




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

__SCREAMING_SNAKE_CASE			:      Dict      			=logging.get_logger(__name__)

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

__SCREAMING_SNAKE_CASE			:      str      			={
    """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""",
    },
}

__SCREAMING_SNAKE_CASE			:      str      			={
    """gpt2""": 1_024,
    """gpt2-medium""": 1_024,
    """gpt2-large""": 1_024,
    """gpt2-xl""": 1_024,
    """distilgpt2""": 1_024,
}

@lru_cache()
def 	lowerCAmelCase_	(     )    ->	Dict:


      '''simple docstring'''




      lowerCamelCase__					     = (
          list(range(ord('!'  )   , ord('~'  ) + 1  )  ) + list(range(ord('¡'  )   , ord('¬'  ) + 1  )  ) + list(range(ord('®'  )   , ord('ÿ'  ) + 1  )  )
      )
      lowerCamelCase__					     = bs[:]
      lowerCamelCase__					     = 0
      for b in range(2**8  ):
            if b not in bs:
                  bs.append(lowerCamelCase  )
                  cs.append(2**8 + n  )
                  n += 1
      lowerCamelCase__					     = [chr(lowerCamelCase  ) for n in cs]
      return dict(zip(lowerCamelCase   , lowerCamelCase  )  )

def 	lowerCAmelCase_	(     lowerCamelCase:				Optional[Any]  )    ->	int:


      '''simple docstring'''




      lowerCamelCase__					     = set()
      lowerCamelCase__					     = word[0]
      for char in word[1:]:
            pairs.add((prev_char, char)  )
            lowerCamelCase__					     = char
      return pairs






class 	A	(						_lowerCAmelCase						):
     """simple docstring"""

     _snake_case:		Dict   =VOCAB_FILES_NAMES
     _snake_case:		Dict   =PRETRAINED_VOCAB_FILES_MAP
     _snake_case:		Tuple   =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _snake_case:		Tuple   =["input_ids", "attention_mask"]




     def __init__(			self     :      Any		,     A__     :      Tuple		,     A__     :      Optional[Any]		,     A__     :      int="replace"		,     A__     :      str="<|endoftext|>"		,     A__     :      Any="<|endoftext|>"		,     A__     :      Any="<|endoftext|>"		,     A__     :      Tuple=None		,     A__     :      Tuple=False		,     A__     :      Tuple=False		,     **A__     :      Tuple		,     ) ->				Dict:
           lowerCamelCase__					     = AddedToken(A__		,     lstrip=A__		,     rstrip=A__	) if isinstance(A__		,     A__	) else bos_token
           lowerCamelCase__					     = AddedToken(A__		,     lstrip=A__		,     rstrip=A__	) if isinstance(A__		,     A__	) else eos_token
           lowerCamelCase__					     = AddedToken(A__		,     lstrip=A__		,     rstrip=A__	) if isinstance(A__		,     A__	) else unk_token
           lowerCamelCase__					     = AddedToken(A__		,     lstrip=A__		,     rstrip=A__	) if isinstance(A__		,     A__	) else pad_token
           super().__init__(
               errors=A__		,     unk_token=A__		,     bos_token=A__		,     eos_token=A__		,     pad_token=A__		,     add_prefix_space=A__		,     add_bos_token=A__		,     **A__		,     )
           lowerCamelCase__					     = add_bos_token

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

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




     @property
     def        __snake_case      (			self     :      List[str]	) ->				Optional[Any]:
           return len(self.encoder	)




     def        __snake_case      (			self     :      Tuple	) ->				Any:
           return dict(self.encoder		,     **self.added_tokens_encoder	)




     def        __snake_case      (			self     :      List[str]		,     A__     :      Tuple	) ->				Union[str, Any]:
           if token in self.cache:
                 return self.cache[token]
           lowerCamelCase__					     = tuple(A__	)
           lowerCamelCase__					     = get_pairs(A__	)

           if not pairs:
                 return token

           while True:
                 lowerCamelCase__					     = min(A__		,     key=lambda A__	: self.bpe_ranks.get(A__		,     float('inf'	)	)	)
                 if bigram not in self.bpe_ranks:
                       break
                 lowerCamelCase__     ,						lowerCamelCase__					     = bigram
                 lowerCamelCase__					     = []
                 lowerCamelCase__					     = 0
                 while i < len(A__	):
                       try:
                             lowerCamelCase__					     = word.index(A__		,     A__	)
                       except ValueError:
                             new_word.extend(word[i:]	)
                             break
                       else:
                             new_word.extend(word[i:j]	)
                             lowerCamelCase__					     = j

                       if word[i] == first and i < len(A__	) - 1 and word[i + 1] == second:
                             new_word.append(first + second	)
                             i += 2
                       else:
                             new_word.append(word[i]	)
                             i += 1
                 lowerCamelCase__					     = tuple(A__	)
                 lowerCamelCase__					     = new_word
                 if len(A__	) == 1:
                       break
                 else:
                       lowerCamelCase__					     = get_pairs(A__	)
           lowerCamelCase__					     = ' '.join(A__	)
           lowerCamelCase__					     = word
           return word




