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				(		*UpperCAmelCase ,		**UpperCAmelCase       )					->       str:
									pass
def 				lowerCAmelCase(      __lowerCamelCase		):
					__a						   =  hashlib.mda(image.tobytes()		)
					return m.hexdigest()



@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 		a__				(  unittest.TestCase						):
			A__							:  List[Any]														=     MODEL_FOR_DEPTH_ESTIMATION_MAPPING



			def __SCREAMING_SNAKE_CASE				(		self ,		UpperCAmelCase ,		UpperCAmelCase ,		UpperCAmelCase       )					->       Optional[Any]:
								__a						   =  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 ,		UpperCAmelCase ,		UpperCAmelCase       )					->       Tuple:
								__a						   =  depth_estimator('./tests/fixtures/tests_samples/COCO/000000039769.png'       )
								self.assertEqual({'predicted_depth': ANY(torch.Tensor       ), 'depth': ANY(Image.Image       )} ,		__a       )
								import datasets

								__a						   =  datasets.load_dataset('hf-internal-testing/fixtures_image_utils' ,		'image' ,		split='test'       )
								__a						   =  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       )					->       Tuple:
								pass



			@slow
			@require_torch
			def __SCREAMING_SNAKE_CASE				(		self       )					->       Dict:
								__a						   =  "Intel/dpt-large"
								__a						   =  pipeline('depth-estimation' ,		model=__a       )
								__a						   =  depth_estimator('http://images.cocodataset.org/val2017/000000039769.jpg'       )
								__a						   =  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 __SCREAMING_SNAKE_CASE				(		self       )					->       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'       )











"""simple docstring"""


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


lowerCamelCase__						= logging.get_logger(__name__)

lowerCamelCase__						= {
    "google/mobilenet_v1_1.0_224": "https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json",
    "google/mobilenet_v1_0.75_192": "https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json",
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}



class   __SCREAMING_SNAKE_CASE     (    _UpperCamelCase ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__				:int						  =       "mobilenet_v1"

  def __init__(		self :							Optional[int]       , __a :							List[str]=3       , __a :							Union[str, Any]=224       , __a :							Tuple=1.0       , __a :							List[Any]=8       , __a :							Union[str, Any]="relu6"       , __a :							Dict=True       , __a :							Tuple=0.9_99       , __a :							Dict=0.02       , __a :							Any=0.0_01       , **__a :							Any       , )		->					int:
        super().__init__(**__a						)

        if depth_multiplier <= 0:
              raise ValueError("depth_multiplier must be greater than zero."						)

        _UpperCamelCase   :						Any    =							num_channels
        _UpperCamelCase   :						Optional[Any]    =							image_size
        _UpperCamelCase   :						Optional[Any]    =							depth_multiplier
        _UpperCamelCase   :						int    =							min_depth
        _UpperCamelCase   :						List[str]    =							hidden_act
        _UpperCamelCase   :						Union[str, Any]    =							tf_padding
        _UpperCamelCase   :						int    =							classifier_dropout_prob
        _UpperCamelCase   :						Optional[int]    =							initializer_range
        _UpperCamelCase   :						Union[str, Any]    =							layer_norm_eps





class   __SCREAMING_SNAKE_CASE     (    _UpperCamelCase ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__				:List[Any]						  =       version.parse("1.11" )

  @property
  def  __SCREAMING_SNAKE_CASE					(		self :							List[str]						)		->					Mapping[str, Mapping[int, str]]:
        return OrderedDict([("pixel_values", {0: "batch"})]						)

  @property
  def  __SCREAMING_SNAKE_CASE					(		self :							Optional[int]						)		->					Mapping[str, Mapping[int, str]]:
        if self.task == "image-classification":
              return OrderedDict([("logits", {0: "batch"})]						)
        else:
              return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})]						)


  @property
  def  __SCREAMING_SNAKE_CASE					(		self :							Tuple						)		->					float:
        return 1e-4
