





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

import torch
from transformers import CLIPTextModel, CLIPTokenizer

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils import logging
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


__snake_case      :      Optional[int]             =       logging.get_logger(__name__)  # pylint: disable=invalid-name


class       __SCREAMING_SNAKE_CASE   (				a__						,   a__):






						@register_to_config
						def __init__(		self       ,  _UpperCamelCase       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None				):






												"""simple docstring"""
												super().__init__()

												lowerCAmelCase__        =  learnable

												if self.learnable:
																		assert hidden_size is not None, "learnable=True requires `hidden_size` to be set"
																		assert length is not None, "learnable=True requires `length` to be set"

																		lowerCAmelCase__        =  torch.zeros(lowerCAmelCase__       ,  lowerCAmelCase__				)
												else:
																		lowerCAmelCase__        =  None

												lowerCAmelCase__        =  torch.nn.Parameter(lowerCAmelCase__				)




class       __SCREAMING_SNAKE_CASE   (				a__):
						_SCREAMING_SNAKE_CASE		:    VQModel
						_SCREAMING_SNAKE_CASE		:    CLIPTextModel
						_SCREAMING_SNAKE_CASE		:    CLIPTokenizer
						_SCREAMING_SNAKE_CASE		:    TransformeraDModel
						_SCREAMING_SNAKE_CASE		:    LearnedClassifierFreeSamplingEmbeddings
						_SCREAMING_SNAKE_CASE		:    VQDiffusionScheduler






						def __init__(		self       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  ):






												"""simple docstring"""
												super().__init__()

												self.register_modules(
												    vqvae=lowerCAmelCase__       ,  transformer=lowerCAmelCase__       ,  text_encoder=lowerCAmelCase__       ,  tokenizer=lowerCAmelCase__       ,  scheduler=lowerCAmelCase__       ,  learned_classifier_free_sampling_embeddings=lowerCAmelCase__       ,  )






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase				):






												"""simple docstring"""
												lowerCAmelCase__        =  len(lowerCAmelCase__				) if isinstance(lowerCAmelCase__       ,  lowerCAmelCase__				) else 1

												# get prompt text embeddings
												lowerCAmelCase__        =  self.tokenizer(
												    lowerCAmelCase__       ,  padding='max_length'       ,  max_length=self.tokenizer.model_max_length       ,  return_tensors='pt'       ,  )
												lowerCAmelCase__        =  text_inputs.input_ids

												if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
																		lowerCAmelCase__        =  self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]				)
																		logger.warning(
																		    'The following part of your input was truncated because CLIP can only handle sequences up to'
																		    F" {self.tokenizer.model_max_length} tokens: {removed_text}"				)
																		lowerCAmelCase__        =  text_input_ids[:, : self.tokenizer.model_max_length]
												lowerCAmelCase__        =  self.text_encoder(text_input_ids.to(self.device				)				)[0]

												# NOTE: This additional step of normalizing the text embeddings is from VQ-Diffusion.
												# While CLIP does normalize the pooled output of the text transformer when combining
												# the image and text embeddings, CLIP does not directly normalize the last hidden state.
												#
												# CLIP normalizing the pooled output.
												# https://github.com/huggingface/transformers/blob/d92e22d1f28324f513f3080e5c47c071a3916721/src/transformers/models/clip/modeling_clip.py#L1052-L1053
												lowerCAmelCase__        =  prompt_embeds / prompt_embeds.norm(dim=-1       ,  keepdim=lowerCAmelCase__				)

												# duplicate text embeddings for each generation per prompt
												lowerCAmelCase__        =  prompt_embeds.repeat_interleave(lowerCAmelCase__       ,  dim=0				)

												if do_classifier_free_guidance:
																		if self.learned_classifier_free_sampling_embeddings.learnable:
																								lowerCAmelCase__        =  self.learned_classifier_free_sampling_embeddings.embeddings
																								lowerCAmelCase__        =  negative_prompt_embeds.unsqueeze(0				).repeat(lowerCAmelCase__       ,  1       ,  1				)
																		else:
																								lowerCAmelCase__        =  [""] * batch_size

