import inspect
import unittest

import requests

from transformers import MgpstrConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available

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


if is_torch_available():
   import torch
   from torch import nn

   from transformers import MgpstrForSceneTextRecognition


if is_vision_available():
   from PIL import Image

   from transformers import MgpstrProcessor







class __snake_case							:




   def __init__(       self     :							List[Any]			,      _lowercase     :							str			,      _lowercase     :							Union[str, Any]=False			,      _lowercase     :							Union[str, Any]=13			,      _lowercase     :							int=(32, 1_28)			,      _lowercase     :							str=4			,      _lowercase     :							Optional[int]=3			,      _lowercase     :							Union[str, Any]=27			,      _lowercase     :							Optional[Any]=38			,      _lowercase     :							int=99			,      _lowercase     :							Union[str, Any]=99			,      _lowercase     :							List[str]=32			,      _lowercase     :							Optional[Any]=5			,      _lowercase     :							Dict=4			,      _lowercase     :							Union[str, Any]=4.0			,      _lowercase     :							Tuple=2_57			,      _lowercase     :							Tuple=None			,      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= parent
     SCREAMING_SNAKE_CASE__     				= is_training
     SCREAMING_SNAKE_CASE__     				= batch_size
     SCREAMING_SNAKE_CASE__     				= image_size
     SCREAMING_SNAKE_CASE__     				= patch_size
     SCREAMING_SNAKE_CASE__     				= num_channels
     SCREAMING_SNAKE_CASE__     				= max_token_length
     SCREAMING_SNAKE_CASE__     				= num_character_labels
     SCREAMING_SNAKE_CASE__     				= num_bpe_labels
     SCREAMING_SNAKE_CASE__     				= num_wordpiece_labels
     SCREAMING_SNAKE_CASE__     				= hidden_size
     SCREAMING_SNAKE_CASE__     				= num_hidden_layers
     SCREAMING_SNAKE_CASE__     				= num_attention_heads
     SCREAMING_SNAKE_CASE__     				= mlp_ratio
     SCREAMING_SNAKE_CASE__     				= patch_embeds_hidden_size
     SCREAMING_SNAKE_CASE__     				= output_hidden_states




   def       __a		(       self     :							List[Any] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]] )
     SCREAMING_SNAKE_CASE__     				= self.get_config()
     return config, pixel_values




   def       __a		(       self     :							List[Any] ):



     """simple docstring"""






     return MgpstrConfig(
         image_size=self.image_size			,      patch_size=self.patch_size			,      num_channels=self.num_channels			,      max_token_length=self.max_token_length			,      num_character_labels=self.num_character_labels			,      num_bpe_labels=self.num_bpe_labels			,      num_wordpiece_labels=self.num_wordpiece_labels			,      hidden_size=self.hidden_size			,      num_hidden_layers=self.num_hidden_layers			,      num_attention_heads=self.num_attention_heads			,      mlp_ratio=self.mlp_ratio			,      output_hidden_states=self.output_hidden_states			,      )




   def       __a		(       self     :							Optional[Any]			,      _lowercase     :							str			,      _lowercase     :							Dict ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= MgpstrForSceneTextRecognition(_lowercase )
     model.to(_lowercase )
     model.eval()
     with torch.no_grad():
       SCREAMING_SNAKE_CASE__     				= model(_lowercase )
     self.parent.assertEqual(
         generated_ids[0][0].shape			,      (self.batch_size, self.max_token_length, self.num_character_labels) )




   def       __a		(       self     :							Optional[Any] ):



     """simple docstring"""






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







@require_torch
class __snake_case							(		lowerCamelCase_  ,						lowerCamelCase_  ,						unittest.TestCase						):
   lowerCAmelCase_								=				(MgpstrForSceneTextRecognition,) if is_torch_available() else ()
   lowerCAmelCase_								=				{"feature-extraction": MgpstrForSceneTextRecognition} if is_torch_available() else {}
   lowerCAmelCase_								=				False

   lowerCAmelCase_								=				False
   lowerCAmelCase_								=				False
   lowerCAmelCase_								=				False
   lowerCAmelCase_								=				False




   def       __a		(       self     :							Optional[Any] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= MgpstrModelTester(self )
     SCREAMING_SNAKE_CASE__     				= ConfigTester(self			,      config_class=_lowercase			,      has_text_modality=_lowercase )




   def       __a		(       self     :							List[Any] ):



