


'''simple docstring'''
import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest
class        lowercase     (							A__ ):

		"""simple docstring"""

		_a   			=    (DDIMParallelScheduler,)
		_a   			=    (('eta', 0.0), ('num_inference_steps', 50))




		def    lowerCAmelCase__    (		self   ,       **UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__       :List[Any]							 =			{
							    '''num_train_timesteps''': 1000,
							    '''beta_start''': 0.0001,
							    '''beta_end''': 0.02,
							    '''beta_schedule''': '''linear''',
							    '''clip_sample''': True,
							}

							config.update(**UpperCamelCase_							)
							return config




		def    lowerCAmelCase__    (		self   ,       **UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__       :str							 =			self.scheduler_classes[0]
							UpperCamelCase__       :Optional[Any]							 =			self.get_scheduler_config(**UpperCamelCase_							)
							UpperCamelCase__       :Optional[Any]							 =			scheduler_class(**UpperCamelCase_							)

							UpperCamelCase__ ,    UpperCamelCase__       :Optional[int]							 =			10, 0.0

							UpperCamelCase__       :List[str]							 =			self.dummy_model()
							UpperCamelCase__       :List[str]							 =			self.dummy_sample_deter

							scheduler.set_timesteps(UpperCamelCase_							)

							for t in scheduler.timesteps:
												UpperCamelCase__       :Optional[int]							 =			model(UpperCamelCase_   ,       UpperCamelCase_							)
												UpperCamelCase__       :List[Any]							 =			scheduler.step(UpperCamelCase_   ,       UpperCamelCase_   ,       UpperCamelCase_   ,       UpperCamelCase_							).prev_sample

							return sample




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							for timesteps in [100, 500, 1000]:
												self.check_over_configs(num_train_timesteps=UpperCamelCase_							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							for steps_offset in [0, 1]:
												self.check_over_configs(steps_offset=UpperCamelCase_							)

							UpperCamelCase__       :List[str]							 =			self.scheduler_classes[0]
							UpperCamelCase__       :List[str]							 =			self.get_scheduler_config(steps_offset=1							)
							UpperCamelCase__       :str							 =			scheduler_class(**UpperCamelCase_							)
							scheduler.set_timesteps(5							)
							assert torch.equal(scheduler.timesteps   ,       torch.LongTensor([801, 601, 401, 201, 1]							)							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1]   ,       [0.002, 0.02, 0.2, 2]							):
												self.check_over_configs(beta_start=UpperCamelCase_   ,       beta_end=UpperCamelCase_							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							for schedule in ["linear", "squaredcos_cap_v2"]:
												self.check_over_configs(beta_schedule=UpperCamelCase_							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


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




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							for clip_sample in [True, False]:
												self.check_over_configs(clip_sample=UpperCamelCase_							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							for timestep_spacing in ["trailing", "leading"]:
												self.check_over_configs(timestep_spacing=UpperCamelCase_							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							for rescale_betas_zero_snr in [True, False]:
												self.check_over_configs(rescale_betas_zero_snr=UpperCamelCase_							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							self.check_over_configs(thresholding=UpperCamelCase_							)
							for threshold in [0.5, 1.0, 2.0]:
												for prediction_type in ["epsilon", "v_prediction"]:
																	self.check_over_configs(
																	    thresholding=UpperCamelCase_   ,       prediction_type=UpperCamelCase_   ,       sample_max_value=UpperCamelCase_   ,       )




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							for t in [1, 10, 49]:
												self.check_over_forward(time_step=UpperCamelCase_							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							for t, num_inference_steps in zip([1, 10, 50]   ,       [10, 50, 500]							):
												self.check_over_forward(time_step=UpperCamelCase_   ,       num_inference_steps=UpperCamelCase_							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							for t, eta in zip([1, 10, 49]   ,       [0.0, 0.5, 1.0]							):
												self.check_over_forward(time_step=UpperCamelCase_   ,       eta=UpperCamelCase_							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :Union[str, Any]							 =			self.scheduler_classes[0]
							UpperCamelCase__       :List[str]							 =			self.get_scheduler_config()
							UpperCamelCase__       :int							 =			scheduler_class(**UpperCamelCase_							)

							assert torch.sum(torch.abs(scheduler._get_variance(0   ,       0							) - 0.0							)							) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(420   ,       400							) - 0.14771							)							) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(980   ,       960							) - 0.32460							)							) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(0   ,       0							) - 0.0							)							) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(487   ,       486							) - 0.00979							)							) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(999   ,       998							) - 0.02							)							) < 1e-5




