





'''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    _A   :
		# setable values
		lowercase__:      Optional[jnp.ndarray]							    =      None
		lowercase__:      Optional[jnp.ndarray]							    =      None
		lowercase__:      Optional[jnp.ndarray]							    =      None

		@classmethod
		def   lowercase__    (      cls				:      Any )				->						Optional[int]:


					"""simple docstring"""

					return cls()





@dataclass
class    _A   (				__lowercase		):
		lowercase__:      ScoreSdeVeSchedulerState
		lowercase__:      jnp.ndarray
		lowercase__:      Optional[jnp.ndarray]							    =      None





class    _A   (				__lowercase	,				__lowercase		):

		@property
		def   lowercase__    (      self				:      int )				->						Union[str, Any]:


					"""simple docstring"""

					return True

		@register_to_config
		def __init__(      self				:      Optional[int]     ,			__magic_name__				:      int = 20_00     ,			__magic_name__				:      float = 0.15     ,			__magic_name__				:      float = 0.01     ,			__magic_name__				:      float = 1348.0     ,			__magic_name__				:      float = 1E-5     ,			__magic_name__				:      int = 1     ,			)				->						Union[str, Any]:


					"""simple docstring"""

					pass

		def   lowercase__    (      self				:      Union[str, Any] )				->						List[Any]:


					"""simple docstring"""

					__snake_case				:			Any       			=    ScoreSdeVeSchedulerState.create()
					return self.set_sigmas(
					    __magic_name__     ,			self.config.num_train_timesteps     ,			self.config.sigma_min     ,			self.config.sigma_max     ,			self.config.sampling_eps     ,			)

		def   lowercase__    (      self				:      List[str]     ,			__magic_name__				:      ScoreSdeVeSchedulerState     ,			__magic_name__				:      int     ,			__magic_name__				:      Tuple = ()     ,			__magic_name__				:      float = None )				->						ScoreSdeVeSchedulerState:


					"""simple docstring"""

					__snake_case				:			Union[str, Any]       			=    sampling_eps if sampling_eps is not None else self.config.sampling_eps

					__snake_case				:			Union[str, Any]       			=    jnp.linspace(1     ,			__magic_name__     ,			__magic_name__ )
					return state.replace(timesteps=__magic_name__ )

		def   lowercase__    (      self				:      Any     ,			__magic_name__				:      ScoreSdeVeSchedulerState     ,			__magic_name__				:      int     ,			__magic_name__				:      float = None     ,			__magic_name__				:      float = None     ,			__magic_name__				:      float = None     ,			)				->						ScoreSdeVeSchedulerState:


					"""simple docstring"""

					__snake_case				:			int       			=    sigma_min if sigma_min is not None else self.config.sigma_min
					__snake_case				:			Optional[int]       			=    sigma_max if sigma_max is not None else self.config.sigma_max
					__snake_case				:			Optional[Any]       			=    sampling_eps if sampling_eps is not None else self.config.sampling_eps
					if state.timesteps is None:
								__snake_case				:			Any       			=    self.set_timesteps(__magic_name__     ,			__magic_name__     ,			__magic_name__ )

					__snake_case				:			Tuple       			=    jnp.exp(jnp.linspace(jnp.log(__magic_name__ )     ,			jnp.log(__magic_name__ )     ,			__magic_name__ ) )
					__snake_case				:			Optional[Any]       			=    jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps] )

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

		def   lowercase__    (      self				:      str     ,			__magic_name__				:      Optional[Any]     ,			__magic_name__				:      List[str]     ,			__magic_name__				:      Union[str, Any] )				->						Union[str, Any]:


					"""simple docstring"""

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

		def   lowercase__    (      self				:      Dict     ,			__magic_name__				:      ScoreSdeVeSchedulerState     ,			__magic_name__				:      jnp.ndarray     ,			__magic_name__				:      int     ,			__magic_name__				:      jnp.ndarray     ,			__magic_name__				:      random.KeyArray     ,			__magic_name__				:      bool = True     ,			)				->						Union[FlaxSdeVeOutput, Tuple]:


					"""simple docstring"""

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

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

					__snake_case				:			int       			=    state.discrete_sigmas[timesteps]
					__snake_case				:			List[str]       			=    self.get_adjacent_sigma(__magic_name__     ,			__magic_name__     ,			__magic_name__ )
					__snake_case				:			int       			=    jnp.zeros_like(__magic_name__ )
					__snake_case				:			Tuple       			=    (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
					__snake_case				:			str       			=    diffusion.flatten()
					__snake_case				:			Optional[int]       			=    broadcast_to_shape_from_left(__magic_name__     ,			sample.shape )
					__snake_case				:			int       			=    drift - diffusion**2 * model_output

