

'''simple docstring'''






import os

import pytest

from datasets import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
)


lowerCAmelCase__      		=			pytest.mark.integration


@pytest.mark.parametrize('''path'''  ,       ['''paws''', '''csv''']   )
def 	_A  (					A__  ,       A__   ):






 """simple docstring"""



 inspect_dataset(A__  ,       A__   )
 __lowercase													=				path + '''.py'''
 assert script_name in os.listdir(A__   )
 assert "__pycache__" not in os.listdir(A__   )


@pytest.mark.filterwarnings('''ignore:inspect_metric is deprecated:FutureWarning'''   )
@pytest.mark.filterwarnings('''ignore:metric_module_factory is deprecated:FutureWarning'''   )
@pytest.mark.parametrize('''path'''  ,       ['''accuracy''']   )
def 	_A  (					A__  ,       A__   ):






 """simple docstring"""



 inspect_metric(A__  ,       A__   )
 __lowercase													=				path + '''.py'''
 assert script_name in os.listdir(A__   )
 assert "__pycache__" not in os.listdir(A__   )


@pytest.mark.parametrize(
    '''path, config_name, expected_splits'''  ,       [
        ('''squad''', '''plain_text''', ['''train''', '''validation''']),
        ('''dalle-mini/wit''', '''dalle-mini--wit''', ['''train''']),
        ('''paws''', '''labeled_final''', ['''train''', '''test''', '''validation''']),
    ]  ,       )
def 	_A  (					A__  ,       A__  ,       A__   ):






 """simple docstring"""



 __lowercase													=				get_dataset_config_info(A__  ,       config_name=A__   )
 assert info.config_name == config_name
 assert list(info.splits.keys()   ) == expected_splits


@pytest.mark.parametrize(
    '''path, config_name, expected_exception'''  ,       [
        ('''paws''', None, ValueError),
    ]  ,       )
def 	_A  (					A__  ,       A__  ,       A__   ):






 """simple docstring"""



 with pytest.raises(A__   ):
  get_dataset_config_info(A__  ,       config_name=A__   )


@pytest.mark.parametrize(
    '''path, expected'''  ,       [
        ('''squad''', '''plain_text'''),
        ('''acronym_identification''', '''default'''),
        ('''lhoestq/squad''', '''plain_text'''),
        ('''lhoestq/test''', '''default'''),
        ('''lhoestq/demo1''', '''lhoestq--demo1'''),
        ('''dalle-mini/wit''', '''dalle-mini--wit'''),
    ]  ,       )
def 	_A  (					A__  ,       A__   ):






 """simple docstring"""



 __lowercase													=				get_dataset_config_names(A__   )
 assert expected in config_names


@pytest.mark.parametrize(
    '''path, expected_configs, expected_splits_in_first_config'''  ,       [
        ('''squad''', ['''plain_text'''], ['''train''', '''validation''']),
        ('''dalle-mini/wit''', ['''dalle-mini--wit'''], ['''train''']),
        ('''paws''', ['''labeled_final''', '''labeled_swap''', '''unlabeled_final'''], ['''train''', '''test''', '''validation''']),
    ]  ,       )
def 	_A  (					A__  ,       A__  ,       A__   ):






 """simple docstring"""



 __lowercase													=				get_dataset_infos(A__   )
 assert list(infos.keys()   ) == expected_configs
 __lowercase													=				expected_configs[0]
 assert expected_config in infos
 __lowercase													=				infos[expected_config]
 assert info.config_name == expected_config
 assert list(info.splits.keys()   ) == expected_splits_in_first_config


@pytest.mark.parametrize(
    '''path, expected_config, expected_splits'''  ,       [
        ('''squad''', '''plain_text''', ['''train''', '''validation''']),
        ('''dalle-mini/wit''', '''dalle-mini--wit''', ['''train''']),
        ('''paws''', '''labeled_final''', ['''train''', '''test''', '''validation''']),
    ]  ,       )
def 	_A  (					A__  ,       A__  ,       A__   ):






 """simple docstring"""



 __lowercase													=				get_dataset_infos(A__   )
 assert expected_config in infos
 __lowercase													=				infos[expected_config]
 assert info.config_name == expected_config
 assert list(info.splits.keys()   ) == expected_splits






