






"""simple docstring"""


from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block




@dataclass
class 	__UpperCamelCase    (    _a	):
					lowercase_						:							torch.FloatTensor




class 	__UpperCamelCase    (    _a      ,    _a	):
					@register_to_config
					def __init__(       self       :  int    ,   UpperCAmelCase       :  Optional[Union[int, Tuple[int, int]]] = None    ,   UpperCAmelCase       :  int = 3    ,   UpperCAmelCase       :  int = 3    ,   UpperCAmelCase       :  bool = False    ,   UpperCAmelCase       :  str = "positional"    ,   UpperCAmelCase       :  int = 0    ,   UpperCAmelCase       :  bool = True    ,   UpperCAmelCase       :  Tuple[str] = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D")    ,   UpperCAmelCase       :  Tuple[str] = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D")    ,   UpperCAmelCase       :  Tuple[int] = (224, 448, 672, 896)    ,   UpperCAmelCase       :  int = 2    ,   UpperCAmelCase       :  float = 1    ,   UpperCAmelCase       :  int = 1    ,   UpperCAmelCase       :  str = "conv"    ,   UpperCAmelCase       :  str = "conv"    ,   UpperCAmelCase       :  str = "silu"    ,   UpperCAmelCase       :  Optional[int] = 8    ,   UpperCAmelCase       :  int = 32    ,   UpperCAmelCase       :  float = 1e-5    ,   UpperCAmelCase       :  str = "default"    ,   UpperCAmelCase       :  bool = True    ,   UpperCAmelCase       :  Optional[str] = None    ,   UpperCAmelCase       :  Optional[int] = None    ,   )  ->					List[Any]:
											super().__init__()

											lowerCAmelCase      :Union[str, Any]				  = sample_size
											lowerCAmelCase      :Optional[Any]				  = block_out_channels[0] * 4

											# Check inputs
											if len(UpperCAmelCase      ) != len(UpperCAmelCase      ):
																	raise ValueError(
																	    f"""Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}."""      )

											if len(UpperCAmelCase      ) != len(UpperCAmelCase      ):
																	raise ValueError(
																	    f"""Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}."""      )

											# input
											lowerCAmelCase      :Optional[Any]				  = nn.Convad(UpperCAmelCase    ,   block_out_channels[0]    ,   kernel_size=3    ,   padding=(1, 1)      )

											# time
											if time_embedding_type == "fourier":
																	lowerCAmelCase      :Union[str, Any]				  = GaussianFourierProjection(embedding_size=block_out_channels[0]    ,   scale=16      )
																	lowerCAmelCase      :Dict				  = 2 * block_out_channels[0]
											elif time_embedding_type == "positional":
																	lowerCAmelCase      :str				  = Timesteps(block_out_channels[0]    ,   UpperCAmelCase    ,   UpperCAmelCase      )
																	lowerCAmelCase      :Optional[Any]				  = block_out_channels[0]

											lowerCAmelCase      :Union[str, Any]				  = TimestepEmbedding(UpperCAmelCase    ,   UpperCAmelCase      )

											# class embedding
											if class_embed_type is None and num_class_embeds is not None:
																	lowerCAmelCase      :Tuple				  = nn.Embedding(UpperCAmelCase    ,   UpperCAmelCase      )
											elif class_embed_type == "timestep":
																	lowerCAmelCase      :Dict				  = TimestepEmbedding(UpperCAmelCase    ,   UpperCAmelCase      )
											elif class_embed_type == "identity":
																	lowerCAmelCase      :int				  = nn.Identity(UpperCAmelCase    ,   UpperCAmelCase      )
											else:
																	lowerCAmelCase      :Optional[int]				  = None

											lowerCAmelCase      :Optional[Any]				  = nn.ModuleList([]      )
											lowerCAmelCase      :Any				  = None
											lowerCAmelCase      :Any				  = nn.ModuleList([]      )

