





'''simple docstring'''


import collections.abc
from typing import Optional, Tuple, Union

import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_poolformer import PoolFormerConfig


lowerCAmelCase					:     List[Any]												=logging.get_logger(__name__)

# General docstring
lowerCAmelCase					:     Union[str, Any]												='''PoolFormerConfig'''

# Base docstring
lowerCAmelCase					:     Union[str, Any]												='''sail/poolformer_s12'''
lowerCAmelCase					:     str												=[1, 512, 7, 7]

# Image classification docstring
lowerCAmelCase					:     Any												='''sail/poolformer_s12'''
lowerCAmelCase					:     Optional[Any]												='''tabby, tabby cat'''

lowerCAmelCase					:     str												=[
    '''sail/poolformer_s12''',
    # See all PoolFormer models at https://huggingface.co/models?filter=poolformer
]
def 							UpperCAmelCase_  (						__lowerCamelCase					:					int	,__lowerCamelCase					:					float = 0.0	,__lowerCamelCase					:					bool = False ):
   if drop_prob == 0.0 or not training:
      return input
   lowercase_			:Optional[int]    			= 1 - drop_prob
   lowercase_			:int    			= (input.shape[0],) + (1,) * (input.ndim - 1)  # work with diff dim tensors, not just 2D ConvNets
   lowercase_			:Any    			= keep_prob + torch.rand(__lowerCamelCase	,dtype=input.dtype	,device=input.device )
   random_tensor.floor_()  # binarize
   lowercase_			:List[str]    			= input.div(__lowerCamelCase ) * random_tensor
   return output
class       a_   (				nn.Module							):





     def __init__( self		:					Tuple	,      lowercase		:					Optional[float] = None						):





        """simple docstring"""




        super().__init__()
        lowercase_			:List[Any]    			= drop_prob





     def 							lowercase__      ( self		:					Dict	,      lowercase		:					torch.Tensor						):





        """simple docstring"""




        return drop_path(lowercase	,      self.drop_prob	,      self.training						)







     def 							lowercase__      ( self		:					int						):





        """simple docstring"""




        return "p={}".format(self.drop_prob						)







class       a_   (				nn.Module							):





     def __init__( self		:					Any	,      lowercase		:					Optional[int]	,      lowercase		:					int	,      lowercase		:					Dict	,      lowercase		:					int	,      lowercase		:					Union[str, Any]	,      lowercase		:					int=None						):





        """simple docstring"""




        super().__init__()
        lowercase_			:Optional[Any]    			= patch_size if isinstance(lowercase	,      collections.abc.Iterable						) else (patch_size, patch_size)
        lowercase_			:List[Any]    			= stride if isinstance(lowercase	,      collections.abc.Iterable						) else (stride, stride)
        lowercase_			:List[Any]    			= padding if isinstance(lowercase	,      collections.abc.Iterable						) else (padding, padding)

        lowercase_			:Optional[Any]    			= nn.Convad(lowercase	,      lowercase	,      kernel_size=lowercase	,      stride=lowercase	,      padding=lowercase						)
        lowercase_			:Tuple    			= norm_layer(lowercase						) if norm_layer else nn.Identity()







     def 							lowercase__      ( self		:					List[Any]	,      lowercase		:					Union[str, Any]						):





        """simple docstring"""




        lowercase_			:Optional[Any]    			= self.projection(lowercase						)
        lowercase_			:Any    			= self.norm(lowercase						)
        return embeddings







class       a_   (				nn.GroupNorm							):





     def __init__( self		:					int	,      lowercase		:					Optional[Any]	,      **lowercase		:					List[Any]						):





        """simple docstring"""




        super().__init__(1	,      lowercase	,      **lowercase						)







class       a_   (				nn.Module							):





     def __init__( self		:					str	,      lowercase		:					List[Any]						):





        """simple docstring"""




        super().__init__()
        lowercase_			:Union[str, Any]    			= nn.AvgPoolad(lowercase	,      stride=1	,      padding=pool_size // 2	,      count_include_pad=lowercase						)







     def 							lowercase__      ( self		:					int	,      lowercase		:					List[Any]						):





