



'''simple docstring'''




import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union

import numpy as np
import torch

from .utils import deprecate, is_transformers_available


if is_transformers_available():
	import transformers



def      a      (			lowerCamelCase__   ):
			'''simple docstring'''





			random.seed(UpperCAmelCase__   )
			np.random.seed(UpperCAmelCase__   )
			torch.manual_seed(UpperCAmelCase__   )
			torch.cuda.manual_seed_all(UpperCAmelCase__   )
			# ^^ safe to call this function even if cuda is not available







class      _lowerCAmelCase							:


	def __init__(self	,				lowercase	,				lowercase = 0.99_99	,				lowercase = 0.0	,				lowercase = 0	,				lowercase = False	,				lowercase = 1.0	,				lowercase = 2 / 3	,				lowercase = None	,				lowercase = None	,				**lowercase	,				):

				if isinstance(UpperCamelCase__	,				torch.nn.Module							):
							A_     :						Optional[Any]     =					(
							    """Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. """
							    """Please pass the parameters of the module instead."""
							)
							deprecate(
							    """passing a `torch.nn.Module` to `ExponentialMovingAverage`"""	,				"""1.0.0"""	,				UpperCamelCase__	,				standard_warn=UpperCamelCase__	,				)
							A_     :						Tuple     =					parameters.parameters()

							# set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
							A_     :						Optional[Any]     =					True

				if kwargs.get("""max_value"""	,				UpperCamelCase__							) is not None:
							A_     :						List[Any]     =					"""The `max_value` argument is deprecated. Please use `decay` instead."""
							deprecate("""max_value"""	,				"""1.0.0"""	,				UpperCamelCase__	,				standard_warn=UpperCamelCase__							)
							A_     :						int     =					kwargs["""max_value"""]

				if kwargs.get("""min_value"""	,				UpperCamelCase__							) is not None:
							A_     :						Optional[Any]     =					"""The `min_value` argument is deprecated. Please use `min_decay` instead."""
							deprecate("""min_value"""	,				"""1.0.0"""	,				UpperCamelCase__	,				standard_warn=UpperCamelCase__							)
							A_     :						List[Any]     =					kwargs["""min_value"""]

				A_     :						List[str]     =					list(UpperCamelCase__							)
				A_     :						List[str]     =					[p.clone().detach() for p in parameters]

				if kwargs.get("""device"""	,				UpperCamelCase__							) is not None:
							A_     :						Optional[Any]     =					"""The `device` argument is deprecated. Please use `to` instead."""
							deprecate("""device"""	,				"""1.0.0"""	,				UpperCamelCase__	,				standard_warn=UpperCamelCase__							)
							self.to(device=kwargs["""device"""]							)

				A_     :						Tuple     =					None

				A_     :						Union[str, Any]     =					decay
				A_     :						Any     =					min_decay
				A_     :						Dict     =					update_after_step
				A_     :						Any     =					use_ema_warmup
				A_     :						Optional[Any]     =					inv_gamma
				A_     :						List[Any]     =					power
				A_     :						Optional[Any]     =					0
				A_     :						str     =					None  # set in `step()`

				A_     :						Optional[int]     =					model_cls
				A_     :						Dict     =					model_config


	@classmethod
	def    _a     (cls	,				lowercase	,				lowercase							):
				A_,   A_     :						Tuple     =					model_cls.load_config(UpperCamelCase__	,				return_unused_kwargs=UpperCamelCase__							)
				A_     :						List[str]     =					model_cls.from_pretrained(UpperCamelCase__							)

				A_     :						Dict     =					cls(model.parameters()	,				model_cls=UpperCamelCase__	,				model_config=model.config							)

				ema_model.load_state_dict(UpperCamelCase__							)
				return ema_model


	def    _a     (self	,				lowercase							):
				if self.model_cls is None:
							raise ValueError("""`save_pretrained` can only be used if `model_cls` was defined at __init__."""							)

				if self.model_config is None:
							raise ValueError("""`save_pretrained` can only be used if `model_config` was defined at __init__."""							)

