




import math
import sys







def 			lowerCamelCase_		(       UpperCamelCase__				:      str      )     ->       str:





				"""simple docstring"""


				__lowerCamelCase		       =    ""
				try:
								with open(_UpperCAmelCase ,     'rb'      ) as binary_file:
												__lowerCamelCase		       =    binary_file.read()
								for dat in data:
												__lowerCamelCase		       =    F"""{dat:08b}"""
												result += curr_byte
								return result
				except OSError:
								print('File not accessible'      )
								sys.exit()







def 			lowerCamelCase_		(       UpperCamelCase__				:      str      )     ->       str:





				"""simple docstring"""


				__lowerCamelCase		       =    {"0": "0", "1": "1"}
				__lowerCamelCase		       =    "", ""
				__lowerCamelCase		       =    len(_UpperCAmelCase      )

				for i in range(len(_UpperCAmelCase      )      ):
								curr_string += data_bits[i]
								if curr_string not in lexicon:
												continue

								__lowerCamelCase		       =    lexicon[curr_string]
								result += last_match_id
								__lowerCamelCase		       =    last_match_id + "0"

								if math.loga(_UpperCAmelCase      ).is_integer():
												__lowerCamelCase		       =    {}
												for curr_key in list(_UpperCAmelCase      ):
																__lowerCamelCase		       =    lexicon.pop(_UpperCAmelCase      )
												__lowerCamelCase		       =    new_lex

								__lowerCamelCase		       =    last_match_id + "1"
								index += 1
								__lowerCamelCase		       =    ""
				return result







def 			lowerCamelCase_		(       UpperCamelCase__				:      str ,     UpperCamelCase__				:      str      )     ->       None:





				"""simple docstring"""


				__lowerCamelCase		       =    8
				try:
								with open(_UpperCAmelCase ,     'wb'      ) as opened_file:
												__lowerCamelCase		       =    [
												    to_write[i : i + byte_length]
												    for i in range(0 ,     len(_UpperCAmelCase      ) ,     _UpperCAmelCase      )
												]

												if len(result_byte_array[-1]      ) % byte_length == 0:
																result_byte_array.append('10000000'      )
												else:
																result_byte_array[-1] += "1" + "0" * (
																    byte_length - len(result_byte_array[-1]      ) - 1
																)

												for elem in result_byte_array[:-1]:
																opened_file.write(int(_UpperCAmelCase ,     2      ).to_bytes(1 ,     byteorder='big'      )      )
				except OSError:
								print('File not accessible'      )
								sys.exit()







def 			lowerCamelCase_		(       UpperCamelCase__				:      str      )     ->       str:





				"""simple docstring"""


				__lowerCamelCase		       =    0
				for letter in data_bits:
								if letter == "1":
												break
								counter += 1

				__lowerCamelCase		       =    data_bits[counter:]
				__lowerCamelCase		       =    data_bits[counter + 1 :]
				return data_bits







def 			lowerCamelCase_		(       UpperCamelCase__				:      str ,     UpperCamelCase__				:      str      )     ->       None:





				"""simple docstring"""


				__lowerCamelCase		       =    read_file_binary(_UpperCAmelCase      )
				__lowerCamelCase		       =    remove_prefix(_UpperCAmelCase      )
				__lowerCamelCase		       =    decompress_data(_UpperCAmelCase      )
				write_file_binary(_UpperCAmelCase ,     _UpperCAmelCase      )


if __name__ == "__main__":
					compress(sys.argv[1], sys.argv[2])



'''simple docstring'''




import math
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput

class 	lowerCamelCase_	(snake_case__							, snake_case__    ):
      '''simple docstring'''

      __UpperCamelCase:  Optional[Any]						=						1




      @register_to_config
      def __init__(       self						:				Optional[int]						,					A						:				int = 1000						,					A						:				Optional[Union[np.ndarray, List[float]]] = None       ):
             # set `betas`, `alphas`, `timesteps`
             self.set_timesteps(A       )

