
from typing import Any, Dict, Optional

import torch
import torch.nn.functional as F
from torch import nn

from ..utils import maybe_allow_in_graph
from .activations import get_activation
from .attention_processor import Attention
from .embeddings import CombinedTimestepLabelEmbeddings



@maybe_allow_in_graph
class       UpperCamelCase_      (    nn.Module ):




							'''simple docstring'''

							def __init__(					self  ,    UpperCamelCase  ,    UpperCamelCase  ,    UpperCamelCase  ,    UpperCamelCase=0.0  ,    UpperCamelCase = None  ,    UpperCamelCase = "geglu"  ,    UpperCamelCase = None  ,    UpperCamelCase = False  ,    UpperCamelCase = False  ,    UpperCamelCase = False  ,    UpperCamelCase = False  ,    UpperCamelCase = True  ,    UpperCamelCase = "layer_norm"  ,    UpperCamelCase = False  ,    )			->						str:
										super().__init__()
										UpperCamelCase__     :		Union[str, Any]	 =					only_cross_attention

										UpperCamelCase__     :		Tuple	 =					(num_embeds_ada_norm is not None) and norm_type == 'ada_norm_zero'
										UpperCamelCase__     :		Any	 =					(num_embeds_ada_norm is not None) and norm_type == 'ada_norm'

										if norm_type in ("ada_norm", "ada_norm_zero") and num_embeds_ada_norm is None:
													raise ValueError(
													    F"""`norm_type` is set to {norm_type}, but `num_embeds_ada_norm` is not defined. Please make sure to"""
													    F""" define `num_embeds_ada_norm` if setting `norm_type` to {norm_type}.""")

										# Define 3 blocks. Each block has its own normalization layer.
										# 1. Self-Attn
										if self.use_ada_layer_norm:
													UpperCamelCase__     :		Optional[Any]	 =					AdaLayerNorm(_UpperCamelCase  ,    _UpperCamelCase)
										elif self.use_ada_layer_norm_zero:
													UpperCamelCase__     :		Union[str, Any]	 =					AdaLayerNormZero(_UpperCamelCase  ,    _UpperCamelCase)
										else:
													UpperCamelCase__     :		List[str]	 =					nn.LayerNorm(_UpperCamelCase  ,    elementwise_affine=_UpperCamelCase)
										UpperCamelCase__     :		List[str]	 =					Attention(
										    query_dim=_UpperCamelCase  ,    heads=_UpperCamelCase  ,    dim_head=_UpperCamelCase  ,    dropout=_UpperCamelCase  ,    bias=_UpperCamelCase  ,    cross_attention_dim=cross_attention_dim if only_cross_attention else None  ,    upcast_attention=_UpperCamelCase  ,    )

										# 2. Cross-Attn
										if cross_attention_dim is not None or double_self_attention:
													# We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
													# I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
													# the second cross attention block.
													UpperCamelCase__     :		Optional[int]	 =					(
													    AdaLayerNorm(_UpperCamelCase  ,    _UpperCamelCase)
													    if self.use_ada_layer_norm
													    else nn.LayerNorm(_UpperCamelCase  ,    elementwise_affine=_UpperCamelCase)
													)
													UpperCamelCase__     :		Tuple	 =					Attention(
													    query_dim=_UpperCamelCase  ,    cross_attention_dim=cross_attention_dim if not double_self_attention else None  ,    heads=_UpperCamelCase  ,    dim_head=_UpperCamelCase  ,    dropout=_UpperCamelCase  ,    bias=_UpperCamelCase  ,    upcast_attention=_UpperCamelCase  ,    )  # is self-attn if encoder_hidden_states is none
										else:
													UpperCamelCase__     :		Dict	 =					None
													UpperCamelCase__     :		Tuple	 =					None

										# 3. Feed-forward
										UpperCamelCase__     :		List[str]	 =					nn.LayerNorm(_UpperCamelCase  ,    elementwise_affine=_UpperCamelCase)
										UpperCamelCase__     :		Dict	 =					FeedForward(_UpperCamelCase  ,    dropout=_UpperCamelCase  ,    activation_fn=_UpperCamelCase  ,    final_dropout=_UpperCamelCase)

