"""simple docstring"""






import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot import BlenderbotTokenizer


if TYPE_CHECKING:
				from transformers.pipelines.conversational import Conversation

UpperCamelCase         =  logging.get_logger(__name__)


UpperCamelCase         =  {
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
    'tokenizer_config_file': 'tokenizer_config.json',
}

UpperCamelCase         =  {
    '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         =  {'facebook/blenderbot-3B': 1_28}
class 	UpperCAmelCase__					(     SCREAMING_SNAKE_CASE__      ):


	"""simple docstring"""
	lowerCAmelCase__     :       List[Any]       =				VOCAB_FILES_NAMES
	lowerCAmelCase__     :       Optional[int]       =				PRETRAINED_VOCAB_FILES_MAP
	lowerCAmelCase__     :       List[Any]       =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowerCAmelCase__     :       List[Any]       =				['''input_ids''', '''attention_mask''']
	lowerCAmelCase__     :       Optional[int]       =				BlenderbotTokenizer







	def __init__(      self							,  _SCREAMING_SNAKE_CASE=None							,  _SCREAMING_SNAKE_CASE=None							,  _SCREAMING_SNAKE_CASE=None							,  _SCREAMING_SNAKE_CASE="replace"							,  _SCREAMING_SNAKE_CASE="<s>"							,  _SCREAMING_SNAKE_CASE="</s>"							,  _SCREAMING_SNAKE_CASE="</s>"							,  _SCREAMING_SNAKE_CASE="<s>"							,  _SCREAMING_SNAKE_CASE="<unk>"							,  _SCREAMING_SNAKE_CASE="<pad>"							,  _SCREAMING_SNAKE_CASE="<mask>"							,  _SCREAMING_SNAKE_CASE=False							,  _SCREAMING_SNAKE_CASE=True							,  **_SCREAMING_SNAKE_CASE							,  )     ->						List[Any]:
							super().__init__(
							    __lowerCAmelCase							,  __lowerCAmelCase							,  tokenizer_file=__lowerCAmelCase							,  errors=__lowerCAmelCase							,  bos_token=__lowerCAmelCase							,  eos_token=__lowerCAmelCase							,  sep_token=__lowerCAmelCase							,  cls_token=__lowerCAmelCase							,  unk_token=__lowerCAmelCase							,  pad_token=__lowerCAmelCase							,  mask_token=__lowerCAmelCase							,  add_prefix_space=__lowerCAmelCase							,  trim_offsets=__lowerCAmelCase							,  **__lowerCAmelCase							,  )

							a_				:      int  		=       json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()			)
							if pre_tok_state.get("add_prefix_space"							,  __lowerCAmelCase			) != add_prefix_space:
													a_				:      Union[str, Any]  		=       getattr(__lowerCAmelCase							,  pre_tok_state.pop("type"			)			)
													a_				:      int  		=       add_prefix_space
													a_				:      int  		=       pre_tok_class(**__lowerCAmelCase			)

							a_				:      List[str]  		=       add_prefix_space

							a_				:      Any  		=       """post_processor"""
							a_				:      Any  		=       getattr(self.backend_tokenizer							,  __lowerCAmelCase							,  __lowerCAmelCase			)
							if tokenizer_component_instance:
													a_				:      int  		=       json.loads(tokenizer_component_instance.__getstate__()			)

													# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
													if "sep" in state:
																			a_				:      str  		=       tuple(state["sep"]			)
													if "cls" in state:
																			a_				:      Any  		=       tuple(state["cls"]			)

													a_				:      int  		=       False

													if state.get("add_prefix_space"							,  __lowerCAmelCase			) != add_prefix_space:
																			a_				:      Optional[int]  		=       add_prefix_space
																			a_				:      Dict  		=       True

													if state.get("trim_offsets"							,  __lowerCAmelCase			) != trim_offsets:
																			a_				:      List[str]  		=       trim_offsets
																			a_				:      Optional[int]  		=       True

													if changes_to_apply:
																			a_				:      List[Any]  		=       getattr(__lowerCAmelCase							,  state.pop("type"			)			)
																			a_				:      Any  		=       component_class(**__lowerCAmelCase			)
																			setattr(self.backend_tokenizer							,  __lowerCAmelCase							,  __lowerCAmelCase			)







	@property
	# Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
	def    A     (      self			)     ->						Optional[int]:
							if self._mask_token is None:
													if self.verbose:
																			logger.error("Using mask_token, but it is not set yet."			)
													return None
							return str(self._mask_token			)







