
"""simple docstring"""




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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging


if is_torch_available():
    import torch


if TYPE_CHECKING:
    from transformers.pipelines.conversational import Conversation


A__      :	Any          =    logging.get_logger(__name__)
A__      :	List[Any]          =    {"""vocab_file""": """spiece.model"""}

A__      :	Optional[Any]          =    {
    """vocab_file""": {
        """AI-Sweden/gpt-sw3-126m""": """https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-350m""": """https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-1.6b""": """https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-6.7b""": """https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model""",
        """AI-Sweden/gpt-sw3-20b""": """https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model""",
    }
}

A__      :	int          =    {
    """AI-Sweden/gpt-sw3-126m""": 2_048,
    """AI-Sweden/gpt-sw3-350m""": 2_048,
    """AI-Sweden/gpt-sw3-1.6b""": 2_048,
    """AI-Sweden/gpt-sw3-6.7b""": 2_048,
    """AI-Sweden/gpt-sw3-20b""": 2_048,
}







class   _lowercase						(       lowerCAmelCase_				):


     '''simple docstring'''





     _A								=							VOCAB_FILES_NAMES
     _A								=							PRETRAINED_VOCAB_FILES_MAP
     _A								=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _A								=							['input_ids', 'attention_mask']



     def __init__(      self ,			__UpperCamelCase ,			__UpperCamelCase=False ,			__UpperCamelCase=False ,			__UpperCamelCase=False ,			__UpperCamelCase=None ,			__UpperCamelCase=None ,			__UpperCamelCase=None ,			__UpperCamelCase=None ,			__UpperCamelCase = None ,			**__UpperCamelCase ,			)->					None:
          UpperCAmelCase__							: List[Any] 							=						{} if sp_model_kwargs is None else sp_model_kwargs

          UpperCAmelCase__							: Any 							=						kwargs.get("name_or_path"  )
          if name_or_path is None:
               logger.warning(
                   "name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,"
                   " you are testing the model, this can safely be ignored"  )
               UpperCAmelCase__							: List[Any] 							=						"None"

          # Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
          UpperCAmelCase__							: Any 							=						"<|endoftext|>" if eos_token is None else eos_token
          UpperCAmelCase__							: Union[str, Any] 							=						"<unk>" if unk_token is None else unk_token
          if "gpt-sw3-7b" in name_or_path:
               UpperCAmelCase__							: int 							=						unk_token if pad_token is None else pad_token
               UpperCAmelCase__							: List[Any] 							=						eos_token if bos_token is None else bos_token
          else:
               UpperCAmelCase__							: List[str] 							=						"<pad>" if pad_token is None else pad_token
               UpperCAmelCase__							: str 							=						"<s>" if bos_token is None else bos_token

          super().__init__(
              do_lower_case=__UpperCamelCase ,			remove_space=__UpperCamelCase ,			keep_accents=__UpperCamelCase ,			bos_token=__UpperCamelCase ,			eos_token=__UpperCamelCase ,			unk_token=__UpperCamelCase ,			pad_token=__UpperCamelCase ,			sp_model_kwargs=self.sp_model_kwargs ,			**__UpperCamelCase ,			)

          UpperCAmelCase__							: Optional[int] 							=						do_lower_case
          UpperCAmelCase__							: int 							=						remove_space
          UpperCAmelCase__							: Union[str, Any] 							=						keep_accents
          UpperCAmelCase__							: Any 							=						vocab_file

          UpperCAmelCase__							: List[str] 							=						spm.SentencePieceProcessor(**self.sp_model_kwargs  )
          self.sp_model.Load(__UpperCamelCase  )

          # Used for whitespace normalization in input texts
          # fmt : off
          UpperCAmelCase__							: Optional[Any] 							=						{" ", " ", " ", " ", " ", "　", " ", " ", " ", " ", "￼", ""}
          # fmt : on

          # Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
          UpperCAmelCase__							: int 							=						re.compile(
              F"[{''.join(map(__UpperCamelCase ,			list(range(0 ,			9  )  ) + list(range(11 ,			32  )  ) + list(range(1_27 ,			1_60  )  ) + [1_60, 1_73, 82_03]  )  )}]"  )



     def __getstate__(      self  )->					List[str]:
          UpperCAmelCase__							: Optional[int] 							=						self.__dict__.copy()
          UpperCAmelCase__							: Dict 							=						None
          return state



     def __setstate__(      self ,			__UpperCamelCase  )->					Any:
          UpperCAmelCase__							: List[str] 							=						d

