






import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


A__      :     Any       =					logging.get_logger(__name__)


A__      :     Optional[int]       =					{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt'}

# See all BART models at https://huggingface.co/models?filter=bart
A__      :     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',
    },
}

A__      :     Tuple       =					{
    '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,
}






@lru_cache()
def     a     (		):






				'''simple docstring'''
				lowercase__  					=     (
				    list(range(ord('''!'''							)						,  ord('''~'''							) + 1							)							) + list(range(ord('''¡'''							)						,  ord('''¬'''							) + 1							)							) + list(range(ord('''®'''							)						,  ord('''ÿ'''							) + 1							)							)
				)
				lowercase__  					=     bs[:]
				lowercase__  					=     0
				for b in range(2**8							):
								if b not in bs:
												bs.append(lowerCamelCase_							)
												cs.append(2**8 + n							)
												n += 1
				lowercase__  					=     [chr(lowerCamelCase_							) for n in cs]
				return dict(zip(lowerCamelCase_						,  lowerCamelCase_							)							)






def     a     (		lowerCamelCase_							):






				'''simple docstring'''
				lowercase__  					=     set()
				lowercase__  					=     word[0]
				for char in word[1:]:
								pairs.add((prev_char, char)							)
								lowercase__  					=     char
				return pairs




class  _UpperCAmelCase   (			A__      ):

				"""simple docstring"""




				lowercase__											=			VOCAB_FILES_NAMES
				lowercase__											=			PRETRAINED_VOCAB_FILES_MAP
				lowercase__											=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowercase__											=			["""input_ids""", """attention_mask"""]




				def __init__(	self		:       Optional[Any],    lowerCamelCase		:       Optional[Any],    lowerCamelCase		:       List[str],    lowerCamelCase		:       List[Any]="replace",    lowerCamelCase		:       Union[str, Any]="<s>",    lowerCamelCase		:       Optional[Any]="</s>",    lowerCamelCase		:       Optional[Any]="</s>",    lowerCamelCase		:       int="<s>",    lowerCamelCase		:       Optional[int]="<unk>",    lowerCamelCase		:       int="<pad>",    lowerCamelCase		:       List[str]="<mask>",    lowerCamelCase		:       str=False,    **lowerCamelCase		:       Dict,    ):

								'''simple docstring'''
								lowercase__  					=     AddedToken(lowerCamelCase,    lstrip=lowerCamelCase,    rstrip=lowerCamelCase    ) if isinstance(lowerCamelCase,    lowerCamelCase    ) else bos_token
								lowercase__  					=     AddedToken(lowerCamelCase,    lstrip=lowerCamelCase,    rstrip=lowerCamelCase    ) if isinstance(lowerCamelCase,    lowerCamelCase    ) else eos_token
								lowercase__  					=     AddedToken(lowerCamelCase,    lstrip=lowerCamelCase,    rstrip=lowerCamelCase    ) if isinstance(lowerCamelCase,    lowerCamelCase    ) else sep_token
								lowercase__  					=     AddedToken(lowerCamelCase,    lstrip=lowerCamelCase,    rstrip=lowerCamelCase    ) if isinstance(lowerCamelCase,    lowerCamelCase    ) else cls_token
								lowercase__  					=     AddedToken(lowerCamelCase,    lstrip=lowerCamelCase,    rstrip=lowerCamelCase    ) if isinstance(lowerCamelCase,    lowerCamelCase    ) else unk_token
								lowercase__  					=     AddedToken(lowerCamelCase,    lstrip=lowerCamelCase,    rstrip=lowerCamelCase    ) if isinstance(lowerCamelCase,    lowerCamelCase    ) else pad_token

								# Mask token behave like a normal word, i.e. include the space before it
								lowercase__  					=     AddedToken(lowerCamelCase,    lstrip=lowerCamelCase,    rstrip=lowerCamelCase    ) if isinstance(lowerCamelCase,    lowerCamelCase    ) else mask_token

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

								with open(lowerCamelCase,    encoding='''utf-8'''    ) as vocab_handle:
												lowercase__  					=     json.load(lowerCamelCase    )
								lowercase__  					=     {v: k for k, v in self.encoder.items()}
								lowercase__  					=     errors  # how to handle errors in decoding
								lowercase__  					=     bytes_to_unicode()
								lowercase__  					=     {v: k for k, v in self.byte_encoder.items()}
								with open(lowerCamelCase,    encoding='''utf-8'''    ) as merges_handle:
												lowercase__  					=     merges_handle.read().split('''\n'''    )[1:-1]
								lowercase__  					=     [tuple(merge.split()    ) for merge in bpe_merges]
								lowercase__  					=     dict(zip(lowerCamelCase,    range(len(lowerCamelCase    )    )    )    )
								lowercase__  					=     {}
								lowercase__  					=     add_prefix_space

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




				@property
				def 							lowercase__  (	self		:       Dict    ):

