
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     lowercase :



							@staticmethod
							def __UpperCamelCase			(			*A_    ,				**A_	)  -> Any:
									"""simple docstring"""
									pass


def 				A	( lowercase			)       ->  str:
		'''simple docstring'''

		UpperCamelCase          =					hashlib.mda(image.tobytes()			)
		return m.hexdigest()


@is_pipeline_test
@require_vision
@require_timm
@require_torch
class     lowercase (		unittest.TestCase       ):
		__lowercase	:	Any     					=    MODEL_FOR_DEPTH_ESTIMATION_MAPPING



		def __UpperCamelCase			(			self    ,				A_    ,				A_    ,				A_	)  -> Optional[Any]:
				"""simple docstring"""
				UpperCamelCase          =					DepthEstimationPipeline(model=A_    ,				image_processor=A_	)
				return depth_estimator, [
				    "./tests/fixtures/tests_samples/COCO/000000039769.png",
				    "./tests/fixtures/tests_samples/COCO/000000039769.png",
				]



		def __UpperCamelCase			(			self    ,				A_    ,				A_	)  -> Any:
				"""simple docstring"""
				UpperCamelCase          =					depth_estimator('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
				self.assertEqual({'predicted_depth': ANY(torch.Tensor	), 'depth': ANY(Image.Image	)}    ,				A_	)
				import datasets

				UpperCamelCase          =					datasets.load_dataset('hf-internal-testing/fixtures_image_utils'    ,				'image'    ,				split='test'	)
				UpperCamelCase          =					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 __UpperCamelCase			(			self	)  -> Optional[Any]:
				"""simple docstring"""
				pass



		@slow
		@require_torch
		def __UpperCamelCase			(			self	)  -> Optional[int]:
				"""simple docstring"""
				UpperCamelCase          =					'Intel/dpt-large'
				UpperCamelCase          =					pipeline('depth-estimation'    ,				model=A_	)
				UpperCamelCase          =					depth_estimator('http://images.cocodataset.org/val2017/000000039769.jpg'	)
				UpperCamelCase          =					hashimage(outputs['depth']	)

				# This seems flaky.
				# self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
				self.assertEqual(nested_simplify(outputs['predicted_depth'].max().item()	)    ,				29.304	)
				self.assertEqual(nested_simplify(outputs['predicted_depth'].min().item()	)    ,				2.662	)



		@require_torch
		def __UpperCamelCase			(			self	)  -> Optional[Any]:
				"""simple docstring"""
				# 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 typing import Optional, Union

import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin


class     lowercase (		_SCREAMING_SNAKE_CASE  ,       _SCREAMING_SNAKE_CASE       ):



		@register_to_config
		def __init__(			self    ,				A_ = 768    ,				)  -> List[Any]:
				"""simple docstring"""
				super().__init__()

				UpperCamelCase          =					nn.Parameter(torch.zeros(1    ,				A_	)	)
				UpperCamelCase          =					nn.Parameter(torch.ones(1    ,				A_	)	)



		def __UpperCamelCase			(			self    ,				A_ = None    ,				A_ = None    ,				)  -> Any:
				"""simple docstring"""
				UpperCamelCase          =					nn.Parameter(self.mean.to(A_	).to(A_	)	)
				UpperCamelCase          =					nn.Parameter(self.std.to(A_	).to(A_	)	)
				return self



		def __UpperCamelCase			(			self    ,				A_	)  -> Dict:
				"""simple docstring"""
				UpperCamelCase          =					(embeds - self.mean) * 1.0 / self.std
				return embeds



		def __UpperCamelCase			(			self    ,				A_	)  -> Dict:
				"""simple docstring"""
				UpperCamelCase          =					(embeds * self.std) + self.mean
				return embeds
