


"""simple docstring"""







import gc
import importlib.metadata
import tempfile
import unittest

from packaging import version

from transformers import (
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    BitsAndBytesConfig,
    pipeline,
)
from transformers.testing_utils import (
    is_torch_available,
    require_accelerate,
    require_bitsandbytes,
    require_torch,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)


def      lowercase			(				A_       )->      int:
  '''simple docstring'''







  if model.config.model_type == "gpt2":
    return model.transformer.h[0].mlp.c_fc
  return model.transformer.h[0].mlp.dense_ah_to_h


if is_torch_available():
 import torch
 import torch.nn as nn






 class 						_A  (  nn.Module  ):







     """simple docstring"""




     def __init__(					self				:    List[str]				, __UpperCAmelCase				:    nn.Module				, __UpperCAmelCase				:    int):
       super().__init__()
       a						:		Tuple      							=			module
       a						:		List[str]      							=			nn.Sequential(
           nn.Linear(module.in_features				, __UpperCAmelCase				, bias=__UpperCAmelCase)				, nn.Linear(__UpperCAmelCase				, module.out_features				, bias=__UpperCAmelCase)				, )
       a						:		Dict      							=			(2.0 / (5 * min(module.in_features				, module.out_features))) ** 0.5
       nn.init.normal_(self.adapter[0].weight				, std=__UpperCAmelCase)
       nn.init.zeros_(self.adapter[1].weight)
       self.adapter.to(module.weight.device)






     def 	__snake_case							(					self				:    List[Any]				, __UpperCAmelCase				:    Optional[int]				, *__UpperCAmelCase				:    str				, **__UpperCAmelCase				:    Union[str, Any]):
       return self.module(__UpperCAmelCase				, *__UpperCAmelCase				, **__UpperCAmelCase) + self.adapter(__UpperCAmelCase)






@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class 						_A  (  unittest.TestCase  ):







    """simple docstring"""




    UpperCAmelCase       :							Optional[Any]	=       """bigscience/bloom-1b7"""

    # Constant values
    UpperCAmelCase       :							Tuple	=       2.109_6595_5269_2574

    UpperCAmelCase       :							str	=       """Hello my name is"""
    UpperCAmelCase       :							Any	=       set()
    EXPECTED_OUTPUTS.add("""Hello my name is John and I am a professional photographer. I"""  )
    EXPECTED_OUTPUTS.add("""Hello my name is John.\nI am a friend of your father.\n"""  )
    EXPECTED_OUTPUTS.add("""Hello my name is John Doe, I am a student at the University"""  )
    UpperCAmelCase       :							str	=       1_0






    def 	__snake_case							(					self				:    Union[str, Any]):
      a						:		int      							=			AutoTokenizer.from_pretrained(self.model_name)






class 						_A  (  _lowerCAmelCase  ):







    """simple docstring"""




    def 	__snake_case							(					self				:    List[str]):
      super().setUp()

      # Models and tokenizer
      a						:		str      							=			AutoModelForCausalLM.from_pretrained(
          self.model_name				, torch_dtype=torch.floataa				, device_map="auto")
      a						:		Optional[Any]      							=			AutoModelForCausalLM.from_pretrained(self.model_name				, load_in_abit=__UpperCAmelCase				, device_map="auto")






    def 	__snake_case							(					self				:    Tuple):
      del self.model_fpaa
      del self.model_abit

      gc.collect()
      torch.cuda.empty_cache()






    def 	__snake_case							(					self				:    Union[str, Any]):
      a						:		Union[str, Any]      							=			self.model_abit.config

      self.assertTrue(hasattr(__UpperCAmelCase				, "quantization_config"))

      a						:		Tuple      							=			config.to_dict()
      a						:		Union[str, Any]      							=			config.to_diff_dict()

      a						:		Optional[int]      							=			config.to_json_string()






    def 	__snake_case							(					self				:    Union[str, Any]):
      from bitsandbytes.nn import Paramsabit

      a						:		Dict      							=			self.model_fpaa.get_memory_footprint()
      a						:		Union[str, Any]      							=			self.model_abit.get_memory_footprint()

