






'''simple docstring'''

import json
from typing import 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_mvp import MvpTokenizer


UpperCamelCase      					=logging.get_logger(__name__)


UpperCamelCase      					={"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

# See all MVP models at https://huggingface.co/models?filter=mvp
UpperCamelCase      					={
    "vocab_file": {
        "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json",
    },
    "added_tokens.json": {
        "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json",
    },
    "merges_file": {
        "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/tokenizer.json",
    },
}

UpperCamelCase      					={
    "RUCAIBox/mvp": 1024,
}




class      A     (   SCREAMING_SNAKE_CASE__    ):






 """simple docstring"""





 __a      :    List[str]			  =    VOCAB_FILES_NAMES
 __a      :    str			  =    PRETRAINED_VOCAB_FILES_MAP
 __a      :    Union[str, Any]			  =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __a      :    Optional[int]			  =    ['''input_ids''', '''attention_mask''']
 __a      :    Optional[int]			  =    MvpTokenizer
 def __init__(				self      ,  __lowerCAmelCase=None      ,  __lowerCAmelCase=None      ,  __lowerCAmelCase=None      ,  __lowerCAmelCase="replace"      ,  __lowerCAmelCase="<s>"      ,  __lowerCAmelCase="</s>"      ,  __lowerCAmelCase="</s>"      ,  __lowerCAmelCase="<s>"      ,  __lowerCAmelCase="<unk>"      ,  __lowerCAmelCase="<pad>"      ,  __lowerCAmelCase="<mask>"      ,  __lowerCAmelCase=False      ,  __lowerCAmelCase=True      ,  **__lowerCAmelCase      ,  ):
       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      ,  )

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

       UpperCamelCase_					: Any            =				add_prefix_space

       # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
       UpperCamelCase_					: Optional[Any]            =				"""post_processor"""
       UpperCamelCase_					: str            =				getattr(self.backend_tokenizer      ,  __lowerCAmelCase      ,  __lowerCAmelCase			)
       if tokenizer_component_instance:
             UpperCamelCase_					: str            =				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:
                   UpperCamelCase_					: Dict            =				tuple(state["""sep"""]			)
             if "cls" in state:
                   UpperCamelCase_					: List[Any]            =				tuple(state["""cls"""]			)

             UpperCamelCase_					: str            =				False

             if state.get("""add_prefix_space"""      ,  __lowerCAmelCase			) != add_prefix_space:
                   UpperCamelCase_					: List[Any]            =				add_prefix_space
                   UpperCamelCase_					: Tuple            =				True

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

             if changes_to_apply:
                   UpperCamelCase_					: List[Any]            =				getattr(__lowerCAmelCase      ,  state.pop("""type"""			)			)
                   UpperCamelCase_					: List[str]            =				component_class(**__lowerCAmelCase			)
                   setattr(self.backend_tokenizer      ,  __lowerCAmelCase      ,  __lowerCAmelCase			)
 @property
 def 	_UpperCAmelCase							(				self			):
       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 	_UpperCAmelCase							(				self      ,  __lowerCAmelCase			):
       UpperCamelCase_					: Any            =				AddedToken(__lowerCAmelCase      ,  lstrip=__lowerCAmelCase      ,  rstrip=__lowerCAmelCase			) if isinstance(__lowerCAmelCase      ,  __lowerCAmelCase			) else value
       UpperCamelCase_					: List[str]            =				value
 def 	_UpperCAmelCase							(				self      ,  *__lowerCAmelCase      ,  **__lowerCAmelCase			):
       UpperCamelCase_					: Optional[int]            =				kwargs.get("""is_split_into_words"""      ,  __lowerCAmelCase			)

       if is_split_into_words and not self.add_prefix_space:
             raise ValueError(
                 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 	_UpperCAmelCase							(				self      ,  *__lowerCAmelCase      ,  **__lowerCAmelCase			):
       UpperCamelCase_					: List[str]            =				kwargs.get("""is_split_into_words"""      ,  __lowerCAmelCase			)

       if is_split_into_words and not self.add_prefix_space:
             raise ValueError(
                 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 	_UpperCAmelCase							(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase = None			):
       UpperCamelCase_					: str            =				self._tokenizer.model.save(__lowerCAmelCase      ,  name=__lowerCAmelCase			)
       return tuple(__lowerCAmelCase			)
 def 	_UpperCAmelCase							(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase=None			):
       UpperCamelCase_					: Dict            =				[self.bos_token_id] + token_ids_a + [self.eos_token_id]
       if token_ids_a is None:
             return output

       return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]





 def 	_UpperCAmelCase							(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase = None			):
       UpperCamelCase_					: Union[str, Any]            =				[self.sep_token_id]
       UpperCamelCase_					: List[str]            =				[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]














'''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			)