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :Tuple							 =			self.scheduler_classes[0]
							UpperCamelCase__       :Dict							 =			self.get_scheduler_config()
							UpperCamelCase__       :Any							 =			scheduler_class(**UpperCamelCase_							)

							UpperCamelCase__ ,    UpperCamelCase__       :List[str]							 =			10, 0.0
							scheduler.set_timesteps(UpperCamelCase_							)

							UpperCamelCase__       :Optional[int]							 =			self.dummy_model()
							UpperCamelCase__       :List[Any]							 =			self.dummy_sample_deter
							UpperCamelCase__       :int							 =			self.dummy_sample_deter + 0.1
							UpperCamelCase__       :List[str]							 =			self.dummy_sample_deter - 0.1

							UpperCamelCase__       :str							 =			samplea.shape[0]
							UpperCamelCase__       :Optional[Any]							 =			torch.stack([samplea, samplea, samplea]   ,       dim=0							)
							UpperCamelCase__       :List[Any]							 =			torch.arange(UpperCamelCase_							)[0:3, None].repeat(1   ,       UpperCamelCase_							)

							UpperCamelCase__       :Dict							 =			model(samples.flatten(0   ,       1							)   ,       timesteps.flatten(0   ,       1							)							)
							UpperCamelCase__       :Any							 =			scheduler.batch_step_no_noise(UpperCamelCase_   ,       timesteps.flatten(0   ,       1							)   ,       samples.flatten(0   ,       1							)   ,       UpperCamelCase_							)

							UpperCamelCase__       :Dict							 =			torch.sum(torch.abs(UpperCamelCase_							)							)
							UpperCamelCase__       :Any							 =			torch.mean(torch.abs(UpperCamelCase_							)							)

							assert abs(result_sum.item() - 1147.7904							) < 1e-2
							assert abs(result_mean.item() - 0.4982							) < 1e-3




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :int							 =			self.full_loop()

							UpperCamelCase__       :List[str]							 =			torch.sum(torch.abs(UpperCamelCase_							)							)
							UpperCamelCase__       :Any							 =			torch.mean(torch.abs(UpperCamelCase_							)							)

							assert abs(result_sum.item() - 172.0067							) < 1e-2
							assert abs(result_mean.item() - 0.223967							) < 1e-3




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :Any							 =			self.full_loop(prediction_type='''v_prediction'''							)

							UpperCamelCase__       :Dict							 =			torch.sum(torch.abs(UpperCamelCase_							)							)
							UpperCamelCase__       :str							 =			torch.mean(torch.abs(UpperCamelCase_							)							)

							assert abs(result_sum.item() - 52.5302							) < 1e-2
							assert abs(result_mean.item() - 0.0684							) < 1e-3




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :Dict							 =			self.full_loop(set_alpha_to_one=UpperCamelCase_   ,       beta_start=0.01							)
							UpperCamelCase__       :str							 =			torch.sum(torch.abs(UpperCamelCase_							)							)
							UpperCamelCase__       :int							 =			torch.mean(torch.abs(UpperCamelCase_							)							)

							assert abs(result_sum.item() - 149.8295							) < 1e-2
							assert abs(result_mean.item() - 0.1951							) < 1e-3




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :List[str]							 =			self.full_loop(set_alpha_to_one=UpperCamelCase_   ,       beta_start=0.01							)
							UpperCamelCase__       :Dict							 =			torch.sum(torch.abs(UpperCamelCase_							)							)
							UpperCamelCase__       :List[str]							 =			torch.mean(torch.abs(UpperCamelCase_							)							)

							assert abs(result_sum.item() - 149.0784							) < 1e-2
							assert abs(result_mean.item() - 0.1941							) < 1e-3



