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 __snake_case				:




      @staticmethod
      def 	__a						(    *_lowercase			:				Union[str, Any]       ,	**_lowercase			:				Union[str, Any]      ):



        """simple docstring"""






        pass



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       Image	)							->					str:







  """simple docstring"""





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







@is_pipeline_test
@require_vision
@require_timm
@require_torch
class __snake_case				(						unittest.TestCase		):
   lowerCAmelCase_														=							MODEL_FOR_DEPTH_ESTIMATION_MAPPING




   def 	__a						(    self			:				Tuple       ,	_lowercase			:				Union[str, Any]       ,	_lowercase			:				Any       ,	_lowercase			:				Optional[int]      ):



     """simple docstring"""






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




   def 	__a						(    self			:				int       ,	_lowercase			:				List[str]       ,	_lowercase			:				Union[str, Any]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      depth_estimator("""./tests/fixtures/tests_samples/COCO/000000039769.png"""      )
     self.assertEqual({"""predicted_depth""": ANY(torch.Tensor      ), """depth""": ANY(Image.Image      )}       ,	_lowercase      )
     import datasets

     SCREAMING_SNAKE_CASE__										=      datasets.load_dataset("""hf-internal-testing/fixtures_image_utils"""       ,	"""image"""       ,	split="""test"""      )
     SCREAMING_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      )},
         ]       ,	_lowercase       ,	)




   @require_tf
   @unittest.skip("""Depth estimation is not implemented in TF"""      )
   def 	__a						(    self			:				Union[str, Any]      ):



     """simple docstring"""






     pass




   @slow
   @require_torch
   def 	__a						(    self			:				List[Any]      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      """Intel/dpt-large"""
     SCREAMING_SNAKE_CASE__										=      pipeline("""depth-estimation"""       ,	model=_lowercase      )
     SCREAMING_SNAKE_CASE__										=      depth_estimator("""http://images.cocodataset.org/val2017/000000039769.jpg"""      )
     SCREAMING_SNAKE_CASE__										=      hashimage(outputs["""depth"""]      )

     # This seems flaky.
     # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
     self.assertEqual(nested_simplify(outputs["""predicted_depth"""].max().item()      )       ,	29.3_04      )
     self.assertEqual(nested_simplify(outputs["""predicted_depth"""].min().item()      )       ,	2.6_62      )




   @require_torch
   def 	__a						(    self			:				Any      ):



     """simple docstring"""






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

import doctest
from collections import deque

import numpy as np







class __snake_case				:




   def __init__(    self			:				Dict      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      [2, 1, 2, -1]
     SCREAMING_SNAKE_CASE__										=      [1, 2, 3, 4]




   def 	__a						(    self			:				int      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      len(self.first_signal      )
     SCREAMING_SNAKE_CASE__										=      len(self.second_signal      )

     SCREAMING_SNAKE_CASE__										=      max(_lowercase       ,	_lowercase      )

     # create a zero matrix of max_length x max_length
     SCREAMING_SNAKE_CASE__										=      [[0] * max_length for i in range(_lowercase      )]

     # fills the smaller signal with zeros to make both signals of same length
     if length_first_signal < length_second_signal:
       self.first_signal += [0] * (max_length - length_first_signal)
     elif length_first_signal > length_second_signal:
       self.second_signal += [0] * (max_length - length_second_signal)
     for i in range(_lowercase      ):
       SCREAMING_SNAKE_CASE__										=      deque(self.second_signal      )
       rotated_signal.rotate(_lowercase      )
       for j, item in enumerate(_lowercase      ):
         matrix[i][j] += item

        # multiply the matrix with the first signal
     SCREAMING_SNAKE_CASE__										=      np.matmul(np.transpose(_lowercase      )       ,	np.transpose(self.first_signal      )      )

     # rounding-off to two decimal places
     return [round(_lowercase       ,	2      ) for i in final_signal]


if __name__ == "__main__":
   doctest.testmod()