										# let chunk size default to None
										UpperCamelCase__     :		str	 =					None
										UpperCamelCase__     :		Optional[int]	 =					0
							def 							lowerCAmelCase__			(					self  ,    UpperCamelCase  ,    UpperCamelCase)			->						Tuple:
										UpperCamelCase__     :		str	 =					chunk_size
										UpperCamelCase__     :		Dict	 =					dim






							def 							lowerCAmelCase__			(					self  ,    UpperCamelCase  ,    UpperCamelCase = None  ,    UpperCamelCase = None  ,    UpperCamelCase = None  ,    UpperCamelCase = None  ,    UpperCamelCase = None  ,    UpperCamelCase = None  ,    )			->						str:


										if self.use_ada_layer_norm:
													UpperCamelCase__     :		List[Any]	 =					self.norma(_UpperCamelCase  ,    _UpperCamelCase)
										elif self.use_ada_layer_norm_zero:
													UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__     :		Union[str, Any]	 =					self.norma(
													    _UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase  ,    hidden_dtype=hidden_states.dtype)
										else:
													UpperCamelCase__     :		Union[str, Any]	 =					self.norma(_UpperCamelCase)

										UpperCamelCase__     :		Dict	 =					cross_attention_kwargs if cross_attention_kwargs is not None else {}

										UpperCamelCase__     :		Optional[int]	 =					self.attna(
										    _UpperCamelCase  ,    encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None  ,    attention_mask=_UpperCamelCase  ,    **_UpperCamelCase  ,    )
										if self.use_ada_layer_norm_zero:
													UpperCamelCase__     :		List[Any]	 =					gate_msa.unsqueeze(1) * attn_output
										UpperCamelCase__     :		Dict	 =					attn_output + hidden_states

										# 2. Cross-Attention
										if self.attna is not None:
													UpperCamelCase__     :		Dict	 =					(
													    self.norma(_UpperCamelCase  ,    _UpperCamelCase) if self.use_ada_layer_norm else self.norma(_UpperCamelCase)
													)

													UpperCamelCase__     :		List[Any]	 =					self.attna(
													    _UpperCamelCase  ,    encoder_hidden_states=_UpperCamelCase  ,    attention_mask=_UpperCamelCase  ,    **_UpperCamelCase  ,    )
													UpperCamelCase__     :		List[Any]	 =					attn_output + hidden_states

										# 3. Feed-forward
										UpperCamelCase__     :		str	 =					self.norma(_UpperCamelCase)

										if self.use_ada_layer_norm_zero:
													UpperCamelCase__     :		int	 =					norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]

										if self._chunk_size is not None:
													# "feed_forward_chunk_size" can be used to save memory
													if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0:
																raise ValueError(
																    F"""`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`.""")

													UpperCamelCase__     :		Optional[int]	 =					norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
													UpperCamelCase__     :		Optional[int]	 =					torch.cat(
													    [self.ff(_UpperCamelCase) for hid_slice in norm_hidden_states.chunk(_UpperCamelCase  ,    dim=self._chunk_dim)]  ,    dim=self._chunk_dim  ,    )
										else:
													UpperCamelCase__     :		Tuple	 =					self.ff(_UpperCamelCase)

										if self.use_ada_layer_norm_zero:
													UpperCamelCase__     :		Dict	 =					gate_mlp.unsqueeze(1) * ff_output

										UpperCamelCase__     :		Optional[Any]	 =					ff_output + hidden_states

										return hidden_states



class       UpperCamelCase_      (    nn.Module ):




							'''simple docstring'''

							def __init__(					self  ,    UpperCamelCase  ,    UpperCamelCase = None  ,    UpperCamelCase = 4  ,    UpperCamelCase = 0.0  ,    UpperCamelCase = "geglu"  ,    UpperCamelCase = False  ,    )			->						int:
										super().__init__()
										UpperCamelCase__     :		Optional[Any]	 =					int(dim * mult)
										UpperCamelCase__     :		List[str]	 =					dim_out if dim_out is not None else dim

										if activation_fn == "gelu":
													UpperCamelCase__     :		List[Any]	 =					GELU(_UpperCamelCase  ,    _UpperCamelCase)
										if activation_fn == "gelu-approximate":
													UpperCamelCase__     :		Union[str, Any]	 =					GELU(_UpperCamelCase  ,    _UpperCamelCase  ,    approximate='tanh')
										elif activation_fn == "geglu":
													UpperCamelCase__     :		Union[str, Any]	 =					GEGLU(_UpperCamelCase  ,    _UpperCamelCase)
										elif activation_fn == "geglu-approximate":
													UpperCamelCase__     :		List[Any]	 =					ApproximateGELU(_UpperCamelCase  ,    _UpperCamelCase)

