





"""simple docstring"""
import math
from typing import Optional

import numpy as np
import torch
from torch import nn

from .activations import get_activation






def    A__							(     UpperCamelCase   ,    UpperCamelCase   ,    UpperCamelCase = False   ,    UpperCamelCase = 1   ,    UpperCamelCase = 1   ,    UpperCamelCase = 10_000   ,    ):
							assert len(timesteps.shape			) == 1, "Timesteps should be a 1d-array"

							A						=    embedding_dim // 2
							A						=    -math.log(UpperCamelCase			) * torch.arange(
							    start=0   ,    end=UpperCamelCase   ,    dtype=torch.floataa   ,    device=timesteps.device			)
							A						=    exponent / (half_dim - downscale_freq_shift)

							A						=    torch.exp(UpperCamelCase			)
							A						=    timesteps[:, None].float() * emb[None, :]

							# scale embeddings
							A						=    scale * emb

							# concat sine and cosine embeddings
							A						=    torch.cat([torch.sin(UpperCamelCase			), torch.cos(UpperCamelCase			)]   ,    dim=-1			)

							# flip sine and cosine embeddings
							if flip_sin_to_cos:
														A						=    torch.cat([emb[:, half_dim:], emb[:, :half_dim]]   ,    dim=-1			)

							# zero pad
							if embedding_dim % 2 == 1:
														A						=    torch.nn.functional.pad(UpperCamelCase   ,    (0, 1, 0, 0)			)
							return emb






def    A__							(     UpperCamelCase   ,    UpperCamelCase   ,    UpperCamelCase=False   ,    UpperCamelCase=0			):
							A						=    np.arange(UpperCamelCase   ,    dtype=np.floataa			)
							A						=    np.arange(UpperCamelCase   ,    dtype=np.floataa			)
							A						=    np.meshgrid(UpperCamelCase   ,    UpperCamelCase			)  # here w goes first
							A						=    np.stack(UpperCamelCase   ,    axis=0			)

							A						=    grid.reshape([2, 1, grid_size, grid_size]			)
							A						=    get_ad_sincos_pos_embed_from_grid(UpperCamelCase   ,    UpperCamelCase			)
							if cls_token and extra_tokens > 0:
														A						=    np.concatenate([np.zeros([extra_tokens, embed_dim]			), pos_embed]   ,    axis=0			)
							return pos_embed






def    A__							(     UpperCamelCase   ,    UpperCamelCase			):
							if embed_dim % 2 != 0:
														raise ValueError("embed_dim must be divisible by 2"			)

							# use half of dimensions to encode grid_h
							A						=    get_ad_sincos_pos_embed_from_grid(embed_dim // 2   ,    grid[0]			)  # (H*W, D/2)
							A						=    get_ad_sincos_pos_embed_from_grid(embed_dim // 2   ,    grid[1]			)  # (H*W, D/2)

							A						=    np.concatenate([emb_h, emb_w]   ,    axis=1			)  # (H*W, D)
							return emb






def    A__							(     UpperCamelCase   ,    UpperCamelCase			):
							if embed_dim % 2 != 0:
														raise ValueError("embed_dim must be divisible by 2"			)

							A						=    np.arange(embed_dim // 2   ,    dtype=np.floataa			)
							omega /= embed_dim / 2.0
							A						=    1.0 / 10_000**omega  # (D/2,)

							A						=    pos.reshape(-1			)  # (M,)
							A						=    np.einsum("m,d->md"   ,    UpperCamelCase   ,    UpperCamelCase			)  # (M, D/2), outer product

							A						=    np.sin(UpperCamelCase			)  # (M, D/2)
							A						=    np.cos(UpperCamelCase			)  # (M, D/2)

							A						=    np.concatenate([emb_sin, emb_cos]   ,    axis=1			)  # (M, D)
							return emb






class    _UpperCAmelCase  (      nn.Module					):


					def __init__(   self     :Dict	,			__UpperCamelCase     :List[Any]=2_24	,			__UpperCamelCase     :List[str]=2_24	,			__UpperCamelCase     :List[str]=16	,			__UpperCamelCase     :int=3	,			__UpperCamelCase     :int=7_68	,			__UpperCamelCase     :Union[str, Any]=False	,			__UpperCamelCase     :Optional[Any]=True	,			__UpperCamelCase     :str=True	,			):
												super().__init__()

