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     _A   :






       @staticmethod
       def 		_a					(*SCREAMING_SNAKE_CASE_   ,				**SCREAMING_SNAKE_CASE_	)		->      Optional[Any]:





            '''simple docstring'''





            pass







def  __UpperCamelCase    (						A							):
     UpperCamelCase__										=      hashlib.mda(image.tobytes()							)
     return m.hexdigest()



@is_pipeline_test
@require_vision
@require_timm
@require_torch
class     _A   (    unittest.TestCase							):
  SCREAMING_SNAKE_CASE_	:	Tuple						=MODEL_FOR_DEPTH_ESTIMATION_MAPPING






  def 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)		->      Union[str, Any]:





       '''simple docstring'''





       UpperCamelCase__										=      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 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_	)		->      Dict:





       '''simple docstring'''





       UpperCamelCase__										=      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

       UpperCamelCase__										=      datasets.load_dataset('''hf-internal-testing/fixtures_image_utils'''   ,				'''image'''   ,				split='''test'''	)
       UpperCamelCase__										=      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 		_a					(self	)		->      str:





       '''simple docstring'''





       pass






  @slow
  @require_torch
  def 		_a					(self	)		->      Dict:





       '''simple docstring'''





       UpperCamelCase__										=      '''Intel/dpt-large'''
       UpperCamelCase__										=      pipeline('''depth-estimation'''   ,				model=SCREAMING_SNAKE_CASE_	)
       UpperCamelCase__										=      depth_estimator('''http://images.cocodataset.org/val2017/000000039769.jpg'''	)
       UpperCamelCase__										=      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 		_a					(self	)		->      Dict:





       '''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


__magic_name__								=logging.get_logger(__name__)

__magic_name__								={
    '''facebook/vit-mae-base''': '''https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json''',
    # See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}



class     _A   (    __UpperCamelCase							):
  SCREAMING_SNAKE_CASE_	:	Dict						="vit_mae"






  def __init__(self   ,				SCREAMING_SNAKE_CASE_=768   ,				SCREAMING_SNAKE_CASE_=12   ,				SCREAMING_SNAKE_CASE_=12   ,				SCREAMING_SNAKE_CASE_=3072   ,				SCREAMING_SNAKE_CASE_="gelu"   ,				SCREAMING_SNAKE_CASE_=0.0   ,				SCREAMING_SNAKE_CASE_=0.0   ,				SCREAMING_SNAKE_CASE_=0.02   ,				SCREAMING_SNAKE_CASE_=1E-12   ,				SCREAMING_SNAKE_CASE_=224   ,				SCREAMING_SNAKE_CASE_=16   ,				SCREAMING_SNAKE_CASE_=3   ,				SCREAMING_SNAKE_CASE_=True   ,				SCREAMING_SNAKE_CASE_=16   ,				SCREAMING_SNAKE_CASE_=512   ,				SCREAMING_SNAKE_CASE_=8   ,				SCREAMING_SNAKE_CASE_=2048   ,				SCREAMING_SNAKE_CASE_=0.75   ,				SCREAMING_SNAKE_CASE_=False   ,				**SCREAMING_SNAKE_CASE_   ,				)		->      Optional[Any]:





       '''simple docstring'''





       super().__init__(**SCREAMING_SNAKE_CASE_	)

       UpperCamelCase__										=      hidden_size
       UpperCamelCase__										=      num_hidden_layers
       UpperCamelCase__										=      num_attention_heads
       UpperCamelCase__										=      intermediate_size
       UpperCamelCase__										=      hidden_act
       UpperCamelCase__										=      hidden_dropout_prob
       UpperCamelCase__										=      attention_probs_dropout_prob
       UpperCamelCase__										=      initializer_range
       UpperCamelCase__										=      layer_norm_eps
       UpperCamelCase__										=      image_size
       UpperCamelCase__										=      patch_size
       UpperCamelCase__										=      num_channels
       UpperCamelCase__										=      qkv_bias
       UpperCamelCase__										=      decoder_num_attention_heads
       UpperCamelCase__										=      decoder_hidden_size
       UpperCamelCase__										=      decoder_num_hidden_layers
       UpperCamelCase__										=      decoder_intermediate_size
       UpperCamelCase__										=      mask_ratio
       UpperCamelCase__										=      norm_pix_loss