																								lowerCAmelCase__        =  text_input_ids.shape[-1]
																								lowerCAmelCase__        =  self.tokenizer(
																								    lowerCAmelCase__       ,  padding='max_length'       ,  max_length=lowerCAmelCase__       ,  truncation=lowerCAmelCase__       ,  return_tensors='pt'       ,  )
																								lowerCAmelCase__        =  self.text_encoder(uncond_input.input_ids.to(self.device				)				)[0]
																								# See comment for normalizing text embeddings
																								lowerCAmelCase__        =  negative_prompt_embeds / negative_prompt_embeds.norm(dim=-1       ,  keepdim=lowerCAmelCase__				)

																		# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
																		lowerCAmelCase__        =  negative_prompt_embeds.shape[1]
																		lowerCAmelCase__        =  negative_prompt_embeds.repeat(1       ,  lowerCAmelCase__       ,  1				)
																		lowerCAmelCase__        =  negative_prompt_embeds.view(batch_size * num_images_per_prompt       ,  lowerCAmelCase__       ,  -1				)

																		# For classifier free guidance, we need to do two forward passes.
																		# Here we concatenate the unconditional and text embeddings into a single batch
																		# to avoid doing two forward passes
																		lowerCAmelCase__        =  torch.cat([negative_prompt_embeds, prompt_embeds]				)

												return prompt_embeds






						@torch.no_grad()
						def __call__(		self       ,  _UpperCamelCase       ,  _UpperCamelCase = 1_00       ,  _UpperCamelCase = 5.0       ,  _UpperCamelCase = 1.0       ,  _UpperCamelCase = 1       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  _UpperCamelCase = "pil"       ,  _UpperCamelCase = True       ,  _UpperCamelCase = None       ,  _UpperCamelCase = 1       ,  ):






												"""simple docstring"""
												if isinstance(lowerCAmelCase__       ,  lowerCAmelCase__				):
																		lowerCAmelCase__        =  1
												elif isinstance(lowerCAmelCase__       ,  lowerCAmelCase__				):
																		lowerCAmelCase__        =  len(lowerCAmelCase__				)
												else:
																		raise ValueError(F"`prompt` has to be of type `str` or `list` but is {type(lowerCAmelCase__				)}"				)

												lowerCAmelCase__        =  batch_size * num_images_per_prompt

												lowerCAmelCase__        =  guidance_scale > 1.0

												lowerCAmelCase__        =  self._encode_prompt(lowerCAmelCase__       ,  lowerCAmelCase__       ,  lowerCAmelCase__				)

												if (callback_steps is None) or (
												    callback_steps is not None and (not isinstance(lowerCAmelCase__       ,  lowerCAmelCase__				) or callback_steps <= 0)
												):
																		raise ValueError(
																		    F"`callback_steps` has to be a positive integer but is {callback_steps} of type"
																		    F" {type(lowerCAmelCase__				)}."				)

												# get the initial completely masked latents unless the user supplied it

												lowerCAmelCase__        =  (batch_size, self.transformer.num_latent_pixels)
												if latents is None:
																		lowerCAmelCase__        =  self.transformer.num_vector_embeds - 1
																		lowerCAmelCase__        =  torch.full(lowerCAmelCase__       ,  lowerCAmelCase__				).to(self.device				)
												else:
																		if latents.shape != latents_shape:
																								raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {latents_shape}"				)
																		if (latents < 0).any() or (latents >= self.transformer.num_vector_embeds).any():
																								raise ValueError(
																								    'Unexpected latents value(s). All latents be valid embedding indices i.e. in the range 0,'
																								    F" {self.transformer.num_vector_embeds - 1} (inclusive)."				)
																		lowerCAmelCase__        =  latents.to(self.device				)

												# set timesteps
												self.scheduler.set_timesteps(lowerCAmelCase__       ,  device=self.device				)

												lowerCAmelCase__        =  self.scheduler.timesteps.to(self.device				)

												lowerCAmelCase__        =  latents

												for i, t in enumerate(self.progress_bar(lowerCAmelCase__				)				):
																		# expand the sample if we are doing classifier free guidance
																		lowerCAmelCase__        =  torch.cat([sample] * 2				) if do_classifier_free_guidance else sample

