



"""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    (       *__lowerCamelCase    :	Union[str, Any]							,       **__lowerCamelCase    :	Optional[Any]				):



          """simple docstring"""
          pass




def 			snake_case		(		lowerCAmelCase_	)						->				str:
  _snake_case   	=      hashlib.mda(image.tobytes()	)
  return m.hexdigest()







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



   def    __UpperCAmelCase    (       self    :	int							,       __lowerCamelCase    :	Optional[Any]							,       __lowerCamelCase    :	List[Any]							,       __lowerCamelCase    :	Tuple				):



     """simple docstring"""
     _snake_case   	=      DepthEstimationPipeline(model=__lowerCamelCase							,       image_processor=__lowerCamelCase				)
     return depth_estimator, [
         "./tests/fixtures/tests_samples/COCO/000000039769.png",
         "./tests/fixtures/tests_samples/COCO/000000039769.png",
     ]



   def    __UpperCAmelCase    (       self    :	List[str]							,       __lowerCamelCase    :	List[Any]							,       __lowerCamelCase    :	Optional[int]				):



     """simple docstring"""
     _snake_case   	=      depth_estimator('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				)
     self.assertEqual({'''predicted_depth''': ANY(torch.Tensor				), '''depth''': ANY(Image.Image				)}							,       __lowerCamelCase				)
     import datasets

     _snake_case   	=      datasets.load_dataset('''hf-internal-testing/fixtures_image_utils'''							,       '''image'''							,       split='''test'''				)
     _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				)},
         ]							,       __lowerCamelCase							,       )



   @require_tf
   @unittest.skip('''Depth estimation is not implemented in TF'''				)
   def    __UpperCAmelCase    (       self    :	List[str]				):



     """simple docstring"""
     pass



   @slow
   @require_torch
   def    __UpperCAmelCase    (       self    :	List[Any]				):



     """simple docstring"""
     _snake_case   	=      '''Intel/dpt-large'''
     _snake_case   	=      pipeline('''depth-estimation'''							,       model=__lowerCamelCase				)
     _snake_case   	=      depth_estimator('''http://images.cocodataset.org/val2017/000000039769.jpg'''				)
     _snake_case   	=      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    __UpperCAmelCase    (       self    :	Tuple				):



     """simple docstring"""
     # This is highly irregular to have no small tests.
     self.skipTest('''There is not hf-internal-testing tiny model for either GLPN nor DPT'''				)













import torch

from transformers import AutoModel






class 					SCREAMING_SNAKE_CASE_  (       torch.nn.Module ):
       """simple docstring"""


       def __init__(				self	:Dict,      snake_case	:str="sayef/fsner-bert-base-uncased"):



          """simple docstring"""

          super(snake_case,      self).__init__()

          _lowercase              =AutoModel.from_pretrained(snake_case,      return_dict=snake_case)
          _lowercase              =torch.nn.CosineSimilarity(3,      1e-0_8)
          _lowercase              =torch.nn.Softmax(dim=1)





       def 				UpperCamelCase__						(				self	:str,      **snake_case	:int):



          """simple docstring"""

          return self.bert(**snake_case).last_hidden_state





       def 				UpperCamelCase__						(				self	:Union[str, Any],      snake_case	:Optional[Any]):



          """simple docstring"""

          return token_embeddings.sum(2,      keepdim=snake_case)





       def 				UpperCamelCase__						(				self	:List[Any],      snake_case	:int,      snake_case	:Dict,      snake_case	:Dict=1):



          """simple docstring"""

          return self.softmax(T * self.cos(snake_case,      snake_case))





       def 				UpperCamelCase__						(				self	:List[str],      snake_case	:int,      snake_case	:List[str]):



          """simple docstring"""

          _lowercase              =W_supports['sizes'].tolist()
          _lowercase              =W_supports['start_token_id'].item()
          _lowercase              =W_supports['end_token_id'].item()

          del W_supports["sizes"]
          del W_supports["start_token_id"]
          del W_supports["end_token_id"]

          _lowercase              =self.BERT(**snake_case)
          _lowercase              =self.BERT(**snake_case)

          _lowercase              =None
          _lowercase              =None

          _lowercase              =W_supports['input_ids'] == start_token_id
          _lowercase              =W_supports['input_ids'] == end_token_id

          for i, size in enumerate(snake_case):
             if i == 0:
                _lowercase              =0
             else:
                _lowercase              =support_sizes[i - 1]

             _lowercase              =S[s : s + size][start_token_masks[s : s + size]]
             _lowercase              =S[s : s + size][end_token_masks[s : s + size]]

             _lowercase              =torch.matmul(q[i],      s_start.T).sum(1).softmax(0)
             _lowercase              =torch.matmul(q[i],      s_end.T).sum(1).softmax(0)

             if p_starts is not None:
                _lowercase              =torch.vstack((p_starts, p_start))
                _lowercase              =torch.vstack((p_ends, p_end))
             else:
                _lowercase              =p_start
                _lowercase              =p_end

          return p_starts, p_ends