     def        __snake_case      (			self     :      int		,     A__     :      Any		,     A__     :      str=None	) ->				List[Any]:
           if self.add_bos_token:
                 lowerCamelCase__					     = [self.bos_token_id]
           else:
                 lowerCamelCase__					     = []

           lowerCamelCase__					     = bos_token_ids + token_ids_a

           if token_ids_a is None:
                 return output

           return output + bos_token_ids + token_ids_a




     def        __snake_case      (			self     :      Optional[int]		,     A__     :      List[int]		,     A__     :      Optional[List[int]] = None		,     A__     :      bool = False	) ->				List[int]:
           if already_has_special_tokens:
                 return super().get_special_tokens_mask(
                     token_ids_a=A__		,     token_ids_a=A__		,     already_has_special_tokens=A__	)

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

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




     def        __snake_case      (			self     :      List[Any]		,     A__     :      str	) ->				Tuple:
           lowerCamelCase__					     = []
           for token in re.findall(self.pat		,     A__	):
                 lowerCamelCase__					     = ''.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(A__	).split(' '	)	)
           return bpe_tokens




     def        __snake_case      (			self     :      Any		,     A__     :      str	) ->				Optional[Any]:
           return self.encoder.get(A__		,     self.encoder.get(self.unk_token	)	)




     def        __snake_case      (			self     :      str		,     A__     :      Optional[int]	) ->				Optional[Any]:
           return self.decoder.get(A__	)




     def        __snake_case      (			self     :      Tuple		,     A__     :      Optional[int]	) ->				Optional[Any]:
           lowerCamelCase__					     = ''.join(A__	)
           lowerCamelCase__					     = bytearray([self.byte_decoder[c] for c in text]	).decode('utf-8'		,     errors=self.errors	)
           return text




     def        __snake_case      (			self     :      Optional[int]		,     A__     :      str		,     A__     :      Optional[str] = None	) ->				Tuple[str]:
           if not os.path.isdir(A__	):
                 logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""	)
                 return
           lowerCamelCase__					     = os.path.join(
               A__		,     (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']	)
           lowerCamelCase__					     = os.path.join(
               A__		,     (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']	)

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

           lowerCamelCase__					     = 0
           with open(A__		,     '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 A__	: 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!'	)
                             lowerCamelCase__					     = token_index
                       writer.write(' '.join(A__	) + '\n'	)
                       index += 1

           return vocab_file, merge_file




     def        __snake_case      (			self     :      Tuple		,     A__     :      Tuple		,     A__     :      Optional[int]=False		,     **A__     :      Optional[Any]	) ->				List[Any]:
           lowerCamelCase__					     = kwargs.pop('add_prefix_space'		,     self.add_prefix_space	)
           if is_split_into_words or add_prefix_space:
                 lowerCamelCase__					     = ' ' + text
           return (text, kwargs)




     def        __snake_case      (			self     :      List[str]		,     A__     :      "Conversation"	) ->				List[int]:
           lowerCamelCase__					     = []
           for is_user, text in conversation.iter_texts():
                 input_ids.extend(self.encode(A__		,     add_special_tokens=A__	) + [self.eos_token_id]	)
           if len(A__	) > self.model_max_length:
                 lowerCamelCase__					     = input_ids[-self.model_max_length :]
           return input_ids





# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion

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

import flax
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 	A	:
     """simple docstring"""

     _snake_case:		CommonSchedulerState
     _snake_case:		jnp.ndarray

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




     @classmethod
     def        __snake_case      (			cls     :      Optional[int]		,     A__     :      CommonSchedulerState		,     A__     :      jnp.ndarray		,     A__     :      jnp.ndarray		,     A__     :      jnp.ndarray		,     ) ->				Any:
           return cls(
               common=A__		,     final_alpha_cumprod=A__		,     init_noise_sigma=A__		,     timesteps=A__		,     )






@dataclass
class 	A	(						_lowerCAmelCase						):
     """simple docstring"""

     _snake_case:		DDIMSchedulerState






class 	A	(						_lowerCAmelCase	,  _lowerCAmelCase						):
     """simple docstring"""

     _snake_case:		List[str]   =[e.name for e in FlaxKarrasDiffusionSchedulers]

     _snake_case:		jnp.dtype




     @property
     def        __snake_case      (			self     :      Optional[int]	) ->				List[str]:
           return True




     @register_to_config
     def __init__(			self     :      Optional[Any]		,     A__     :      int = 1_0_0_0		,     A__     :      float = 0.0001		,     A__     :      float = 0.02		,     A__     :      str = "linear"		,     A__     :      Optional[jnp.ndarray] = None		,     A__     :      bool = True		,     A__     :      int = 0		,     A__     :      str = "epsilon"		,     A__     :      jnp.dtype = jnp.floataa		,     ) ->				Optional[int]:
           lowerCamelCase__					     = dtype




     def        __snake_case      (			self     :      Optional[Any]		,     A__     :      Optional[CommonSchedulerState] = None	) ->				DDIMSchedulerState:
           if common is None:
                 lowerCamelCase__					     = CommonSchedulerState.create(self	)