      self.assertAlmostEqual(mem_fpaa / mem_abit				, self.EXPECTED_RELATIVE_DIFFERENCE)
      a						:		Optional[Any]      							=			get_some_linear_layer(self.model_abit)
      self.assertTrue(linear.weight.__class__ == Paramsabit)






    def 	__snake_case							(					self				:    int):
      from transformers import TaPreTrainedModel

      self.model_fpaa.get_memory_footprint()
      self.model_abit.get_memory_footprint()

      for name, module in self.model_abit.named_modules():
        if isinstance(__UpperCAmelCase				, torch.nn.Linear):
          if name not in ["lm_head"] + TaPreTrainedModel._keep_in_fpaa_modules:
            # 4-bit parameters are packed in uint8 variables
            self.assertTrue(module.weight.dtype == torch.uinta)






    def 	__snake_case							(					self				:    Tuple):
      a						:		Dict      							=			self.tokenizer(self.input_text				, return_tensors="pt")
      a						:		str      							=			self.model_abit.generate(input_ids=encoded_input["input_ids"].to(0)				, max_new_tokens=10)

      self.assertIn(self.tokenizer.decode(output_sequences[0]				, skip_special_tokens=__UpperCAmelCase)				, self.EXPECTED_OUTPUTS)






    def 	__snake_case							(					self				:    Any):
      a						:		Dict      							=			BitsAndBytesConfig()
      a						:		List[str]      							=			True

      a						:		Union[str, Any]      							=			AutoModelForCausalLM.from_pretrained(
          self.model_name				, quantization_config=__UpperCAmelCase				, device_map="auto")

      a						:		Optional[Any]      							=			self.tokenizer(self.input_text				, return_tensors="pt")
      a						:		int      							=			model_abit_from_config.generate(
          input_ids=encoded_input["input_ids"].to(0)				, max_new_tokens=10)

      self.assertIn(self.tokenizer.decode(output_sequences[0]				, skip_special_tokens=__UpperCAmelCase)				, self.EXPECTED_OUTPUTS)






    def 	__snake_case							(					self				:    Dict):




      with self.assertRaises(__UpperCAmelCase), tempfile.TemporaryDirectory() as tmpdirname:
        self.model_abit.save_pretrained(__UpperCAmelCase)






    def 	__snake_case							(					self				:    str):
      a						:		Dict      							=			BitsAndBytesConfig()

      with self.assertRaises(__UpperCAmelCase):
        a						:		Tuple      							=			AutoModelForCausalLM.from_pretrained(
            self.model_name				, quantization_config=__UpperCAmelCase				, load_in_abit=__UpperCAmelCase				, device_map="auto"				, bnb_abit_quant_type="nf4"				, )






    def 	__snake_case							(					self				:    Dict):




      with self.assertRaises(__UpperCAmelCase):
        # Tries with `str`
        self.model_abit.to("cpu")

      with self.assertRaises(__UpperCAmelCase):
        # Tries with a `dtype``
        self.model_abit.to(torch.floataa)

      with self.assertRaises(__UpperCAmelCase):
        # Tries with a `device`
        self.model_abit.to(torch.device("cuda:0"))

      with self.assertRaises(__UpperCAmelCase):
        # Tries with a `device`
        self.model_abit.float()

      with self.assertRaises(__UpperCAmelCase):
        # Tries with a `device`
        self.model_abit.half()

      # Test if we did not break anything
      a						:		int      							=			self.tokenizer(self.input_text				, return_tensors="pt")

      a						:		int      							=			self.model_fpaa.to(torch.floataa)
      a						:		Tuple      							=			self.model_fpaa.generate(input_ids=encoded_input["input_ids"].to(0)				, max_new_tokens=10)