				A_     :						Optional[Any]     =					self.model_cls.from_config(self.model_config							)
				A_     :						str     =					self.state_dict()
				state_dict.pop("""shadow_params"""	,				UpperCamelCase__							)

				model.register_to_config(**UpperCamelCase__							)
				self.copy_to(model.parameters()							)
				model.save_pretrained(UpperCamelCase__							)


	def    _a     (self	,				lowercase							):
				A_     :						Union[str, Any]     =					max(0	,				optimization_step - self.update_after_step - 1							)

				if step <= 0:
							return 0.0

				if self.use_ema_warmup:
							A_     :						List[Any]     =					1 - (1 + step / self.inv_gamma) ** -self.power
				else:
							A_     :						Tuple     =					(1 + step) / (10 + step)

				A_     :						List[Any]     =					min(UpperCamelCase__	,				self.decay							)
				# make sure decay is not smaller than min_decay
				A_     :						List[Any]     =					max(UpperCamelCase__	,				self.min_decay							)
				return cur_decay_value


	@torch.no_grad()
	def    _a     (self	,				lowercase							):
				if isinstance(UpperCamelCase__	,				torch.nn.Module							):
							A_     :						Tuple     =					(
							    """Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. """
							    """Please pass the parameters of the module instead."""
							)
							deprecate(
							    """passing a `torch.nn.Module` to `ExponentialMovingAverage.step`"""	,				"""1.0.0"""	,				UpperCamelCase__	,				standard_warn=UpperCamelCase__	,				)
							A_     :						Any     =					parameters.parameters()

				A_     :						List[str]     =					list(UpperCamelCase__							)

				self.optimization_step += 1

				# Compute the decay factor for the exponential moving average.
				A_     :						Union[str, Any]     =					self.get_decay(self.optimization_step							)
				A_     :						Optional[Any]     =					decay
				A_     :						Optional[int]     =					1 - decay

				A_     :						Tuple     =					contextlib.nullcontext
				if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
							import deepspeed

				for s_param, param in zip(self.shadow_params	,				UpperCamelCase__							):
							if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
										A_     :						List[str]     =					deepspeed.zero.GatheredParameters(UpperCamelCase__	,				modifier_rank=UpperCamelCase__							)

							with context_manager():
										if param.requires_grad:
													s_param.sub_(one_minus_decay * (s_param - param)							)
										else:
													s_param.copy_(UpperCamelCase__							)


	def    _a     (self	,				lowercase							):
				A_     :						int     =					list(UpperCamelCase__							)
				for s_param, param in zip(self.shadow_params	,				UpperCamelCase__							):
							param.data.copy_(s_param.to(param.device							).data							)


	def    _a     (self	,				lowercase=None	,				lowercase=None							):
				A_     :						Optional[Any]     =					[
				    p.to(device=UpperCamelCase__	,				dtype=UpperCamelCase__							) if p.is_floating_point() else p.to(device=UpperCamelCase__							)
				    for p in self.shadow_params
				]


	def    _a     (self							):
				return {
				    "decay": self.decay,
				    "min_decay": self.min_decay,
				    "optimization_step": self.optimization_step,
				    "update_after_step": self.update_after_step,
				    "use_ema_warmup": self.use_ema_warmup,
				    "inv_gamma": self.inv_gamma,
				    "power": self.power,
				    "shadow_params": self.shadow_params,
				}


	def    _a     (self	,				lowercase							):
				A_     :						Optional[int]     =					[param.detach().cpu().clone() for param in parameters]


	def    _a     (self	,				lowercase							):
				if self.temp_stored_params is None:
							raise RuntimeError("""This ExponentialMovingAverage has no `store()`ed weights """ """to `restore()`"""							)
				for c_param, param in zip(self.temp_stored_params	,				UpperCamelCase__							):
							param.data.copy_(c_param.data							)

				# Better memory-wise.
				A_     :						Tuple     =					None


	def    _a     (self	,				lowercase							):
				A_     :						List[Any]     =					copy.deepcopy(UpperCamelCase__							)

				A_     :						Optional[Any]     =					state_dict.get("""decay"""	,				self.decay							)
				if self.decay < 0.0 or self.decay > 1.0:
							raise ValueError("""Decay must be between 0 and 1"""							)

				A_     :						Tuple     =					state_dict.get("""min_decay"""	,				self.min_decay							)
				if not isinstance(self.min_decay	,				UpperCamelCase__							):
							raise ValueError("""Invalid min_decay"""							)

