


import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging


__lowerCAmelCase	: int  				=logging.get_logger(__name__)

__lowerCAmelCase	: str  				='▁'

__lowerCAmelCase	: Tuple  				={'vocab_file': 'sentencepiece.bpe.model'}

__lowerCAmelCase	: Union[str, Any]  				={
    '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'
        ),
    }
}

__lowerCAmelCase	: int  				={
    'facebook/mbart-large-50-one-to-many-mmt': 1_0_2_4,
}

# fmt: off
__lowerCAmelCase	: Optional[Any]  				=['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        _lowercase      (			lowerCamelCase_       ):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__				:			Optional[Any]						=		VOCAB_FILES_NAMES
				SCREAMING_SNAKE_CASE__				:			Dict						=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				SCREAMING_SNAKE_CASE__				:			str						=		PRETRAINED_VOCAB_FILES_MAP
				SCREAMING_SNAKE_CASE__				:			Dict						=		['''input_ids''', '''attention_mask''']

				SCREAMING_SNAKE_CASE__				:			int						=		[]
				SCREAMING_SNAKE_CASE__				:			List[Any]						=		[]


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

										__SCREAMING_SNAKE_CASE						:     Dict					=		{} if sp_model_kwargs is None else sp_model_kwargs

										__SCREAMING_SNAKE_CASE						:     Tuple					=		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__(
										    src_lang=__a			,			tgt_lang=__a			,			eos_token=__a			,			unk_token=__a			,			sep_token=__a			,			cls_token=__a			,			pad_token=__a			,			mask_token=__a			,			sp_model_kwargs=self.sp_model_kwargs			,			**__a			,			)

										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		spm.SentencePieceProcessor(**self.sp_model_kwargs	)
										self.sp_model.Load(str(__a	)	)
										__SCREAMING_SNAKE_CASE						:     int					=		vocab_file

										# Original fairseq vocab and spm vocab must be "aligned":
										# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
										# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
										# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
										# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

										# Mimic fairseq token-to-id alignment for the first 4 token
										__SCREAMING_SNAKE_CASE						:     Any					=		{'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

										# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
										__SCREAMING_SNAKE_CASE						:     int					=		1

										__SCREAMING_SNAKE_CASE						:     int					=		len(self.sp_model	)
										__SCREAMING_SNAKE_CASE						:     Tuple					=		{
										    code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(__a	)
										}
										__SCREAMING_SNAKE_CASE						:     List[str]					=		{v: k for k, v in self.lang_code_to_id.items()}
										__SCREAMING_SNAKE_CASE						:     Dict					=		len(self.sp_model	) + len(self.lang_code_to_id	) + self.fairseq_offset

										self.fairseq_tokens_to_ids.update(self.lang_code_to_id	)
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		{v: k for k, v in self.fairseq_tokens_to_ids.items()}

										__SCREAMING_SNAKE_CASE						:     Any					=		src_lang if src_lang is not None else '''en_XX'''
										__SCREAMING_SNAKE_CASE						:     List[str]					=		self.lang_code_to_id[self._src_lang]
										__SCREAMING_SNAKE_CASE						:     List[str]					=		tgt_lang
										self.set_src_lang_special_tokens(self._src_lang	)


				@property
				def 		__magic_name__(       self			:int	)		->			Union[str, Any]:
										return len(self.sp_model	) + len(self.lang_code_to_id	) + self.fairseq_offset + 1  # Plus 1 for the mask token


				@property
				def 		__magic_name__(       self			:Optional[int]	)		->			List[str]:
										return self._src_lang


				@src_lang.setter
				def 		__magic_name__(       self			:Optional[Any]			,			lowerCAmelCase__			:str	)		->			Any:
										__SCREAMING_SNAKE_CASE						:     List[str]					=		new_src_lang
										self.set_src_lang_special_tokens(self._src_lang	)


				def __getstate__(       self			:List[str]	)		->			List[Any]:
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		self.__dict__.copy()
										__SCREAMING_SNAKE_CASE						:     Tuple					=		None
										return state


				def __setstate__(       self			:Union[str, Any]			,			lowerCAmelCase__			:Dict	)		->			List[Any]:
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		d

										# for backward compatibility
										if not hasattr(self			,			'''sp_model_kwargs'''	):
																__SCREAMING_SNAKE_CASE						:     Dict					=		{}

