"""simple docstring"""





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


_UpperCamelCase     :		str											=					logging.get_logger(__name__)

_UpperCamelCase     :		List[Any]											=					{
    "caidas/swin2sr-classicalsr-x2-64": (
        "https://huggingface.co/caidas/swin2sr-classicalsr-x2-64/resolve/main/config.json"
    ),
}


class   UpperCAmelCase_			(					_a):
      lowerCamelCase__	:					Dict										=						"swin2sr"

      lowerCamelCase__	:					Any										=						{
          "hidden_size": "embed_dim",
          "num_attention_heads": "num_heads",
          "num_hidden_layers": "num_layers",
      }


      def __init__(     self	,    a=6_4	,    a=1	,    a=3	,    a=1_8_0	,    a=[6, 6, 6, 6, 6, 6]	,    a=[6, 6, 6, 6, 6, 6]	,    a=8	,    a=2.0	,    a=True	,    a=0.0	,    a=0.0	,    a=0.1	,    a="gelu"	,    a=False	,    a=0.02	,    a=1e-5	,    a=2	,    a=1.0	,    a="1conv"	,    a="pixelshuffle"	,    **a	,    ) ->      Optional[Any]:
             super().__init__(**a							)

             lowercase__    :	str     					=			image_size
             lowercase__    :	Tuple     					=			patch_size
             lowercase__    :	List[Any]     					=			num_channels
             lowercase__    :	Tuple     					=			embed_dim
             lowercase__    :	int     					=			depths
             lowercase__    :	int     					=			len(a							)
             lowercase__    :	Dict     					=			num_heads
             lowercase__    :	List[str]     					=			window_size
             lowercase__    :	Dict     					=			mlp_ratio
             lowercase__    :	List[Any]     					=			qkv_bias
             lowercase__    :	Dict     					=			hidden_dropout_prob
             lowercase__    :	str     					=			attention_probs_dropout_prob
             lowercase__    :	Optional[int]     					=			drop_path_rate
             lowercase__    :	int     					=			hidden_act
             lowercase__    :	Any     					=			use_absolute_embeddings
             lowercase__    :	Optional[Any]     					=			layer_norm_eps
             lowercase__    :	str     					=			initializer_range
             lowercase__    :	List[Any]     					=			upscale
             lowercase__    :	List[Any]     					=			img_range
             lowercase__    :	str     					=			resi_connection
             lowercase__    :	Any     					=			upsampler


"""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_			:


             @staticmethod
             def 	_UpperCAmelCase (     *a	,    **a							) ->      int:
                    pass





def        a_  (   _lowerCAmelCase						:				Image  ):





       '''simple docstring'''

       lowercase__    :	List[str]     					=			hashlib.mda(image.tobytes()  )
       return m.hexdigest()






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


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a							) ->      Dict:
             lowercase__    :	Union[str, Any]     					=			DepthEstimationPipeline(model=a	,    image_processor=a							)
             return depth_estimator, [
                 "./tests/fixtures/tests_samples/COCO/000000039769.png",
                 "./tests/fixtures/tests_samples/COCO/000000039769.png",
             ]


      def 	_UpperCAmelCase (     self	,    a	,    a							) ->      Optional[int]:
             lowercase__    :	Tuple     					=			depth_estimator('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
             self.assertEqual({'predicted_depth': ANY(torch.Tensor							), 'depth': ANY(Image.Image							)}	,    a							)
             import datasets

             lowercase__    :	Tuple     					=			datasets.load_dataset('hf-internal-testing/fixtures_image_utils'	,    'image'	,    split='test'							)
             lowercase__    :	List[Any]     					=			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 	_UpperCAmelCase (     self							) ->      Optional[int]:
             pass


      @slow
      @require_torch
      def 	_UpperCAmelCase (     self							) ->      Tuple:
             lowercase__    :	Tuple     					=			'Intel/dpt-large'
             lowercase__    :	Optional[int]     					=			pipeline('depth-estimation'	,    model=a							)
             lowercase__    :	List[Any]     					=			depth_estimator('http://images.cocodataset.org/val2017/000000039769.jpg'							)
             lowercase__    :	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 	_UpperCAmelCase (     self							) ->      Optional[int]:
             # This is highly irregular to have no small tests.
             self.skipTest('There is not hf-internal-testing tiny model for either GLPN nor DPT'							)