										UpperCamelCase__     :		Optional[int]	 =					nn.ModuleList([])
										# project in
										self.net.append(_UpperCamelCase)
										# project dropout
										self.net.append(nn.Dropout(_UpperCamelCase))
										# project out
										self.net.append(nn.Linear(_UpperCamelCase  ,    _UpperCamelCase))
										# FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout
										if final_dropout:
													self.net.append(nn.Dropout(_UpperCamelCase))






							def 							lowerCAmelCase__			(					self  ,    UpperCamelCase)			->						Any:


										for module in self.net:
													UpperCamelCase__     :		Optional[int]	 =					module(_UpperCamelCase)
										return hidden_states



class       UpperCamelCase_      (    nn.Module ):




							'''simple docstring'''

							def __init__(					self  ,    UpperCamelCase  ,    UpperCamelCase  ,    UpperCamelCase = "none")			->						Dict:
										super().__init__()
										UpperCamelCase__     :		str	 =					nn.Linear(_UpperCamelCase  ,    _UpperCamelCase)
										UpperCamelCase__     :		List[str]	 =					approximate
							def 							lowerCAmelCase__			(					self  ,    UpperCamelCase)			->						Any:


										if gate.device.type != "mps":
													return F.gelu(_UpperCamelCase  ,    approximate=self.approximate)
										# mps: gelu is not implemented for float16
										return F.gelu(gate.to(dtype=torch.floataa)  ,    approximate=self.approximate).to(dtype=gate.dtype)






							def 							lowerCAmelCase__			(					self  ,    UpperCamelCase)			->						Dict:
										UpperCamelCase__     :		str	 =					self.proj(_UpperCamelCase)
										UpperCamelCase__     :		List[str]	 =					self.gelu(_UpperCamelCase)
										return hidden_states



class       UpperCamelCase_      (    nn.Module ):




							'''simple docstring'''

							def __init__(					self  ,    UpperCamelCase  ,    UpperCamelCase)			->						List[Any]:
										super().__init__()
										UpperCamelCase__     :		Tuple	 =					nn.Linear(_UpperCamelCase  ,    dim_out * 2)
							def 							lowerCAmelCase__			(					self  ,    UpperCamelCase)			->						str:


										if gate.device.type != "mps":
													return F.gelu(_UpperCamelCase)
										# mps: gelu is not implemented for float16
										return F.gelu(gate.to(dtype=torch.floataa)).to(dtype=gate.dtype)






							def 							lowerCAmelCase__			(					self  ,    UpperCamelCase)			->						int:
										UpperCamelCase__ ,      UpperCamelCase__     :		Optional[Any]	 =					self.proj(_UpperCamelCase).chunk(2  ,    dim=-1)
										return hidden_states * self.gelu(_UpperCamelCase)



class       UpperCamelCase_      (    nn.Module ):




							'''simple docstring'''

							def __init__(					self  ,    UpperCamelCase  ,    UpperCamelCase)			->						int:
										super().__init__()
										UpperCamelCase__     :		List[Any]	 =					nn.Linear(_UpperCamelCase  ,    _UpperCamelCase)






							def 							lowerCAmelCase__			(					self  ,    UpperCamelCase)			->						List[Any]:
										UpperCamelCase__     :		List[Any]	 =					self.proj(_UpperCamelCase)
										return x * torch.sigmoid(1.702 * x)



class       UpperCamelCase_      (    nn.Module ):




