'''simple docstring'''
import unittest

import numpy as np

from transformers import BlipTextConfig
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.utils import is_torch_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask


if is_torch_available():
 import torch

 from transformers import BlipTextModel
 from transformers.models.blip.modeling_blip import BLIP_PRETRAINED_MODEL_ARCHIVE_LIST




class _lowerCAmelCase			:







      def __init__(  self  ,    lowercase  ,    lowercase=12  ,    lowercase=7  ,    lowercase=True  ,    lowercase=True  ,    lowercase=True  ,    lowercase=99  ,    lowercase=32  ,    lowercase=32  ,    lowercase=5  ,    lowercase=4  ,    lowercase=37  ,    lowercase=0.1  ,    lowercase=0.1  ,    lowercase=512  ,    lowercase=0.02  ,    lowercase=0  ,    lowercase=None  ,    ):
        lowercase				:							Dict									=   parent
        lowercase				:							Optional[int]									=   batch_size
        lowercase				:							Union[str, Any]									=   seq_length
        lowercase				:							Any									=   is_training
        lowercase				:							List[Any]									=   use_input_mask
        lowercase				:							str									=   use_labels
        lowercase				:							Union[str, Any]									=   vocab_size
        lowercase				:							List[Any]									=   hidden_size
        lowercase				:							List[Any]									=   projection_dim
        lowercase				:							Any									=   num_hidden_layers
        lowercase				:							Optional[int]									=   num_attention_heads
        lowercase				:							Optional[int]									=   intermediate_size
        lowercase				:							Dict									=   dropout
        lowercase				:							Any									=   attention_dropout
        lowercase				:							Optional[int]									=   max_position_embeddings
        lowercase				:							Union[str, Any]									=   initializer_range
        lowercase				:							Optional[Any]									=   scope
        lowercase				:							List[Any]									=   bos_token_id







      def      lowerCamelCase__      (  self     ):
        lowercase				:							List[Any]									=   ids_tensor([self.batch_size, self.seq_length]  ,    self.vocab_size     )

        lowercase				:							List[Any]									=   None
        if self.use_input_mask:
          lowercase				:							Optional[Any]									=   random_attention_mask([self.batch_size, self.seq_length]     )

        if input_mask is not None:
          lowercase				,      lowercase				:							Optional[Any]									=   input_mask.shape
          lowercase				:							List[Any]									=   np.random.randint(1  ,    seq_length - 1  ,    size=(batch_size,)     )
          for batch_idx, start_index in enumerate(lowercase     ):
            lowercase				:							Tuple									=   1
            lowercase				:							Tuple									=   0

        lowercase				:							Dict									=   self.get_config()

        return config, input_ids, input_mask







      def      lowerCamelCase__      (  self     ):
        return BlipTextConfig(
            vocab_size=self.vocab_size  ,    hidden_size=self.hidden_size  ,    projection_dim=self.projection_dim  ,    num_hidden_layers=self.num_hidden_layers  ,    num_attention_heads=self.num_attention_heads  ,    intermediate_size=self.intermediate_size  ,    dropout=self.dropout  ,    attention_dropout=self.attention_dropout  ,    max_position_embeddings=self.max_position_embeddings  ,    initializer_range=self.initializer_range  ,    bos_token_id=self.bos_token_id  ,    )







      def      lowerCamelCase__      (  self  ,    lowercase  ,    lowercase  ,    lowercase     ):
        lowercase				:							Dict									=   BlipTextModel(config=lowercase     )
        model.to(lowercase     )
        model.eval()
        with torch.no_grad():
          lowercase				:							Tuple									=   model(lowercase  ,    attention_mask=lowercase     )
          lowercase				:							List[Any]									=   model(lowercase     )
        self.parent.assertEqual(result.last_hidden_state.shape  ,    (self.batch_size, self.seq_length, self.hidden_size)     )
        self.parent.assertEqual(result.pooler_output.shape  ,    (self.batch_size, self.hidden_size)     )







      def      lowerCamelCase__      (  self     ):
        lowercase				:							Dict									=   self.prepare_config_and_inputs()
        lowercase				,      lowercase				,      lowercase				:							Optional[int]									=   config_and_inputs
        lowercase				:							Tuple									=   {'''input_ids''': input_ids, '''attention_mask''': input_mask}
        return config, inputs_dict





@require_torch
class _lowerCAmelCase			(	snake_case							,   unittest.TestCase					):
      UpperCAmelCase_	:						Dict	 =       (BlipTextModel,) if is_torch_available() else ()
      UpperCAmelCase_	:						List[str]	 =       False
      UpperCAmelCase_	:						str	 =       False
      UpperCAmelCase_	:						Dict	 =       False







      def      lowerCamelCase__      (  self     ):
        lowercase				:							Dict									=   BlipTextModelTester(self     )
        lowercase				:							Any									=   ConfigTester(self  ,    config_class=lowercase  ,    hidden_size=37     )







      def      lowerCamelCase__      (  self     ):
        self.config_tester.run_common_tests()







      def      lowerCamelCase__      (  self     ):
        lowercase				:							Optional[Any]									=   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowercase     )







      def      lowerCamelCase__      (  self     ):
        pass







      def      lowerCamelCase__      (  self     ):
        pass







      @unittest.skip(reason='''Blip does not use inputs_embeds'''     )
      def      lowerCamelCase__      (  self     ):
        pass







      @unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING'''     )
      def      lowerCamelCase__      (  self     ):
        pass







      @unittest.skip(reason='''BlipTextModel has no base class and is not available in MODEL_MAPPING'''     )
      def      lowerCamelCase__      (  self     ):
        pass







      @slow
      def      lowerCamelCase__      (  self     ):
        for model_name in BLIP_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          lowercase				:							List[Any]									=   BlipTextModel.from_pretrained(lowercase     )
          self.assertIsNotNone(lowercase     )







      def      lowerCamelCase__      (  self     ):
        super().test_pt_tf_model_equivalence(allow_missing_keys=lowercase     )
'''simple docstring'''
from __future__ import annotations

import math
import random







def 				__UpperCamelCase (					__UpperCAmelCase  :					int ,   __UpperCAmelCase  :					int ,   __UpperCAmelCase  :					int			)							->	list[int]:
  lowercase				:							Union[str, Any]									=   d * e - 1
  lowercase				:							List[str]									=   0
  lowercase				:							Tuple									=   0
  while p == 0:
    lowercase				:							Optional[Any]									=   random.randint(2 ,   n - 1			)
    lowercase				:							Dict									=   k
    while True:
      if t % 2 == 0:
        lowercase				:							List[Any]									=   t // 2
        lowercase				:							Union[str, Any]									=   (g**t) % n
        lowercase				:							Optional[Any]									=   math.gcd(x - 1 ,   __UpperCAmelCase			)
        if x > 1 and y > 1:
          lowercase				:							Dict									=   y
          lowercase				:							Any									=   n // y
          break  # find the correct factors
      else:
        break  # t is not divisible by 2, break and choose another g
  return sorted([p, q]			)


if __name__ == "__main__":
 import doctest

 doctest.testmod()