											# down
											lowerCAmelCase      :Any				  = block_out_channels[0]
											for i, down_block_type in enumerate(UpperCAmelCase      ):
																	lowerCAmelCase      :int				  = output_channel
																	lowerCAmelCase      :Tuple				  = block_out_channels[i]
																	lowerCAmelCase      :List[str]				  = i == len(UpperCAmelCase      ) - 1

																	lowerCAmelCase      :Union[str, Any]				  = get_down_block(
																	    UpperCAmelCase    ,   num_layers=UpperCAmelCase    ,   in_channels=UpperCAmelCase    ,   out_channels=UpperCAmelCase    ,   temb_channels=UpperCAmelCase    ,   add_downsample=not is_final_block    ,   resnet_eps=UpperCAmelCase    ,   resnet_act_fn=UpperCAmelCase    ,   resnet_groups=UpperCAmelCase    ,   attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel    ,   downsample_padding=UpperCAmelCase    ,   resnet_time_scale_shift=UpperCAmelCase    ,   downsample_type=UpperCAmelCase    ,   )
																	self.down_blocks.append(UpperCAmelCase      )

											# mid
											lowerCAmelCase      :Dict				  = UNetMidBlockaD(
											    in_channels=block_out_channels[-1]    ,   temb_channels=UpperCAmelCase    ,   resnet_eps=UpperCAmelCase    ,   resnet_act_fn=UpperCAmelCase    ,   output_scale_factor=UpperCAmelCase    ,   resnet_time_scale_shift=UpperCAmelCase    ,   attention_head_dim=attention_head_dim if attention_head_dim is not None else block_out_channels[-1]    ,   resnet_groups=UpperCAmelCase    ,   add_attention=UpperCAmelCase    ,   )

											# up
											lowerCAmelCase      :Any				  = list(reversed(UpperCAmelCase      )      )
											lowerCAmelCase      :Any				  = reversed_block_out_channels[0]
											for i, up_block_type in enumerate(UpperCAmelCase      ):
																	lowerCAmelCase      :Tuple				  = output_channel
																	lowerCAmelCase      :str				  = reversed_block_out_channels[i]
																	lowerCAmelCase      :Dict				  = reversed_block_out_channels[min(i + 1    ,   len(UpperCAmelCase      ) - 1      )]

																	lowerCAmelCase      :List[str]				  = i == len(UpperCAmelCase      ) - 1

																	lowerCAmelCase      :str				  = get_up_block(
																	    UpperCAmelCase    ,   num_layers=layers_per_block + 1    ,   in_channels=UpperCAmelCase    ,   out_channels=UpperCAmelCase    ,   prev_output_channel=UpperCAmelCase    ,   temb_channels=UpperCAmelCase    ,   add_upsample=not is_final_block    ,   resnet_eps=UpperCAmelCase    ,   resnet_act_fn=UpperCAmelCase    ,   resnet_groups=UpperCAmelCase    ,   attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel    ,   resnet_time_scale_shift=UpperCAmelCase    ,   upsample_type=UpperCAmelCase    ,   )
																	self.up_blocks.append(UpperCAmelCase      )
																	lowerCAmelCase      :Union[str, Any]				  = output_channel

											# out
											lowerCAmelCase      :Tuple				  = norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4    ,   32      )
											lowerCAmelCase      :Any				  = nn.GroupNorm(num_channels=block_out_channels[0]    ,   num_groups=UpperCAmelCase    ,   eps=UpperCAmelCase      )
											lowerCAmelCase      :Optional[int]				  = nn.SiLU()
											lowerCAmelCase      :Optional[int]				  = nn.Convad(block_out_channels[0]    ,   UpperCAmelCase    ,   kernel_size=3    ,   padding=1      )
					def 					UpperCAmelCase__   (       self       :  List[Any]    ,   UpperCAmelCase       :  torch.FloatTensor    ,   UpperCAmelCase       :  Union[torch.Tensor, float, int]    ,   UpperCAmelCase       :  Optional[torch.Tensor] = None    ,   UpperCAmelCase       :  bool = True    ,   )  ->					Union[UNetaDOutput, Tuple]:
											# 0. center input if necessary
											if self.config.center_input_sample:
																	lowerCAmelCase      :str				  = 2 * sample - 1.0

