



"""simple docstring"""






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


lowerCAmelCase__        =				logging.get_logger(__name__)  # pylint: disable=invalid-name






class        __snake_case       (	_lowercase  ,     _lowercase):




  @register_to_config
  def __init__( self     :						int						,					__lowerCAmelCase     :						bool						,					__lowerCAmelCase     :						Optional[int] = None						,					__lowerCAmelCase     :						Optional[int] = None		):






         """simple docstring"""






         super().__init__()

         _lowerCamelCase						:				Tuple    			= 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						:				Dict    			= torch.zeros(__lowerCAmelCase						,					__lowerCAmelCase		)
         else:
                _lowerCamelCase						:				Dict    			= None

         _lowerCamelCase						:				List[Any]    			= torch.nn.Parameter(__lowerCAmelCase		)






class        __snake_case       (	_lowercase):
  snake_case__				:   VQModel
  snake_case__				:   CLIPTextModel
  snake_case__				:   CLIPTokenizer
  snake_case__				:   TransformeraDModel
  snake_case__				:   LearnedClassifierFreeSamplingEmbeddings
  snake_case__				:   VQDiffusionScheduler




  def __init__( self     :						List[str]						,					__lowerCAmelCase     :						VQModel						,					__lowerCAmelCase     :						CLIPTextModel						,					__lowerCAmelCase     :						CLIPTokenizer						,					__lowerCAmelCase     :						TransformeraDModel						,					__lowerCAmelCase     :						VQDiffusionScheduler						,					__lowerCAmelCase     :						LearnedClassifierFreeSamplingEmbeddings						,					):






         """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 						SCREAMING_SNAKE_CASE				( self     :						Optional[int]						,					__lowerCAmelCase     :						int						,					__lowerCAmelCase     :						Optional[Any]						,					__lowerCAmelCase     :						Tuple		):






         """simple docstring"""






         _lowerCamelCase						:				Any    			= len(__lowerCAmelCase		) if isinstance(__lowerCAmelCase						,					__lowerCAmelCase		) else 1

         # get prompt text embeddings
         _lowerCamelCase						:				Optional[int]    			= self.tokenizer(
             __lowerCAmelCase						,					padding='''max_length'''						,					max_length=self.tokenizer.model_max_length						,					return_tensors='''pt'''						,					)
         _lowerCamelCase						:				Dict    			= text_inputs.input_ids

         if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
                _lowerCamelCase						:				int    			= 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						:				Optional[Any]    			= text_input_ids[:, : self.tokenizer.model_max_length]
         _lowerCamelCase						:				Union[str, Any]    			= 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						:				Tuple    			= prompt_embeds / prompt_embeds.norm(dim=-1						,					keepdim=__lowerCAmelCase		)

         # duplicate text embeddings for each generation per prompt
         _lowerCamelCase						:				Any    			= prompt_embeds.repeat_interleave(__lowerCAmelCase						,					dim=0		)

         if do_classifier_free_guidance:
                if self.learned_classifier_free_sampling_embeddings.learnable:
                       _lowerCamelCase						:				str    			= self.learned_classifier_free_sampling_embeddings.embeddings
                       _lowerCamelCase						:				Any    			= negative_prompt_embeds.unsqueeze(0		).repeat(__lowerCAmelCase						,					1						,					1		)
                else:
                       _lowerCamelCase						:				str    			= [''''''] * batch_size

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

                # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
                _lowerCamelCase						:				Any    			= negative_prompt_embeds.shape[1]
                _lowerCamelCase						:				Optional[int]    			= negative_prompt_embeds.repeat(1						,					__lowerCAmelCase						,					1		)
                _lowerCamelCase						:				Any    			= 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						:				Tuple    			= torch.cat([negative_prompt_embeds, prompt_embeds]		)

         return prompt_embeds




  @torch.no_grad()
  def __call__( self     :						Optional[Any]						,					__lowerCAmelCase     :						Union[str, List[str]]						,					__lowerCAmelCase     :						int = 1_0_0						,					__lowerCAmelCase     :						float = 5.0						,					__lowerCAmelCase     :						float = 1.0						,					__lowerCAmelCase     :						int = 1						,					__lowerCAmelCase     :						Optional[Union[torch.Generator, List[torch.Generator]]] = None						,					__lowerCAmelCase     :						Optional[torch.FloatTensor] = None						,					__lowerCAmelCase     :						Optional[str] = "pil"						,					__lowerCAmelCase     :						bool = True						,					__lowerCAmelCase     :						Optional[Callable[[int, int, torch.FloatTensor], None]] = None						,					__lowerCAmelCase     :						int = 1						,					):






         """simple docstring"""






         if isinstance(__lowerCAmelCase						,					__lowerCAmelCase		):
                _lowerCamelCase						:				str    			= 1
         elif isinstance(__lowerCAmelCase						,					__lowerCAmelCase		):
                _lowerCamelCase						:				str    			= len(__lowerCAmelCase		)
         else:
                raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(__lowerCAmelCase		)}'''		)

