






'''simple docstring'''





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


_UpperCamelCase						:						Union[str, Any]  =					logging.get_logger(__name__)


_UpperCamelCase						:						Dict  =					{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt'}

_UpperCamelCase						:						Dict  =					{
    'vocab_file': {
        'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json',
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt',
        'allenai/longformer-large-4096': (
            'https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt'
        ),
        'allenai/longformer-large-4096-finetuned-triviaqa': (
            'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt'
        ),
        'allenai/longformer-base-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt'
        ),
        'allenai/longformer-large-4096-extra.pos.embd.only': (
            'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt'
        ),
    },
}

_UpperCamelCase						:						Optional[int]  =					{
    'allenai/longformer-base-4096': 4_096,
    'allenai/longformer-large-4096': 4_096,
    'allenai/longformer-large-4096-finetuned-triviaqa': 4_096,
    'allenai/longformer-base-4096-extra.pos.embd.only': 4_096,
    'allenai/longformer-large-4096-extra.pos.embd.only': 4_096,
}
@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def __UpperCAmelCase   (					)    ->   str:
			UpperCAmelCase_      :       int          =       (
			    list(range(ord('''!'''		)			,       ord('''~'''		) + 1		)		) + list(range(ord('''¡'''		)			,       ord('''¬'''		) + 1		)		) + list(range(ord('''®'''		)			,       ord('''ÿ'''		) + 1		)		)
			)
			UpperCAmelCase_      :       Any          =       bs[:]
			UpperCAmelCase_      :       str          =       0
			for b in range(2**8		):
						if b not in bs:
									bs.append(A		)
									cs.append(2**8 + n		)
									n += 1
			UpperCAmelCase_      :       str          =       [chr(A		) for n in cs]
			return dict(zip(A			,       A		)		)
def __UpperCAmelCase   (					A					:			int		)    ->   Tuple:
			UpperCAmelCase_      :       Optional[Any]          =       set()
			UpperCAmelCase_      :       Dict          =       word[0]
			for char in word[1:]:
						pairs.add((prev_char, char)		)
						UpperCAmelCase_      :       Optional[int]          =       char
			return pairs







class  snake_case__      (					snake_case_):
					a_        =					VOCAB_FILES_NAMES
					a_        =					PRETRAINED_VOCAB_FILES_MAP
					a_        =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					a_        =					['''input_ids''', '''attention_mask''']




					def __init__(  self  :		Dict				, _A  :		List[str]				, _A  :		Union[str, Any]				, _A  :		str="replace"				, _A  :		int="<s>"				, _A  :		Any="</s>"				, _A  :		Any="</s>"				, _A  :		Optional[int]="<s>"				, _A  :		Any="<unk>"				, _A  :		str="<pad>"				, _A  :		Optional[Any]="<mask>"				, _A  :		Tuple=False				, **_A  :		str				, )    ->							Union[str, Any]:
								UpperCAmelCase_      :       Any          =       AddedToken(UpperCamelCase__				, lstrip=UpperCamelCase__				, rstrip=UpperCamelCase__      ) if isinstance(UpperCamelCase__				, UpperCamelCase__      ) else bos_token
								UpperCAmelCase_      :       int          =       AddedToken(UpperCamelCase__				, lstrip=UpperCamelCase__				, rstrip=UpperCamelCase__      ) if isinstance(UpperCamelCase__				, UpperCamelCase__      ) else eos_token
								UpperCAmelCase_      :       Dict          =       AddedToken(UpperCamelCase__				, lstrip=UpperCamelCase__				, rstrip=UpperCamelCase__      ) if isinstance(UpperCamelCase__				, UpperCamelCase__      ) else sep_token
								UpperCAmelCase_      :       Optional[int]          =       AddedToken(UpperCamelCase__				, lstrip=UpperCamelCase__				, rstrip=UpperCamelCase__      ) if isinstance(UpperCamelCase__				, UpperCamelCase__      ) else cls_token
								UpperCAmelCase_      :       Dict          =       AddedToken(UpperCamelCase__				, lstrip=UpperCamelCase__				, rstrip=UpperCamelCase__      ) if isinstance(UpperCamelCase__				, UpperCamelCase__      ) else unk_token
								UpperCAmelCase_      :       Tuple          =       AddedToken(UpperCamelCase__				, lstrip=UpperCamelCase__				, rstrip=UpperCamelCase__      ) if isinstance(UpperCamelCase__				, UpperCamelCase__      ) else pad_token

