"""simple docstring"""







import inspect
import unittest

from transformers import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import MODEL_MAPPING, GLPNConfig, GLPNForDepthEstimation, GLPNModel
     from transformers.models.glpn.modeling_glpn import GLPN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from PIL import Image

     from transformers import GLPNImageProcessor






class      _SCREAMING_SNAKE_CASE							(			UpperCAmelCase				):



   '''simple docstring'''



   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.config_class(**self.inputs_dict	)
      self.parent.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,				'hidden_sizes'	)	)
      self.parent.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,				'num_attention_heads'	)	)
      self.parent.assertTrue(hasattr(_SCREAMING_SNAKE_CASE ,				'num_encoder_blocks'	)	)






class      _SCREAMING_SNAKE_CASE							:



   '''simple docstring'''



   def __init__(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE=13 ,				_SCREAMING_SNAKE_CASE=64 ,				_SCREAMING_SNAKE_CASE=3 ,				_SCREAMING_SNAKE_CASE=4 ,				_SCREAMING_SNAKE_CASE=[2, 2, 2, 2] ,				_SCREAMING_SNAKE_CASE=[8, 4, 2, 1] ,				_SCREAMING_SNAKE_CASE=[16, 32, 64, 128] ,				_SCREAMING_SNAKE_CASE=[1, 4, 8, 16] ,				_SCREAMING_SNAKE_CASE=[1, 2, 4, 8] ,				_SCREAMING_SNAKE_CASE=True ,				_SCREAMING_SNAKE_CASE=True ,				_SCREAMING_SNAKE_CASE="gelu" ,				_SCREAMING_SNAKE_CASE=0.1 ,				_SCREAMING_SNAKE_CASE=0.1 ,				_SCREAMING_SNAKE_CASE=0.02 ,				_SCREAMING_SNAKE_CASE=16 ,				_SCREAMING_SNAKE_CASE=3 ,				_SCREAMING_SNAKE_CASE=None ,				):


      '''simple docstring'''

      __lowerCamelCase    		=					parent
      __lowerCamelCase    		=					batch_size
      __lowerCamelCase    		=					image_size
      __lowerCamelCase    		=					num_channels
      __lowerCamelCase    		=					num_encoder_blocks
      __lowerCamelCase    		=					sr_ratios
      __lowerCamelCase    		=					depths
      __lowerCamelCase    		=					hidden_sizes
      __lowerCamelCase    		=					downsampling_rates
      __lowerCamelCase    		=					num_attention_heads
      __lowerCamelCase    		=					is_training
      __lowerCamelCase    		=					use_labels
      __lowerCamelCase    		=					hidden_act
      __lowerCamelCase    		=					hidden_dropout_prob
      __lowerCamelCase    		=					attention_probs_dropout_prob
      __lowerCamelCase    		=					initializer_range
      __lowerCamelCase    		=					decoder_hidden_size
      __lowerCamelCase    		=					num_labels
      __lowerCamelCase    		=					scope







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

      __lowerCamelCase    		=					None
      if self.use_labels:
         __lowerCamelCase    		=					ids_tensor([self.batch_size, self.image_size, self.image_size] ,				self.num_labels	)

      __lowerCamelCase    		=					self.get_config()
      return config, pixel_values, labels







   def    _A				(	self	):


      '''simple docstring'''

      return GLPNConfig(
          image_size=self.image_size ,				num_channels=self.num_channels ,				num_encoder_blocks=self.num_encoder_blocks ,				depths=self.depths ,				hidden_sizes=self.hidden_sizes ,				num_attention_heads=self.num_attention_heads ,				hidden_act=self.hidden_act ,				hidden_dropout_prob=self.hidden_dropout_prob ,				attention_probs_dropout_prob=self.attention_probs_dropout_prob ,				initializer_range=self.initializer_range ,				decoder_hidden_size=self.decoder_hidden_size ,				)







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      __lowerCamelCase    		=					GLPNModel(config=_SCREAMING_SNAKE_CASE	)
      model.to(_SCREAMING_SNAKE_CASE	)
      model.eval()
      __lowerCamelCase    		=					model(_SCREAMING_SNAKE_CASE	)
      __lowerCamelCase    		=					__lowerCamelCase    		=					self.image_size // (self.downsampling_rates[-1] * 2)
      self.parent.assertEqual(
          result.last_hidden_state.shape ,				(self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)	)







