import inspect
import unittest

from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, 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,
      SegformerForImageClassification,
      SegformerForSemanticSegmentation,
      SegformerModel,
  )
  from transformers.models.segformer.modeling_segformer import SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import SegformerImageProcessor
class    A_ (       a_ ):

  def 		_UpperCAmelCase   (		self :      Tuple       ):
   __a       =  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    A_ :

  def __init__(		self :      int     ,							__SCREAMING_SNAKE_CASE :      str     ,							__SCREAMING_SNAKE_CASE :      Tuple=13     ,							__SCREAMING_SNAKE_CASE :      List[str]=64     ,							__SCREAMING_SNAKE_CASE :      List[str]=3     ,							__SCREAMING_SNAKE_CASE :      str=4     ,							__SCREAMING_SNAKE_CASE :      Tuple=[2, 2, 2, 2]     ,							__SCREAMING_SNAKE_CASE :      List[str]=[8, 4, 2, 1]     ,							__SCREAMING_SNAKE_CASE :      Tuple=[16, 32, 64, 1_28]     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=[1, 4, 8, 16]     ,							__SCREAMING_SNAKE_CASE :      str=[1, 2, 4, 8]     ,							__SCREAMING_SNAKE_CASE :      List[str]=True     ,							__SCREAMING_SNAKE_CASE :      Any=True     ,							__SCREAMING_SNAKE_CASE :      Tuple="gelu"     ,							__SCREAMING_SNAKE_CASE :      List[str]=0.1     ,							__SCREAMING_SNAKE_CASE :      str=0.1     ,							__SCREAMING_SNAKE_CASE :      Optional[int]=0.02     ,							__SCREAMING_SNAKE_CASE :      Dict=3     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=None     ,							):
   __a       =  parent
   __a       =  batch_size
   __a       =  image_size
   __a       =  num_channels
   __a       =  num_encoder_blocks
   __a       =  sr_ratios
   __a       =  depths
   __a       =  hidden_sizes
   __a       =  downsampling_rates
   __a       =  num_attention_heads
   __a       =  is_training
   __a       =  use_labels
   __a       =  hidden_act
   __a       =  hidden_dropout_prob
   __a       =  attention_probs_dropout_prob
   __a       =  initializer_range
   __a       =  num_labels
   __a       =  scope

  def 		_UpperCAmelCase   (		self :      Optional[int]       ):
   __a       =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

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

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

  def 		_UpperCAmelCase   (		self :      Dict       ):
   return SegformerConfig(
       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     ,							)

  def 		_UpperCAmelCase   (		self :      Any     ,							__SCREAMING_SNAKE_CASE :      Optional[int]     ,							__SCREAMING_SNAKE_CASE :      str     ,							__SCREAMING_SNAKE_CASE :      Dict       ):
   __a       =  SegformerModel(config=__SCREAMING_SNAKE_CASE       )
   model.to(__SCREAMING_SNAKE_CASE       )
   model.eval()
   __a       =  model(__SCREAMING_SNAKE_CASE       )
   __a       =  __a       =  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 		_UpperCAmelCase   (		self :      Union[str, Any]     ,							__SCREAMING_SNAKE_CASE :      Tuple     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]     ,							__SCREAMING_SNAKE_CASE :      List[Any]       ):
   __a       =  self.num_labels
   __a       =  SegformerForSemanticSegmentation(__SCREAMING_SNAKE_CASE       )
   model.to(__SCREAMING_SNAKE_CASE       )
   model.eval()
   __a       =  model(__SCREAMING_SNAKE_CASE       )
   self.parent.assertEqual(
       result.logits.shape     ,							(self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)       )
   __a       =  model(__SCREAMING_SNAKE_CASE     ,							labels=__SCREAMING_SNAKE_CASE       )
   self.parent.assertEqual(
       result.logits.shape     ,							(self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)       )
   self.parent.assertGreater(result.loss     ,							0.0       )

  def 		_UpperCAmelCase   (		self :      Union[str, Any]     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]     ,							__SCREAMING_SNAKE_CASE :      str     ,							__SCREAMING_SNAKE_CASE :      List[Any]       ):
   __a       =  1
   __a       =  SegformerForSemanticSegmentation(config=__SCREAMING_SNAKE_CASE       )
   model.to(__SCREAMING_SNAKE_CASE       )
   model.eval()
   __a       =  torch.randint(0     ,							1     ,							(self.batch_size, self.image_size, self.image_size)       ).to(__SCREAMING_SNAKE_CASE       )
   __a       =  model(__SCREAMING_SNAKE_CASE     ,							labels=__SCREAMING_SNAKE_CASE       )
   self.parent.assertGreater(result.loss     ,							0.0       )