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

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

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

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




					@property
					def 		A      (  self  :		List[str]      )    ->							Optional[Any]:
								return len(self.encoder      )




					def 		A      (  self  :		Any      )    ->							str:
								return dict(self.encoder				, **self.added_tokens_encoder      )




					def 		A      (  self  :		Dict				, _A  :		List[str]      )    ->							Tuple:




								if token in self.cache:
											return self.cache[token]
								UpperCAmelCase_      :       Optional[Any]          =       tuple(UpperCamelCase__      )
								UpperCAmelCase_      :       str          =       get_pairs(UpperCamelCase__      )

								if not pairs:
											return token

								while True:
											UpperCAmelCase_      :       str          =       min(UpperCamelCase__				, key=lambda _A      : self.bpe_ranks.get(UpperCamelCase__				, float('''inf'''      )      )      )
											if bigram not in self.bpe_ranks:
														break
											UpperCAmelCase_      :       List[Any]          =       bigram
											UpperCAmelCase_      :       str          =       []
											UpperCAmelCase_      :       Tuple          =       0
											while i < len(UpperCamelCase__      ):
														try:
																	UpperCAmelCase_      :       Union[str, Any]          =       word.index(UpperCamelCase__				, UpperCamelCase__      )
														except ValueError:
																	new_word.extend(word[i:]      )
																	break
														else:
																	new_word.extend(word[i:j]      )
																	UpperCAmelCase_      :       Dict          =       j

														if word[i] == first and i < len(UpperCamelCase__      ) - 1 and word[i + 1] == second:
																	new_word.append(first + second      )
																	i += 2
														else:
																	new_word.append(word[i]      )
																	i += 1
											UpperCAmelCase_      :       Optional[Any]          =       tuple(UpperCamelCase__      )
											UpperCAmelCase_      :       int          =       new_word
											if len(UpperCamelCase__      ) == 1:
														break
											else:
														UpperCAmelCase_      :       str          =       get_pairs(UpperCamelCase__      )
								UpperCAmelCase_      :       List[Any]          =       " ".join(UpperCamelCase__      )
								UpperCAmelCase_      :       List[Any]          =       word
								return word




					def 		A      (  self  :		Union[str, Any]				, _A  :		Dict      )    ->							List[Any]:
								UpperCAmelCase_      :       Optional[int]          =       []
								for token in re.findall(self.pat				, UpperCamelCase__      ):
											UpperCAmelCase_      :       Any          =       "".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(UpperCamelCase__      ).split(''' '''      )      )
								return bpe_tokens




					def 		A      (  self  :		Dict				, _A  :		str      )    ->							List[Any]:
								return self.encoder.get(UpperCamelCase__				, self.encoder.get(self.unk_token      )      )




					def 		A      (  self  :		Optional[int]				, _A  :		Optional[Any]      )    ->							List[str]:
								return self.decoder.get(UpperCamelCase__      )




					def 		A      (  self  :		List[Any]				, _A  :		int      )    ->							str:
								UpperCAmelCase_      :       int          =       "".join(UpperCamelCase__      )
								UpperCAmelCase_      :       Dict          =       bytearray([self.byte_decoder[c] for c in text]      ).decode('''utf-8'''				, errors=self.errors      )
								return text




					def 		A      (  self  :		Dict				, _A  :		List[str]				, _A  :		Any = None      )    ->							Tuple[str]:




								if not os.path.isdir(UpperCamelCase__      ):
											logger.error(F"Vocabulary path ({save_directory}) should be a directory"      )
											return
								UpperCAmelCase_      :       Dict          =       os.path.join(
								    UpperCamelCase__				, (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']      )
								UpperCAmelCase_      :       List[Any]          =       os.path.join(
								    UpperCamelCase__				, (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']      )

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

								UpperCAmelCase_      :       List[Any]          =       0
								with open(UpperCamelCase__				, '''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 _A      : 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!'''      )
																	UpperCAmelCase_      :       Optional[Any]          =       token_index
														writer.write(''' '''.join(UpperCamelCase__      ) + '''\n'''      )
														index += 1

								return vocab_file, merge_file




					def 		A      (  self  :		Union[str, Any]				, _A  :		int				, _A  :		Any = None      )    ->							List[int]:




								if token_ids_a is None:
											return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
								UpperCAmelCase_      :       Union[str, Any]          =       [self.cls_token_id]
								UpperCAmelCase_      :       Optional[int]          =       [self.sep_token_id]
								return cls + token_ids_a + sep + sep + token_ids_a + sep




					def 		A      (  self  :		List[Any]				, _A  :		Any				, _A  :		Any = None				, _A  :		Tuple = False      )    ->							List[int]:




								if already_has_special_tokens:
											return super().get_special_tokens_mask(
											    token_ids_a=UpperCamelCase__				, token_ids_a=UpperCamelCase__				, already_has_special_tokens=UpperCamelCase__      )

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




					def 		A      (  self  :		List[Any]				, _A  :		Tuple				, _A  :		Union[str, Any] = None      )    ->							List[int]:
								UpperCAmelCase_      :       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]





					def 		A      (  self  :		List[Any]				, _A  :		List[str]				, _A  :		Dict=False				, **_A  :		int      )    ->							Any:
								UpperCAmelCase_      :       Optional[Any]          =       kwargs.pop('''add_prefix_space'''				, self.add_prefix_space      )
								if (is_split_into_words or add_prefix_space) and (len(UpperCamelCase__      ) > 0 and not text[0].isspace()):
											UpperCAmelCase_      :       str          =       " " + text
								return (text, kwargs)