             # standard deviation of the initial noise distribution
             _UpperCAmelCase     :  int							      =    1.0

             # For now we only support F-PNDM, i.e. the runge-kutta method
             # For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
             # mainly at formula (9), (12), (13) and the Algorithm 2.
             _UpperCAmelCase     :  int							      =    4

             # running values
             _UpperCAmelCase     :  Dict							      =    []




      def      _A	(       self						:				Optional[int]						,					A						:				int						,					A						:				Union[str, torch.device] = None       ):
             _UpperCAmelCase     :  int							      =    num_inference_steps
             _UpperCAmelCase     :  Union[str, Any]							      =    torch.linspace(1						,					0						,					num_inference_steps + 1       )[:-1]
             _UpperCAmelCase     :  Any							      =    torch.cat([steps, torch.tensor([0.0]       )]       )

             if self.config.trained_betas is not None:
                    _UpperCAmelCase     :  str							      =    torch.tensor(self.config.trained_betas						,					dtype=torch.floataa       )
             else:
                    _UpperCAmelCase     :  Dict							      =    torch.sin(steps * math.pi / 2       ) ** 2

             _UpperCAmelCase     :  List[Any]							      =    (1.0 - self.betas**2) ** 0.5

             _UpperCAmelCase     :  List[str]							      =    (torch.atana(self.betas						,					self.alphas       ) / math.pi * 2)[:-1]
             _UpperCAmelCase     :  Dict							      =    timesteps.to(A       )

             _UpperCAmelCase     :  Dict							      =    []




      def      _A	(       self						:				Optional[int]						,					A						:				torch.FloatTensor						,					A						:				int						,					A						:				torch.FloatTensor						,					A						:				bool = True						,					):
             if self.num_inference_steps is None:
                    raise ValueError(
                        "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"       )

             _UpperCAmelCase     :  Tuple							      =    (self.timesteps == timestep).nonzero().item()
             _UpperCAmelCase     :  Optional[Any]							      =    timestep_index + 1

             _UpperCAmelCase     :  int							      =    sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
             self.ets.append(A       )

             if len(self.ets       ) == 1:
                    _UpperCAmelCase     :  List[Any]							      =    self.ets[-1]
             elif len(self.ets       ) == 2:
                    _UpperCAmelCase     :  str							      =    (3 * self.ets[-1] - self.ets[-2]) / 2
             elif len(self.ets       ) == 3:
                    _UpperCAmelCase     :  Tuple							      =    (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
             else:
                    _UpperCAmelCase     :  Union[str, Any]							      =    (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])

             _UpperCAmelCase     :  Union[str, Any]							      =    self._get_prev_sample(A						,					A						,					A						,					A       )

             if not return_dict:
                    return (prev_sample,)

             return SchedulerOutput(prev_sample=A       )




      def      _A	(       self						:				Union[str, Any]						,					A						:				torch.FloatTensor						,					*A						:				Union[str, Any]						,					**A						:				Dict       ):
             return sample




      def      _A	(       self						:				Optional[Any]						,					A						:				Optional[int]						,					A						:				int						,					A						:				Optional[Any]						,					A						:				List[str]       ):
             _UpperCAmelCase     :  List[str]							      =    self.alphas[timestep_index]
             _UpperCAmelCase     :  List[Any]							      =    self.betas[timestep_index]

             _UpperCAmelCase     :  Optional[Any]							      =    self.alphas[prev_timestep_index]
             _UpperCAmelCase     :  Dict							      =    self.betas[prev_timestep_index]

             _UpperCAmelCase     :  Tuple							      =    (sample - sigma * ets) / max(A						,					1E-8       )
             _UpperCAmelCase     :  List[str]							      =    next_alpha * pred + ets * next_sigma

             return prev_sample





      def __len__(       self						:				Union[str, Any]       ):
             return self.config.num_train_timesteps

