


'''simple docstring'''

import inspect
import unittest

from transformers import is_torch_available, is_vision_available
from transformers.models.auto import get_values
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 transformers import MODEL_MAPPING, GLPNConfig, GLPNForDepthEstimation, GLPNModel
       from transformers.models.glpn.modeling_glpn import GLPN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from PIL import Image

       from transformers import GLPNImageProcessor







class 			UpperCAmelCase			(							SCREAMING_SNAKE_CASE__):

 def 		lowercase_    (			self			:						str       ):





    """simple docstring"""



    UpperCamelCase__			    =    self.config_class(**self.inputs_dict       )
    self.parent.assertTrue(hasattr(a_,       "hidden_sizes"       )       )
    self.parent.assertTrue(hasattr(a_,       "num_attention_heads"       )       )
    self.parent.assertTrue(hasattr(a_,       "num_encoder_blocks"       )       )







class 			UpperCAmelCase			:

 def __init__(			self			:						Optional[int],       a_			:						Any,       a_			:						Tuple=13,       a_			:						int=64,       a_			:						List[str]=3,       a_			:						Optional[int]=4,       a_			:						List[Any]=[2, 2, 2, 2],       a_			:						Optional[int]=[8, 4, 2, 1],       a_			:						int=[16, 32, 64, 128],       a_			:						Any=[1, 4, 8, 16],       a_			:						List[str]=[1, 2, 4, 8],       a_			:						Union[str, Any]=True,       a_			:						List[str]=True,       a_			:						Tuple="gelu",       a_			:						Union[str, Any]=0.1,       a_			:						List[str]=0.1,       a_			:						Union[str, Any]=0.02,       a_			:						Union[str, Any]=16,       a_			:						Dict=3,       a_			:						List[Any]=None,       ):





    """simple docstring"""



    UpperCamelCase__			    =    parent
    UpperCamelCase__			    =    batch_size
    UpperCamelCase__			    =    image_size
    UpperCamelCase__			    =    num_channels
    UpperCamelCase__			    =    num_encoder_blocks
    UpperCamelCase__			    =    sr_ratios
    UpperCamelCase__			    =    depths
    UpperCamelCase__			    =    hidden_sizes
    UpperCamelCase__			    =    downsampling_rates
    UpperCamelCase__			    =    num_attention_heads
    UpperCamelCase__			    =    is_training
    UpperCamelCase__			    =    use_labels
    UpperCamelCase__			    =    hidden_act
    UpperCamelCase__			    =    hidden_dropout_prob
    UpperCamelCase__			    =    attention_probs_dropout_prob
    UpperCamelCase__			    =    initializer_range
    UpperCamelCase__			    =    decoder_hidden_size
    UpperCamelCase__			    =    num_labels
    UpperCamelCase__			    =    scope

 def 		lowercase_    (			self			:						List[str]       ):





    """simple docstring"""



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

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

    UpperCamelCase__			    =    self.get_config()
    return config, pixel_values, labels

 def 		lowercase_    (			self			:						Optional[int]       ):





    """simple docstring"""



    return GLPNConfig(
        image_size=self.image_size,       num_channels=self.num_channels,       num_encoder_blocks=self.num_encoder_blocks,       depths=self.depths,       hidden_sizes=self.hidden_sizes,       num_attention_heads=self.num_attention_heads,       hidden_act=self.hidden_act,       hidden_dropout_prob=self.hidden_dropout_prob,       attention_probs_dropout_prob=self.attention_probs_dropout_prob,       initializer_range=self.initializer_range,       decoder_hidden_size=self.decoder_hidden_size,       )

 def 		lowercase_    (			self			:						List[Any],       a_			:						Union[str, Any],       a_			:						Optional[Any],       a_			:						Optional[int]       ):





    """simple docstring"""