																		# predict the un-noised image
																		# model_output == `log_p_x_0`
																		lowerCAmelCase__        =  self.transformer(lowerCAmelCase__       ,  encoder_hidden_states=lowerCAmelCase__       ,  timestep=lowerCAmelCase__				).sample

																		if do_classifier_free_guidance:
																								lowerCAmelCase__        =  model_output.chunk(2				)
																								lowerCAmelCase__        =  model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
																								model_output -= torch.logsumexp(lowerCAmelCase__       ,  dim=1       ,  keepdim=lowerCAmelCase__				)

																		lowerCAmelCase__        =  self.truncate(lowerCAmelCase__       ,  lowerCAmelCase__				)

																		# remove `log(0)`'s (`-inf`s)
																		lowerCAmelCase__        =  model_output.clamp(-70				)

																		# compute the previous noisy sample x_t -> x_t-1
																		lowerCAmelCase__        =  self.scheduler.step(lowerCAmelCase__       ,  timestep=lowerCAmelCase__       ,  sample=lowerCAmelCase__       ,  generator=lowerCAmelCase__				).prev_sample

																		# call the callback, if provided
																		if callback is not None and i % callback_steps == 0:
																								callback(lowerCAmelCase__       ,  lowerCAmelCase__       ,  lowerCAmelCase__				)

												lowerCAmelCase__        =  self.vqvae.config.vq_embed_dim
												lowerCAmelCase__        =  (batch_size, self.transformer.height, self.transformer.width, embedding_channels)
												lowerCAmelCase__        =  self.vqvae.quantize.get_codebook_entry(lowerCAmelCase__       ,  shape=lowerCAmelCase__				)
												lowerCAmelCase__        =  self.vqvae.decode(lowerCAmelCase__       ,  force_not_quantize=lowerCAmelCase__				).sample

												lowerCAmelCase__        =  (image / 2 + 0.5).clamp(0       ,  1				)
												lowerCAmelCase__        =  image.cpu().permute(0       ,  2       ,  3       ,  1				).numpy()

												if output_type == "pil":
																		lowerCAmelCase__        =  self.numpy_to_pil(lowerCAmelCase__				)

												if not return_dict:
																		return (image,)

												return ImagePipelineOutput(images=lowerCAmelCase__				)







						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase				):






												"""simple docstring"""
												lowerCAmelCase__        =  torch.sort(lowerCAmelCase__       ,  1       ,  descending=lowerCAmelCase__				)
												lowerCAmelCase__        =  torch.exp(lowerCAmelCase__				)
												lowerCAmelCase__        =  sorted_p_x_0.cumsum(dim=1				) < truncation_rate

												# Ensure that at least the largest probability is not zeroed out
												lowerCAmelCase__        =  torch.full_like(keep_mask[:, 0:1, :]       ,  lowerCAmelCase__				)
												lowerCAmelCase__        =  torch.cat((all_true, keep_mask)       ,  dim=1				)
												lowerCAmelCase__        =  keep_mask[:, :-1, :]

												lowerCAmelCase__        =  keep_mask.gather(1       ,  indices.argsort(1				)				)

												lowerCAmelCase__        =  log_p_x_0.clone()

												lowerCAmelCase__        =  -torch.inf  # -inf = log(0)

												return rv













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

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging


__snake_case      :      Any             =       logging.get_logger(__name__)


__snake_case      :      Union[str, Any]             =       {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt"""}

# See all LED models at https://huggingface.co/models?filter=LED
__snake_case      :      Optional[Any]             =       {
    """vocab_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json""",
    },
}

__snake_case      :      List[str]             =       {
    """allenai/led-base-16384""": 1_63_84,
}







@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def 		_UpperCamelCase				(      )					->				int:

						"""simple docstring"""



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







def 		_UpperCamelCase				(      UpperCamelCase_    :   Optional[Any]						)					->				Dict:

						"""simple docstring"""



						lowerCAmelCase__        =  set()
						lowerCAmelCase__        =  word[0]
						for char in word[1:]:
												pairs.add((prev_char, char)						)
												lowerCAmelCase__        =  char
						return pairs


class       __SCREAMING_SNAKE_CASE   (				__lowercase):
						_SCREAMING_SNAKE_CASE		:    Optional[int]						=   VOCAB_FILES_NAMES
						_SCREAMING_SNAKE_CASE		:    Optional[int]						=   PRETRAINED_VOCAB_FILES_MAP
						_SCREAMING_SNAKE_CASE		:    Optional[Any]						=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_SCREAMING_SNAKE_CASE		:    Any						=   ['''input_ids''', '''attention_mask''']






						def __init__(		self       ,  _UpperCamelCase       ,  _UpperCamelCase       ,  _UpperCamelCase="replace"       ,  _UpperCamelCase="<s>"       ,  _UpperCamelCase="</s>"       ,  _UpperCamelCase="</s>"       ,  _UpperCamelCase="<s>"       ,  _UpperCamelCase="<unk>"       ,  _UpperCamelCase="<pad>"       ,  _UpperCamelCase="<mask>"       ,  _UpperCamelCase=False       ,  **_UpperCamelCase       ,  ):






												"""simple docstring"""
												lowerCAmelCase__        =  AddedToken(_UpperCamelCase       ,  lstrip=_UpperCamelCase       ,  rstrip=_UpperCamelCase				) if isinstance(_UpperCamelCase       ,  _UpperCamelCase				) else bos_token
												lowerCAmelCase__        =  AddedToken(_UpperCamelCase       ,  lstrip=_UpperCamelCase       ,  rstrip=_UpperCamelCase				) if isinstance(_UpperCamelCase       ,  _UpperCamelCase				) else eos_token
												lowerCAmelCase__        =  AddedToken(_UpperCamelCase       ,  lstrip=_UpperCamelCase       ,  rstrip=_UpperCamelCase				) if isinstance(_UpperCamelCase       ,  _UpperCamelCase				) else sep_token
												lowerCAmelCase__        =  AddedToken(_UpperCamelCase       ,  lstrip=_UpperCamelCase       ,  rstrip=_UpperCamelCase				) if isinstance(_UpperCamelCase       ,  _UpperCamelCase				) else cls_token
												lowerCAmelCase__        =  AddedToken(_UpperCamelCase       ,  lstrip=_UpperCamelCase       ,  rstrip=_UpperCamelCase				) if isinstance(_UpperCamelCase       ,  _UpperCamelCase				) else unk_token
												lowerCAmelCase__        =  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
												lowerCAmelCase__        =  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:
																		lowerCAmelCase__        =  json.load(_UpperCamelCase				)
												lowerCAmelCase__        =  {v: k for k, v in self.encoder.items()}
												lowerCAmelCase__        =  errors  # how to handle errors in decoding
												lowerCAmelCase__        =  bytes_to_unicode()
												lowerCAmelCase__        =  {v: k for k, v in self.byte_encoder.items()}
												with open(_UpperCamelCase       ,  encoding='utf-8'				) as merges_handle:
																		lowerCAmelCase__        =  merges_handle.read().split('\n'				)[1:-1]
												lowerCAmelCase__        =  [tuple(merge.split()				) for merge in bpe_merges]
												lowerCAmelCase__        =  dict(zip(_UpperCamelCase       ,  range(len(_UpperCamelCase				)				)				)				)
												lowerCAmelCase__        =  {}
												lowerCAmelCase__        =  add_prefix_space

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






						@property
						# Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
						def  UpperCamelCase__ (		self				):






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






						def  UpperCamelCase__ (		self				):






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






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase				):