     """simple docstring"""






     self.config_tester.run_common_tests()




   def       __a		(       self     :							Optional[Any] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*_lowercase )




   @unittest.skip(reason="""MgpstrModel does not use inputs_embeds""" )
   def       __a		(       self     :							Tuple ):



     """simple docstring"""






     pass




   def       __a		(       self     :							Union[str, Any] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__     				= self.model_tester.prepare_config_and_inputs_for_common()

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




   def       __a		(       self     :							Optional[Any] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__     				= self.model_tester.prepare_config_and_inputs_for_common()

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

       SCREAMING_SNAKE_CASE__     				= ["""pixel_values"""]
       self.assertListEqual(arg_names[:1]			,      _lowercase )




   @unittest.skip(reason="""MgpstrModel does not support feedforward chunking""" )
   def       __a		(       self     :							Tuple ):



     """simple docstring"""






     pass




   def       __a		(       self     :							Tuple ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__     				= self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
       if not model_class.supports_gradient_checkpointing:
         continue

       SCREAMING_SNAKE_CASE__     				= True
       SCREAMING_SNAKE_CASE__     				= model_class(_lowercase )
       self.assertTrue(model.is_gradient_checkpointing )




   def       __a		(       self     :							List[Any] ):



     """simple docstring"""






     def check_hidden_states_output(_lowercase     :							Union[str, Any]			,      _lowercase     :							Union[str, Any]			,      _lowercase     :							Any ):
       SCREAMING_SNAKE_CASE__     				= model_class(_lowercase )
       model.to(_lowercase )
       model.eval()

       with torch.no_grad():
         SCREAMING_SNAKE_CASE__     				= model(**self._prepare_for_class(_lowercase			,      _lowercase ) )

       SCREAMING_SNAKE_CASE__     				= outputs.hidden_states

       SCREAMING_SNAKE_CASE__     				= getattr(
           self.model_tester			,      """expected_num_hidden_layers"""			,      self.model_tester.num_hidden_layers + 1 )
       self.assertEqual(len(_lowercase )			,      _lowercase )

       self.assertListEqual(
           list(hidden_states[0].shape[-2:] )			,      [self.model_tester.patch_embeds_hidden_size, self.model_tester.hidden_size]			,      )

     SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__     				= self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
       SCREAMING_SNAKE_CASE__     				= True
       check_hidden_states_output(_lowercase			,      _lowercase			,      _lowercase )

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

       check_hidden_states_output(_lowercase			,      _lowercase			,      _lowercase )




   def       __a		(       self     :							Optional[Any] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__     				= self.model_tester.prepare_config_and_inputs_for_common()

     SCREAMING_SNAKE_CASE__     				= _config_zero_init(_lowercase )
     for model_class in self.all_model_classes:
       SCREAMING_SNAKE_CASE__     				= model_class(config=_lowercase )
       for name, param in model.named_parameters():
         if isinstance(_lowercase			,      (nn.Linear, nn.Convad, nn.LayerNorm) ):
           if param.requires_grad:
             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(reason="""Retain_grad is tested in individual model tests""" )
   def       __a		(       self     :							str ):



     """simple docstring"""






     pass





def       __SCREAMING_SNAKE_CASE    (   )				->   Dict:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__     				= """https://i.postimg.cc/ZKwLg2Gw/367-14.png"""
  SCREAMING_SNAKE_CASE__     				= Image.open(requests.get(__UpperCamelCase					,	stream=__UpperCamelCase      ).raw      ).convert("""RGB"""      )
  return im







@require_vision
@require_torch
class __snake_case							(		unittest.TestCase						):




   @slow
   def       __a		(       self     :							List[Any] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= """alibaba-damo/mgp-str-base"""
     SCREAMING_SNAKE_CASE__     				= MgpstrForSceneTextRecognition.from_pretrained(_lowercase ).to(_lowercase )
     SCREAMING_SNAKE_CASE__     				= MgpstrProcessor.from_pretrained(_lowercase )

