

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__				:    Union[str, Any]      , **A__				:    List[str]		)   ->					Dict:
         pass



def 				snake_case_(_UpperCamelCase					)							->     str:



  """simple docstring"""






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



@is_pipeline_test
@require_vision
@require_timm
@require_torch
class   lowercase_   (  unittest.TestCase					):
      UpperCamelCase_      :		Any										=    MODEL_FOR_DEPTH_ESTIMATION_MAPPING



      def 						UpperCamelCase_		(      self				:    Any      , A__				:    Union[str, Any]      , A__				:    Optional[Any]      , A__				:    Tuple		)   ->					Dict:
        _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				:    int      , A__				:    Any      , A__				:    Any		)   ->					Tuple:
        _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				:    int		)   ->					List[Any]:
        pass



      @slow
      @require_torch
      def 						UpperCamelCase_		(      self				:    Optional[int]		)   ->					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()		)      , 29.304		)
        self.assertEqual(nested_simplify(outputs['''predicted_depth'''].min().item()		)      , 2.662		)







      @require_torch
      def 						UpperCamelCase_		(      self				:    Dict		)   ->					List[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'''		)




__A      		=     '''ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'''



def 				snake_case_(_UpperCamelCase					)							->     bytes:



  """simple docstring"""






  if not isinstance(_UpperCamelCase			,	_UpperCamelCase					):
    _snake_case						=							F"""a bytes-like object is required, not '{data.__class__.__name__}'"""
    raise TypeError(_UpperCamelCase					)

  _snake_case						=							''''''.join(bin(_UpperCamelCase					)[2:].zfill(8					) for byte in data					)

  _snake_case						=							len(_UpperCamelCase					) % 6 != 0

  if padding_needed:
    # The padding that will be added later
    _snake_case						=							b'''=''' * ((6 - len(_UpperCamelCase					) % 6) // 2)

    # Append binary_stream with arbitrary binary digits (0's by default) to make its
    # length a multiple of 6.
    binary_stream += "0" * (6 - len(_UpperCamelCase					) % 6)
  else:
    _snake_case						=							b''''''

  # Encode every 6 binary digits to their corresponding Base64 character
  return (
      "".join(
          B64_CHARSET[int(binary_stream[index : index + 6]			,	2					)]
          for index in range(0			,	len(_UpperCamelCase					)			,	6					)					).encode()
      + padding
  )



def 				snake_case_(_UpperCamelCase					)							->     bytes:



  """simple docstring"""






  if not isinstance(_UpperCamelCase			,	_UpperCamelCase					) and not isinstance(_UpperCamelCase			,	_UpperCamelCase					):
    _snake_case						=							(
        '''argument should be a bytes-like object or ASCII string, '''
        F"""not '{encoded_data.__class__.__name__}'"""
    )
    raise TypeError(_UpperCamelCase					)

  # In case encoded_data is a bytes-like object, make sure it contains only
  # ASCII characters so we convert it to a string object
  if isinstance(_UpperCamelCase			,	_UpperCamelCase					):
    try:
      _snake_case						=							encoded_data.decode('''utf-8'''					)
    except UnicodeDecodeError:
      raise ValueError('''base64 encoded data should only contain ASCII characters'''					)

  _snake_case						=							encoded_data.count('''='''					)

  # Check if the encoded string contains non base64 characters
  if padding:
    assert all(
        char in B64_CHARSET for char in encoded_data[:-padding]					), "Invalid base64 character(s) found."
  else:
    assert all(
        char in B64_CHARSET for char in encoded_data					), "Invalid base64 character(s) found."

  # Check the padding
  assert len(_UpperCamelCase					) % 4 == 0 and padding < 3, "Incorrect padding"

  if padding:
    # Remove padding if there is one
    _snake_case						=							encoded_data[:-padding]

    _snake_case						=							''''''.join(
        bin(B64_CHARSET.index(_UpperCamelCase					)					)[2:].zfill(6					) for char in encoded_data					)[: -padding * 2]
  else:
    _snake_case						=							''''''.join(
        bin(B64_CHARSET.index(_UpperCamelCase					)					)[2:].zfill(6					) for char in encoded_data					)

  _snake_case						=							[
      int(binary_stream[index : index + 8]			,	2					)
      for index in range(0			,	len(_UpperCamelCase					)			,	8					)
  ]

  return bytes(_UpperCamelCase					)


if __name__ == "__main__":
 import doctest

 doctest.testmod()