										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		spm.SentencePieceProcessor(**self.sp_model_kwargs	)
										self.sp_model.Load(self.vocab_file	)


				def 		__magic_name__(       self			:List[str]	)		->			Dict:
										__SCREAMING_SNAKE_CASE						:     Optional[int]					=		{self.convert_ids_to_tokens(__a	): i for i in range(self.vocab_size	)}
										vocab.update(self.added_tokens_encoder	)
										return vocab


				def 		__magic_name__(       self			:Tuple			,			lowerCAmelCase__			:str	)		->			Tuple:
										return self.sp_model.encode(__a			,			out_type=__a	)


				def 		__magic_name__(       self			:Dict			,			lowerCAmelCase__			:str	)		->			Optional[Any]:
										if token in self.fairseq_tokens_to_ids:
																return self.fairseq_tokens_to_ids[token]
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		self.sp_model.PieceToId(__a	)

										# Need to return unknown token if the SP model returned 0
										return spm_id + self.fairseq_offset if spm_id else self.unk_token_id


				def 		__magic_name__(       self			:Tuple			,			lowerCAmelCase__			:int	)		->			Any:
										if index in self.fairseq_ids_to_tokens:
																return self.fairseq_ids_to_tokens[index]
										return self.sp_model.IdToPiece(index - self.fairseq_offset	)


				def 		__magic_name__(       self			:Dict			,			lowerCAmelCase__			:Dict	)		->			List[str]:
										__SCREAMING_SNAKE_CASE						:     Dict					=		[]
										__SCREAMING_SNAKE_CASE						:     Tuple					=		''''''
										__SCREAMING_SNAKE_CASE						:     Any					=		False
										for token in tokens:
																# make sure that special tokens are not decoded using sentencepiece model
																if token in self.all_special_tokens:
																						if not prev_is_special:
																												out_string += " "
																						out_string += self.sp_model.decode(__a	) + token
																						__SCREAMING_SNAKE_CASE						:     List[Any]					=		True
																						__SCREAMING_SNAKE_CASE						:     int					=		[]
																else:
																						current_sub_tokens.append(__a	)
																						__SCREAMING_SNAKE_CASE						:     int					=		False
										out_string += self.sp_model.decode(__a	)
										return out_string.strip()


				def 		__magic_name__(       self			:List[str]			,			lowerCAmelCase__			:str			,			lowerCAmelCase__			:Optional[str] = None	)		->			Tuple:
										if not os.path.isdir(__a	):
																logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''	)
																return
										__SCREAMING_SNAKE_CASE						:     Dict					=		os.path.join(
										    __a			,			(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']	)

										if os.path.abspath(self.vocab_file	) != os.path.abspath(__a	) and os.path.isfile(self.vocab_file	):
																copyfile(self.vocab_file			,			__a	)
										elif not os.path.isfile(self.vocab_file	):
																with open(__a			,			'''wb'''	) as fi:
																						__SCREAMING_SNAKE_CASE						:     List[str]					=		self.sp_model.serialized_model_proto()
																						fi.write(__a	)

										return (out_vocab_file,)


				def 		__magic_name__(       self			:Optional[int]			,			lowerCAmelCase__			:List[int]			,			lowerCAmelCase__			:Optional[List[int]] = None			,			lowerCAmelCase__			:bool = False	)		->			int:

										if already_has_special_tokens:
																return super().get_special_tokens_mask(
																    token_ids_a=__a			,			token_ids_a=__a			,			already_has_special_tokens=__a	)

										__SCREAMING_SNAKE_CASE						:     int					=		[1] * len(self.prefix_tokens	)
										__SCREAMING_SNAKE_CASE						:     str					=		[1] * len(self.suffix_tokens	)
										if token_ids_a is None:
																return prefix_ones + ([0] * len(__a	)) + suffix_ones
										return prefix_ones + ([0] * len(__a	)) + ([0] * len(__a	)) + suffix_ones


				def 		__magic_name__(       self			:Dict			,			lowerCAmelCase__			:List[int]			,			lowerCAmelCase__			:Optional[List[int]] = None	)		->			Optional[int]:
										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 		__magic_name__(       self			:str			,			lowerCAmelCase__			:str			,			lowerCAmelCase__			:str			,			lowerCAmelCase__			:Optional[str]			,			lowerCAmelCase__			:Optional[str]			,			**lowerCAmelCase__			:Tuple	)		->			List[str]:
										if src_lang is None or tgt_lang is None:
																raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model'''	)
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		src_lang
										__SCREAMING_SNAKE_CASE						:     List[str]					=		self(__a			,			add_special_tokens=__a			,			return_tensors=__a			,			**__a	)
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		self.convert_tokens_to_ids(__a	)
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		tgt_lang_id
										return inputs