     SCREAMING_SNAKE_CASE__     				= prepare_img()
     SCREAMING_SNAKE_CASE__     				= processor(images=_lowercase			,      return_tensors="""pt""" ).pixel_values.to(_lowercase )

     # forward pass
     with torch.no_grad():
       SCREAMING_SNAKE_CASE__     				= model(_lowercase )

     # verify the logits
     self.assertEqual(outputs.logits[0].shape			,      torch.Size((1, 27, 38) ) )

     SCREAMING_SNAKE_CASE__     				= processor.batch_decode(outputs.logits )
     SCREAMING_SNAKE_CASE__     				= """ticket"""

     self.assertEqual(out_strs["""generated_text"""][0]			,      _lowercase )

     SCREAMING_SNAKE_CASE__     				= torch.tensor(
         [[[-39.53_97, -44.40_24, -36.18_44], [-61.47_09, -63.86_39, -58.34_54], [-74.02_25, -68.54_94, -71.21_64]]]			,      device=_lowercase			,      )

     self.assertTrue(torch.allclose(outputs.logits[0][:, 1:4, 1:4]			,      _lowercase			,      atol=1E-4 ) )

import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionModelEditingPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()







@skip_mps
class __snake_case							(		lowerCamelCase_  ,						lowerCamelCase_  ,						lowerCamelCase_  ,						unittest.TestCase						):
   lowerCAmelCase_								=				StableDiffusionModelEditingPipeline
   lowerCAmelCase_								=				TEXT_TO_IMAGE_PARAMS
   lowerCAmelCase_								=				TEXT_TO_IMAGE_BATCH_PARAMS
   lowerCAmelCase_								=				TEXT_TO_IMAGE_IMAGE_PARAMS
   lowerCAmelCase_								=				TEXT_TO_IMAGE_IMAGE_PARAMS




   def       __a		(       self     :							List[str] ):



     """simple docstring"""






     torch.manual_seed(0 )
     SCREAMING_SNAKE_CASE__     				= UNetaDConditionModel(
         block_out_channels=(32, 64)			,      layers_per_block=2			,      sample_size=32			,      in_channels=4			,      out_channels=4			,      down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")			,      up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")			,      cross_attention_dim=32			,      )
     SCREAMING_SNAKE_CASE__     				= DDIMScheduler()
     torch.manual_seed(0 )
     SCREAMING_SNAKE_CASE__     				= AutoencoderKL(
         block_out_channels=[32, 64]			,      in_channels=3			,      out_channels=3			,      down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]			,      up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]			,      latent_channels=4			,      )
     torch.manual_seed(0 )
     SCREAMING_SNAKE_CASE__     				= CLIPTextConfig(
         bos_token_id=0			,      eos_token_id=2			,      hidden_size=32			,      intermediate_size=37			,      layer_norm_eps=1E-05			,      num_attention_heads=4			,      num_hidden_layers=5			,      pad_token_id=1			,      vocab_size=10_00			,      )
     SCREAMING_SNAKE_CASE__     				= CLIPTextModel(_lowercase )
     SCREAMING_SNAKE_CASE__     				= CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )

     SCREAMING_SNAKE_CASE__     				= {
         """unet""": unet,
         """scheduler""": scheduler,
         """vae""": vae,
         """text_encoder""": text_encoder,
         """tokenizer""": tokenizer,
         """safety_checker""": None,
         """feature_extractor""": None,
     }
     return components




   def       __a		(       self     :							Optional[int]			,      _lowercase     :							Optional[int]			,      _lowercase     :							Union[str, Any]=0 ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= torch.manual_seed(_lowercase )
     SCREAMING_SNAKE_CASE__     				= {
         """prompt""": """A field of roses""",
         """generator""": generator,
         # Setting height and width to None to prevent OOMs on CPU.
         """height""": None,
         """width""": None,
         """num_inference_steps""": 2,
         """guidance_scale""": 6.0,
         """output_type""": """numpy""",
     }
     return inputs




   def       __a		(       self     :							Dict ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= """cpu"""  # ensure determinism for the device-dependent torch.Generator
     SCREAMING_SNAKE_CASE__     				= self.get_dummy_components()
     SCREAMING_SNAKE_CASE__     				= StableDiffusionModelEditingPipeline(**_lowercase )
     SCREAMING_SNAKE_CASE__     				= sd_pipe.to(_lowercase )
     sd_pipe.set_progress_bar_config(disable=_lowercase )

