
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


SCREAMING_SNAKE_CASE							:int										=    logging.get_logger(__name__)


SCREAMING_SNAKE_CASE							: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
SCREAMING_SNAKE_CASE							:Optional[int]										=    {
    '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',
    },
}

SCREAMING_SNAKE_CASE							:int										=    {
    'facebook/bart-base': 1024,
    'facebook/bart-large': 1024,
    'facebook/bart-large-mnli': 1024,
    'facebook/bart-large-cnn': 1024,
    'facebook/bart-large-xsum': 1024,
    'yjernite/bart_eli5': 1024,
}





class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_    =					VOCAB_FILES_NAMES
	snake_case_    =					PRETRAINED_VOCAB_FILES_MAP
	snake_case_    =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	snake_case_    =					["input_ids", "attention_mask"]
	snake_case_    =					BartTokenizer






	def __init__(	self :			Any							,A :			Dict=None							,A :			Any=None							,A :			List[str]=None							,A :			Any="replace"							,A :			Dict="<s>"							,A :			Optional[int]="</s>"							,A :			Any="</s>"							,A :			Union[str, Any]="<s>"							,A :			int="<unk>"							,A :			int="<pad>"							,A :			Optional[Any]="<mask>"							,A :			List[str]=False							,A :			str=True							,**A :			Tuple							,):
					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             =			json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()			)
					if pre_tok_state.get("add_prefix_space"							,A			) != add_prefix_space:
									__A             =			getattr(A							,pre_tok_state.pop("type"			)			)
									__A             =			add_prefix_space
									__A             =			pre_tok_class(**A			)

					__A             =			add_prefix_space

					# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
					__A             =			"post_processor"
					__A             =			getattr(self.backend_tokenizer							,A							,A			)
					if tokenizer_component_instance:
									__A             =			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             =			tuple(state["sep"]			)
									if "cls" in state:
													__A             =			tuple(state["cls"]			)

									__A             =			False

									if state.get("add_prefix_space"							,A			) != add_prefix_space:
													__A             =			add_prefix_space
													__A             =			True

									if state.get("trim_offsets"							,A			) != trim_offsets:
													__A             =			trim_offsets
													__A             =			True

									if changes_to_apply:
													__A             =			getattr(A							,state.pop("type"			)			)
													__A             =			component_class(**A			)
													setattr(self.backend_tokenizer							,A							,A			)






	@property
	def     UpperCamelCase_  (	self :			Any			):
					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 :			List[str]							,A :			List[Any]			):
					__A             =			AddedToken(A							,lstrip=A							,rstrip=A			) if isinstance(A							,A			) else value
					__A             =			value






	def     UpperCamelCase_  (	self :			Optional[Any]							,*A :			List[Any]							,**A :			Optional[int]			):
					__A             =			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 :			Tuple							,*A :			int							,**A :			Dict			):
					__A             =			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 :			Union[str, Any]							,A :			str							,A :			Optional[str] = None			):
					__A             =			self._tokenizer.model.save(A							,name=A			)
					return tuple(A			)






	def     UpperCamelCase_  (	self :			Union[str, Any]							,A :			Tuple							,A :			str=None			):
					__A             =			[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 :			Optional[Any]							,A :			List[int]							,A :			Optional[List[int]] = None			):
					__A             =			[self.sep_token_id]
					__A             =			[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]


from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	def __init__(	self :			List[str]							,A :			Optional[Any]							,A :			List[Any]			):
					super().__init__()

					# make sure scheduler can always be converted to DDIM
					__A             =			DDIMScheduler.from_config(scheduler.config			)

					self.register_modules(unet=A							,scheduler=A			)







	@torch.no_grad()
	def __call__(	self :			Tuple							,A :			int = 1							,A :			Optional[Union[torch.Generator, List[torch.Generator]]] = None							,A :			float = 0.0							,A :			int = 50							,A :			Optional[bool] = None							,A :			Optional[str] = "pil"							,A :			bool = True							,):

					# Sample gaussian noise to begin loop
					if isinstance(self.unet.config.sample_size							,A			):
									__A             =			(
									    batch_size,
									    self.unet.config.in_channels,
									    self.unet.config.sample_size,
									    self.unet.config.sample_size,
									)
					else:
									__A             =			(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

					if isinstance(A							,A			) and len(A			) != batch_size:
									raise ValueError(
									    f'''You have passed a list of generators of length {len(A			)}, but requested an effective batch'''
									    f''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''			)

					__A             =			randn_tensor(A							,generator=A							,device=self.device							,dtype=self.unet.dtype			)

					# set step values
					self.scheduler.set_timesteps(A			)

					for t in self.progress_bar(self.scheduler.timesteps			):
									# 1. predict noise model_output
									__A             =			self.unet(A							,A			).sample

									# 2. predict previous mean of image x_t-1 and add variance depending on eta
									# eta corresponds to η in paper and should be between [0, 1]
									# do x_t -> x_t-1
									__A             =			self.scheduler.step(
									    A							,A							,A							,eta=A							,use_clipped_model_output=A							,generator=A			).prev_sample

					__A             =			(image / 2 + 0.5).clamp(0							,1			)
					__A             =			image.cpu().permute(0							,2							,3							,1			).numpy()
					if output_type == "pil":
									__A             =			self.numpy_to_pil(A			)

					if not return_dict:
									return (image,)

					return ImagePipelineOutput(images=A			)