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.num_labels
      __lowerCamelCase    		=					GLPNForDepthEstimation(_SCREAMING_SNAKE_CASE	)
      model.to(_SCREAMING_SNAKE_CASE	)
      model.eval()
      __lowerCamelCase    		=					model(_SCREAMING_SNAKE_CASE	)
      self.parent.assertEqual(result.predicted_depth.shape ,				(self.batch_size, self.image_size, self.image_size)	)
      __lowerCamelCase    		=					model(_SCREAMING_SNAKE_CASE ,				labels=_SCREAMING_SNAKE_CASE	)
      self.parent.assertEqual(result.predicted_depth.shape ,				(self.batch_size, self.image_size, self.image_size)	)







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.prepare_config_and_inputs()
      __lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase    		=					config_and_inputs
      __lowerCamelCase    		=					{'pixel_values': pixel_values}
      return config, inputs_dict






@require_torch
class      _SCREAMING_SNAKE_CASE							(			UpperCAmelCase    ,				UpperCAmelCase    ,				unittest.TestCase				):



   '''simple docstring'''



   __UpperCAmelCase	:							Union[str, Any]    =    (GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
   __UpperCAmelCase	:							Any    =    (
       {"depth-estimation": GLPNForDepthEstimation, "feature-extraction": GLPNModel} if is_torch_available() else {}
   )

   __UpperCAmelCase	:							Optional[Any]    =    False
   __UpperCAmelCase	:							Optional[int]    =    False
   __UpperCAmelCase	:							int    =    False







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					GLPNModelTester(self	)
      __lowerCamelCase    		=					GLPNConfigTester(self ,				config_class=_SCREAMING_SNAKE_CASE	)







   def    _A				(	self	):


      '''simple docstring'''

      self.config_tester.run_common_tests()







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE	)







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_depth_estimation(*_SCREAMING_SNAKE_CASE	)







   @unittest.skip('GLPN does not use inputs_embeds'	)
   def    _A				(	self	):


      '''simple docstring'''

      pass







   @unittest.skip('GLPN does not have get_input_embeddings method and get_output_embeddings methods'	)
   def    _A				(	self	):


      '''simple docstring'''

      pass







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase		,__lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
         __lowerCamelCase    		=					model_class(_SCREAMING_SNAKE_CASE	)
         __lowerCamelCase    		=					inspect.signature(model.forward	)
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         __lowerCamelCase    		=					[*signature.parameters.keys()]

         __lowerCamelCase    		=					['pixel_values']
         self.assertListEqual(arg_names[:1] ,				_SCREAMING_SNAKE_CASE	)







   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase		,__lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs_for_common()
      __lowerCamelCase    		=					True

      for model_class in self.all_model_classes:
         __lowerCamelCase    		=					True
         __lowerCamelCase    		=					False
         __lowerCamelCase    		=					True
         __lowerCamelCase    		=					model_class(_SCREAMING_SNAKE_CASE	)
         model.to(_SCREAMING_SNAKE_CASE	)
         model.eval()
         with torch.no_grad():
            __lowerCamelCase    		=					model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)	)
         __lowerCamelCase    		=					outputs.attentions

         __lowerCamelCase    		=					sum(self.model_tester.depths	)
         self.assertEqual(len(_SCREAMING_SNAKE_CASE	) ,				_SCREAMING_SNAKE_CASE	)

         # check that output_attentions also work using config
         del inputs_dict["output_attentions"]
         __lowerCamelCase    		=					True
         __lowerCamelCase    		=					model_class(_SCREAMING_SNAKE_CASE	)
         model.to(_SCREAMING_SNAKE_CASE	)
         model.eval()
         with torch.no_grad():
            __lowerCamelCase    		=					model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)	)
         __lowerCamelCase    		=					outputs.attentions

         self.assertEqual(len(_SCREAMING_SNAKE_CASE	) ,				_SCREAMING_SNAKE_CASE	)