												A						=    (height // patch_size) * (width // patch_size)
												A						=    flatten
												A						=    layer_norm

												A						=    nn.Convad(
												    __UpperCamelCase	,			__UpperCamelCase	,			kernel_size=(patch_size, patch_size)	,			stride=__UpperCamelCase	,			bias=__UpperCamelCase						)
												if layer_norm:
																			A						=    nn.LayerNorm(__UpperCamelCase	,			elementwise_affine=__UpperCamelCase	,			eps=1e-6						)
												else:
																			A						=    None

												A						=    get_ad_sincos_pos_embed(__UpperCamelCase	,			int(num_patches**0.5						)						)
												self.register_buffer("pos_embed"	,			torch.from_numpy(__UpperCamelCase						).float().unsqueeze(0						)	,			persistent=__UpperCamelCase						)


					def       lowerCamelCase			(   self     :str	,			__UpperCamelCase     :Dict						):
												A						=    self.proj(__UpperCamelCase						)
												if self.flatten:
																			A						=    latent.flatten(2						).transpose(1	,			2						)  # BCHW -> BNC
												if self.layer_norm:
																			A						=    self.norm(__UpperCamelCase						)
												return latent + self.pos_embed







class    _UpperCAmelCase  (      nn.Module					):


					def __init__(   self     :Optional[int]	,			__UpperCamelCase     :int	,			__UpperCamelCase     :int	,			__UpperCamelCase     :str = "silu"	,			__UpperCamelCase     :int = None	,			__UpperCamelCase     :Optional[str] = None	,			__UpperCamelCase     :List[str]=None	,			):
												super().__init__()

												A						=    nn.Linear(__UpperCamelCase	,			__UpperCamelCase						)

												if cond_proj_dim is not None:
																			A						=    nn.Linear(__UpperCamelCase	,			__UpperCamelCase	,			bias=__UpperCamelCase						)
												else:
																			A						=    None

												A						=    get_activation(__UpperCamelCase						)

												if out_dim is not None:
																			A						=    out_dim
												else:
																			A						=    time_embed_dim
												A						=    nn.Linear(__UpperCamelCase	,			__UpperCamelCase						)

												if post_act_fn is None:
																			A						=    None
												else:
																			A						=    get_activation(__UpperCamelCase						)


					def       lowerCamelCase			(   self     :Any	,			__UpperCamelCase     :int	,			__UpperCamelCase     :List[str]=None						):
												if condition is not None:
																			A						=    sample + self.cond_proj(__UpperCamelCase						)
												A						=    self.linear_a(__UpperCamelCase						)

												if self.act is not None:
																			A						=    self.act(__UpperCamelCase						)

												A						=    self.linear_a(__UpperCamelCase						)

												if self.post_act is not None:
																			A						=    self.post_act(__UpperCamelCase						)
												return sample







class    _UpperCAmelCase  (      nn.Module					):


					def __init__(   self     :Tuple	,			__UpperCamelCase     :int	,			__UpperCamelCase     :bool	,			__UpperCamelCase     :float						):
												super().__init__()
												A						=    num_channels
												A						=    flip_sin_to_cos
												A						=    downscale_freq_shift


					def       lowerCamelCase			(   self     :List[str]	,			__UpperCamelCase     :Union[str, Any]						):
												A						=    get_timestep_embedding(
												    __UpperCamelCase	,			self.num_channels	,			flip_sin_to_cos=self.flip_sin_to_cos	,			downscale_freq_shift=self.downscale_freq_shift	,			)
												return t_emb







class    _UpperCAmelCase  (      nn.Module					):


					def __init__(   self     :List[Any]	,			__UpperCamelCase     :int = 2_56	,			__UpperCamelCase     :float = 1.0	,			__UpperCamelCase     :Tuple=True	,			__UpperCamelCase     :Any=True	,			__UpperCamelCase     :List[str]=False						):
												super().__init__()
												A						=    nn.Parameter(torch.randn(__UpperCamelCase						) * scale	,			requires_grad=__UpperCamelCase						)
												A						=    log
												A						=    flip_sin_to_cos

												if set_W_to_weight:
																			# to delete later
																			A						=    nn.Parameter(torch.randn(__UpperCamelCase						) * scale	,			requires_grad=__UpperCamelCase						)

																			A						=    self.W


					def       lowerCamelCase			(   self     :Dict	,			__UpperCamelCase     :Any						):
												if self.log:
																			A						=    torch.log(__UpperCamelCase						)

