






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__	:    List[Any]			 = logging.get_logger(__name__)

A__	:    int			 = {'vocab_file': 'vocab.txt'}

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

A__	:    Optional[int]			 = {
    'openbmb/cpm-ant-10b': 1_024,
}

def 			_snake_case    (					lowerCamelCase__     :   Tuple  )    ->     str:
							lowerCamelCase_	:							Any									=collections.OrderedDict()
							with open(a__    ,						"r"    ,						encoding="utf-8"  ) as reader:
														lowerCamelCase_	:							Tuple									=reader.readlines()
							for index, token in enumerate(a__  ):
														lowerCamelCase_	:							Any									=token.rstrip("\n"  )
														lowerCamelCase_	:							Any									=index
							return vocab







class        lowercase__      (							_a       ):


			def __init__(				self					:  Union[str, Any]       ,	snake_case__					:  Tuple       ,	snake_case__					:  Optional[int]="<unk>"       ,	snake_case__					:  int=200 ):
										lowerCamelCase_	:							Tuple									=vocab
										lowerCamelCase_	:							List[str]									=unk_token
										lowerCamelCase_	:							Optional[int]									=max_input_chars_per_word


			def      UpperCAmelCase__						(				self					:  Dict       ,	snake_case__					:  Optional[Any] ):
										lowerCamelCase_	:							Union[str, Any]									=list(__lowerCAmelCase )
										if len(__lowerCAmelCase ) > self.max_input_chars_per_word:
																	return [self.unk_token]

										lowerCamelCase_	:							int									=0
										lowerCamelCase_	:							str									=[]
										while start < len(__lowerCAmelCase ):
																	lowerCamelCase_	:							str									=len(__lowerCAmelCase )
																	lowerCamelCase_	:							Dict									=None
																	while start < end:
																								lowerCamelCase_	:							str									="".join(chars[start:end] )
																								if substr in self.vocab:
																															lowerCamelCase_	:							Optional[int]									=substr
																															break
																								end -= 1
																	if cur_substr is None:
																								sub_tokens.append(self.unk_token )
																								start += 1
																	else:
																								sub_tokens.append(__lowerCAmelCase )
																								lowerCamelCase_	:							str									=end

										return sub_tokens







class        lowercase__      (							_a       ):
			_UpperCAmelCase		:Optional[int]             =			VOCAB_FILES_NAMES
			_UpperCAmelCase		:Optional[Any]             =			PRETRAINED_VOCAB_FILES_MAP
			_UpperCAmelCase		:List[Any]             =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			_UpperCAmelCase		:Optional[int]             =			["""input_ids""", """attention_mask"""]
			_UpperCAmelCase		:int             =			False


			def __init__(				self					:  Tuple       ,	snake_case__					:  Any       ,	snake_case__					:  List[str]="<d>"       ,	snake_case__					:  Tuple="</d>"       ,	snake_case__					:  Any="<s>"       ,	snake_case__					:  Dict="</s>"       ,	snake_case__					:  Any="<pad>"       ,	snake_case__					:  Dict="<unk>"       ,	snake_case__					:  str="</n>"       ,	snake_case__					:  Dict="</_>"       ,	snake_case__					:  str="left"       ,	**snake_case__					:  str       ,	):
										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       ,	)
										lowerCamelCase_	:							Optional[int]									=bod_token
										lowerCamelCase_	:							Optional[Any]									=eod_token
										lowerCamelCase_	:							Dict									=load_vocab(__lowerCAmelCase )
										lowerCamelCase_	:							str									=self.encoder[space_token]
										lowerCamelCase_	:							str									=self.encoder[line_token]

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

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

										lowerCamelCase_	:							str									=WordpieceTokenizer(vocab=self.encoder       ,	unk_token=self.unk_token )


			@property
			def      UpperCAmelCase__						(				self					:  List[str] ):
										return self.encoder[self.bod_token]


			@property
			def      UpperCAmelCase__						(				self					:  Dict ):
										return self.encoder[self.eod_token]


			@property
			def      UpperCAmelCase__						(				self					:  Optional[Any] ):
										return self.encoder["\n"]