											# 1. time
											lowerCAmelCase      :str				  = timestep
											if not torch.is_tensor(UpperCAmelCase      ):
																	lowerCAmelCase      :Optional[Any]				  = torch.tensor([timesteps]    ,   dtype=torch.long    ,   device=sample.device      )
											elif torch.is_tensor(UpperCAmelCase      ) and len(timesteps.shape      ) == 0:
																	lowerCAmelCase      :List[str]				  = timesteps[None].to(sample.device      )

											# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
											lowerCAmelCase      :Dict				  = timesteps * torch.ones(sample.shape[0]    ,   dtype=timesteps.dtype    ,   device=timesteps.device      )

											lowerCAmelCase      :Optional[int]				  = self.time_proj(UpperCAmelCase      )

											# timesteps does not contain any weights and will always return f32 tensors
											# but time_embedding might actually be running in fp16. so we need to cast here.
											# there might be better ways to encapsulate this.
											lowerCAmelCase      :Any				  = t_emb.to(dtype=self.dtype      )
											lowerCAmelCase      :Tuple				  = self.time_embedding(UpperCAmelCase      )

											if self.class_embedding is not None:
																	if class_labels is None:
																							raise ValueError('class_labels should be provided when doing class conditioning'      )

																	if self.config.class_embed_type == "timestep":
																							lowerCAmelCase      :int				  = self.time_proj(UpperCAmelCase      )

																	lowerCAmelCase      :int				  = self.class_embedding(UpperCAmelCase      ).to(dtype=self.dtype      )
																	lowerCAmelCase      :Dict				  = emb + class_emb

											# 2. pre-process
											lowerCAmelCase      :List[str]				  = sample
											lowerCAmelCase      :List[str]				  = self.conv_in(UpperCAmelCase      )

											# 3. down
											lowerCAmelCase      :List[str]				  = (sample,)
											for downsample_block in self.down_blocks:
																	if hasattr(UpperCAmelCase    ,   'skip_conv'      ):
																							lowerCAmelCase  ,   lowerCAmelCase  ,   lowerCAmelCase      :Any				  = downsample_block(
																							    hidden_states=UpperCAmelCase    ,   temb=UpperCAmelCase    ,   skip_sample=UpperCAmelCase      )
																	else:
																							lowerCAmelCase  ,   lowerCAmelCase      :List[str]				  = downsample_block(hidden_states=UpperCAmelCase    ,   temb=UpperCAmelCase      )

																	down_block_res_samples += res_samples

											# 4. mid
											lowerCAmelCase      :Optional[int]				  = self.mid_block(UpperCAmelCase    ,   UpperCAmelCase      )

											# 5. up
											lowerCAmelCase      :Union[str, Any]				  = None
											for upsample_block in self.up_blocks:
																	lowerCAmelCase      :List[Any]				  = down_block_res_samples[-len(upsample_block.resnets      ) :]
																	lowerCAmelCase      :Dict				  = down_block_res_samples[: -len(upsample_block.resnets      )]

																	if hasattr(UpperCAmelCase    ,   'skip_conv'      ):
																							lowerCAmelCase  ,   lowerCAmelCase      :List[Any]				  = upsample_block(UpperCAmelCase    ,   UpperCAmelCase    ,   UpperCAmelCase    ,   UpperCAmelCase      )
																	else:
																							lowerCAmelCase      :str				  = upsample_block(UpperCAmelCase    ,   UpperCAmelCase    ,   UpperCAmelCase      )

																	# 6. post-process
											lowerCAmelCase      :str				  = self.conv_norm_out(UpperCAmelCase      )
											lowerCAmelCase      :Optional[Any]				  = self.conv_act(UpperCAmelCase      )
											lowerCAmelCase      :str				  = self.conv_out(UpperCAmelCase      )

											if skip_sample is not None:
																	sample += skip_sample

											if self.config.time_embedding_type == "fourier":
																	lowerCAmelCase      :Dict				  = timesteps.reshape((sample.shape[0], *([1] * len(sample.shape[1:]      )))      )
																	lowerCAmelCase      :List[Any]				  = sample / timesteps

											if not return_dict:
																	return (sample,)

											return UNetaDOutput(sample=UpperCAmelCase      )