    UpperCamelCase__			    =    GLPNModel(config=a_       )
    model.to(a_       )
    model.eval()
    UpperCamelCase__			    =    model(a_       )
    UpperCamelCase__			    =    UpperCamelCase__			    =    self.image_size // (self.downsampling_rates[-1] * 2)
    self.parent.assertEqual(
        result.last_hidden_state.shape,       (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)       )

 def 		lowercase_    (			self			:						int,       a_			:						List[str],       a_			:						Optional[Any],       a_			:						Tuple       ):





    """simple docstring"""



    UpperCamelCase__			    =    self.num_labels
    UpperCamelCase__			    =    GLPNForDepthEstimation(a_       )
    model.to(a_       )
    model.eval()
    UpperCamelCase__			    =    model(a_       )
    self.parent.assertEqual(result.predicted_depth.shape,       (self.batch_size, self.image_size, self.image_size)       )
    UpperCamelCase__			    =    model(a_,       labels=a_       )
    self.parent.assertEqual(result.predicted_depth.shape,       (self.batch_size, self.image_size, self.image_size)       )

 def 		lowercase_    (			self			:						Optional[int]       ):





    """simple docstring"""



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







@require_torch
class 			UpperCAmelCase			(							SCREAMING_SNAKE_CASE__		,      SCREAMING_SNAKE_CASE__		,      unittest.TestCase):
 _lowerCamelCase			:       str            =       (GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
 _lowerCamelCase			:       Any            =       (
     {'depth-estimation': GLPNForDepthEstimation, 'feature-extraction': GLPNModel} if is_torch_available() else {}
 )

 _lowerCamelCase			:       Optional[Any]            =       False
 _lowerCamelCase			:       Any            =       False
 _lowerCamelCase			:       Tuple            =       False

 def 		lowercase_    (			self			:						Dict       ):





    """simple docstring"""



    UpperCamelCase__			    =    GLPNModelTester(self       )
    UpperCamelCase__			    =    GLPNConfigTester(self,       config_class=a_       )

 def 		lowercase_    (			self			:						str       ):





    """simple docstring"""



    self.config_tester.run_common_tests()

 def 		lowercase_    (			self			:						Optional[int]       ):





    """simple docstring"""



    UpperCamelCase__			    =    self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*a_       )

 def 		lowercase_    (			self			:						List[str]       ):





    """simple docstring"""



    UpperCamelCase__			    =    self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_depth_estimation(*a_       )

 @unittest.skip("GLPN does not use inputs_embeds"       )
 def 		lowercase_    (			self			:						Union[str, Any]       ):





    """simple docstring"""



    pass

 @unittest.skip("GLPN does not have get_input_embeddings method and get_output_embeddings methods"       )
 def 		lowercase_    (			self			:						Optional[Any]       ):





    """simple docstring"""



    pass

 def 		lowercase_    (			self			:						str       ):





    """simple docstring"""



    UpperCamelCase__    ,		UpperCamelCase__			    =    self.model_tester.prepare_config_and_inputs_for_common()

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

       UpperCamelCase__			    =    ["pixel_values"]
       self.assertListEqual(arg_names[:1],       a_       )

 def 		lowercase_    (			self			:						Any       ):





    """simple docstring"""



    UpperCamelCase__    ,		UpperCamelCase__			    =    self.model_tester.prepare_config_and_inputs_for_common()
    UpperCamelCase__			    =    True

    for model_class in self.all_model_classes:
       UpperCamelCase__			    =    True
       UpperCamelCase__			    =    False
       UpperCamelCase__			    =    True
       UpperCamelCase__			    =    model_class(a_       )
       model.to(a_       )
       model.eval()
       with torch.no_grad():
          UpperCamelCase__			    =    model(**self._prepare_for_class(a_,       a_       )       )
       UpperCamelCase__			    =    outputs.attentions

       UpperCamelCase__			    =    sum(self.model_tester.depths       )
       self.assertEqual(len(a_       ),       a_       )

       # check that output_attentions also work using config
       del inputs_dict["output_attentions"]
       UpperCamelCase__			    =    True
       UpperCamelCase__			    =    model_class(a_       )
       model.to(a_       )
       model.eval()
       with torch.no_grad():
          UpperCamelCase__			    =    model(**self._prepare_for_class(a_,       a_       )       )
       UpperCamelCase__			    =    outputs.attentions

       self.assertEqual(len(a_       ),       a_       )

