

"""simple docstring"""






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


lowerCamelCase__					:	Any				    =	logging.get_logger(__name__)

enable_full_determinism()






class      _UpperCAmelCase      (		a_     ,  a_     ,  unittest.TestCase):
			__a			:							str					       =				UNetaDModel
			__a			:							str					       =				"""sample"""
			@property
			def 				__snake_case			(		self  )     ->     Any:






				'''simple docstring'''







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

				_UpperCAmelCase     :				Tuple          =       floats_tensor((batch_size, num_channels) + sizes  ).to(lowercase_  )
				_UpperCAmelCase     :				List[str]          =       torch.tensor([10]  ).to(lowercase_  )

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






				'''simple docstring'''







				return (3, 32, 32)
			@property
			def 				__snake_case			(		self  )     ->     List[Any]:






				'''simple docstring'''







				return (3, 32, 32)
			def 				__snake_case			(		self  )     ->     int:






				'''simple docstring'''







				_UpperCAmelCase     :				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,
				}
				_UpperCAmelCase     :				Optional[int]          =       self.dummy_input
				return init_dict, inputs_dict






class      _UpperCAmelCase      (		a_     ,  a_     ,  unittest.TestCase):
			__a			:							List[Any]					       =				UNetaDModel
			__a			:							Any					       =				"""sample"""
			@property
			def 				__snake_case			(		self  )     ->     Any:






				'''simple docstring'''







				_UpperCAmelCase     :				Optional[int]          =       4
				_UpperCAmelCase     :				str          =       4
				_UpperCAmelCase     :				List[Any]          =       (32, 32)

				_UpperCAmelCase     :				Tuple          =       floats_tensor((batch_size, num_channels) + sizes  ).to(lowercase_  )
				_UpperCAmelCase     :				Tuple          =       torch.tensor([10]  ).to(lowercase_  )

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






				'''simple docstring'''







				return (4, 32, 32)
			@property
			def 				__snake_case			(		self  )     ->     Union[str, Any]:






				'''simple docstring'''







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






				'''simple docstring'''







				_UpperCAmelCase     :				Dict          =       {
				    '''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'''),
				}
				_UpperCAmelCase     :				Optional[int]          =       self.dummy_input
				return init_dict, inputs_dict
			def 				__snake_case			(		self  )     ->     Dict:






				'''simple docstring'''







				_UpperCAmelCase     :				List[Any]          =       UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""   ,		output_loading_info=lowercase_  )

				self.assertIsNotNone(lowercase_  )
				self.assertEqual(len(loading_info["""missing_keys"""]  )   ,		0  )

				model.to(lowercase_  )
				_UpperCAmelCase     :				Dict          =       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 				__snake_case			(		self  )     ->     Optional[int]:






				'''simple docstring'''







				_UpperCAmelCase     :				str          =       UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""   ,		output_loading_info=lowercase_  )
				model.to(lowercase_  )
				_UpperCAmelCase     :				Union[str, 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 				__snake_case			(		self  )     ->     str:






				'''simple docstring'''







				_UpperCAmelCase     :				Any          =       UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""   ,		output_loading_info=lowercase_  )
				model_accelerate.to(lowercase_  )
				model_accelerate.eval()

				_UpperCAmelCase     :				Optional[Any]          =       torch.randn(
				    1   ,		model_accelerate.config.in_channels   ,		model_accelerate.config.sample_size   ,		model_accelerate.config.sample_size   ,		generator=torch.manual_seed(0  )   ,		)
				_UpperCAmelCase     :				Tuple          =       noise.to(lowercase_  )
				_UpperCAmelCase     :				Optional[int]          =       torch.tensor([10] * noise.shape[0]  ).to(lowercase_  )

				_UpperCAmelCase     :				Any          =       model_accelerate(lowercase_   ,		lowercase_  )['''sample''']

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

				_UpperCAmelCase     :				Union[str, Any]          =       UNetaDModel.from_pretrained(
				    """fusing/unet-ldm-dummy-update"""   ,		output_loading_info=lowercase_   ,		low_cpu_mem_usage=lowercase_  )
				model_normal_load.to(lowercase_  )
				model_normal_load.eval()
				_UpperCAmelCase     :				Tuple          =       model_normal_load(lowercase_   ,		lowercase_  )['''sample''']

				assert torch_all_close(lowercase_   ,		lowercase_   ,		rtol=1e-3  )
			def 				__snake_case			(		self  )     ->     int:






				'''simple docstring'''







