





'''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 		UpperCAmelCase   :


        """simple docstring"""







        @staticmethod
        def   UpperCamelCase__      (				*UpperCamelCase__    :					int							,	**UpperCamelCase__    :					Dict  )     ->						List[str]:
          pass


def 					_a   (__SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

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







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


  """simple docstring"""







  lowerCAmelCase_					=				MODEL_FOR_DEPTH_ESTIMATION_MAPPING


  def   UpperCamelCase__      (				self    :					List[Any]							,	UpperCamelCase__    :					Optional[Any]							,	UpperCamelCase__    :					Optional[int]							,	UpperCamelCase__    :					Optional[Any]  )     ->						List[Any]:
    _UpperCamelCase       			=DepthEstimationPipeline(model=UpperCamelCase__							,	image_processor=UpperCamelCase__  )
    return depth_estimator, [
        "./tests/fixtures/tests_samples/COCO/000000039769.png",
        "./tests/fixtures/tests_samples/COCO/000000039769.png",
    ]


  def   UpperCamelCase__      (				self    :					Optional[int]							,	UpperCamelCase__    :					int							,	UpperCamelCase__    :					Dict  )     ->						Any:
    _UpperCamelCase       			=depth_estimator('''./tests/fixtures/tests_samples/COCO/000000039769.png'''  )
    self.assertEqual({'''predicted_depth''': ANY(torch.Tensor  ), '''depth''': ANY(Image.Image  )}							,	UpperCamelCase__  )
    import datasets

    _UpperCamelCase       			=datasets.load_dataset('''hf-internal-testing/fixtures_image_utils'''							,	'''image'''							,	split='''test'''  )
    _UpperCamelCase       			=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  )},
        ]							,	UpperCamelCase__							,	)


  @require_tf
  @unittest.skip('''Depth estimation is not implemented in TF'''  )
  def   UpperCamelCase__      (				self    :					Union[str, Any]  )     ->						int:
    pass


  @slow
  @require_torch
  def   UpperCamelCase__      (				self    :					Optional[Any]  )     ->						Optional[int]:
    _UpperCamelCase       			='''Intel/dpt-large'''
    _UpperCamelCase       			=pipeline('''depth-estimation'''							,	model=UpperCamelCase__  )
    _UpperCamelCase       			=depth_estimator('''http://images.cocodataset.org/val2017/000000039769.jpg'''  )
    _UpperCamelCase       			=hashimage(outputs['''depth''']  )

    # This seems flaky.
    # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
    self.assertEqual(nested_simplify(outputs['''predicted_depth'''].max().item()  )							,	29.304  )
    self.assertEqual(nested_simplify(outputs['''predicted_depth'''].min().item()  )							,	2.662  )




  @require_torch
  def   UpperCamelCase__      (				self    :					Any  )     ->						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'''  )













'''simple docstring'''






import contextlib
from multiprocessing import Pool, RLock

from tqdm.auto import tqdm

from ..utils import experimental, logging


__lowerCamelCase						: Union[str, Any]		=   logging.get_logger(__name__)







class 		UpperCAmelCase   :


  """simple docstring"""







  lowerCAmelCase_					=				None




@experimental
def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  if ParallelBackendConfig.backend_name is None:
    return _map_with_multiprocessing_pool(
        __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )

  return _map_with_joblib(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )


def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  _UpperCamelCase       			=num_proc if num_proc <= len(__SCREAMING_SNAKE_CASE   ) else len(__SCREAMING_SNAKE_CASE   )
  _UpperCamelCase       			=[]  # We organize the splits ourselve (contiguous splits)
  for index in range(__SCREAMING_SNAKE_CASE   ):
    _UpperCamelCase       			=len(__SCREAMING_SNAKE_CASE   ) // num_proc
    _UpperCamelCase       			=len(__SCREAMING_SNAKE_CASE   ) % num_proc
    _UpperCamelCase       			=div * index + min(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )
    _UpperCamelCase       			=start + div + (1 if index < mod else 0)
    split_kwds.append((function, iterable[start:end], types, index, disable_tqdm, desc)   )

  if len(__SCREAMING_SNAKE_CASE   ) != sum(len(i[1]   ) for i in split_kwds   ):
    raise ValueError(
        f'''Error dividing inputs iterable among processes. '''
        f'''Total number of objects {len(__SCREAMING_SNAKE_CASE   )}, '''
        f'''length: {sum(len(i[1]   ) for i in split_kwds   )}'''   )

  logger.info(
      f'''Spawning {num_proc} processes for {len(__SCREAMING_SNAKE_CASE   )} objects in slices of {[len(i[1]   ) for i in split_kwds]}'''   )
  _UpperCamelCase			,     _UpperCamelCase       			=None, None
  if not disable_tqdm:
    _UpperCamelCase			,     _UpperCamelCase       			=(RLock(),), tqdm.set_lock
  with Pool(__SCREAMING_SNAKE_CASE  ,      initargs=__SCREAMING_SNAKE_CASE  ,      initializer=__SCREAMING_SNAKE_CASE   ) as pool:
    _UpperCamelCase       			=pool.map(__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   )
  logger.info(f'''Finished {num_proc} processes'''   )
  _UpperCamelCase       			=[obj for proc_res in mapped for obj in proc_res]
  logger.info(f'''Unpacked {len(__SCREAMING_SNAKE_CASE   )} objects'''   )

  return mapped


def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  import joblib

  with joblib.parallel_backend(ParallelBackendConfig.backend_name  ,      n_jobs=__SCREAMING_SNAKE_CASE   ):
    return joblib.Parallel()(
        joblib.delayed(__SCREAMING_SNAKE_CASE   )((function, obj, types, None, True, None)   ) for obj in iterable   )


@experimental
@contextlib.contextmanager
def 					_a   (__SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  _UpperCamelCase       			=backend_name

  if backend_name == "spark":
    from joblibspark import register_spark

    register_spark()

    # TODO: call create_cache_and_write_probe if "download" in steps
    # TODO: raise NotImplementedError when Dataset.map etc is called

  try:
    yield
  finally:
    _UpperCamelCase       			=None






