"""simple docstring"""




import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest
class      UpperCamelCase__(      lowercase_			):
						lowerCAmelCase__	:    Dict    				=		(EulerAncestralDiscreteScheduler,)
						lowerCAmelCase__	:    List[Any]    				=		10





						def 					snake_case__						(							self		,**__UpperCAmelCase  )       ->					Optional[Any]:
													A__						  =				{
													    "num_train_timesteps": 11_00,
													    "beta_start": 0.0_0_0_1,
													    "beta_end": 0.0_2,
													    "beta_schedule": "linear",
													}

													config.update(**UpperCAmelCase_  )
													return config





						def 					snake_case__						(							self  )       ->					List[str]:




													for timesteps in [10, 50, 1_00, 10_00]:
																				self.check_over_configs(num_train_timesteps=UpperCAmelCase_  )





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




													for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]		,[0.0_0_0_2, 0.0_0_2, 0.0_2]  ):
																				self.check_over_configs(beta_start=UpperCAmelCase_		,beta_end=UpperCAmelCase_  )





						def 					snake_case__						(							self  )       ->					Dict:




													for schedule in ["linear", "scaled_linear"]:
																				self.check_over_configs(beta_schedule=UpperCAmelCase_  )





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




													for prediction_type in ["epsilon", "v_prediction"]:
																				self.check_over_configs(prediction_type=UpperCAmelCase_  )





						def 					snake_case__						(							self  )       ->					List[Any]:
													A__						  =				self.scheduler_classes[0]
													A__						  =				self.get_scheduler_config()
													A__						  =				scheduler_class(**UpperCAmelCase_  )

													scheduler.set_timesteps(self.num_inference_steps  )

													A__						  =				torch.manual_seed(0  )

													A__						  =				self.dummy_model()
													A__						  =				self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
													A__						  =				sample.to(UpperCAmelCase_  )

													for i, t in enumerate(scheduler.timesteps  ):
																				A__						  =				scheduler.scale_model_input(UpperCAmelCase_		,UpperCAmelCase_  )

																				A__						  =				model(UpperCAmelCase_		,UpperCAmelCase_  )

																				A__						  =				scheduler.step(UpperCAmelCase_		,UpperCAmelCase_		,UpperCAmelCase_		,generator=UpperCAmelCase_  )
																				A__						  =				output.prev_sample

													A__						  =				torch.sum(torch.abs(UpperCAmelCase_  )  )
													A__						  =				torch.mean(torch.abs(UpperCAmelCase_  )  )

													assert abs(result_sum.item() - 1_52.31_92  ) < 1e-2
													assert abs(result_mean.item() - 0.1_9_8_3  ) < 1e-3





						def 					snake_case__						(							self  )       ->					int:
													A__						  =				self.scheduler_classes[0]
													A__						  =				self.get_scheduler_config(prediction_type='v_prediction'  )
													A__						  =				scheduler_class(**UpperCAmelCase_  )

													scheduler.set_timesteps(self.num_inference_steps  )

													A__						  =				torch.manual_seed(0  )

													A__						  =				self.dummy_model()
													A__						  =				self.dummy_sample_deter * scheduler.init_noise_sigma
													A__						  =				sample.to(UpperCAmelCase_  )

													for i, t in enumerate(scheduler.timesteps  ):
																				A__						  =				scheduler.scale_model_input(UpperCAmelCase_		,UpperCAmelCase_  )

																				A__						  =				model(UpperCAmelCase_		,UpperCAmelCase_  )

																				A__						  =				scheduler.step(UpperCAmelCase_		,UpperCAmelCase_		,UpperCAmelCase_		,generator=UpperCAmelCase_  )
																				A__						  =				output.prev_sample

													A__						  =				torch.sum(torch.abs(UpperCAmelCase_  )  )
													A__						  =				torch.mean(torch.abs(UpperCAmelCase_  )  )

													assert abs(result_sum.item() - 1_08.44_39  ) < 1e-2
													assert abs(result_mean.item() - 0.1_4_1_2  ) < 1e-3





						def 					snake_case__						(							self  )       ->					Union[str, Any]:
													A__						  =				self.scheduler_classes[0]
													A__						  =				self.get_scheduler_config()
													A__						  =				scheduler_class(**UpperCAmelCase_  )

													scheduler.set_timesteps(self.num_inference_steps		,device=UpperCAmelCase_  )
													A__						  =				torch.manual_seed(0  )

													A__						  =				self.dummy_model()
													A__						  =				self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
													A__						  =				sample.to(UpperCAmelCase_  )

													for t in scheduler.timesteps:
																				A__						  =				scheduler.scale_model_input(UpperCAmelCase_		,UpperCAmelCase_  )