        """simple docstring"""




        return self.pool(lowercase						) - hidden_states







class       a_   (				nn.Module							):





     def __init__( self		:					str	,      lowercase		:					Any	,      lowercase		:					Tuple	,      lowercase		:					List[Any]	,      lowercase		:					Union[str, Any]						):





        """simple docstring"""




        super().__init__()
        lowercase_			:List[str]    			= nn.Convad(lowercase	,      lowercase	,      1						)
        lowercase_			:str    			= nn.Convad(lowercase	,      lowercase	,      1						)
        lowercase_			:Optional[int]    			= PoolFormerDropPath(lowercase						)
        if isinstance(config.hidden_act	,      lowercase						):
           lowercase_			:Dict    			= ACTaFN[config.hidden_act]
        else:
           lowercase_			:List[str]    			= config.hidden_act







     def 							lowercase__      ( self		:					Optional[Any]	,      lowercase		:					Tuple						):





        """simple docstring"""




        lowercase_			:Union[str, Any]    			= self.conva(lowercase						)
        lowercase_			:Tuple    			= self.act_fn(lowercase						)
        lowercase_			:Dict    			= self.drop(lowercase						)
        lowercase_			:Any    			= self.conva(lowercase						)
        lowercase_			:List[str]    			= self.drop(lowercase						)

        return hidden_states







class       a_   (				nn.Module							):





     def __init__( self		:					Optional[Any]	,      lowercase		:					Union[str, Any]	,      lowercase		:					str	,      lowercase		:					Tuple	,      lowercase		:					Any	,      lowercase		:					List[Any]	,      lowercase		:					Any						):





        """simple docstring"""




        super().__init__()
        lowercase_			:Any    			= PoolFormerPooling(lowercase						)
        lowercase_			:Any    			= PoolFormerOutput(lowercase	,      lowercase	,      lowercase	,      lowercase						)
        lowercase_			:Union[str, Any]    			= PoolFormerGroupNorm(lowercase						)
        lowercase_			:Any    			= PoolFormerGroupNorm(lowercase						)

        # Useful for training neural nets
        lowercase_			:str    			= PoolFormerDropPath(lowercase						) if drop_path > 0.0 else nn.Identity()
        lowercase_			:str    			= config.use_layer_scale
        if config.use_layer_scale:
           lowercase_			:Dict    			= nn.Parameter(
               config.layer_scale_init_value * torch.ones((lowercase)						)	,      requires_grad=lowercase						)
           lowercase_			:Dict    			= nn.Parameter(
               config.layer_scale_init_value * torch.ones((lowercase)						)	,      requires_grad=lowercase						)







     def 							lowercase__      ( self		:					str	,      lowercase		:					Tuple						):





        """simple docstring"""




        if self.use_layer_scale:
           lowercase_			:Any    			= self.pooling(self.before_norm(lowercase						)						)
           lowercase_			:List[Any]    			= self.layer_scale_a.unsqueeze(-1						).unsqueeze(-1						) * pooling_output
           # First residual connection
           lowercase_			:Dict    			= hidden_states + self.drop_path(lowercase						)
           lowercase_			:str    			= ()

           lowercase_			:Tuple    			= self.output(self.after_norm(lowercase						)						)
           lowercase_			:List[str]    			= self.layer_scale_a.unsqueeze(-1						).unsqueeze(-1						) * layer_output
           # Second residual connection
           lowercase_			:int    			= hidden_states + self.drop_path(lowercase						)

           lowercase_			:List[str]    			= (output,) + outputs
           return outputs

        else:
           lowercase_			:Tuple    			= self.drop_path(self.pooling(self.before_norm(lowercase						)						)						)
           # First residual connection
           lowercase_			:List[Any]    			= pooling_output + hidden_states
           lowercase_			:Union[str, Any]    			= ()

