



"""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   snake_case__	(      *SCREAMING_SNAKE_CASE__						,	**SCREAMING_SNAKE_CASE__		)      ->	List[str]:
							pass

def       _lowerCamelCase				(							UpperCAmelCase_ :       Image     )	-> str:






	"""simple docstring"""





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






@is_pipeline_test
@require_vision
@require_timm
@require_torch
class       UpperCamelCase__			(   unittest.TestCase      ):

			"""simple docstring"""


			A__	:					int							  =     MODEL_FOR_DEPTH_ESTIMATION_MAPPING


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)      ->	int:
				A__											=					DepthEstimationPipeline(model=SCREAMING_SNAKE_CASE__						,	image_processor=SCREAMING_SNAKE_CASE__		)
				return depth_estimator, [
				    "./tests/fixtures/tests_samples/COCO/000000039769.png",
				    "./tests/fixtures/tests_samples/COCO/000000039769.png",
				]


			def   snake_case__	(      self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)      ->	List[Any]:
				A__											=					depth_estimator("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
				self.assertEqual({"predicted_depth": ANY(torch.Tensor		), "depth": ANY(Image.Image		)}						,	SCREAMING_SNAKE_CASE__		)
				import datasets

				A__											=					datasets.load_dataset("hf-internal-testing/fixtures_image_utils"						,	"image"						,	split="test"		)
				A__											=					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		)},
				    ]						,	SCREAMING_SNAKE_CASE__						,	)


			@require_tf
			@unittest.skip("Depth estimation is not implemented in TF"		)
			def   snake_case__	(      self		)      ->	Dict:
				pass


			@slow
			@require_torch
			def   snake_case__	(      self		)      ->	Optional[int]:
				A__											=					"Intel/dpt-large"
				A__											=					pipeline("depth-estimation"						,	model=SCREAMING_SNAKE_CASE__		)
				A__											=					depth_estimator("http://images.cocodataset.org/val2017/000000039769.jpg"		)
				A__											=					hashimage(outputs["depth"]		)

				# This seems flaky.
				# self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
				self.assertEqual(nested_simplify(outputs["predicted_depth"].max().item()		)						,	2_9.3_0_4		)
				self.assertEqual(nested_simplify(outputs["predicted_depth"].min().item()		)						,	2.6_6_2		)







			@require_torch
			def   snake_case__	(      self		)      ->	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"		)






"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_					     =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_					     =	{
    '''alibaba-damo/mgp-str-base''': '''https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json''',
}
class       lowerCAmelCase_		(				A__ ):



							'''simple docstring'''





							_snake_case						      =		'''mgp-str'''




							def __init__( self    ,				snake_case_=[32, 128]    ,				snake_case_=4    ,				snake_case_=3    ,				snake_case_=27    ,				snake_case_=38    ,				snake_case_=50_257    ,				snake_case_=30_522    ,				snake_case_=768    ,				snake_case_=12    ,				snake_case_=12    ,				snake_case_=4.0    ,				snake_case_=True    ,				snake_case_=False    ,				snake_case_=1e-5    ,				snake_case_=0.0    ,				snake_case_=0.0    ,				snake_case_=0.0    ,				snake_case_=False    ,				snake_case_=0.02    ,				**snake_case_    ,				)       ->  List[Any]:
													super().__init__(**snake_case_				)

													__lowerCAmelCase     =  image_size
													__lowerCAmelCase     =  patch_size
													__lowerCAmelCase     =  num_channels
													__lowerCAmelCase     =  max_token_length
													__lowerCAmelCase     =  num_character_labels
													__lowerCAmelCase     =  num_bpe_labels
													__lowerCAmelCase     =  num_wordpiece_labels
													__lowerCAmelCase     =  hidden_size
													__lowerCAmelCase     =  num_hidden_layers
													__lowerCAmelCase     =  num_attention_heads
													__lowerCAmelCase     =  mlp_ratio
													__lowerCAmelCase     =  distilled
													__lowerCAmelCase     =  layer_norm_eps
													__lowerCAmelCase     =  drop_rate
													__lowerCAmelCase     =  qkv_bias
													__lowerCAmelCase     =  attn_drop_rate
													__lowerCAmelCase     =  drop_path_rate
													__lowerCAmelCase     =  output_aa_attentions
													__lowerCAmelCase     =  initializer_range