  def 		_UpperCAmelCase   (		self :      Optional[Any]       ):
   __a       =  self.prepare_config_and_inputs()
   __a       ,     __a       ,     __a       =  config_and_inputs
   __a       =  {"pixel_values": pixel_values}
   return config, inputs_dict
@require_torch
class    A_ (       a_			,       a_			,       unittest.TestCase ):
  _SCREAMING_SNAKE_CASE    				=			(
      (
          SegformerModel,
          SegformerForSemanticSegmentation,
          SegformerForImageClassification,
      )
      if is_torch_available()
      else ()
  )
  _SCREAMING_SNAKE_CASE    				=			(
      {
          """feature-extraction""": SegformerModel,
          """image-classification""": SegformerForImageClassification,
          """image-segmentation""": SegformerForSemanticSegmentation,
      }
      if is_torch_available()
      else {}
  )

  _SCREAMING_SNAKE_CASE    				=			True
  _SCREAMING_SNAKE_CASE    				=			False
  _SCREAMING_SNAKE_CASE    				=			False
  _SCREAMING_SNAKE_CASE    				=			False

  def 		_UpperCAmelCase   (		self :      Optional[Any]       ):
   __a       =  SegformerModelTester(self       )
   __a       =  SegformerConfigTester(self     ,							config_class=__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      Union[str, Any]       ):
   self.config_tester.run_common_tests()

  def 		_UpperCAmelCase   (		self :      str       ):
   __a       =  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      Dict       ):
   __a       =  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_binary_image_segmentation(*__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      int       ):
   __a       =  self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_image_segmentation(*__SCREAMING_SNAKE_CASE       )

  @unittest.skip("SegFormer does not use inputs_embeds"       )
  def 		_UpperCAmelCase   (		self :      Dict       ):
   pass

  @unittest.skip("SegFormer does not have get_input_embeddings method and get_output_embeddings methods"       )
  def 		_UpperCAmelCase   (		self :      Optional[Any]       ):
   pass

  def 		_UpperCAmelCase   (		self :      int       ):
   __a       ,     __a       =  self.model_tester.prepare_config_and_inputs_for_common()

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

    __a       =  ["pixel_values"]
    self.assertListEqual(arg_names[:1]     ,							__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      List[Any]       ):
   __a       ,     __a       =  self.model_tester.prepare_config_and_inputs_for_common()
   __a       =  True

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

    __a       =  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"]
    __a       =  True
    __a       =  model_class(__SCREAMING_SNAKE_CASE       )
    model.to(__SCREAMING_SNAKE_CASE       )
    model.eval()
    with torch.no_grad():
     __a       =  model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )       )
    __a       =  outputs.attentions

    self.assertEqual(len(__SCREAMING_SNAKE_CASE       )     ,							__SCREAMING_SNAKE_CASE       )

    # verify the first attentions (first block, first layer)
    __a       =  (self.model_tester.image_size // 4) ** 2
    __a       =  (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)
    __a       =  (self.model_tester.image_size // 32) ** 2
    __a       =  (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]     ,							)
    __a       =  len(__SCREAMING_SNAKE_CASE       )

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

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

    __a       =  outputs.attentions

    self.assertEqual(len(__SCREAMING_SNAKE_CASE       )     ,							__SCREAMING_SNAKE_CASE       )
    # verify the first attentions (first block, first layer)
    __a       =  (self.model_tester.image_size // 4) ** 2
    __a       =  (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 		_UpperCAmelCase   (		self :      str       ):
   def check_hidden_states_output(__SCREAMING_SNAKE_CASE :      List[Any]     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]     ,							__SCREAMING_SNAKE_CASE :      Dict       ):
    __a       =  model_class(__SCREAMING_SNAKE_CASE       )
    model.to(__SCREAMING_SNAKE_CASE       )
    model.eval()

    with torch.no_grad():
     __a       =  model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )       )

    __a       =  outputs.hidden_states

    __a       =  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,
        ]     ,							)

   __a       ,     __a       =  self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
    __a       =  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"]
    __a       =  True

    check_hidden_states_output(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      Tuple       ):
   if not self.model_tester.is_training:
    return