           # Second residual connection inside the PoolFormerOutput block
           lowercase_			:Tuple    			= self.drop_path(self.output(self.after_norm(lowercase						)						)						)
           lowercase_			:Optional[Any]    			= hidden_states + layer_output

           lowercase_			:List[str]    			= (output,) + outputs
           return outputs







class       a_   (				nn.Module							):





     def __init__( self		:					Union[str, Any]	,      lowercase		:					int						):





        """simple docstring"""




        super().__init__()
        lowercase_			:str    			= config
        # stochastic depth decay rule
        lowercase_			:int    			= [x.item() for x in torch.linspace(0	,      config.drop_path_rate	,      sum(config.depths						)						)]

        # patch embeddings
        lowercase_			:Dict    			= []
        for i in range(config.num_encoder_blocks						):
           embeddings.append(
               PoolFormerEmbeddings(
                   patch_size=config.patch_sizes[i]	,      stride=config.strides[i]	,      padding=config.padding[i]	,      num_channels=config.num_channels if i == 0 else config.hidden_sizes[i - 1]	,      hidden_size=config.hidden_sizes[i]	,      )						)
        lowercase_			:Union[str, Any]    			= nn.ModuleList(lowercase						)

        # Transformer blocks
        lowercase_			:List[str]    			= []
        lowercase_			:List[Any]    			= 0
        for i in range(config.num_encoder_blocks						):
           # each block consists of layers
           lowercase_			:List[Any]    			= []
           if i != 0:
              cur += config.depths[i - 1]
           for j in range(config.depths[i]						):
              layers.append(
                  PoolFormerLayer(
                      lowercase	,      num_channels=config.hidden_sizes[i]	,      pool_size=config.pool_size	,      hidden_size=config.hidden_sizes[i]	,      intermediate_size=int(config.hidden_sizes[i] * config.mlp_ratio						)	,      drop_path=dpr[cur + j]	,      )						)
           blocks.append(nn.ModuleList(lowercase						)						)

        lowercase_			:int    			= nn.ModuleList(lowercase						)







     def 							lowercase__      ( self		:					Tuple	,      lowercase		:					List[str]	,      lowercase		:					Tuple=False	,      lowercase		:					int=True						):





        """simple docstring"""




        lowercase_			:Optional[Any]    			= () if output_hidden_states else None

        lowercase_			:Optional[int]    			= pixel_values
        for idx, layers in enumerate(zip(self.patch_embeddings	,      self.block						)						):
           lowercase_			,							lowercase_			:Optional[int]    			= layers
           # Get patch embeddings from hidden_states
           lowercase_			:Any    			= embedding_layer(lowercase						)
           # Send the embeddings through the blocks
           for _, blk in enumerate(lowercase						):
              lowercase_			:int    			= blk(lowercase						)
              lowercase_			:Union[str, Any]    			= layer_outputs[0]

           if output_hidden_states:
              lowercase_			:Tuple    			= all_hidden_states + (hidden_states,)

        if not return_dict:
           return tuple(v for v in [hidden_states, all_hidden_states] if v is not None						)

        return BaseModelOutputWithNoAttention(last_hidden_state=lowercase	,      hidden_states=lowercase						)







class       a_   (				_lowerCAmelCase							):
     __A      =			PoolFormerConfig
     __A      =			"poolformer"
     __A      =			"pixel_values"
     __A      =			True





     def 							lowercase__      ( self		:					str	,      lowercase		:					int						):





        """simple docstring"""




        if isinstance(lowercase	,      (nn.Linear, nn.Convad)						):
           module.weight.data.normal_(mean=0.0	,      std=self.config.initializer_range						)
           if module.bias is not None:
              module.bias.data.zero_()
        elif isinstance(lowercase	,      nn.LayerNorm						):
           module.bias.data.zero_()
           module.weight.data.fill_(1.0						)







     def 							lowercase__      ( self		:					str	,      lowercase		:					Any	,      lowercase		:					Optional[Any]=False						):





        """simple docstring"""




        if isinstance(lowercase	,      lowercase						):
           lowercase_			:List[Any]    			= value







lowerCAmelCase					:     Optional[int]												=r'''
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
    it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.