"""simple docstring"""


import json
import os
import re
import unicodedata
from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__SCREAMING_SNAKE_CASE      =      logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE      =      {
    'src_vocab_file': 'vocab-src.json',
    'tgt_vocab_file': 'vocab-tgt.json',
    'merges_file': 'merges.txt',
}

__SCREAMING_SNAKE_CASE      =      {
    'src_vocab_file': {
        'stas/tiny-wmt19-en-de': 'https://huggingface.co/stas/tiny-wmt19-en-de/resolve/main/vocab-src.json'
    },
    'tgt_vocab_file': {
        'stas/tiny-wmt19-en-de': 'https://huggingface.co/stas/tiny-wmt19-en-de/resolve/main/vocab-tgt.json'
    },
    'merges_file': {'stas/tiny-wmt19-en-de': 'https://huggingface.co/stas/tiny-wmt19-en-de/resolve/main/merges.txt'},
}

__SCREAMING_SNAKE_CASE      =      {'stas/tiny-wmt19-en-de': 10_24}
__SCREAMING_SNAKE_CASE      =      {
    'stas/tiny-wmt19-en-de': {
        'langs': ['en', 'de'],
        'model_max_length': 10_24,
        'special_tokens_map_file': None,
        'full_tokenizer_file': None,
    }
}


def 					UpperCAmelCase			(				a__   ):





						'''simple docstring'''






						lowerCAmelCase      :int				  = set()
						lowerCAmelCase      :Optional[Any]				  = word[0]
						for char in word[1:]:
												pairs.add((prev_char, char)   )
												lowerCAmelCase      :int				  = char
						return pairs


def 					UpperCAmelCase			(				a__   ):





						'''simple docstring'''






						lowerCAmelCase      :List[str]				  = text.replace('，'    ,			','   )
						lowerCAmelCase      :str				  = re.sub(R'。\s*'    ,			'. '    ,			a__   )
						lowerCAmelCase      :int				  = text.replace('、'    ,			','   )
						lowerCAmelCase      :str				  = text.replace('”'    ,			'"'   )
						lowerCAmelCase      :Union[str, Any]				  = text.replace('“'    ,			'"'   )
						lowerCAmelCase      :List[Any]				  = text.replace('∶'    ,			':'   )
						lowerCAmelCase      :List[Any]				  = text.replace('：'    ,			':'   )
						lowerCAmelCase      :Optional[Any]				  = text.replace('？'    ,			'?'   )
						lowerCAmelCase      :Tuple				  = text.replace('《'    ,			'"'   )
						lowerCAmelCase      :Dict				  = text.replace('》'    ,			'"'   )
						lowerCAmelCase      :List[str]				  = text.replace('）'    ,			')'   )
						lowerCAmelCase      :Dict				  = text.replace('！'    ,			'!'   )
						lowerCAmelCase      :Optional[Any]				  = text.replace('（'    ,			'('   )
						lowerCAmelCase      :Optional[Any]				  = text.replace('；'    ,			';'   )
						lowerCAmelCase      :Any				  = text.replace('１'    ,			'1'   )
						lowerCAmelCase      :Union[str, Any]				  = text.replace('」'    ,			'"'   )
						lowerCAmelCase      :Any				  = text.replace('「'    ,			'"'   )
						lowerCAmelCase      :str				  = text.replace('０'    ,			'0'   )
						lowerCAmelCase      :Dict				  = text.replace('３'    ,			'3'   )
						lowerCAmelCase      :Union[str, Any]				  = text.replace('２'    ,			'2'   )
						lowerCAmelCase      :str				  = text.replace('５'    ,			'5'   )
						lowerCAmelCase      :Tuple				  = text.replace('６'    ,			'6'   )
						lowerCAmelCase      :Optional[int]				  = text.replace('９'    ,			'9'   )
						lowerCAmelCase      :Optional[Any]				  = text.replace('７'    ,			'7'   )
						lowerCAmelCase      :Union[str, Any]				  = text.replace('８'    ,			'8'   )
						lowerCAmelCase      :Optional[Any]				  = text.replace('４'    ,			'4'   )
						lowerCAmelCase      :int				  = re.sub(R'．\s*'    ,			'. '    ,			a__   )
						lowerCAmelCase      :List[Any]				  = text.replace('～'    ,			'~'   )
						lowerCAmelCase      :int				  = text.replace('’'    ,			'\''   )
						lowerCAmelCase      :Dict				  = text.replace('…'    ,			'...'   )
						lowerCAmelCase      :Any				  = text.replace('━'    ,			'-'   )
						lowerCAmelCase      :Union[str, Any]				  = text.replace('〈'    ,			'<'   )
						lowerCAmelCase      :Any				  = text.replace('〉'    ,			'>'   )
						lowerCAmelCase      :str				  = text.replace('【'    ,			'['   )
						lowerCAmelCase      :Any				  = text.replace('】'    ,			']'   )
						lowerCAmelCase      :int				  = text.replace('％'    ,			'%'   )
						return text