'''simple docstring'''
import collections
import os
from typing import List, Optional, Tuple

from transformers.utils import is_jieba_available, requires_backends


if is_jieba_available():
		import jieba

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__snake_case											=					logging.get_logger(__name__)

__snake_case											=					{'''vocab_file''': '''vocab.txt'''}

__snake_case											=					{
    '''vocab_file''': {
        '''openbmb/cpm-ant-10b''': '''https://huggingface.co/openbmb/cpm-ant-10b/blob/main/vocab.txt''',
    },
}

__snake_case											=					{
    '''openbmb/cpm-ant-10b''': 1024,
}


def       a				(      __a       )  ->     Tuple:



					'''simple docstring'''






					UpperCamelCase__       :List[str]							 =			collections.OrderedDict()
					with open(__a			, '''r'''			, encoding='''utf-8'''       ) as reader:
										UpperCamelCase__       :Dict							 =			reader.readlines()
					for index, token in enumerate(__a       ):
										UpperCamelCase__       :str							 =			token.rstrip('''\n'''       )
										UpperCamelCase__       :Optional[int]							 =			index
					return vocab
class        lowercase     (							A__ ):

		"""simple docstring"""

		def __init__(		self   ,       UpperCamelCase_   ,       UpperCamelCase_="<unk>"   ,       UpperCamelCase_=200							):

							'''simple docstring'''


							UpperCamelCase__       :Tuple							 =			vocab
							UpperCamelCase__       :List[str]							 =			unk_token
							UpperCamelCase__       :Tuple							 =			max_input_chars_per_word




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__       :Optional[int]							 =			list(UpperCamelCase_							)
							if len(UpperCamelCase_							) > self.max_input_chars_per_word:
												return [self.unk_token]

							UpperCamelCase__       :List[Any]							 =			0
							UpperCamelCase__       :str							 =			[]
							while start < len(UpperCamelCase_							):
												UpperCamelCase__       :int							 =			len(UpperCamelCase_							)
												UpperCamelCase__       :List[Any]							 =			None
												while start < end:
																	UpperCamelCase__       :int							 =			''''''.join(chars[start:end]							)
																	if substr in self.vocab:
																						UpperCamelCase__       :List[Any]							 =			substr
																						break
																	end -= 1
												if cur_substr is None:
																	sub_tokens.append(self.unk_token							)
																	start += 1
												else:
																	sub_tokens.append(UpperCamelCase_							)
																	UpperCamelCase__       :Any							 =			end

							return sub_tokens



class        lowercase     (							A__ ):

		"""simple docstring"""

		_a   			=    VOCAB_FILES_NAMES
		_a   			=    PRETRAINED_VOCAB_FILES_MAP
		_a   			=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		_a   			=    ['input_ids', 'attention_mask']
		_a   			=    False




		def __init__(		self   ,       UpperCamelCase_   ,       UpperCamelCase_="<d>"   ,       UpperCamelCase_="</d>"   ,       UpperCamelCase_="<s>"   ,       UpperCamelCase_="</s>"   ,       UpperCamelCase_="<pad>"   ,       UpperCamelCase_="<unk>"   ,       UpperCamelCase_="</n>"   ,       UpperCamelCase_="</_>"   ,       UpperCamelCase_="left"   ,       **UpperCamelCase_   ,       ):

							'''simple docstring'''


							requires_backends(self   ,       ['''jieba''']							)
							super().__init__(
							    bod_token=UpperCamelCase_   ,       eod_token=UpperCamelCase_   ,       bos_token=UpperCamelCase_   ,       eos_token=UpperCamelCase_   ,       pad_token=UpperCamelCase_   ,       unk_token=UpperCamelCase_   ,       line_token=UpperCamelCase_   ,       space_token=UpperCamelCase_   ,       padding_side=UpperCamelCase_   ,       **UpperCamelCase_   ,       )
							UpperCamelCase__       :Tuple							 =			bod_token
							UpperCamelCase__       :Dict							 =			eod_token
							UpperCamelCase__       :Optional[int]							 =			load_vocab(UpperCamelCase_							)
							UpperCamelCase__       :Tuple							 =			self.encoder[space_token]
							UpperCamelCase__       :List[Any]							 =			self.encoder[line_token]

							del self.encoder[space_token]
							del self.encoder[line_token]