    Parameters:
        config ([`PoolFormerConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
'''

lowerCAmelCase					:     Union[str, Any]												=r'''
    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`PoolFormerImageProcessor.__call__`] for details.
'''
@add_start_docstrings(
    "The bare PoolFormer Model transformer outputting raw hidden-states without any specific head on top."	,						_lowerCAmelCase	,						)
class       a_   (				_lowerCAmelCase							):





     def __init__( self		:					Union[str, Any]	,      lowercase		:					Any						):





        """simple docstring"""




        super().__init__(lowercase						)
        lowercase_			:int    			= config

        lowercase_			:str    			= PoolFormerEncoder(lowercase						)

        # Initialize weights and apply final processing
        self.post_init()





     def 							lowercase__      ( self		:					Optional[int]						):





        """simple docstring"""




        return self.embeddings.patch_embeddings







     @add_start_docstrings_to_model_forward(lowercase						)
     @add_code_sample_docstrings(
         checkpoint=_CHECKPOINT_FOR_DOC	,      output_type=lowercase	,      config_class=_CONFIG_FOR_DOC	,      modality="vision"	,      expected_output=_EXPECTED_OUTPUT_SHAPE	,      )
     def 							lowercase__      ( self		:					int	,      lowercase		:					Optional[torch.FloatTensor] = None	,      lowercase		:					Optional[bool] = None	,      lowercase		:					Optional[bool] = None	,      ):





        """simple docstring"""




        lowercase_			:Tuple    			= (
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        lowercase_			:List[Any]    			= return_dict if return_dict is not None else self.config.use_return_dict

        if pixel_values is None:
           raise ValueError("You have to specify pixel_values"						)

        lowercase_			:List[Any]    			= self.encoder(
            lowercase	,      output_hidden_states=lowercase	,      return_dict=lowercase	,      )
        lowercase_			:Optional[int]    			= encoder_outputs[0]

        if not return_dict:
           return (sequence_output, None) + encoder_outputs[1:]

        return BaseModelOutputWithNoAttention(
            last_hidden_state=lowercase	,      hidden_states=encoder_outputs.hidden_states	,      )







class       a_   (				nn.Module							):





     def __init__( self		:					Tuple	,      lowercase		:					Any						):





        """simple docstring"""




        super().__init__()
        lowercase_			:Optional[Any]    			= nn.Linear(config.hidden_size	,      config.hidden_size						)







     def 							lowercase__      ( self		:					Tuple	,      lowercase		:					int						):





        """simple docstring"""




        lowercase_			:Any    			= self.dense(lowercase						)
        return output







@add_start_docstrings(
    "\n    PoolFormer Model transformer with an image classification head on top\n    "	,						_lowerCAmelCase	,						)
class       a_   (				_lowerCAmelCase							):





     def __init__( self		:					List[str]	,      lowercase		:					Union[str, Any]						):





        """simple docstring"""




        super().__init__(lowercase						)
        lowercase_			:Dict    			= config.num_labels
        lowercase_			:List[Any]    			= PoolFormerModel(lowercase						)

        # Final norm
        lowercase_			:Union[str, Any]    			= PoolFormerGroupNorm(config.hidden_sizes[-1]						)
        # Classifier head
        lowercase_			:List[Any]    			= (
            nn.Linear(config.hidden_sizes[-1]	,      config.num_labels						) if config.num_labels > 0 else nn.Identity()
        )

        # Initialize weights and apply final processing
        self.post_init()







     @add_start_docstrings_to_model_forward(lowercase						)
     @add_code_sample_docstrings(
         checkpoint=_IMAGE_CLASS_CHECKPOINT	,      output_type=lowercase	,      config_class=_CONFIG_FOR_DOC	,      expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT	,      )
     def 							lowercase__      ( self		:					int	,      lowercase		:					Optional[torch.FloatTensor] = None	,      lowercase		:					Optional[torch.LongTensor] = None	,      lowercase		:					Optional[bool] = None	,      lowercase		:					Optional[bool] = None	,      ):