def 					UpperCAmelCase			(				a__   ):





						'''simple docstring'''






						lowerCAmelCase      :Optional[int]				  = []
						for char in text:
												lowerCAmelCase      :Optional[Any]				  = unicodedata.category(a__   )
												if cat.startswith('C'   ):
																		continue
												output.append(a__   )
						return "".join(a__   )







class 	__UpperCamelCase    (    UpperCamelCase	):
					lowercase_						:							Dict			=     VOCAB_FILES_NAMES
					lowercase_						:							Any			=     PRETRAINED_VOCAB_FILES_MAP
					lowercase_						:							Dict			=     PRETRAINED_INIT_CONFIGURATION
					lowercase_						:							Optional[Any]			=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowercase_						:							Union[str, Any]			=     ["""input_ids""", """attention_mask"""]
					def __init__(       self       :  int    ,   UpperCAmelCase       :  Tuple=None    ,   UpperCAmelCase       :  Dict=None    ,   UpperCAmelCase       :  Dict=None    ,   UpperCAmelCase       :  Tuple=None    ,   UpperCAmelCase       :  str=False    ,   UpperCAmelCase       :  Optional[int]="<unk>"    ,   UpperCAmelCase       :  List[Any]="<s>"    ,   UpperCAmelCase       :  List[str]="</s>"    ,   UpperCAmelCase       :  Optional[int]="<pad>"    ,   **UpperCAmelCase       :  int    ,   )  ->					int:
											super().__init__(
											    langs=UpperCAmelCase    ,   src_vocab_file=UpperCAmelCase    ,   tgt_vocab_file=UpperCAmelCase    ,   merges_file=UpperCAmelCase    ,   do_lower_case=UpperCAmelCase    ,   unk_token=UpperCAmelCase    ,   bos_token=UpperCAmelCase    ,   sep_token=UpperCAmelCase    ,   pad_token=UpperCAmelCase    ,   **UpperCAmelCase    ,   )

											try:
																	import sacremoses
											except ImportError:
																	raise ImportError(
																	    'You need to install sacremoses to use XLMTokenizer. '
																	    'See https://pypi.org/project/sacremoses/ for installation.'      )

											lowerCAmelCase      :str				  = sacremoses

											lowerCAmelCase      :str				  = src_vocab_file
											lowerCAmelCase      :List[str]				  = tgt_vocab_file
											lowerCAmelCase      :str				  = merges_file
											lowerCAmelCase      :Optional[Any]				  = do_lower_case

