
"""simple docstring"""

import inspect
import unittest

from transformers import DPTConfig
from transformers.file_utils 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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch
      from torch import nn

      from transformers import MODEL_MAPPING, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
      from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image

      from transformers import DPTImageProcessor

class __lowerCAmelCase	:





     def __init__(			self			,  __UpperCAmelCase			,  __UpperCAmelCase=2			,  __UpperCAmelCase=32			,  __UpperCAmelCase=16			,  __UpperCAmelCase=3			,  __UpperCAmelCase=True			,  __UpperCAmelCase=True			,  __UpperCAmelCase=32			,  __UpperCAmelCase=4			,  __UpperCAmelCase=[0, 1, 2, 3]			,  __UpperCAmelCase=4			,  __UpperCAmelCase=37			,  __UpperCAmelCase="gelu"			,  __UpperCAmelCase=0.1			,  __UpperCAmelCase=0.1			,  __UpperCAmelCase=0.0_2			,  __UpperCAmelCase=3			,  __UpperCAmelCase=[1, 384, 24, 24]			,  __UpperCAmelCase=True			,  __UpperCAmelCase=None			,  ):

      '''simple docstring'''




      __UpperCamelCase       						= parent
      __UpperCamelCase       						= batch_size
      __UpperCamelCase       						= image_size
      __UpperCamelCase       						= patch_size
      __UpperCamelCase       						= num_channels
      __UpperCamelCase       						= is_training
      __UpperCamelCase       						= use_labels
      __UpperCamelCase       						= hidden_size
      __UpperCamelCase       						= num_hidden_layers
      __UpperCamelCase       						= backbone_out_indices
      __UpperCamelCase       						= num_attention_heads
      __UpperCamelCase       						= intermediate_size
      __UpperCamelCase       						= hidden_act
      __UpperCamelCase       						= hidden_dropout_prob
      __UpperCamelCase       						= attention_probs_dropout_prob
      __UpperCamelCase       						= initializer_range
      __UpperCamelCase       						= num_labels
      __UpperCamelCase       						= backbone_featmap_shape
      __UpperCamelCase       						= scope
      __UpperCamelCase       						= is_hybrid
      # sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
      __UpperCamelCase       						= (image_size // patch_size) ** 2
      __UpperCamelCase       						= num_patches + 1





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




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

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

      __UpperCamelCase       						= self.get_config()

      return config, pixel_values, labels





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= {
          'global_padding': 'same',
          'layer_type': 'bottleneck',
          'depths': [3, 4, 9],
          'out_features': ['stage1', 'stage2', 'stage3'],
          'embedding_dynamic_padding': True,
          'hidden_sizes': [96, 192, 384, 768],
          'num_groups': 2,
      }

      return DPTConfig(
          image_size=self.image_size			,  patch_size=self.patch_size			,  num_channels=self.num_channels			,  hidden_size=self.hidden_size			,  num_hidden_layers=self.num_hidden_layers			,  backbone_out_indices=self.backbone_out_indices			,  num_attention_heads=self.num_attention_heads			,  intermediate_size=self.intermediate_size			,  hidden_act=self.hidden_act			,  hidden_dropout_prob=self.hidden_dropout_prob			,  attention_probs_dropout_prob=self.attention_probs_dropout_prob			,  is_decoder=__UpperCAmelCase			,  initializer_range=self.initializer_range			,  is_hybrid=self.is_hybrid			,  backbone_config=__UpperCAmelCase			,  backbone_featmap_shape=self.backbone_featmap_shape			,  )





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase   ):

      '''simple docstring'''




      __UpperCamelCase       						= DPTModel(config=__UpperCAmelCase   )
      model.to(__UpperCAmelCase   )
      model.eval()
      __UpperCamelCase       						= model(__UpperCAmelCase   )
      self.parent.assertEqual(result.last_hidden_state.shape			,  (self.batch_size, self.seq_length, self.hidden_size)   )





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.num_labels
      __UpperCamelCase       						= DPTForDepthEstimation(__UpperCAmelCase   )
      model.to(__UpperCAmelCase   )
      model.eval()
      __UpperCamelCase       						= model(__UpperCAmelCase   )
      self.parent.assertEqual(result.predicted_depth.shape			,  (self.batch_size, self.image_size, self.image_size)   )