         # verify the first attentions (first block, first layer)
         __lowerCamelCase    		=					(self.model_tester.image_size // 4) ** 2
         __lowerCamelCase    		=					(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
         self.assertListEqual(
             list(attentions[0].shape[-3:]	) ,				[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len] ,				)

         # verify the last attentions (last block, last layer)
         __lowerCamelCase    		=					(self.model_tester.image_size // 32) ** 2
         __lowerCamelCase    		=					(self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
         self.assertListEqual(
             list(attentions[-1].shape[-3:]	) ,				[self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len] ,				)
         __lowerCamelCase    		=					len(_SCREAMING_SNAKE_CASE	)

         # Check attention is always last and order is fine
         __lowerCamelCase    		=					True
         __lowerCamelCase    		=					True
         __lowerCamelCase    		=					model_class(_SCREAMING_SNAKE_CASE	)
         model.to(_SCREAMING_SNAKE_CASE	)
         model.eval()
         with torch.no_grad():
            __lowerCamelCase    		=					model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)	)

         self.assertEqual(out_len + 1 ,				len(_SCREAMING_SNAKE_CASE	)	)

         __lowerCamelCase    		=					outputs.attentions

         self.assertEqual(len(_SCREAMING_SNAKE_CASE	) ,				_SCREAMING_SNAKE_CASE	)
         # verify the first attentions (first block, first layer)
         __lowerCamelCase    		=					(self.model_tester.image_size // 4) ** 2
         __lowerCamelCase    		=					(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
         self.assertListEqual(
             list(self_attentions[0].shape[-3:]	) ,				[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len] ,				)







   def    _A				(	self	):


      '''simple docstring'''

      def check_hidden_states_output(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	):
         __lowerCamelCase    		=					model_class(_SCREAMING_SNAKE_CASE	)
         model.to(_SCREAMING_SNAKE_CASE	)
         model.eval()

         with torch.no_grad():
            __lowerCamelCase    		=					model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)	)

         __lowerCamelCase    		=					outputs.hidden_states

         __lowerCamelCase    		=					self.model_tester.num_encoder_blocks
         self.assertEqual(len(_SCREAMING_SNAKE_CASE	) ,				_SCREAMING_SNAKE_CASE	)

         # verify the first hidden states (first block)
         self.assertListEqual(
             list(hidden_states[0].shape[-3:]	) ,				[
                 self.model_tester.hidden_sizes[0],
                 self.model_tester.image_size // 4,
                 self.model_tester.image_size // 4,
             ] ,				)

      __lowerCamelCase		,__lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
         __lowerCamelCase    		=					True
         check_hidden_states_output(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         __lowerCamelCase    		=					True

         check_hidden_states_output(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)







   def    _A				(	self	):


      '''simple docstring'''

      if not self.model_tester.is_training:
         return

      __lowerCamelCase		,__lowerCamelCase    		=					self.model_tester.prepare_config_and_inputs_for_common()
      __lowerCamelCase    		=					True

      for model_class in self.all_model_classes:
         if model_class in get_values(_SCREAMING_SNAKE_CASE	):
            continue
         # TODO: remove the following 3 lines once we have a MODEL_FOR_DEPTH_ESTIMATION_MAPPING
         # this can then be incorporated into _prepare_for_class in test_modeling_common.py
         if model_class.__name__ == "GLPNForDepthEstimation":
            __lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase    		=					inputs_dict['pixel_values'].shape
            __lowerCamelCase    		=					torch.zeros(
                [self.model_tester.batch_size, height, width] ,				device=_SCREAMING_SNAKE_CASE	).long()
         __lowerCamelCase    		=					model_class(_SCREAMING_SNAKE_CASE	)
         model.to(_SCREAMING_SNAKE_CASE	)
         model.train()
         __lowerCamelCase    		=					self._prepare_for_class(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				return_labels=_SCREAMING_SNAKE_CASE	)
         __lowerCamelCase    		=					model(**_SCREAMING_SNAKE_CASE	).loss
         loss.backward()







   @slow
   def    _A				(	self	):