        """simple docstring"""




        lowercase_			:Dict    			= return_dict if return_dict is not None else self.config.use_return_dict

        lowercase_			:Optional[Any]    			= self.poolformer(
            lowercase	,      output_hidden_states=lowercase	,      return_dict=lowercase	,      )

        lowercase_			:Optional[int]    			= outputs[0]

        lowercase_			:Tuple    			= self.classifier(self.norm(lowercase						).mean([-2, -1]						)						)

        lowercase_			:List[str]    			= None
        if labels is not None:
           if self.config.problem_type is None:
              if self.num_labels == 1:
                 lowercase_			:Union[str, Any]    			= "regression"
              elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
                 lowercase_			:int    			= "single_label_classification"
              else:
                 lowercase_			:Optional[Any]    			= "multi_label_classification"

           if self.config.problem_type == "regression":
              lowercase_			:List[Any]    			= MSELoss()
              if self.num_labels == 1:
                 lowercase_			:List[Any]    			= loss_fct(logits.squeeze()	,      labels.squeeze()						)
              else:
                 lowercase_			:Any    			= loss_fct(lowercase	,      lowercase						)
           elif self.config.problem_type == "single_label_classification":
              lowercase_			:List[Any]    			= CrossEntropyLoss()
              lowercase_			:List[Any]    			= loss_fct(logits.view(-1	,      self.num_labels						)	,      labels.view(-1						)						)
           elif self.config.problem_type == "multi_label_classification":
              lowercase_			:Optional[Any]    			= BCEWithLogitsLoss()
              lowercase_			:Optional[Any]    			= loss_fct(lowercase	,      lowercase						)

        if not return_dict:
           lowercase_			:Union[str, Any]    			= (logits,) + outputs[2:]
           return ((loss,) + output) if loss is not None else output

        return ImageClassifierOutputWithNoAttention(loss=lowercase	,      logits=lowercase	,      hidden_states=outputs.hidden_states						)









'''simple docstring'''


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 BatchEncoding, PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase					:     int												=logging.get_logger(__name__)

lowerCAmelCase					:     List[str]												='''▁'''

lowerCAmelCase					:     List[str]												={
    '''vocab_file''': '''vocab.json''',
    '''spm_file''': '''sentencepiece.bpe.model''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

lowerCAmelCase					:     Optional[Any]												={
    '''vocab_file''': {
        '''facebook/m2m100_418M''': '''https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json''',
        '''facebook/m2m100_1.2B''': '''https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json''',
    },
    '''spm_file''': {
        '''facebook/m2m100_418M''': '''https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model''',
        '''facebook/m2m100_1.2B''': '''https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model''',
    },
    '''tokenizer_config_file''': {
        '''facebook/m2m100_418M''': '''https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json''',
        '''facebook/m2m100_1.2B''': '''https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json''',
    },
}

lowerCAmelCase					:     int												={
    '''facebook/m2m100_418M''': 1_024,
}

