





"""simple docstring"""





import os
from collections import deque

import torch
from torch.utils.data import Dataset
class 		SCREAMING_SNAKE_CASE_							(    snake_case__   ):
				"""simple docstring"""




				def __init__(     self  :Tuple						,    __lowercase  :Union[str, Any]=""						,    __lowercase  :List[str]="train"			):
											assert os.path.isdir(__lowercase			)

											__lowerCamelCase      :      Dict         =[]
											__lowerCamelCase      :      str         =os.listdir(__lowercase			)
											for story_filename in story_filenames_list:
																		if "summary" in story_filename:
																									continue
																		__lowerCamelCase      :      int         =os.path.join(__lowercase						,    __lowercase			)
																		if not os.path.isfile(__lowercase			):
																									continue
																		self.documents.append(__lowercase			)




				def __len__(     self  :Any			):
											return len(self.documents			)




				def __getitem__(     self  :Tuple						,    __lowercase  :Union[str, Any]			):
											__lowerCamelCase      :      Any         =self.documents[idx]
											__lowerCamelCase      :      Optional[Any]         =document_path.split('''/'''			)[-1]
											with open(__lowercase						,    encoding='''utf-8'''			) as source:
																		__lowerCamelCase      :      Union[str, Any]         =source.read()
																		__lowerCamelCase    ,						__lowerCamelCase      :      Tuple         =process_story(__lowercase			)
											return document_name, story_lines, summary_lines



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Union[str, Any]   ):


							'''simple docstring'''







							__lowerCamelCase      :      List[Any]         =list(filter(lambda SCREAMING_SNAKE_CASE   : len(SCREAMING_SNAKE_CASE   ) != 0    ,						[line.strip() for line in raw_story.split('''\n'''   )]   )   )

							# for some unknown reason some lines miss a period, add it
							__lowerCamelCase      :      Optional[int]         =[_add_missing_period(SCREAMING_SNAKE_CASE   ) for line in nonempty_lines]

							# gather article lines
							__lowerCamelCase      :      Union[str, Any]         =[]
							__lowerCamelCase      :      Union[str, Any]         =deque(SCREAMING_SNAKE_CASE   )
							while True:
														try:
																					__lowerCamelCase      :      Optional[int]         =lines.popleft()
																					if element.startswith('''@highlight'''   ):
																												break
																					story_lines.append(SCREAMING_SNAKE_CASE   )
														except IndexError:
																					# if "@highlight" is absent from the file we pop
																					# all elements until there is None, raising an exception.
																					return story_lines, []

    # gather summary lines
							__lowerCamelCase      :      Dict         =list(filter(lambda SCREAMING_SNAKE_CASE   : not t.startswith('''@highlight'''   )    ,						SCREAMING_SNAKE_CASE   )   )

							return story_lines, summary_lines



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							int   ):


							'''simple docstring'''







							__lowerCamelCase      :      Union[str, Any]         =['''.''', '''!''', '''?''', '''...''', '''\'''', '''`''', '''"''', '''\u2019''', '''\u2019''', ''')''']
							if line.startswith('''@highlight'''   ):
														return line
							if line[-1] in END_TOKENS:
														return line
							return line + "."



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							str    ,						SCREAMING_SNAKE_CASE       :							Optional[int]    ,						SCREAMING_SNAKE_CASE       :							str   ):


							'''simple docstring'''







							if len(SCREAMING_SNAKE_CASE   ) > block_size:
														return sequence[:block_size]
							else:
														sequence.extend([pad_token_id] * (block_size - len(SCREAMING_SNAKE_CASE   ))   )
														return sequence



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							str    ,						SCREAMING_SNAKE_CASE       :							Union[str, Any]   ):


							'''simple docstring'''







							__lowerCamelCase      :      Optional[int]         =torch.ones_like(SCREAMING_SNAKE_CASE   )
							__lowerCamelCase      :      str         =sequence == pad_token_id
							__lowerCamelCase      :      Optional[Any]         =0
							return mask



