




from __future__ import annotations

from collections.abc import Sequence
from typing import Literal







def      _a				(						SCREAMING_SNAKE_CASE_							:    str				,				SCREAMING_SNAKE_CASE_							:    str					):
     __lowerCAmelCase           =	list(__UpperCamelCase					)
     __lowerCAmelCase           =	list(__UpperCamelCase					)
     __lowerCAmelCase           =	0
     for i in range(len(__UpperCamelCase					)					):
          if lista[i] != lista[i]:
               count += 1
               __lowerCAmelCase           =	"_"
     if count > 1:
          return False
     else:
          return "".join(__UpperCamelCase					)







def      _a				(						SCREAMING_SNAKE_CASE_							:    list[str]					):
     __lowerCAmelCase           =	[]
     while True:
          __lowerCAmelCase           =	["$"] * len(__UpperCamelCase					)
          __lowerCAmelCase           =	[]
          for i in range(len(__UpperCamelCase					)					):
               for j in range(i + 1				,				len(__UpperCamelCase					)					):
                    __lowerCAmelCase           =	compare_string(binary[i]				,				binary[j]					)
                    if k is False:
                         __lowerCAmelCase           =	"*"
                         __lowerCAmelCase           =	"*"
                         temp.append("X"					)
          for i in range(len(__UpperCamelCase					)					):
               if checka[i] == "$":
                    pi.append(binary[i]					)
          if len(__UpperCamelCase					) == 0:
               return pi
          __lowerCAmelCase           =	list(set(__UpperCamelCase					)					)







def      _a				(						SCREAMING_SNAKE_CASE_							:    int				,				SCREAMING_SNAKE_CASE_							:    Sequence[float]					):
     __lowerCAmelCase           =	[]
     for minterm in minterms:
          __lowerCAmelCase           =	""
          for _ in range(__UpperCamelCase					):
               __lowerCAmelCase           =	str(minterm % 2					) + string
               minterm //= 2
          temp.append(__UpperCamelCase					)
     return temp







def      _a				(						SCREAMING_SNAKE_CASE_							:    str				,				SCREAMING_SNAKE_CASE_							:    str				,				SCREAMING_SNAKE_CASE_							:    int					):
     __lowerCAmelCase           =	list(__UpperCamelCase					)
     __lowerCAmelCase           =	list(__UpperCamelCase					)
     __lowerCAmelCase           =	0
     for i in range(len(__UpperCamelCase					)					):
          if lista[i] != lista[i]:
               count_n += 1
     return count_n == count







def      _a				(						SCREAMING_SNAKE_CASE_							:    list[list[int]]				,				SCREAMING_SNAKE_CASE_							:    list[str]					):
     __lowerCAmelCase           =	[]
     __lowerCAmelCase           =	[0] * len(__UpperCamelCase					)
     for i in range(len(chart[0]					)					):
          __lowerCAmelCase           =	0
          __lowerCAmelCase           =	-1
          for j in range(len(__UpperCamelCase					)					):
               if chart[j][i] == 1:
                    count += 1
                    __lowerCAmelCase           =	j
          if count == 1:
               __lowerCAmelCase           =	1
     for i in range(len(__UpperCamelCase					)					):
          if select[i] == 1:
               for j in range(len(chart[0]					)					):
                    if chart[i][j] == 1:
                         for k in range(len(__UpperCamelCase					)					):
                              __lowerCAmelCase           =	0
               temp.append(prime_implicants[i]					)
     while True:
          __lowerCAmelCase           =	0
          __lowerCAmelCase           =	-1
          __lowerCAmelCase           =	0
          for i in range(len(__UpperCamelCase					)					):
               __lowerCAmelCase           =	chart[i].count(1					)
               if count_n > max_n:
                    __lowerCAmelCase           =	count_n
                    __lowerCAmelCase           =	i

          if max_n == 0:
               return temp

          temp.append(prime_implicants[rem]					)

          for i in range(len(chart[0]					)					):
               if chart[rem][i] == 1:
                    for j in range(len(__UpperCamelCase					)					):
                         __lowerCAmelCase           =	0







def      _a				(						SCREAMING_SNAKE_CASE_							:    list[str]				,				SCREAMING_SNAKE_CASE_							:    list[str]					):
     __lowerCAmelCase           =	[[0 for x in range(len(__UpperCamelCase					)					)] for x in range(len(__UpperCamelCase					)					)]
     for i in range(len(__UpperCamelCase					)					):
          __lowerCAmelCase           =	prime_implicants[i].count("_"					)
          for j in range(len(__UpperCamelCase					)					):
               if is_for_table(prime_implicants[i]				,				binary[j]				,				__UpperCamelCase					):
                    __lowerCAmelCase           =	1

     return chart







def      _a				(						):
     __lowerCAmelCase           =	int(input("Enter the no. of variables\n"					)					)
     __lowerCAmelCase           =	[
         float(__UpperCamelCase					)
         for x in input(
             "Enter the decimal representation of Minterms \'Spaces Separated\'\n"					).split()
     ]
     __lowerCAmelCase           =	decimal_to_binary(__UpperCamelCase				,				__UpperCamelCase					)

     __lowerCAmelCase           =	check(__UpperCamelCase					)
     print("Prime Implicants are:"					)
     print(__UpperCamelCase					)
     __lowerCAmelCase           =	prime_implicant_chart(__UpperCamelCase				,				__UpperCamelCase					)

     __lowerCAmelCase           =	selection(__UpperCamelCase				,				__UpperCamelCase					)
     print("Essential Prime Implicants are:"					)
     print(__UpperCamelCase					)


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     main()










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       a__   :






											@staticmethod
											def     __SCREAMING_SNAKE_CASE(       *_A		,      **_A      ):




																"""simple docstring"""



																pass







def      _a				(						SCREAMING_SNAKE_CASE_							:    Image					):
					__lowerCAmelCase           =	hashlib.mda(image.tobytes()					)
					return m.hexdigest()


@is_pipeline_test
@require_vision
@require_timm
@require_torch
class       a__   ( unittest.TestCase				):
						_a							:   Tuple =   MODEL_FOR_DEPTH_ESTIMATION_MAPPING






						def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A		,      _A      ):




											"""simple docstring"""



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






						def     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A      ):




											"""simple docstring"""



											__lowerCAmelCase           =	depth_estimator("./tests/fixtures/tests_samples/COCO/000000039769.png"      )
											self.assertEqual({"predicted_depth": ANY(torch.Tensor      ), "depth": ANY(Image.Image      )}		,      _A      )
											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      )},
											    ]		,      _A		,      )






						@require_tf
						@unittest.skip("Depth estimation is not implemented in TF"      )
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											pass






						@slow
						@require_torch
						def     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	"Intel/dpt-large"
											__lowerCAmelCase           =	pipeline("depth-estimation"		,      model=_A      )
											__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     __SCREAMING_SNAKE_CASE(       self      ):




											"""simple docstring"""



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




