
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		:



      @staticmethod
      def 				_SCREAMING_SNAKE_CASE   (*snake_case__		:					Union[str, Any]    ,						**snake_case__		:					Any )		->	List[str]:


             '''simple docstring'''


             pass


def 			UpperCamelCase					(    __lowerCamelCase     : Image    ):
       snake_case       :   Any							=      hashlib.mda(image.tobytes()    )
       return m.hexdigest()






@is_pipeline_test
@require_vision
@require_timm
@require_torch
class   UpperCAmelCase		(						unittest.TestCase ):
   A__					:		Union[str, Any]						     =     MODEL_FOR_DEPTH_ESTIMATION_MAPPING



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[Any]    ,						snake_case__		:					Optional[Any]    ,						snake_case__		:					Union[str, Any]    ,						snake_case__		:					Tuple )		->	Union[str, Any]:


          '''simple docstring'''


          snake_case       :   Optional[Any]							=      DepthEstimationPipeline(model=snake_case__    ,						image_processor=snake_case__ )
          return depth_estimator, [
              "./tests/fixtures/tests_samples/COCO/000000039769.png",
              "./tests/fixtures/tests_samples/COCO/000000039769.png",
          ]



   def 				_SCREAMING_SNAKE_CASE   (self		:					Any    ,						snake_case__		:					Union[str, Any]    ,						snake_case__		:					Tuple )		->	Tuple:


          '''simple docstring'''


          snake_case       :   List[str]							=      depth_estimator("./tests/fixtures/tests_samples/COCO/000000039769.png" )
          self.assertEqual({"predicted_depth": ANY(torch.Tensor ), "depth": ANY(Image.Image )}    ,						snake_case__ )
          import datasets

          snake_case       :   Any							=      datasets.load_dataset("hf-internal-testing/fixtures_image_utils"    ,						"image"    ,						split="test" )
          snake_case       :   Dict							=      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 )},
              ]    ,						snake_case__    ,						)



   @require_tf
   @unittest.skip("Depth estimation is not implemented in TF" )
   def 				_SCREAMING_SNAKE_CASE   (self		:					str )		->	List[str]:


          '''simple docstring'''


          pass



   @slow
   @require_torch
   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int] )		->	Union[str, Any]:


          '''simple docstring'''


          snake_case       :   List[Any]							=      "Intel/dpt-large"
          snake_case       :   Tuple							=      pipeline("depth-estimation"    ,						model=snake_case__ )
          snake_case       :   List[str]							=      depth_estimator("http://images.cocodataset.org/val2017/000000039769.jpg" )
          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 				_SCREAMING_SNAKE_CASE   (self		:					Union[str, Any] )		->	List[Any]:


          '''simple docstring'''


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







from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCamelCase							=       logging.get_logger(__name__)

__lowerCamelCase							=       {
    # See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}






class   UpperCAmelCase		(						A_ ):
   A__					:		List[str]						     =     "megatron-bert"



   def __init__(self		:					Optional[int]    ,						snake_case__		:					List[str]=2_90_56    ,						snake_case__		:					List[Any]=10_24    ,						snake_case__		:					str=24    ,						snake_case__		:					Tuple=16    ,						snake_case__		:					Union[str, Any]=40_96    ,						snake_case__		:					str="gelu"    ,						snake_case__		:					str=0.1    ,						snake_case__		:					Optional[int]=0.1    ,						snake_case__		:					Tuple=5_12    ,						snake_case__		:					Union[str, Any]=2    ,						snake_case__		:					Dict=0.02    ,						snake_case__		:					List[Any]=1e-12    ,						snake_case__		:					int=0    ,						snake_case__		:					Tuple="absolute"    ,						snake_case__		:					Any=True    ,						**snake_case__		:					Union[str, Any]    ,						)		->	Optional[Any]:


          '''simple docstring'''


          super().__init__(pad_token_id=snake_case__    ,						**snake_case__ )

          snake_case       :   Tuple							=      vocab_size
          snake_case       :   str							=      hidden_size
          snake_case       :   str							=      num_hidden_layers
          snake_case       :   str							=      num_attention_heads
          snake_case       :   Optional[int]							=      hidden_act
          snake_case       :   int							=      intermediate_size
          snake_case       :   List[str]							=      hidden_dropout_prob
          snake_case       :   Union[str, Any]							=      attention_probs_dropout_prob
          snake_case       :   Dict							=      max_position_embeddings
          snake_case       :   List[str]							=      type_vocab_size
          snake_case       :   List[str]							=      initializer_range
          snake_case       :   Tuple							=      layer_norm_eps
          snake_case       :   int							=      position_embedding_type
          snake_case       :   str							=      use_cache