				_UpperCAmelCase     :				str          =       UNetaDModel.from_pretrained("""fusing/unet-ldm-dummy-update"""  )
				model.eval()
				model.to(lowercase_  )

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

				with torch.no_grad():
					_UpperCAmelCase     :				int          =       model(lowercase_   ,		lowercase_  ).sample

				_UpperCAmelCase     :				List[Any]          =       output[0, -1, -3:, -3:].flatten().cpu()
				# fmt: off
				_UpperCAmelCase     :				Any          =       torch.tensor([-13.3258, -20.1100, -15.9873, -17.6617, -23.0596, -17.9419, -13.3675, -16.1889, -12.3800]  )
				# fmt: on

				self.assertTrue(torch_all_close(lowercase_   ,		lowercase_   ,		rtol=1e-3  )  )






class      _UpperCAmelCase      (		a_     ,  a_     ,  unittest.TestCase):
			__a			:							Optional[Any]					       =				UNetaDModel
			__a			:							List[Any]					       =				"""sample"""
			@property
			def 				__snake_case			(		self   ,		_A=(32, 32)  )     ->     Optional[int]:






				'''simple docstring'''







				_UpperCAmelCase     :				List[str]          =       4
				_UpperCAmelCase     :				Union[str, Any]          =       3

				_UpperCAmelCase     :				List[Any]          =       floats_tensor((batch_size, num_channels) + sizes  ).to(lowercase_  )
				_UpperCAmelCase     :				List[Any]          =       torch.tensor(batch_size * [10]  ).to(dtype=torch.intaa   ,		device=lowercase_  )

				return {"sample": noise, "timestep": time_step}
			@property
			def 				__snake_case			(		self  )     ->     Optional[Any]:






				'''simple docstring'''







				return (3, 32, 32)
			@property
			def 				__snake_case			(		self  )     ->     Optional[int]:






				'''simple docstring'''







				return (3, 32, 32)
			def 				__snake_case			(		self  )     ->     Dict:






				'''simple docstring'''







				_UpperCAmelCase     :				int          =       {
				    '''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''',
				    ],
				}
				_UpperCAmelCase     :				Tuple          =       self.dummy_input
				return init_dict, inputs_dict
			@slow
			def 				__snake_case			(		self  )     ->     List[str]:






				'''simple docstring'''







				_UpperCAmelCase     :				Dict          =       UNetaDModel.from_pretrained("""google/ncsnpp-celebahq-256"""   ,		output_loading_info=lowercase_  )
				self.assertIsNotNone(lowercase_  )
				self.assertEqual(len(loading_info["""missing_keys"""]  )   ,		0  )

				model.to(lowercase_  )
				_UpperCAmelCase     :				Any          =       self.dummy_input
				_UpperCAmelCase     :				str          =       floats_tensor((4, 3) + (2_56, 2_56)  ).to(lowercase_  )
				_UpperCAmelCase     :				List[str]          =       noise
				_UpperCAmelCase     :				List[str]          =       model(**lowercase_  )

				assert image is not None, "Make sure output is not None"
			@slow
			def 				__snake_case			(		self  )     ->     Any:






				'''simple docstring'''







				_UpperCAmelCase     :				List[str]          =       UNetaDModel.from_pretrained("""google/ncsnpp-celebahq-256"""  )
				model.to(lowercase_  )

				_UpperCAmelCase     :				str          =       4
				_UpperCAmelCase     :				Any          =       3
				_UpperCAmelCase     :				int          =       (2_56, 2_56)

				_UpperCAmelCase     :				Any          =       torch.ones((batch_size, num_channels) + sizes  ).to(lowercase_  )
				_UpperCAmelCase     :				Optional[Any]          =       torch.tensor(batch_size * [1e-4]  ).to(lowercase_  )

				with torch.no_grad():
					_UpperCAmelCase     :				List[str]          =       model(lowercase_   ,		lowercase_  ).sample

				_UpperCAmelCase     :				str          =       output[0, -3:, -3:, -1].flatten().cpu()
				# fmt: off
				_UpperCAmelCase     :				Any          =       torch.tensor([-4842.8691, -6499.6631, -3800.1953, -7978.2686, -1_09_80.71_29, -2_00_28.85_35, 8148.2822, 2342.2905, 567.7608]  )
				# fmt: on

				self.assertTrue(torch_all_close(lowercase_   ,		lowercase_   ,		rtol=1e-2  )  )
			def 				__snake_case			(		self  )     ->     List[Any]:






				'''simple docstring'''