# fmt: off
lowerCAmelCase					:     str												={
    '''m2m100''': ['''af''', '''am''', '''ar''', '''ast''', '''az''', '''ba''', '''be''', '''bg''', '''bn''', '''br''', '''bs''', '''ca''', '''ceb''', '''cs''', '''cy''', '''da''', '''de''', '''el''', '''en''', '''es''', '''et''', '''fa''', '''ff''', '''fi''', '''fr''', '''fy''', '''ga''', '''gd''', '''gl''', '''gu''', '''ha''', '''he''', '''hi''', '''hr''', '''ht''', '''hu''', '''hy''', '''id''', '''ig''', '''ilo''', '''is''', '''it''', '''ja''', '''jv''', '''ka''', '''kk''', '''km''', '''kn''', '''ko''', '''lb''', '''lg''', '''ln''', '''lo''', '''lt''', '''lv''', '''mg''', '''mk''', '''ml''', '''mn''', '''mr''', '''ms''', '''my''', '''ne''', '''nl''', '''no''', '''ns''', '''oc''', '''or''', '''pa''', '''pl''', '''ps''', '''pt''', '''ro''', '''ru''', '''sd''', '''si''', '''sk''', '''sl''', '''so''', '''sq''', '''sr''', '''ss''', '''su''', '''sv''', '''sw''', '''ta''', '''th''', '''tl''', '''tn''', '''tr''', '''uk''', '''ur''', '''uz''', '''vi''', '''wo''', '''xh''', '''yi''', '''yo''', '''zh''', '''zu'''],
    '''wmt21''': ['''en''', '''ha''', '''is''', '''ja''', '''cs''', '''ru''', '''zh''', '''de''']
}
class       a_   (				_lowerCAmelCase							):
     __A      =			VOCAB_FILES_NAMES
     __A      =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __A      =			PRETRAINED_VOCAB_FILES_MAP
     __A      =			["input_ids", "attention_mask"]

     __A      =			[]
     __A      =			[]





     def __init__( self		:					Any	,      lowercase		:					Any	,      lowercase		:					List[Any]	,      lowercase		:					int=None	,      lowercase		:					Optional[Any]=None	,      lowercase		:					Union[str, Any]="<s>"	,      lowercase		:					Any="</s>"	,      lowercase		:					Optional[int]="</s>"	,      lowercase		:					List[Any]="<pad>"	,      lowercase		:					Optional[int]="<unk>"	,      lowercase		:					Optional[int]="m2m100"	,      lowercase		:					Optional[Dict[str, Any]] = None	,      lowercase		:					Any=8	,      **lowercase		:					int	,      ):





        """simple docstring"""




        lowercase_			:Optional[int]    			= {} if sp_model_kwargs is None else sp_model_kwargs

        lowercase_			:Optional[Any]    			= language_codes
        lowercase_			:Tuple    			= FAIRSEQ_LANGUAGE_CODES[language_codes]
        lowercase_			:List[Any]    			= {lang_code: F'__{lang_code}__' for lang_code in fairseq_language_code}

        lowercase_			:Union[str, Any]    			= kwargs.get("additional_special_tokens"	,      []						)
        kwargs["additional_special_tokens"] += [
            self.get_lang_token(lowercase						)
            for lang_code in fairseq_language_code
            if self.get_lang_token(lowercase						) not in kwargs["additional_special_tokens"]
        ]

        super().__init__(
            src_lang=lowercase	,      tgt_lang=lowercase	,      bos_token=lowercase	,      eos_token=lowercase	,      sep_token=lowercase	,      unk_token=lowercase	,      pad_token=lowercase	,      language_codes=lowercase	,      sp_model_kwargs=self.sp_model_kwargs	,      num_madeup_words=lowercase	,      **lowercase	,      )

        lowercase_			:Optional[int]    			= vocab_file
        lowercase_			:Any    			= load_json(lowercase						)
        lowercase_			:Optional[Any]    			= {v: k for k, v in self.encoder.items()}
        lowercase_			:List[str]    			= spm_file
        lowercase_			:List[str]    			= load_spm(lowercase	,      self.sp_model_kwargs						)

        lowercase_			:Optional[int]    			= len(self.encoder						)

        lowercase_			:int    			= {
            self.get_lang_token(lowercase						): self.encoder_size + i for i, lang_code in enumerate(lowercase						)
        }
        lowercase_			:List[Any]    			= {lang_code: self.encoder_size + i for i, lang_code in enumerate(lowercase						)}
        lowercase_			:List[Any]    			= {v: k for k, v in self.lang_token_to_id.items()}

        lowercase_			:int    			= src_lang if src_lang is not None else "en"
        lowercase_			:Union[str, Any]    			= tgt_lang
        lowercase_			:List[Any]    			= self.get_lang_id(self._src_lang						)
        self.set_src_lang_special_tokens(self._src_lang						)

        lowercase_			:int    			= num_madeup_words





     @property
     def 							lowercase__      ( self		:					List[str]						):





