
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase						:      List[str]									= logging.get_logger(__name__)

lowercase						:      Optional[int]									= {
    'microsoft/beit-base-patch16-224-pt22k': (
        'https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json'
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}




class 				lowerCamelCase__							(       __lowercase):




 '''simple docstring'''




 _A			=  'beit'







 def __init__(      self	:str  ,     a	:Any=8_1_9_2  ,     a	:List[Any]=7_6_8  ,     a	:Optional[int]=1_2  ,     a	:Optional[Any]=1_2  ,     a	:Union[str, Any]=3_0_7_2  ,     a	:Optional[int]="gelu"  ,     a	:Optional[int]=0.0  ,     a	:Optional[Any]=0.0  ,     a	:str=0.02  ,     a	:List[Any]=1E-1_2  ,     a	:List[Any]=2_2_4  ,     a	:Optional[Any]=1_6  ,     a	:Dict=3  ,     a	:Tuple=False  ,     a	:Dict=False  ,     a	:Optional[int]=False  ,     a	:Optional[Any]=False  ,     a	:Any=0.1  ,     a	:str=0.1  ,     a	:str=True  ,     a	:Union[str, Any]=[3, 5, 7, 1_1]  ,     a	:Dict=[1, 2, 3, 6]  ,     a	:Union[str, Any]=True  ,     a	:int=0.4  ,     a	:List[Any]=2_5_6  ,     a	:List[Any]=1  ,     a	:int=False  ,     a	:List[str]=2_5_5  ,     **a	:List[Any]  ,     )	->				Union[str, Any]:
        super().__init__(**a		)

        __UpperCamelCase			:     List[str]         =      vocab_size
        __UpperCamelCase			:     Union[str, Any]         =      hidden_size
        __UpperCamelCase			:     Dict         =      num_hidden_layers
        __UpperCamelCase			:     Optional[Any]         =      num_attention_heads
        __UpperCamelCase			:     List[Any]         =      intermediate_size
        __UpperCamelCase			:     str         =      hidden_act
        __UpperCamelCase			:     List[str]         =      hidden_dropout_prob
        __UpperCamelCase			:     Any         =      attention_probs_dropout_prob
        __UpperCamelCase			:     Dict         =      initializer_range
        __UpperCamelCase			:     Dict         =      layer_norm_eps

        __UpperCamelCase			:     Tuple         =      image_size
        __UpperCamelCase			:     Union[str, Any]         =      patch_size
        __UpperCamelCase			:     List[str]         =      num_channels
        __UpperCamelCase			:     int         =      use_mask_token
        __UpperCamelCase			:     Optional[Any]         =      use_absolute_position_embeddings
        __UpperCamelCase			:     str         =      use_relative_position_bias
        __UpperCamelCase			:     Dict         =      use_shared_relative_position_bias
        __UpperCamelCase			:     Optional[Any]         =      layer_scale_init_value
        __UpperCamelCase			:     int         =      drop_path_rate
        __UpperCamelCase			:     Any         =      use_mean_pooling
        # decode head attributes (semantic segmentation)
        __UpperCamelCase			:     Any         =      out_indices
        __UpperCamelCase			:     List[Any]         =      pool_scales
        # auxiliary head attributes (semantic segmentation)
        __UpperCamelCase			:     List[Any]         =      use_auxiliary_head
        __UpperCamelCase			:     int         =      auxiliary_loss_weight
        __UpperCamelCase			:     Any         =      auxiliary_channels
        __UpperCamelCase			:     Tuple         =      auxiliary_num_convs
        __UpperCamelCase			:     Tuple         =      auxiliary_concat_input
        __UpperCamelCase			:     Any         =      semantic_loss_ignore_index




class 				lowerCamelCase__							(       __lowercase):




 '''simple docstring'''




 _A			=  version.parse('1.11')







 @property
 def      _lowerCamelCase       (      self	:List[Any]		)	->				Mapping[str, Mapping[int, str]]:
        return OrderedDict(
            [
                ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
            ]		)







 @property
 def      _lowerCamelCase       (      self	:Optional[int]		)	->				float:
        return 1E-4

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 				lowerCamelCase__							:




			'''simple docstring'''




			@staticmethod
			def      _lowerCamelCase       (      *a	:Union[str, Any]  ,     **a	:int		)	->				str:
										pass




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Image)      ->					str:






							'''simple docstring'''





							__UpperCamelCase			:     Optional[int]         =      hashlib.mda(image.tobytes())
							return m.hexdigest()




@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 				lowerCamelCase__							(       unittest.TestCase):




	'''simple docstring'''




	_A			=  MODEL_FOR_DEPTH_ESTIMATION_MAPPING







	def      _lowerCamelCase       (      self	:Union[str, Any]  ,     a	:Optional[Any]  ,     a	:Optional[Any]  ,     a	:Optional[int]		)	->				Optional[Any]:
								__UpperCamelCase			:     Any         =      DepthEstimationPipeline(model=a  ,     image_processor=a		)
								return depth_estimator, [
								    "./tests/fixtures/tests_samples/COCO/000000039769.png",
								    "./tests/fixtures/tests_samples/COCO/000000039769.png",
								]







	def      _lowerCamelCase       (      self	:Union[str, Any]  ,     a	:Optional[Any]  ,     a	:List[Any]		)	->				Dict:
								__UpperCamelCase			:     str         =      depth_estimator("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
								self.assertEqual({"predicted_depth": ANY(torch.Tensor		), "depth": ANY(Image.Image		)}  ,     a		)
								import datasets

								__UpperCamelCase			:     List[Any]         =      datasets.load_dataset("hf-internal-testing/fixtures_image_utils"  ,     "image"  ,     split="test"		)
								__UpperCamelCase			:     int         =      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      _lowerCamelCase       (      self	:str		)	->				Optional[Any]:
								pass







	@slow
	@require_torch
	def      _lowerCamelCase       (      self	:List[Any]		)	->				List[Any]:
								__UpperCamelCase			:     Dict         =      "Intel/dpt-large"
								__UpperCamelCase			:     int         =      pipeline("depth-estimation"  ,     model=a		)
								__UpperCamelCase			:     Optional[int]         =      depth_estimator("http://images.cocodataset.org/val2017/000000039769.jpg"		)
								__UpperCamelCase			:     Optional[int]         =      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      _lowerCamelCase       (      self	:List[Any]		)	->				List[str]:
								# This is highly irregular to have no small tests.
								self.skipTest("There is not hf-internal-testing tiny model for either GLPN nor DPT"		)