												A						=    x[:, None] * self.weight[None, :] * 2 * np.pi

												if self.flip_sin_to_cos:
																			A						=    torch.cat([torch.cos(__UpperCamelCase						), torch.sin(__UpperCamelCase						)]	,			dim=-1						)
												else:
																			A						=    torch.cat([torch.sin(__UpperCamelCase						), torch.cos(__UpperCamelCase						)]	,			dim=-1						)
												return out







class    _UpperCAmelCase  (      nn.Module					):


					def __init__(   self     :List[str]	,			__UpperCamelCase     :int	,			__UpperCamelCase     :int	,			__UpperCamelCase     :int	,			__UpperCamelCase     :int	,			):
												super().__init__()

												A						=    height
												A						=    width
												A						=    num_embed
												A						=    embed_dim

												A						=    nn.Embedding(self.num_embed	,			__UpperCamelCase						)
												A						=    nn.Embedding(self.height	,			__UpperCamelCase						)
												A						=    nn.Embedding(self.width	,			__UpperCamelCase						)


					def       lowerCamelCase			(   self     :Optional[int]	,			__UpperCamelCase     :Optional[Any]						):
												A						=    self.emb(__UpperCamelCase						)

												A						=    self.height_emb(torch.arange(self.height	,			device=index.device						).view(1	,			self.height						)						)

												# 1 x H x D -> 1 x H x 1 x D
												A						=    height_emb.unsqueeze(2						)

												A						=    self.width_emb(torch.arange(self.width	,			device=index.device						).view(1	,			self.width						)						)

												# 1 x W x D -> 1 x 1 x W x D
												A						=    width_emb.unsqueeze(1						)

												A						=    height_emb + width_emb

												# 1 x H x W x D -> 1 x L xD
												A						=    pos_emb.view(1	,			self.height * self.width	,			-1						)

												A						=    emb + pos_emb[:, : emb.shape[1], :]

												return emb







class    _UpperCAmelCase  (      nn.Module					):


					def __init__(   self     :Optional[int]	,			__UpperCamelCase     :List[Any]	,			__UpperCamelCase     :str	,			__UpperCamelCase     :Optional[Any]						):
												super().__init__()
												A						=    dropout_prob > 0
												A						=    nn.Embedding(num_classes + use_cfg_embedding	,			__UpperCamelCase						)
												A						=    num_classes
												A						=    dropout_prob


					def       lowerCamelCase			(   self     :int	,			__UpperCamelCase     :List[str]	,			__UpperCamelCase     :Union[str, Any]=None						):
												if force_drop_ids is None:
																			A						=    torch.rand(labels.shape[0]	,			device=labels.device						) < self.dropout_prob
												else:
																			A						=    torch.tensor(force_drop_ids == 1						)
												A						=    torch.where(__UpperCamelCase	,			self.num_classes	,			__UpperCamelCase						)
												return labels


					def       lowerCamelCase			(   self     :Tuple	,			__UpperCamelCase     :torch.LongTensor	,			__UpperCamelCase     :Optional[Any]=None						):
												A						=    self.dropout_prob > 0
												if (self.training and use_dropout) or (force_drop_ids is not None):
																			A						=    self.token_drop(__UpperCamelCase	,			__UpperCamelCase						)
												A						=    self.embedding_table(__UpperCamelCase						)
												return embeddings







class    _UpperCAmelCase  (      nn.Module					):


					def __init__(   self     :Optional[int]	,			__UpperCamelCase     :int = 10_24	,			__UpperCamelCase     :int = 7_68	,			__UpperCamelCase     :int = 7_68	,			__UpperCamelCase     :int = 10	,			):
												super().__init__()

												A						=    num_image_text_embeds
												A						=    nn.Linear(__UpperCamelCase	,			self.num_image_text_embeds * cross_attention_dim						)
												A						=    nn.Linear(__UpperCamelCase	,			__UpperCamelCase						)


					def       lowerCamelCase			(   self     :Dict	,			__UpperCamelCase     :torch.FloatTensor	,			__UpperCamelCase     :torch.FloatTensor						):
												A						=    text_embeds.shape[0]

												# image
												A						=    self.image_embeds(__UpperCamelCase						)
												A						=    image_text_embeds.reshape(__UpperCamelCase	,			self.num_image_text_embeds	,			-1						)

