




'''simple docstring'''




import argparse
import inspect
import logging
from typing import Tuple

import numpy as np
import torch

from transformers import (
    AutoTokenizer,
    BloomForCausalLM,
    BloomTokenizerFast,
    CTRLLMHeadModel,
    CTRLTokenizer,
    GenerationMixin,
    GPTaLMHeadModel,
    GPTaTokenizer,
    GPTJForCausalLM,
    LlamaForCausalLM,
    LlamaTokenizer,
    OpenAIGPTLMHeadModel,
    OpenAIGPTTokenizer,
    OPTForCausalLM,
    TransfoXLLMHeadModel,
    TransfoXLTokenizer,
    XLMTokenizer,
    XLMWithLMHeadModel,
    XLNetLMHeadModel,
    XLNetTokenizer,
)
from transformers.modeling_outputs import CausalLMOutputWithPast


logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""",
    datefmt="""%m/%d/%Y %H:%M:%S""",
    level=logging.INFO,
)
a_   :	Tuple							   = logging.getLogger(__name__)

a_   :	Union[str, Any]							   = int(1_00_00)  # Hardcoded max length to avoid infinite loop

a_   :	Optional[Any]							   = {
    """gpt2""": (GPTaLMHeadModel, GPTaTokenizer),
    """ctrl""": (CTRLLMHeadModel, CTRLTokenizer),
    """openai-gpt""": (OpenAIGPTLMHeadModel, OpenAIGPTTokenizer),
    """xlnet""": (XLNetLMHeadModel, XLNetTokenizer),
    """transfo-xl""": (TransfoXLLMHeadModel, TransfoXLTokenizer),
    """xlm""": (XLMWithLMHeadModel, XLMTokenizer),
    """gptj""": (GPTJForCausalLM, AutoTokenizer),
    """bloom""": (BloomForCausalLM, BloomTokenizerFast),
    """llama""": (LlamaForCausalLM, LlamaTokenizer),
    """opt""": (OPTForCausalLM, GPTaTokenizer),
}

# Padding text to help Transformer-XL and XLNet with short prompts as proposed by Aman Rusia
# in https://github.com/rusiaaman/XLNet-gen#methodology
# and https://medium.com/@amanrusia/xlnet-speaks-comparison-to-gpt-2-ea1a4e9ba39e
a_   :	int							   = """In 1991, the remains of Russian Tsar Nicholas II and his family
(except for Alexei and Maria) are discovered.
The voice of Nicholas's young son, Tsarevich Alexei Nikolaevich, narrates the
remainder of the story. 1883 Western Siberia,
a young Grigori Rasputin is asked by his father and a group of men to perform magic.
Rasputin has a vision and denounces one of the men as a horse thief. Although his
father initially slaps him for making such an accusation, Rasputin watches as the
man is chased outside and beaten. Twenty years later, Rasputin sees a vision of
the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous,
with people, even a bishop, begging for his blessing. <eod> </s> <eos>"""





def   a_							(   __snake_case							:    Any     )							->		Optional[Any]:



       """simple docstring"""
       np.random.seed(args.seed     )
       torch.manual_seed(args.seed     )
       if args.n_gpu > 0:
              torch.cuda.manual_seed_all(args.seed     )





def   a_							(   __snake_case							:    int     , __snake_case							:    Dict     , __snake_case							:    Tuple     , __snake_case							:    int     )							->		Optional[Any]:



       """simple docstring"""
       if args.temperature > 0.7:
              logger.info('''CTRL typically works better with lower temperatures (and lower top_k).'''     )

       lowerCamelCase_							=tokenizer.encode(__snake_case     , add_special_tokens=__snake_case     )
       if not any(encoded_prompt[0] == x for x in tokenizer.control_codes.values()     ):
              logger.info('''WARNING! You are not starting your generation from a control code so you won\'t get good results'''     )
       return prompt_text





def   a_							(   __snake_case							:    List[str]     , __snake_case							:    Optional[Any]     , __snake_case							:    Tuple     , __snake_case							:    int     )							->		Any:



