

import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class 						__magic_name__      (							UpperCAmelCase__							):

			'''simple docstring'''




			lowerCamelCase__			:     Tuple    =       (EulerAncestralDiscreteScheduler,)
			lowerCamelCase__			:     str    =       10



			def 			_UpperCAmelCase			(    self,   **lowercase_	)							->   Optional[Any]:






					"""simple docstring"""






					a__  ={
					    'num_train_timesteps': 1100,
					    'beta_start': 0.0001,
					    'beta_end': 0.02,
					    'beta_schedule': 'linear',
					}

					config.update(**snake_case_	)
					return config



			def 			_UpperCAmelCase			(    self	)							->   Union[str, Any]:






					"""simple docstring"""






					for timesteps in [10, 50, 100, 1000]:
							self.check_over_configs(num_train_timesteps=snake_case_	)



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






					"""simple docstring"""






					for beta_start, beta_end in zip([0.00001, 0.0001, 0.001],   [0.0002, 0.002, 0.02]	):
							self.check_over_configs(beta_start=snake_case_,   beta_end=snake_case_	)



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






					"""simple docstring"""






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



			def 			_UpperCAmelCase			(    self	)							->   int:






					"""simple docstring"""






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



			def 			_UpperCAmelCase			(    self	)							->   Union[str, Any]:






					"""simple docstring"""






					a__  =self.scheduler_classes[0]
					a__  =self.get_scheduler_config()
					a__  =scheduler_class(**snake_case_	)

					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(snake_case_	)

					for i, t in enumerate(scheduler.timesteps	):
							a__  =scheduler.scale_model_input(snake_case_,   snake_case_	)

							a__  =model(snake_case_,   snake_case_	)

							a__  =scheduler.step(snake_case_,   snake_case_,   snake_case_,   generator=snake_case_	)
							a__  =output.prev_sample

					a__  =torch.sum(torch.abs(snake_case_	)	)
					a__  =torch.mean(torch.abs(snake_case_	)	)

					assert abs(result_sum.item() - 152.3192	) < 1E-2
					assert abs(result_mean.item() - 0.1983	) < 1E-3



			def 			_UpperCAmelCase			(    self	)							->   str:






					"""simple docstring"""






					a__  =self.scheduler_classes[0]
					a__  =self.get_scheduler_config(prediction_type='''v_prediction'''	)
					a__  =scheduler_class(**snake_case_	)

					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(snake_case_	)

					for i, t in enumerate(scheduler.timesteps	):
							a__  =scheduler.scale_model_input(snake_case_,   snake_case_	)

							a__  =model(snake_case_,   snake_case_	)

							a__  =scheduler.step(snake_case_,   snake_case_,   snake_case_,   generator=snake_case_	)
							a__  =output.prev_sample

					a__  =torch.sum(torch.abs(snake_case_	)	)
					a__  =torch.mean(torch.abs(snake_case_	)	)

					assert abs(result_sum.item() - 108.4439	) < 1E-2
					assert abs(result_mean.item() - 0.1412	) < 1E-3



			def 			_UpperCAmelCase			(    self	)							->   str:






					"""simple docstring"""






					a__  =self.scheduler_classes[0]
					a__  =self.get_scheduler_config()
					a__  =scheduler_class(**snake_case_	)

					scheduler.set_timesteps(self.num_inference_steps,   device=snake_case_	)
					a__  =torch.manual_seed(0	)

					a__  =self.dummy_model()
					a__  =self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
					a__  =sample.to(snake_case_	)

					for t in scheduler.timesteps:
							a__  =scheduler.scale_model_input(snake_case_,   snake_case_	)

							a__  =model(snake_case_,   snake_case_	)

							a__  =scheduler.step(snake_case_,   snake_case_,   snake_case_,   generator=snake_case_	)
							a__  =output.prev_sample

					a__  =torch.sum(torch.abs(snake_case_	)	)
					a__  =torch.mean(torch.abs(snake_case_	)	)

					assert abs(result_sum.item() - 152.3192	) < 1E-2
					assert abs(result_mean.item() - 0.1983	) < 1E-3



from dataclasses import dataclass

lowerCamelCase									= '''0.1'''
lowerCamelCase									= '''Lucia Harcekova'''







@dataclass
class 						__magic_name__      :

   '''simple docstring'''




   lowerCamelCase__			:     int
   lowerCamelCase__			:     int
   lowerCamelCase__			:     str



   def __repr__(    self	)							->   str:






     """simple docstring"""






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







class 						__magic_name__      :

   '''simple docstring'''




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






     """simple docstring"""






     a__  =window_size
     a__  =lookahead_buffer_size
     a__  =self.window_size - self.lookahead_buffer_size



   def 			_UpperCAmelCase			(    self,   lowercase_	)							->   list[Token]:






     """simple docstring"""






     a__  =[]
     a__  =''''''

     # 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)
       a__  =self._find_encoding_token(lowercase_,   lowercase_	)

       # 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(lowercase_	) > self.search_buffer_size:
         a__  =search_buffer[-self.search_buffer_size :]

       # update the text
       a__  =text[token.length + 1 :]

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

     return output



   def 			_UpperCAmelCase			(    self,   lowercase_	)							->   str:






     """simple docstring"""






     a__  =''''''

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

     return output



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_	)							->   Token:






     """simple docstring"""






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

     # Initialise result parameters to default values
     a__,				a__  =0, 0

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

     for i, character in enumerate(lowercase_	):
       a__  =len(lowercase_	) - i
       if character == text[0]:
         a__  =self._match_length_from_index(lowercase_,   lowercase_,   0,   lowercase_	)
         # 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:
           a__,				a__  =found_offset, found_length

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



   def 			_UpperCAmelCase			(    self,   lowercase_,   lowercase_,   lowercase_,   lowercase_	)							->   int:






     """simple docstring"""






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


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

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

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