												# text
												A						=    self.text_proj(__UpperCamelCase						)

												return torch.cat([image_text_embeds, text_embeds]	,			dim=1						)







class    _UpperCAmelCase  (      nn.Module					):


					def __init__(   self     :Union[str, Any]	,			__UpperCamelCase     :int = 7_68	,			__UpperCamelCase     :int = 7_68	,			__UpperCamelCase     :int = 32	,			):
												super().__init__()

												A						=    num_image_text_embeds
												A						=    nn.Linear(__UpperCamelCase	,			self.num_image_text_embeds * cross_attention_dim						)
												A						=    nn.LayerNorm(__UpperCamelCase						)


					def       lowerCamelCase			(   self     :int	,			__UpperCamelCase     :torch.FloatTensor						):
												A						=    image_embeds.shape[0]

												# image
												A						=    self.image_embeds(__UpperCamelCase						)
												A						=    image_embeds.reshape(__UpperCamelCase	,			self.num_image_text_embeds	,			-1						)
												A						=    self.norm(__UpperCamelCase						)
												return image_embeds







class    _UpperCAmelCase  (      nn.Module					):


					def __init__(   self     :Dict	,			__UpperCamelCase     :Union[str, Any]	,			__UpperCamelCase     :Tuple	,			__UpperCamelCase     :List[Any]=0.1						):
												super().__init__()

												A						=    Timesteps(num_channels=2_56	,			flip_sin_to_cos=__UpperCamelCase	,			downscale_freq_shift=1						)
												A						=    TimestepEmbedding(in_channels=2_56	,			time_embed_dim=__UpperCamelCase						)
												A						=    LabelEmbedding(__UpperCamelCase	,			__UpperCamelCase	,			__UpperCamelCase						)


					def       lowerCamelCase			(   self     :Tuple	,			__UpperCamelCase     :Optional[int]	,			__UpperCamelCase     :Dict	,			__UpperCamelCase     :int=None						):
												A						=    self.time_proj(__UpperCamelCase						)
												A						=    self.timestep_embedder(timesteps_proj.to(dtype=__UpperCamelCase						)						)  # (N, D)

												A						=    self.class_embedder(__UpperCamelCase						)  # (N, D)

												A						=    timesteps_emb + class_labels  # (N, D)

												return conditioning







class    _UpperCAmelCase  (      nn.Module					):


					def __init__(   self     :Optional[Any]	,			__UpperCamelCase     :int	,			__UpperCamelCase     :int	,			__UpperCamelCase     :int = 64						):
												super().__init__()
												A						=    nn.LayerNorm(__UpperCamelCase						)
												A						=    AttentionPooling(__UpperCamelCase	,			__UpperCamelCase						)
												A						=    nn.Linear(__UpperCamelCase	,			__UpperCamelCase						)
												A						=    nn.LayerNorm(__UpperCamelCase						)


					def       lowerCamelCase			(   self     :List[Any]	,			__UpperCamelCase     :int						):
												A						=    self.norma(__UpperCamelCase						)
												A						=    self.pool(__UpperCamelCase						)
												A						=    self.proj(__UpperCamelCase						)
												A						=    self.norma(__UpperCamelCase						)
												return hidden_states







class    _UpperCAmelCase  (      nn.Module					):


					def __init__(   self     :Any	,			__UpperCamelCase     :int = 7_68	,			__UpperCamelCase     :int = 7_68	,			__UpperCamelCase     :int = 15_36						):
												super().__init__()
												A						=    nn.Linear(__UpperCamelCase	,			__UpperCamelCase						)
												A						=    nn.LayerNorm(__UpperCamelCase						)
												A						=    nn.Linear(__UpperCamelCase	,			__UpperCamelCase						)


					def       lowerCamelCase			(   self     :List[Any]	,			__UpperCamelCase     :torch.FloatTensor	,			__UpperCamelCase     :torch.FloatTensor						):
												# text
												A						=    self.text_proj(__UpperCamelCase						)
												A						=    self.text_norm(__UpperCamelCase						)