      '''simple docstring'''

      for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         __lowerCamelCase    		=					GLPNModel.from_pretrained(_SCREAMING_SNAKE_CASE	)
         self.assertIsNotNone(_SCREAMING_SNAKE_CASE	)



def       A__    (	)  ->							Union[str, Any]:
   __lowerCamelCase    		=					Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
   return image






@require_torch
@require_vision
@slow
class      _SCREAMING_SNAKE_CASE							(			unittest.TestCase				):



   '''simple docstring'''



   @slow
   def    _A				(	self	):


      '''simple docstring'''

      __lowerCamelCase    		=					GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]	)
      __lowerCamelCase    		=					GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]	).to(_SCREAMING_SNAKE_CASE	)

      __lowerCamelCase    		=					prepare_img()
      __lowerCamelCase    		=					image_processor(images=_SCREAMING_SNAKE_CASE ,				return_tensors='pt'	).to(_SCREAMING_SNAKE_CASE	)

      # forward pass
      with torch.no_grad():
         __lowerCamelCase    		=					model(**_SCREAMING_SNAKE_CASE	)

      # verify the predicted depth
      __lowerCamelCase    		=					torch.Size([1, 480, 640]	)
      self.assertEqual(outputs.predicted_depth.shape ,				_SCREAMING_SNAKE_CASE	)

      __lowerCamelCase    		=					torch.tensor(
          [[3.4291, 2.7865, 2.5151], [3.2841, 2.7021, 2.3502], [3.1147, 2.4625, 2.2481]]	).to(_SCREAMING_SNAKE_CASE	)

      self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3] ,				_SCREAMING_SNAKE_CASE ,				atol=1E-4	)	)







"""simple docstring"""


import qiskit
from qiskit.providers import Backend







def        __a (		A     ,					A   ):






     '''simple docstring'''



     lowercase__			,    lowercase__								=				bin(A   )[2:], bin(A   )[2:]  # Remove leading '0b'

     # Ensure that both strings are of the same length
     if len(A   ) > len(A   ):
          lowercase__								=				y.zfill(len(A   )   )
     else:
          lowercase__								=				x.zfill(len(A   )   )

     # We need (3 * number of bits in the larger number)+1 qBits
     # The second parameter is the number of classical registers, to measure the result
     lowercase__								=				qiskit.QuantumCircuit((len(A   ) * 3) + 1     ,					len(A   ) + 1   )

     # We are essentially "not-ing" the bits that are 1
     # Reversed because it's easier to perform ops on more significant bits
     for i in range(len(A   )   ):
          if x[::-1][i] == "1":
               circuit.x(A   )
     for j in range(len(A   )   ):
          if y[::-1][j] == "1":
               circuit.x(len(A   ) + j   )

     return circuit, x, y







def        __a (		A     ,					A     ,					A     ,					A     ,					A     ,					):






     '''simple docstring'''



     circuit.ccx(A     ,					A     ,					A   )
     circuit.cx(A     ,					A   )
     circuit.ccx(A     ,					A     ,					A   )
     circuit.cx(A     ,					A   )
     circuit.cx(A     ,					A   )







def        __a (		A     ,					A     ,					A = qiskit.Aer.get_backend("aer_simulator"   )     ,					):






     '''simple docstring'''



     if vala < 0 or vala < 0:
          raise ValueError("Both Integers must be positive!"   )

     # Store the Integers
     lowercase__			,    lowercase__			,    lowercase__								=				store_two_classics(A     ,					A   )
     for i in range(len(A   )   ):
          full_adder(A     ,					A     ,					len(A   ) + i     ,					len(A   ) + len(A   ) + i     ,					len(A   ) + len(A   ) + i + 1   )
          circuit.barrier()  # Optional, just for aesthetics

     # Measure the resultant qBits
     for i in range(len(A   ) + 1   ):
          circuit.measure([(len(A   ) * 2) + i]     ,					[i]   )

     lowercase__								=				qiskit.execute(A     ,					A     ,					shots=1   ).result()

     # The result is in binary. Convert it back to int
     return int(list(res.get_counts()   )[0]     ,					2   )


if __name__ == "__main__":
 import doctest

 doctest.testmod()




