




from timeit import timeit

UpperCamelCase__         =      {
    """MALAYALAM""": True,
    """String""": False,
    """rotor""": True,
    """level""": True,
    """A""": True,
    """BB""": True,
    """ABC""": False,
    """amanaplanacanalpanama""": True,  # "a man a plan a canal panama"
}
# Ensure our test data is valid
assert all((key == key[::-1]) is value for key, value in test_data.items())







def      _a				(						SCREAMING_SNAKE_CASE_							:    str					):
					__lowerCAmelCase           =	0
					__lowerCAmelCase           =	len(SCREAMING_SNAKE_CASE_					) - 1
					while start_i < end_i:
										if s[start_i] == s[end_i]:
															start_i += 1
															end_i -= 1
										else:
															return False
					return True







def      _a				(						SCREAMING_SNAKE_CASE_							:    str					):
					__lowerCAmelCase           =	len(SCREAMING_SNAKE_CASE_					) // 2
					__lowerCAmelCase           =	len(SCREAMING_SNAKE_CASE_					)

					# We need to traverse till half of the length of string
					# as we can get access of the i'th last element from
					# i'th index.
					# eg: [0,1,2,3,4,5] => 4th index can be accessed
					# with the help of 1st index (i==n-i-1)
					# where n is length of string
					return all(s[i] == s[n - i - 1] for i in range(SCREAMING_SNAKE_CASE_					)					)







def      _a				(						SCREAMING_SNAKE_CASE_							:    str					):
					if len(SCREAMING_SNAKE_CASE_					) <= 2:
										return True
					if s[0] == s[len(SCREAMING_SNAKE_CASE_					) - 1]:
										return is_palindrome_recursive(s[1:-1]					)
					else:
										return False







def      _a				(						SCREAMING_SNAKE_CASE_							:    str					):
					return s == s[::-1]







def      _a				(						SCREAMING_SNAKE_CASE_							:    str					):
					__lowerCAmelCase           =	F"""all({name}(key) is value for key, value in test_data.items())"""
					__lowerCAmelCase           =	F"""from __main__ import test_data, {name}"""
					__lowerCAmelCase           =	50_00_00
					__lowerCAmelCase           =	timeit(stmt=SCREAMING_SNAKE_CASE_				,				setup=SCREAMING_SNAKE_CASE_				,				number=SCREAMING_SNAKE_CASE_					)
					print(F"""{name:<35} finished {number:,} runs in {result:.5f} seconds"""					)


if __name__ == "__main__":
					for key, value in test_data.items():
										assert is_palindrome(key) is is_palindrome_recursive(key)
										assert is_palindrome(key) is is_palindrome_slice(key)
										print(f'''{key:21} {value}''')
					print("""a man a plan a canal panama""")

					# finished 500,000 runs in 0.46793 seconds
					benchmark_function("""is_palindrome_slice""")
					# finished 500,000 runs in 0.85234 seconds
					benchmark_function("""is_palindrome""")
					# finished 500,000 runs in 1.32028 seconds
					benchmark_function("""is_palindrome_recursive""")
					# finished 500,000 runs in 2.08679 seconds
					benchmark_function("""is_palindrome_traversal""")










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"      )




