




import inspect
import unittest

from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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 RegNetForImageClassification, RegNetModel
  from transformers.models.regnet.modeling_regnet import REGNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import AutoImageProcessor


class 							UpperCamelCase_       :

    '''simple docstring'''






    def __init__(     self :      Union[str, Any]   ,					UpperCAmelCase__ :      Optional[int]   ,					UpperCAmelCase__ :      int=3   ,					UpperCAmelCase__ :      List[Any]=32   ,					UpperCAmelCase__ :      Any=3   ,					UpperCAmelCase__ :      Tuple=10   ,					UpperCAmelCase__ :      List[Any]=[10, 20, 30, 40]   ,					UpperCAmelCase__ :      str=[1, 1, 2, 1]   ,					UpperCAmelCase__ :      Union[str, Any]=True   ,					UpperCAmelCase__ :      List[str]=True   ,					UpperCAmelCase__ :      Any="relu"   ,					UpperCAmelCase__ :      Optional[Any]=3   ,					UpperCAmelCase__ :      Dict=None   ,					)		->Tuple:





           '''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(UpperCAmelCase__)


    def 			SCREAMING_SNAKE_CASE				(     self :      List[Any])		->List[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 			SCREAMING_SNAKE_CASE				(     self :      str)		->List[str]:





           '''simple docstring'''

           return RegNetConfig(
               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   ,					)


    def 			SCREAMING_SNAKE_CASE				(     self :      Tuple   ,					UpperCAmelCase__ :      Dict   ,					UpperCAmelCase__ :      str   ,					UpperCAmelCase__ :      List[Any])		->str:





           '''simple docstring'''

           A__ 					=       RegNetModel(config=UpperCAmelCase__)
           model.to(UpperCAmelCase__)
           model.eval()
           A__ 					=       model(UpperCAmelCase__)
           # expected last hidden states: B, C, H // 32, W // 32
           self.parent.assertEqual(
               result.last_hidden_state.shape   ,					(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)   ,					)


    def 			SCREAMING_SNAKE_CASE				(     self :      Dict   ,					UpperCAmelCase__ :      Optional[Any]   ,					UpperCAmelCase__ :      int   ,					UpperCAmelCase__ :      List[Any])		->int:





           '''simple docstring'''

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





    def 			SCREAMING_SNAKE_CASE				(     self :      Optional[int])		->Tuple:





           '''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_       (					UpperCAmelCase__						,   UpperCAmelCase__						,   unittest.TestCase	):

    '''simple docstring'''






    UpperCAmelCase__				    = (RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
    UpperCAmelCase__				    = (
        {'''feature-extraction''': RegNetModel, '''image-classification''': RegNetForImageClassification}
        if is_torch_available()
        else {}
    )

    UpperCAmelCase__				    = False
    UpperCAmelCase__				    = False
    UpperCAmelCase__				    = False
    UpperCAmelCase__				    = False


    def 			SCREAMING_SNAKE_CASE				(     self :      Any)		->Any:





           '''simple docstring'''

           A__ 					=       RegNetModelTester(self)
           A__ 					=       ConfigTester(self   ,					config_class=UpperCAmelCase__   ,					has_text_modality=UpperCAmelCase__)


    def 			SCREAMING_SNAKE_CASE				(     self :      Optional[Any])		->Tuple:





           '''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 :      Optional[int])		->Union[str, Any]:





           '''simple docstring'''

           return


    @unittest.skip(reason='''RegNet does not use inputs_embeds''')
    def 			SCREAMING_SNAKE_CASE				(     self :      Optional[int])		->Any:





           '''simple docstring'''

           pass


    @unittest.skip(reason='''RegNet does not support input and output embeddings''')
    def 			SCREAMING_SNAKE_CASE				(     self :      int)		->str:





           '''simple docstring'''

           pass


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


    def 			SCREAMING_SNAKE_CASE				(     self :      Any)		->int:





           '''simple docstring'''