				def 		__magic_name__(       self			:List[str]			,			lowerCAmelCase__			:List[str]			,			lowerCAmelCase__			:str = "en_XX"			,			lowerCAmelCase__			:Optional[List[str]] = None			,			lowerCAmelCase__			:str = "ro_RO"			,			**lowerCAmelCase__			:int			,			)		->			Tuple:
										__SCREAMING_SNAKE_CASE						:     str					=		src_lang
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		tgt_lang
										return super().prepare_seqaseq_batch(__a			,			__a			,			**__a	)


				def 		__magic_name__(       self			:str	)		->			Dict:
										return self.set_src_lang_special_tokens(self.src_lang	)


				def 		__magic_name__(       self			:int	)		->			Dict:
										return self.set_tgt_lang_special_tokens(self.tgt_lang	)


				def 		__magic_name__(       self			:List[Any]			,			lowerCAmelCase__			:str	)		->			Tuple:
										__SCREAMING_SNAKE_CASE						:     Tuple					=		self.lang_code_to_id[src_lang]
										__SCREAMING_SNAKE_CASE						:     List[Any]					=		[self.cur_lang_code_id]
										__SCREAMING_SNAKE_CASE						:     Any					=		[self.eos_token_id]



				def 		__magic_name__(       self			:Optional[Any]			,			lowerCAmelCase__			:str	)		->			List[str]:
										__SCREAMING_SNAKE_CASE						:     str					=		self.lang_code_to_id[tgt_lang]
										__SCREAMING_SNAKE_CASE						:     Union[str, Any]					=		[self.cur_lang_code_id]
										__SCREAMING_SNAKE_CASE						:     str					=		[self.eos_token_id]







'''simple docstring'''







import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_       ,							lowerCamelCase_      ):






				"""simple docstring"""







				@register_to_config
				def __init__(  self     :					Dict					,				*,
				    __a     :					int = 4					,				__a     :					int = 7_68					,				__a     :					int					,				__a     :					int					,				):
								super().__init__()

								_a								= nn.Parameter(torch.zeros(__a					)					)

								# parameters for additional clip time embeddings
								_a								= nn.Linear(__a					,				__a					)
								_a								= nn.Linear(__a					,				__a					)

								# parameters for encoder hidden states
								_a								= clip_extra_context_tokens
								_a								= nn.Linear(
								    __a					,				self.clip_extra_context_tokens * cross_attention_dim					)
								_a								= nn.Linear(__a					,				__a					)
								_a								= nn.LayerNorm(__a					)




				def   UpperCamelCase__   (  self     :					Optional[Any]					,				*, __a     :					Tuple					,				__a     :					Union[str, Any]					,				__a     :					Any					,				__a     :					List[Any]					):
								if do_classifier_free_guidance:
												# Add the classifier free guidance embeddings to the image embeddings
												_a								= image_embeddings.shape[0]
												_a								= self.learned_classifier_free_guidance_embeddings.unsqueeze(0					)
												_a								= classifier_free_guidance_embeddings.expand(
												    __a					,				-1					)
												_a								= torch.cat([classifier_free_guidance_embeddings, image_embeddings]					,				dim=0					)

								# The image embeddings batch size and the text embeddings batch size are equal
								assert image_embeddings.shape[0] == prompt_embeds.shape[0]

								_a								= prompt_embeds.shape[0]

								# "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
								# adding CLIP embeddings to the existing timestep embedding, ...
								_a								= self.embedding_proj(__a					)
								_a								= self.clip_image_embeddings_project_to_time_embeddings(__a					)
								_a								= time_projected_image_embeddings + time_projected_prompt_embeds

								# ... and by projecting CLIP embeddings into four
								# extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
								_a								= self.clip_extra_context_tokens_proj(__a					)
								_a								= clip_extra_context_tokens.reshape(__a					,				-1					,				self.clip_extra_context_tokens					)
								_a								= clip_extra_context_tokens.permute(0					,				2					,				1					)

								_a								= self.encoder_hidden_states_proj(__a					)
								_a								= self.text_encoder_hidden_states_norm(__a					)
								_a								= torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]					,				dim=1					)

								return text_encoder_hidden_states, additive_clip_time_embeddings
