

import copy
import inspect
import unittest

from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor


if is_torch_available():
   import torch

   from transformers import TimmBackbone, TimmBackboneConfig

from ...test_pipeline_mixin import PipelineTesterMixin



class SCREAMING_SNAKE_CASE     :
  """simple docstring"""






  def __init__(      self			:   int	,							lowerCAmelCase			:   Dict	,							lowerCAmelCase			:   List[str]=None	,							lowerCAmelCase			:   Union[str, Any]=None	,							lowerCAmelCase			:   Optional[int]=None	,							lowerCAmelCase			:   Optional[int]="resnet50"	,							lowerCAmelCase			:   str=3	,							lowerCAmelCase			:   Union[str, Any]=32	,							lowerCAmelCase			:   Union[str, Any]=3	,							lowerCAmelCase			:   Any=True	,							lowerCAmelCase			:   List[str]=True	,							)							->     Union[str, Any]:






     """simple docstring"""







     __lowerCAmelCase  :      Tuple    =						parent
     __lowerCAmelCase  :      int    =						out_indices if out_indices is not None else [4]
     __lowerCAmelCase  :      int    =						stage_names
     __lowerCAmelCase  :      Dict    =						out_features
     __lowerCAmelCase  :      Optional[int]    =						backbone
     __lowerCAmelCase  :      Dict    =						batch_size
     __lowerCAmelCase  :      Optional[Any]    =						image_size
     __lowerCAmelCase  :      int    =						num_channels
     __lowerCAmelCase  :      Union[str, Any]    =						use_pretrained_backbone
     __lowerCAmelCase  :      int    =						is_training







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[str]   )							->     int:






     """simple docstring"""







     __lowerCAmelCase  :      str    =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )
     __lowerCAmelCase  :      List[Any]    =						self.get_config()

     return config, pixel_values







  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict   )							->     List[str]:






     """simple docstring"""







     return TimmBackboneConfig(
         image_size=self.image_size	,							num_channels=self.num_channels	,							out_features=self.out_features	,							out_indices=self.out_indices	,							stage_names=self.stage_names	,							use_pretrained_backbone=self.use_pretrained_backbone	,							backbone=self.backbone	,							)







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[Any]	,							lowerCAmelCase			:   Tuple	,							lowerCAmelCase			:   Dict   )							->     Optional[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      List[Any]    =						TimmBackbone(config=lowerCAmelCase   )
     model.to(lowerCAmelCase   )
     model.eval()
     with torch.no_grad():
        __lowerCAmelCase  :      Optional[Any]    =						model(lowerCAmelCase   )
     self.parent.assertEqual(
         result.feature_map[-1].shape	,							(self.batch_size, model.channels[-1], 14, 14)	,							)







  def 	SCREAMING_SNAKE_CASE   (      self			:   int   )							->     int:






     """simple docstring"""







     __lowerCAmelCase  :      str    =						self.prepare_config_and_inputs()
     __lowerCAmelCase   ,__lowerCAmelCase  :      Any    =						config_and_inputs
     __lowerCAmelCase  :      str    =						{"""pixel_values""": pixel_values}
     return config, inputs_dict






@require_torch
@require_timm
class SCREAMING_SNAKE_CASE     (  a_       ,							a_       ,							a_       ,							unittest.TestCase			):
  """simple docstring"""






  lowerCamelCase   :      Tuple										=(TimmBackbone,) if is_torch_available() else ()
  lowerCamelCase   :      Optional[Any]										={"feature-extraction": TimmBackbone} if is_torch_available() else {}
  lowerCamelCase   :      Dict										=False
  lowerCamelCase   :      Tuple										=False
  lowerCamelCase   :      Union[str, Any]										=False
  lowerCamelCase   :      List[str]										=False







  def 	SCREAMING_SNAKE_CASE   (      self			:   int   )							->     Optional[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      List[Any]    =						TimmBackboneModelTester(self   )
     __lowerCAmelCase  :      Dict    =						ConfigTester(self	,							config_class=lowerCAmelCase	,							has_text_modality=lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[Any]   )							->     List[Any]:






     """simple docstring"""







     self.config_tester.create_and_test_config_to_json_string()
     self.config_tester.create_and_test_config_to_json_file()
     self.config_tester.create_and_test_config_from_and_save_pretrained()
     self.config_tester.create_and_test_config_with_num_labels()
     self.config_tester.check_config_can_be_init_without_params()
     self.config_tester.check_config_arguments_init()







  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[int]   )							->     Any:






     """simple docstring"""







     __lowerCAmelCase  :      Tuple    =						"""resnet18"""
     __lowerCAmelCase  :      Optional[Any]    =						"""microsoft/resnet-18"""

