





'''simple docstring'''







from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_tf_available():
   import tensorflow as tf


_lowercase       						=			logging.get_logger(__name__)
@dataclass
class 			_lowercase						(       __a					):
     _UpperCAmelCase										=					[
         '''no_inference''',
         '''no_cuda''',
         '''no_tpu''',
         '''no_speed''',
         '''no_memory''',
         '''no_env_print''',
         '''no_multi_process''',
     ]







     def __init__( self    ,   **A__    ) ->	Optional[int]:

      '''simple docstring'''





      for deprecated_arg in self.deprecated_args:
       if deprecated_arg in kwargs:
        snake_case				=			deprecated_arg[3:]
        snake_case				=			not kwargs.pop(A__    )
        logger.warning(
            F"""{deprecated_arg} is depreciated. Please use --no-{positive_arg} or"""
            F""" {positive_arg}={kwargs[positive_arg]}"""    )
      snake_case				=			kwargs.pop('''tpu_name'''    ,   self.tpu_name    )
      snake_case				=			kwargs.pop('''device_idx'''    ,   self.device_idx    )
      snake_case				=			kwargs.pop('''eager_mode'''    ,   self.eager_mode    )
      snake_case				=			kwargs.pop('''use_xla'''    ,   self.use_xla    )
      super().__init__(**A__    )

     _UpperCAmelCase										=					field(
         default=__a				,		metadata={'''help''': '''Name of TPU'''}				,		)
     _UpperCAmelCase										=					field(
         default=0				,		metadata={'''help''': '''CPU / GPU device index. Defaults to 0.'''}				,		)
     _UpperCAmelCase										=					field(default=__a				,		metadata={'''help''': '''Benchmark models in eager model.'''}					)
     _UpperCAmelCase										=					field(
         default=__a				,		metadata={
             '''help''': '''Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`.'''
         }				,		)







     @cached_property
     def     UpperCamelCase			( self    ) ->	Tuple["tf.distribute.cluster_resolver.TPUClusterResolver"]:

      '''simple docstring'''





      requires_backends(self    ,   ['''tf''']    )
      snake_case				=			None
      if self.tpu:
       try:
        if self.tpu_name:
         snake_case				=			tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name    )
        else:
         snake_case				=			tf.distribute.cluster_resolver.TPUClusterResolver()
       except ValueError:
        snake_case				=			None
      return tpu







     @cached_property
     def     UpperCamelCase			( self    ) ->	Tuple["tf.distribute.Strategy", "tf.distribute.cluster_resolver.TPUClusterResolver"]:

      '''simple docstring'''





      requires_backends(self    ,   ['''tf''']    )
      if self.is_tpu:
       tf.config.experimental_connect_to_cluster(self._setup_tpu    )
       tf.tpu.experimental.initialize_tpu_system(self._setup_tpu    )

       snake_case				=			tf.distribute.TPUStrategy(self._setup_tpu    )
      else:
       # currently no multi gpu is allowed
       if self.is_gpu:
        # TODO: Currently only single GPU is supported
        tf.config.set_visible_devices(self.gpu_list[self.device_idx]    ,   '''GPU'''    )
        snake_case				=			tf.distribute.OneDeviceStrategy(device=F"""/gpu:{self.device_idx}"""    )
       else:
        tf.config.set_visible_devices([]    ,   '''GPU'''    )  # disable GPU
        snake_case				=			tf.distribute.OneDeviceStrategy(device=F"""/cpu:{self.device_idx}"""    )

      return strategy







     @property
     def     UpperCamelCase			( self    ) ->	bool:

      '''simple docstring'''





      requires_backends(self    ,   ['''tf''']    )
      return self._setup_tpu is not None







     @property
     def     UpperCamelCase			( self    ) ->	"tf.distribute.Strategy":

      '''simple docstring'''





      requires_backends(self    ,   ['''tf''']    )
      return self._setup_strategy







     @property
     def     UpperCamelCase			( self    ) ->	Optional[Any]:

      '''simple docstring'''





      requires_backends(self    ,   ['''tf''']    )
      return tf.config.list_physical_devices('''GPU'''    )







     @property
     def     UpperCamelCase			( self    ) ->	int:

      '''simple docstring'''





      requires_backends(self    ,   ['''tf''']    )
      if self.cuda:
       return len(self.gpu_list    )
      return 0







     @property
     def     UpperCamelCase			( self    ) ->	bool:

      '''simple docstring'''





      return self.n_gpu > 0








'''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						:







        @staticmethod
        def     UpperCamelCase			( *A__    ,   **A__    ) ->	List[Any]:
         pass

def    __UpperCamelCase	(	a    : Image     ) ->str:
 snake_case				=			hashlib.mda(image.tobytes()     )
 return m.hexdigest()






@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 			_lowercase						(       unittest.TestCase					):
     _UpperCAmelCase										=					MODEL_FOR_DEPTH_ESTIMATION_MAPPING







     def     UpperCamelCase			( self    ,   A__    ,   A__    ,   A__    ) ->	Union[str, Any]:
      snake_case				=			DepthEstimationPipeline(model=A__    ,   image_processor=A__    )
      return depth_estimator, [
          "./tests/fixtures/tests_samples/COCO/000000039769.png",
          "./tests/fixtures/tests_samples/COCO/000000039769.png",
      ]







     def     UpperCamelCase			( self    ,   A__    ,   A__    ) ->	List[Any]:
      snake_case				=			depth_estimator('''./tests/fixtures/tests_samples/COCO/000000039769.png'''    )
      self.assertEqual({'''predicted_depth''': ANY(torch.Tensor    ), '''depth''': ANY(Image.Image    )}    ,   A__    )
      import datasets

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







     @require_tf
     @unittest.skip('''Depth estimation is not implemented in TF'''    )
     def     UpperCamelCase			( self    ) ->	Optional[Any]:
      pass







     @slow
     @require_torch
     def     UpperCamelCase			( self    ) ->	Dict:
      snake_case				=			'''Intel/dpt-large'''
      snake_case				=			pipeline('''depth-estimation'''    ,   model=A__    )
      snake_case				=			depth_estimator('''http://images.cocodataset.org/val2017/000000039769.jpg'''    )
      snake_case				=			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     UpperCamelCase			( self    ) ->	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'''    )