      # Check this does not throw an error
      a						:		int      							=			self.model_fpaa.to("cpu")

      # Check this does not throw an error
      a						:		Dict      							=			self.model_fpaa.half()

      # Check this does not throw an error
      a						:		List[str]      							=			self.model_fpaa.float()






    def 	__snake_case							(					self				:    Dict):
      a						:		Optional[Any]      							=			AutoModelForSeqaSeqLM.from_pretrained("t5-small"				, load_in_abit=__UpperCAmelCase				, device_map="auto")
      self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wo.weight.dtype == torch.floataa)






@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class 						_A  (  unittest.TestCase  ):







    """simple docstring"""




    @classmethod
    def 	__snake_case							(					cls				:    Any):
      a						:		List[str]      							=			"t5-small"
      a						:		Optional[Any]      							=			"google/flan-t5-small"  # flan-t5 uses dense-act instead of dense-relu-dense
      a						:		str      							=			AutoTokenizer.from_pretrained(cls.model_name)
      a						:		int      							=			"Translate in German: Hello, my dog is cute"






    def 	__snake_case							(					self				:    str):
      gc.collect()
      torch.cuda.empty_cache()






    def 	__snake_case							(					self				:    List[str]):
      from transformers import TaForConditionalGeneration

      a						:		List[Any]      							=			TaForConditionalGeneration._keep_in_fpaa_modules
      a						:		Optional[int]      							=			None

      # test with `t5-small`
      a						:		List[Any]      							=			TaForConditionalGeneration.from_pretrained(self.model_name				, load_in_abit=__UpperCAmelCase				, device_map="auto")
      a						:		List[Any]      							=			self.tokenizer(self.input_text				, return_tensors="pt").to(0)
      a						:		str      							=			model.generate(**__UpperCAmelCase)

      # test with `flan-t5-small`
      a						:		Optional[Any]      							=			TaForConditionalGeneration.from_pretrained(
          self.dense_act_model_name				, load_in_abit=__UpperCAmelCase				, device_map="auto")
      a						:		str      							=			self.tokenizer(self.input_text				, return_tensors="pt").to(0)
      a						:		int      							=			model.generate(**__UpperCAmelCase)
      a						:		List[str]      							=			modules






    def 	__snake_case							(					self				:    List[str]):
      import bitsandbytes as bnb

      from transformers import TaForConditionalGeneration

      # test with `t5-small`
      a						:		Union[str, Any]      							=			TaForConditionalGeneration.from_pretrained(self.model_name				, load_in_abit=__UpperCAmelCase				, device_map="auto")

      # there was a bug with decoders - this test checks that it is fixed
      self.assertTrue(isinstance(model.decoder.block[0].layer[0].SelfAttention.q				, bnb.nn.Linearabit))

      a						:		int      							=			self.tokenizer(self.input_text				, return_tensors="pt").to(0)
      a						:		Tuple      							=			model.generate(**__UpperCAmelCase)

      # test with `flan-t5-small`
      a						:		int      							=			TaForConditionalGeneration.from_pretrained(
          self.dense_act_model_name				, load_in_abit=__UpperCAmelCase				, device_map="auto")
      a						:		Dict      							=			self.tokenizer(self.input_text				, return_tensors="pt").to(0)
      a						:		Dict      							=			model.generate(**__UpperCAmelCase)






class 						_A  (  _lowerCAmelCase  ):







    """simple docstring"""




    def 	__snake_case							(					self				:    List[str]):
      super().setUp()
      # model_name
      a						:		List[Any]      							=			"bigscience/bloom-560m"
      a						:		str      							=			"t5-small"

      # Different types of model

      a						:		Optional[Any]      							=			AutoModel.from_pretrained(self.model_name				, load_in_abit=__UpperCAmelCase				, device_map="auto")
      # Sequence classification model
      a						:		List[str]      							=			AutoModelForSequenceClassification.from_pretrained(
          self.model_name				, load_in_abit=__UpperCAmelCase				, device_map="auto")
      # CausalLM model
      a						:		Optional[Any]      							=			AutoModelForCausalLM.from_pretrained(self.model_name				, load_in_abit=__UpperCAmelCase				, device_map="auto")
      # Seq2seq model
      a						:		Tuple      							=			AutoModelForSeqaSeqLM.from_pretrained(
          self.seq_to_seq_name				, load_in_abit=__UpperCAmelCase				, device_map="auto")






    def 	__snake_case							(					self				:    List[str]):
      del self.base_model
      del self.sequence_model
      del self.model_abit
      del self.seq_to_seq_model

      gc.collect()
      torch.cuda.empty_cache()






    def 	__snake_case							(					self				:    Tuple):
      from bitsandbytes.nn import Paramsabit

      self.assertTrue(self.base_model.h[-1].mlp.dense_ah_to_h.weight.__class__ == Paramsabit)