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Optional[int]    ,						SCREAMING_SNAKE_CASE       :							Optional[int]    ,						SCREAMING_SNAKE_CASE       :							Tuple   ):


							'''simple docstring'''







							__lowerCamelCase      :      Union[str, Any]         =[tokenizer.encode(SCREAMING_SNAKE_CASE   ) for line in story_lines]
							__lowerCamelCase      :      List[str]         =[token for sentence in story_lines_token_ids for token in sentence]
							__lowerCamelCase      :      int         =[tokenizer.encode(SCREAMING_SNAKE_CASE   ) for line in summary_lines]
							__lowerCamelCase      :      Union[str, Any]         =[token for sentence in summary_lines_token_ids for token in sentence]

							return story_token_ids, summary_token_ids



def 				lowerCAmelCase_			(      SCREAMING_SNAKE_CASE       :							Dict    ,						SCREAMING_SNAKE_CASE       :							Dict   ):


							'''simple docstring'''







							__lowerCamelCase      :      int         =[]
							for sequence in batch:
														__lowerCamelCase      :      str         =-1
														__lowerCamelCase      :      str         =[]
														for s in sequence:
																					if s == separator_token_id:
																												sentence_num += 1
																					embeddings.append(sentence_num % 2   )
														batch_embeddings.append(SCREAMING_SNAKE_CASE   )
							return torch.tensor(SCREAMING_SNAKE_CASE   )











"""simple docstring"""





import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest
class 		SCREAMING_SNAKE_CASE_							(    snake_case__   ):
				"""simple docstring"""
				__snake_case   :       Optional[Any]	 =		(UnCLIPScheduler,)




				def 					__lowercase  (     self  :Any						,    **__lowercase  :str			):
											__lowerCamelCase      :      Optional[int]         ={
											    '''num_train_timesteps''': 1000,
											    '''variance_type''': '''fixed_small_log''',
											    '''clip_sample''': True,
											    '''clip_sample_range''': 1.0,
											    '''prediction_type''': '''epsilon''',
											}

											config.update(**__lowercase			)
											return config




				def 					__lowercase  (     self  :Tuple			):
											for timesteps in [1, 5, 100, 1000]:
																		self.check_over_configs(num_train_timesteps=__lowercase			)




				def 					__lowercase  (     self  :Tuple			):
											for variance in ["fixed_small_log", "learned_range"]:
																		self.check_over_configs(variance_type=__lowercase			)




				def 					__lowercase  (     self  :List[str]			):
											for clip_sample in [True, False]:
																		self.check_over_configs(clip_sample=__lowercase			)




				def 					__lowercase  (     self  :List[Any]			):
											for clip_sample_range in [1, 5, 10, 20]:
																		self.check_over_configs(clip_sample_range=__lowercase			)




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




				def 					__lowercase  (     self  :Union[str, Any]			):
											for time_step in [0, 500, 999]:
																		for prev_timestep in [None, 5, 100, 250, 500, 750]:
																									if prev_timestep is not None and prev_timestep >= time_step:
																																continue

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




				def 					__lowercase  (     self  :Union[str, Any]			):
											__lowerCamelCase      :      List[Any]         =self.scheduler_classes[0]
											__lowerCamelCase      :      str         =self.get_scheduler_config(variance_type='''fixed_small_log'''			)
											__lowerCamelCase      :      Tuple         =scheduler_class(**__lowercase			)

											assert torch.sum(torch.abs(scheduler._get_variance(0			) - 1.0_0_0_0e-1_0			)			) < 1e-5
											assert torch.sum(torch.abs(scheduler._get_variance(487			) - 0.0549625			)			) < 1e-5
											assert torch.sum(torch.abs(scheduler._get_variance(999			) - 0.9994987			)			) < 1e-5




				def 					__lowercase  (     self  :Optional[Any]			):
											__lowerCamelCase      :      Tuple         =self.scheduler_classes[0]
											__lowerCamelCase      :      Optional[Any]         =self.get_scheduler_config(variance_type='''learned_range'''			)
											__lowerCamelCase      :      Optional[Any]         =scheduler_class(**__lowercase			)

