
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     _SCREAMING_SNAKE_CASE     :



				@staticmethod
				def     SCREAMING_SNAKE_CASE_(    *lowercase						,  **lowercase )				->       int:
								pass





def 			lowerCamelCase_ (				lowerCamelCase__    ):
				lowerCamelCase_						=			hashlib.mda(image.tobytes()    )
				return m.hexdigest()





@is_pipeline_test
@require_vision
@require_timm
@require_torch
class     _SCREAMING_SNAKE_CASE     (      unittest.TestCase						):
	lowerCAmelCase__         = MODEL_FOR_DEPTH_ESTIMATION_MAPPING



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase						,  lowercase )				->       str:
					lowerCamelCase_						=			DepthEstimationPipeline(model=lowercase						,  image_processor=lowercase )
					return depth_estimator, [
					    "./tests/fixtures/tests_samples/COCO/000000039769.png",
					    "./tests/fixtures/tests_samples/COCO/000000039769.png",
					]



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase )				->       Optional[Any]:
					lowerCamelCase_						=			depth_estimator("./tests/fixtures/tests_samples/COCO/000000039769.png" )
					self.assertEqual({"predicted_depth": ANY(torch.Tensor ), "depth": ANY(Image.Image )}						,  lowercase )
					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 )},
					    ]						,  lowercase						,  )



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



	@slow
	@require_torch
	def     SCREAMING_SNAKE_CASE_(    self )				->       List[Any]:
					lowerCamelCase_						=			"Intel/dpt-large"
					lowerCamelCase_						=			pipeline("depth-estimation"						,  model=lowercase )
					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() )						,  2_9.3_0_4 )
					self.assertEqual(nested_simplify(outputs["predicted_depth"].min().item() )						,  2.6_6_2 )



	@require_torch
	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[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" )








import string

# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
__A        =    {
    "E": 1_2.7_0,
    "T": 9.0_6,
    "A": 8.1_7,
    "O": 7.5_1,
    "I": 6.9_7,
    "N": 6.7_5,
    "S": 6.3_3,
    "H": 6.0_9,
    "R": 5.9_9,
    "D": 4.2_5,
    "L": 4.0_3,
    "C": 2.7_8,
    "U": 2.7_6,
    "M": 2.4_1,
    "W": 2.3_6,
    "F": 2.2_3,
    "G": 2.0_2,
    "Y": 1.9_7,
    "P": 1.9_3,
    "B": 1.2_9,
    "V": 0.9_8,
    "K": 0.7_7,
    "J": 0.1_5,
    "X": 0.1_5,
    "Q": 0.1_0,
    "Z": 0.0_7,
}
__A        =    "ETAOINSHRDLCUMWFGYPBVKJXQZ"
__A        =    "ABCDEFGHIJKLMNOPQRSTUVWXYZ"







def 			lowerCamelCase_		(       UpperCamelCase__				:      str      )     ->       dict[str, int]:





				"""simple docstring"""


				__lowerCamelCase		       =    {letter: 0 for letter in string.ascii_uppercase}
				for letter in message.upper():
								if letter in LETTERS:
												letter_count[letter] += 1

				return letter_count







def 			lowerCamelCase_		(       UpperCamelCase__				:      tuple      )     ->       str:





				"""simple docstring"""


				return x[0]







def 			lowerCamelCase_		(       UpperCamelCase__				:      str      )     ->       str:





				"""simple docstring"""


				__lowerCamelCase		       =    get_letter_count(UpperCamelCase__      )
				__lowerCamelCase		       =    {
				    freq: [] for letter, freq in letter_to_freq.items()
				}
				for letter in LETTERS:
								freq_to_letter[letter_to_freq[letter]].append(UpperCamelCase__      )

				__lowerCamelCase		       =    {}

				for freq in freq_to_letter:
								freq_to_letter[freq].sort(key=ETAOIN.find ,     reverse=UpperCamelCase__      )
								__lowerCamelCase		       =    ''.join(freq_to_letter[freq]      )

				__lowerCamelCase		       =    list(freq_to_letter_str.items()      )
				freq_pairs.sort(key=UpperCamelCase__ ,     reverse=UpperCamelCase__      )

				__lowerCamelCase		       =    [freq_pair[1] for freq_pair in freq_pairs]

				return "".join(UpperCamelCase__      )







def 			lowerCamelCase_		(       UpperCamelCase__				:      str      )     ->       int:





				"""simple docstring"""


				__lowerCamelCase		       =    get_frequency_order(UpperCamelCase__      )
				__lowerCamelCase		       =    0
				for common_letter in ETAOIN[:6]:
								if common_letter in freq_order[:6]:
												match_score += 1

				for uncommon_letter in ETAOIN[-6:]:
								if uncommon_letter in freq_order[-6:]:
												match_score += 1

				return match_score


if __name__ == "__main__":
					import doctest

					doctest.testmod()


