

"""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			=					logging.get_logger(__name__)


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

__UpperCAmelCase			=					{
    '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			=					{
    'allenai/longformer-base-4096': 4096,
    'allenai/longformer-large-4096': 4096,
    'allenai/longformer-large-4096-finetuned-triviaqa': 4096,
    'allenai/longformer-base-4096-extra.pos.embd.only': 4096,
    'allenai/longformer-large-4096-extra.pos.embd.only': 4096,
}
@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def 	lowerCAmelCase    (       ):
 '''simple docstring'''





 UpperCAmelCase__       :	Optional[Any]        =       (
     list(range(ord("""!"""   )				,     ord("""~"""   ) + 1   )   ) + list(range(ord("""¡"""   )				,     ord("""¬"""   ) + 1   )   ) + list(range(ord("""®"""   )				,     ord("""ÿ"""   ) + 1   )   )
 )
 UpperCAmelCase__       :	Optional[Any]        =       bs[:]
 UpperCAmelCase__       :	Union[str, Any]        =       0
 for b in range(2**8   ):
  if b not in bs:
   bs.append(__UpperCamelCase   )
   cs.append(2**8 + n   )
   n += 1
 UpperCAmelCase__       :	Any        =       [chr(__UpperCamelCase   ) for n in cs]
 return dict(zip(__UpperCamelCase				,     __UpperCamelCase   )   )
def 	lowerCAmelCase    (       __UpperCamelCase   ):
 '''simple docstring'''





 UpperCAmelCase__       :	int        =       set()
 UpperCAmelCase__       :	Union[str, Any]        =       word[0]
 for char in word[1:]:
  pairs.add((prev_char, char)   )
  UpperCAmelCase__       :	Tuple        =       char
 return pairs







class   __lowercase      (     __lowerCamelCase       ):
  snake_case_												=							VOCAB_FILES_NAMES
  snake_case_												=							PRETRAINED_VOCAB_FILES_MAP
  snake_case_												=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  snake_case_												=							["""input_ids""", """attention_mask"""]

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






   '''simple docstring'''
   UpperCAmelCase__       :	List[Any]        =       AddedToken(A       ,lstrip=A       ,rstrip=A     ) if isinstance(A       ,A     ) else bos_token
   UpperCAmelCase__       :	Union[str, Any]        =       AddedToken(A       ,lstrip=A       ,rstrip=A     ) if isinstance(A       ,A     ) else eos_token
   UpperCAmelCase__       :	str        =       AddedToken(A       ,lstrip=A       ,rstrip=A     ) if isinstance(A       ,A     ) else sep_token
   UpperCAmelCase__       :	Union[str, Any]        =       AddedToken(A       ,lstrip=A       ,rstrip=A     ) if isinstance(A       ,A     ) else cls_token
   UpperCAmelCase__       :	str        =       AddedToken(A       ,lstrip=A       ,rstrip=A     ) if isinstance(A       ,A     ) else unk_token
   UpperCAmelCase__       :	str        =       AddedToken(A       ,lstrip=A       ,rstrip=A     ) if isinstance(A       ,A     ) else pad_token

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

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

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

   # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
   UpperCAmelCase__       :	str        =       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		:      List[Any]     ):






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

  def  __lowercase							(	self		:      Optional[Any]     ):






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

  def  __lowercase							(	self		:      Optional[int]       ,A		:      Union[str, Any]     ):






   '''simple docstring'''
   if token in self.cache:
    return self.cache[token]
   UpperCAmelCase__       :	Optional[int]        =       tuple(A     )
   UpperCAmelCase__       :	int        =       get_pairs(A     )

   if not pairs:
    return token

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

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

  def  __lowercase							(	self		:      List[Any]       ,A		:      List[str]     ):






   '''simple docstring'''
   UpperCAmelCase__       :	str        =       []
   for token in re.findall(self.pat       ,A     ):
    UpperCAmelCase__       :	List[str]        =       """""".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(A     ).split(""" """     )     )
   return bpe_tokens

  def  __lowercase							(	self		:      Dict       ,A		:      List[Any]     ):






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

  def  __lowercase							(	self		:      Optional[int]       ,A		:      Any     ):






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

  def  __lowercase							(	self		:      Dict       ,A		:      List[str]     ):






   '''simple docstring'''
   UpperCAmelCase__       :	Dict        =       """""".join(A     )
   UpperCAmelCase__       :	Tuple        =       bytearray([self.byte_decoder[c] for c in text]     ).decode("""utf-8"""       ,errors=self.errors     )
   return text

  def  __lowercase							(	self		:      List[str]       ,A		:      str       ,A		:      Optional[str] = None     ):






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

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

   UpperCAmelCase__       :	str        =       0
   with open(A       ,"""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[int]        =       token_index
     writer.write(""" """.join(A     ) + """\n"""     )
     index += 1

   return vocab_file, merge_file

  def  __lowercase							(	self		:      str       ,A		:      List[int]       ,A		:      Optional[List[int]] = None     ):






   '''simple docstring'''
   if token_ids_a is None:
    return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
   UpperCAmelCase__       :	Optional[Any]        =       [self.cls_token_id]
   UpperCAmelCase__       :	List[str]        =       [self.sep_token_id]
   return cls + token_ids_a + sep + sep + token_ids_a + sep

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






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

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

  def  __lowercase							(	self		:      List[str]       ,A		:      List[int]       ,A		:      Optional[List[int]] = None     ):






   '''simple docstring'''
   UpperCAmelCase__       :	List[Any]        =       [self.sep_token_id]
   UpperCAmelCase__       :	Dict        =       [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       ,A		:      Union[str, Any]       ,A		:      int=False       ,**A		:      List[Any]     ):






   '''simple docstring'''
   UpperCAmelCase__       :	Optional[int]        =       kwargs.pop("""add_prefix_space"""       ,self.add_prefix_space     )
   if (is_split_into_words or add_prefix_space) and (len(A     ) > 0 and not text[0].isspace()):
    UpperCAmelCase__       :	List[str]        =       """ """ + 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    )




