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


__a  :     Optional[int]       =  logging.get_logger(__name__)

__a  :     Any       =  {"""vocab_file""": """vocab.txt"""}

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

__a  :     int       =  {
    """openbmb/cpm-ant-10b""": 1_0_2_4,
}





def 				UpperCAmelCase						(    lowercase							):







				"""simple docstring"""



				__lowercase 			=							collections.OrderedDict()
				with open(lowercase							,			'''r'''							,			encoding='''utf-8'''							) as reader:
								__lowercase 			=							reader.readlines()
				for index, token in enumerate(lowercase							):
								__lowercase 			=							token.rstrip('''\n'''							)
								__lowercase 			=							index
				return vocab




class    _UpperCamelCase      (						_UpperCAmelCase ):


			"""simple docstring"""






			def __init__(   self      ,   lowerCAmelCase__      ,   lowerCAmelCase__="<unk>"      ,   lowerCAmelCase__=2_00						)     ->					Optional[int]:

							'''simple docstring'''


							__lowercase 			=							vocab
							__lowercase 			=							unk_token
							__lowercase 			=							max_input_chars_per_word





			def        _SCREAMING_SNAKE_CASE			(   self      ,   lowerCAmelCase__						)     ->					int:

							'''simple docstring'''


							__lowercase 			=							list(lowerCAmelCase__						)
							if len(lowerCAmelCase__						) > self.max_input_chars_per_word:
											return [self.unk_token]

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

							return sub_tokens




class    _UpperCamelCase      (						_UpperCAmelCase ):


			"""simple docstring"""






			__a  : Optional[Any]			 =						VOCAB_FILES_NAMES
			__a  : Any			 =						PRETRAINED_VOCAB_FILES_MAP
			__a  : Tuple			 =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			__a  : Optional[int]			 =						['''input_ids''', '''attention_mask''']
			__a  : Optional[int]			 =						False





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

							'''simple docstring'''


							requires_backends(self      ,   ['''jieba''']						)
							super().__init__(
							    bod_token=lowerCAmelCase__      ,   eod_token=lowerCAmelCase__      ,   bos_token=lowerCAmelCase__      ,   eos_token=lowerCAmelCase__      ,   pad_token=lowerCAmelCase__      ,   unk_token=lowerCAmelCase__      ,   line_token=lowerCAmelCase__      ,   space_token=lowerCAmelCase__      ,   padding_side=lowerCAmelCase__      ,   **lowerCAmelCase__      ,   )
							__lowercase 			=							bod_token
							__lowercase 			=							eod_token
							__lowercase 			=							load_vocab(lowerCAmelCase__						)
							__lowercase 			=							self.encoder[space_token]
							__lowercase 			=							self.encoder[line_token]

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

							__lowercase 			=							collections.OrderedDict(sorted(self.encoder.items()      ,   key=lambda lowerCAmelCase__						: x[1]						)						)
							__lowercase 			=							{v: k for k, v in self.encoder.items()}

							__lowercase 			=							WordpieceTokenizer(vocab=self.encoder      ,   unk_token=self.unk_token						)





			@property
			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					List[str]:

							'''simple docstring'''


							return self.encoder[self.bod_token]





			@property
			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Any:

							'''simple docstring'''


							return self.encoder[self.eod_token]





			@property
			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Any:

							'''simple docstring'''


							return self.encoder["\n"]





			@property
			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					int:

							'''simple docstring'''


							return len(self.encoder						)





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

							'''simple docstring'''


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





			def        _SCREAMING_SNAKE_CASE			(   self      ,   lowerCAmelCase__						)     ->					Optional[Any]:

							'''simple docstring'''


							__lowercase 			=							[]
							for x in jieba.cut(lowerCAmelCase__      ,   cut_all=lowerCAmelCase__						):
											output_tokens.extend(self.wordpiece_tokenizer.tokenize(lowerCAmelCase__						)						)
							return output_tokens





			def        _SCREAMING_SNAKE_CASE			(   self      ,   lowerCAmelCase__      ,   **lowerCAmelCase__						)     ->					Any:

							'''simple docstring'''


							__lowercase 			=							[i for i in token_ids if i >= 0]
							__lowercase 			=							[
							    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(lowerCAmelCase__      ,   **lowerCAmelCase__						)





			def        _SCREAMING_SNAKE_CASE			(   self      ,   lowerCAmelCase__						)     ->					int:

							'''simple docstring'''


							return token in self.encoder





			def        _SCREAMING_SNAKE_CASE			(   self      ,   lowerCAmelCase__						)     ->					str:

							'''simple docstring'''


							return "".join(lowerCAmelCase__						)





			def        _SCREAMING_SNAKE_CASE			(   self      ,   lowerCAmelCase__						)     ->					Optional[Any]:

							'''simple docstring'''


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





			def        _SCREAMING_SNAKE_CASE			(   self      ,   lowerCAmelCase__						)     ->					List[Any]:

							'''simple docstring'''


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





			def        _SCREAMING_SNAKE_CASE			(   self      ,   lowerCAmelCase__      ,   lowerCAmelCase__ = None						)     ->					Tuple[str]:

							'''simple docstring'''


							if os.path.isdir(lowerCAmelCase__						):
											__lowercase 			=							os.path.join(
											    lowerCAmelCase__      ,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']						)
							else:
											__lowercase 			=							(filename_prefix + '''-''' if filename_prefix else '''''') + save_directory
							__lowercase 			=							0
							if " " in self.encoder:
											__lowercase 			=							self.encoder[''' ''']
											del self.encoder[" "]
							if "\n" in self.encoder:
											__lowercase 			=							self.encoder['''\n''']
											del self.encoder["\n"]
							__lowercase 			=							collections.OrderedDict(sorted(self.encoder.items()      ,   key=lambda lowerCAmelCase__						: x[1]						)						)
							with open(lowerCAmelCase__      ,   '''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!'''						)
																			__lowercase 			=							token_index
															writer.write(token + '''\n'''						)
															index += 1
							return (vocab_file,)





			def        _SCREAMING_SNAKE_CASE			(   self      ,   lowerCAmelCase__      ,   lowerCAmelCase__ = None						)     ->					List[int]:

							'''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        _SCREAMING_SNAKE_CASE			(   self      ,   lowerCAmelCase__      ,   lowerCAmelCase__ = None      ,   lowerCAmelCase__ = False						)     ->					List[int]:

							'''simple docstring'''


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

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

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class    _UpperCamelCase      (						_UpperCAmelCase ):


			"""simple docstring"""






			__a  : Optional[Any]			 =						(KDPMaDiscreteScheduler,)
			__a  : Dict			 =						10





			def        _SCREAMING_SNAKE_CASE			(   self      ,   **lowerCAmelCase__						)     ->					int:

							'''simple docstring'''


							__lowercase 			=							{
							    '''num_train_timesteps''': 11_00,
							    '''beta_start''': 0.0001,
							    '''beta_end''': 0.02,
							    '''beta_schedule''': '''linear''',
							}

							config.update(**lowerCAmelCase__						)
							return config





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					List[str]:

							'''simple docstring'''


							for timesteps in [10, 50, 1_00, 10_00]:
											self.check_over_configs(num_train_timesteps=lowerCAmelCase__						)





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Any:

							'''simple docstring'''


							for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]      ,   [0.0002, 0.002, 0.02]						):
											self.check_over_configs(beta_start=lowerCAmelCase__      ,   beta_end=lowerCAmelCase__						)





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

							'''simple docstring'''


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





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

							'''simple docstring'''


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





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Tuple:

							'''simple docstring'''


							__lowercase 			=							self.scheduler_classes[0]
							__lowercase 			=							self.get_scheduler_config(prediction_type='''v_prediction'''						)
							__lowercase 			=							scheduler_class(**lowerCAmelCase__						)

							scheduler.set_timesteps(self.num_inference_steps						)

							__lowercase 			=							self.dummy_model()
							__lowercase 			=							self.dummy_sample_deter * scheduler.init_noise_sigma
							__lowercase 			=							sample.to(lowerCAmelCase__						)

							for i, t in enumerate(scheduler.timesteps						):
											__lowercase 			=							scheduler.scale_model_input(lowerCAmelCase__      ,   lowerCAmelCase__						)