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


    def 			SCREAMING_SNAKE_CASE				(     self :      Optional[Any])		->Optional[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=UpperCAmelCase__)
                  for name, module in model.named_modules():
                         if isinstance(UpperCAmelCase__   ,					(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[Any])		->List[str]:





           '''simple docstring'''

           def check_hidden_states_output(UpperCAmelCase__ :      Any   ,					UpperCAmelCase__ :      Optional[Any]   ,					UpperCAmelCase__ :      Dict):
                  A__ 					=       model_class(UpperCAmelCase__)
                  model.to(UpperCAmelCase__)
                  model.eval()

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

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

                  A__ 					=       self.model_tester.num_stages
                  self.assertEqual(len(UpperCAmelCase__)   ,					expected_num_stages + 1)

                  # RegNet'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 // 2, self.model_tester.image_size // 2]   ,					)

           A__					,				A__ 					=       self.model_tester.prepare_config_and_inputs_for_common()
           A__ 					=       ['''basic''', '''bottleneck''']
           for model_class in self.all_model_classes:
                  for layer_type in layers_type:
                         A__ 					=       layer_type
                         A__ 					=       True
                         check_hidden_states_output(UpperCAmelCase__   ,					UpperCAmelCase__   ,					UpperCAmelCase__)

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

                         check_hidden_states_output(UpperCAmelCase__   ,					UpperCAmelCase__   ,					UpperCAmelCase__)


    def 			SCREAMING_SNAKE_CASE				(     self :      Any)		->Any:





           '''simple docstring'''

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





    @slow
    def 			SCREAMING_SNAKE_CASE				(     self :      Tuple)		->int:





           '''simple docstring'''

           for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                  A__ 					=       RegNetModel.from_pretrained(UpperCAmelCase__)
                  self.assertIsNotNone(UpperCAmelCase__)







def SCREAMING_SNAKE_CASE							(			)  ->			str:



       """simple docstring"""

       A__ 					=       Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
       return image


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

    '''simple docstring'''






    @cached_property
    def 			SCREAMING_SNAKE_CASE				(     self :      Union[str, Any])		->Optional[int]:





           '''simple docstring'''

           return (
               AutoImageProcessor.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0])
               if is_vision_available()
               else None
           )





    @slow
    def 			SCREAMING_SNAKE_CASE				(     self :      int)		->Tuple:





           '''simple docstring'''

           A__ 					=       RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(UpperCAmelCase__)

           A__ 					=       self.default_image_processor
           A__ 					=       prepare_img()
           A__ 					=       image_processor(images=UpperCAmelCase__   ,					return_tensors='''pt''').to(UpperCAmelCase__)

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

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

           A__ 					=       torch.tensor([-0.4180, -1.5051, -3.4836]).to(UpperCAmelCase__)

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




from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef

import datasets


_lowercase											=    """\
@inproceedings{wang2019glue,
  title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},
  author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},
  note={In the Proceedings of ICLR.},
  year={2019}
}
"""

_lowercase											=    """\
GLUE, the General Language Understanding Evaluation benchmark
(https://gluebenchmark.com/) is a collection of resources for training,
evaluating, and analyzing natural language understanding systems.
"""

_lowercase											=    """
Compute GLUE evaluation metric associated to each GLUE dataset.
Args:
    predictions: list of predictions to score.
        Each translation should be tokenized into a list of tokens.
    references: list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
Returns: depending on the GLUE subset, one or several of:
    \"accuracy\": Accuracy
    \"f1\": F1 score
    \"pearson\": Pearson Correlation
    \"spearmanr\": Spearman Correlation
    \"matthews_correlation\": Matthew Correlation
Examples:

    >>> glue_metric = datasets.load_metric('glue', 'sst2')  # 'sst2' or any of [\"mnli\", \"mnli_mismatched\", \"mnli_matched\", \"qnli\", \"rte\", \"wnli\", \"hans\"]
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0}

