
from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
		from PIL import Image

		from ..image_utils import load_image

if is_tf_available():
		import tensorflow as tf

		from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
		from ..tf_utils import stable_softmax

if is_torch_available():
		from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING

lowercase						:      int									= logging.get_logger(__name__)




@add_end_docstrings(__lowercase)
class 				lowerCamelCase__							(       __lowercase):




	'''simple docstring'''




	def __init__(      self	:str  ,     *a	:Tuple  ,     **a	:Dict		)	->				int:
								super().__init__(*a  ,     **a		)
								requires_backends(self  ,     "vision"		)
								self.check_model_type(
								    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
								    if self.framework == "tf"
								    else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING		)







	def      _lowerCamelCase       (      self	:List[Any]  ,     a	:Any=None		)	->				Dict:
								__UpperCamelCase			:     Dict         =      {}
								if top_k is not None:
															__UpperCamelCase			:     Optional[int]         =      top_k
								return {}, {}, postprocess_params







	def __call__(      self	:Tuple  ,     a	:Union[str, List[str], "Image.Image", List["Image.Image"]]  ,     **a	:int		)	->				Dict:
								return super().__call__(a  ,     **a		)







	def      _lowerCamelCase       (      self	:Any  ,     a	:Optional[int]		)	->				Union[str, Any]:
								__UpperCamelCase			:     int         =      load_image(a		)
								__UpperCamelCase			:     str         =      self.image_processor(images=a  ,     return_tensors=self.framework		)
								return model_inputs







	def      _lowerCamelCase       (      self	:int  ,     a	:Any		)	->				Dict:
								__UpperCamelCase			:     Any         =      self.model(**a		)
								return model_outputs







	def      _lowerCamelCase       (      self	:List[Any]  ,     a	:str  ,     a	:List[Any]=5		)	->				Union[str, Any]:
								if top_k > self.model.config.num_labels:
															__UpperCamelCase			:     Dict         =      self.model.config.num_labels

								if self.framework == "pt":
															__UpperCamelCase			:     Tuple         =      model_outputs.logits.softmax(-1		)[0]
															__UpperCamelCase     ,		__UpperCamelCase			:     int         =      probs.topk(a		)
								elif self.framework == "tf":
															__UpperCamelCase			:     Optional[int]         =      stable_softmax(model_outputs.logits  ,     axis=-1		)[0]
															__UpperCamelCase			:     Optional[Any]         =      tf.math.top_k(a  ,     k=a		)
															__UpperCamelCase     ,		__UpperCamelCase			:     Any         =      topk.values.numpy(), topk.indices.numpy()
								else:
															raise ValueError(f'Unsupported framework: {self.framework}'		)

								__UpperCamelCase			:     int         =      scores.tolist()
								__UpperCamelCase			:     Optional[int]         =      ids.tolist()
								return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(a  ,     a		)]

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"		)