											__lowercase 			=							model(lowerCAmelCase__      ,   lowerCAmelCase__						)

											__lowercase 			=							scheduler.step(lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__						)
											__lowercase 			=							output.prev_sample

							__lowercase 			=							torch.sum(torch.abs(lowerCAmelCase__						)						)
							__lowercase 			=							torch.mean(torch.abs(lowerCAmelCase__						)						)

							if torch_device in ["cpu", "mps"]:
											assert abs(result_sum.item() - 4.6934E-07						) < 1E-2
											assert abs(result_mean.item() - 6.1112E-10						) < 1E-3
							else:
											# CUDA
											assert abs(result_sum.item() - 4.693_4286_5017_0972E-07						) < 1E-2
											assert abs(result_mean.item() - 0.0002						) < 1E-3





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					str:

							'''simple docstring'''


							if torch_device == "mps":
											return
							__lowercase 			=							self.scheduler_classes[0]
							__lowercase 			=							self.get_scheduler_config()
							__lowercase 			=							scheduler_class(**lowerCAmelCase__						)

							scheduler.set_timesteps(self.num_inference_steps						)

							__lowercase 			=							self.dummy_model()
							__lowercase 			=							self.dummy_sample_deter * scheduler.init_noise_sigma
							__lowercase 			=							sample.to(lowerCAmelCase__						)

							for i, t in enumerate(scheduler.timesteps						):
											__lowercase 			=							scheduler.scale_model_input(lowerCAmelCase__      ,   lowerCAmelCase__						)

											__lowercase 			=							model(lowerCAmelCase__      ,   lowerCAmelCase__						)

											__lowercase 			=							scheduler.step(lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__						)
											__lowercase 			=							output.prev_sample

							__lowercase 			=							torch.sum(torch.abs(lowerCAmelCase__						)						)
							__lowercase 			=							torch.mean(torch.abs(lowerCAmelCase__						)						)

							if torch_device in ["cpu", "mps"]:
											assert abs(result_sum.item() - 20.4125						) < 1E-2
											assert abs(result_mean.item() - 0.0266						) < 1E-3
							else:
											# CUDA
											assert abs(result_sum.item() - 20.4125						) < 1E-2
											assert abs(result_mean.item() - 0.0266						) < 1E-3





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Dict:

							'''simple docstring'''


							if torch_device == "mps":
											return
							__lowercase 			=							self.scheduler_classes[0]
							__lowercase 			=							self.get_scheduler_config()
							__lowercase 			=							scheduler_class(**lowerCAmelCase__						)

							scheduler.set_timesteps(self.num_inference_steps      ,   device=lowerCAmelCase__						)

							__lowercase 			=							self.dummy_model()
							__lowercase 			=							self.dummy_sample_deter.to(lowerCAmelCase__						) * scheduler.init_noise_sigma

							for t in scheduler.timesteps:
											__lowercase 			=							scheduler.scale_model_input(lowerCAmelCase__      ,   lowerCAmelCase__						)

											__lowercase 			=							model(lowerCAmelCase__      ,   lowerCAmelCase__						)

											__lowercase 			=							scheduler.step(lowerCAmelCase__      ,   lowerCAmelCase__      ,   lowerCAmelCase__						)
											__lowercase 			=							output.prev_sample

							__lowercase 			=							torch.sum(torch.abs(lowerCAmelCase__						)						)
							__lowercase 			=							torch.mean(torch.abs(lowerCAmelCase__						)						)

							if str(lowerCAmelCase__						).startswith('''cpu'''						):
											# The following sum varies between 148 and 156 on mps. Why?
											assert abs(result_sum.item() - 20.4125						) < 1E-2
											assert abs(result_mean.item() - 0.0266						) < 1E-3
							else:
											# CUDA
											assert abs(result_sum.item() - 20.4125						) < 1E-2
											assert abs(result_mean.item() - 0.0266						) < 1E-3