											# cache of sm.MosesPunctNormalizer instance
											lowerCAmelCase      :str				  = {}
											# cache of sm.MosesTokenizer instance
											lowerCAmelCase      :Any				  = {}
											lowerCAmelCase      :List[str]				  = {}

											if langs and len(UpperCAmelCase      ) == 2:
																	lowerCAmelCase  ,   lowerCAmelCase      :List[Any]				  = langs
											else:
																	raise ValueError(
																	    f"""arg `langs` needs to be a list of 2 langs, e.g. ['en', 'ru'], but got {langs}. """
																	    'Usually that means that tokenizer can\'t find a mapping for the given model path '
																	    'in PRETRAINED_VOCAB_FILES_MAP, and other maps of this tokenizer.'      )

											with open(UpperCAmelCase    ,   encoding='utf-8'      ) as src_vocab_handle:
																	lowerCAmelCase      :Tuple				  = json.load(UpperCAmelCase      )
											with open(UpperCAmelCase    ,   encoding='utf-8'      ) as tgt_vocab_handle:
																	lowerCAmelCase      :str				  = json.load(UpperCAmelCase      )
																	lowerCAmelCase      :int				  = {v: k for k, v in tgt_vocab.items()}
											with open(UpperCAmelCase    ,   encoding='utf-8'      ) as merges_handle:
																	lowerCAmelCase      :Optional[int]				  = merges_handle.read().split('\n'      )[:-1]
											lowerCAmelCase      :List[Any]				  = [tuple(merge.split()[:2]      ) for merge in merges]
											lowerCAmelCase      :int				  = dict(zip(UpperCAmelCase    ,   range(len(UpperCAmelCase      )      )      )      )
											lowerCAmelCase      :str				  = {}
					def 					UpperCAmelCase__   (       self       :  Optional[Any]      )  ->					Dict[str, int]:
											return self.get_src_vocab()
					@property
					def 					UpperCAmelCase__   (       self       :  Dict      )  ->					int:
											return self.src_vocab_size
					def 					UpperCAmelCase__   (       self       :  int    ,   UpperCAmelCase       :  Tuple    ,   UpperCAmelCase       :  Tuple      )  ->					List[Any]:
											if lang not in self.cache_moses_punct_normalizer:
																	lowerCAmelCase      :str				  = self.sm.MosesPunctNormalizer(lang=UpperCAmelCase      )
																	lowerCAmelCase      :List[str]				  = punct_normalizer
											return self.cache_moses_punct_normalizer[lang].normalize(UpperCAmelCase      )
					def 					UpperCAmelCase__   (       self       :  Tuple    ,   UpperCAmelCase       :  str    ,   UpperCAmelCase       :  Optional[int]      )  ->					str:
											if lang not in self.cache_moses_tokenizer:
																	lowerCAmelCase      :Optional[int]				  = self.sm.MosesTokenizer(lang=UpperCAmelCase      )
																	lowerCAmelCase      :Any				  = moses_tokenizer
											return self.cache_moses_tokenizer[lang].tokenize(
											    UpperCAmelCase    ,   aggressive_dash_splits=UpperCAmelCase    ,   return_str=UpperCAmelCase    ,   escape=UpperCAmelCase      )
					def 					UpperCAmelCase__   (       self       :  Optional[Any]    ,   UpperCAmelCase       :  int    ,   UpperCAmelCase       :  str      )  ->					List[Any]:
											if lang not in self.cache_moses_detokenizer:
																	lowerCAmelCase      :Any				  = self.sm.MosesDetokenizer(lang=UpperCAmelCase      )
																	lowerCAmelCase      :str				  = moses_detokenizer
											return self.cache_moses_detokenizer[lang].detokenize(UpperCAmelCase      )
					def 					UpperCAmelCase__   (       self       :  Optional[int]    ,   UpperCAmelCase       :  str    ,   UpperCAmelCase       :  List[Any]      )  ->					int:
											lowerCAmelCase      :Dict				  = replace_unicode_punct(UpperCAmelCase      )
											lowerCAmelCase      :Union[str, Any]				  = self.moses_punct_norm(UpperCAmelCase    ,   UpperCAmelCase      )
											lowerCAmelCase      :Dict				  = remove_non_printing_char(UpperCAmelCase      )
											return text
					@property
					def 					UpperCAmelCase__   (       self       :  str      )  ->					Union[str, Any]:
											return len(self.encoder      )
					@property
					def 					UpperCAmelCase__   (       self       :  int      )  ->					Optional[int]:
											return len(self.decoder      )
					def 					UpperCAmelCase__   (       self       :  List[Any]      )  ->					Tuple:
											return dict(self.encoder    ,   **self.added_tokens_encoder      )
					def 					UpperCAmelCase__   (       self       :  Any      )  ->					str:
											return dict(self.decoder    ,   **self.added_tokens_decoder      )
					def 					UpperCAmelCase__   (       self       :  List[str]    ,   UpperCAmelCase       :  List[str]      )  ->					Tuple:
											lowerCAmelCase      :List[str]				  = tuple(token[:-1]      ) + (token[-1] + '</w>',)
											if token in self.cache:
																	return self.cache[token]
											lowerCAmelCase      :Dict				  = get_pairs(UpperCAmelCase      )