        """simple docstring"""




        return len(self.encoder						) + len(self.lang_token_to_id						)





     @property
     def 							lowercase__      ( self		:					Any						):





        """simple docstring"""




        return self._src_lang





     @src_lang.setter
     def 							lowercase__      ( self		:					Optional[int]	,      lowercase		:					str						):





        """simple docstring"""




        lowercase_			:str    			= new_src_lang
        self.set_src_lang_special_tokens(self._src_lang						)





     def 							lowercase__      ( self		:					Dict	,      lowercase		:					str						):





        """simple docstring"""




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





     def 							lowercase__      ( self		:					Tuple	,      lowercase		:					Dict						):





        """simple docstring"""




        if token in self.lang_token_to_id:
           return self.lang_token_to_id[token]
        return self.encoder.get(lowercase	,      self.encoder[self.unk_token]						)





     def 							lowercase__      ( self		:					Any	,      lowercase		:					int						):





        """simple docstring"""




        if index in self.id_to_lang_token:
           return self.id_to_lang_token[index]
        return self.decoder.get(lowercase	,      self.unk_token						)





     def 							lowercase__      ( self		:					int	,      lowercase		:					int						):





        """simple docstring"""




        lowercase_			:Optional[Any]    			= []
        lowercase_			:Any    			= ""
        for token in tokens:
           # make sure that special tokens are not decoded using sentencepiece model
           if token in self.all_special_tokens:
              out_string += self.sp_model.decode(lowercase						) + token
              lowercase_			:str    			= []
           else:
              current_sub_tokens.append(lowercase						)
        out_string += self.sp_model.decode(lowercase						)
        return out_string.strip()





     def 							lowercase__      ( self		:					Any	,      lowercase		:					List[int]	,      lowercase		:					Optional[List[int]] = None	,      lowercase		:					bool = False						):





        """simple docstring"""




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

        lowercase_			:List[Any]    			= [1] * len(self.prefix_tokens						)
        lowercase_			:List[Any]    			= [1] * len(self.suffix_tokens						)
        if token_ids_a is None:
           return prefix_ones + ([0] * len(lowercase						)) + suffix_ones
        return prefix_ones + ([0] * len(lowercase						)) + ([0] * len(lowercase						)) + suffix_ones





     def 							lowercase__      ( self		:					Union[str, Any]	,      lowercase		:					List[int]	,      lowercase		:					Optional[List[int]] = None						):





        """simple docstring"""




        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 							lowercase__      ( self		:					Union[str, Any]						):





        """simple docstring"""




        lowercase_			:str    			= {self.convert_ids_to_tokens(lowercase						): i for i in range(self.vocab_size						)}
        vocab.update(self.added_tokens_encoder						)
        return vocab





     def __getstate__( self		:					str						):





        """simple docstring"""




        lowercase_			:Any    			= self.__dict__.copy()
        lowercase_			:str    			= None
        return state





     def __setstate__( self		:					Tuple	,      lowercase		:					Dict						):





        """simple docstring"""




        lowercase_			:int    			= d

        # for backward compatibility
        if not hasattr(self	,      "sp_model_kwargs"						):
           lowercase_			:List[str]    			= {}

        lowercase_			:List[Any]    			= load_spm(self.spm_file	,      self.sp_model_kwargs						)





     def 							lowercase__      ( self		:					str	,      lowercase		:					str	,      lowercase		:					Optional[str] = None						):