											__lowerCamelCase      :      Optional[int]         =0.5

											assert scheduler._get_variance(1						,    predicted_variance=__lowercase			) - -10.1712790 < 1e-5
											assert scheduler._get_variance(487						,    predicted_variance=__lowercase			) - -5.7998052 < 1e-5
											assert scheduler._get_variance(999						,    predicted_variance=__lowercase			) - -0.0010011 < 1e-5




				def 					__lowercase  (     self  :Optional[Any]			):
											__lowerCamelCase      :      Any         =self.scheduler_classes[0]
											__lowerCamelCase      :      Optional[int]         =self.get_scheduler_config()
											__lowerCamelCase      :      str         =scheduler_class(**__lowercase			)

											__lowerCamelCase      :      Tuple         =scheduler.timesteps

											__lowerCamelCase      :      Any         =self.dummy_model()
											__lowerCamelCase      :      Any         =self.dummy_sample_deter
											__lowerCamelCase      :      List[Any]         =torch.manual_seed(0			)

											for i, t in enumerate(__lowercase			):
																		# 1. predict noise residual
																		__lowerCamelCase      :      Tuple         =model(__lowercase						,    __lowercase			)

																		# 2. predict previous mean of sample x_t-1
																		__lowerCamelCase      :      List[Any]         =scheduler.step(__lowercase						,    __lowercase						,    __lowercase						,    generator=__lowercase			).prev_sample

																		__lowerCamelCase      :      List[Any]         =pred_prev_sample

											__lowerCamelCase      :      Optional[Any]         =torch.sum(torch.abs(__lowercase			)			)
											__lowerCamelCase      :      Optional[int]         =torch.mean(torch.abs(__lowercase			)			)

											assert abs(result_sum.item() - 252.2682495			) < 1e-2
											assert abs(result_mean.item() - 0.3284743			) < 1e-3




				def 					__lowercase  (     self  :int			):
											__lowerCamelCase      :      Optional[Any]         =self.scheduler_classes[0]
											__lowerCamelCase      :      Any         =self.get_scheduler_config()
											__lowerCamelCase      :      Optional[Any]         =scheduler_class(**__lowercase			)

											scheduler.set_timesteps(25			)

											__lowerCamelCase      :      Dict         =scheduler.timesteps

											__lowerCamelCase      :      List[str]         =self.dummy_model()
											__lowerCamelCase      :      List[str]         =self.dummy_sample_deter
											__lowerCamelCase      :      Any         =torch.manual_seed(0			)

											for i, t in enumerate(__lowercase			):
																		# 1. predict noise residual
																		__lowerCamelCase      :      Optional[Any]         =model(__lowercase						,    __lowercase			)

																		if i + 1 == timesteps.shape[0]:
																									__lowerCamelCase      :      Any         =None
																		else:
																									__lowerCamelCase      :      Union[str, Any]         =timesteps[i + 1]

																		# 2. predict previous mean of sample x_t-1
																		__lowerCamelCase      :      List[str]         =scheduler.step(
																		    __lowercase						,    __lowercase						,    __lowercase						,    prev_timestep=__lowercase						,    generator=__lowercase			).prev_sample

																		__lowerCamelCase      :      int         =pred_prev_sample

											__lowerCamelCase      :      List[Any]         =torch.sum(torch.abs(__lowercase			)			)
											__lowerCamelCase      :      List[str]         =torch.mean(torch.abs(__lowercase			)			)

											assert abs(result_sum.item() - 258.2044983			) < 1e-2
											assert abs(result_mean.item() - 0.3362038			) < 1e-3




				def 					__lowercase  (     self  :List[str]			):
											pass




				def 					__lowercase  (     self  :Tuple			):
											pass