																				A__						  =				model(UpperCAmelCase_		,UpperCAmelCase_  )

																				A__						  =				scheduler.step(UpperCAmelCase_		,UpperCAmelCase_		,UpperCAmelCase_		,generator=UpperCAmelCase_  )
																				A__						  =				output.prev_sample

													A__						  =				torch.sum(torch.abs(UpperCAmelCase_  )  )
													A__						  =				torch.mean(torch.abs(UpperCAmelCase_  )  )

													assert abs(result_sum.item() - 1_52.31_92  ) < 1e-2
													assert abs(result_mean.item() - 0.1_9_8_3  ) < 1e-3













"""simple docstring"""





from dataclasses import dataclass

UpperCamelCase					=     """0.1"""
UpperCamelCase					=     """Lucia Harcekova"""







@dataclass
class 						_a  :





   '''simple docstring'''






   UpperCamelCase__     	=							42
   UpperCamelCase__     	=							42
   UpperCamelCase__     	=							42





   def __repr__(       self)			->   str:




          '''simple docstring'''




          return F"""({self.offset}, {self.length}, {self.indicator})"""







class 						_a  :





   '''simple docstring'''






   def __init__(       self					, UpperCAmelCase_ = 13					, UpperCAmelCase_ = 6)			->   None:




          '''simple docstring'''




          lowercase__:							Union[str, Any]         =     window_size
          lowercase__:							Any         =     lookahead_buffer_size
          lowercase__:							Optional[Any]         =     self.window_size - self.lookahead_buffer_size





   def __lowercase							(       self					, UpperCAmelCase_)			->   list[Token]:




          '''simple docstring'''




          lowercase__:							Any         =     []
          lowercase__:							Union[str, Any]         =     ""

          # while there are still characters in text to compress
          while text:
                 # find the next encoding phrase
                 # - triplet with offset, length, indicator (the next encoding character)
                 lowercase__:							Optional[Any]         =     self._find_encoding_token(UpperCAmelCase_					, UpperCAmelCase_)

                 # update the search buffer:
                 # - add new characters from text into it
                 # - check if size exceed the max search buffer size, if so, drop the
                 #   oldest elements
                 search_buffer += text[: token.length + 1]
                 if len(UpperCAmelCase_) > self.search_buffer_size:
                        lowercase__:							List[Any]         =     search_buffer[-self.search_buffer_size :]

                 # update the text
                 lowercase__:							List[str]         =     text[token.length + 1 :]

                 # append the token to output
                 output.append(UpperCAmelCase_)

          return output





   def __lowercase							(       self					, UpperCAmelCase_)			->   str:




          '''simple docstring'''




          lowercase__:							int         =     ""

          for token in tokens:
                 for _ in range(token.length):
                        output += output[-token.offset]
                 output += token.indicator

          return output





   def __lowercase							(       self					, UpperCAmelCase_					, UpperCAmelCase_)			->   Token:




          '''simple docstring'''




          if not text:
                 raise ValueError("We need some text to work with.")

          # Initialise result parameters to default values
          lowercase__    ,      lowercase__:							Optional[int]         =     0, 0

          if not search_buffer:
                 return Token(UpperCAmelCase_					, UpperCAmelCase_					, text[length])

          for i, character in enumerate(UpperCAmelCase_):
                 lowercase__:							Union[str, Any]         =     len(UpperCAmelCase_) - i
                 if character == text[0]:
                        lowercase__:							Optional[Any]         =     self._match_length_from_index(UpperCAmelCase_					, UpperCAmelCase_					, 0					, UpperCAmelCase_)
                        # if the found length is bigger than the current or if it's equal,
                        # which means it's offset is smaller: update offset and length
                        if found_length >= length:
                               lowercase__    ,      lowercase__:							Any         =     found_offset, found_length

          return Token(UpperCAmelCase_					, UpperCAmelCase_					, text[length])





   def __lowercase							(       self					, UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_					, UpperCAmelCase_)			->   int:




          '''simple docstring'''




          if not text or text[text_index] != window[window_index]:
                 return 0
          return 1 + self._match_length_from_index(
              UpperCAmelCase_					, window + text[text_index]					, text_index + 1					, window_index + 1)


if __name__ == "__main__":
  from doctest import testmod

  testmod()
  # Initialize compressor class
  UpperCamelCase					=     LZaaCompressor(window_size=13, lookahead_buffer_size=6)

  # Example
  UpperCamelCase					=     """cabracadabrarrarrad"""
  UpperCamelCase					=     lzaa_compressor.compress(TEXT)
  print(lzaa_compressor.compress("""ababcbababaa"""))
  UpperCamelCase					=     lzaa_compressor.decompress(compressed_text)
  assert decompressed_text == TEXT, "The LZ77 algorithm returned the invalid result."






