



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        _snake_case    (*__magic_name__							,    **__magic_name__				):
										pass






def      _A				(UpperCamelCase		:      Image )  ->str:



			'''simple docstring'''







			lowerCamelCase__ :      List[str]								=		hashlib.mda(image.tobytes() )
			return m.hexdigest()
@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 				__A					( unittest.TestCase      ):
			UpperCamelCase					:Dict     =			MODEL_FOR_DEPTH_ESTIMATION_MAPPING





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__							,    __magic_name__				):
						lowerCamelCase__ :      List[str]								=		DepthEstimationPipeline(model=__magic_name__							,    image_processor=__magic_name__				)
						return depth_estimator, [
						    "./tests/fixtures/tests_samples/COCO/000000039769.png",
						    "./tests/fixtures/tests_samples/COCO/000000039769.png",
						]





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__				):
						lowerCamelCase__ :      Tuple								=		depth_estimator("""./tests/fixtures/tests_samples/COCO/000000039769.png"""				)
						self.assertEqual({"""predicted_depth""": ANY(torch.Tensor				), """depth""": ANY(Image.Image				)}							,    __magic_name__				)
						import datasets

						lowerCamelCase__ :      Dict								=		datasets.load_dataset("""hf-internal-testing/fixtures_image_utils"""							,    """image"""							,    split="""test"""				)
						lowerCamelCase__ :      Union[str, Any]								=		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				)},
						    ]							,    __magic_name__							,    )





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





			@slow
			@require_torch
			def        _snake_case    (self				):
						lowerCamelCase__ :      List[Any]								=		"""Intel/dpt-large"""
						lowerCamelCase__ :      str								=		pipeline("""depth-estimation"""							,    model=__magic_name__				)
						lowerCamelCase__ :      Dict								=		depth_estimator("""http://images.cocodataset.org/val2017/000000039769.jpg"""				)
						lowerCamelCase__ :      List[str]								=		hashimage(outputs["""depth"""]				)

						# This seems flaky.
						# self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
						self.assertEqual(nested_simplify(outputs["""predicted_depth"""].max().item()				)							,    29.3_04				)
						self.assertEqual(nested_simplify(outputs["""predicted_depth"""].min().item()				)							,    2.6_62				)





			@require_torch
			def        _snake_case    (self				):
						# This is highly irregular to have no small tests.
						self.skipTest("""There is not hf-internal-testing tiny model for either GLPN nor DPT"""				)








import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=A_      )
class 				__A					( A_      ):
			UpperCamelCase					:str     =			field(default='''automatic-speech-recognition'''					, metadata={'''include_in_asdict_even_if_is_default''': True}      )
			UpperCamelCase					:ClassVar[Features]     =			Features({'''audio''': Audio()}      )
			UpperCamelCase					:ClassVar[Features]     =			Features({'''transcription''': Value('''string'''      )}      )
			UpperCamelCase					:str     =			"audio"
			UpperCamelCase					:str     =			"transcription"





			def        _snake_case    (self							,    __magic_name__				):
						if self.audio_column not in features:
									raise ValueError(f"Column {self.audio_column} is not present in features."				)
						if not isinstance(features[self.audio_column]							,    __magic_name__				):
									raise ValueError(f"Column {self.audio_column} is not an Audio type."				)
						lowerCamelCase__ :      int								=		copy.deepcopy(self				)
						lowerCamelCase__ :      Tuple								=		self.input_schema.copy()
						lowerCamelCase__ :      Tuple								=		features[self.audio_column]
						lowerCamelCase__ :      List[str]								=		input_schema
						return task_template





			@property
			def        _snake_case    (self				):
						return {self.audio_column: "audio", self.transcription_column: "transcription"}