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

   for model_class in self.all_model_classes:
    if model_class in get_values(__SCREAMING_SNAKE_CASE       ):
     continue

    __a       =  model_class(__SCREAMING_SNAKE_CASE       )
    model.to(__SCREAMING_SNAKE_CASE       )
    model.train()
    __a       =  self._prepare_for_class(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							return_labels=__SCREAMING_SNAKE_CASE       )
    __a       =  model(**__SCREAMING_SNAKE_CASE       ).loss
    loss.backward()

  @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."       )
  def 		_UpperCAmelCase   (		self :      List[Any]       ):
   pass

  @slow
  def 		_UpperCAmelCase   (		self :      int       ):
   for model_name in SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
    __a       =  SegformerModel.from_pretrained(__SCREAMING_SNAKE_CASE       )
    self.assertIsNotNone(__SCREAMING_SNAKE_CASE       )



def   __A		(			):



 """simple docstring"""


 __a       =  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"					)
 return image






@require_torch
class    A_ (       unittest.TestCase ):

  @slow
  def 		_UpperCAmelCase   (		self :      Optional[int]       ):
   # only resize + normalize
   __a       =  SegformerImageProcessor(
       image_scale=(5_12, 5_12)     ,							keep_ratio=__SCREAMING_SNAKE_CASE     ,							align=__SCREAMING_SNAKE_CASE     ,							do_random_crop=__SCREAMING_SNAKE_CASE       )
   __a       =  SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512"       ).to(
       __SCREAMING_SNAKE_CASE       )

   __a       =  prepare_img()
   __a       =  image_processor(images=__SCREAMING_SNAKE_CASE     ,							return_tensors="pt"       )
   __a       =  encoded_inputs.pixel_values.to(__SCREAMING_SNAKE_CASE       )

   with torch.no_grad():
    __a       =  model(__SCREAMING_SNAKE_CASE       )

   __a       =  torch.Size((1, model.config.num_labels, 1_28, 1_28)       )
   self.assertEqual(outputs.logits.shape     ,							__SCREAMING_SNAKE_CASE       )

   __a       =  torch.tensor(
       [
           [[-4.63_10, -5.52_32, -6.23_56], [-5.19_21, -6.14_44, -6.59_96], [-5.44_24, -6.27_90, -6.75_74]],
           [[-12.13_91, -13.31_22, -13.95_54], [-12.87_32, -13.93_52, -14.35_63], [-12.94_38, -13.82_26, -14.25_13]],
           [[-12.51_34, -13.46_86, -14.49_15], [-12.86_69, -14.43_43, -14.77_58], [-13.25_23, -14.58_19, -15.06_94]],
       ]       ).to(__SCREAMING_SNAKE_CASE       )
   self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]     ,							__SCREAMING_SNAKE_CASE     ,							atol=1E-4       )       )

  @slow
  def 		_UpperCAmelCase   (		self :      Dict       ):
   # only resize + normalize
   __a       =  SegformerImageProcessor(
       image_scale=(5_12, 5_12)     ,							keep_ratio=__SCREAMING_SNAKE_CASE     ,							align=__SCREAMING_SNAKE_CASE     ,							do_random_crop=__SCREAMING_SNAKE_CASE       )
   __a       =  SegformerForSemanticSegmentation.from_pretrained(
       "nvidia/segformer-b1-finetuned-cityscapes-1024-1024"       ).to(__SCREAMING_SNAKE_CASE       )

   __a       =  prepare_img()
   __a       =  image_processor(images=__SCREAMING_SNAKE_CASE     ,							return_tensors="pt"       )
   __a       =  encoded_inputs.pixel_values.to(__SCREAMING_SNAKE_CASE       )

   with torch.no_grad():
    __a       =  model(__SCREAMING_SNAKE_CASE       )

   __a       =  torch.Size((1, model.config.num_labels, 1_28, 1_28)       )
   self.assertEqual(outputs.logits.shape     ,							__SCREAMING_SNAKE_CASE       )