       """simple docstring"""
       lowerCamelCase_							=hasattr(model.config     , '''use_lang_emb'''     ) and model.config.use_lang_emb
       if hasattr(model.config     , '''lang2id'''     ) and use_lang_emb:
              lowerCamelCase_							=model.config.langaid.keys()
              if args.xlm_language in available_languages:
                     lowerCamelCase_							=args.xlm_language
              else:
                     lowerCamelCase_							=None
                     while language not in available_languages:
                            lowerCamelCase_							=input('''Using XLM. Select language in ''' + str(list(__snake_case     )     ) + ''' >>> '''     )

              lowerCamelCase_							=model.config.langaid[language]
              # kwargs["language"] = tokenizer.lang2id[language]

       # TODO fix mask_token_id setup when configurations will be synchronized between models and tokenizers
       # XLM masked-language modeling (MLM) models need masked token
       # is_xlm_mlm = "mlm" in args.model_name_or_path
       # if is_xlm_mlm:
       #     kwargs["mask_token_id"] = tokenizer.mask_token_id

       return prompt_text





def   a_							(   __snake_case							:    int     , __snake_case							:    Tuple     , __snake_case							:    Optional[int]     , __snake_case							:    Any     )							->		Tuple:



       """simple docstring"""
       lowerCamelCase_							=args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
       lowerCamelCase_							=prefix + prompt_text
       return prompt_text





def   a_							(   __snake_case							:    Any     , __snake_case							:    Optional[Any]     , __snake_case							:    Any     , __snake_case							:    Optional[Any]     )							->		Any:



       """simple docstring"""
       lowerCamelCase_							=args.prefix if args.prefix else args.padding_text if args.padding_text else PREFIX
       lowerCamelCase_							=prefix + prompt_text
       return prompt_text


a_   :	Tuple							   = {
    """ctrl""": prepare_ctrl_input,
    """xlm""": prepare_xlm_input,
    """xlnet""": prepare_xlnet_input,
    """transfo-xl""": prepare_transfoxl_input,
}





def   a_							(   __snake_case							:    int     , __snake_case							:    int     )							->		Any:



       """simple docstring"""
       if length < 0 and max_sequence_length > 0:
              lowerCamelCase_							=max_sequence_length
       elif 0 < max_sequence_length < length:
              lowerCamelCase_							=max_sequence_length  # No generation bigger than model size
       elif length < 0:
              lowerCamelCase_							=MAX_LENGTH  # avoid infinite loop
       return length





def   a_							(   __snake_case							:    Optional[Any]     )							->		List[Any]:



       """simple docstring"""
       lowerCamelCase_							=None
       if hasattr(__snake_case     , '''hidden_size'''     ):
              lowerCamelCase_							=model_config.hidden_size
       elif hasattr(__snake_case     , '''n_embed'''     ):
              lowerCamelCase_							=model_config.n_embed
       elif hasattr(__snake_case     , '''n_embd'''     ):
              lowerCamelCase_							=model_config.n_embd

       lowerCamelCase_							=None
       if hasattr(__snake_case     , '''num_attention_heads'''     ):
              lowerCamelCase_							=model_config.num_attention_heads
       elif hasattr(__snake_case     , '''n_head'''     ):
              lowerCamelCase_							=model_config.n_head

       if embedding_size is None or num_head is None or num_head == 0:
              raise ValueError('''Check the model config'''     )

       lowerCamelCase_							=int(embedding_size / num_head     )
       if hasattr(__snake_case     , '''n_layer'''     ):
              lowerCamelCase_							=model_config.n_layer
       elif hasattr(__snake_case     , '''num_hidden_layers'''     ):
              lowerCamelCase_							=model_config.num_hidden_layers
       else:
              raise ValueError('''Number of hidden layers couldn\'t be determined from the model config'''     )

       return num_layer, num_head, num_embedding_size_per_head





def   a_							(   __snake_case							:    int     , __snake_case							:    List[str]     , __snake_case							:    str     )							->		List[str]:



       """simple docstring"""
       lowerCamelCase_,   lowerCamelCase_,   lowerCamelCase_							=sparse_model_config(model.config     )
       if model.config.model_type == "bloom":
              lowerCamelCase_							=tuple(
                  (
                      torch.empty(int(num_attention_heads * batch_size     )     , __snake_case     , __snake_case     )
                      .to(model.dtype     )
                      .to(model.device     ),
                      torch.empty(int(num_attention_heads * batch_size     )     , __snake_case     , __snake_case     )
                      .to(model.dtype     )
                      .to(model.device     ),
                  )
                  for _ in range(__snake_case     )     )
       else:
              lowerCamelCase_							=tuple(
                  (
                      torch.empty(__snake_case     , __snake_case     , __snake_case     , __snake_case     )
                      .to(model.dtype     )
                      .to(model.device     ),
                      torch.empty(__snake_case     , __snake_case     , __snake_case     , __snake_case     )
                      .to(model.dtype     )
                      .to(model.device     ),
                  )
                  for _ in range(__snake_case     )     )
       return past_key_values





def   a_							(   __snake_case							:    Tuple     , __snake_case							:    Union[str, Any]     , __snake_case							:    Dict     )							->		str:



       """simple docstring"""
       lowerCamelCase_							=len(__snake_case     )
       lowerCamelCase_							=tokenizer.batch_encode_plus(__snake_case     , return_tensors='''pt'''     )
       lowerCamelCase_							=dummy_input.to(model.device     )
       if model.config.use_cache:
              lowerCamelCase_							=generate_past_key_values(__snake_case     , __snake_case     , 1     )
       lowerCamelCase_							=torch.cat(
           [
               torch.zeros(dummy_input['''attention_mask'''].shape[0]     , 1     )
               .to(dummy_input['''attention_mask'''].dtype     )
               .to(model.device     ),
               dummy_input['''attention_mask'''],
           ]     , -1     , )
       return dummy_input


class 						__UpperCamelCase (				lowerCamelCase__       ):
       lowercase			:					List[Any]   		=('_optimized', '_default')






       def __init__(							self,	lowerCAmelCase,	lowerCAmelCase						):



              """simple docstring"""







              lowerCamelCase_							=optimized
              lowerCamelCase_							=default






       def __call__(							self,	*lowerCAmelCase,	**lowerCAmelCase						):



              """simple docstring"""







              if kwargs["past_key_values"] is None and self._default.config.use_cache:
                     lowerCamelCase_							=generate_past_key_values(self._default,	kwargs['''input_ids'''].shape[0],	0						)
              kwargs.pop('''position_ids''',	lowerCAmelCase						)
              for k in list(kwargs.keys()						):
                     if kwargs[k] is None or isinstance(kwargs[k],	lowerCAmelCase						):
                            kwargs.pop(lowerCAmelCase						)
              lowerCamelCase_							=self._optimized(**lowerCAmelCase						)
              lowerCamelCase_							=outputs[0]
              lowerCamelCase_							=outputs[1]
              lowerCamelCase_							=CausalLMOutputWithPast(
                  loss=lowerCAmelCase,	logits=lowerCAmelCase,	past_key_values=lowerCAmelCase,	hidden_states=lowerCAmelCase,	attentions=lowerCAmelCase,	)
              return fixed_output






       def __getattr__(							self,	lowerCAmelCase						):



              """simple docstring"""







              return getattr(self._default,	lowerCAmelCase						)






       def   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase=None,	lowerCAmelCase=None,	lowerCAmelCase=None,	**lowerCAmelCase						):



              """simple docstring"""







              return self._default.prepare_inputs_for_generation(
                  lowerCAmelCase,	past_key_values=lowerCAmelCase,	inputs_embeds=lowerCAmelCase,	use_cache=lowerCAmelCase,	**lowerCAmelCase						)






       def   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase						):



              """simple docstring"""







              return self._default._reorder_cache(lowerCAmelCase,	lowerCAmelCase						)





def   a_							(   )							->		List[str]:



