


'''simple docstring'''
import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
		from transformers.pipelines.conversational import Conversation


__snake_case							   =  logging.get_logger(__name__)

__snake_case							   =  {'''tokenizer_file''': '''tokenizer.json'''}

__snake_case							   =  {
    '''tokenizer_file''': {
        '''bigscience/tokenizer''': '''https://huggingface.co/bigscience/tokenizer/blob/main/tokenizer.json''',
        '''bigscience/bloom-560m''': '''https://huggingface.co/bigscience/bloom-560m/blob/main/tokenizer.json''',
        '''bigscience/bloom-1b1''': '''https://huggingface.co/bigscience/bloom-1b1/blob/main/tokenizer.json''',
        '''bigscience/bloom-1b7''': '''https://huggingface.co/bigscience/bloom-1b7/blob/main/tokenizer.json''',
        '''bigscience/bloom-3b''': '''https://huggingface.co/bigscience/bloom-3b/blob/main/tokenizer.json''',
        '''bigscience/bloom-7b1''': '''https://huggingface.co/bigscience/bloom-7b1/blob/main/tokenizer.json''',
        '''bigscience/bloom''': '''https://huggingface.co/bigscience/bloom/blob/main/tokenizer.json''',
    },
}
class      lowercase			(							A__      ):

		"""simple docstring"""

		_a									=       VOCAB_FILES_NAMES
		_a									=       PRETRAINED_VOCAB_FILES_MAP
		_a									=       ['input_ids', 'attention_mask']
		_a									=       None




		def __init__(	self					,     UpperCamelCase_=None					,     UpperCamelCase_=None					,     UpperCamelCase_=None					,     UpperCamelCase_="<unk>"					,     UpperCamelCase_="<s>"					,     UpperCamelCase_="</s>"					,     UpperCamelCase_="<pad>"					,     UpperCamelCase_=False					,     UpperCamelCase_=False					,     **UpperCamelCase_					,     ):

							'''simple docstring'''


							super().__init__(
							    UpperCamelCase_					,     UpperCamelCase_					,     tokenizer_file=UpperCamelCase_					,     unk_token=UpperCamelCase_					,     bos_token=UpperCamelCase_					,     eos_token=UpperCamelCase_					,     pad_token=UpperCamelCase_					,     add_prefix_space=UpperCamelCase_					,     clean_up_tokenization_spaces=UpperCamelCase_					,     **UpperCamelCase_					,     )
							UpperCamelCase__     :List[Any]							      =				json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()							)
							if pre_tok_state.get('''add_prefix_space'''					,     UpperCamelCase_							) != add_prefix_space:
												UpperCamelCase__     :Union[str, Any]							      =				getattr(UpperCamelCase_					,     pre_tok_state.pop('''type'''							)							)
												UpperCamelCase__     :int							      =				add_prefix_space
												UpperCamelCase__     :List[Any]							      =				pre_tok_class(**UpperCamelCase_							)

							UpperCamelCase__     :Optional[Any]							      =				add_prefix_space




		def 					lowerCAmelCase__       (	self					,     *UpperCamelCase_					,     **UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :int							      =				kwargs.get('''is_split_into_words'''					,     UpperCamelCase_							)
							if not (self.add_prefix_space or not is_split_into_words):
												raise Exception(
												    F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with'''
												    ''' pretokenized inputs.'''							)

							return super()._batch_encode_plus(*UpperCamelCase_					,     **UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     *UpperCamelCase_					,     **UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :List[Any]							      =				kwargs.get('''is_split_into_words'''					,     UpperCamelCase_							)

							if not (self.add_prefix_space or not is_split_into_words):
												raise Exception(
												    F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with'''
												    ''' pretokenized inputs.'''							)

							return super()._encode_plus(*UpperCamelCase_					,     **UpperCamelCase_							)




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

							'''simple docstring'''


							UpperCamelCase__     :Dict							      =				self._tokenizer.model.save(UpperCamelCase_					,     name=UpperCamelCase_							)
							return tuple(UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :Tuple							      =				[]
							for is_user, text in conversation.iter_texts():
												input_ids.extend(self.encode(UpperCamelCase_					,     add_special_tokens=UpperCamelCase_							) + [self.eos_token_id]							)

							if len(UpperCamelCase_							) > self.model_max_length:
												UpperCamelCase__     :List[str]							      =				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

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin
class      lowercase			(							A__			,	A__      ):

		"""simple docstring"""

		_a									=       1




		@register_to_config
		def __init__(	self					,     UpperCamelCase_=2000					,     UpperCamelCase_=0.1					,     UpperCamelCase_=20					,     UpperCamelCase_=1e-3							):

							'''simple docstring'''


							UpperCamelCase__     :Dict							      =				None
							UpperCamelCase__     :Any							      =				None
							UpperCamelCase__     :List[str]							      =				None




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

							'''simple docstring'''


							UpperCamelCase__     :Union[str, Any]							      =				torch.linspace(1					,     self.config.sampling_eps					,     UpperCamelCase_					,     device=UpperCamelCase_							)




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

							'''simple docstring'''


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

							# TODO(Patrick) better comments + non-PyTorch
							# postprocess model score
							UpperCamelCase__     :Optional[int]							      =				(
							    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
							)
							UpperCamelCase__     :Tuple							      =				torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff							)							)
							UpperCamelCase__     :Tuple							      =				std.flatten()
							while len(std.shape							) < len(score.shape							):
												UpperCamelCase__     :int							      =				std.unsqueeze(-1							)
							UpperCamelCase__     :List[Any]							      =				-score / std

							# compute
							UpperCamelCase__     :List[str]							      =				-1.0 / len(self.timesteps							)

							UpperCamelCase__     :Optional[int]							      =				self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
							UpperCamelCase__     :Any							      =				beta_t.flatten()
							while len(beta_t.shape							) < len(x.shape							):
												UpperCamelCase__     :Any							      =				beta_t.unsqueeze(-1							)
							UpperCamelCase__     :Optional[int]							      =				-0.5 * beta_t * x

							UpperCamelCase__     :int							      =				torch.sqrt(UpperCamelCase_							)
							UpperCamelCase__     :List[str]							      =				drift - diffusion**2 * score
							UpperCamelCase__     :Dict							      =				x + drift * dt

							# add noise
							UpperCamelCase__     :List[Any]							      =				randn_tensor(x.shape					,     layout=x.layout					,     generator=UpperCamelCase_					,     device=x.device					,     dtype=x.dtype							)
							UpperCamelCase__     :int							      =				x_mean + diffusion * math.sqrt(-dt							) * noise

							return x, x_mean




		def __len__(	self							):

							'''simple docstring'''


							return self.config.num_train_timesteps