






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 		_snake_case	:






							@staticmethod
							def  SCREAMING_SNAKE_CASE__     (							*a		,       **a)   ->							Dict:
										pass






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







@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 		_snake_case	(	unittest.TestCase       ):
			_lowercase		:     Optional[int]        =				MODEL_FOR_DEPTH_ESTIMATION_MAPPING






			def  SCREAMING_SNAKE_CASE__     (							self		,       a		,       a		,       a)   ->							Any:
						SCREAMING_SNAKE_CASE							=			DepthEstimationPipeline(model=lowerCamelCase__		,       image_processor=lowerCamelCase__)
						return depth_estimator, [
						    "./tests/fixtures/tests_samples/COCO/000000039769.png",
						    "./tests/fixtures/tests_samples/COCO/000000039769.png",
						]






			def  SCREAMING_SNAKE_CASE__     (							self		,       a		,       a)   ->							List[str]:
						SCREAMING_SNAKE_CASE							=			depth_estimator('./tests/fixtures/tests_samples/COCO/000000039769.png')
						self.assertEqual({'predicted_depth': ANY(torch.Tensor), 'depth': ANY(Image.Image)}		,       lowerCamelCase__)
						import datasets

						SCREAMING_SNAKE_CASE							=			datasets.load_dataset('hf-internal-testing/fixtures_image_utils'		,       'image'		,       split='test')
						SCREAMING_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)},
						    ]		,       lowerCamelCase__		,       )






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






			@slow
			@require_torch
			def  SCREAMING_SNAKE_CASE__     (							self)   ->							Tuple:
						SCREAMING_SNAKE_CASE							=			'Intel/dpt-large'
						SCREAMING_SNAKE_CASE							=			pipeline('depth-estimation'		,       model=lowerCamelCase__)
						SCREAMING_SNAKE_CASE							=			depth_estimator('http://images.cocodataset.org/val2017/000000039769.jpg')
						SCREAMING_SNAKE_CASE							=			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)   ->							Optional[Any]:
						self.skipTest('There is not hf-internal-testing tiny model for either GLPN nor DPT')










from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase







def      lowerCamelCase_						(			UpperCamelCase__							: str )	->			None:





				"""simple docstring"""


				__lowerCamelCase			,		__lowerCamelCase									=       analyze_text(UpperCamelCase__ )
				__lowerCamelCase									=       list(' ' + ascii_lowercase )
				# what is our total sum of probabilities.
				__lowerCamelCase									=       sum(single_char_strings.values() )

				# one length string
				__lowerCamelCase									=       0
				# for each alpha we go in our dict and if it is in it we calculate entropy
				for ch in my_alphas:
								if ch in single_char_strings:
												__lowerCamelCase									=       single_char_strings[ch]
												__lowerCamelCase									=       my_str / all_sum
												my_fir_sum += prob * math.loga(UpperCamelCase__ )  # entropy formula.

    # print entropy
				print(F"""{round(-1 * my_fir_sum ):.1f}""" )

				# two len string
				__lowerCamelCase									=       sum(two_char_strings.values() )
				__lowerCamelCase									=       0
				# for each alpha (two in size) calculate entropy.
				for cha in my_alphas:
								for cha in my_alphas:
												__lowerCamelCase									=       cha + cha
												if sequence in two_char_strings:
																__lowerCamelCase									=       two_char_strings[sequence]
																__lowerCamelCase									=       int(UpperCamelCase__ ) / all_sum
																my_sec_sum += prob * math.loga(UpperCamelCase__ )

    # print second entropy
				print(F"""{round(-1 * my_sec_sum ):.1f}""" )

				# print the difference between them
				print(F"""{round((-1 * my_sec_sum) - (-1 * my_fir_sum) ):.1f}""" )







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





				"""simple docstring"""


				__lowerCamelCase									=       Counter()  # type: ignore
				__lowerCamelCase									=       Counter()  # type: ignore
				single_char_strings[text[-1]] += 1

				# first case when we have space at start.
				two_char_strings[" " + text[0]] += 1
				for i in range(0					,	len(UpperCamelCase__ ) - 1 ):
								single_char_strings[text[i]] += 1
								two_char_strings[text[i : i + 2]] += 1
				return single_char_strings, two_char_strings







def      lowerCamelCase_						(			)	->			Dict:





				"""simple docstring"""


				import doctest

				doctest.testmod()
				# text = (
				#     "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
				#     "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
				#     "jointure saw horrible. He private he on be imagine suppose. Fertile "
				#     "beloved evident through no service elderly is. Blind there if every no so "
				#     "at. Own neglected you preferred way sincerity delivered his attempted. To "
				#     "of message cottage windows do besides against uncivil.  Delightful "
				#     "unreserved impossible few estimating men favourable see entreaties. She "
				#     "propriety immediate was improving. He or entrance humoured likewise "
				#     "moderate. Much nor game son say feel. Fat make met can must form into "
				#     "gate. Me we offending prevailed discovery. "
				# )

				# calculate_prob(text)


if __name__ == "__main__":
					main()