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

          UpperCAmelCase__							: Optional[int] 							=						spm.SentencePieceProcessor(**self.sp_model_kwargs  )
          self.sp_model.Load(self.vocab_file  )



     @property
     # Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
     def  lowerCAmelCase__       (      self  )->					int:
          return len(self.sp_model  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					str:
          UpperCAmelCase__							: Optional[Any] 							=						self.non_printing_characters_re.sub("" ,			__UpperCamelCase  )

          # Normalize whitespaces
          UpperCAmelCase__							: Optional[Any] 							=						"".join([char if char not in self.whitespaces else " " for char in text]  )

          # NFC Unicode normalization
          UpperCAmelCase__							: int 							=						unicodedata.normalize("NFC" ,			__UpperCamelCase  )
          return text



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			**__UpperCamelCase  )->					List[str]:
          UpperCAmelCase__							: List[Any] 							=						self.preprocess_text(__UpperCamelCase  )
          return self.sp_model.encode(__UpperCamelCase ,			out_type=__UpperCamelCase  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					int:
          return self.sp_model.PieceToId(__UpperCamelCase  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					str:
          return self.sp_model.IdToPiece(__UpperCamelCase  )



     @staticmethod
     def  lowerCAmelCase__       (      __UpperCamelCase  )->					str:
          return out_string



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					str:
          UpperCAmelCase__							: Any 							=						[]
          UpperCAmelCase__							: Optional[Any] 							=						""
          UpperCAmelCase__							: str 							=						False
          for token in tokens:
               # make sure that special tokens are not decoded using sentencepiece model
               if token in self.all_special_tokens:
                    # TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
                    if not prev_is_special:
                         out_string += " "

                    out_string += self.sp_model.decode(__UpperCamelCase  ) + token
                    UpperCAmelCase__							: Optional[Any] 							=						True
                    UpperCAmelCase__							: Optional[Any] 							=						[]
               else:
                    current_sub_tokens.append(__UpperCamelCase  )
                    UpperCAmelCase__							: List[str] 							=						False
          out_string += self.sp_model.decode(__UpperCamelCase  )

          return out_string



     def  lowerCAmelCase__       (      self  )->					Dict[str, int]:
          UpperCAmelCase__							: str 							=						{self.convert_ids_to_tokens(__UpperCamelCase  ): i for i in range(self.vocab_size  )}
          vocab.update(self.added_tokens_encoder  )
          return vocab



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase = None  )->					Tuple[str]:
          if not os.path.isdir(__UpperCamelCase  ):
               logger.error(F"Vocabulary path ({save_directory}) should be a directory"  )
               return
          UpperCAmelCase__							: Union[str, Any] 							=						os.path.join(
              __UpperCamelCase ,			(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]  )

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

          return (out_vocab_file,)



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase = False  )->					Union[List[int], List[List[int]], "torch.Tensor"]:

          if isinstance(__UpperCamelCase ,			__UpperCamelCase  ):
               UpperCAmelCase__							: List[Any] 							=						self.preprocess_text(__UpperCamelCase  )
               UpperCAmelCase__							: int 							=						self.sp_model.encode(__UpperCamelCase  )
          else:
               UpperCAmelCase__							: Optional[Any] 							=						[self.preprocess_text(__UpperCamelCase  ) for t in text]
               UpperCAmelCase__							: Tuple 							=						self.sp_model.encode(__UpperCamelCase  )

          if return_tensors is True or return_tensors == "pt":
               UpperCAmelCase__							: Union[str, Any] 							=						torch.tensor(__UpperCamelCase  )

          return token_ids



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					str:
          return self.sp_model.decode(__UpperCamelCase  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					List[int]:
          UpperCAmelCase__							: Optional[int] 							=						[F"User: {text}" if is_user else F"Bot: {text}" for is_user, text in conversation.iter_texts()]
          UpperCAmelCase__							: Optional[int] 							=						(
              F"{self.eos_token}{self.bos_token}" + F"{self.bos_token}".join(__UpperCamelCase  ) + F"{self.bos_token}Bot:"
          )
          return self.encode(text=__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