       """simple docstring"""
       lowerCamelCase_							=argparse.ArgumentParser()
       parser.add_argument(
           '''--model_type'''     , default=__snake_case     , type=__snake_case     , required=__snake_case     , help='''Model type selected in the list: ''' + ''', '''.join(MODEL_CLASSES.keys()     )     , )
       parser.add_argument(
           '''--model_name_or_path'''     , default=__snake_case     , type=__snake_case     , required=__snake_case     , help='''Path to pre-trained model or shortcut name selected in the list: ''' + ''', '''.join(MODEL_CLASSES.keys()     )     , )

       parser.add_argument('''--prompt'''     , type=__snake_case     , default=''''''     )
       parser.add_argument('''--length'''     , type=__snake_case     , default=20     )
       parser.add_argument('''--stop_token'''     , type=__snake_case     , default=__snake_case     , help='''Token at which text generation is stopped'''     )

       parser.add_argument(
           '''--temperature'''     , type=__snake_case     , default=1.0     , help='''temperature of 1.0 has no effect, lower tend toward greedy sampling'''     , )
       parser.add_argument(
           '''--repetition_penalty'''     , type=__snake_case     , default=1.0     , help='''primarily useful for CTRL model; in that case, use 1.2'''     )
       parser.add_argument('''--k'''     , type=__snake_case     , default=0     )
       parser.add_argument('''--p'''     , type=__snake_case     , default=0.9     )

       parser.add_argument('''--prefix'''     , type=__snake_case     , default=''''''     , help='''Text added prior to input.'''     )
       parser.add_argument('''--padding_text'''     , type=__snake_case     , default=''''''     , help='''Deprecated, the use of `--prefix` is preferred.'''     )
       parser.add_argument('''--xlm_language'''     , type=__snake_case     , default=''''''     , help='''Optional language when used with the XLM model.'''     )

       parser.add_argument('''--seed'''     , type=__snake_case     , default=42     , help='''random seed for initialization'''     )
       parser.add_argument('''--no_cuda'''     , action='''store_true'''     , help='''Avoid using CUDA when available'''     )
       parser.add_argument('''--num_return_sequences'''     , type=__snake_case     , default=1     , help='''The number of samples to generate.'''     )
       parser.add_argument(
           '''--fp16'''     , action='''store_true'''     , help='''Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit'''     , )
       parser.add_argument('''--jit'''     , action='''store_true'''     , help='''Whether or not to use jit trace to accelerate inference'''     )
       lowerCamelCase_							=parser.parse_args()

       lowerCamelCase_							=torch.device('''cuda''' if torch.cuda.is_available() and not args.no_cuda else '''cpu'''     )
       lowerCamelCase_							=0 if args.no_cuda else torch.cuda.device_count()

       logger.warning(F'''device: {args.device}, n_gpu: {args.n_gpu}, 16-bits training: {args.fpaa}'''     )

       set_seed(__snake_case     )

       # Initialize the model and tokenizer
       try:
              lowerCamelCase_							=args.model_type.lower()
              lowerCamelCase_,   lowerCamelCase_							=MODEL_CLASSES[args.model_type]
       except KeyError:
              raise KeyError('''the model {} you specified is not supported. You are welcome to add it and open a PR :)'''     )

       lowerCamelCase_							=tokenizer_class.from_pretrained(args.model_name_or_path     )
       if tokenizer.pad_token is None:
              lowerCamelCase_							=tokenizer.eos_token
       lowerCamelCase_							=model_class.from_pretrained(args.model_name_or_path     )
       model.to(args.device     )

       if args.fpaa:
              model.half()
       lowerCamelCase_							=getattr(model.config     , '''max_position_embeddings'''     , 0     )
       lowerCamelCase_							=adjust_length_to_model(args.length     , max_sequence_length=__snake_case     )
       logger.info(__snake_case     )

       lowerCamelCase_							=args.prompt if args.prompt else input('''Model prompt >>> '''     )