			@property
			def      UpperCAmelCase__						(				self					:  List[Any] ):
										return len(self.encoder )


			def      UpperCAmelCase__						(				self					:  Dict ):
										return dict(self.encoder       ,	**self.added_tokens_encoder )


			def      UpperCAmelCase__						(				self					:  Dict       ,	snake_case__					:  Union[str, Any] ):
										lowerCamelCase_	:							str									=[]
										for x in jieba.cut(__lowerCAmelCase       ,	cut_all=__lowerCAmelCase ):
																	output_tokens.extend(self.wordpiece_tokenizer.tokenize(__lowerCAmelCase ) )
										return output_tokens


			def      UpperCAmelCase__						(				self					:  int       ,	snake_case__					:  Optional[int]       ,	**snake_case__					:  List[Any] ):
										lowerCamelCase_	:							Any									=[i for i in token_ids if i >= 0]
										lowerCamelCase_	:							List[str]									=[
										    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      UpperCAmelCase__						(				self					:  List[str]       ,	snake_case__					:  Any ):
										return token in self.encoder


			def      UpperCAmelCase__						(				self					:  Optional[int]       ,	snake_case__					:  Dict ):
										return "".join(__lowerCAmelCase )


			def      UpperCAmelCase__						(				self					:  Optional[Any]       ,	snake_case__					:  str ):
										return self.encoder.get(__lowerCAmelCase       ,	self.encoder.get(self.unk_token ) )


			def      UpperCAmelCase__						(				self					:  List[Any]       ,	snake_case__					:  List[str] ):
										return self.decoder.get(__lowerCAmelCase       ,	self.unk_token )


			def      UpperCAmelCase__						(				self					:  List[str]       ,	snake_case__					:  str       ,	snake_case__					:  Union[str, Any] = None ):
										if os.path.isdir(__lowerCAmelCase ):
																	lowerCamelCase_	:							Union[str, Any]									=os.path.join(
																	    __lowerCAmelCase       ,	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
										else:
																	lowerCamelCase_	:							Tuple									=(filename_prefix + "-" if filename_prefix else "") + save_directory
										lowerCamelCase_	:							Optional[Any]									=0
										if " " in self.encoder:
																	lowerCamelCase_	:							Any									=self.encoder[" "]
																	del self.encoder[" "]
										if "\n" in self.encoder:
																	lowerCamelCase_	:							Union[str, Any]									=self.encoder["\n"]
																	del self.encoder["\n"]
										lowerCamelCase_	:							Optional[Any]									=collections.OrderedDict(sorted(self.encoder.items()       ,	key=lambda snake_case__ : 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!" )
																															lowerCamelCase_	:							Any									=token_index
																								writer.write(token + "\n" )
																								index += 1
										return (vocab_file,)


			def      UpperCAmelCase__						(				self					:  List[Any]       ,	snake_case__					:  Dict       ,	snake_case__					:  Optional[int] = None ):
										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      UpperCAmelCase__						(				self					:  List[Any]       ,	snake_case__					:  Dict       ,	snake_case__					:  Dict = None       ,	snake_case__					:  List[Any] = False ):

										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 ))











"""simple docstring"""



# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)







@flax.struct.dataclass
class        lowercase__      :
			_UpperCAmelCase		:CommonSchedulerState

			# setable values
			_UpperCAmelCase		:jnp.ndarray
			_UpperCAmelCase		:jnp.ndarray
			_UpperCAmelCase		:Optional[int]             =			None


			@classmethod
			def      UpperCAmelCase__						(				cls					:  int       ,	snake_case__					:  CommonSchedulerState       ,	snake_case__					:  jnp.ndarray       ,	snake_case__					:  jnp.ndarray ):
										return cls(common=snake_case__       ,	init_noise_sigma=snake_case__       ,	timesteps=snake_case__ )







@dataclass
class        lowercase__      (							snake_case__       ):
			_UpperCAmelCase		:DDPMSchedulerState







class        lowercase__      (							snake_case__,       snake_case__       ):
			_UpperCAmelCase		:Any             =			[e.name for e in FlaxKarrasDiffusionSchedulers]

