


'''simple docstring'''


import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

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


if TYPE_CHECKING:
      from transformers.pipelines.conversational import Conversation

_UpperCamelCase          =     logging.get_logger(__name__)

_UpperCamelCase          =     {
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
}

_UpperCamelCase          =     {
    """vocab_file""": {
        """gpt2""": """https://huggingface.co/gpt2/resolve/main/vocab.json""",
        """gpt2-medium""": """https://huggingface.co/gpt2-medium/resolve/main/vocab.json""",
        """gpt2-large""": """https://huggingface.co/gpt2-large/resolve/main/vocab.json""",
        """gpt2-xl""": """https://huggingface.co/gpt2-xl/resolve/main/vocab.json""",
        """distilgpt2""": """https://huggingface.co/distilgpt2/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """gpt2""": """https://huggingface.co/gpt2/resolve/main/merges.txt""",
        """gpt2-medium""": """https://huggingface.co/gpt2-medium/resolve/main/merges.txt""",
        """gpt2-large""": """https://huggingface.co/gpt2-large/resolve/main/merges.txt""",
        """gpt2-xl""": """https://huggingface.co/gpt2-xl/resolve/main/merges.txt""",
        """distilgpt2""": """https://huggingface.co/distilgpt2/resolve/main/merges.txt""",
    },
}

_UpperCamelCase          =     {
    """gpt2""": 1024,
    """gpt2-medium""": 1024,
    """gpt2-large""": 1024,
    """gpt2-xl""": 1024,
    """distilgpt2""": 1024,
}


@lru_cache()
def 		_lowercase   ():




     '''simple docstring'''





     __A  :					List[Any]						     =       (
         list(range(ord("!"       )					,				ord("~"       ) + 1       )       ) + list(range(ord("¡"       )					,				ord("¬"       ) + 1       )       ) + list(range(ord("®"       )					,				ord("ÿ"       ) + 1       )       )
     )
     __A  :					Tuple						     =       bs[:]
     __A  :					Dict						     =       0
     for b in range(2**8       ):
          if b not in bs:
               bs.append(SCREAMING_SNAKE_CASE       )
               cs.append(2**8 + n       )
               n += 1
     __A  :					List[Any]						     =       [chr(SCREAMING_SNAKE_CASE       ) for n in cs]
     return dict(zip(SCREAMING_SNAKE_CASE					,				SCREAMING_SNAKE_CASE       )       )


def 		_lowercase   (SCREAMING_SNAKE_CASE       ):




     '''simple docstring'''





     __A  :					Tuple						     =       set()
     __A  :					List[Any]						     =       word[0]
     for char in word[1:]:
          pairs.add((prev_char, char)       )
          __A  :					int						     =       char
     return pairs







class        __magic_name__  (					lowerCAmelCase   ):

     """simple docstring"""






     lowerCamelCase__     :   List[str]						=							VOCAB_FILES_NAMES
     lowerCamelCase__     :   Union[str, Any]						=							PRETRAINED_VOCAB_FILES_MAP
     lowerCamelCase__     :   List[str]						=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCamelCase__     :   Dict						=							['input_ids', 'attention_mask']


     def __init__(      self ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase="replace" ,    lowerCamelCase="<|endoftext|>" ,    lowerCamelCase="<|endoftext|>" ,    lowerCamelCase="<|endoftext|>" ,    lowerCamelCase=None ,    lowerCamelCase=False ,    lowerCamelCase=False ,    **lowerCamelCase ,    ):


          '''simple docstring'''





          __A  :					int						     =       AddedToken(lowerCamelCase ,    lstrip=lowerCamelCase ,    rstrip=lowerCamelCase		) if isinstance(lowerCamelCase ,    lowerCamelCase		) else bos_token
          __A  :					Union[str, Any]						     =       AddedToken(lowerCamelCase ,    lstrip=lowerCamelCase ,    rstrip=lowerCamelCase		) if isinstance(lowerCamelCase ,    lowerCamelCase		) else eos_token
          __A  :					Dict						     =       AddedToken(lowerCamelCase ,    lstrip=lowerCamelCase ,    rstrip=lowerCamelCase		) if isinstance(lowerCamelCase ,    lowerCamelCase		) else unk_token
          __A  :					Dict						     =       AddedToken(lowerCamelCase ,    lstrip=lowerCamelCase ,    rstrip=lowerCamelCase		) if isinstance(lowerCamelCase ,    lowerCamelCase		) else pad_token
          super().__init__(
              errors=lowerCamelCase ,    unk_token=lowerCamelCase ,    bos_token=lowerCamelCase ,    eos_token=lowerCamelCase ,    pad_token=lowerCamelCase ,    add_prefix_space=lowerCamelCase ,    add_bos_token=lowerCamelCase ,    **lowerCamelCase ,    )
          __A  :					List[Any]						     =       add_bos_token