     __lowerCAmelCase  :      Dict    =						AutoBackbone.from_pretrained(lowerCAmelCase	,							use_timm_backbone=lowerCAmelCase   )
     __lowerCAmelCase  :      List[Any]    =						AutoBackbone.from_pretrained(lowerCAmelCase   )

     self.assertEqual(len(timm_model.out_features   )	,							len(transformers_model.out_features   )   )
     self.assertEqual(len(timm_model.stage_names   )	,							len(transformers_model.stage_names   )   )
     self.assertEqual(timm_model.channels	,							transformers_model.channels   )
     # Out indices are set to the last layer by default. For timm models, we don't know
     # the number of layers in advance, so we set it to (-1,), whereas for transformers
     # models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
     self.assertEqual(timm_model.out_indices	,							(-1,)   )
     self.assertEqual(transformers_model.out_indices	,							[len(timm_model.stage_names   ) - 1]   )

     __lowerCAmelCase  :      Tuple    =						AutoBackbone.from_pretrained(lowerCAmelCase	,							use_timm_backbone=lowerCAmelCase	,							out_indices=[1, 2, 3]   )
     __lowerCAmelCase  :      Optional[Any]    =						AutoBackbone.from_pretrained(lowerCAmelCase	,							out_indices=[1, 2, 3]   )

     self.assertEqual(timm_model.out_indices	,							transformers_model.out_indices   )
     self.assertEqual(len(timm_model.out_features   )	,							len(transformers_model.out_features   )   )
     self.assertEqual(timm_model.channels	,							transformers_model.channels   )







  @unittest.skip("""TimmBackbone doesn't support feed forward chunking"""   )
  def 	SCREAMING_SNAKE_CASE   (      self			:   str   )							->     Dict:






     """simple docstring"""







     pass







  @unittest.skip("""TimmBackbone doesn't have num_hidden_layers attribute"""   )
  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict   )							->     Optional[Any]:






     """simple docstring"""







     pass







  @unittest.skip("""TimmBackbone initialization is managed on the timm side"""   )
  def 	SCREAMING_SNAKE_CASE   (      self			:   int   )							->     int:






     """simple docstring"""







     pass







  @unittest.skip("""TimmBackbone models doesn't have inputs_embeds"""   )
  def 	SCREAMING_SNAKE_CASE   (      self			:   List[Any]   )							->     Tuple:






     """simple docstring"""







     pass







  @unittest.skip("""TimmBackbone models doesn't have inputs_embeds"""   )
  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[Any]   )							->     int:






     """simple docstring"""







     pass







  @unittest.skip("""TimmBackbone model cannot be created without specifying a backbone checkpoint"""   )
  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[Any]   )							->     Dict:






     """simple docstring"""







     pass







  @unittest.skip("""Only checkpoints on timm can be loaded into TimmBackbone"""   )
  def 	SCREAMING_SNAKE_CASE   (      self			:   Union[str, Any]   )							->     str:






     """simple docstring"""







     pass







  @unittest.skip("""model weights aren't tied in TimmBackbone."""   )
  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     Optional[Any]:






     """simple docstring"""







     pass







  @unittest.skip("""model weights aren't tied in TimmBackbone."""   )
  def 	SCREAMING_SNAKE_CASE   (      self			:   Any   )							->     Dict:






     """simple docstring"""







     pass







  @unittest.skip("""Only checkpoints on timm can be loaded into TimmBackbone"""   )
  def 	SCREAMING_SNAKE_CASE   (      self			:   str   )							->     Union[str, Any]:






     """simple docstring"""







     pass







  @unittest.skip("""Only checkpoints on timm can be loaded into TimmBackbone"""   )
  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     Dict:






     """simple docstring"""







     pass







  @unittest.skip("""TimmBackbone doesn't have hidden size info in its configuration."""   )
  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict   )							->     Tuple:






     """simple docstring"""







     pass







  @unittest.skip("""TimmBackbone doesn't support output_attentions."""   )
  def 	SCREAMING_SNAKE_CASE   (      self			:   Union[str, Any]   )							->     Any:






     """simple docstring"""







     pass







  @unittest.skip("""Safetensors is not supported by timm."""   )
  def 	SCREAMING_SNAKE_CASE   (      self			:   Union[str, Any]   )							->     List[str]:






     """simple docstring"""







     pass







  @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""   )
  def 	SCREAMING_SNAKE_CASE   (      self			:   Optional[Any]   )							->     str:






     """simple docstring"""







     pass







  def 	SCREAMING_SNAKE_CASE   (      self			:   str   )							->     List[Any]:






     """simple docstring"""







     __lowerCAmelCase   ,__lowerCAmelCase  :      Optional[Any]    =						self.model_tester.prepare_config_and_inputs_for_common()

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

        __lowerCAmelCase  :      Optional[Any]    =						["""pixel_values"""]
        self.assertListEqual(arg_names[:1]	,							lowerCAmelCase   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     str:






     """simple docstring"""







     __lowerCAmelCase   ,__lowerCAmelCase  :      Any    =						self.model_tester.prepare_config_and_inputs_for_common()
     __lowerCAmelCase  :      Any    =						True
     __lowerCAmelCase  :      Union[str, Any]    =						self.has_attentions