					#  equation 6: sample noise for the diffusion term of
					__snake_case				:			Optional[Any]       			=    random.split(__magic_name__     ,			num=1 )
					__snake_case				:			str       			=    random.normal(key=__magic_name__     ,			shape=sample.shape )
					__snake_case				:			List[Any]       			=    sample - drift  # subtract because `dt` is a small negative timestep
					# TODO is the variable diffusion the correct scaling term for the noise?
					__snake_case				:			Tuple       			=    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=__magic_name__     ,			prev_sample_mean=__magic_name__     ,			state=__magic_name__ )

		def   lowercase__    (      self				:      Tuple     ,			__magic_name__				:      ScoreSdeVeSchedulerState     ,			__magic_name__				:      jnp.ndarray     ,			__magic_name__				:      jnp.ndarray     ,			__magic_name__				:      random.KeyArray     ,			__magic_name__				:      bool = True     ,			)				->						Union[FlaxSdeVeOutput, Tuple]:


					"""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
					__snake_case				:			Any       			=    random.split(__magic_name__     ,			num=1 )
					__snake_case				:			Optional[Any]       			=    random.normal(key=__magic_name__     ,			shape=sample.shape )

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

					# compute corrected sample: model_output term and noise term
					__snake_case				:			str       			=    step_size.flatten()
					__snake_case				:			Dict       			=    broadcast_to_shape_from_left(__magic_name__     ,			sample.shape )
					__snake_case				:			Any       			=    sample + step_size * model_output
					__snake_case				:			str       			=    prev_sample_mean + ((step_size * 2) ** 0.5) * noise

					if not return_dict:
								return (prev_sample, state)

					return FlaxSdeVeOutput(prev_sample=__magic_name__     ,			state=__magic_name__ )

		def __len__(      self				:      List[Any] )				->						Optional[int]:


					"""simple docstring"""

					return self.config.num_train_timesteps









'''simple docstring'''






import os
from shutil import copyfile
from typing import TYPE_CHECKING, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
from ...utils.versions import require_version


if TYPE_CHECKING:
	from transformers.pipelines.conversational import Conversation

require_version("tokenizers>=0.13.3")

if is_sentencepiece_available():
	from .tokenization_llama import LlamaTokenizer
else:
	__UpperCamelCase										=  None

__UpperCamelCase										=  logging.get_logger(__name__)
__UpperCamelCase										=  {"vocab_file": "tokenizer.model", "tokenizer_file": "tokenizer.json"}

__UpperCamelCase						,		__UpperCamelCase										=  "[INST]", "[/INST]"
__UpperCamelCase						,		__UpperCamelCase										=  "<<SYS>>\n", "\n<</SYS>>\n\n"

# fmt: off
__UpperCamelCase										=  "You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your\\nanswers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\\nthat your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not\\ncorrect. If you don't know the answer to a question, please don't share false information."




class    _A   (				__lowercase		):
		lowercase__:      Union[str, Any]							    =      VOCAB_FILES_NAMES
		lowercase__:      Tuple							    =      LlamaTokenizer
		lowercase__:      List[Any]							    =      '''left'''
		lowercase__:      int							    =      ['''input_ids''', '''attention_mask''']

		def __init__(      self				:      Optional[int]     ,			__magic_name__				:      str=None     ,			__magic_name__				:      Any=None     ,			__magic_name__				:      int=False     ,			__magic_name__				:      Optional[Any]="<unk>"     ,			__magic_name__				:      Dict="<s>"     ,			__magic_name__				:      Dict="</s>"     ,			__magic_name__				:      int=True     ,			__magic_name__				:      List[str]=False     ,			**__magic_name__				:      Optional[Any]     ,			)				->						Optional[int]:


					"""simple docstring"""

					super().__init__(
					    vocab_file=__magic_name__     ,			tokenizer_file=__magic_name__     ,			clean_up_tokenization_spaces=__magic_name__     ,			unk_token=__magic_name__     ,			bos_token=__magic_name__     ,			eos_token=__magic_name__     ,			**__magic_name__     ,			)
					__snake_case				:			List[Any]       			=    add_bos_token
					__snake_case				:			int       			=    add_eos_token
					self.update_post_processor()