				A_     :						Optional[Any]     =					state_dict.get("""optimization_step"""	,				self.optimization_step							)
				if not isinstance(self.optimization_step	,				UpperCamelCase__							):
							raise ValueError("""Invalid optimization_step"""							)

				A_     :						Tuple     =					state_dict.get("""update_after_step"""	,				self.update_after_step							)
				if not isinstance(self.update_after_step	,				UpperCamelCase__							):
							raise ValueError("""Invalid update_after_step"""							)

				A_     :						Union[str, Any]     =					state_dict.get("""use_ema_warmup"""	,				self.use_ema_warmup							)
				if not isinstance(self.use_ema_warmup	,				UpperCamelCase__							):
							raise ValueError("""Invalid use_ema_warmup"""							)

				A_     :						Tuple     =					state_dict.get("""inv_gamma"""	,				self.inv_gamma							)
				if not isinstance(self.inv_gamma	,				(float, int)							):
							raise ValueError("""Invalid inv_gamma"""							)

				A_     :						Optional[int]     =					state_dict.get("""power"""	,				self.power							)
				if not isinstance(self.power	,				(float, int)							):
							raise ValueError("""Invalid power"""							)

				A_     :						Union[str, Any]     =					state_dict.get("""shadow_params"""	,				UpperCamelCase__							)
				if shadow_params is not None:
							A_     :						List[str]     =					shadow_params
							if not isinstance(self.shadow_params	,				UpperCamelCase__							):
										raise ValueError("""shadow_params must be a list"""							)
							if not all(isinstance(UpperCamelCase__	,				torch.Tensor							) for p in self.shadow_params							):
										raise ValueError("""shadow_params must all be Tensors"""							)




'''simple docstring'''




import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging


lowerCamelCase							:Union[str, Any]       = logging.get_logger(__name__)

lowerCamelCase							:Union[str, Any]       = {'''vocab_file''': '''spiece.model'''}

lowerCamelCase							:Tuple       = {
    '''vocab_file''': {
        '''TsinghuaAI/CPM-Generate''': '''https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model''',
    }
}







class      _lowerCAmelCase							(      __UpperCAmelCase			):


	def __init__(self	,				lowercase	,				lowercase=False	,				lowercase=True	,				lowercase=False	,				lowercase="<s>"	,				lowercase="</s>"	,				lowercase="<unk>"	,				lowercase="<sep>"	,				lowercase="<pad>"	,				lowercase="<cls>"	,				lowercase="<mask>"	,				lowercase=["<eop>", "<eod>"]	,				lowercase = None	,				**lowercase	,				):
				A_     :						Optional[int]     =					AddedToken(lowercase	,				lstrip=lowercase	,				rstrip=lowercase							) if isinstance(lowercase	,				lowercase							) else mask_token

				A_     :						str     =					{} if sp_model_kwargs is None else sp_model_kwargs

				super().__init__(
				    do_lower_case=lowercase	,				remove_space=lowercase	,				keep_accents=lowercase	,				bos_token=lowercase	,				eos_token=lowercase	,				unk_token=lowercase	,				sep_token=lowercase	,				pad_token=lowercase	,				cls_token=lowercase	,				mask_token=lowercase	,				additional_special_tokens=lowercase	,				sp_model_kwargs=self.sp_model_kwargs	,				**lowercase	,				)

				A_     :						List[str]     =					3

				A_     :						List[str]     =					do_lower_case
				A_     :						Tuple     =					remove_space
				A_     :						Tuple     =					keep_accents
				A_     :						Tuple     =					vocab_file

				A_     :						int     =					spm.SentencePieceProcessor(**self.sp_model_kwargs							)
				self.sp_model.Load(lowercase							)

				try:
							import jieba
				except ModuleNotFoundError as error:
							raise error.__class__(
							    """You need to install jieba to use CpmTokenizer or CpmTokenizerFast. """
							    """See https://pypi.org/project/jieba/ for installation."""							)
				A_     :						Tuple     =					jieba
				A_     :						int     =					str.maketrans(""" \n"""	,				"""\u2582\u2583"""							)


	@property
	# Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
	def    _a     (self							):
				return len(self.sp_model							)


	def    _a     (self							):
				A_     :						str     =					{self.convert_ids_to_tokens(lowercase							): i for i in range(self.vocab_size							)}
				vocab.update(self.added_tokens_encoder							)
				return vocab


	def __getstate__(self							):
				A_     :						Optional[Any]     =					self.__dict__.copy()
				A_     :						Tuple     =					None
				return state


	def __setstate__(self	,				lowercase							):
				A_     :						Tuple     =					d