			_UpperCAmelCase		:jnp.dtype


			@property
			def      UpperCAmelCase__						(				self					:  Optional[Any] ):
										return True


			@register_to_config
			def __init__(				self					:  Optional[int]       ,	snake_case__					:  int = 1000       ,	snake_case__					:  float = 0.0_001       ,	snake_case__					:  float = 0.02       ,	snake_case__					:  str = "linear"       ,	snake_case__					:  Optional[jnp.ndarray] = None       ,	snake_case__					:  str = "fixed_small"       ,	snake_case__					:  bool = True       ,	snake_case__					:  str = "epsilon"       ,	snake_case__					:  jnp.dtype = jnp.floataa       ,	):
										lowerCamelCase_	:							str									=dtype


			def      UpperCAmelCase__						(				self					:  List[str]       ,	snake_case__					:  Optional[CommonSchedulerState] = None ):
										if common is None:
																	lowerCamelCase_	:							int									=CommonSchedulerState.create(self )

										# standard deviation of the initial noise distribution
										lowerCamelCase_	:							Optional[Any]									=jnp.array(1.0       ,	dtype=self.dtype )

										lowerCamelCase_	:							str									=jnp.arange(0       ,	self.config.num_train_timesteps ).round()[::-1]

										return DDPMSchedulerState.create(
										    common=snake_case__       ,	init_noise_sigma=snake_case__       ,	timesteps=snake_case__       ,	)


			def      UpperCAmelCase__						(				self					:  Optional[int]       ,	snake_case__					:  DDPMSchedulerState       ,	snake_case__					:  jnp.ndarray       ,	snake_case__					:  Optional[int] = None ):
										return sample


			def      UpperCAmelCase__						(				self					:  Optional[Any]       ,	snake_case__					:  DDPMSchedulerState       ,	snake_case__					:  int       ,	snake_case__					:  Tuple = () ):
										lowerCamelCase_	:							Any									=self.config.num_train_timesteps // num_inference_steps
										# creates integer timesteps by multiplying by ratio
										# rounding to avoid issues when num_inference_step is power of 3
										lowerCamelCase_	:							List[str]									=(jnp.arange(0       ,	snake_case__ ) * step_ratio).round()[::-1]

										return state.replace(
										    num_inference_steps=snake_case__       ,	timesteps=snake_case__       ,	)


			def      UpperCAmelCase__						(				self					:  Union[str, Any]       ,	snake_case__					:  DDPMSchedulerState       ,	snake_case__					:  Union[str, Any]       ,	snake_case__					:  List[Any]=None       ,	snake_case__					:  Any=None ):
										lowerCamelCase_	:							List[str]									=state.common.alphas_cumprod[t]
										lowerCamelCase_	:							Union[str, Any]									=jnp.where(t > 0       ,	state.common.alphas_cumprod[t - 1]       ,	jnp.array(1.0       ,	dtype=self.dtype ) )

										# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
										# and sample from it to get previous sample
										# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
										lowerCamelCase_	:							Tuple									=(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

										if variance_type is None:
																	lowerCamelCase_	:							List[Any]									=self.config.variance_type

										# hacks - were probably added for training stability
										if variance_type == "fixed_small":
																	lowerCamelCase_	:							List[str]									=jnp.clip(snake_case__       ,	a_min=1E-20 )
										# for rl-diffuser https://arxiv.org/abs/2205.09991
										elif variance_type == "fixed_small_log":
																	lowerCamelCase_	:							Dict									=jnp.log(jnp.clip(snake_case__       ,	a_min=1E-20 ) )
										elif variance_type == "fixed_large":
																	lowerCamelCase_	:							Optional[Any]									=state.common.betas[t]
										elif variance_type == "fixed_large_log":
																	# Glide max_log
																	lowerCamelCase_	:							Any									=jnp.log(state.common.betas[t] )
										elif variance_type == "learned":
																	return predicted_variance
										elif variance_type == "learned_range":
																	lowerCamelCase_	:							List[str]									=variance
																	lowerCamelCase_	:							Optional[int]									=state.common.betas[t]
																	lowerCamelCase_	:							Dict									=(predicted_variance + 1) / 2
																	lowerCamelCase_	:							Dict									=frac * max_log + (1 - frac) * min_log