   __a       =  torch.tensor(
       [
           [[-13.57_48, -13.91_11, -12.65_00], [-14.35_00, -15.36_83, -14.23_28], [-14.75_32, -16.04_24, -15.60_87]],
           [[-17.16_51, -15.87_25, -12.96_53], [-17.25_80, -17.37_18, -14.82_23], [-16.60_58, -16.87_83, -16.74_52]],
           [[-3.64_56, -3.02_09, -1.42_03], [-3.07_97, -3.19_59, -2.00_00], [-1.87_57, -1.92_17, -1.69_97]],
       ]       ).to(__SCREAMING_SNAKE_CASE       )
   self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]     ,							__SCREAMING_SNAKE_CASE     ,							atol=1E-1       )       )

  @slow
  def 		_UpperCAmelCase   (		self :      int       ):
   # only resize + normalize
   __a       =  SegformerImageProcessor(
       image_scale=(5_12, 5_12)     ,							keep_ratio=__SCREAMING_SNAKE_CASE     ,							align=__SCREAMING_SNAKE_CASE     ,							do_random_crop=__SCREAMING_SNAKE_CASE       )
   __a       =  SegformerForSemanticSegmentation.from_pretrained("nvidia/segformer-b0-finetuned-ade-512-512"       ).to(
       __SCREAMING_SNAKE_CASE       )

   __a       =  prepare_img()
   __a       =  image_processor(images=__SCREAMING_SNAKE_CASE     ,							return_tensors="pt"       )
   __a       =  encoded_inputs.pixel_values.to(__SCREAMING_SNAKE_CASE       )

   with torch.no_grad():
    __a       =  model(__SCREAMING_SNAKE_CASE       )

   __a       =  outputs.logits.detach().cpu()

   __a       =  image_processor.post_process_semantic_segmentation(outputs=__SCREAMING_SNAKE_CASE     ,							target_sizes=[(5_00, 3_00)]       )
   __a       =  torch.Size((5_00, 3_00)       )
   self.assertEqual(segmentation[0].shape     ,							__SCREAMING_SNAKE_CASE       )

   __a       =  image_processor.post_process_semantic_segmentation(outputs=__SCREAMING_SNAKE_CASE       )
   __a       =  torch.Size((1_28, 1_28)       )
   self.assertEqual(segmentation[0].shape     ,							__SCREAMING_SNAKE_CASE       )