          with open(lowerCamelCase ,    encoding="utf-8"		) as vocab_handle:
               __A  :					Optional[int]						     =       json.load(lowerCamelCase		)
          __A  :					str						     =       {v: k for k, v in self.encoder.items()}
          __A  :					Optional[Any]						     =       errors  # how to handle errors in decoding
          __A  :					Tuple						     =       bytes_to_unicode()
          __A  :					Any						     =       {v: k for k, v in self.byte_encoder.items()}
          with open(lowerCamelCase ,    encoding="utf-8"		) as merges_handle:
               __A  :					Any						     =       merges_handle.read().split("\n"		)[1:-1]
          __A  :					List[str]						     =       [tuple(merge.split()		) for merge in bpe_merges]
          __A  :					Any						     =       dict(zip(lowerCamelCase ,    range(len(lowerCamelCase		)		)		)		)
          __A  :					Tuple						     =       {}
          __A  :					List[Any]						     =       add_prefix_space

          # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
          __A  :					List[str]						     =       re.compile(r"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"		)


     @property
     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          return len(self.encoder		)


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          return dict(self.encoder ,    **self.added_tokens_encoder		)


     def lowerCAmelCase__     (      self ,    lowerCamelCase		):


          '''simple docstring'''





          if token in self.cache:
               return self.cache[token]
          __A  :					Tuple						     =       tuple(lowerCamelCase		)
          __A  :					str						     =       get_pairs(lowerCamelCase		)

          if not pairs:
               return token

          while True:
               __A  :					List[Any]						     =       min(lowerCamelCase ,    key=lambda lowerCamelCase		: self.bpe_ranks.get(lowerCamelCase ,    float("inf"		)		)		)
               if bigram not in self.bpe_ranks:
                    break
               __A  :					Optional[int]						     =       bigram
               __A  :					Tuple						     =       []
               __A  :					Optional[int]						     =       0
               while i < len(lowerCamelCase		):
                    try:
                         __A  :					str						     =       word.index(lowerCamelCase ,    lowerCamelCase		)
                    except ValueError:
                         new_word.extend(word[i:]		)
                         break
                    else:
                         new_word.extend(word[i:j]		)
                         __A  :					Dict						     =       j

                    if word[i] == first and i < len(lowerCamelCase		) - 1 and word[i + 1] == second:
                         new_word.append(first + second		)
                         i += 2
                    else:
                         new_word.append(word[i]		)
                         i += 1
               __A  :					int						     =       tuple(lowerCamelCase		)
               __A  :					Optional[Any]						     =       new_word
               if len(lowerCamelCase		) == 1:
                    break
               else:
                    __A  :					Optional[int]						     =       get_pairs(lowerCamelCase		)
          __A  :					str						     =       " ".join(lowerCamelCase		)
          __A  :					Union[str, Any]						     =       word
          return word


     def lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase=None		):


          '''simple docstring'''





          if self.add_bos_token:
               __A  :					Union[str, Any]						     =       [self.bos_token_id]
          else:
               __A  :					Optional[int]						     =       []

          __A  :					List[Any]						     =       bos_token_ids + token_ids_a

          if token_ids_a is None:
               return output

          return output + bos_token_ids + token_ids_a


     def lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase = None ,    lowerCamelCase = False		):


          '''simple docstring'''





          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 not self.add_bos_token:
               return super().get_special_tokens_mask(
                   token_ids_a=lowerCamelCase ,    token_ids_a=lowerCamelCase ,    already_has_special_tokens=lowerCamelCase		)

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


     def lowerCAmelCase__     (      self ,    lowerCamelCase		):


          '''simple docstring'''





          __A  :					List[Any]						     =       []
          for token in re.findall(self.pat ,    lowerCamelCase		):
               __A  :					Optional[int]						     =       "".join(
                   self.byte_encoder[b] for b in token.encode("utf-8"		)		)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
               bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowerCamelCase		).split(" "		)		)
          return bpe_tokens


     def lowerCAmelCase__     (      self ,    lowerCamelCase		):