     # no need to test all models as different heads yield the same functionality
     __lowerCAmelCase  :      List[Any]    =						self.all_model_classes[0]
     __lowerCAmelCase  :      Dict    =						model_class(lowerCAmelCase   )
     model.to(lowerCAmelCase   )

     __lowerCAmelCase  :      Union[str, Any]    =						self._prepare_for_class(lowerCAmelCase	,							lowerCAmelCase   )
     __lowerCAmelCase  :      str    =						model(**lowerCAmelCase   )
     __lowerCAmelCase  :      Dict    =						outputs[0][-1]

     # Encoder-/Decoder-only models
     __lowerCAmelCase  :      Union[str, Any]    =						outputs.hidden_states[0]
     hidden_states.retain_grad()

     if self.has_attentions:
        __lowerCAmelCase  :      Dict    =						outputs.attentions[0]
        attentions.retain_grad()

     output.flatten()[0].backward(retain_graph=lowerCAmelCase   )

     self.assertIsNotNone(hidden_states.grad   )

     if self.has_attentions:
        self.assertIsNotNone(attentions.grad   )







  def 	SCREAMING_SNAKE_CASE   (      self			:   str   )							->     Any:






     """simple docstring"""







     __lowerCAmelCase   ,__lowerCAmelCase  :      List[Any]    =						self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        __lowerCAmelCase  :      Optional[int]    =						model_class(lowerCAmelCase   )
        model.to(lowerCAmelCase   )
        model.eval()
        __lowerCAmelCase  :      int    =						model(**lowerCAmelCase   )

        self.assertEqual(len(result.feature_maps   )	,							len(config.out_indices   )   )
        self.assertEqual(len(model.channels   )	,							len(config.out_indices   )   )

        # Check output of last stage is taken if out_features=None, out_indices=None
        __lowerCAmelCase  :      Any    =						copy.deepcopy(lowerCAmelCase   )
        __lowerCAmelCase  :      List[Any]    =						None
        __lowerCAmelCase  :      Dict    =						model_class(lowerCAmelCase   )
        model.to(lowerCAmelCase   )
        model.eval()
        __lowerCAmelCase  :      Union[str, Any]    =						model(**lowerCAmelCase   )

        self.assertEqual(len(result.feature_maps   )	,							1   )
        self.assertEqual(len(model.channels   )	,							1   )

        # Check backbone can be initialized with fresh weights
        __lowerCAmelCase  :      Any    =						copy.deepcopy(lowerCAmelCase   )
        __lowerCAmelCase  :      Tuple    =						False
        __lowerCAmelCase  :      Union[str, Any]    =						model_class(lowerCAmelCase   )
        model.to(lowerCAmelCase   )
        model.eval()
        __lowerCAmelCase  :      Union[str, Any]    =						model(**lowerCAmelCase   )





import numpy as np
import qiskit





def      snake_case_				(__A					:						int = 8 ,       __A					:						int | None = None					)      ->     str:
   __lowerCAmelCase  :      List[Any]    =						np.random.default_rng(seed=__A					)

   # Roughly 25% of the qubits will contribute to the key.
   # So we take more than we need.
   __lowerCAmelCase  :      Tuple    =						6 * key_len
   # Measurement basis for Alice's qubits.
   __lowerCAmelCase  :      List[Any]    =						rng.integers(2 ,       size=__A					)
   # The set of states Alice will prepare.
   __lowerCAmelCase  :      List[str]    =						rng.integers(2 ,       size=__A					)
   # Measurement basis for Bob's qubits.
   __lowerCAmelCase  :      List[Any]    =						rng.integers(2 ,       size=__A					)

   # Quantum Circuit to simulate BB84
   __lowerCAmelCase  :      int    =						qiskit.QuantumCircuit(__A ,       name="""BB84"""					)

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

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

   bbaa_circ.barrier()
   bbaa_circ.measure_all()

   # Simulate the quantum circuit.
   __lowerCAmelCase  :      Optional[Any]    =						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.
   __lowerCAmelCase  :      Optional[Any]    =						qiskit.execute(__A ,       __A ,       shots=1 ,       seed_simulator=__A					)
   # Returns the result of measurement.
   __lowerCAmelCase  :      List[Any]    =						job.result().get_counts(__A					).most_frequent()

   # Extracting the generated key from the simulation results.
   # Only keep measurement results where Alice and Bob chose the same basis.
   __lowerCAmelCase  :      Optional[int]    =						"""""".join(
       [
           result_bit
           for alice_basis_bit, bob_basis_bit, result_bit in zip(
               __A ,       __A ,       __A					)
           if alice_basis_bit == bob_basis_bit
       ]					)

   # Get final key. Pad with 0 if too short, otherwise truncate.
   __lowerCAmelCase  :      Tuple    =						gen_key[:key_len] if len(__A					) >= key_len else gen_key.ljust(__A ,       """0"""					)
   return key


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

   testmod()