           # At every step in ddim, we are looking into the previous alphas_cumprod
           # For the final step, there is no previous alphas_cumprod because we are already at 0
           # `set_alpha_to_one` decides whether we set this parameter simply to one or
           # whether we use the final alpha of the "non-previous" one.
           lowerCamelCase__					     = (
               jnp.array(1.0		,     dtype=self.dtype	) if self.config.set_alpha_to_one else common.alphas_cumprod[0]
           )

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

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

           return DDIMSchedulerState.create(
               common=A__		,     final_alpha_cumprod=A__		,     init_noise_sigma=A__		,     timesteps=A__		,     )




     def        __snake_case      (			self     :      Optional[Any]		,     A__     :      DDIMSchedulerState		,     A__     :      jnp.ndarray		,     A__     :      Optional[int] = None	) ->				jnp.ndarray:
           return sample




     def        __snake_case      (			self     :      Union[str, Any]		,     A__     :      DDIMSchedulerState		,     A__     :      int		,     A__     :      Tuple = ()	) ->				DDIMSchedulerState:
           lowerCamelCase__					     = 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__					     = (jnp.arange(0		,     A__	) * step_ratio).round()[::-1] + self.config.steps_offset

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




     def        __snake_case      (			self     :      Optional[int]		,     A__     :      DDIMSchedulerState		,     A__     :      List[str]		,     A__     :      Optional[Any]	) ->				int:
           lowerCamelCase__					     = state.common.alphas_cumprod[timestep]
           lowerCamelCase__					     = jnp.where(
               prev_timestep >= 0		,     state.common.alphas_cumprod[prev_timestep]		,     state.final_alpha_cumprod	)
           lowerCamelCase__					     = 1 - alpha_prod_t
           lowerCamelCase__					     = 1 - alpha_prod_t_prev

           lowerCamelCase__					     = (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

           return variance




     def        __snake_case      (			self     :      List[str]		,     A__     :      DDIMSchedulerState		,     A__     :      jnp.ndarray		,     A__     :      int		,     A__     :      jnp.ndarray		,     A__     :      float = 0.0		,     A__     :      bool = True		,     ) ->				Union[FlaxDDIMSchedulerOutput, Tuple]:
           if state.num_inference_steps is None:
                 raise ValueError(
                     'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'	)

           # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
           # Ideally, read DDIM paper in-detail understanding

           # Notation (<variable name> -> <name in paper>
           # - pred_noise_t -> e_theta(x_t, t)
           # - pred_original_sample -> f_theta(x_t, t) or x_0
           # - std_dev_t -> sigma_t
           # - eta -> η
           # - pred_sample_direction -> "direction pointing to x_t"
           # - pred_prev_sample -> "x_t-1"

           # 1. get previous step value (=t-1)
           lowerCamelCase__					     = timestep - self.config.num_train_timesteps // state.num_inference_steps

           lowerCamelCase__					     = state.common.alphas_cumprod
           lowerCamelCase__					     = state.final_alpha_cumprod

           # 2. compute alphas, betas
           lowerCamelCase__					     = alphas_cumprod[timestep]
           lowerCamelCase__					     = jnp.where(prev_timestep >= 0		,     alphas_cumprod[prev_timestep]		,     A__	)

           lowerCamelCase__					     = 1 - alpha_prod_t

           # 3. compute predicted original sample from predicted noise also called
           # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
           if self.config.prediction_type == "epsilon":
                 lowerCamelCase__					     = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
                 lowerCamelCase__					     = model_output
           elif self.config.prediction_type == "sample":
                 lowerCamelCase__					     = model_output
                 lowerCamelCase__					     = (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
           elif self.config.prediction_type == "v_prediction":
                 lowerCamelCase__					     = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
                 lowerCamelCase__					     = (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
           else:
                 raise ValueError(
                     F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"""
                     ' `v_prediction`'	)

           # 4. compute variance: "sigma_t(η)" -> see formula (16)
           # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
           lowerCamelCase__					     = self._get_variance(A__		,     A__		,     A__	)
           lowerCamelCase__					     = eta * variance ** 0.5

           # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
           lowerCamelCase__					     = (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * pred_epsilon

           # 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
           lowerCamelCase__					     = alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

           if not return_dict:
                 return (prev_sample, state)

           return FlaxDDIMSchedulerOutput(prev_sample=A__		,     state=A__	)




     def        __snake_case      (			self     :      Tuple		,     A__     :      DDIMSchedulerState		,     A__     :      jnp.ndarray		,     A__     :      jnp.ndarray		,     A__     :      jnp.ndarray		,     ) ->				jnp.ndarray:
           return add_noise_common(state.common		,     A__		,     A__		,     A__	)




     def        __snake_case      (			self     :      List[Any]		,     A__     :      DDIMSchedulerState		,     A__     :      jnp.ndarray		,     A__     :      jnp.ndarray		,     A__     :      jnp.ndarray		,     ) ->				jnp.ndarray:
           return get_velocity_common(state.common		,     A__		,     A__		,     A__	)




     def __len__(			self     :      Any	) ->				Union[str, Any]:
           return self.config.num_train_timesteps