from binascii import hexlify
from hashlib import shaaaa
from os import urandom

# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526

SCREAMING_SNAKE_CASE :	Tuple							     =		{
    # 1536-bit
    5: {
        """prime""": int(
            """FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"""
            + """29024E088A67CC74020BBEA63B139B22514A08798E3404DD"""
            + """EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"""
            + """E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"""
            + """EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"""
            + """C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"""
            + """83655D23DCA3AD961C62F356208552BB9ED529077096966D"""
            + """670C354E4ABC9804F1746C08CA237327FFFFFFFFFFFFFFFF""",
            base=16,
        ),
        """generator""": 2,
    },
    # 2048-bit
    14: {
        """prime""": int(
            """FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"""
            + """29024E088A67CC74020BBEA63B139B22514A08798E3404DD"""
            + """EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"""
            + """E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"""
            + """EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"""
            + """C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"""
            + """83655D23DCA3AD961C62F356208552BB9ED529077096966D"""
            + """670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"""
            + """E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"""
            + """DE2BCBF6955817183995497CEA956AE515D2261898FA0510"""
            + """15728E5A8AACAA68FFFFFFFFFFFFFFFF""",
            base=16,
        ),
        """generator""": 2,
    },
    # 3072-bit
    15: {
        """prime""": int(
            """FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"""
            + """29024E088A67CC74020BBEA63B139B22514A08798E3404DD"""
            + """EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"""
            + """E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"""
            + """EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"""
            + """C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"""
            + """83655D23DCA3AD961C62F356208552BB9ED529077096966D"""
            + """670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"""
            + """E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"""
            + """DE2BCBF6955817183995497CEA956AE515D2261898FA0510"""
            + """15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"""
            + """ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"""
            + """ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"""
            + """F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"""
            + """BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"""
            + """43DB5BFCE0FD108E4B82D120A93AD2CAFFFFFFFFFFFFFFFF""",
            base=16,
        ),
        """generator""": 2,
    },
    # 4096-bit
    16: {
        """prime""": int(
            """FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"""
            + """29024E088A67CC74020BBEA63B139B22514A08798E3404DD"""
            + """EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"""
            + """E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"""
            + """EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"""
            + """C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"""
            + """83655D23DCA3AD961C62F356208552BB9ED529077096966D"""
            + """670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"""
            + """E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"""
            + """DE2BCBF6955817183995497CEA956AE515D2261898FA0510"""
            + """15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"""
            + """ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"""
            + """ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"""
            + """F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"""
            + """BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"""
            + """43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7"""
            + """88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA"""
            + """2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6"""
            + """287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED"""
            + """1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9"""
            + """93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934063199"""
            + """FFFFFFFFFFFFFFFF""",
            base=16,
        ),
        """generator""": 2,
    },
    # 6144-bit
    17: {
        """prime""": int(
            """FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD129024E08"""
            + """8A67CC74020BBEA63B139B22514A08798E3404DDEF9519B3CD3A431B"""
            + """302B0A6DF25F14374FE1356D6D51C245E485B576625E7EC6F44C42E9"""
            + """A637ED6B0BFF5CB6F406B7EDEE386BFB5A899FA5AE9F24117C4B1FE6"""
            + """49286651ECE45B3DC2007CB8A163BF0598DA48361C55D39A69163FA8"""
            + """FD24CF5F83655D23DCA3AD961C62F356208552BB9ED529077096966D"""
            + """670C354E4ABC9804F1746C08CA18217C32905E462E36CE3BE39E772C"""
            + """180E86039B2783A2EC07A28FB5C55DF06F4C52C9DE2BCBF695581718"""
            + """3995497CEA956AE515D2261898FA051015728E5A8AAAC42DAD33170D"""
            + """04507A33A85521ABDF1CBA64ECFB850458DBEF0A8AEA71575D060C7D"""
            + """B3970F85A6E1E4C7ABF5AE8CDB0933D71E8C94E04A25619DCEE3D226"""
            + """1AD2EE6BF12FFA06D98A0864D87602733EC86A64521F2B18177B200C"""
            + """BBE117577A615D6C770988C0BAD946E208E24FA074E5AB3143DB5BFC"""
            + """E0FD108E4B82D120A92108011A723C12A787E6D788719A10BDBA5B26"""
            + """99C327186AF4E23C1A946834B6150BDA2583E9CA2AD44CE8DBBBC2DB"""
            + """04DE8EF92E8EFC141FBECAA6287C59474E6BC05D99B2964FA090C3A2"""
            + """233BA186515BE7ED1F612970CEE2D7AFB81BDD762170481CD0069127"""
            + """D5B05AA993B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492"""
            + """36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BDF8FF9406"""
            + """AD9E530EE5DB382F413001AEB06A53ED9027D831179727B0865A8918"""
            + """DA3EDBEBCF9B14ED44CE6CBACED4BB1BDB7F1447E6CC254B33205151"""
            + """2BD7AF426FB8F401378CD2BF5983CA01C64B92ECF032EA15D1721D03"""
            + """F482D7CE6E74FEF6D55E702F46980C82B5A84031900B1C9E59E7C97F"""
            + """BEC7E8F323A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA"""
            + """CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE32806A1D58B"""
            + """B7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55CDA56C9EC2EF29632"""
            + """387FE8D76E3C0468043E8F663F4860EE12BF2D5B0B7474D6E694F91E"""
            + """6DCC4024FFFFFFFFFFFFFFFF""",
            base=16,
        ),
        """generator""": 2,
    },
    # 8192-bit
    18: {
        """prime""": int(
            """FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"""
            + """29024E088A67CC74020BBEA63B139B22514A08798E3404DD"""
            + """EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"""
            + """E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"""
            + """EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"""
            + """C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"""
            + """83655D23DCA3AD961C62F356208552BB9ED529077096966D"""
            + """670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"""
            + """E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"""
            + """DE2BCBF6955817183995497CEA956AE515D2261898FA0510"""
            + """15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"""
            + """ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"""
            + """ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"""
            + """F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"""
            + """BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"""
            + """43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7"""
            + """88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA"""
            + """2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6"""
            + """287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED"""
            + """1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9"""
            + """93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492"""
            + """36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BD"""
            + """F8FF9406AD9E530EE5DB382F413001AEB06A53ED9027D831"""
            + """179727B0865A8918DA3EDBEBCF9B14ED44CE6CBACED4BB1B"""
            + """DB7F1447E6CC254B332051512BD7AF426FB8F401378CD2BF"""
            + """5983CA01C64B92ECF032EA15D1721D03F482D7CE6E74FEF6"""
            + """D55E702F46980C82B5A84031900B1C9E59E7C97FBEC7E8F3"""
            + """23A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA"""
            + """CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE328"""
            + """06A1D58BB7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55C"""
            + """DA56C9EC2EF29632387FE8D76E3C0468043E8F663F4860EE"""
            + """12BF2D5B0B7474D6E694F91E6DBE115974A3926F12FEE5E4"""
            + """38777CB6A932DF8CD8BEC4D073B931BA3BC832B68D9DD300"""
            + """741FA7BF8AFC47ED2576F6936BA424663AAB639C5AE4F568"""
            + """3423B4742BF1C978238F16CBE39D652DE3FDB8BEFC848AD9"""
            + """22222E04A4037C0713EB57A81A23F0C73473FC646CEA306B"""
            + """4BCBC8862F8385DDFA9D4B7FA2C087E879683303ED5BDD3A"""
            + """062B3CF5B3A278A66D2A13F83F44F82DDF310EE074AB6A36"""
            + """4597E899A0255DC164F31CC50846851DF9AB48195DED7EA1"""
            + """B1D510BD7EE74D73FAF36BC31ECFA268359046F4EB879F92"""
            + """4009438B481C6CD7889A002ED5EE382BC9190DA6FC026E47"""
            + """9558E4475677E9AA9E3050E2765694DFC81F56E880B96E71"""
            + """60C980DD98EDD3DFFFFFFFFFFFFFFFFF""",
            base=16,
        ),
        """generator""": 2,
    },
}
class    A_ :