       # verify the first attentions (first block, first layer)
       UpperCamelCase__			    =    (self.model_tester.image_size // 4) ** 2
       UpperCamelCase__			    =    (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
       self.assertListEqual(
           list(attentions[0].shape[-3:]       ),       [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len],       )

       # verify the last attentions (last block, last layer)
       UpperCamelCase__			    =    (self.model_tester.image_size // 32) ** 2
       UpperCamelCase__			    =    (self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
       self.assertListEqual(
           list(attentions[-1].shape[-3:]       ),       [self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len],       )
       UpperCamelCase__			    =    len(a_       )

       # Check attention is always last and order is fine
       UpperCamelCase__			    =    True
       UpperCamelCase__			    =    True
       UpperCamelCase__			    =    model_class(a_       )
       model.to(a_       )
       model.eval()
       with torch.no_grad():
          UpperCamelCase__			    =    model(**self._prepare_for_class(a_,       a_       )       )

       self.assertEqual(out_len + 1,       len(a_       )       )

       UpperCamelCase__			    =    outputs.attentions

       self.assertEqual(len(a_       ),       a_       )
       # verify the first attentions (first block, first layer)
       UpperCamelCase__			    =    (self.model_tester.image_size // 4) ** 2
       UpperCamelCase__			    =    (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
       self.assertListEqual(
           list(self_attentions[0].shape[-3:]       ),       [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len],       )

 def 		lowercase_    (			self			:						List[str]       ):





    """simple docstring"""



    def check_hidden_states_output(a_			:						Union[str, Any],       a_			:						int,       a_			:						List[Any]       ):
       UpperCamelCase__			    =    model_class(a_       )
       model.to(a_       )
       model.eval()

       with torch.no_grad():
          UpperCamelCase__			    =    model(**self._prepare_for_class(a_,       a_       )       )

       UpperCamelCase__			    =    outputs.hidden_states

       UpperCamelCase__			    =    self.model_tester.num_encoder_blocks
       self.assertEqual(len(a_       ),       a_       )

       # verify the first hidden states (first block)
       self.assertListEqual(
           list(hidden_states[0].shape[-3:]       ),       [
               self.model_tester.hidden_sizes[0],
               self.model_tester.image_size // 4,
               self.model_tester.image_size // 4,
           ],       )

    UpperCamelCase__    ,		UpperCamelCase__			    =    self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
       UpperCamelCase__			    =    True
       check_hidden_states_output(a_,       a_,       a_       )

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

       check_hidden_states_output(a_,       a_,       a_       )

 def 		lowercase_    (			self			:						Any       ):





    """simple docstring"""



    if not self.model_tester.is_training:
       return

    UpperCamelCase__    ,		UpperCamelCase__			    =    self.model_tester.prepare_config_and_inputs_for_common()
    UpperCamelCase__			    =    True

    for model_class in self.all_model_classes:
       if model_class in get_values(a_       ):
          continue
       # TODO: remove the following 3 lines once we have a MODEL_FOR_DEPTH_ESTIMATION_MAPPING
       # this can then be incorporated into _prepare_for_class in test_modeling_common.py
       if model_class.__name__ == "GLPNForDepthEstimation":
          UpperCamelCase__    ,		UpperCamelCase__    ,		UpperCamelCase__    ,		UpperCamelCase__			    =    inputs_dict["pixel_values"].shape
          UpperCamelCase__			    =    torch.zeros(
              [self.model_tester.batch_size, height, width],       device=a_       ).long()
       UpperCamelCase__			    =    model_class(a_       )
       model.to(a_       )
       model.train()
       UpperCamelCase__			    =    self._prepare_for_class(a_,       a_,       return_labels=a_       )
       UpperCamelCase__			    =    model(**a_       ).loss
       loss.backward()

 @slow
 def 		lowercase_    (			self			:						Union[str, Any]       ):





