




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_		:					int          =  logging.get_logger(__name__)


UpperCAmelCase_		:					str          =  {
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

UpperCAmelCase_		:					Any          =  {
    "vocab_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json"},
    "merges_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt"},
    "tokenizer_config_file": {
        "facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json"
    },
}

UpperCAmelCase_		:					Optional[Any]          =  {"facebook/blenderbot-3B": 128}




@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def 		lowerCAmelCase_ (						):
				__magic_name__						:						Tuple										=(
				    list(range(ord("""!""" )						,  ord("""~""" ) + 1 ) ) + list(range(ord("""¡""" )						,  ord("""¬""" ) + 1 ) ) + list(range(ord("""®""" )						,  ord("""ÿ""" ) + 1 ) )
				)
				__magic_name__						:						Dict										=bs[:]
				__magic_name__						:						List[Any]										=0
				for b in range(2**8 ):
								if b not in bs:
												bs.append(lowerCamelCase )
												cs.append(2**8 + n )
												n += 1
				__magic_name__						:						List[str]										=[chr(lowerCamelCase ) for n in cs]
				return dict(zip(lowerCamelCase						,  lowerCamelCase ) )




def 		lowerCAmelCase_ (						lowerCamelCase ):
				__magic_name__						:						Tuple										=set()
				__magic_name__						:						Dict										=word[0]
				for char in word[1:]:
								pairs.add((prev_char, char) )
								__magic_name__						:						List[Any]										=char
				return pairs




class 		__A   (      UpperCamelCase__      ):
						UpperCamelCase      =       VOCAB_FILES_NAMES
						UpperCamelCase      =       PRETRAINED_VOCAB_FILES_MAP
						UpperCamelCase      =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						UpperCamelCase      =       ["""input_ids""", """attention_mask"""]

						def __init__(		self :Any     ,			__snake_case :Tuple     ,			__snake_case :str     ,			__snake_case :Union[str, Any]="replace"     ,			__snake_case :Union[str, Any]="<s>"     ,			__snake_case :Optional[int]="</s>"     ,			__snake_case :List[str]="</s>"     ,			__snake_case :Optional[int]="<s>"     ,			__snake_case :int="<unk>"     ,			__snake_case :Optional[Any]="<pad>"     ,			__snake_case :List[Any]="<mask>"     ,			__snake_case :Union[str, Any]=False     ,			**__snake_case :int     ,			):
										'''simple docstring'''

										__magic_name__						:						List[str]										=AddedToken(__snake_case     ,			lstrip=__snake_case     ,			rstrip=__snake_case			) if isinstance(__snake_case     ,			__snake_case			) else bos_token
										__magic_name__						:						Dict										=AddedToken(__snake_case     ,			lstrip=__snake_case     ,			rstrip=__snake_case			) if isinstance(__snake_case     ,			__snake_case			) else eos_token
										__magic_name__						:						Tuple										=AddedToken(__snake_case     ,			lstrip=__snake_case     ,			rstrip=__snake_case			) if isinstance(__snake_case     ,			__snake_case			) else sep_token
										__magic_name__						:						List[str]										=AddedToken(__snake_case     ,			lstrip=__snake_case     ,			rstrip=__snake_case			) if isinstance(__snake_case     ,			__snake_case			) else cls_token
										__magic_name__						:						Tuple										=AddedToken(__snake_case     ,			lstrip=__snake_case     ,			rstrip=__snake_case			) if isinstance(__snake_case     ,			__snake_case			) else unk_token
										__magic_name__						:						List[str]										=AddedToken(__snake_case     ,			lstrip=__snake_case     ,			rstrip=__snake_case			) if isinstance(__snake_case     ,			__snake_case			) else pad_token

										# Mask token behave like a normal word, i.e. include the space before it
										__magic_name__						:						Optional[int]										=AddedToken(__snake_case     ,			lstrip=__snake_case     ,			rstrip=__snake_case			) if isinstance(__snake_case     ,			__snake_case			) else mask_token

										super().__init__(
										    errors=__snake_case     ,			bos_token=__snake_case     ,			eos_token=__snake_case     ,			unk_token=__snake_case     ,			sep_token=__snake_case     ,			cls_token=__snake_case     ,			pad_token=__snake_case     ,			mask_token=__snake_case     ,			add_prefix_space=__snake_case     ,			**__snake_case     ,			)

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

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

						@property
						# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
						def      A__    (		self :Dict			):
										'''simple docstring'''

										return len(self.encoder			)

						def      A__    (		self :List[Any]			):
										'''simple docstring'''

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

						def      A__    (		self :Tuple     ,			__snake_case :Dict			):
										'''simple docstring'''

										if token in self.cache:
														return self.cache[token]
										__magic_name__						:						List[Any]										=tuple(__snake_case			)
										__magic_name__						:						Union[str, Any]										=get_pairs(__snake_case			)

										if not pairs:
														return token

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

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

						def      A__    (		self :Dict     ,			__snake_case :str			):
										'''simple docstring'''

