


"""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 			_UpperCAmelCase :






					'''simple docstring'''

					@staticmethod
					def 				SCREAMING_SNAKE_CASE  (*a_				,       **a_					):





						'''simple docstring'''

						pass






def 		lowercase							(     _snake_case						:      Image     )     ->str:
	"""simple docstring"""





	__snake_case	:						Optional[int]						=				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 				SCREAMING_SNAKE_CASE  (self				,       a_				,       a_				,       a_					):





			'''simple docstring'''

			__snake_case	:						str						=				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				,       a_				,       a_					):





			'''simple docstring'''

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

			__snake_case	:						List[Any]						=				datasets.load_dataset('''hf-internal-testing/fixtures_image_utils'''				,       '''image'''				,       split='''test'''					)
			__snake_case	:						List[str]						=				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					):





			'''simple docstring'''

			pass



		@slow
		@require_torch
		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Union[str, Any]						=				'''Intel/dpt-large'''
			__snake_case	:						List[Any]						=				pipeline('''depth-estimation'''				,       model=a_					)
			__snake_case	:						Optional[int]						=				depth_estimator('''http://images.cocodataset.org/val2017/000000039769.jpg'''					)
			__snake_case	:						Optional[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 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

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










"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_lowercase : List[Any]      						=						{
    "configuration_tapas": ["TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP", "TapasConfig"],
    "tokenization_tapas": ["TapasTokenizer"],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase : Optional[Any]      						=						[
		    "TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "TapasForMaskedLM",
		    "TapasForQuestionAnswering",
		    "TapasForSequenceClassification",
		    "TapasModel",
		    "TapasPreTrainedModel",
		    "load_tf_weights_in_tapas",
		]
try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase : str      						=						[
		    "TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "TFTapasForMaskedLM",
		    "TFTapasForQuestionAnswering",
		    "TFTapasForSequenceClassification",
		    "TFTapasModel",
		    "TFTapasPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
		from .tokenization_tapas import TapasTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tapas import (
				    TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TapasForMaskedLM,
				    TapasForQuestionAnswering,
				    TapasForSequenceClassification,
				    TapasModel,
				    TapasPreTrainedModel,
				    load_tf_weights_in_tapas,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_tapas import (
				    TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFTapasForMaskedLM,
				    TFTapasForQuestionAnswering,
				    TFTapasForSequenceClassification,
				    TFTapasModel,
				    TFTapasPreTrainedModel,
				)


else:
		import sys

		_lowercase : List[str]      						=						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




