
"""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 __lowerCAmelCase		:





           @staticmethod
           def 						UpperCAmelCase (     *__UpperCAmelCase     ,					**__UpperCAmelCase				):

            '''simple docstring'''




            pass






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





@is_pipeline_test
@require_vision
@require_timm
@require_torch
class __lowerCAmelCase		(				unittest.TestCase      ):
     lowercase						       =						MODEL_FOR_DEPTH_ESTIMATION_MAPPING





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				):

      '''simple docstring'''




      __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     ,					__UpperCAmelCase     ,					__UpperCAmelCase				):

      '''simple docstring'''




      __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				):

      '''simple docstring'''




      pass





     @slow
     @require_torch
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __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()				)     ,					2_9.3_0_4				)
      self.assertEqual(nested_simplify(outputs['predicted_depth'].min().item()				)     ,					2.6_6_2				)







     @require_torch
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      self.skipTest('There is not hf-internal-testing tiny model for either GLPN nor DPT'				)





"""simple docstring"""

from typing import Union

import fire
import torch
from tqdm import tqdm






def 							A						(    snake_case				:str    , snake_case				:str = "cpu"    , snake_case				:Union[str, None] = None		)				->     None:
 __UpperCamelCase      =	torch.load(snake_case    , map_location=snake_case		)
 for k, v in tqdm(state_dict.items()		):
  if not isinstance(snake_case    , torch.Tensor		):
   raise TypeError('FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin'		)
  __UpperCamelCase      =	v.half()
 if save_path is None:  # overwrite src_path
  __UpperCamelCase      =	src_path
 torch.save(snake_case    , snake_case		)


if __name__ == "__main__":
      fire.Fire(convert)