         _lowerCamelCase						:				List[str]    			= batch_size * num_images_per_prompt

         _lowerCamelCase						:				Optional[Any]    			= guidance_scale > 1.0

         _lowerCamelCase						:				Tuple    			= 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						:				int    			= (batch_size, self.transformer.num_latent_pixels)
         if latents is None:
                _lowerCamelCase						:				int    			= self.transformer.num_vector_embeds - 1
                _lowerCamelCase						:				Optional[int]    			= 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						:				Any    			= latents.to(self.device		)

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

         _lowerCamelCase						:				List[str]    			= self.scheduler.timesteps.to(self.device		)

         _lowerCamelCase						:				int    			= latents

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

                # predict the un-noised image
                # model_output == `log_p_x_0`
                _lowerCamelCase						:				Optional[int]    			= self.transformer(__lowerCAmelCase						,					encoder_hidden_states=__lowerCAmelCase						,					timestep=__lowerCAmelCase		).sample

                if do_classifier_free_guidance:
                       _lowerCamelCase			,    _lowerCamelCase						:				Any    			= model_output.chunk(2		)
                       _lowerCamelCase						:				List[Any]    			= model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
                       model_output -= torch.logsumexp(__lowerCAmelCase						,					dim=1						,					keepdim=__lowerCAmelCase		)

                _lowerCamelCase						:				Union[str, Any]    			= self.truncate(__lowerCAmelCase						,					__lowerCAmelCase		)

                # remove `log(0)`'s (`-inf`s)
                _lowerCamelCase						:				int    			= model_output.clamp(-7_0		)

                # compute the previous noisy sample x_t -> x_t-1
                _lowerCamelCase						:				Optional[int]    			= 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						:				Tuple    			= self.vqvae.config.vq_embed_dim
         _lowerCamelCase						:				List[str]    			= (batch_size, self.transformer.height, self.transformer.width, embedding_channels)
         _lowerCamelCase						:				List[Any]    			= self.vqvae.quantize.get_codebook_entry(__lowerCAmelCase						,					shape=__lowerCAmelCase		)
         _lowerCamelCase						:				Optional[int]    			= self.vqvae.decode(__lowerCAmelCase						,					force_not_quantize=__lowerCAmelCase		).sample

         _lowerCamelCase						:				List[str]    			= (image / 2 + 0.5).clamp(0						,					1		)
         _lowerCamelCase						:				Dict    			= image.cpu().permute(0						,					2						,					3						,					1		).numpy()

         if output_type == "pil":
                _lowerCamelCase						:				List[str]    			= self.numpy_to_pil(__lowerCAmelCase		)

         if not return_dict:
                return (image,)

         return ImagePipelineOutput(images=__lowerCAmelCase		)




  def 						SCREAMING_SNAKE_CASE				( self     :						Optional[int]						,					__lowerCAmelCase     :						torch.FloatTensor						,					__lowerCAmelCase     :						float		):






         """simple docstring"""






         _lowerCamelCase			,    _lowerCamelCase						:				str    			= torch.sort(__lowerCAmelCase						,					1						,					descending=__lowerCAmelCase		)
         _lowerCamelCase						:				List[Any]    			= torch.exp(__lowerCAmelCase		)
         _lowerCamelCase						:				Tuple    			= sorted_p_x_0.cumsum(dim=1		) < truncation_rate

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

         _lowerCamelCase						:				List[str]    			= keep_mask.gather(1						,					indices.argsort(1		)		)

         _lowerCamelCase						:				Union[str, Any]    			= log_p_x_0.clone()

         _lowerCamelCase						:				Optional[Any]    			= -torch.inf  # -inf = log(0)

         return rv







'''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__       :	Optional[int]    = logging.get_logger(__name__)


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

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

UpperCamelCase__       :	List[Any]    = {
    '''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 				lowerCAmelCase_   (	):
		__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		(
		    list(range(ord("""!"""					)       ,      ord("""~"""					) + 1					)					) + list(range(ord("""¡"""					)       ,      ord("""¬"""					) + 1					)					) + list(range(ord("""®"""					)       ,      ord("""ÿ"""					) + 1					)					)
		)
		__SCREAMING_SNAKE_CASE       :		int           =		bs[:]
		__SCREAMING_SNAKE_CASE       :		Any           =		0
		for b in range(2**8					):
				if b not in bs:
						bs.append(_lowerCamelCase					)
						cs.append(2**8 + n					)
						n += 1
		__SCREAMING_SNAKE_CASE       :		Dict           =		[chr(_lowerCamelCase					) for n in cs]
		return dict(zip(_lowerCamelCase       ,      _lowerCamelCase					)					)


def 				lowerCAmelCase_   (	_lowerCamelCase:   int					):
		__SCREAMING_SNAKE_CASE       :		List[str]           =		set()
		__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		word[0]
		for char in word[1:]:
				pairs.add((prev_char, char)					)
				__SCREAMING_SNAKE_CASE       :		Optional[int]           =		char
		return pairs






class 						_UpperCamelCase  (							lowerCamelCase__    ):


