





# 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     __SCREAMING_SNAKE_CASE   :
							snake_case       : CommonSchedulerState

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






							@classmethod
							def 			_lowerCamelCase						(  cls					,	__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					):
														return cls(common=__lowerCAmelCase					,	init_noise_sigma=__lowerCAmelCase					,	timesteps=__lowerCAmelCase					)






@dataclass
class     __SCREAMING_SNAKE_CASE   (       _a    ):
							snake_case       : DDPMSchedulerState






class     __SCREAMING_SNAKE_CASE   (       _a				,						_a    ):
							snake_case       : str         =    [e.name for e in FlaxKarrasDiffusionSchedulers]

							snake_case       : jnp.dtype






							@property
							def 			_lowerCamelCase						(  self					):
														return True






							@register_to_config
							def __init__(  self					,	__lowerCAmelCase = 1000					,	__lowerCAmelCase = 0.0001					,	__lowerCAmelCase = 0.02					,	__lowerCAmelCase = "linear"					,	__lowerCAmelCase = None					,	__lowerCAmelCase = "fixed_small"					,	__lowerCAmelCase = True					,	__lowerCAmelCase = "epsilon"					,	__lowerCAmelCase = jnp.floataa					,	):
														UpperCamelCase__			       =     dtype






							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase = None					):
														if common is None:
																					UpperCamelCase__			       =     CommonSchedulerState.create(self					)

														# standard deviation of the initial noise distribution
														UpperCamelCase__			       =     jnp.array(1.0					,	dtype=self.dtype					)

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

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






							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase = None					):
														return sample






							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase = ()					):
														UpperCamelCase__			       =     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
														UpperCamelCase__			       =     (jnp.arange(0					,	__lowerCAmelCase					) * step_ratio).round()[::-1]

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






							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase=None					,	__lowerCAmelCase=None					):
														UpperCamelCase__			       =     state.common.alphas_cumprod[t]
														UpperCamelCase__			       =     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
														UpperCamelCase__			       =     (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

														if variance_type is None:
																					UpperCamelCase__			       =     self.config.variance_type

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

														return variance






							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase = None					,	__lowerCAmelCase = True					,	):
														UpperCamelCase__			       =     timestep

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

														if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
																					UpperCamelCase__       ,       UpperCamelCase__			       =     jnp.split(__lowerCAmelCase					,	sample.shape[1]					,	axis=1					)
														else:
																					UpperCamelCase__			       =     None

														# 1. compute alphas, betas
														UpperCamelCase__			       =     state.common.alphas_cumprod[t]
														UpperCamelCase__			       =     jnp.where(t > 0					,	state.common.alphas_cumprod[t - 1]					,	jnp.array(1.0					,	dtype=self.dtype					)					)
														UpperCamelCase__			       =     1 - alpha_prod_t
														UpperCamelCase__			       =     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":
																					UpperCamelCase__			       =     (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
														elif self.config.prediction_type == "sample":
																					UpperCamelCase__			       =     model_output
														elif self.config.prediction_type == "v_prediction":
																					UpperCamelCase__			       =     (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:
																					UpperCamelCase__			       =     jnp.clip(__lowerCAmelCase					,	-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
														UpperCamelCase__			       =     (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
														UpperCamelCase__			       =     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
														UpperCamelCase__			       =     pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

														# 6. Add noise
														def random_variance():
																					UpperCamelCase__			       =     jax.random.split(__lowerCAmelCase					,	num=1					)
																					UpperCamelCase__			       =     jax.random.normal(__lowerCAmelCase					,	shape=model_output.shape					,	dtype=self.dtype					)
																					return (self._get_variance(__lowerCAmelCase					,	__lowerCAmelCase					,	predicted_variance=__lowerCAmelCase					) ** 0.5) * noise

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

														UpperCamelCase__			       =     pred_prev_sample + variance

														if not return_dict:
																					return (pred_prev_sample, state)

														return FlaxDDPMSchedulerOutput(prev_sample=__lowerCAmelCase					,	state=__lowerCAmelCase					)






							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					,	):
														return add_noise_common(state.common					,	__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					)