	@mask_token.setter
	def    A     (      self							,  _SCREAMING_SNAKE_CASE			)     ->						str:
							a_				:      Optional[int]  		=       AddedToken(__lowerCAmelCase							,  lstrip=__lowerCAmelCase							,  rstrip=__lowerCAmelCase			) if isinstance(__lowerCAmelCase							,  __lowerCAmelCase			) else value
							a_				:      Optional[int]  		=       value







	def    A     (      self							,  *_SCREAMING_SNAKE_CASE							,  **_SCREAMING_SNAKE_CASE			)     ->						Dict:
							a_				:      Union[str, Any]  		=       kwargs.get("is_split_into_words"							,  __lowerCAmelCase			)
							assert self.add_prefix_space or not is_split_into_words, (
							    f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
							    "to use it with pretokenized inputs."
							)

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







	def    A     (      self							,  *_SCREAMING_SNAKE_CASE							,  **_SCREAMING_SNAKE_CASE			)     ->						Any:
							a_				:      str  		=       kwargs.get("is_split_into_words"							,  __lowerCAmelCase			)

							assert self.add_prefix_space or not is_split_into_words, (
							    f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
							    "to use it with pretokenized inputs."
							)

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







	def    A     (      self							,  _SCREAMING_SNAKE_CASE							,  _SCREAMING_SNAKE_CASE = None			)     ->						Optional[Any]:
							a_				:      str  		=       self._tokenizer.model.save(__lowerCAmelCase							,  name=__lowerCAmelCase			)
							return tuple(__lowerCAmelCase			)







	def    A     (      self							,  _SCREAMING_SNAKE_CASE							,  _SCREAMING_SNAKE_CASE = None			)     ->						Dict:
							a_				:      Tuple  		=       [self.sep_token_id]
							a_				:      int  		=       [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							,  _SCREAMING_SNAKE_CASE							,  _SCREAMING_SNAKE_CASE = None			)     ->						Tuple:
							return token_ids_a + [self.eos_token_id]







	def    A     (      self							,  _SCREAMING_SNAKE_CASE			)     ->						Optional[Any]:
							a_				:      List[str]  		=       []
							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(__lowerCAmelCase			)

							a_				:      List[str]  		=       """  """.join(__lowerCAmelCase			)
							a_				:      Any  		=       self.encode(__lowerCAmelCase			)
							if len(__lowerCAmelCase			) > self.model_max_length:
													a_				:      List[str]  		=       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












'''simple docstring'''

from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class      A     (   SCREAMING_SNAKE_CASE__    ):






 """simple docstring"""





 __a      :    UNetaDModel
 __a      :    ScoreSdeVeScheduler
 def __init__(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase			):
       super().__init__()
       self.register_modules(unet=__lowerCAmelCase      ,  scheduler=__lowerCAmelCase			)





 @torch.no_grad()
 def __call__(				self      ,  __lowerCAmelCase = 1      ,  __lowerCAmelCase = 20_00      ,  __lowerCAmelCase = None      ,  __lowerCAmelCase = "pil"      ,  __lowerCAmelCase = True      ,  **__lowerCAmelCase      ,  ):
       UpperCamelCase_					: str            =				self.unet.config.sample_size
       UpperCamelCase_					: int            =				(batch_size, 3, img_size, img_size)

       UpperCamelCase_					: List[Any]            =				self.unet

       UpperCamelCase_					: Union[str, Any]            =				randn_tensor(__lowerCAmelCase      ,  generator=__lowerCAmelCase			) * self.scheduler.init_noise_sigma
       UpperCamelCase_					: Union[str, Any]            =				sample.to(self.device			)

       self.scheduler.set_timesteps(__lowerCAmelCase			)
       self.scheduler.set_sigmas(__lowerCAmelCase			)

       for i, t in enumerate(self.progress_bar(self.scheduler.timesteps			)			):
             UpperCamelCase_					: Union[str, Any]            =				self.scheduler.sigmas[i] * torch.ones(shape[0]      ,  device=self.device			)

             # correction step
             for _ in range(self.scheduler.config.correct_steps			):
                   UpperCamelCase_					: List[str]            =				self.unet(__lowerCAmelCase      ,  __lowerCAmelCase			).sample
                   UpperCamelCase_					: List[str]            =				self.scheduler.step_correct(__lowerCAmelCase      ,  __lowerCAmelCase      ,  generator=__lowerCAmelCase			).prev_sample

             # prediction step
             UpperCamelCase_					: List[Any]            =				model(__lowerCAmelCase      ,  __lowerCAmelCase			).sample
             UpperCamelCase_					: Tuple            =				self.scheduler.step_pred(__lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  generator=__lowerCAmelCase			)

             UpperCamelCase_     ,      UpperCamelCase_					: List[Any]            =				output.prev_sample, output.prev_sample_mean

       UpperCamelCase_					: Tuple            =				sample_mean.clamp(0      ,  1			)
       UpperCamelCase_					: str            =				sample.cpu().permute(0      ,  2      ,  3      ,  1			).numpy()
       if output_type == "pil":
             UpperCamelCase_					: Dict            =				self.numpy_to_pil(__lowerCAmelCase			)

       if not return_dict:
             return (sample,)

       return ImagePipelineOutput(images=__lowerCAmelCase			)






