


import gc
import math
import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


SCREAMING_SNAKE_CASE			: List[str]				   =					logging.get_logger(__name__)

enable_full_determinism()






class _lowerCamelCase(		_a,					_a,					unittest.TestCase  ):
       lowercase_					:      Optional[int]       					=     UNetaDModel
       lowercase_					:      List[str]       					=     """sample"""
       @property
       def      UpperCamelCase	(   self)			->  int:




           """simple docstring"""

           _lowercase			:				Tuple 						=						4
           _lowercase			:				Any 						=						3
           _lowercase			:				List[str] 						=						(32, 32)

           _lowercase			:				str 						=						floats_tensor((batch_size, num_channels) + sizes).to(lowerCamelCase)
           _lowercase			:				List[str] 						=						torch.tensor([10]).to(lowerCamelCase)

           return {"sample": noise, "timestep": time_step}
       @property
       def      UpperCamelCase	(   self)			->  int:




           """simple docstring"""

           return (3, 32, 32)
       @property
       def      UpperCamelCase	(   self)			->  Union[str, Any]:




           """simple docstring"""

           return (3, 32, 32)




       def      UpperCamelCase	(   self)			->  Any:




           """simple docstring"""

           _lowercase			:				List[Any] 						=						{
               'block_out_channels': (32, 64),
               'down_block_types': ('DownBlock2D', 'AttnDownBlock2D'),
               'up_block_types': ('AttnUpBlock2D', 'UpBlock2D'),
               'attention_head_dim': 3,
               'out_channels': 3,
               'in_channels': 3,
               'layers_per_block': 2,
               'sample_size': 32,
           }
           _lowercase			:				int 						=						self.dummy_input
           return init_dict, inputs_dict






class _lowerCamelCase(		_a,					_a,					unittest.TestCase  ):
       lowercase_					:      List[str]       					=     UNetaDModel
       lowercase_					:      List[Any]       					=     """sample"""
       @property
       def      UpperCamelCase	(   self)			->  str:




           """simple docstring"""

           _lowercase			:				Tuple 						=						4
           _lowercase			:				int 						=						4
           _lowercase			:				Tuple 						=						(32, 32)

           _lowercase			:				Any 						=						floats_tensor((batch_size, num_channels) + sizes).to(lowerCamelCase)
           _lowercase			:				Any 						=						torch.tensor([10]).to(lowerCamelCase)

           return {"sample": noise, "timestep": time_step}
       @property
       def      UpperCamelCase	(   self)			->  Dict:




           """simple docstring"""

           return (4, 32, 32)
       @property
       def      UpperCamelCase	(   self)			->  Optional[Any]:




           """simple docstring"""

           return (4, 32, 32)
       def      UpperCamelCase	(   self)			->  Optional[int]:




           """simple docstring"""

           _lowercase			:				Tuple 						=						{
               'sample_size': 32,
               'in_channels': 4,
               'out_channels': 4,
               'layers_per_block': 2,
               'block_out_channels': (32, 64),
               'attention_head_dim': 32,
               'down_block_types': ('DownBlock2D', 'DownBlock2D'),
               'up_block_types': ('UpBlock2D', 'UpBlock2D'),
           }
           _lowercase			:				int 						=						self.dummy_input
           return init_dict, inputs_dict
       def      UpperCamelCase	(   self)			->  List[str]:




           """simple docstring"""

           _lowercase						,							_lowercase			:				str 						=						UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update', output_loading_info=lowerCamelCase)

           self.assertIsNotNone(lowerCamelCase)
           self.assertEqual(len(loading_info['missing_keys']), 0)

           model.to(lowerCamelCase)
           _lowercase			:				List[str] 						=						model(**self.dummy_input).sample

           assert image is not None, "Make sure output is not None"
       @unittest.skipIf(torch_device != 'cuda', 'This test is supposed to run on GPU')
       def      UpperCamelCase	(   self)			->  Union[str, Any]:




           """simple docstring"""

           _lowercase						,							_lowercase			:				Dict 						=						UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update', output_loading_info=lowerCamelCase)
           model.to(lowerCamelCase)
           _lowercase			:				Optional[Any] 						=						model(**self.dummy_input).sample

           assert image is not None, "Make sure output is not None"
       @unittest.skipIf(torch_device != 'cuda', 'This test is supposed to run on GPU')
       def      UpperCamelCase	(   self)			->  Union[str, Any]:




           """simple docstring"""