				_UpperCAmelCase     :				Optional[int]          =       UNetaDModel.from_pretrained("""fusing/ncsnpp-ffhq-ve-dummy-update"""  )
				model.to(lowercase_  )

				_UpperCAmelCase     :				Tuple          =       4
				_UpperCAmelCase     :				Optional[int]          =       3
				_UpperCAmelCase     :				List[Any]          =       (32, 32)

				_UpperCAmelCase     :				Tuple          =       torch.ones((batch_size, num_channels) + sizes  ).to(lowercase_  )
				_UpperCAmelCase     :				List[str]          =       torch.tensor(batch_size * [1e-4]  ).to(lowercase_  )

				with torch.no_grad():
					_UpperCAmelCase     :				Dict          =       model(lowercase_   ,		lowercase_  ).sample

				_UpperCAmelCase     :				List[str]          =       output[0, -3:, -3:, -1].flatten().cpu()
				# fmt: off
				_UpperCAmelCase     :				str          =       torch.tensor([-0.0325, -0.0900, -0.0869, -0.0332, -0.0725, -0.0270, -0.0101, 0.0227, 0.0256]  )
				# fmt: on

				self.assertTrue(torch_all_close(lowercase_   ,		lowercase_   ,		rtol=1e-2  )  )
			def 				__snake_case			(		self  )     ->     Tuple:






				'''simple docstring'''







				pass








"""simple docstring"""





from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase

def     __SCREAMING_SNAKE_CASE    ( A_					):
	lowerCAmelCase__							,lowerCAmelCase__      :		Dict    							=      analyze_text(A_					)
	lowerCAmelCase__      :		Any    							=      list(''' ''' + ascii_lowercase					)
	# what is our total sum of probabilities.
	lowerCAmelCase__      :		Optional[Any]    							=      sum(single_char_strings.values()					)

	# one length string
	lowerCAmelCase__      :		int    							=      0
	# for each alpha we go in our dict and if it is in it we calculate entropy
	for ch in my_alphas:
		if ch in single_char_strings:
			lowerCAmelCase__      :		Union[str, Any]    							=      single_char_strings[ch]
			lowerCAmelCase__      :		Dict    							=      my_str / all_sum
			my_fir_sum += prob * math.loga(A_					)  # entropy formula.

    # print entropy
	print(f'{round(-1 * my_fir_sum					):.1f}'					)

	# two len string
	lowerCAmelCase__      :		List[Any]    							=      sum(two_char_strings.values()					)
	lowerCAmelCase__      :		Optional[int]    							=      0
	# for each alpha (two in size) calculate entropy.
	for cha in my_alphas:
		for cha in my_alphas:
			lowerCAmelCase__      :		int    							=      cha + cha
			if sequence in two_char_strings:
				lowerCAmelCase__      :		Optional[Any]    							=      two_char_strings[sequence]
				lowerCAmelCase__      :		int    							=      int(A_					) / all_sum
				my_sec_sum += prob * math.loga(A_					)

    # print second entropy
	print(f'{round(-1 * my_sec_sum					):.1f}'					)

	# print the difference between them
	print(f'{round((-1 * my_sec_sum) - (-1 * my_fir_sum)					):.1f}'					)

def     __SCREAMING_SNAKE_CASE    ( A_					):
	lowerCAmelCase__      :		List[str]    							=      Counter()  # type: ignore
	lowerCAmelCase__      :		Optional[int]    							=      Counter()  # type: ignore
	single_char_strings[text[-1]] += 1

	# first case when we have space at start.
	two_char_strings[" " + text[0]] += 1
	for i in range(0		,	len(A_					) - 1					):
		single_char_strings[text[i]] += 1
		two_char_strings[text[i : i + 2]] += 1
	return single_char_strings, two_char_strings

def     __SCREAMING_SNAKE_CASE    ( ):
	import doctest

	doctest.testmod()
	# text = (
	#     "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
	#     "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
	#     "jointure saw horrible. He private he on be imagine suppose. Fertile "
	#     "beloved evident through no service elderly is. Blind there if every no so "
	#     "at. Own neglected you preferred way sincerity delivered his attempted. To "
	#     "of message cottage windows do besides against uncivil.  Delightful "
	#     "unreserved impossible few estimating men favourable see entreaties. She "
	#     "propriety immediate was improving. He or entrance humoured likewise "
	#     "moderate. Much nor game son say feel. Fat make met can must form into "
	#     "gate. Me we offending prevailed discovery. "
	# )

	# calculate_prob(text)


if __name__ == "__main__":
							main()