												# image
												A						=    self.image_proj(__UpperCamelCase						)

												return time_image_embeds + time_text_embeds







class    _UpperCAmelCase  (      nn.Module					):


					def __init__(   self     :Any	,			__UpperCamelCase     :int = 7_68	,			__UpperCamelCase     :int = 15_36						):
												super().__init__()
												A						=    nn.Linear(__UpperCamelCase	,			__UpperCamelCase						)
												A						=    nn.LayerNorm(__UpperCamelCase						)


					def       lowerCamelCase			(   self     :int	,			__UpperCamelCase     :torch.FloatTensor						):
												# image
												A						=    self.image_proj(__UpperCamelCase						)
												A						=    self.image_norm(__UpperCamelCase						)
												return time_image_embeds







class    _UpperCAmelCase  (      nn.Module					):


					def __init__(   self     :Dict	,			__UpperCamelCase     :int = 7_68	,			__UpperCamelCase     :int = 15_36						):
												super().__init__()
												A						=    nn.Linear(__UpperCamelCase	,			__UpperCamelCase						)
												A						=    nn.LayerNorm(__UpperCamelCase						)
												A						=    nn.Sequential(
												    nn.Convad(3	,			16	,			3	,			padding=1						)	,			nn.SiLU()	,			nn.Convad(16	,			16	,			3	,			padding=1						)	,			nn.SiLU()	,			nn.Convad(16	,			32	,			3	,			padding=1	,			stride=2						)	,			nn.SiLU()	,			nn.Convad(32	,			32	,			3	,			padding=1						)	,			nn.SiLU()	,			nn.Convad(32	,			96	,			3	,			padding=1	,			stride=2						)	,			nn.SiLU()	,			nn.Convad(96	,			96	,			3	,			padding=1						)	,			nn.SiLU()	,			nn.Convad(96	,			2_56	,			3	,			padding=1	,			stride=2						)	,			nn.SiLU()	,			nn.Convad(2_56	,			4	,			3	,			padding=1						)	,			)


					def       lowerCamelCase			(   self     :Any	,			__UpperCamelCase     :torch.FloatTensor	,			__UpperCamelCase     :torch.FloatTensor						):
												# image
												A						=    self.image_proj(__UpperCamelCase						)
												A						=    self.image_norm(__UpperCamelCase						)
												A						=    self.input_hint_block(__UpperCamelCase						)
												return time_image_embeds, hint







class    _UpperCAmelCase  (      nn.Module					):


					def __init__(   self     :Dict	,			__UpperCamelCase     :Union[str, Any]	,			__UpperCamelCase     :List[str]	,			__UpperCamelCase     :Union[str, Any]=None						):
												super().__init__()
												A						=    dtype
												A						=    nn.Parameter(torch.randn(1	,			__UpperCamelCase						) / embed_dim**0.5						)
												A						=    nn.Linear(__UpperCamelCase	,			__UpperCamelCase	,			dtype=self.dtype						)
												A						=    nn.Linear(__UpperCamelCase	,			__UpperCamelCase	,			dtype=self.dtype						)
												A						=    nn.Linear(__UpperCamelCase	,			__UpperCamelCase	,			dtype=self.dtype						)
												A						=    num_heads
												A						=    embed_dim // self.num_heads


					def       lowerCamelCase			(   self     :Optional[int]	,			__UpperCamelCase     :int						):
												A,      A,      A						=    x.size()

												def shape(__UpperCamelCase     :int						):
																			# (bs, length, width) --> (bs, length, n_heads, dim_per_head)
																			A						=    x.view(__UpperCamelCase	,			-1	,			self.num_heads	,			self.dim_per_head						)
																			# (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)
																			A						=    x.transpose(1	,			2						)
																			# (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
																			A						=    x.reshape(bs * self.num_heads	,			-1	,			self.dim_per_head						)
																			# (bs*n_heads, length, dim_per_head) --> (bs*n_heads, dim_per_head, length)
																			A						=    x.transpose(1	,			2						)
																			return x

												A						=    x.mean(dim=1	,			keepdim=__UpperCamelCase						) + self.positional_embedding.to(x.dtype						)
												A						=    torch.cat([class_token, x]	,			dim=1						)  # (bs, length+1, width)

												# (bs*n_heads, class_token_length, dim_per_head)
												A						=    shape(self.q_proj(__UpperCamelCase						)						)
												# (bs*n_heads, length+class_token_length, dim_per_head)
												A						=    shape(self.k_proj(__UpperCamelCase						)						)
												A						=    shape(self.v_proj(__UpperCamelCase						)						)