							'''simple docstring'''

							def __init__(					self  ,    UpperCamelCase  ,    UpperCamelCase)			->						Dict:
										super().__init__()
										UpperCamelCase__     :		int	 =					nn.Embedding(_UpperCamelCase  ,    _UpperCamelCase)
										UpperCamelCase__     :		List[Any]	 =					nn.SiLU()
										UpperCamelCase__     :		str	 =					nn.Linear(_UpperCamelCase  ,    embedding_dim * 2)
										UpperCamelCase__     :		str	 =					nn.LayerNorm(_UpperCamelCase  ,    elementwise_affine=_UpperCamelCase)






							def 							lowerCAmelCase__			(					self  ,    UpperCamelCase  ,    UpperCamelCase)			->						Optional[int]:
										UpperCamelCase__     :		Optional[int]	 =					self.linear(self.silu(self.emb(_UpperCamelCase)))
										UpperCamelCase__ ,      UpperCamelCase__     :		List[str]	 =					torch.chunk(_UpperCamelCase  ,    2)
										UpperCamelCase__     :		str	 =					self.norm(_UpperCamelCase) * (1 + scale) + shift
										return x



class       UpperCamelCase_      (    nn.Module ):




							'''simple docstring'''

							def __init__(					self  ,    UpperCamelCase  ,    UpperCamelCase)			->						List[str]:
										super().__init__()

										UpperCamelCase__     :		Optional[int]	 =					CombinedTimestepLabelEmbeddings(_UpperCamelCase  ,    _UpperCamelCase)

										UpperCamelCase__     :		Optional[int]	 =					nn.SiLU()
										UpperCamelCase__     :		str	 =					nn.Linear(_UpperCamelCase  ,    6 * embedding_dim  ,    bias=_UpperCamelCase)
										UpperCamelCase__     :		Optional[Any]	 =					nn.LayerNorm(_UpperCamelCase  ,    elementwise_affine=_UpperCamelCase  ,    eps=1E-6)






							def 							lowerCAmelCase__			(					self  ,    UpperCamelCase  ,    UpperCamelCase  ,    UpperCamelCase  ,    UpperCamelCase=None)			->						Optional[Any]:
										UpperCamelCase__     :		int	 =					self.linear(self.silu(self.emb(_UpperCamelCase  ,    _UpperCamelCase  ,    hidden_dtype=_UpperCamelCase)))
										UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__     :		Union[str, Any]	 =					emb.chunk(6  ,    dim=1)
										UpperCamelCase__     :		int	 =					self.norm(_UpperCamelCase) * (1 + scale_msa[:, None]) + shift_msa[:, None]
										return x, gate_msa, shift_mlp, scale_mlp, gate_mlp



class       UpperCamelCase_      (    nn.Module ):




							'''simple docstring'''

							def __init__(					self  ,    UpperCamelCase  ,    UpperCamelCase  ,    UpperCamelCase  ,    UpperCamelCase = None  ,    UpperCamelCase = 1E-5)			->						List[str]:
										super().__init__()
										UpperCamelCase__     :		Dict	 =					num_groups
										UpperCamelCase__     :		str	 =					eps

										if act_fn is None:
													UpperCamelCase__     :		Union[str, Any]	 =					None
										else:
													UpperCamelCase__     :		Tuple	 =					get_activation(_UpperCamelCase)

										UpperCamelCase__     :		Optional[int]	 =					nn.Linear(_UpperCamelCase  ,    out_dim * 2)






							def 							lowerCAmelCase__			(					self  ,    UpperCamelCase  ,    UpperCamelCase)			->						Optional[Any]:


										if self.act:
													UpperCamelCase__     :		int	 =					self.act(_UpperCamelCase)
										UpperCamelCase__     :		Union[str, Any]	 =					self.linear(_UpperCamelCase)
										UpperCamelCase__     :		str	 =					emb[:, :, None, None]
										UpperCamelCase__ ,      UpperCamelCase__     :		Dict	 =					emb.chunk(2  ,    dim=1)

										UpperCamelCase__     :		List[Any]	 =					F.group_norm(_UpperCamelCase  ,    self.num_groups  ,    eps=self.eps)
										UpperCamelCase__     :		List[str]	 =					x * (1 + scale) + shift
										return x






import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

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


UpperCamelCase__		 =     logging.get_logger(__name__)

UpperCamelCase__		 =     '''▁'''

UpperCamelCase__		 =     {
    '''vocab_file''': '''vocab.json''',
    '''spm_file''': '''sentencepiece.bpe.model''',
}