       # Different models need different input formatting and/or extra arguments
       lowerCamelCase_							=args.model_type in PREPROCESSING_FUNCTIONS.keys()
       if requires_preprocessing:
              lowerCamelCase_							=PREPROCESSING_FUNCTIONS.get(args.model_type     )
              lowerCamelCase_							=prepare_input(__snake_case     , __snake_case     , __snake_case     , __snake_case     )

              if model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
                     lowerCamelCase_							={'''add_space_before_punct_symbol''': True}
              else:
                     lowerCamelCase_							={}

              lowerCamelCase_							=tokenizer.encode(
                  __snake_case     , add_special_tokens=__snake_case     , return_tensors='''pt'''     , **__snake_case     )
       else:
              lowerCamelCase_							=args.prefix if args.prefix else args.padding_text
              lowerCamelCase_							=tokenizer.encode(prefix + prompt_text     , add_special_tokens=__snake_case     , return_tensors='''pt'''     )
       lowerCamelCase_							=encoded_prompt.to(args.device     )

       if encoded_prompt.size()[-1] == 0:
              lowerCamelCase_							=None
       else:
              lowerCamelCase_							=encoded_prompt

       if args.jit:
              lowerCamelCase_							=['''enable jit''']
              lowerCamelCase_							=prepare_jit_inputs(__snake_case     , __snake_case     , __snake_case     )
              torch._C._jit_set_texpr_fuser_enabled(__snake_case     )
              lowerCamelCase_							=False
              if hasattr(__snake_case     , '''forward'''     ):
                     lowerCamelCase_							=inspect.signature(model.forward     )
              else:
                     lowerCamelCase_							=inspect.signature(model.__call__     )
              lowerCamelCase_							=tuple(jit_inputs[key] for key in sig.parameters if jit_inputs.get(__snake_case     , __snake_case     ) is not None     )
              lowerCamelCase_							=torch.jit.trace(__snake_case     , __snake_case     , strict=__snake_case     )
              lowerCamelCase_							=torch.jit.freeze(traced_model.eval()     )
              traced_model(*__snake_case     )
              traced_model(*__snake_case     )

              lowerCamelCase_							=_ModelFallbackWrapper(__snake_case     , __snake_case     )

       lowerCamelCase_							=model.generate(
           input_ids=__snake_case     , max_length=args.length + len(encoded_prompt[0]     )     , temperature=args.temperature     , top_k=args.k     , top_p=args.p     , repetition_penalty=args.repetition_penalty     , do_sample=__snake_case     , num_return_sequences=args.num_return_sequences     , )

       # Remove the batch dimension when returning multiple sequences
       if len(output_sequences.shape     ) > 2:
              output_sequences.squeeze_()

       lowerCamelCase_							=[]

       for generated_sequence_idx, generated_sequence in enumerate(__snake_case     ):
              print(F'''=== GENERATED SEQUENCE {generated_sequence_idx + 1} ==='''     )
              lowerCamelCase_							=generated_sequence.tolist()

              # Decode text
              lowerCamelCase_							=tokenizer.decode(__snake_case     , clean_up_tokenization_spaces=__snake_case     )

              # Remove all text after the stop token
              lowerCamelCase_							=text[: text.find(args.stop_token     ) if args.stop_token else None]

              # Add the prompt at the beginning of the sequence. Remove the excess text that was used for pre-processing
              lowerCamelCase_							=(
                  prompt_text + text[len(tokenizer.decode(encoded_prompt[0]     , clean_up_tokenization_spaces=__snake_case     )     ) :]
              )

              generated_sequences.append(__snake_case     )
              print(__snake_case     )

       return generated_sequences


if __name__ == "__main__":
     main()










'''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:
     a_   :	Tuple							   = None


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

a_   :	Any							   = {"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

a_   :	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"""
        ),
    },
    """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"""
        ),
    },
}

a_   :	Dict							   = {
    """facebook/mbart-large-50-one-to-many-mmt""": 10_24,
}