							UpperCamelCase__       :Union[str, Any]							 =			collections.OrderedDict(sorted(self.encoder.items()   ,       key=lambda UpperCamelCase_							: x[1]							)							)
							UpperCamelCase__       :Union[str, Any]							 =			{v: k for k, v in self.encoder.items()}

							UpperCamelCase__       :List[Any]							 =			WordpieceTokenizer(vocab=self.encoder   ,       unk_token=self.unk_token							)




		@property
		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							return self.encoder[self.bod_token]




		@property
		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							return self.encoder[self.eod_token]




		@property
		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							return self.encoder["\n"]




		@property
		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							return len(self.encoder							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							return dict(self.encoder   ,       **self.added_tokens_encoder							)




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__       :List[Any]							 =			[]
							for x in jieba.cut(UpperCamelCase_   ,       cut_all=UpperCamelCase_							):
												output_tokens.extend(self.wordpiece_tokenizer.tokenize(UpperCamelCase_							)							)
							return output_tokens




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_   ,       **UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__       :List[Any]							 =			[i for i in token_ids if i >= 0]
							UpperCamelCase__       :Optional[int]							 =			[
							    x for x in token_ids if x != self.pad_token_id and x != self.eos_token_id and x != self.bos_token_id
							]
							return super()._decode(UpperCamelCase_   ,       **UpperCamelCase_							)




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_							):

							'''simple docstring'''


							return token in self.encoder




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_							):

							'''simple docstring'''


							return "".join(UpperCamelCase_							)




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_							):

							'''simple docstring'''


							return self.encoder.get(UpperCamelCase_   ,       self.encoder.get(self.unk_token							)							)




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_							):

							'''simple docstring'''


							return self.decoder.get(UpperCamelCase_   ,       self.unk_token							)




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_   ,       UpperCamelCase_ = None							):

							'''simple docstring'''


							if os.path.isdir(UpperCamelCase_							):
												UpperCamelCase__       :int							 =			os.path.join(
												    UpperCamelCase_   ,       (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']							)
							else:
												UpperCamelCase__       :str							 =			(filename_prefix + '''-''' if filename_prefix else '''''') + save_directory
							UpperCamelCase__       :Any							 =			0
							if " " in self.encoder:
												UpperCamelCase__       :Dict							 =			self.encoder[''' ''']
												del self.encoder[" "]
							if "\n" in self.encoder:
												UpperCamelCase__       :List[str]							 =			self.encoder['''\n''']
												del self.encoder["\n"]
							UpperCamelCase__       :List[str]							 =			collections.OrderedDict(sorted(self.encoder.items()   ,       key=lambda UpperCamelCase_							: x[1]							)							)
							with open(UpperCamelCase_   ,       '''w'''   ,       encoding='''utf-8'''							) as writer:
												for token, token_index in self.encoder.items():
																	if index != token_index:
																						logger.warning(
																						    F'''Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.'''
																						    ''' Please check that the vocabulary is not corrupted!'''							)
																						UpperCamelCase__       :Any							 =			token_index
																	writer.write(token + '''\n'''							)
																	index += 1
							return (vocab_file,)




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_   ,       UpperCamelCase_ = None							):

							'''simple docstring'''


							if token_ids_a is None:
												return [self.bos_token_id] + token_ids_a
							return [self.bos_token_id] + token_ids_a + [self.bos_token_id] + token_ids_a




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_   ,       UpperCamelCase_ = None   ,       UpperCamelCase_ = False							):

							'''simple docstring'''


							if already_has_special_tokens:
												return super().get_special_tokens_mask(
												    token_ids_a=UpperCamelCase_   ,       token_ids_a=UpperCamelCase_   ,       already_has_special_tokens=UpperCamelCase_							)

							if token_ids_a is not None:
												return [1] + ([0] * len(UpperCamelCase_							)) + [1] + ([0] * len(UpperCamelCase_							))
							return [1] + ([0] * len(UpperCamelCase_							))