          '''simple docstring'''





          return self.encoder.get(lowerCamelCase ,    self.encoder.get(self.unk_token		)		)


     def lowerCAmelCase__     (      self ,    lowerCamelCase		):


          '''simple docstring'''





          return self.decoder.get(lowerCamelCase		)


     def lowerCAmelCase__     (      self ,    lowerCamelCase		):


          '''simple docstring'''





          __A  :					Union[str, Any]						     =       "".join(lowerCamelCase		)
          __A  :					int						     =       bytearray([self.byte_decoder[c] for c in text]		).decode("utf-8" ,    errors=self.errors		)
          return text


     def lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase = None		):


          '''simple docstring'''





          if not os.path.isdir(lowerCamelCase		):
               logger.error(f"Vocabulary path ({save_directory}) should be a directory"		)
               return
          __A  :					Union[str, Any]						     =       os.path.join(
              lowerCamelCase ,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)
          __A  :					int						     =       os.path.join(
              lowerCamelCase ,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]		)

          with open(lowerCamelCase ,    "w" ,    encoding="utf-8"		) as f:
               f.write(json.dumps(self.encoder ,    indent=2 ,    sort_keys=lowerCamelCase ,    ensure_ascii=lowerCamelCase		) + "\n"		)

          __A  :					Optional[int]						     =       0
          with open(lowerCamelCase ,    "w" ,    encoding="utf-8"		) as writer:
               writer.write("#version: 0.2\n"		)
               for bpe_tokens, token_index in sorted(self.bpe_ranks.items() ,    key=lambda lowerCamelCase		: kv[1]		):
                    if index != token_index:
                         logger.warning(
                             f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
                             " Please check that the tokenizer is not corrupted!"		)
                         __A  :					Optional[int]						     =       token_index
                    writer.write(" ".join(lowerCamelCase		) + "\n"		)
                    index += 1

          return vocab_file, merge_file


     def lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase=False ,    **lowerCamelCase		):


          '''simple docstring'''





          __A  :					Union[str, Any]						     =       kwargs.pop("add_prefix_space" ,    self.add_prefix_space		)
          if is_split_into_words or add_prefix_space:
               __A  :					Tuple						     =       " " + text
          return (text, kwargs)


     def lowerCAmelCase__     (      self ,    lowerCamelCase		):


          '''simple docstring'''





          __A  :					Union[str, Any]						     =       []
          for is_user, text in conversation.iter_texts():
               input_ids.extend(self.encode(lowerCamelCase ,    add_special_tokens=lowerCamelCase		) + [self.eos_token_id]		)
          if len(lowerCamelCase		) > self.model_max_length:
               __A  :					Any						     =       input_ids[-self.model_max_length :]
          return input_ids





'''simple docstring'''


# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

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

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import FlaxSchedulerMixin, FlaxSchedulerOutput, broadcast_to_shape_from_left







@flax.struct.dataclass
class        __magic_name__  :

     """simple docstring"""






     lowerCamelCase__						=							None
     lowerCamelCase__						=							None
     lowerCamelCase__						=							None


     @classmethod
     def lowerCAmelCase__     (      cls		):


          '''simple docstring'''





          return cls()







@dataclass
class        __magic_name__  (					lowerCAmelCase   ):

     """simple docstring"""






     lowerCamelCase__						=							42
     lowerCamelCase__						=							42
     lowerCamelCase__						=							None







class        __magic_name__  (					lowerCAmelCase						,		lowerCAmelCase   ):

     """simple docstring"""






     @property
     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          return True


     @register_to_config
     def __init__(      self ,    lowerCamelCase = 2000 ,    lowerCamelCase = 0.15 ,    lowerCamelCase = 0.01 ,    lowerCamelCase = 1348.0 ,    lowerCamelCase = 1E-5 ,    lowerCamelCase = 1 ,    ):


          '''simple docstring'''





          pass


     def lowerCAmelCase__     (      self		):


          '''simple docstring'''





          __A  :					List[Any]						     =       ScoreSdeVeSchedulerState.create()
          return self.set_sigmas(
              lowerCamelCase ,    self.config.num_train_timesteps ,    self.config.sigma_min ,    self.config.sigma_max ,    self.config.sampling_eps ,    )


     def lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase = () ,    lowerCamelCase = None		):


          '''simple docstring'''





          __A  :					Tuple						     =       sampling_eps if sampling_eps is not None else self.config.sampling_eps

          __A  :					Optional[int]						     =       jnp.linspace(1 ,    lowerCamelCase ,    lowerCamelCase		)
          return state.replace(timesteps=lowerCamelCase		)


     def lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase = None ,    lowerCamelCase = None ,    lowerCamelCase = None ,    ):