								'''simple docstring'''
								return len(self.encoder    )




				def 							lowercase__  (	self		:       List[Any]    ):

								'''simple docstring'''
								return dict(self.encoder,    **self.added_tokens_encoder    )




				def 							lowercase__  (	self		:       Dict,    lowerCamelCase		:       int    ):

								'''simple docstring'''
								if token in self.cache:
												return self.cache[token]
								lowercase__  					=     tuple(lowerCamelCase    )
								lowercase__  					=     get_pairs(lowerCamelCase    )

								if not pairs:
												return token

								while True:
												lowercase__  					=     min(lowerCamelCase,    key=lambda lowerCamelCase    : self.bpe_ranks.get(lowerCamelCase,    float('''inf'''    )    )    )
												if bigram not in self.bpe_ranks:
																break
												lowercase__					,		lowercase__  					=     bigram
												lowercase__  					=     []
												lowercase__  					=     0
												while i < len(lowerCamelCase    ):
																try:
																				lowercase__  					=     word.index(lowerCamelCase,    lowerCamelCase    )
																except ValueError:
																				new_word.extend(word[i:]    )
																				break
																else:
																				new_word.extend(word[i:j]    )
																				lowercase__  					=     j

																if word[i] == first and i < len(lowerCamelCase    ) - 1 and word[i + 1] == second:
																				new_word.append(first + second    )
																				i += 2
																else:
																				new_word.append(word[i]    )
																				i += 1
												lowercase__  					=     tuple(lowerCamelCase    )
												lowercase__  					=     new_word
												if len(lowerCamelCase    ) == 1:
																break
												else:
																lowercase__  					=     get_pairs(lowerCamelCase    )
								lowercase__  					=     ''' '''.join(lowerCamelCase    )
								lowercase__  					=     word
								return word




				def 							lowercase__  (	self		:       Dict,    lowerCamelCase		:       str    ):

								'''simple docstring'''
								lowercase__  					=     []
								for token in re.findall(self.pat,    lowerCamelCase    ):
												lowercase__  					=     ''''''.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(lowerCamelCase    ).split(''' '''    )    )
								return bpe_tokens




				def 							lowercase__  (	self		:       Dict,    lowerCamelCase		:       Dict    ):

								'''simple docstring'''
								return self.encoder.get(lowerCamelCase,    self.encoder.get(self.unk_token    )    )




				def 							lowercase__  (	self		:       Any,    lowerCamelCase		:       Optional[Any]    ):

								'''simple docstring'''
								return self.decoder.get(lowerCamelCase    )




				def 							lowercase__  (	self		:       List[str],    lowerCamelCase		:       Optional[int]    ):

								'''simple docstring'''
								lowercase__  					=     ''''''.join(lowerCamelCase    )
								lowercase__  					=     bytearray([self.byte_decoder[c] for c in text]    ).decode('''utf-8''',    errors=self.errors    )
								return text




				def 							lowercase__  (	self		:       str,    lowerCamelCase		:       str,    lowerCamelCase		:       Optional[str] = None    ):

								'''simple docstring'''
								if not os.path.isdir(lowerCamelCase    ):
												logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""    )
												return
								lowercase__  					=     os.path.join(
								    lowerCamelCase,    (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']    )
								lowercase__  					=     os.path.join(
								    lowerCamelCase,    (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']    )

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

								lowercase__  					=     0
								with open(lowerCamelCase,    '''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 lowerCamelCase    : 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!'''    )
																				lowercase__  					=     token_index
																writer.write(''' '''.join(lowerCamelCase    ) + '''\n'''    )
																index += 1

								return vocab_file, merge_file




				def 							lowercase__  (	self		:       str,    lowerCamelCase		:       List[int],    lowerCamelCase		:       Optional[List[int]] = None    ):

								'''simple docstring'''
								if token_ids_a is None:
												return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
								lowercase__  					=     [self.cls_token_id]
								lowercase__  					=     [self.sep_token_id]
								return cls + token_ids_a + sep + sep + token_ids_a + sep




				def 							lowercase__  (	self		:       List[Any],    lowerCamelCase		:       List[int],    lowerCamelCase		:       Optional[List[int]] = None,    lowerCamelCase		:       bool = False    ):

								'''simple docstring'''
								if already_has_special_tokens:
												return super().get_special_tokens_mask(
												    token_ids_a=lowerCamelCase,    token_ids_a=lowerCamelCase,    already_has_special_tokens=lowerCamelCase    )

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




				def 							lowercase__  (	self		:       List[Any],    lowerCamelCase		:       List[int],    lowerCamelCase		:       Optional[List[int]] = None    ):

								'''simple docstring'''
								lowercase__  					=     [self.sep_token_id]
								lowercase__  					=     [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 							lowercase__  (	self		:       str,    lowerCamelCase		:       List[Any],    lowerCamelCase		:       Optional[Any]=False,    **lowerCamelCase		:       Union[str, Any]    ):

