'''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 		lowerCAmelCase__					(				*lowerCamelCase_					,**lowerCamelCase_							)			->			List[Any]:



           '''simple docstring'''



           pass







def __UpperCamelCase(   _A					:				Union[str, Any]    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Optional[int]   				=  hashlib.mda(image.tobytes()    )
   return m.hexdigest()


@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 				_lowercase	(      unittest.TestCase   ):
      '''simple docstring'''


      UpperCAmelCase_      :      List[str]      							=   MODEL_FOR_DEPTH_ESTIMATION_MAPPING




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_							)			->			Tuple:



         '''simple docstring'''



         UpperCAmelCase__							:				Tuple   				=  DepthEstimationPipeline(model=_lowercase					,image_processor=_lowercase							)
         return depth_estimator, [
             "./tests/fixtures/tests_samples/COCO/000000039769.png",
             "./tests/fixtures/tests_samples/COCO/000000039769.png",
         ]




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_							)			->			Tuple:



         '''simple docstring'''



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

         UpperCAmelCase__							:				str   				=  datasets.load_dataset('''hf-internal-testing/fixtures_image_utils'''					,'''image'''					,split='''test'''							)
         UpperCAmelCase__							:				str   				=  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 		lowerCAmelCase__					(				self							)			->			str:



         '''simple docstring'''



         pass




      @slow
      @require_torch
      def 		lowerCAmelCase__					(				self							)			->			List[str]:



         '''simple docstring'''



         UpperCAmelCase__							:				Union[str, Any]   				=  '''Intel/dpt-large'''
         UpperCAmelCase__							:				Optional[Any]   				=  pipeline('''depth-estimation'''					,model=_lowercase							)
         UpperCAmelCase__							:				int   				=  depth_estimator('''http://images.cocodataset.org/val2017/000000039769.jpg'''							)
         UpperCAmelCase__							:				Dict   				=  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 		lowerCAmelCase__					(				self							)			->			List[str]:



         '''simple docstring'''



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





'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import _LazyModule


UpperCamelCase__     :	Union[str, Any]				=			{'tokenization_byt5': ['ByT5Tokenizer']}


if TYPE_CHECKING:
  from .tokenization_byta import ByTaTokenizer
else:
  import sys

  UpperCamelCase__     :	List[str]				=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




