





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 				_UpperCAmelCase    :

       """simple docstring"""


       @staticmethod
       def 				lowercase		(				*lowerCAmelCase_ :		Optional[int]					,  **lowerCAmelCase_ :		str     )							->						List[Any]:
          pass

def   a_    ( lowerCAmelCase_      :			Image	):
   __lowerCAmelCase								=	hashlib.mda(image.tobytes()	)
   return m.hexdigest()




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

  """simple docstring"""


  a_        = MODEL_FOR_DEPTH_ESTIMATION_MAPPING






  def 				lowercase		(				self :		List[Any]					,  lowerCAmelCase_ :		Tuple					,  lowerCAmelCase_ :		Union[str, Any]					,  lowerCAmelCase_ :		List[str]     )							->						Union[str, Any]:
     __lowerCAmelCase								=	DepthEstimationPipeline(model=lowerCAmelCase_					,  image_processor=lowerCAmelCase_     )
     return depth_estimator, [
         "./tests/fixtures/tests_samples/COCO/000000039769.png",
         "./tests/fixtures/tests_samples/COCO/000000039769.png",
     ]






  def 				lowercase		(				self :		Any					,  lowerCAmelCase_ :		Any					,  lowerCAmelCase_ :		str     )							->						Any:
     __lowerCAmelCase								=	depth_estimator('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
     self.assertEqual({'predicted_depth': ANY(torch.Tensor     ), 'depth': ANY(Image.Image     )}					,  lowerCAmelCase_     )
     import datasets

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






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






  @slow
  @require_torch
  def 				lowercase		(				self :		Any     )							->						Any:
     __lowerCAmelCase								=	'Intel/dpt-large'
     __lowerCAmelCase								=	pipeline('depth-estimation'					,  model=lowerCAmelCase_     )
     __lowerCAmelCase								=	depth_estimator('http://images.cocodataset.org/val2017/000000039769.jpg'     )
     __lowerCAmelCase								=	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 				lowercase		(				self :		List[Any]     )							->						Union[str, 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'     )








from __future__ import annotations

import string
from itertools import cycle, product
from pathlib import Path

_snake_case  :    str						    =  (
    string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
_snake_case  :    list[int]						    =  [ord(letter) for letter in string.ascii_lowercase]
_snake_case  :    set[int]						    =  {ord(char) for char in VALID_CHARS}

_snake_case  :    list[str]						    =  ["the", "be", "to", "of", "and", "in", "that", "have"]

def   a_    ( lowerCAmelCase_      :			list[int],	lowerCAmelCase_      :			tuple[int, ...]	):
   __lowerCAmelCase								=	""
   __lowerCAmelCase								=	42
   __lowerCAmelCase								=	42
   __lowerCAmelCase								=	42

   for keychar, cipherchar in zip(cycle(lowerCAmelCase_	),	lowerCAmelCase_	):
      __lowerCAmelCase								=	cipherchar ^ keychar
      if decodedchar not in VALID_INTS:
         return None
      decoded += chr(lowerCAmelCase_	)

   return decoded

def   a_    ( lowerCAmelCase_      :			list[int]	):
   __lowerCAmelCase								=	[]
   for key in product(lowerCAmelCase_,	repeat=3	):
      __lowerCAmelCase								=	try_key(lowerCAmelCase_,	lowerCAmelCase_	)
      if encoded is not None:
         possibles.append(lowerCAmelCase_	)
   return possibles

def   a_    ( lowerCAmelCase_      :			list[str],	lowerCAmelCase_      :			str	):
   return [possible for possible in possibles if common_word in possible.lower()]

def   a_    ( lowerCAmelCase_      :			str = "p059_cipher.txt"	):
   __lowerCAmelCase								=	42
   __lowerCAmelCase								=	42
   __lowerCAmelCase								=	42
   __lowerCAmelCase								=	42
   __lowerCAmelCase								=	Path(lowerCAmelCase_	).parent.joinpath(lowerCAmelCase_	).read_text(encoding='utf-8'	)

   __lowerCAmelCase								=	[int(lowerCAmelCase_	) for number in data.strip().split(','	)]

   __lowerCAmelCase								=	filter_valid_chars(lowerCAmelCase_	)
   for common_word in COMMON_WORDS:
      __lowerCAmelCase								=	filter_common_word(lowerCAmelCase_,	lowerCAmelCase_	)
      if len(lowerCAmelCase_	) == 1:
         break

   __lowerCAmelCase								=	possibles[0]
   return sum(ord(lowerCAmelCase_	) for char in decoded_text	)


if __name__ == "__main__":
     print(F"""{solution() = }""")