								'''simple docstring'''
								lowercase__  					=     kwargs.pop('''add_prefix_space''',    self.add_prefix_space    )
								if (is_split_into_words or add_prefix_space) and (len(lowerCamelCase    ) > 0 and not text[0].isspace()):
												lowercase__  					=     ''' ''' + text
								return (text, kwargs)












from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

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


A__      :     Union[str, Any]       =					transforms.Compose(
    [
        transforms.Resize((2_56, 2_56)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)






def     a     (		lowerCamelCase_							):






				'''simple docstring'''
				if isinstance(lowerCamelCase_						,  torch.Tensor							):
								return image
				elif isinstance(lowerCamelCase_						,  PIL.Image.Image							):
								lowercase__  					=     [image]

				lowercase__  					=     [trans(img.convert('''RGB'''							)							) for img in image]
				lowercase__  					=     torch.stack(lowerCamelCase_							)
				return image




class  _UpperCAmelCase   (			A__      ):

				"""simple docstring"""




				def __init__(	self		:       Tuple,    lowerCamelCase		:       Dict,    lowerCamelCase		:       str    ):

								'''simple docstring'''
								super().__init__()

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

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




				def 							lowercase__  (	self		:       int,    lowerCamelCase		:       Optional[int]    ):

								'''simple docstring'''
								if strength < 0 or strength > 1:
												raise ValueError(F"""The value of strength should in [0.0, 1.0] but is {strength}"""    )




				def 							lowercase__  (	self		:       Optional[int],    lowerCamelCase		:       Union[str, Any],    lowerCamelCase		:       Dict,    lowerCamelCase		:       List[str]    ):

								'''simple docstring'''
								# get the original timestep using init_timestep
								lowercase__  					=     min(int(num_inference_steps * strength    ),    lowerCamelCase    )

								lowercase__  					=     max(num_inference_steps - init_timestep,    0    )
								lowercase__  					=     self.scheduler.timesteps[t_start:]

								return timesteps, num_inference_steps - t_start




				def 							lowercase__  (	self		:       Optional[Any],    lowerCamelCase		:       List[Any],    lowerCamelCase		:       int,    lowerCamelCase		:       Dict,    lowerCamelCase		:       Any,    lowerCamelCase		:       Optional[Any],    lowerCamelCase		:       Any=None    ):

								'''simple docstring'''
								if not isinstance(lowerCamelCase,    (torch.Tensor, PIL.Image.Image, list)    ):
												raise ValueError(
												    F"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(lowerCamelCase    )}"""    )

								lowercase__  					=     image.to(device=lowerCamelCase,    dtype=lowerCamelCase    )

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

								lowercase__  					=     init_latents.shape
								lowercase__  					=     randn_tensor(lowerCamelCase,    generator=lowerCamelCase,    device=lowerCamelCase,    dtype=lowerCamelCase    )

								# get latents
								print('''add noise to latents at timestep''',    lowerCamelCase    )
								lowercase__  					=     self.scheduler.add_noise(lowerCamelCase,    lowerCamelCase,    lowerCamelCase    )
								lowercase__  					=     init_latents

								return latents




				@torch.no_grad()
				def __call__(	self		:       Dict,    lowerCamelCase		:       Union[torch.FloatTensor, PIL.Image.Image] = None,    lowerCamelCase		:       float = 0.8,    lowerCamelCase		:       int = 1,    lowerCamelCase		:       Optional[Union[torch.Generator, List[torch.Generator]]] = None,    lowerCamelCase		:       float = 0.0,    lowerCamelCase		:       int = 50,    lowerCamelCase		:       Optional[bool] = None,    lowerCamelCase		:       Optional[str] = "pil",    lowerCamelCase		:       bool = True,    ):

								'''simple docstring'''
								self.check_inputs(lowerCamelCase    )

								# 2. Preprocess image
								lowercase__  					=     preprocess(lowerCamelCase    )

								# 3. set timesteps
								self.scheduler.set_timesteps(lowerCamelCase,    device=self.device    )
								lowercase__					,		lowercase__  					=     self.get_timesteps(lowerCamelCase,    lowerCamelCase,    self.device    )
								lowercase__  					=     timesteps[:1].repeat(lowerCamelCase    )

								# 4. Prepare latent variables
								lowercase__  					=     self.prepare_latents(lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    self.unet.dtype,    self.device,    lowerCamelCase    )
								lowercase__  					=     latents

								# 5. Denoising loop
								for t in self.progress_bar(lowerCamelCase    ):
												# 1. predict noise model_output
												lowercase__  					=     self.unet(lowerCamelCase,    lowerCamelCase    ).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
												lowercase__  					=     self.scheduler.step(
												    lowerCamelCase,    lowerCamelCase,    lowerCamelCase,    eta=lowerCamelCase,    use_clipped_model_output=lowerCamelCase,    generator=lowerCamelCase,    ).prev_sample

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

								if not return_dict:
												return (image, latent_timestep.item())

								return ImagePipelineOutput(images=lowerCamelCase    )