												# (bs*n_heads, class_token_length, length+class_token_length):
												A						=    1 / math.sqrt(math.sqrt(self.dim_per_head						)						)
												A						=    torch.einsum("bct,bcs->bts"	,			q * scale	,			k * scale						)  # More stable with f16 than dividing afterwards
												A						=    torch.softmax(weight.float()	,			dim=-1						).type(weight.dtype						)

												# (bs*n_heads, dim_per_head, class_token_length)
												A						=    torch.einsum("bts,bcs->bct"	,			__UpperCamelCase	,			__UpperCamelCase						)

												# (bs, length+1, width)
												A						=    a.reshape(__UpperCamelCase	,			-1	,			1						).transpose(1	,			2						)

												return a[:, 0, :]  # cls_token









"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_mbartaa import MBartaaTokenizer
else:
		_snake_case	:						Dict					=					None


_snake_case	:						int					=					logging.get_logger(__name__)

_snake_case	:						Optional[Any]					=					{'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

_snake_case	:						str					=					{
    'vocab_file': {
        'facebook/mbart-large-50-one-to-many-mmt': (
            'https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'facebook/mbart-large-50-one-to-many-mmt': (
            'https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/tokenizer.json'
        ),
    },
}

_snake_case	:						Union[str, Any]					=					{
    'facebook/mbart-large-50-one-to-many-mmt': 1024,
}

# fmt: off
_snake_case	:						int					=					['ar_AR', 'cs_CZ', 'de_DE', 'en_XX', 'es_XX', 'et_EE', 'fi_FI', 'fr_XX', 'gu_IN', 'hi_IN', 'it_IT', 'ja_XX', 'kk_KZ', 'ko_KR', 'lt_LT', 'lv_LV', 'my_MM', 'ne_NP', 'nl_XX', 'ro_RO', 'ru_RU', 'si_LK', 'tr_TR', 'vi_VN', 'zh_CN', 'af_ZA', 'az_AZ', 'bn_IN', 'fa_IR', 'he_IL', 'hr_HR', 'id_ID', 'ka_GE', 'km_KH', 'mk_MK', 'ml_IN', 'mn_MN', 'mr_IN', 'pl_PL', 'ps_AF', 'pt_XX', 'sv_SE', 'sw_KE', 'ta_IN', 'te_IN', 'th_TH', 'tl_XX', 'uk_UA', 'ur_PK', 'xh_ZA', 'gl_ES', 'sl_SI']






class    _UpperCAmelCase  (      lowercase_					):
					UpperCamelCase						=				VOCAB_FILES_NAMES
					UpperCamelCase						=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					UpperCamelCase						=				PRETRAINED_VOCAB_FILES_MAP
					UpperCamelCase						=				['''input_ids''', '''attention_mask''']
					UpperCamelCase						=				MBartaaTokenizer

					UpperCamelCase						=				[]
					UpperCamelCase						=				[]


					def __init__(   self     :Any	,			__UpperCamelCase     :str=None	,			__UpperCamelCase     :Any=None	,			__UpperCamelCase     :int=None	,			__UpperCamelCase     :Union[str, Any]=None	,			__UpperCamelCase     :Optional[Any]="</s>"	,			__UpperCamelCase     :Tuple="</s>"	,			__UpperCamelCase     :Any="<s>"	,			__UpperCamelCase     :Optional[int]="<unk>"	,			__UpperCamelCase     :str="<pad>"	,			__UpperCamelCase     :int="<mask>"	,			**__UpperCamelCase     :List[str]	,			):
												# Mask token behave like a normal word, i.e. include the space before it
												A						=    AddedToken(__UpperCamelCase	,			lstrip=__UpperCamelCase	,			rstrip=__UpperCamelCase						) if isinstance(__UpperCamelCase	,			__UpperCamelCase						) else mask_token

												A						=    kwargs.get("additional_special_tokens"	,			[]						)
												kwargs["additional_special_tokens"] += [
												    code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
												]

												super().__init__(
												    __UpperCamelCase	,			src_lang=__UpperCamelCase	,			tgt_lang=__UpperCamelCase	,			tokenizer_file=__UpperCamelCase	,			eos_token=__UpperCamelCase	,			sep_token=__UpperCamelCase	,			cls_token=__UpperCamelCase	,			unk_token=__UpperCamelCase	,			pad_token=__UpperCamelCase	,			mask_token=__UpperCamelCase	,			**__UpperCamelCase	,			)

												A						=    vocab_file
												A						=    False if not self.vocab_file else True

