
"""simple docstring"""







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 							__magic_name__     :





											@staticmethod
											def 							lowerCAmelCase      (				*snake_case_   :							Optional[int]							,						**snake_case_   :							Tuple    ):
																		pass
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     str:







							"""simple docstring"""


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







@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 							__magic_name__     (		unittest.TestCase   ):
					_SCREAMING_SNAKE_CASE       :					Any     							=   MODEL_FOR_DEPTH_ESTIMATION_MAPPING





					def 							lowerCAmelCase      (				self   :							Optional[int]							,						snake_case_   :							List[Any]							,						snake_case_   :							int							,						snake_case_   :							str    ):
												__snake_case     			=       DepthEstimationPipeline(model=snake_case_							,						image_processor=snake_case_    )
												return depth_estimator, [
												    "./tests/fixtures/tests_samples/COCO/000000039769.png",
												    "./tests/fixtures/tests_samples/COCO/000000039769.png",
												]





					def 							lowerCAmelCase      (				self   :							int							,						snake_case_   :							int							,						snake_case_   :							List[Any]    ):
												__snake_case     			=       depth_estimator("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
												self.assertEqual({"predicted_depth": ANY(torch.Tensor    ), "depth": ANY(Image.Image    )}							,						snake_case_    )
												import datasets

												__snake_case     			=       datasets.load_dataset("hf-internal-testing/fixtures_image_utils"							,						"image"							,						split="test"    )
												__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    )},
												    ]							,						snake_case_							,						)





					@require_tf
					@unittest.skip("Depth estimation is not implemented in TF"    )
					def 							lowerCAmelCase      (				self   :							str    ):
												pass





					@slow
					@require_torch
					def 							lowerCAmelCase      (				self   :							Optional[int]    ):
												__snake_case     			=       "Intel/dpt-large"
												__snake_case     			=       pipeline("depth-estimation"							,						model=snake_case_    )
												__snake_case     			=       depth_estimator("http://images.cocodataset.org/val2017/000000039769.jpg"    )
												__snake_case     			=       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   :							Any    ):
												# 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 UserDict
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_torch_available():
		from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING

if is_tf_available():
		from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
		from ..tf_utils import stable_softmax

_SCREAMING_SNAKE_CASE	       =      logging.get_logger(__name__)







@add_end_docstrings(lowercase__   )
class 							__magic_name__     (		lowercase__   ):





					def __init__(				self   :							Tuple							,						**snake_case_   :							str    ):
												super().__init__(**snake_case_    )

												requires_backends(self							,						"vision"    )
												self.check_model_type(
												    TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
												    if self.framework == "tf"
												    else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING    )





					def __call__(				self   :							Optional[Any]							,						snake_case_   :							Union[str, List[str], "Image", List["Image"]]							,						**snake_case_   :							Optional[int]    ):
												return super().__call__(snake_case_							,						**snake_case_    )





					def 							lowerCAmelCase      (				self   :							Tuple							,						**snake_case_   :							Any    ):
												__snake_case     			=       {}
												if "candidate_labels" in kwargs:
																			__snake_case     			=       kwargs["candidate_labels"]
												if "hypothesis_template" in kwargs:
																			__snake_case     			=       kwargs["hypothesis_template"]

												return preprocess_params, {}, {}





					def 							lowerCAmelCase      (				self   :							int							,						snake_case_   :							List[str]							,						snake_case_   :							List[str]=None							,						snake_case_   :							int="This is a photo of {}."    ):
												__snake_case     			=       load_image(snake_case_    )
												__snake_case     			=       self.image_processor(images=[image]							,						return_tensors=self.framework    )
												__snake_case     			=       candidate_labels
												__snake_case     			=       [hypothesis_template.format(snake_case_    ) for x in candidate_labels]
												__snake_case     			=       self.tokenizer(snake_case_							,						return_tensors=self.framework							,						padding=snake_case_    )
												__snake_case     			=       [text_inputs]
												return inputs





					def 							lowerCAmelCase      (				self   :							Any							,						snake_case_   :							Dict    ):
												__snake_case     			=       model_inputs.pop("candidate_labels"    )
												__snake_case     			=       model_inputs.pop("text_inputs"    )
												if isinstance(text_inputs[0]							,						snake_case_    ):
																			__snake_case     			=       text_inputs[0]
												else:
																			# Batching case.
																			__snake_case     			=       text_inputs[0][0]

												__snake_case     			=       self.model(**snake_case_							,						**snake_case_    )

												__snake_case     			=       {
												    "candidate_labels": candidate_labels,
												    "logits": outputs.logits_per_image,
												}
												return model_outputs





					def 							lowerCAmelCase      (				self   :							Optional[int]							,						snake_case_   :							Any    ):
												__snake_case     			=       model_outputs.pop("candidate_labels"    )
												__snake_case     			=       model_outputs["logits"][0]
												if self.framework == "pt":
																			__snake_case     			=       logits.softmax(dim=-1    ).squeeze(-1    )
																			__snake_case     			=       probs.tolist()
																			if not isinstance(snake_case_							,						snake_case_    ):
																										__snake_case     			=       [scores]
												elif self.framework == "tf":
																			__snake_case     			=       stable_softmax(snake_case_							,						axis=-1    )
																			__snake_case     			=       probs.numpy().tolist()
												else:
																			raise ValueError(F'''Unsupported framework: {self.framework}'''    )

												__snake_case     			=       [
												    {"score": score, "label": candidate_label}
												    for score, candidate_label in sorted(zip(snake_case_							,						snake_case_    )							,						key=lambda snake_case_    : -x[0]    )
												]
												return result