										return variance


			def      UpperCAmelCase__						(				self					:  int       ,	snake_case__					:  DDPMSchedulerState       ,	snake_case__					:  jnp.ndarray       ,	snake_case__					:  int       ,	snake_case__					:  jnp.ndarray       ,	snake_case__					:  Optional[jax.random.KeyArray] = None       ,	snake_case__					:  bool = True       ,	):
										lowerCamelCase_	:							Union[str, Any]									=timestep

										if key is None:
																	lowerCamelCase_	:							Dict									=jax.random.PRNGKey(0 )

										if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
																	lowerCamelCase_							,						lowerCamelCase_	:							Optional[Any]									=jnp.split(snake_case__       ,	sample.shape[1]       ,	axis=1 )
										else:
																	lowerCamelCase_	:							List[str]									=None

										# 1. compute alphas, betas
										lowerCamelCase_	:							Union[str, Any]									=state.common.alphas_cumprod[t]
										lowerCamelCase_	:							Dict									=jnp.where(t > 0       ,	state.common.alphas_cumprod[t - 1]       ,	jnp.array(1.0       ,	dtype=self.dtype ) )
										lowerCamelCase_	:							Any									=1 - alpha_prod_t
										lowerCamelCase_	:							List[str]									=1 - alpha_prod_t_prev

										# 2. compute predicted original sample from predicted noise also called
										# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
										if self.config.prediction_type == "epsilon":
																	lowerCamelCase_	:							int									=(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
										elif self.config.prediction_type == "sample":
																	lowerCamelCase_	:							List[Any]									=model_output
										elif self.config.prediction_type == "v_prediction":
																	lowerCamelCase_	:							Tuple									=(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
										else:
																	raise ValueError(
																	    F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` """
																	    " for the FlaxDDPMScheduler." )

										# 3. Clip "predicted x_0"
										if self.config.clip_sample:
																	lowerCamelCase_	:							List[Any]									=jnp.clip(snake_case__       ,	-1       ,	1 )

										# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
										# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
										lowerCamelCase_	:							int									=(alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
										lowerCamelCase_	:							Optional[Any]									=state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

										# 5. Compute predicted previous sample µ_t
										# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
										lowerCamelCase_	:							Any									=pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

										# 6. Add noise
										def random_variance():
																	lowerCamelCase_	:							Union[str, Any]									=jax.random.split(snake_case__       ,	num=1 )
																	lowerCamelCase_	:							List[Any]									=jax.random.normal(snake_case__       ,	shape=model_output.shape       ,	dtype=self.dtype )
																	return (self._get_variance(snake_case__       ,	snake_case__       ,	predicted_variance=snake_case__ ) ** 0.5) * noise

										lowerCamelCase_	:							Tuple									=jnp.where(t > 0       ,	random_variance()       ,	jnp.zeros(model_output.shape       ,	dtype=self.dtype ) )

										lowerCamelCase_	:							str									=pred_prev_sample + variance

										if not return_dict:
																	return (pred_prev_sample, state)

										return FlaxDDPMSchedulerOutput(prev_sample=snake_case__       ,	state=snake_case__ )


			def      UpperCAmelCase__						(				self					:  Dict       ,	snake_case__					:  DDPMSchedulerState       ,	snake_case__					:  jnp.ndarray       ,	snake_case__					:  jnp.ndarray       ,	snake_case__					:  jnp.ndarray       ,	):
										return add_noise_common(state.common       ,	snake_case__       ,	snake_case__       ,	snake_case__ )


			def      UpperCAmelCase__						(				self					:  int       ,	snake_case__					:  DDPMSchedulerState       ,	snake_case__					:  jnp.ndarray       ,	snake_case__					:  jnp.ndarray       ,	snake_case__					:  jnp.ndarray       ,	):
										return get_velocity_common(state.common       ,	snake_case__       ,	snake_case__       ,	snake_case__ )


			def __len__(				self					:  Tuple ):
										return self.config.num_train_timesteps