    >>> glue_metric = datasets.load_metric('glue', 'mrpc')  # 'mrpc' or 'qqp'
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0, 'f1': 1.0}

    >>> glue_metric = datasets.load_metric('glue', 'stsb')
    >>> references = [0., 1., 2., 3., 4., 5.]
    >>> predictions = [0., 1., 2., 3., 4., 5.]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print({\"pearson\": round(results[\"pearson\"], 2), \"spearmanr\": round(results[\"spearmanr\"], 2)})
    {'pearson': 1.0, 'spearmanr': 1.0}

    >>> glue_metric = datasets.load_metric('glue', 'cola')
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'matthews_correlation': 1.0}
"""
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     List[Any]			,							UpperCAmelCase_	:     Union[str, Any]				)			->						int:
       return float((preds == labels).mean()				)
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     int			,							UpperCAmelCase_	:     str				)			->						Union[str, Any]:
       SCREAMING_SNAKE_CASE_							:				Tuple		=simple_accuracy(UpperCAmelCase_			,							UpperCAmelCase_				)
       SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=float(fa_score(y_true=UpperCAmelCase_			,							y_pred=UpperCAmelCase_				)				)
       return {
           "accuracy": acc,
           "f1": fa,
       }
def 							SCREAMING_SNAKE_CASE_	(					UpperCAmelCase_	:     Optional[int]			,							UpperCAmelCase_	:     Dict				)			->						List[str]:
       SCREAMING_SNAKE_CASE_							:				str		=float(pearsonr(UpperCAmelCase_			,							UpperCAmelCase_				)[0]				)
       SCREAMING_SNAKE_CASE_							:				List[str]		=float(spearmanr(UpperCAmelCase_			,							UpperCAmelCase_				)[0]				)
       return {
           "pearson": pearson_corr,
           "spearmanr": spearman_corr,
       }


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION  ,      _KWARGS_DESCRIPTION       )
class 			lowercase_		(      datasets.Metric       ):


       def 	_snake_case				(    self				)						->    str:
              if self.config_name not in [
                  "sst2",
                  "mnli",
                  "mnli_mismatched",
                  "mnli_matched",
                  "cola",
                  "stsb",
                  "mrpc",
                  "qqp",
                  "qnli",
                  "rte",
                  "wnli",
                  "hans",
              ]:
                     raise KeyError(
                         '''You should supply a configuration name selected in '''
                         '''["sst2", "mnli", "mnli_mismatched", "mnli_matched", '''
                         '''"cola", "stsb", "mrpc", "qqp", "qnli", "rte", "wnli", "hans"]'''				)
              return datasets.MetricInfo(
                  description=_DESCRIPTION						,       citation=_CITATION						,       inputs_description=_KWARGS_DESCRIPTION						,       features=datasets.Features(
                      {
                          '''predictions''': datasets.Value('''int64''' if self.config_name != '''stsb''' else '''float32'''				),
                          '''references''': datasets.Value('''int64''' if self.config_name != '''stsb''' else '''float32'''				),
                      }				)						,       codebase_urls=[]						,       reference_urls=[]						,       format='''numpy'''						,       )





       def 	_snake_case				(    self						,       __A						,       __A				)						->    List[Any]:
              if self.config_name == "cola":
                     return {"matthews_correlation": matthews_corrcoef(__A						,       __A				)}
              elif self.config_name == "stsb":
                     return pearson_and_spearman(__A						,       __A				)
              elif self.config_name in ["mrpc", "qqp"]:
                     return acc_and_fa(__A						,       __A				)
              elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
                     return {"accuracy": simple_accuracy(__A						,       __A				)}
              else:
                     raise KeyError(
                         '''You should supply a configuration name selected in '''
                         '''["sst2", "mnli", "mnli_mismatched", "mnli_matched", '''
                         '''"cola", "stsb", "mrpc", "qqp", "qnli", "rte", "wnli", "hans"]'''				)