@pytest.mark.parametrize(
    '''path, config_name, expected_exception'''  ,       [
        ('''paws''', None, ValueError),
    ]  ,       )
def 	_A  (					A__  ,       A__  ,       A__   ):






 """simple docstring"""



 with pytest.raises(A__   ):
  get_dataset_split_names(A__  ,       config_name=A__   )




'''simple docstring'''






import hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image
else:






 class     lowercase_			:




   """simple docstring"""




   @staticmethod
   def       SCREAMING_SNAKE_CASE   (   *lowercase__     :  Union[str, Any]      ,**lowercase__     :  Tuple    ):
    pass


def 	_A  (					A__   ):






 """simple docstring"""



 __lowercase													=				hashlib.mda(image.tobytes()   )
 return m.hexdigest()






@is_pipeline_test
@require_vision
@require_timm
@require_torch
class     lowercase_			(unittest.TestCase   ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE					:						Optional[Any]           =    MODEL_FOR_DEPTH_ESTIMATION_MAPPING






  def       SCREAMING_SNAKE_CASE   (   self     :  List[Any]      ,lowercase__     :  int      ,lowercase__     :  List[str]      ,lowercase__     :  int    ):
   __lowercase													=				DepthEstimationPipeline(model=lowercase__      ,image_processor=lowercase__    )
   return depth_estimator, [
       "./tests/fixtures/tests_samples/COCO/000000039769.png",
       "./tests/fixtures/tests_samples/COCO/000000039769.png",
   ]






  def       SCREAMING_SNAKE_CASE   (   self     :  List[Any]      ,lowercase__     :  List[str]      ,lowercase__     :  List[str]    ):
   __lowercase													=				depth_estimator('''./tests/fixtures/tests_samples/COCO/000000039769.png'''    )
   self.assertEqual({'''predicted_depth''': ANY(torch.Tensor    ), '''depth''': ANY(Image.Image    )}      ,lowercase__    )
   import datasets

   __lowercase													=				datasets.load_dataset('''hf-internal-testing/fixtures_image_utils'''      ,'''image'''      ,split='''test'''    )
   __lowercase													=				depth_estimator(
       [
           Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''    ),
           '''http://images.cocodataset.org/val2017/000000039769.jpg''',
           # RGBA
           dataset[0]['''file'''],
           # LA
           dataset[1]['''file'''],
           # L
           dataset[2]['''file'''],
       ]    )
   self.assertEqual(
       [
           {'''predicted_depth''': ANY(torch.Tensor    ), '''depth''': ANY(Image.Image    )},
           {'''predicted_depth''': ANY(torch.Tensor    ), '''depth''': ANY(Image.Image    )},
           {'''predicted_depth''': ANY(torch.Tensor    ), '''depth''': ANY(Image.Image    )},
           {'''predicted_depth''': ANY(torch.Tensor    ), '''depth''': ANY(Image.Image    )},
           {'''predicted_depth''': ANY(torch.Tensor    ), '''depth''': ANY(Image.Image    )},
       ]      ,lowercase__      ,)






  @require_tf
  @unittest.skip('''Depth estimation is not implemented in TF'''    )
  def       SCREAMING_SNAKE_CASE   (   self     :  Dict    ):
   pass






  @slow
  @require_torch
  def       SCREAMING_SNAKE_CASE   (   self     :  List[str]    ):
   __lowercase													=				'''Intel/dpt-large'''
   __lowercase													=				pipeline('''depth-estimation'''      ,model=lowercase__    )
   __lowercase													=				depth_estimator('''http://images.cocodataset.org/val2017/000000039769.jpg'''    )
   __lowercase													=				hashimage(outputs['''depth''']    )

   # This seems flaky.
   # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
   self.assertEqual(nested_simplify(outputs['''predicted_depth'''].max().item()    )      ,2_9.3_0_4    )
   self.assertEqual(nested_simplify(outputs['''predicted_depth'''].min().item()    )      ,2.6_6_2    )






  @require_torch
  def       SCREAMING_SNAKE_CASE   (   self     :  List[Any]    ):
   # This is highly irregular to have no small tests.
   self.skipTest('''There is not hf-internal-testing tiny model for either GLPN nor DPT'''    )