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase			,  __UpperCAmelCase   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.num_labels
      __UpperCamelCase       						= DPTForSemanticSegmentation(__UpperCAmelCase   )
      model.to(__UpperCAmelCase   )
      model.eval()
      __UpperCamelCase       						= model(__UpperCAmelCase			,  labels=__UpperCAmelCase   )
      self.parent.assertEqual(
          result.logits.shape			,  (self.batch_size, self.num_labels, self.image_size, self.image_size)   )







     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




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


@require_torch
class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ,						__SCREAMING_SNAKE_CASE    ,						unittest.TestCase    ):
     lowercase		     =		(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
     lowercase		     =		(
         {
             "depth-estimation": DPTForDepthEstimation,
             "feature-extraction": DPTModel,
             "image-segmentation": DPTForSemanticSegmentation,
         }
         if is_torch_available()
         else {}
     )

     lowercase		     =		False
     lowercase		     =		False
     lowercase		     =		False





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= DPTModelTester(self   )
      __UpperCamelCase       						= ConfigTester(self			,  config_class=__UpperCAmelCase			,  has_text_modality=__UpperCAmelCase			,  hidden_size=37   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      self.config_tester.run_common_tests()





     @unittest.skip(reason='DPT does not use inputs_embeds'   )
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      pass





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase						,			__UpperCamelCase       						= self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
       __UpperCamelCase       						= model_class(__UpperCAmelCase   )
       self.assertIsInstance(model.get_input_embeddings()			,  (nn.Module)   )
       __UpperCamelCase       						= model.get_output_embeddings()
       self.assertTrue(x is None or isinstance(__UpperCAmelCase			,  nn.Linear   )   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase						,			__UpperCamelCase       						= self.model_tester.prepare_config_and_inputs_for_common()

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

       __UpperCamelCase       						= ['pixel_values']
       self.assertListEqual(arg_names[:1]			,  __UpperCAmelCase   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*__UpperCAmelCase   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_depth_estimation(*__UpperCAmelCase   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_semantic_segmentation(*__UpperCAmelCase   )





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      for model_class in self.all_model_classes:
       if model_class.__name__ == "DPTForDepthEstimation":
        continue

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

       if model_class in get_values(__UpperCAmelCase   ):
        continue

       __UpperCamelCase       						= model_class(__UpperCAmelCase   )
       model.to(__UpperCAmelCase   )
       model.train()
       __UpperCamelCase       						= self._prepare_for_class(__UpperCAmelCase			,  __UpperCAmelCase			,  return_labels=__UpperCAmelCase   )
       __UpperCamelCase       						= model(**__UpperCAmelCase   ).loss
       loss.backward()





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      for model_class in self.all_model_classes:
       if model_class.__name__ == "DPTForDepthEstimation":
        continue

       __UpperCamelCase						,			__UpperCamelCase       						= self.model_tester.prepare_config_and_inputs_for_common()
       __UpperCamelCase       						= False
       __UpperCamelCase       						= True

       if model_class in get_values(__UpperCAmelCase   ) or not model_class.supports_gradient_checkpointing:
        continue
       __UpperCamelCase       						= model_class(__UpperCAmelCase   )
       model.to(__UpperCAmelCase   )
       model.gradient_checkpointing_enable()
       model.train()
       __UpperCamelCase       						= self._prepare_for_class(__UpperCAmelCase			,  __UpperCAmelCase			,  return_labels=__UpperCAmelCase   )
       __UpperCamelCase       						= model(**__UpperCAmelCase   ).loss
       loss.backward()





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase						,			__UpperCamelCase       						= self.model_tester.prepare_config_and_inputs_for_common()

      __UpperCamelCase       						= _config_zero_init(__UpperCAmelCase   )
      for model_class in self.all_model_classes:
       __UpperCamelCase       						= model_class(config=__UpperCAmelCase   )
       # Skip the check for the backbone
       __UpperCamelCase       						= []
       for name, module in model.named_modules():
        if module.__class__.__name__ == "DPTViTHybridEmbeddings":
         __UpperCamelCase       						= [F'{name}.{key}' for key in module.state_dict().keys()]
         break

       for name, param in model.named_parameters():
        if param.requires_grad:
         if name in backbone_params:
          continue
         self.assertIn(
             ((param.data.mean() * 1E9).round() / 1E9).item()			,  [0.0, 1.0]			,  msg=F'Parameter {name} of model {model_class} seems not properly initialized'			,  )





     @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'   )
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      pass





     @slow
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
       __UpperCamelCase       						= DPTModel.from_pretrained(__UpperCAmelCase   )
       self.assertIsNotNone(__UpperCAmelCase   )