			'''simple docstring'''

			_A      :		List[str]			    =		VOCAB_FILES_NAMES
			_A      :		Tuple			    =		PRETRAINED_VOCAB_FILES_MAP
			_A      :		Optional[int]			    =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			_A      :		int			    =		['''input_ids''', '''attention_mask''']

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





					"""simple docstring"""






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

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

			@property
			def UpperCamelCase__					(							self     : Optional[int]						):





					"""simple docstring"""






					return len(self.encoder						)

			def UpperCamelCase__					(							self     : List[str]						):





					"""simple docstring"""






					return dict(self.encoder							,     **self.added_tokens_encoder						)

			def UpperCamelCase__					(							self     : Any							,     lowerCAmelCase__     : Tuple						):





					"""simple docstring"""






					if token in self.cache:
							return self.cache[token]
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		tuple(lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE       :		Dict           =		get_pairs(lowerCAmelCase__						)

					if not pairs:
							return token

					while True:
							__SCREAMING_SNAKE_CASE       :		Optional[int]           =		min(lowerCAmelCase__							,     key=lambda lowerCAmelCase__						: self.bpe_ranks.get(lowerCAmelCase__							,     float("""inf"""						)						)						)
							if bigram not in self.bpe_ranks:
									break
							__SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       :		Union[str, Any]           =		bigram
							__SCREAMING_SNAKE_CASE       :		int           =		[]
							__SCREAMING_SNAKE_CASE       :		List[str]           =		0
							while i < len(lowerCAmelCase__						):
									try:
											__SCREAMING_SNAKE_CASE       :		Any           =		word.index(lowerCAmelCase__							,     lowerCAmelCase__						)
									except ValueError:
											new_word.extend(word[i:]						)
											break
									else:
											new_word.extend(word[i:j]						)
											__SCREAMING_SNAKE_CASE       :		Dict           =		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
							__SCREAMING_SNAKE_CASE       :		Optional[int]           =		tuple(lowerCAmelCase__						)
							__SCREAMING_SNAKE_CASE       :		Optional[int]           =		new_word
							if len(lowerCAmelCase__						) == 1:
									break
							else:
									__SCREAMING_SNAKE_CASE       :		Tuple           =		get_pairs(lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		""" """.join(lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE       :		Dict           =		word
					return word

			def UpperCamelCase__					(							self     : Optional[int]							,     lowerCAmelCase__     : Optional[int]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		[]
					for token in re.findall(self.pat							,     lowerCAmelCase__						):
							__SCREAMING_SNAKE_CASE       :		Optional[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(lowerCAmelCase__						).split(""" """						)						)
					return bpe_tokens

			def UpperCamelCase__					(							self     : Dict							,     lowerCAmelCase__     : Tuple						):





					"""simple docstring"""






					return self.encoder.get(lowerCAmelCase__							,     self.encoder.get(self.unk_token						)						)

			def UpperCamelCase__					(							self     : Any							,     lowerCAmelCase__     : Tuple						):





					"""simple docstring"""






					return self.decoder.get(lowerCAmelCase__						)

			def UpperCamelCase__					(							self     : Dict							,     lowerCAmelCase__     : Optional[Any]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		List[Any]           =		"""""".join(lowerCAmelCase__						)
					__SCREAMING_SNAKE_CASE       :		Dict           =		bytearray([self.byte_decoder[c] for c in text]						).decode("""utf-8"""							,     errors=self.errors						)
					return text

			def UpperCamelCase__					(							self     : Optional[Any]							,     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
					__SCREAMING_SNAKE_CASE       :		str           =		os.path.join(
					    lowerCAmelCase__							,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]						)
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		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"""						)

					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		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!"""						)
											__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		token_index
									writer.write(""" """.join(lowerCAmelCase__						) + """\n"""						)
									index += 1

					return vocab_file, merge_file

			def UpperCamelCase__					(							self     : Optional[int]							,     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]
					__SCREAMING_SNAKE_CASE       :		List[Any]           =		[self.cls_token_id]
					__SCREAMING_SNAKE_CASE       :		Optional[int]           =		[self.sep_token_id]
					return cls + token_ids_a + sep + sep + token_ids_a + sep

			def UpperCamelCase__					(							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 UpperCamelCase__					(							self     : Optional[Any]							,     lowerCAmelCase__     : List[int]							,     lowerCAmelCase__     : Optional[List[int]] = None						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Tuple           =		[self.sep_token_id]
					__SCREAMING_SNAKE_CASE       :		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 UpperCamelCase__					(							self     : Dict							,     lowerCAmelCase__     : str							,     lowerCAmelCase__     : Dict=False							,     **lowerCAmelCase__     : List[str]						):





					"""simple docstring"""






					__SCREAMING_SNAKE_CASE       :		Optional[Any]           =		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()):
							__SCREAMING_SNAKE_CASE       :		List[str]           =		""" """ + text
					return (text, kwargs)