          '''simple docstring'''





          __A  :					str						     =       sigma_min if sigma_min is not None else self.config.sigma_min
          __A  :					Union[str, Any]						     =       sigma_max if sigma_max is not None else self.config.sigma_max
          __A  :					Dict						     =       sampling_eps if sampling_eps is not None else self.config.sampling_eps
          if state.timesteps is None:
               __A  :					Union[str, Any]						     =       self.set_timesteps(lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase		)

          __A  :					Dict						     =       jnp.exp(jnp.linspace(jnp.log(lowerCamelCase		) ,    jnp.log(lowerCamelCase		) ,    lowerCamelCase		)		)
          __A  :					int						     =       jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]		)

          return state.replace(discrete_sigmas=lowerCamelCase ,    sigmas=lowerCamelCase		)


     def lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase		):


          '''simple docstring'''





          return jnp.where(timesteps == 0 ,    jnp.zeros_like(lowerCamelCase		) ,    state.discrete_sigmas[timesteps - 1]		)


     def lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase = True ,    ):


          '''simple docstring'''





          if state.timesteps is None:
               raise ValueError(
                   "`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"		)

          __A  :					Any						     =       timestep * jnp.ones(
              sample.shape[0] ,    )
          __A  :					Any						     =       (timestep * (len(state.timesteps		) - 1)).long()

          __A  :					Any						     =       state.discrete_sigmas[timesteps]
          __A  :					Union[str, Any]						     =       self.get_adjacent_sigma(lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase		)
          __A  :					List[str]						     =       jnp.zeros_like(lowerCamelCase		)
          __A  :					Union[str, Any]						     =       (sigma**2 - adjacent_sigma**2) ** 0.5

          # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
          # also equation 47 shows the analog from SDE models to ancestral sampling methods
          __A  :					List[Any]						     =       diffusion.flatten()
          __A  :					List[Any]						     =       broadcast_to_shape_from_left(lowerCamelCase ,    sample.shape		)
          __A  :					str						     =       drift - diffusion**2 * model_output

          #  equation 6: sample noise for the diffusion term of
          __A  :					Any						     =       random.split(lowerCamelCase ,    num=1		)
          __A  :					Union[str, Any]						     =       random.normal(key=lowerCamelCase ,    shape=sample.shape		)
          __A  :					Dict						     =       sample - drift  # subtract because `dt` is a small negative timestep
          # TODO is the variable diffusion the correct scaling term for the noise?
          __A  :					int						     =       prev_sample_mean + diffusion * noise  # add impact of diffusion field g

          if not return_dict:
               return (prev_sample, prev_sample_mean, state)

          return FlaxSdeVeOutput(prev_sample=lowerCamelCase ,    prev_sample_mean=lowerCamelCase ,    state=lowerCamelCase		)


     def lowerCAmelCase__     (      self ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase ,    lowerCamelCase = True ,    ):


          '''simple docstring'''





          if state.timesteps is None:
               raise ValueError(
                   "`state.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler"		)

          # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
          # sample noise for correction
          __A  :					int						     =       random.split(lowerCamelCase ,    num=1		)
          __A  :					int						     =       random.normal(key=lowerCamelCase ,    shape=sample.shape		)

          # compute step size from the model_output, the noise, and the snr
          __A  :					Optional[Any]						     =       jnp.linalg.norm(lowerCamelCase		)
          __A  :					Dict						     =       jnp.linalg.norm(lowerCamelCase		)
          __A  :					Any						     =       (self.config.snr * noise_norm / grad_norm) ** 2 * 2
          __A  :					List[str]						     =       step_size * jnp.ones(sample.shape[0]		)

          # compute corrected sample: model_output term and noise term
          __A  :					Optional[Any]						     =       step_size.flatten()
          __A  :					Optional[int]						     =       broadcast_to_shape_from_left(lowerCamelCase ,    sample.shape		)
          __A  :					Dict						     =       sample + step_size * model_output
          __A  :					List[Any]						     =       prev_sample_mean + ((step_size * 2) ** 0.5) * noise

          if not return_dict:
               return (prev_sample, state)

          return FlaxSdeVeOutput(prev_sample=lowerCamelCase ,    state=lowerCamelCase		)


     def __len__(      self		):


          '''simple docstring'''





          return self.config.num_train_timesteps