     SCREAMING_SNAKE_CASE__     				= self.get_dummy_inputs(_lowercase )
     SCREAMING_SNAKE_CASE__     				= sd_pipe(**_lowercase ).images
     SCREAMING_SNAKE_CASE__     				= image[0, -3:, -3:, -1]
     assert image.shape == (1, 64, 64, 3)

     SCREAMING_SNAKE_CASE__     				= np.array([0.47_55, 0.51_32, 0.49_76, 0.39_04, 0.35_54, 0.47_65, 0.51_39, 0.51_58, 0.48_89] )

     assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2




   def       __a		(       self     :							List[str] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= """cpu"""  # ensure determinism for the device-dependent torch.Generator
     SCREAMING_SNAKE_CASE__     				= self.get_dummy_components()
     SCREAMING_SNAKE_CASE__     				= StableDiffusionModelEditingPipeline(**_lowercase )
     SCREAMING_SNAKE_CASE__     				= sd_pipe.to(_lowercase )
     sd_pipe.set_progress_bar_config(disable=_lowercase )

     SCREAMING_SNAKE_CASE__     				= self.get_dummy_inputs(_lowercase )
     SCREAMING_SNAKE_CASE__     				= """french fries"""
     SCREAMING_SNAKE_CASE__     				= sd_pipe(**_lowercase			,      negative_prompt=_lowercase )
     SCREAMING_SNAKE_CASE__     				= output.images
     SCREAMING_SNAKE_CASE__     				= image[0, -3:, -3:, -1]

     assert image.shape == (1, 64, 64, 3)

     SCREAMING_SNAKE_CASE__     				= np.array([0.49_92, 0.51_01, 0.50_04, 0.39_49, 0.36_04, 0.47_35, 0.52_16, 0.52_04, 0.49_13] )

     assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2




   def       __a		(       self     :							Union[str, Any] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= """cpu"""  # ensure determinism for the device-dependent torch.Generator
     SCREAMING_SNAKE_CASE__     				= self.get_dummy_components()
     SCREAMING_SNAKE_CASE__     				= EulerAncestralDiscreteScheduler(
         beta_start=0.0_00_85			,      beta_end=0.0_12			,      beta_schedule="""scaled_linear""" )
     SCREAMING_SNAKE_CASE__     				= StableDiffusionModelEditingPipeline(**_lowercase )
     SCREAMING_SNAKE_CASE__     				= sd_pipe.to(_lowercase )
     sd_pipe.set_progress_bar_config(disable=_lowercase )

     SCREAMING_SNAKE_CASE__     				= self.get_dummy_inputs(_lowercase )
     SCREAMING_SNAKE_CASE__     				= sd_pipe(**_lowercase ).images
     SCREAMING_SNAKE_CASE__     				= image[0, -3:, -3:, -1]

     assert image.shape == (1, 64, 64, 3)

     SCREAMING_SNAKE_CASE__     				= np.array([0.47_47, 0.53_72, 0.47_79, 0.49_82, 0.55_43, 0.48_16, 0.52_38, 0.49_04, 0.50_27] )

     assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2




   def       __a		(       self     :							List[Any] ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= """cpu"""  # ensure determinism for the device-dependent torch.Generator
     SCREAMING_SNAKE_CASE__     				= self.get_dummy_components()
     SCREAMING_SNAKE_CASE__     				= PNDMScheduler()
     SCREAMING_SNAKE_CASE__     				= StableDiffusionModelEditingPipeline(**_lowercase )
     SCREAMING_SNAKE_CASE__     				= sd_pipe.to(_lowercase )
     sd_pipe.set_progress_bar_config(disable=_lowercase )

