





"""simple docstring"""
import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest






class 					_UpperCAmelCase   (	lowercase_ ):
					UpperCamelCase      =							(UnCLIPScheduler,)


					def 	lowerCamelCase       (					self			:Tuple     ,       **__UpperCamelCase			:Union[str, Any]		):
												A		=  {
												    "num_train_timesteps": 10_00,
												    "variance_type": "fixed_small_log",
												    "clip_sample": True,
												    "clip_sample_range": 1.0,
												    "prediction_type": "epsilon",
												}

												config.update(**__UpperCamelCase		)
												return config


					def 	lowerCamelCase       (					self			:List[str]		):
												for timesteps in [1, 5, 1_00, 10_00]:
																			self.check_over_configs(num_train_timesteps=__UpperCamelCase		)


					def 	lowerCamelCase       (					self			:str		):
												for variance in ["fixed_small_log", "learned_range"]:
																			self.check_over_configs(variance_type=__UpperCamelCase		)


					def 	lowerCamelCase       (					self			:Optional[int]		):
												for clip_sample in [True, False]:
																			self.check_over_configs(clip_sample=__UpperCamelCase		)


					def 	lowerCamelCase       (					self			:Optional[Any]		):
												for clip_sample_range in [1, 5, 10, 20]:
																			self.check_over_configs(clip_sample_range=__UpperCamelCase		)


					def 	lowerCamelCase       (					self			:List[Any]		):
												for prediction_type in ["epsilon", "sample"]:
																			self.check_over_configs(prediction_type=__UpperCamelCase		)


					def 	lowerCamelCase       (					self			:List[Any]		):
												for time_step in [0, 5_00, 9_99]:
																			for prev_timestep in [None, 5, 1_00, 2_50, 5_00, 7_50]:
																										if prev_timestep is not None and prev_timestep >= time_step:
																																	continue

																										self.check_over_forward(time_step=__UpperCamelCase     ,       prev_timestep=__UpperCamelCase		)


					def 	lowerCamelCase       (					self			:List[str]		):
												A		=  self.scheduler_classes[0]
												A		=  self.get_scheduler_config(variance_type="fixed_small_log"		)
												A		=  scheduler_class(**__UpperCamelCase		)

												assert torch.sum(torch.abs(scheduler._get_variance(0		) - 1.0_000e-10		)		) < 1e-5
												assert torch.sum(torch.abs(scheduler._get_variance(4_87		) - 0.0_549_625		)		) < 1e-5
												assert torch.sum(torch.abs(scheduler._get_variance(9_99		) - 0.9_994_987		)		) < 1e-5


					def 	lowerCamelCase       (					self			:int		):
												A		=  self.scheduler_classes[0]
												A		=  self.get_scheduler_config(variance_type="learned_range"		)
												A		=  scheduler_class(**__UpperCamelCase		)

												A		=  0.5

												assert scheduler._get_variance(1     ,       predicted_variance=__UpperCamelCase		) - -10.1_712_790 < 1e-5
												assert scheduler._get_variance(4_87     ,       predicted_variance=__UpperCamelCase		) - -5.7_998_052 < 1e-5
												assert scheduler._get_variance(9_99     ,       predicted_variance=__UpperCamelCase		) - -0.0_010_011 < 1e-5


					def 	lowerCamelCase       (					self			:List[Any]		):
												A		=  self.scheduler_classes[0]
												A		=  self.get_scheduler_config()
												A		=  scheduler_class(**__UpperCamelCase		)

												A		=  scheduler.timesteps

												A		=  self.dummy_model()
												A		=  self.dummy_sample_deter
												A		=  torch.manual_seed(0		)

												for i, t in enumerate(__UpperCamelCase		):
																			# 1. predict noise residual
																			A		=  model(__UpperCamelCase     ,       __UpperCamelCase		)

																			# 2. predict previous mean of sample x_t-1
																			A		=  scheduler.step(__UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase     ,       generator=__UpperCamelCase		).prev_sample

																			A		=  pred_prev_sample

												A		=  torch.sum(torch.abs(__UpperCamelCase		)		)
												A		=  torch.mean(torch.abs(__UpperCamelCase		)		)

												assert abs(result_sum.item() - 252.2_682_495		) < 1e-2
												assert abs(result_mean.item() - 0.3_284_743		) < 1e-3


					def 	lowerCamelCase       (					self			:int		):
												A		=  self.scheduler_classes[0]
												A		=  self.get_scheduler_config()
												A		=  scheduler_class(**__UpperCamelCase		)

												scheduler.set_timesteps(25		)

												A		=  scheduler.timesteps

												A		=  self.dummy_model()
												A		=  self.dummy_sample_deter
												A		=  torch.manual_seed(0		)

												for i, t in enumerate(__UpperCamelCase		):
																			# 1. predict noise residual
																			A		=  model(__UpperCamelCase     ,       __UpperCamelCase		)

																			if i + 1 == timesteps.shape[0]:
																										A		=  None
																			else:
																										A		=  timesteps[i + 1]

																			# 2. predict previous mean of sample x_t-1
																			A		=  scheduler.step(
																			    __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase     ,       prev_timestep=__UpperCamelCase     ,       generator=__UpperCamelCase		).prev_sample

																			A		=  pred_prev_sample

												A		=  torch.sum(torch.abs(__UpperCamelCase		)		)
												A		=  torch.mean(torch.abs(__UpperCamelCase		)		)

												assert abs(result_sum.item() - 258.2_044_983		) < 1e-2
												assert abs(result_mean.item() - 0.3_362_038		) < 1e-3


					def 	lowerCamelCase       (					self			:Any		):
												pass


					def 	lowerCamelCase       (					self			:Dict		):
												pass









"""simple docstring"""
import math
import sys






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






def 					A__				(			UpperCamelCase       ):
							A		=  {"0": "0", "1": "1"}
							A,	A		=  "", ""
							A		=  len(UpperCamelCase       )

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

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

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

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






def 					A__				(			UpperCamelCase					,  UpperCamelCase       ):
							A		=  8
							try:
														with open(UpperCamelCase					,  "wb"       ) as opened_file:
																					A		=  [
																					    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 					A__				(			UpperCamelCase       ):
							A		=  0
							for letter in data_bits:
														if letter == "1":
																					break
														counter += 1

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






def 					A__				(			UpperCamelCase					,  UpperCamelCase       ):
							A		=  read_file_binary(UpperCamelCase       )
							A		=  remove_prefix(UpperCamelCase       )
							A		=  decompress_data(UpperCamelCase       )
							write_file_binary(UpperCamelCase					,  UpperCamelCase       )


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