												"""simple docstring"""
												if token in self.cache:
																		return self.cache[token]
												lowerCAmelCase__        =  tuple(_UpperCamelCase				)
												lowerCAmelCase__        =  get_pairs(_UpperCamelCase				)

												if not pairs:
																		return token

												while True:
																		lowerCAmelCase__        =  min(_UpperCamelCase       ,  key=lambda _UpperCamelCase				: self.bpe_ranks.get(_UpperCamelCase       ,  float('inf'				)				)				)
																		if bigram not in self.bpe_ranks:
																								break
																		lowerCAmelCase__  ,    lowerCAmelCase__        =  bigram
																		lowerCAmelCase__        =  []
																		lowerCAmelCase__        =  0
																		while i < len(_UpperCamelCase				):
																								try:
																														lowerCAmelCase__        =  word.index(_UpperCamelCase       ,  _UpperCamelCase				)
																								except ValueError:
																														new_word.extend(word[i:]				)
																														break
																								else:
																														new_word.extend(word[i:j]				)
																														lowerCAmelCase__        =  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
																		lowerCAmelCase__        =  tuple(_UpperCamelCase				)
																		lowerCAmelCase__        =  new_word
																		if len(_UpperCamelCase				) == 1:
																								break
																		else:
																								lowerCAmelCase__        =  get_pairs(_UpperCamelCase				)
												lowerCAmelCase__        =  ' '.join(_UpperCamelCase				)
												lowerCAmelCase__        =  word
												return word






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase				):






												"""simple docstring"""
												lowerCAmelCase__        =  []
												for token in re.findall(self.pat       ,  _UpperCamelCase				):
																		lowerCAmelCase__        =  ''.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  UpperCamelCase__ (		self       ,  _UpperCamelCase				):






												"""simple docstring"""
												return self.encoder.get(_UpperCamelCase       ,  self.encoder.get(self.unk_token				)				)






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase				):






												"""simple docstring"""
												return self.decoder.get(_UpperCamelCase				)






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase				):






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






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase = None				):






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

												lowerCAmelCase__        =  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 _UpperCamelCase				: 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!'				)
																														lowerCAmelCase__        =  token_index
																								writer.write(' '.join(_UpperCamelCase				) + '\n'				)
																								index += 1

												return vocab_file, merge_file






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase = None				):






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






						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase = None       ,  _UpperCamelCase = False				):






												"""simple docstring"""
												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  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase = None				):






												"""simple docstring"""
												lowerCAmelCase__        =  [self.sep_token_id]
												lowerCAmelCase__        =  [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  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase=False       ,  **_UpperCamelCase				):






												"""simple docstring"""
												lowerCAmelCase__        =  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()):
																		lowerCAmelCase__        =  ' ' + text
												return (text, kwargs)







						def  UpperCamelCase__ (		self       ,  _UpperCamelCase       ,  _UpperCamelCase = None       ,  _UpperCamelCase = PaddingStrategy.DO_NOT_PAD       ,  _UpperCamelCase = None       ,  _UpperCamelCase = None       ,  ):






												"""simple docstring"""
												lowerCAmelCase__        =  super()._pad(
												    encoded_inputs=_UpperCamelCase       ,  max_length=_UpperCamelCase       ,  padding_strategy=_UpperCamelCase       ,  pad_to_multiple_of=_UpperCamelCase       ,  return_attention_mask=_UpperCamelCase       ,  )

												# Load from model defaults
												if return_attention_mask is None:
																		lowerCAmelCase__        =  'attention_mask' in self.model_input_names

												if return_attention_mask and "global_attention_mask" in encoded_inputs:
																		lowerCAmelCase__        =  encoded_inputs[self.model_input_names[0]]
																		# `global_attention_mask` need to have the same length as other (sequential) inputs.
																		lowerCAmelCase__        =  len(encoded_inputs['global_attention_mask']				) != len(_UpperCamelCase				)

																		if needs_to_be_padded:
																								lowerCAmelCase__        =  len(_UpperCamelCase				) - len(encoded_inputs['global_attention_mask']				)

																								if self.padding_side == "right":
																														# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
																														lowerCAmelCase__        =  (
																														    encoded_inputs['global_attention_mask'] + [-1] * difference
																														)
																								elif self.padding_side == "left":
																														lowerCAmelCase__        =  [-1] * difference + encoded_inputs[
																														    'global_attention_mask'
																														]
																								else:
																														raise ValueError('Invalid padding strategy:' + str(self.padding_side				)				)

												return encoded_inputs