							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					,	):
														return get_velocity_common(state.common					,	__lowerCAmelCase					,	__lowerCAmelCase					,	__lowerCAmelCase					)







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











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


UpperCamelCase__     =	logging.get_logger(__name__)

UpperCamelCase__     =	{"vocab_file": "vocab.txt"}

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

UpperCamelCase__     =	{
    "openbmb/cpm-ant-10b": 1_024,
}





def       _UpperCamelCase       (a__				:Tuple    ):






							"""simple docstring"""





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

class     __SCREAMING_SNAKE_CASE   (       _a    ):






							def __init__(  self					,	__lowerCAmelCase					,	__lowerCAmelCase="<unk>"					,	__lowerCAmelCase=200					):
														UpperCamelCase__			       =     vocab
														UpperCamelCase__			       =     unk_token
														UpperCamelCase__			       =     max_input_chars_per_word







							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					):
														UpperCamelCase__			       =     list(__lowerCAmelCase					)
														if len(__lowerCAmelCase					) > self.max_input_chars_per_word:
																					return [self.unk_token]

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

														return sub_tokens






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






							def __init__(  self					,	__lowerCAmelCase					,	__lowerCAmelCase="<d>"					,	__lowerCAmelCase="</d>"					,	__lowerCAmelCase="<s>"					,	__lowerCAmelCase="</s>"					,	__lowerCAmelCase="<pad>"					,	__lowerCAmelCase="<unk>"					,	__lowerCAmelCase="</n>"					,	__lowerCAmelCase="</_>"					,	__lowerCAmelCase="left"					,	**__lowerCAmelCase					,	):
														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					,	)
														UpperCamelCase__			       =     bod_token
														UpperCamelCase__			       =     eod_token
														UpperCamelCase__			       =     load_vocab(__lowerCAmelCase					)
														UpperCamelCase__			       =     self.encoder[space_token]
														UpperCamelCase__			       =     self.encoder[line_token]

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

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

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






							@property
							def 			_lowerCamelCase						(  self					):
														return self.encoder[self.bod_token]






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






							@property
							def 			_lowerCamelCase						(  self					):
														return self.encoder["\n"]






							@property
							def 			_lowerCamelCase						(  self					):
														return len(self.encoder					)






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






							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					):
														UpperCamelCase__			       =     []
														for x in jieba.cut(__lowerCAmelCase					,	cut_all=__lowerCAmelCase					):
																					output_tokens.extend(self.wordpiece_tokenizer.tokenize(__lowerCAmelCase					)					)
														return output_tokens






							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					,	**__lowerCAmelCase					):
														UpperCamelCase__			       =     [i for i in token_ids if i >= 0]
														UpperCamelCase__			       =     [
														    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 			_lowerCamelCase						(  self					,	__lowerCAmelCase					):
														return token in self.encoder






							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					):
														return "".join(__lowerCAmelCase					)






							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					):
														return self.encoder.get(__lowerCAmelCase					,	self.encoder.get(self.unk_token					)					)






							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					):
														return self.decoder.get(__lowerCAmelCase					,	self.unk_token					)






							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					,	__lowerCAmelCase = None					):
														if os.path.isdir(__lowerCAmelCase					):
																					UpperCamelCase__			       =     os.path.join(
																					    __lowerCAmelCase					,	(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]					)
														else:
																					UpperCamelCase__			       =     (filename_prefix + """-""" if filename_prefix else """""") + save_directory
														UpperCamelCase__			       =     0
														if " " in self.encoder:
																					UpperCamelCase__			       =     self.encoder[""" """]
																					del self.encoder[" "]
														if "\n" in self.encoder:
																					UpperCamelCase__			       =     self.encoder["""\n"""]
																					del self.encoder["\n"]
														UpperCamelCase__			       =     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!"""					)
																																			UpperCamelCase__			       =     token_index
																												writer.write(token + """\n"""					)
																												index += 1
														return (vocab_file,)






							def 			_lowerCamelCase						(  self					,	__lowerCAmelCase					,	__lowerCAmelCase = 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 			_lowerCamelCase						(  self					,	__lowerCAmelCase					,	__lowerCAmelCase = None					,	__lowerCAmelCase = 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					))