										__magic_name__						:						List[str]										=[]
										for token in re.findall(self.pat     ,			__snake_case			):
														__magic_name__						:						str										="""""".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(__snake_case			).split(""" """			)			)
										return bpe_tokens

						def      A__    (		self :List[Any]     ,			__snake_case :List[str]			):
										'''simple docstring'''

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

						def      A__    (		self :Any     ,			__snake_case :str			):
										'''simple docstring'''

										return self.decoder.get(__snake_case			)

						def      A__    (		self :Tuple     ,			__snake_case :Any			):
										'''simple docstring'''

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

						def      A__    (		self :Any     ,			__snake_case :str     ,			__snake_case :Optional[str] = None			):
										'''simple docstring'''

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

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

										__magic_name__						:						Union[str, Any]										=0
										with open(__snake_case     ,			"""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 __snake_case			: 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!"""			)
																						__magic_name__						:						Tuple										=token_index
																		writer.write(""" """.join(__snake_case			) + """\n"""			)
																		index += 1

										return vocab_file, merge_file

						def      A__    (		self :Any     ,			__snake_case :List[int]     ,			__snake_case :Optional[List[int]] = None     ,			__snake_case :bool = False			):
										'''simple docstring'''

										if already_has_special_tokens:
														return super().get_special_tokens_mask(
														    token_ids_a=__snake_case     ,			token_ids_a=__snake_case     ,			already_has_special_tokens=__snake_case			)

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

						def      A__    (		self :Dict     ,			__snake_case :List[int]     ,			__snake_case :Optional[List[int]] = None			):
										'''simple docstring'''

										__magic_name__						:						List[str]										=[self.sep_token_id]
										__magic_name__						:						List[Any]										=[self.cls_token_id]

										if token_ids_a is None:
														return len(cls + token_ids_a + sep			) * [0]
										return len(cls + token_ids_a + sep + sep + token_ids_a + sep			) * [0]

						def      A__    (		self :Any     ,			__snake_case :Union[str, Any]     ,			__snake_case :Union[str, Any]=False     ,			**__snake_case :List[str]			):
										'''simple docstring'''

										__magic_name__						:						Tuple										=kwargs.pop("""add_prefix_space"""     ,			self.add_prefix_space			)
										if (is_split_into_words or add_prefix_space) and (len(__snake_case			) > 0 and not text[0].isspace()):
														__magic_name__						:						Dict										=""" """ + text
										return (text, kwargs)

						def      A__    (		self :Optional[int]     ,			__snake_case :List[int]     ,			__snake_case :Optional[List[int]] = None			):
										'''simple docstring'''

										return token_ids_a + [self.eos_token_id]


						def      A__    (		self :int     ,			__snake_case :"Conversation"			):
										'''simple docstring'''

										__magic_name__						:						int										=[]
										for is_user, text in conversation.iter_texts():
														if is_user:
																		# We need to space prefix as it's being done within blenderbot
																		inputs.append(""" """ + text			)
														else:
																		# Generated responses should contain them already.
																		inputs.append(__snake_case			)

										__magic_name__						:						Optional[Any]										="""  """.join(__snake_case			)
										__magic_name__						:						int										=self.encode(__snake_case			)
										if len(__snake_case			) > self.model_max_length:
														__magic_name__						:						Union[str, Any]										=input_ids[-self.model_max_length :]
														logger.warning(f"Trimmed input from conversation as it was longer than {self.model_max_length} tokens."			)
										return input_ids










# 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 		__A   (      UpperCamelCase__       ,    UpperCamelCase__      ):
						UpperCamelCase      =       1

						@register_to_config
						def __init__(		self :Any     ,			__snake_case :Tuple=20_00     ,			__snake_case :Optional[Any]=0.1     ,			__snake_case :Any=20     ,			__snake_case :Optional[int]=1E-3			):
										'''simple docstring'''

										__magic_name__						:						Dict										=None
										__magic_name__						:						List[str]										=None
										__magic_name__						:						str										=None

						def      A__    (		self :Dict     ,			__snake_case :Optional[int]     ,			__snake_case :Union[str, torch.device] = None			):
										'''simple docstring'''

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

						def      A__    (		self :List[str]     ,			__snake_case :List[str]     ,			__snake_case :int     ,			__snake_case :int     ,			__snake_case :List[str]=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
										__magic_name__						:						int										=(
										    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
										)
										__magic_name__						:						Optional[int]										=torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff			)			)
										__magic_name__						:						str										=std.flatten()
										while len(std.shape			) < len(score.shape			):
														__magic_name__						:						List[str]										=std.unsqueeze(-1			)
										__magic_name__						:						Union[str, Any]										=-score / std

										# compute
										__magic_name__						:						Tuple										=-1.0 / len(self.timesteps			)

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

										__magic_name__						:						Optional[int]										=torch.sqrt(__snake_case			)
										__magic_name__						:						int										=drift - diffusion**2 * score
										__magic_name__						:						List[str]										=x + drift * dt

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

										return x, x_mean


						def __len__(		self :List[Any]			):
										'''simple docstring'''

										return self.config.num_train_timesteps