"""simple docstring"""



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

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







def     __lowerCAmelCase		(		lowercase    :    Optional[int]					)      ->		Union[str, Any]:


    """simple docstring"""





    snake_case   ,snake_case       : List[str]    =       image.size
    snake_case   ,snake_case       : int    =       (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
    snake_case       : Any    =       image.resize((w, h)		,				resample=PIL_INTERPOLATION["lanczos"]					)
    snake_case       : Dict    =       np.array(lowercase					).astype(np.floataa					) / 255.0
    snake_case       : List[str]    =       image[None].transpose(0		,				3		,				1		,				2					)
    snake_case       : List[Any]    =       torch.from_numpy(lowercase					)
    return 2.0 * image - 1.0







class _lowerCAmelCase							( snake_case_      ):







   def __init__(      self		,      UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      )      ->						Optional[Any]:

       '''simple docstring'''




       super().__init__()
       self.register_modules(vqvae=UpperCamelCase__		,      unet=UpperCamelCase__		,      scheduler=UpperCamelCase__		)







   @torch.no_grad()
   def __call__(      self		,      UpperCamelCase__ = None		,      UpperCamelCase__ = 1		,      UpperCamelCase__ = 100		,      UpperCamelCase__ = 0.0		,      UpperCamelCase__ = None		,      UpperCamelCase__ = "pil"		,      UpperCamelCase__ = True		,      )      ->						Union[Tuple, ImagePipelineOutput]:

       '''simple docstring'''




       if isinstance(UpperCamelCase__		,      PIL.Image.Image		):
           snake_case       : Tuple    =       1
       elif isinstance(UpperCamelCase__		,      torch.Tensor		):
           snake_case       : str    =       image.shape[0]
       else:
           raise ValueError(F'`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(UpperCamelCase__		)}'		)

       if isinstance(UpperCamelCase__		,      PIL.Image.Image		):
           snake_case       : Optional[int]    =       preprocess(UpperCamelCase__		)

       snake_case   ,snake_case       : List[str]    =       image.shape[-2:]

       # in_channels should be 6: 3 for latents, 3 for low resolution image
       snake_case       : Any    =       (batch_size, self.unet.config.in_channels // 2, height, width)
       snake_case       : List[str]    =       next(self.unet.parameters()		).dtype

       snake_case       : int    =       randn_tensor(UpperCamelCase__		,      generator=UpperCamelCase__		,      device=self.device		,      dtype=UpperCamelCase__		)

       snake_case       : List[Any]    =       image.to(device=self.device		,      dtype=UpperCamelCase__		)

       # set timesteps and move to the correct device
       self.scheduler.set_timesteps(UpperCamelCase__		,      device=self.device		)
       snake_case       : Optional[Any]    =       self.scheduler.timesteps

       # scale the initial noise by the standard deviation required by the scheduler
       snake_case       : Optional[int]    =       latents * self.scheduler.init_noise_sigma

       # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
       # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
       # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
       # and should be between [0, 1]
       snake_case       : Optional[int]    =       "eta" in set(inspect.signature(self.scheduler.step		).parameters.keys()		)
       snake_case       : Union[str, Any]    =       {}
       if accepts_eta:
           snake_case       : List[str]    =       eta

       for t in self.progress_bar(UpperCamelCase__		):
           # concat latents and low resolution image in the channel dimension.
           snake_case       : int    =       torch.cat([latents, image]		,      dim=1		)
           snake_case       : List[Any]    =       self.scheduler.scale_model_input(UpperCamelCase__		,      UpperCamelCase__		)
           # predict the noise residual
           snake_case       : int    =       self.unet(UpperCamelCase__		,      UpperCamelCase__		).sample
           # compute the previous noisy sample x_t -> x_t-1
           snake_case       : List[Any]    =       self.scheduler.step(UpperCamelCase__		,      UpperCamelCase__		,      UpperCamelCase__		,      **UpperCamelCase__		).prev_sample

       # decode the image latents with the VQVAE
       snake_case       : Optional[int]    =       self.vqvae.decode(UpperCamelCase__		).sample
       snake_case       : Tuple    =       torch.clamp(UpperCamelCase__		,      -1.0		,      1.0		)
       snake_case       : Union[str, Any]    =       image / 2 + 0.5
       snake_case       : Optional[Any]    =       image.cpu().permute(0		,      2		,      3		,      1		).numpy()

       if output_type == "pil":
           snake_case       : Union[str, Any]    =       self.numpy_to_pil(UpperCamelCase__		)

       if not return_dict:
           return (image,)

       return ImagePipelineOutput(images=UpperCamelCase__		)





