

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 _UpperCamelCase				:
						'''simple docstring'''







						@staticmethod
						def 				__UpperCamelCase			(      *a				:			Any   ,   **a				:			str						)				->							Any:

							"""simple docstring"""

							pass

def 			lowerCamelCase__			(       _a):
	SCREAMING_SNAKE_CASE :   str					  =							hashlib.mda(image.tobytes())
	return m.hexdigest()






@is_pipeline_test
@require_vision
@require_timm
@require_torch
class _UpperCamelCase				(				unittest.TestCase ):
			'''simple docstring'''







			lowerCamelCase__							     =MODEL_FOR_DEPTH_ESTIMATION_MAPPING
			def 				__UpperCamelCase			(      self				:			Tuple   ,   a				:			int   ,   a				:			Any   ,   a				:			Any						)				->							Union[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   List[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 				__UpperCamelCase			(      self				:			int   ,   a				:			List[str]   ,   a				:			List[Any]						)				->							str:

				"""simple docstring"""

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

				SCREAMING_SNAKE_CASE :   Tuple					  =							datasets.load_dataset("hf-internal-testing/fixtures_image_utils"   ,   "image"   ,   split="test"						)
				SCREAMING_SNAKE_CASE :   List[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						)},
				    ]   ,   a   ,   )
			@require_tf
			@unittest.skip("Depth estimation is not implemented in TF"						)
			def 				__UpperCamelCase			(      self				:			List[Any]						)				->							Optional[int]:

				"""simple docstring"""

				pass
			@slow
			@require_torch
			def 				__UpperCamelCase			(      self				:			Optional[Any]						)				->							Tuple:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE :   Tuple					  =							"Intel/dpt-large"
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							pipeline("depth-estimation"   ,   model=a						)
				SCREAMING_SNAKE_CASE :   str					  =							depth_estimator("http://images.cocodataset.org/val2017/000000039769.jpg"						)
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							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 				__UpperCamelCase			(      self				:			Tuple						)				->							Union[str, Any]:

				"""simple docstring"""

				self.skipTest("There is not hf-internal-testing tiny model for either GLPN nor DPT"						)


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A			:					Dict						=			{
    'configuration_bigbird_pegasus': [
        'BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BigBirdPegasusConfig',
        'BigBirdPegasusOnnxConfig',
    ],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __A			:					int						=			[
        'BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST',
        'BigBirdPegasusForCausalLM',
        'BigBirdPegasusForConditionalGeneration',
        'BigBirdPegasusForQuestionAnswering',
        'BigBirdPegasusForSequenceClassification',
        'BigBirdPegasusModel',
        'BigBirdPegasusPreTrainedModel',
    ]


if TYPE_CHECKING:
    from .configuration_bigbird_pegasus import (
        BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
        BigBirdPegasusConfig,
        BigBirdPegasusOnnxConfig,
    )

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_bigbird_pegasus import (
            BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
            BigBirdPegasusForCausalLM,
            BigBirdPegasusForConditionalGeneration,
            BigBirdPegasusForQuestionAnswering,
            BigBirdPegasusForSequenceClassification,
            BigBirdPegasusModel,
            BigBirdPegasusPreTrainedModel,
        )


else:
    import sys

    __A			:					List[Any]						=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)