				# for backward compatibility
				if not hasattr(self	,				"""sp_model_kwargs"""							):
							A_     :						List[Any]     =					{}

				A_     :						int     =					spm.SentencePieceProcessor(**self.sp_model_kwargs							)
				self.sp_model.Load(self.vocab_file							)


	def    _a     (self	,				lowercase							):
				if self.remove_space:
							A_     :						Union[str, Any]     =					""" """.join(inputs.strip().split()							)
				else:
							A_     :						Optional[int]     =					inputs
				A_     :						List[str]     =					outputs.replace("""``"""	,				"""\""""							).replace("""''"""	,				"""\""""							)

				if not self.keep_accents:
							A_     :						str     =					unicodedata.normalize("""NFKD"""	,				lowercase							)
							A_     :						Union[str, Any]     =					"""""".join([c for c in outputs if not unicodedata.combining(lowercase							)]							)
				if self.do_lower_case:
							A_     :						int     =					outputs.lower()

				return outputs


	def    _a     (self	,				lowercase							):
				A_     :						Optional[int]     =					self.preprocess_text(lowercase							)
				A_     :						Dict     =					self.sp_model.encode(lowercase	,				out_type=lowercase							)
				A_     :						List[Any]     =					[]
				for piece in pieces:
							if len(lowercase							) > 1 and piece[-1] == str(""","""							) and piece[-2].isdigit():
										A_     :						Union[str, Any]     =					self.sp_model.EncodeAsPieces(piece[:-1].replace(lowercase	,				""""""							)							)
										if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
													if len(cur_pieces[0]							) == 1:
																A_     :						Dict     =					cur_pieces[1:]
													else:
																A_     :						str     =					cur_pieces[0][1:]
										cur_pieces.append(piece[-1]							)
										new_pieces.extend(lowercase							)
							else:
										new_pieces.append(lowercase							)

				return new_pieces


	def    _a     (self	,				lowercase							):
				return self.sp_model.PieceToId(lowercase							)


	def    _a     (self	,				lowercase							):
				return self.sp_model.IdToPiece(lowercase							)


	def    _a     (self	,				lowercase							):
				A_     :						int     =					"""""".join(lowercase							).replace(lowercase	,				""" """							).strip()
				return out_string


	def    _a     (self	,				lowercase	,				lowercase = None							):
				A_     :						List[str]     =					[self.sep_token_id]
				A_     :						Any     =					[self.cls_token_id]
				if token_ids_a is None:
							return token_ids_a + sep + cls
				return token_ids_a + sep + token_ids_a + sep + cls


	def    _a     (self	,				lowercase	,				lowercase = None	,				lowercase = False							):

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

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


	def    _a     (self	,				lowercase	,				lowercase = None							):
				A_     :						str     =					[self.sep_token_id]
				A_     :						str     =					[2]

				if token_ids_a is None:
							return len(token_ids_a + sep							) * [0] + cls_segment_id
				return len(token_ids_a + sep							) * [0] + len(token_ids_a + sep							) * [1] + cls_segment_id


	def    _a     (self	,				lowercase	,				lowercase = None							):
				if not os.path.isdir(lowercase							):
							logger.error(F'Vocabulary path ({save_directory}) should be a directory'							)
							return
				A_     :						Tuple     =					os.path.join(
				    lowercase	,				(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]							)

				if os.path.abspath(self.vocab_file							) != os.path.abspath(lowercase							) and os.path.isfile(self.vocab_file							):
							copyfile(self.vocab_file	,				lowercase							)
				elif not os.path.isfile(self.vocab_file							):
							with open(lowercase	,				"""wb"""							) as fi:
										A_     :						Dict     =					self.sp_model.serialized_model_proto()
										fi.write(lowercase							)

				return (out_vocab_file,)


	def    _a     (self	,				*lowercase	,				**lowercase							):
				A_     :						Any     =					super()._decode(*lowercase	,				**lowercase							)
				A_     :						int     =					text.replace(""" """	,				""""""							).replace("""\u2582"""	,				""" """							).replace("""\u2583"""	,				"""\n"""							)
				return text