  }




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  warnings.warn(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )
  requires_backends(SCREAMING_SNAKE_CASE__					,	"sklearn"       )
  _snake_case     		=				pearsonr(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )[0]
  _snake_case     		=				spearmanr(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )[0]
  return {
      "pearson": pearson_corr,
      "spearmanr": spearman_corr,
      "corr": (pearson_corr + spearman_corr) / 2,
  }




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  warnings.warn(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )
  requires_backends(SCREAMING_SNAKE_CASE__					,	"sklearn"       )
  assert len(SCREAMING_SNAKE_CASE__       ) == len(SCREAMING_SNAKE_CASE__       ), f'''Predictions and labels have mismatched lengths {len(SCREAMING_SNAKE_CASE__       )} and {len(SCREAMING_SNAKE_CASE__       )}'''
  if task_name == "cola":
    return {"mcc": matthews_corrcoef(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )}
  elif task_name == "sst-2":
    return {"acc": simple_accuracy(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )}
  elif task_name == "mrpc":
    return acc_and_fa(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )
  elif task_name == "sts-b":
    return pearson_and_spearman(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )
  elif task_name == "qqp":
    return acc_and_fa(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )
  elif task_name == "mnli":
    return {"mnli/acc": simple_accuracy(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )}
  elif task_name == "mnli-mm":
    return {"mnli-mm/acc": simple_accuracy(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )}
  elif task_name == "qnli":
    return {"acc": simple_accuracy(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )}
  elif task_name == "rte":
    return {"acc": simple_accuracy(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )}
  elif task_name == "wnli":
    return {"acc": simple_accuracy(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )}
  elif task_name == "hans":
    return {"acc": simple_accuracy(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )}
  else:
    raise KeyError(SCREAMING_SNAKE_CASE__       )




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



  warnings.warn(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )
  requires_backends(SCREAMING_SNAKE_CASE__					,	"sklearn"       )
  if len(SCREAMING_SNAKE_CASE__       ) != len(SCREAMING_SNAKE_CASE__       ):
    raise ValueError(f'''Predictions and labels have mismatched lengths {len(SCREAMING_SNAKE_CASE__       )} and {len(SCREAMING_SNAKE_CASE__       )}'''       )
  if task_name == "xnli":
    return {"acc": simple_accuracy(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )}
  else:
    raise KeyError(SCREAMING_SNAKE_CASE__       )