# fmt: off
a_   :	List[str]							   = ["""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 (				lowerCamelCase__       ):
       lowercase			:					Any   		=VOCAB_FILES_NAMES
       lowercase			:					str   		=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowercase			:					Dict   		=PRETRAINED_VOCAB_FILES_MAP
       lowercase			:					str   		=['input_ids', 'attention_mask']
       lowercase			:					List[Any]   		=MBartaaTokenizer

       lowercase			:					List[int]   		=[]
       lowercase			:					List[int]   		=[]






       def __init__(							self,	lowerCAmelCase=None,	lowerCAmelCase=None,	lowerCAmelCase=None,	lowerCAmelCase=None,	lowerCAmelCase="</s>",	lowerCAmelCase="</s>",	lowerCAmelCase="<s>",	lowerCAmelCase="<unk>",	lowerCAmelCase="<pad>",	lowerCAmelCase="<mask>",	**lowerCAmelCase,	):



              """simple docstring"""







              lowerCamelCase_							=AddedToken(lowerCAmelCase,	lstrip=lowerCAmelCase,	rstrip=lowerCAmelCase						) if isinstance(lowerCAmelCase,	lowerCAmelCase						) else mask_token

              lowerCamelCase_							=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__(
                  lowerCAmelCase,	src_lang=lowerCAmelCase,	tgt_lang=lowerCAmelCase,	tokenizer_file=lowerCAmelCase,	eos_token=lowerCAmelCase,	sep_token=lowerCAmelCase,	cls_token=lowerCAmelCase,	unk_token=lowerCAmelCase,	pad_token=lowerCAmelCase,	mask_token=lowerCAmelCase,	**lowerCAmelCase,	)

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

              lowerCamelCase_							={
                  lang_code: self.convert_tokens_to_ids(lowerCAmelCase						) for lang_code in FAIRSEQ_LANGUAGE_CODES
              }

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






       @property
       def   lowercase__  (							self						):



              """simple docstring"""







              return self._src_lang






       @src_lang.setter
       def   lowercase__  (							self,	lowerCAmelCase						):



              """simple docstring"""







              lowerCamelCase_							=new_src_lang
              self.set_src_lang_special_tokens(self._src_lang						)






       def   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase = 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,	lowerCAmelCase,	lowerCAmelCase = "en_XX",	lowerCAmelCase = None,	lowerCAmelCase = "ro_RO",	**lowerCAmelCase,	):



              """simple docstring"""







              lowerCamelCase_							=src_lang
              lowerCamelCase_							=tgt_lang
              return super().prepare_seqaseq_batch(lowerCAmelCase,	lowerCAmelCase,	**lowerCAmelCase						)






       def   lowercase__  (							self						):



              """simple docstring"""







              return self.set_src_lang_special_tokens(self.src_lang						)






       def   lowercase__  (							self						):



              """simple docstring"""







              return self.set_tgt_lang_special_tokens(self.tgt_lang						)






       def   lowercase__  (							self,	lowerCAmelCase						):



              """simple docstring"""







              lowerCamelCase_							=self.convert_tokens_to_ids(lowerCAmelCase						)
              lowerCamelCase_							=[self.cur_lang_code_id]
              lowerCamelCase_							=[self.eos_token_id]

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

              lowerCamelCase_							=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   lowercase__  (							self,	lowerCAmelCase						):



              """simple docstring"""







              lowerCamelCase_							=self.convert_tokens_to_ids(lowerCAmelCase						)
              lowerCamelCase_							=[self.cur_lang_code_id]
              lowerCamelCase_							=[self.eos_token_id]

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

              lowerCamelCase_							=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   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase,	lowerCAmelCase,	**lowerCAmelCase						):



              """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'''						)
              lowerCamelCase_							=src_lang
              lowerCamelCase_							=self(lowerCAmelCase,	add_special_tokens=lowerCAmelCase,	return_tensors=lowerCAmelCase,	**lowerCAmelCase						)
              lowerCamelCase_							=self.convert_tokens_to_ids(lowerCAmelCase						)
              lowerCamelCase_							=tgt_lang_id
              return inputs






       def   lowercase__  (							self,	lowerCAmelCase,	lowerCAmelCase = None						):



              """simple docstring"""







              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(lowerCAmelCase						):
                     logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''						)
                     return
              lowerCamelCase_							=os.path.join(
                  lowerCAmelCase,	(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']						)

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

              return (out_vocab_file,)