  def __init__(		self :      List[Any]     ,							__SCREAMING_SNAKE_CASE :      int = 14       ):
   if group not in primes:
    raise ValueError("Unsupported Group"       )
   __a       =  primes[group]["prime"]
   __a       =  primes[group]["generator"]

   __a       =  int(hexlify(urandom(32       )       )     ,							base=16       )

  def 		_UpperCAmelCase   (		self :      Tuple       ):
   return hex(self.__private_key       )[2:]

  def 		_UpperCAmelCase   (		self :      Optional[Any]       ):
   __a       =  pow(self.generator     ,							self.__private_key     ,							self.prime       )
   return hex(__SCREAMING_SNAKE_CASE       )[2:]

  def 		_UpperCAmelCase   (		self :      Tuple     ,							__SCREAMING_SNAKE_CASE :      int       ):
   # check if the other public key is valid based on NIST SP800-56
   return (
       2 <= key <= self.prime - 2
       and pow(__SCREAMING_SNAKE_CASE     ,							(self.prime - 1) // 2     ,							self.prime       ) == 1
   )

  def 		_UpperCAmelCase   (		self :      str     ,							__SCREAMING_SNAKE_CASE :      str       ):
   __a       =  int(__SCREAMING_SNAKE_CASE     ,							base=16       )
   if not self.is_valid_public_key(__SCREAMING_SNAKE_CASE       ):
    raise ValueError("Invalid public key"       )
   __a       =  pow(__SCREAMING_SNAKE_CASE     ,							self.__private_key     ,							self.prime       )
   return shaaaa(str(__SCREAMING_SNAKE_CASE       ).encode()       ).hexdigest()

  @staticmethod
  def 		_UpperCAmelCase   (		__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      int       ):
   # check if the other public key is valid based on NIST SP800-56
   return (
       2 <= remote_public_key_str <= prime - 2
       and pow(__SCREAMING_SNAKE_CASE     ,							(prime - 1) // 2     ,							__SCREAMING_SNAKE_CASE       ) == 1
   )

  @staticmethod
  def 		_UpperCAmelCase   (		__SCREAMING_SNAKE_CASE :      str     ,							__SCREAMING_SNAKE_CASE :      str     ,							__SCREAMING_SNAKE_CASE :      int = 14       ):
   __a       =  int(__SCREAMING_SNAKE_CASE     ,							base=16       )
   __a       =  int(__SCREAMING_SNAKE_CASE     ,							base=16       )
   __a       =  primes[group]["prime"]
   if not DiffieHellman.is_valid_public_key_static(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       ):
    raise ValueError("Invalid public key"       )
   __a       =  pow(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )
   return shaaaa(str(__SCREAMING_SNAKE_CASE       ).encode()       ).hexdigest()


if __name__ == "__main__":
  import doctest

  doctest.testmod()