UpperCamelCase__		 =     {
    '''vocab_file''': {
        '''facebook/s2t-small-librispeech-asr''': (
            '''https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/vocab.json'''
        ),
    },
    '''spm_file''': {
        '''facebook/s2t-small-librispeech-asr''': (
            '''https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/sentencepiece.bpe.model'''
        )
    },
}

UpperCamelCase__		 =     {
    '''facebook/s2t-small-librispeech-asr''': 1024,
}

UpperCamelCase__		 =     ['''pt''', '''fr''', '''ru''', '''nl''', '''ro''', '''it''', '''es''', '''de''']

UpperCamelCase__		 =     {'''mustc''': MUSTC_LANGS}

class     __snake_case    (				snake_case__  ):
       """simple docstring"""







       __SCREAMING_SNAKE_CASE				    =							VOCAB_FILES_NAMES
       __SCREAMING_SNAKE_CASE				    =							PRETRAINED_VOCAB_FILES_MAP
       __SCREAMING_SNAKE_CASE				    =							MAX_MODEL_INPUT_SIZES
       __SCREAMING_SNAKE_CASE				    =							["input_ids", "attention_mask"]

       __SCREAMING_SNAKE_CASE				    =							[]


       def __init__(		self      , _UpperCamelCase      , _UpperCamelCase      , _UpperCamelCase="<s>"      , _UpperCamelCase="</s>"      , _UpperCamelCase="<pad>"      , _UpperCamelCase="<unk>"      , _UpperCamelCase=False      , _UpperCamelCase=False      , _UpperCamelCase=None      , _UpperCamelCase=None      , _UpperCamelCase = None      , **_UpperCamelCase      , )      ->							None:






              """simple docstring"""


              __snake_case       	=							{} if sp_model_kwargs is None else sp_model_kwargs

              super().__init__(
                  bos_token=_UpperCamelCase      , eos_token=_UpperCamelCase      , unk_token=_UpperCamelCase      , pad_token=_UpperCamelCase      , do_upper_case=_UpperCamelCase      , do_lower_case=_UpperCamelCase      , tgt_lang=_UpperCamelCase      , lang_codes=_UpperCamelCase      , sp_model_kwargs=self.sp_model_kwargs      , **_UpperCamelCase      , )
              __snake_case       	=							do_upper_case
              __snake_case       	=							do_lower_case

              __snake_case       	=							load_json(_UpperCamelCase   )
              __snake_case       	=							{v: k for k, v in self.encoder.items()}
              __snake_case       	=							spm_file
              __snake_case       	=							load_spm(_UpperCamelCase      , self.sp_model_kwargs   )

              if lang_codes is not None:
                     __snake_case       	=							lang_codes
                     __snake_case       	=							LANGUAGES[lang_codes]
                     __snake_case       	=							[F'<lang:{lang}>' for lang in self.langs]
                     __snake_case       	=							{lang: self.sp_model.PieceToId(F'<lang:{lang}>'   ) for lang in self.langs}

                     __snake_case       	=							self.lang_tokens
                     __snake_case       	=							tgt_lang if tgt_lang is not None else self.langs[0]

                     self.set_tgt_lang_special_tokens(self._tgt_lang   )
              else:
                     __snake_case       	=							{}


       @property
       def 			a		(		self   )      ->							int:






              """simple docstring"""


              return len(self.encoder   )


       @property
       def 			a		(		self   )      ->							str:






              """simple docstring"""


              return self._tgt_lang


       @tgt_lang.setter
       def 			a		(		self      , _UpperCamelCase   )      ->							None:






              """simple docstring"""


              __snake_case       	=							new_tgt_lang
              self.set_tgt_lang_special_tokens(_UpperCamelCase   )


       def 			a		(		self      , _UpperCamelCase   )      ->							None:






              """simple docstring"""


              __snake_case       	=							self.lang_code_to_id[tgt_lang]
              __snake_case       	=							[lang_code_id]


       def 			a		(		self      , _UpperCamelCase   )      ->							List[str]:






              """simple docstring"""


              return self.sp_model.encode(_UpperCamelCase      , out_type=_UpperCamelCase   )


       def 			a		(		self      , _UpperCamelCase   )      ->							Optional[Any]:






              """simple docstring"""


              return self.encoder.get(_UpperCamelCase      , self.encoder[self.unk_token]   )


       def 			a		(		self      , _UpperCamelCase   )      ->							str:






              """simple docstring"""


              return self.decoder.get(_UpperCamelCase      , self.unk_token   )


       def 			a		(		self      , _UpperCamelCase   )      ->							str:






              """simple docstring"""


              __snake_case       	=							[]
              __snake_case       	=							""""""
              for token in tokens:
                     # make sure that special tokens are not decoded using sentencepiece model
                     if token in self.all_special_tokens:
                            __snake_case       	=							self.sp_model.decode(_UpperCamelCase   )
                            out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
                            __snake_case       	=							[]
                     else:
                            current_sub_tokens.append(_UpperCamelCase   )
              __snake_case       	=							self.sp_model.decode(_UpperCamelCase   )
              out_string += decoded.upper() if self.do_upper_case else decoded
              return out_string.strip()


       def 			a		(		self      , _UpperCamelCase      , _UpperCamelCase=None   )      ->							List[int]:






              """simple docstring"""


              if token_ids_a is None:
                     return self.prefix_tokens + token_ids_a + [self.eos_token_id]
              # 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.eos_token_id]


       def 			a		(		self      , _UpperCamelCase      , _UpperCamelCase = None      , _UpperCamelCase = False   )      ->							List[int]:






              """simple docstring"""


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

              __snake_case       	=							[1] * len(self.prefix_tokens   )
              __snake_case       	=							[1]
              if token_ids_a is None:
                     return prefix_ones + ([0] * len(_UpperCamelCase   )) + suffix_ones
              return prefix_ones + ([0] * len(_UpperCamelCase   )) + ([0] * len(_UpperCamelCase   )) + suffix_ones


       def 			a		(		self   )      ->							Dict:






              """simple docstring"""


              __snake_case       	=							self.encoder.copy()
              vocab.update(self.added_tokens_encoder   )
              return vocab


       def __getstate__(		self   )      ->							Dict:






              """simple docstring"""


              __snake_case       	=							self.__dict__.copy()
              __snake_case       	=							None
              return state


       def __setstate__(		self      , _UpperCamelCase   )      ->							None:






              """simple docstring"""


              __snake_case       	=							d

              # for backward compatibility
              if not hasattr(self      , """sp_model_kwargs"""   ):
                     __snake_case       	=							{}

              __snake_case       	=							load_spm(self.spm_file      , self.sp_model_kwargs   )






       def 			a		(		self      , _UpperCamelCase      , _UpperCamelCase = None   )      ->							Tuple[str]:






              """simple docstring"""


              __snake_case       	=							Path(_UpperCamelCase   )
              assert save_dir.is_dir(), F'{save_directory} should be a directory'
              __snake_case       	=							save_dir / (
                  (filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""vocab_file"""]
              )
              __snake_case       	=							save_dir / (
                  (filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""spm_file"""]
              )

              save_json(self.encoder      , _UpperCamelCase   )

              if os.path.abspath(self.spm_file   ) != os.path.abspath(_UpperCamelCase   ) and os.path.isfile(self.spm_file   ):
                     copyfile(self.spm_file      , _UpperCamelCase   )
              elif not os.path.isfile(self.spm_file   ):
                     with open(_UpperCamelCase      , """wb"""   ) as fi:
                            __snake_case       	=							self.sp_model.serialized_model_proto()
                            fi.write(_UpperCamelCase   )

              return (str(_UpperCamelCase   ), str(_UpperCamelCase   ))







def 						lowerCamelCase__      ( __A    :str	,__A    :Dict[str, Any]		):


       """simple docstring"""




       __snake_case       	=							sentencepiece.SentencePieceProcessor(**__A		)
       spm.Load(str(__A		)		)
       return spm







def 						lowerCamelCase__      ( __A    :str		):


       """simple docstring"""




       with open(__A	,"""r"""		) as f:
              return json.load(__A		)







def 						lowerCamelCase__      ( __A    :Optional[Any]	,__A    :str		):


       """simple docstring"""




       with open(__A	,"""w"""		) as f:
              json.dump(__A	,__A	,indent=2		)