											if not pairs:
																	return token + "</w>"

											while True:
																	lowerCAmelCase      :Optional[Any]				  = min(UpperCAmelCase    ,   key=lambda UpperCAmelCase      : self.bpe_ranks.get(UpperCAmelCase    ,   float('inf'      )      )      )
																	if bigram not in self.bpe_ranks:
																							break
																	lowerCAmelCase  ,   lowerCAmelCase      :Tuple				  = bigram
																	lowerCAmelCase      :int				  = []
																	lowerCAmelCase      :Union[str, Any]				  = 0
																	while i < len(UpperCAmelCase      ):
																							try:
																													lowerCAmelCase      :List[Any]				  = word.index(UpperCAmelCase    ,   UpperCAmelCase      )
																							except ValueError:
																													new_word.extend(word[i:]      )
																													break
																							else:
																													new_word.extend(word[i:j]      )
																													lowerCAmelCase      :Any				  = 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      :Optional[Any]				  = tuple(UpperCAmelCase      )
																	lowerCAmelCase      :Dict				  = new_word
																	if len(UpperCAmelCase      ) == 1:
																							break
																	else:
																							lowerCAmelCase      :Any				  = get_pairs(UpperCAmelCase      )
											lowerCAmelCase      :Dict				  = ' '.join(UpperCAmelCase      )
											if word == "\n  </w>":
																	lowerCAmelCase      :List[Any]				  = '\n</w>'
											lowerCAmelCase      :Optional[Any]				  = word
											return word
					def 					UpperCAmelCase__   (       self       :  List[Any]    ,   UpperCAmelCase       :  int    ,   UpperCAmelCase       :  List[str]="en"    ,   UpperCAmelCase       :  int=False      )  ->					List[Any]:
											lowerCAmelCase      :Optional[Any]				  = self.src_lang

											if self.do_lower_case:
																	lowerCAmelCase      :Tuple				  = text.lower()

											if bypass_tokenizer:
																	lowerCAmelCase      :List[str]				  = text.split()
											else:
																	lowerCAmelCase      :Tuple				  = self.moses_pipeline(UpperCAmelCase    ,   lang=UpperCAmelCase      )
																	lowerCAmelCase      :Optional[int]				  = self.moses_tokenize(UpperCAmelCase    ,   lang=UpperCAmelCase      )

											lowerCAmelCase      :int				  = []
											for token in text:
																	if token:
																							split_tokens.extend(list(self.bpe(UpperCAmelCase      ).split(' '      )      )      )