     SCREAMING_SNAKE_CASE__     				= self.get_dummy_inputs(_lowercase )
     # the pipeline does not expect pndm so test if it raises error.
     with self.assertRaises(_lowercase ):
       SCREAMING_SNAKE_CASE__     				= sd_pipe(**_lowercase ).images




   def       __a		(       self     :							List[str] ):



     """simple docstring"""






     super().test_inference_batch_single_identical(expected_max_diff=5E-3 )




   def       __a		(       self     :							Any ):



     """simple docstring"""






     super().test_attention_slicing_forward_pass(expected_max_diff=5E-3 )







@slow
@require_torch_gpu
class __snake_case							(		unittest.TestCase						):




   def       __a		(       self     :							str ):



     """simple docstring"""






     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()




   def       __a		(       self     :							List[Any]			,      _lowercase     :							Dict=0 ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= torch.manual_seed(_lowercase )
     SCREAMING_SNAKE_CASE__     				= {
         """prompt""": """A field of roses""",
         """generator""": generator,
         """num_inference_steps""": 3,
         """guidance_scale""": 7.5,
         """output_type""": """numpy""",
     }
     return inputs




   def       __a		(       self     :							Tuple ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= """CompVis/stable-diffusion-v1-4"""
     SCREAMING_SNAKE_CASE__     				= StableDiffusionModelEditingPipeline.from_pretrained(_lowercase			,      safety_checker=_lowercase )
     pipe.to(_lowercase )
     pipe.set_progress_bar_config(disable=_lowercase )
     pipe.enable_attention_slicing()

     SCREAMING_SNAKE_CASE__     				= self.get_inputs()
     SCREAMING_SNAKE_CASE__     				= pipe(**_lowercase ).images
     SCREAMING_SNAKE_CASE__     				= image[0, -3:, -3:, -1].flatten()

     assert image.shape == (1, 5_12, 5_12, 3)

     SCREAMING_SNAKE_CASE__     				= np.array(
         [0.6_74_94_96, 0.6_38_64_53, 0.51_44_32_67, 0.66_09_49_05, 0.61_92_12_15, 0.5_49_13_32, 0.5_74_44_17, 0.58_07_51_06, 0.5_17_46_58] )

     assert np.abs(expected_slice - image_slice ).max() < 1E-2

     # make sure image changes after editing
     pipe.edit_model("""A pack of roses"""			,      """A pack of blue roses""" )

     SCREAMING_SNAKE_CASE__     				= pipe(**_lowercase ).images
     SCREAMING_SNAKE_CASE__     				= image[0, -3:, -3:, -1].flatten()

     assert image.shape == (1, 5_12, 5_12, 3)

     assert np.abs(expected_slice - image_slice ).max() > 1E-1




   def       __a		(       self     :							Any ):



     """simple docstring"""






     torch.cuda.empty_cache()
     torch.cuda.reset_max_memory_allocated()
     torch.cuda.reset_peak_memory_stats()

     SCREAMING_SNAKE_CASE__     				= """CompVis/stable-diffusion-v1-4"""
     SCREAMING_SNAKE_CASE__     				= DDIMScheduler.from_pretrained(_lowercase			,      subfolder="""scheduler""" )
     SCREAMING_SNAKE_CASE__     				= StableDiffusionModelEditingPipeline.from_pretrained(
         _lowercase			,      scheduler=_lowercase			,      safety_checker=_lowercase )
     SCREAMING_SNAKE_CASE__     				= pipe.to(_lowercase )
     pipe.set_progress_bar_config(disable=_lowercase )
     pipe.enable_attention_slicing(1 )
     pipe.enable_sequential_cpu_offload()

     SCREAMING_SNAKE_CASE__     				= self.get_inputs()
     SCREAMING_SNAKE_CASE__     				= pipe(**_lowercase )

     SCREAMING_SNAKE_CASE__     				= torch.cuda.max_memory_allocated()
     # make sure that less than 4.4 GB is allocated
     assert mem_bytes < 4.4 * 10**9