           _lowercase						,							_lowercase			:				Tuple 						=						UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update', output_loading_info=lowerCamelCase)
           model_accelerate.to(lowerCamelCase)
           model_accelerate.eval()

           _lowercase			:				Union[str, Any] 						=						torch.randn(
               1, model_accelerate.config.in_channels, model_accelerate.config.sample_size, model_accelerate.config.sample_size, generator=torch.manual_seed(0), )
           _lowercase			:				List[str] 						=						noise.to(lowerCamelCase)
           _lowercase			:				List[Any] 						=						torch.tensor([10] * noise.shape[0]).to(lowerCamelCase)

           _lowercase			:				str 						=						model_accelerate(lowerCamelCase, lowerCamelCase)['sample']

           # two models don't need to stay in the device at the same time
           del model_accelerate
           torch.cuda.empty_cache()
           gc.collect()

           _lowercase						,							_lowercase			:				Union[str, Any] 						=						UNetaDModel.from_pretrained(
               'fusing/unet-ldm-dummy-update', output_loading_info=lowerCamelCase, low_cpu_mem_usage=lowerCamelCase)
           model_normal_load.to(lowerCamelCase)
           model_normal_load.eval()
           _lowercase			:				Dict 						=						model_normal_load(lowerCamelCase, lowerCamelCase)['sample']

           assert torch_all_close(lowerCamelCase, lowerCamelCase, rtol=1E-3)




       def      UpperCamelCase	(   self)			->  Optional[int]:




           """simple docstring"""

           _lowercase			:				Optional[int] 						=						UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update')
           model.eval()
           model.to(lowerCamelCase)

           _lowercase			:				List[Any] 						=						torch.randn(
               1, model.config.in_channels, model.config.sample_size, model.config.sample_size, generator=torch.manual_seed(0), )
           _lowercase			:				Dict 						=						noise.to(lowerCamelCase)
           _lowercase			:				Union[str, Any] 						=						torch.tensor([10] * noise.shape[0]).to(lowerCamelCase)

           with torch.no_grad():
               _lowercase			:				Dict 						=						model(lowerCamelCase, lowerCamelCase).sample

           _lowercase			:				List[str] 						=						output[0, -1, -3:, -3:].flatten().cpu()
           # fmt: off
           _lowercase			:				Dict 						=						torch.tensor([-1_3.3_2_5_8, -2_0.1_1_0_0, -1_5.9_8_7_3, -1_7.6_6_1_7, -2_3.0_5_9_6, -1_7.9_4_1_9, -1_3.3_6_7_5, -1_6.1_8_8_9, -1_2.3_8_0_0])
           # fmt: on

           self.assertTrue(torch_all_close(lowerCamelCase, lowerCamelCase, rtol=1E-3))






class _lowerCamelCase(		_a,					_a,					unittest.TestCase  ):
       lowercase_					:      str       					=     UNetaDModel
       lowercase_					:      Any       					=     """sample"""
       @property
       def      UpperCamelCase	(   self, lowerCamelCase=(32, 32))			->  List[Any]:




           """simple docstring"""

           _lowercase			:				Union[str, Any] 						=						4
           _lowercase			:				int 						=						3

           _lowercase			:				Union[str, Any] 						=						floats_tensor((batch_size, num_channels) + sizes).to(lowerCamelCase)
           _lowercase			:				List[Any] 						=						torch.tensor(batch_size * [10]).to(dtype=torch.intaa, device=lowerCamelCase)

           return {"sample": noise, "timestep": time_step}
       @property
       def      UpperCamelCase	(   self)			->  Any:




           """simple docstring"""

           return (3, 32, 32)
       @property
       def      UpperCamelCase	(   self)			->  int:




           """simple docstring"""

           return (3, 32, 32)
       def      UpperCamelCase	(   self)			->  str:




           """simple docstring"""

           _lowercase			:				Tuple 						=						{
               'block_out_channels': [32, 64, 64, 64],
               'in_channels': 3,
               'layers_per_block': 1,
               'out_channels': 3,
               'time_embedding_type': 'fourier',
               'norm_eps': 1E-6,
               'mid_block_scale_factor': math.sqrt(2.0),
               'norm_num_groups': None,
               'down_block_types': [
                   'SkipDownBlock2D',
                   'AttnSkipDownBlock2D',
                   'SkipDownBlock2D',
                   'SkipDownBlock2D',
               ],
               'up_block_types': [
                   'SkipUpBlock2D',
                   'SkipUpBlock2D',
                   'AttnSkipUpBlock2D',
                   'SkipUpBlock2D',
               ],
           }
           _lowercase			:				List[Any] 						=						self.dummy_input
           return init_dict, inputs_dict
       @slow
       def      UpperCamelCase	(   self)			->  Optional[Any]:




           """simple docstring"""

           _lowercase						,							_lowercase			:				Optional[int] 						=						UNetaDModel.from_pretrained('google/ncsnpp-celebahq-256', output_loading_info=lowerCamelCase)
           self.assertIsNotNone(lowerCamelCase)
           self.assertEqual(len(loading_info['missing_keys']), 0)

           model.to(lowerCamelCase)
           _lowercase			:				List[str] 						=						self.dummy_input
           _lowercase			:				Dict 						=						floats_tensor((4, 3) + (2_56, 2_56)).to(lowerCamelCase)
           _lowercase			:				Optional[Any] 						=						noise
           _lowercase			:				List[Any] 						=						model(**lowerCamelCase)

           assert image is not None, "Make sure output is not None"
       @slow
       def      UpperCamelCase	(   self)			->  Optional[int]:




           """simple docstring"""

           _lowercase			:				Optional[int] 						=						UNetaDModel.from_pretrained('google/ncsnpp-celebahq-256')
           model.to(lowerCamelCase)

           _lowercase			:				Tuple 						=						4
           _lowercase			:				List[str] 						=						3
           _lowercase			:				str 						=						(2_56, 2_56)

           _lowercase			:				int 						=						torch.ones((batch_size, num_channels) + sizes).to(lowerCamelCase)
           _lowercase			:				List[str] 						=						torch.tensor(batch_size * [1E-4]).to(lowerCamelCase)

           with torch.no_grad():
               _lowercase			:				Dict 						=						model(lowerCamelCase, lowerCamelCase).sample

           _lowercase			:				Optional[Any] 						=						output[0, -3:, -3:, -1].flatten().cpu()
           # fmt: off
           _lowercase			:				Tuple 						=						torch.tensor([-4_8_4_2.8_6_9_1, -6_4_9_9.6_6_3_1, -3_8_0_0.1_9_5_3, -7_9_7_8.2_6_8_6, -1_0_9_8_0.7_1_2_9, -2_0_0_2_8.8_5_3_5, 8_1_4_8.2_8_2_2, 2_3_4_2.2_9_0_5, 5_6_7.7_6_0_8])
           # fmt: on

           self.assertTrue(torch_all_close(lowerCamelCase, lowerCamelCase, rtol=1E-2))
       def      UpperCamelCase	(   self)			->  Union[str, Any]:




           """simple docstring"""

           _lowercase			:				int 						=						UNetaDModel.from_pretrained('fusing/ncsnpp-ffhq-ve-dummy-update')
           model.to(lowerCamelCase)

           _lowercase			:				Optional[int] 						=						4
           _lowercase			:				Optional[Any] 						=						3
           _lowercase			:				Optional[int] 						=						(32, 32)

           _lowercase			:				str 						=						torch.ones((batch_size, num_channels) + sizes).to(lowerCamelCase)
           _lowercase			:				Tuple 						=						torch.tensor(batch_size * [1E-4]).to(lowerCamelCase)

           with torch.no_grad():
               _lowercase			:				Optional[int] 						=						model(lowerCamelCase, lowerCamelCase).sample

           _lowercase			:				Optional[int] 						=						output[0, -3:, -3:, -1].flatten().cpu()
           # fmt: off
           _lowercase			:				Tuple 						=						torch.tensor([-0.0_3_2_5, -0.0_9_0_0, -0.0_8_6_9, -0.0_3_3_2, -0.0_7_2_5, -0.0_2_7_0, -0.0_1_0_1, 0.0_2_2_7, 0.0_2_5_6])
           # fmt: on

           self.assertTrue(torch_all_close(lowerCamelCase, lowerCamelCase, rtol=1E-2))




       def      UpperCamelCase	(   self)			->  Optional[Any]:




           """simple docstring"""

           pass








'''simple docstring'''


def 						lowerCamelCase__   (				a							):
						if not all(x.isalpha() for x in string							):
												raise ValueError('String must only contain alphabetic characters.'							)

						__snake_case						 =   sorted(string.lower()							)
						return len(a							) == len(set(a							)							)


if __name__ == "__main__":
	_lowercase  =			input("""Enter a string """).strip()

	_lowercase  =			is_isogram(input_str)
	print(f'''{input_str} is {'an' if isogram else 'not an'} isogram.''')