												A						=    {
												    lang_code: self.convert_tokens_to_ids(__UpperCamelCase						) for lang_code in FAIRSEQ_LANGUAGE_CODES
												}

												A						=    src_lang if src_lang is not None else "en_XX"
												A						=    tgt_lang
												A						=    self.lang_code_to_id[self._src_lang]
												self.set_src_lang_special_tokens(self._src_lang						)


					@property
					def       lowerCamelCase			(   self     :int						):
												return self._src_lang


					@src_lang.setter
					def       lowerCamelCase			(   self     :Optional[int]	,			__UpperCamelCase     :str						):
												A						=    new_src_lang
												self.set_src_lang_special_tokens(self._src_lang						)


					def       lowerCamelCase			(   self     :Any	,			__UpperCamelCase     :List[int]	,			__UpperCamelCase     :Optional[List[int]] = None						):
												if token_ids_a is None:
																			return self.prefix_tokens + token_ids_a + self.suffix_tokens
												# We don't expect to process pairs, but leave the pair logic for API consistency
												return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens


					def       lowerCamelCase			(   self     :Optional[Any]	,			__UpperCamelCase     :List[str]	,			__UpperCamelCase     :str = "en_XX"	,			__UpperCamelCase     :Optional[List[str]] = None	,			__UpperCamelCase     :str = "ro_RO"	,			**__UpperCamelCase     :Tuple	,			):
												A						=    src_lang
												A						=    tgt_lang
												return super().prepare_seqaseq_batch(__UpperCamelCase	,			__UpperCamelCase	,			**__UpperCamelCase						)


					def       lowerCamelCase			(   self     :str						):
												return self.set_src_lang_special_tokens(self.src_lang						)


					def       lowerCamelCase			(   self     :Optional[Any]						):
												return self.set_tgt_lang_special_tokens(self.tgt_lang						)


					def       lowerCamelCase			(   self     :Any	,			__UpperCamelCase     :str						):
												A						=    self.convert_tokens_to_ids(__UpperCamelCase						)
												A						=    [self.cur_lang_code_id]
												A						=    [self.eos_token_id]

												A						=    self.convert_ids_to_tokens(self.prefix_tokens						)
												A						=    self.convert_ids_to_tokens(self.suffix_tokens						)

												A						=    processors.TemplateProcessing(
												    single=prefix_tokens_str + ["$A"] + suffix_tokens_str	,			pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str	,			special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str	,			self.prefix_tokens + self.suffix_tokens						)						)	,			)


					def       lowerCamelCase			(   self     :Optional[Any]	,			__UpperCamelCase     :str						):
												A						=    self.convert_tokens_to_ids(__UpperCamelCase						)
												A						=    [self.cur_lang_code_id]
												A						=    [self.eos_token_id]

												A						=    self.convert_ids_to_tokens(self.prefix_tokens						)
												A						=    self.convert_ids_to_tokens(self.suffix_tokens						)

												A						=    processors.TemplateProcessing(
												    single=prefix_tokens_str + ["$A"] + suffix_tokens_str	,			pair=prefix_tokens_str + ["$A", "$B"] + suffix_tokens_str	,			special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str	,			self.prefix_tokens + self.suffix_tokens						)						)	,			)


					def       lowerCamelCase			(   self     :Optional[Any]	,			__UpperCamelCase     :Any	,			__UpperCamelCase     :str	,			__UpperCamelCase     :Optional[str]	,			__UpperCamelCase     :Optional[str]	,			**__UpperCamelCase     :Union[str, Any]						):
												if src_lang is None or tgt_lang is None:
																			raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model"						)
												A						=    src_lang
												A						=    self(__UpperCamelCase	,			add_special_tokens=__UpperCamelCase	,			return_tensors=__UpperCamelCase	,			**__UpperCamelCase						)
												A						=    self.convert_tokens_to_ids(__UpperCamelCase						)
												A						=    tgt_lang_id
												return inputs


					def       lowerCamelCase			(   self     :Tuple	,			__UpperCamelCase     :str	,			__UpperCamelCase     :Optional[str] = None						):
												if not self.can_save_slow_tokenizer:
																			raise ValueError(
																			    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
																			    "tokenizer."						)

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

												if os.path.abspath(self.vocab_file						) != os.path.abspath(__UpperCamelCase						):
																			copyfile(self.vocab_file	,			__UpperCamelCase						)

												return (out_vocab_file,)