    """simple docstring"""



    for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       UpperCamelCase__			    =    GLPNModel.from_pretrained(a_       )
       self.assertIsNotNone(a_       )







def 							SCREAMING_SNAKE_CASE__(		)		->       int:





   '''simple docstring'''







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







@require_torch
@require_vision
@slow
class 			UpperCAmelCase			(							unittest.TestCase):

 @slow
 def 		lowercase_    (			self			:						List[str]       ):





    """simple docstring"""



    UpperCamelCase__			    =    GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]       )
    UpperCamelCase__			    =    GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]       ).to(a_       )

    UpperCamelCase__			    =    prepare_img()
    UpperCamelCase__			    =    image_processor(images=a_,       return_tensors="pt"       ).to(a_       )

    # forward pass
    with torch.no_grad():
       UpperCamelCase__			    =    model(**a_       )

    # verify the predicted depth
    UpperCamelCase__			    =    torch.Size([1, 480, 640]       )
    self.assertEqual(outputs.predicted_depth.shape,       a_       )

    UpperCamelCase__			    =    torch.tensor(
        [[3.4_291, 2.7_865, 2.5_151], [3.2_841, 2.7_021, 2.3_502], [3.1_147, 2.4_625, 2.2_481]]       ).to(a_       )

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






from sklearn.metrics import accuracy_score

import datasets


UpperCAmelCase__    :      Optional[Any]       					='''
Accuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:
Accuracy = (TP + TN) / (TP + TN + FP + FN)
 Where:
TP: True positive
TN: True negative
FP: False positive
FN: False negative
'''


UpperCAmelCase__    :      Optional[int]       					='''
Args:
    predictions (`list` of `int`): Predicted labels.
    references (`list` of `int`): Ground truth labels.
    normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.
    sample_weight (`list` of `float`): Sample weights Defaults to None.

Returns:
    accuracy (`float` or `int`): Accuracy score. Minimum possible value is 0. Maximum possible value is 1.0, or the number of examples input, if `normalize` is set to `True`.. A higher score means higher accuracy.

Examples:

    Example 1-A simple example
        >>> accuracy_metric = datasets.load_metric("accuracy")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])
        >>> print(results)
        {\'accuracy\': 0.5}

    Example 2-The same as Example 1, except with `normalize` set to `False`.
        >>> accuracy_metric = datasets.load_metric("accuracy")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)
        >>> print(results)
        {\'accuracy\': 3.0}

    Example 3-The same as Example 1, except with `sample_weight` set.
        >>> accuracy_metric = datasets.load_metric("accuracy")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], sample_weight=[0.5, 2, 0.7, 0.5, 9, 0.4])
        >>> print(results)
        {\'accuracy\': 0.8778625954198473}
'''


UpperCAmelCase__    :      List[str]       					='''
@article{scikit-learn,
  title={Scikit-learn: Machine Learning in {P}ython},
  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
  journal={Journal of Machine Learning Research},
  volume={12},
  pages={2825--2830},
  year={2011}
}
'''

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


				def _snake_case							(		self				):
								return datasets.MetricInfo(
								    description=_DESCRIPTION ,			citation=_CITATION ,			inputs_description=_KWARGS_DESCRIPTION ,			features=datasets.Features(
								        {
								            """predictions""": datasets.Sequence(datasets.Value("""int32"""				)				),
								            """references""": datasets.Sequence(datasets.Value("""int32"""				)				),
								        }
								        if self.config_name == """multilabel"""
								        else {
								            """predictions""": datasets.Value("""int32"""				),
								            """references""": datasets.Value("""int32"""				),
								        }				) ,			reference_urls=["""https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html"""] ,			)


				def _snake_case							(		self ,			UpperCAmelCase_ ,			UpperCAmelCase_ ,			UpperCAmelCase_=True ,			UpperCAmelCase_=None				):
								return {
								    "accuracy": float(
								        accuracy_score(UpperCAmelCase_ ,			UpperCAmelCase_ ,			normalize=UpperCAmelCase_ ,			sample_weight=UpperCAmelCase_				)				)
								}





