





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__  (							*lowerCamelCase__						:   Optional[int]						,**lowerCamelCase__						:   List[str]	)					->		str:




																	'''simple docstring'''


																	pass



def        __lowercase       (				_SCREAMING_SNAKE_CASE     )			->       int:


						'''simple docstring'''


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



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



					'''simple docstring'''

					__snake_case   :     Tuple		     =      MODEL_FOR_DEPTH_ESTIMATION_MAPPING






					def     SCREAMING_SNAKE_CASE__  (							self						:   Dict						,lowerCamelCase__						:   List[str]						,lowerCamelCase__						:   List[Any]						,lowerCamelCase__						:   Union[str, Any]	)					->		List[str]:




											'''simple docstring'''


											SCREAMING_SNAKE_CASE		=						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						:   List[Any]						,lowerCamelCase__						:   Tuple						,lowerCamelCase__						:   Dict	)					->		Dict:




											'''simple docstring'''


											SCREAMING_SNAKE_CASE		=						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		=						datasets.load_dataset("""hf-internal-testing/fixtures_image_utils"""						,"""image"""						,split="""test"""	)
											SCREAMING_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	)},
											    ]						,_A						,)






					@require_tf
					@unittest.skip("""Depth estimation is not implemented in TF"""	)
					def     SCREAMING_SNAKE_CASE__  (							self						:   Tuple	)					->		int:




											'''simple docstring'''


											pass






					@slow
					@require_torch
					def     SCREAMING_SNAKE_CASE__  (							self						:   List[Any]	)					->		List[str]:




											'''simple docstring'''


											SCREAMING_SNAKE_CASE		=						"""Intel/dpt-large"""
											SCREAMING_SNAKE_CASE		=						pipeline("""depth-estimation"""						,model=_A	)
											SCREAMING_SNAKE_CASE		=						depth_estimator("""http://images.cocodataset.org/val2017/000000039769.jpg"""	)
											SCREAMING_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     SCREAMING_SNAKE_CASE__  (							self						:   List[str]	)					->		Optional[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


SCREAMING_SNAKE_CASE_						       =	{
    """configuration_megatron_bert""": ["""MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MegatronBertConfig"""],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE_						       =	[
          """MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """MegatronBertForCausalLM""",
          """MegatronBertForMaskedLM""",
          """MegatronBertForMultipleChoice""",
          """MegatronBertForNextSentencePrediction""",
          """MegatronBertForPreTraining""",
          """MegatronBertForQuestionAnswering""",
          """MegatronBertForSequenceClassification""",
          """MegatronBertForTokenClassification""",
          """MegatronBertModel""",
          """MegatronBertPreTrainedModel""",
      ]

if TYPE_CHECKING:
      from .configuration_megatron_bert import MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, MegatronBertConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_megatron_bert import (
                MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                MegatronBertForCausalLM,
                MegatronBertForMaskedLM,
                MegatronBertForMultipleChoice,
                MegatronBertForNextSentencePrediction,
                MegatronBertForPreTraining,
                MegatronBertForQuestionAnswering,
                MegatronBertForSequenceClassification,
                MegatronBertForTokenClassification,
                MegatronBertModel,
                MegatronBertPreTrainedModel,
            )

else:
      import sys

      SCREAMING_SNAKE_CASE_						       =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


