
"""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


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

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

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

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



def 							a__ (				lowerCAmelCase	:       int		):






     '''simple docstring'''
     UpperCAmelCase__							: List[Any] 							=						collections.OrderedDict()
     with open(lowerCAmelCase       ,    "r"       ,    encoding="utf-8"		) as reader:
          UpperCAmelCase__							: int 							=						reader.readlines()
     for index, token in enumerate(lowerCAmelCase		):
          UpperCAmelCase__							: Union[str, Any] 							=						token.rstrip("\n"		)
          UpperCAmelCase__							: Any 							=						index
     return vocab







class   _lowercase						(       lowerCAmelCase_				):


     '''simple docstring'''





     def __init__(      self ,			__UpperCamelCase ,			__UpperCamelCase="<unk>" ,			__UpperCamelCase=2_00  )->					Optional[Any]:
          UpperCAmelCase__							: Tuple 							=						vocab
          UpperCAmelCase__							: int 							=						unk_token
          UpperCAmelCase__							: str 							=						max_input_chars_per_word



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					Union[str, Any]:
          UpperCAmelCase__							: Dict 							=						list(__UpperCamelCase  )
          if len(__UpperCamelCase  ) > self.max_input_chars_per_word:
               return [self.unk_token]

          UpperCAmelCase__							: List[str] 							=						0
          UpperCAmelCase__							: Union[str, Any] 							=						[]
          while start < len(__UpperCamelCase  ):
               UpperCAmelCase__							: Union[str, Any] 							=						len(__UpperCamelCase  )
               UpperCAmelCase__							: Optional[int] 							=						None
               while start < end:
                    UpperCAmelCase__							: Optional[Any] 							=						"".join(chars[start:end]  )
                    if substr in self.vocab:
                         UpperCAmelCase__							: Union[str, Any] 							=						substr
                         break
                    end -= 1
               if cur_substr is None:
                    sub_tokens.append(self.unk_token  )
                    start += 1
               else:
                    sub_tokens.append(__UpperCamelCase  )
                    UpperCAmelCase__							: int 							=						end

          return sub_tokens







class   _lowercase						(       lowerCAmelCase_				):


     '''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 ,			)->					int:
          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__							: List[Any] 							=						bod_token
          UpperCAmelCase__							: Any 							=						eod_token
          UpperCAmelCase__							: int 							=						load_vocab(__UpperCamelCase  )
          UpperCAmelCase__							: int 							=						self.encoder[space_token]
          UpperCAmelCase__							: Dict 							=						self.encoder[line_token]

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

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

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



     @property
     def  lowerCAmelCase__       (      self  )->					int:
          return self.encoder[self.bod_token]



     @property
     def  lowerCAmelCase__       (      self  )->					int:
          return self.encoder[self.eod_token]



     @property
     def  lowerCAmelCase__       (      self  )->					List[str]:
          return self.encoder["\n"]



     @property
     def  lowerCAmelCase__       (      self  )->					int:
          return len(self.encoder  )



     def  lowerCAmelCase__       (      self  )->					List[str]:
          return dict(self.encoder ,			**self.added_tokens_encoder  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					int:
          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  )->					Any:
          UpperCAmelCase__							: Union[str, 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  )->					List[str]:
          return token in self.encoder



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					str:
          return "".join(__UpperCamelCase  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					Union[str, Any]:
          return self.encoder.get(__UpperCamelCase ,			self.encoder.get(self.unk_token  )  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					List[str]:
          return self.decoder.get(__UpperCamelCase ,			self.unk_token  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase = None  )->					Tuple[str]:
          if os.path.isdir(__UpperCamelCase  ):
               UpperCAmelCase__							: Optional[Any] 							=						os.path.join(
                   __UpperCamelCase ,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]  )
          else:
               UpperCAmelCase__							: List[str] 							=						(filename_prefix + "-" if filename_prefix else "") + save_directory
          UpperCAmelCase__							: List[Any] 							=						0
          if " " in self.encoder:
               UpperCAmelCase__							: int 							=						self.encoder[" "]
               del self.encoder[" "]
          if "\n" in self.encoder:
               UpperCAmelCase__							: int 							=						self.encoder["\n"]
               del self.encoder["\n"]
          UpperCAmelCase__							: 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__							: List[str] 							=						token_index
                    writer.write(token + "\n"  )
                    index += 1
          return (vocab_file,)



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

          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  ))





"""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						:


     '''simple docstring'''





     _A								=							42

     # setable values
     _A								=							42
     _A								=							42
     _A								=							None



     @classmethod
     def  lowerCAmelCase__       (      cls ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase  )->					Union[str, Any]:
          return cls(common=__UpperCamelCase ,			init_noise_sigma=__UpperCamelCase ,			timesteps=__UpperCamelCase  )







@dataclass
class   _lowercase						(       lowerCAmelCase_				):


     '''simple docstring'''





     _A								=							42







class   _lowercase						(       lowerCAmelCase_	,   lowerCAmelCase_				):


     '''simple docstring'''





     _A								=							[e.name for e in FlaxKarrasDiffusionSchedulers]

     _A								=							42



     @property
     def  lowerCAmelCase__       (      self  )->					Optional[int]:
          return True



     @register_to_config
     def __init__(      self ,			__UpperCamelCase = 10_00 ,			__UpperCamelCase = 0.0001 ,			__UpperCamelCase = 0.02 ,			__UpperCamelCase = "linear" ,			__UpperCamelCase = None ,			__UpperCamelCase = "fixed_small" ,			__UpperCamelCase = True ,			__UpperCamelCase = "epsilon" ,			__UpperCamelCase = jnp.floataa ,			)->					List[str]:
          UpperCAmelCase__							: int 							=						dtype



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase = None  )->					DDPMSchedulerState:
          if common is None:
               UpperCAmelCase__							: int 							=						CommonSchedulerState.create(self  )

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

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

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



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase = None  )->					jnp.ndarray:
          return sample



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase = ()  )->					DDPMSchedulerState:
          UpperCAmelCase__							: Dict 							=						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__							: Optional[int] 							=						(jnp.arange(0 ,			__UpperCamelCase  ) * step_ratio).round()[::-1]

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



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase=None ,			__UpperCamelCase=None  )->					Union[str, Any]:
          UpperCAmelCase__							: Optional[Any] 							=						state.common.alphas_cumprod[t]
          UpperCAmelCase__							: int 							=						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__							: Any 							=						(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

          if variance_type is None:
               UpperCAmelCase__							: Union[str, Any] 							=						self.config.variance_type

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

          return variance



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase = None ,			__UpperCamelCase = True ,			)->					Union[FlaxDDPMSchedulerOutput, Tuple]:
          UpperCAmelCase__							: List[str] 							=						timestep

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

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

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

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

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

          UpperCAmelCase__							: Any 							=						pred_prev_sample + variance

          if not return_dict:
               return (pred_prev_sample, state)

          return FlaxDDPMSchedulerOutput(prev_sample=__UpperCamelCase ,			state=__UpperCamelCase  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			)->					jnp.ndarray:
          return add_noise_common(state.common ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase ,			)->					jnp.ndarray:
          return get_velocity_common(state.common ,			__UpperCamelCase ,			__UpperCamelCase ,			__UpperCamelCase  )



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



