


'''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_bart import BartTokenizer


__lowercase	:						List[Any]       =	logging.get_logger(__name__)


__lowercase	:						Union[str, Any]       =	{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

# See all BART models at https://huggingface.co/models?filter=bart
__lowercase	:						Optional[Any]       =	{
    'vocab_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/vocab.json',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/vocab.json',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json',
    },
    'merges_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/merges.txt',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/merges.txt',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json',
    },
}

__lowercase	:						Dict       =	{
    'facebook/bart-base': 10_24,
    'facebook/bart-large': 10_24,
    'facebook/bart-large-mnli': 10_24,
    'facebook/bart-large-cnn': 10_24,
    'facebook/bart-large-xsum': 10_24,
    'yjernite/bart_eli5': 10_24,
}





class 							__UpperCamelCase				(		lowerCAmelCase_					):
	A_				=    VOCAB_FILES_NAMES
	A_				=    PRETRAINED_VOCAB_FILES_MAP
	A_				=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	A_				=    ["input_ids", "attention_mask"]
	A_				=    BartTokenizer




	def __init__(   self						,				__a=None						,				__a=None						,				__a=None						,				__a="replace"						,				__a="<s>"						,				__a="</s>"						,				__a="</s>"						,				__a="<s>"						,				__a="<unk>"						,				__a="<pad>"						,				__a="<mask>"						,				__a=False						,				__a=True						,				**__a						,				):

						'''simple docstring'''





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

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

						__a			:       Union[str, Any]					    =  add_prefix_space

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

											__a			:       str					    =  False

											if state.get('add_prefix_space'						,				__a							) != add_prefix_space:
																__a			:       Dict					    =  add_prefix_space
																__a			:       Tuple					    =  True

											if state.get('trim_offsets'						,				__a							) != trim_offsets:
																__a			:       Optional[Any]					    =  trim_offsets
																__a			:       Dict					    =  True

											if changes_to_apply:
																__a			:       Tuple					    =  getattr(__a						,				state.pop('type'							)							)
																__a			:       Optional[Any]					    =  component_class(**__a							)
																setattr(self.backend_tokenizer						,				__a						,				__a							)




	@property
	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						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						,				__a							):

						'''simple docstring'''





						__a			:       Dict					    =  AddedToken(__a						,				lstrip=__a						,				rstrip=__a							) if isinstance(__a						,				__a							) else value
						__a			:       Union[str, Any]					    =  value




	def       __UpperCAmelCase							(   self						,				*__a						,				**__a							):

						'''simple docstring'''





						__a			:       Optional[int]					    =  kwargs.get('is_split_into_words'						,				__a							)

						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(*__a						,				**__a							)




	def       __UpperCAmelCase							(   self						,				*__a						,				**__a							):

						'''simple docstring'''





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

						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(*__a						,				**__a							)




	def       __UpperCAmelCase							(   self						,				__a						,				__a = None							):

						'''simple docstring'''





						__a			:       str					    =  self._tokenizer.model.save(__a						,				name=__a							)
						return tuple(__a							)




	def       __UpperCAmelCase							(   self						,				__a						,				__a=None							):

						'''simple docstring'''





						__a			:       Optional[int]					    =  [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						,				__a						,				__a = None							):

						'''simple docstring'''





						__a			:       str					    =  [self.sep_token_id]
						__a			:       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"""





import inspect
from typing import List, Optional, Tuple, Union

import torch

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



class 						snake_case_(				a__	):






     def __init__(		self     :    int    , UpperCamelCase_     :    VQModel    , UpperCamelCase_     :    UNetaDModel    , UpperCamelCase_     :    DDIMScheduler ):
           super().__init__()
           self.register_modules(vqvae=UpperCamelCase_    , unet=UpperCamelCase_    , scheduler=UpperCamelCase_ )






     @torch.no_grad()
     def __call__(		self     :    Union[str, Any]    , UpperCamelCase_     :    int = 1    , UpperCamelCase_     :    Optional[Union[torch.Generator, List[torch.Generator]]] = None    , UpperCamelCase_     :    float = 0.0    , UpperCamelCase_     :    int = 5_0    , UpperCamelCase_     :    Optional[str] = "pil"    , UpperCamelCase_     :    bool = True    , **UpperCamelCase_     :    Optional[int]    , ):
           lowerCAmelCase    :		Dict				=      randn_tensor(
               (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)    , generator=UpperCamelCase_    , )
           lowerCAmelCase    :		Optional[int]				=      latents.to(self.device )

           # scale the initial noise by the standard deviation required by the scheduler
           lowerCAmelCase    :		List[str]				=      latents * self.scheduler.init_noise_sigma

           self.scheduler.set_timesteps(UpperCamelCase_ )

           # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
           lowerCAmelCase    :		Any				=      '''eta''' in set(inspect.signature(self.scheduler.step ).parameters.keys() )

           lowerCAmelCase    :		List[str]				=      {}
           if accepts_eta:
                 lowerCAmelCase    :		List[Any]				=      eta

           for t in self.progress_bar(self.scheduler.timesteps ):
                 lowerCAmelCase    :		List[str]				=      self.scheduler.scale_model_input(UpperCamelCase_    , UpperCamelCase_ )
                 # predict the noise residual
                 lowerCAmelCase    :		Tuple				=      self.unet(UpperCamelCase_    , UpperCamelCase_ ).sample
                 # compute the previous noisy sample x_t -> x_t-1
                 lowerCAmelCase    :		Optional[Any]				=      self.scheduler.step(UpperCamelCase_    , UpperCamelCase_    , UpperCamelCase_    , **UpperCamelCase_ ).prev_sample

           # decode the image latents with the VAE
           lowerCAmelCase    :		Dict				=      self.vqvae.decode(UpperCamelCase_ ).sample

           lowerCAmelCase    :		Dict				=      (image / 2 + 0.5).clamp(0    , 1 )
           lowerCAmelCase    :		Dict				=      image.cpu().permute(0    , 2    , 3    , 1 ).numpy()
           if output_type == "pil":
                 lowerCAmelCase    :		List[str]				=      self.numpy_to_pil(UpperCamelCase_ )

           if not return_dict:
                 return (image,)

           return ImagePipelineOutput(images=UpperCamelCase_ )