     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase						,			__UpperCamelCase       						= self.model_tester.prepare_config_and_inputs_for_common()
      __UpperCamelCase       						= 'add'
      with self.assertRaises(__UpperCAmelCase   ):
       __UpperCamelCase       						= DPTForDepthEstimation(__UpperCAmelCase   )






def 							A		(       )   ->			str:
 __UpperCamelCase       						= Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
 return image





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





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= DPTImageProcessor.from_pretrained('Intel/dpt-hybrid-midas'   )
      __UpperCamelCase       						= DPTForDepthEstimation.from_pretrained('Intel/dpt-hybrid-midas'   ).to(__UpperCAmelCase   )

      __UpperCamelCase       						= prepare_img()
      __UpperCamelCase       						= image_processor(images=__UpperCAmelCase			,  return_tensors='pt'   ).to(__UpperCAmelCase   )

      # forward pass
      with torch.no_grad():
       __UpperCamelCase       						= model(**__UpperCAmelCase   )
       __UpperCamelCase       						= outputs.predicted_depth

      # verify the predicted depth
      __UpperCamelCase       						= torch.Size((1, 384, 384)   )
      self.assertEqual(predicted_depth.shape			,  __UpperCAmelCase   )

      __UpperCamelCase       						= torch.tensor(
          [[[5.6_4_3_7, 5.6_1_4_6, 5.6_5_1_1], [5.4_3_7_1, 5.5_6_4_9, 5.5_9_5_8], [5.5_2_1_5, 5.5_1_8_4, 5.5_2_9_3]]]   ).to(__UpperCAmelCase   )

      self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 100			,  __UpperCAmelCase			,  atol=1E-4   )   )





"""simple docstring"""

import numpy as np
import qiskit






def 							A		(       snake_case   :int = 8       ,      snake_case   :int | None = None	)   ->			str:
 __UpperCamelCase       						= np.random.default_rng(seed=snake_case	)

 # Roughly 25% of the qubits will contribute to the key.
 # So we take more than we need.
 __UpperCamelCase       						= 6 * key_len
 # Measurement basis for Alice's qubits.
 __UpperCamelCase       						= rng.integers(2       ,      size=snake_case	)
 # The set of states Alice will prepare.
 __UpperCamelCase       						= rng.integers(2       ,      size=snake_case	)
 # Measurement basis for Bob's qubits.
 __UpperCamelCase       						= rng.integers(2       ,      size=snake_case	)

 # Quantum Circuit to simulate BB84
 __UpperCamelCase       						= qiskit.QuantumCircuit(snake_case       ,      name='BB84'	)

 # Alice prepares her qubits according to rules above.
 for index, _ in enumerate(snake_case	):
  if alice_state[index] == 1:
   bbaa_circ.x(snake_case	)
  if alice_basis[index] == 1:
   bbaa_circ.h(snake_case	)
 bbaa_circ.barrier()

 # Bob measures the received qubits according to rules above.
 for index, _ in enumerate(snake_case	):
  if bob_basis[index] == 1:
   bbaa_circ.h(snake_case	)

 bbaa_circ.barrier()
 bbaa_circ.measure_all()

 # Simulate the quantum circuit.
 __UpperCamelCase       						= qiskit.Aer.get_backend('aer_simulator'	)
 # We only need to run one shot because the key is unique.
 # Multiple shots will produce the same key.
 __UpperCamelCase       						= qiskit.execute(snake_case       ,      snake_case       ,      shots=1       ,      seed_simulator=snake_case	)
 # Returns the result of measurement.
 __UpperCamelCase       						= job.result().get_counts(snake_case	).most_frequent()

 # Extracting the generated key from the simulation results.
 # Only keep measurement results where Alice and Bob chose the same basis.
 __UpperCamelCase       						= ''.join(
     [
         result_bit
         for alice_basis_bit, bob_basis_bit, result_bit in zip(
             snake_case       ,      snake_case       ,      snake_case	)
         if alice_basis_bit == bob_basis_bit
     ]	)

 # Get final key. Pad with 0 if too short, otherwise truncate.
 __UpperCamelCase       						= gen_key[:key_len] if len(snake_case	) >= key_len else gen_key.ljust(snake_case       ,      '0'	)
 return key


if __name__ == "__main__":
      print(f'''The generated key is : {bbaa(8, seed=0)}''')
      from doctest import testmod

      testmod()