					__snake_case				:			Dict       			=    vocab_file
					__snake_case				:			int       			=    False if not self.vocab_file else True

		def   lowercase__    (      self				:      Any )				->						Dict:


					"""simple docstring"""

					__snake_case				:			Any       			=    self.bos_token
					__snake_case				:			Dict       			=    self.bos_token_id

					__snake_case				:			List[str]       			=    self.eos_token
					__snake_case				:			str       			=    self.eos_token_id

					__snake_case				:			List[Any]       			=    f'''{(bos+":0 ") * self.add_bos_token}$A:0{(" "+eos+":0") * self.add_eos_token}'''
					__snake_case				:			str       			=    f'''{single}{(" "+bos+":1") * self.add_bos_token} $B:1{(" "+eos+":1") * self.add_eos_token}'''

					__snake_case				:			List[str]       			=    []
					if self.add_bos_token:
								special_tokens.append((bos, bos_token_id) )
					if self.add_eos_token:
								special_tokens.append((eos, eos_token_id) )
					__snake_case				:			Dict       			=    processors.TemplateProcessing(
					    single=__magic_name__     ,			pair=__magic_name__     ,			special_tokens=__magic_name__ )

		@property
		def   lowercase__    (      self				:      Tuple )				->						List[Any]:


					"""simple docstring"""

					return self._add_eos_token

		@property
		def   lowercase__    (      self				:      Union[str, Any] )				->						str:


					"""simple docstring"""

					return self._add_bos_token

		@add_eos_token.setter
		def   lowercase__    (      self				:      List[str]     ,			__magic_name__				:      Optional[Any] )				->						int:


					"""simple docstring"""

					__snake_case				:			Any       			=    value
					self.update_post_processor()

		@add_bos_token.setter
		def   lowercase__    (      self				:      Union[str, Any]     ,			__magic_name__				:      Optional[Any] )				->						List[str]:


					"""simple docstring"""

					__snake_case				:			Dict       			=    value
					self.update_post_processor()

		def   lowercase__    (      self				:      List[Any]     ,			__magic_name__				:      str     ,			__magic_name__				:      Optional[str] = None )				->						Tuple[str]:


					"""simple docstring"""

					if not self.can_save_slow_tokenizer:
								raise ValueError(
								    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
								    """tokenizer.""" )

					if not os.path.isdir(__magic_name__ ):
								logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
								return
					__snake_case				:			Dict       			=    os.path.join(
					    __magic_name__     ,			(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )

					if os.path.abspath(self.vocab_file ) != os.path.abspath(__magic_name__ ):
								copyfile(self.vocab_file     ,			__magic_name__ )

					return (out_vocab_file,)

		def   lowercase__    (      self				:      int     ,			__magic_name__				:      "Conversation" )				->						int:


					"""simple docstring"""

					__snake_case				:			List[Any]       			=    list(conversation.iter_texts() )
					if not all([is_user for is_user, msg in dialogue[::2]] ) or not all(
					    [not is_user for is_user, msg in dialogue[1::2]] ):
								raise ValueError(
								    """The model only supports 'user' and 'assistant' roles, starting with user and alternating (u/a/u/a/u...)""" )

					__snake_case				:			str       			=    []
					if len(conversation.past_user_inputs ) > 0:
								if not conversation.past_user_inputs[0].startswith(__magic_name__ ) or E_SYS not in conversation.past_user_inputs[0]:
											__snake_case				:			Tuple       			=    (
											    B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
											)
					elif not dialogue[0][1].startswith(__magic_name__ ) or E_SYS not in dialogue[0][1]:
								__snake_case				:			str       			=    (dialogue[0][0], B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + dialogue[0][1])

					dialog_tokens += sum(
					    [
					        [self.bos_token_id]
					        + self.encode(
					            f'''{B_INST} {(prompt[1]).strip()} {E_INST} {(answer[1]).strip()} '''     ,			add_special_tokens=__magic_name__ )
					        + [self.eos_token_id]
					        for prompt, answer in zip(dialogue[::2]     ,			dialogue[1::2] )
					    ]     ,			[]     ,			)
					if not (dialogue[-1][0]):
								raise ValueError(f'''Last message must be from user, got {dialogue[-1]["role"]}''' )
					dialog_tokens += [self.bos_token_id] + self.encode(
					    f'''{B_INST} {(dialogue[-1][1]).strip()} {E_INST}'''     ,			add_special_tokens=__magic_name__ )
					return dialog_tokens