        """simple docstring"""




        lowercase_			:Dict    			= Path(lowercase						)
        if not save_dir.is_dir():
           raise OSError(F'{save_directory} should be a directory'						)
        lowercase_			:Dict    			= save_dir / (
            (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["vocab_file"]
        )
        lowercase_			:Dict    			= save_dir / (
            (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["spm_file"]
        )

        save_json(self.encoder	,      lowercase						)

        if os.path.abspath(self.spm_file						) != os.path.abspath(lowercase						) and os.path.isfile(self.spm_file						):
           copyfile(self.spm_file	,      lowercase						)
        elif not os.path.isfile(self.spm_file						):
           with open(lowercase	,      "wb"						) as fi:
              lowercase_			:List[str]    			= self.sp_model.serialized_model_proto()
              fi.write(lowercase						)

        return (str(lowercase						), str(lowercase						))





     def 							lowercase__      ( self		:					List[str]	,      lowercase		:					List[str]	,      lowercase		:					str = "en"	,      lowercase		:					Optional[List[str]] = None	,      lowercase		:					str = "ro"	,      **lowercase		:					Optional[int]	,      ):





        """simple docstring"""




        lowercase_			:int    			= src_lang
        lowercase_			:Optional[int]    			= tgt_lang
        self.set_src_lang_special_tokens(self.src_lang						)
        return super().prepare_seqaseq_batch(lowercase	,      lowercase	,      **lowercase						)





     def 							lowercase__      ( self		:					List[Any]	,      lowercase		:					Any	,      lowercase		:					Optional[str]	,      lowercase		:					Optional[str]	,      **lowercase		:					Union[str, Any]						):





        """simple docstring"""




        if src_lang is None or tgt_lang is None:
           raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model"						)
        lowercase_			:List[str]    			= src_lang
        lowercase_			:Union[str, Any]    			= self(lowercase	,      add_special_tokens=lowercase	,      **lowercase						)
        lowercase_			:str    			= self.get_lang_id(lowercase						)
        lowercase_			:Union[str, Any]    			= tgt_lang_id
        return inputs





     def 							lowercase__      ( self		:					str						):





        """simple docstring"""




        self.set_src_lang_special_tokens(self.src_lang						)





     def 							lowercase__      ( self		:					Tuple						):





        """simple docstring"""




        self.set_tgt_lang_special_tokens(self.tgt_lang						)





     def 							lowercase__      ( self		:					str	,      lowercase		:					str						):





        """simple docstring"""




        lowercase_			:List[str]    			= self.get_lang_token(lowercase						)
        lowercase_			:List[str]    			= self.lang_token_to_id[lang_token]
        lowercase_			:List[Any]    			= [self.cur_lang_id]
        lowercase_			:str    			= [self.eos_token_id]





     def 							lowercase__      ( self		:					str	,      lowercase		:					str						):





        """simple docstring"""




        lowercase_			:Optional[int]    			= self.get_lang_token(lowercase						)
        lowercase_			:Tuple    			= self.lang_token_to_id[lang_token]
        lowercase_			:Dict    			= [self.cur_lang_id]
        lowercase_			:List[Any]    			= [self.eos_token_id]





     def 							lowercase__      ( self		:					Union[str, Any]	,      lowercase		:					str						):





        """simple docstring"""




        return self.lang_code_to_token[lang]







     def 							lowercase__      ( self		:					Dict	,      lowercase		:					str						):





        """simple docstring"""




        lowercase_			:Union[str, Any]    			= self.get_lang_token(lowercase						)
        return self.lang_token_to_id[lang_token]







def 							UpperCAmelCase_  (						__lowerCamelCase					:					str	,__lowerCamelCase					:					Dict[str, Any] ):
   lowercase_			:List[str]    			= sentencepiece.SentencePieceProcessor(**__lowerCamelCase )
   spm.Load(str(__lowerCamelCase ) )
   return spm
def 							UpperCAmelCase_  (						__lowerCamelCase					:					str ):
   with open(__lowerCamelCase	,"r" ) as f:
      return json.load(__lowerCamelCase )


def 							UpperCAmelCase_  (						__lowerCamelCase					:					int	,__lowerCamelCase					:					str ):
   with open(__lowerCamelCase	,"w" ) as f:
      json.dump(__lowerCamelCase	,__lowerCamelCase	,indent=2 )