											return split_tokens
					def 					UpperCAmelCase__   (       self       :  Dict    ,   UpperCAmelCase       :  Any      )  ->					List[Any]:
											return self.encoder.get(UpperCAmelCase    ,   self.encoder.get(self.unk_token      )      )
					def 					UpperCAmelCase__   (       self       :  Union[str, Any]    ,   UpperCAmelCase       :  Any      )  ->					Dict:
											return self.decoder.get(UpperCAmelCase    ,   self.unk_token      )
					def 					UpperCAmelCase__   (       self       :  List[str]    ,   UpperCAmelCase       :  Optional[Any]      )  ->					int:
											lowerCAmelCase      :Optional[int]				  = [t.replace(' '    ,   ''      ).replace('</w>'    ,   ' '      ) for t in tokens]
											lowerCAmelCase      :Optional[int]				  = ''.join(UpperCAmelCase      ).split()
											# detokenize
											lowerCAmelCase      :Dict				  = self.moses_detokenize(UpperCAmelCase    ,   self.tgt_lang      )
											return text
					def 					UpperCAmelCase__   (       self       :  str    ,   UpperCAmelCase       :  List[int]    ,   UpperCAmelCase       :  Optional[List[int]] = None      )  ->					List[int]:
											lowerCAmelCase      :Union[str, Any]				  = [self.sep_token_id]

											# no bos used in fairseq
											if token_ids_a is None:
																	return token_ids_a + sep
											return token_ids_a + sep + token_ids_a + sep
					def 					UpperCAmelCase__   (       self       :  Dict    ,   UpperCAmelCase       :  List[int]    ,   UpperCAmelCase       :  Optional[List[int]] = None    ,   UpperCAmelCase       :  bool = 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      )
											# no bos used in fairseq
											if token_ids_a is not None:
																	return ([0] * len(UpperCAmelCase      )) + [1] + ([0] * len(UpperCAmelCase      )) + [1]
											return ([0] * len(UpperCAmelCase      )) + [1]
					def 					UpperCAmelCase__   (       self       :  str    ,   UpperCAmelCase       :  List[int]    ,   UpperCAmelCase       :  Optional[List[int]] = None      )  ->					List[int]:
											lowerCAmelCase      :Optional[Any]				  = [self.sep_token_id]

											# no bos used in fairseq
											if token_ids_a is None:
																	return len(token_ids_a + sep      ) * [0]
											return len(token_ids_a + sep      ) * [0] + len(token_ids_a + sep      ) * [1]
					def 					UpperCAmelCase__   (       self       :  Optional[int]    ,   UpperCAmelCase       :  str    ,   UpperCAmelCase       :  Optional[str] = None      )  ->					Tuple[str]:
											if not os.path.isdir(UpperCAmelCase      ):
																	logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""      )
																	return

											lowerCAmelCase      :Any				  = os.path.join(
											    UpperCAmelCase    ,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['src_vocab_file']      )
											lowerCAmelCase      :Tuple				  = os.path.join(
											    UpperCAmelCase    ,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['tgt_vocab_file']      )
											lowerCAmelCase      :Optional[int]				  = 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'      )

											with open(UpperCAmelCase    ,   'w'    ,   encoding='utf-8'      ) as f:
																	lowerCAmelCase      :int				  = {v: k for k, v in self.decoder.items()}
																	f.write(json.dumps(UpperCAmelCase    ,   indent=2    ,   sort_keys=UpperCAmelCase    ,   ensure_ascii=UpperCAmelCase      ) + '\n'      )

											lowerCAmelCase      :Optional[Any]				  = 0
											with open(UpperCAmelCase    ,   'w'    ,   encoding='utf-8'      ) as writer:
																	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 {merges_file}: BPE merge indices are not consecutive."""
																													    ' Please check that the tokenizer is not corrupted!'      )
																													lowerCAmelCase      :List[Any]				  = token_index
																							writer.write(' '.join(UpperCAmelCase      ) + '\n'      )
																							index += 1

											return src_vocab_file, tgt_vocab_file, merges_file
					def __getstate__(       self       :  Union[str, Any]      )  ->					int:
											lowerCAmelCase      :Any				  = self.__dict__.copy()
											lowerCAmelCase      :Tuple				  = None
											return state
					def __setstate__(       self       :  Any    ,   UpperCAmelCase       :  Union[str, Any]      )  ->					List[Any]:
											lowerCAmelCase      :Tuple				  = d

											try:
																	import sacremoses
											except ImportError:
																	raise ImportError(
																	    'You need to install sacremoses to use XLMTokenizer. '
																	    'See https://pypi.org/project/sacremoses/ for installation.'      )

											lowerCAmelCase      :str				  = sacremoses