      # Other heads should be nn.Parameter
      self.assertTrue(self.model_abit.lm_head.weight.__class__ == torch.nn.Parameter)
      self.assertTrue(self.sequence_model.score.weight.__class__ == torch.nn.Parameter)
      self.assertTrue(self.seq_to_seq_model.lm_head.weight.__class__ == torch.nn.Parameter)






class 						_A  (  _lowerCAmelCase  ):







    """simple docstring"""




    def 	__snake_case							(					self				:    Any):
      super().setUp()






    def 	__snake_case							(					self				:    Tuple):
      del self.pipe

      gc.collect()
      torch.cuda.empty_cache()






    def 	__snake_case							(					self				:    str):
      a						:		Any      							=			pipeline(
          "text-generation"				, model=self.model_name				, model_kwargs={"device_map": "auto", "load_in_4bit": True, "torch_dtype": torch.floataa}				, max_new_tokens=self.MAX_NEW_TOKENS				, )

      # Real second forward pass
      a						:		str      							=			self.pipe(self.input_text)
      self.assertIn(pipeline_output[0]["generated_text"]				, self.EXPECTED_OUTPUTS)






@require_torch_multi_gpu
class 						_A  (  _lowerCAmelCase  ):







    """simple docstring"""




    def 	__snake_case							(					self				:    int):
      super().setUp()






    def 	__snake_case							(					self				:    Optional[int]):
      a						:		Optional[Any]      							=			AutoModelForCausalLM.from_pretrained(
          self.model_name				, load_in_abit=__UpperCAmelCase				, device_map="balanced")

      # Check correct device map
      self.assertEqual(set(model_parallel.hf_device_map.values())				, {0, 1})

      # Check that inference pass works on the model
      a						:		List[Any]      							=			self.tokenizer(self.input_text				, return_tensors="pt")

      # Second real batch
      a						:		Dict      							=			model_parallel.generate(input_ids=encoded_input["input_ids"].to(0)				, max_new_tokens=10)
      self.assertIn(self.tokenizer.decode(output_parallel[0]				, skip_special_tokens=__UpperCAmelCase)				, self.EXPECTED_OUTPUTS)






class 						_A  (  _lowerCAmelCase  ):







    """simple docstring"""




    def 	__snake_case							(					self				:    Optional[Any]):
      a						:		Dict      							=			"facebook/opt-350m"
      super().setUp()






    def 	__snake_case							(					self				:    str):




      if version.parse(importlib.metadata.version("bitsandbytes")) < version.parse("0.37.0"):
        return

      # Step 1: freeze all parameters
      a						:		Optional[int]      							=			AutoModelForCausalLM.from_pretrained(self.model_name				, load_in_abit=__UpperCAmelCase)

      self.assertEqual(set(model.hf_device_map.values())				, {torch.cuda.current_device()})

      for param in model.parameters():
        a						:		Optional[int]      							=			False  # freeze the model - train adapters later
        if param.ndim == 1:
          # cast the small parameters (e.g. layernorm) to fp32 for stability
          a						:		Union[str, Any]      							=			param.data.to(torch.floataa)

        # Step 2: add adapters
      for _, module in model.named_modules():
        if "OPTAttention" in repr(type(__UpperCAmelCase)):
          a						:		List[str]      							=			LoRALayer(module.q_proj				, rank=16)
          a						:		Optional[int]      							=			LoRALayer(module.k_proj				, rank=16)
          a						:		Optional[int]      							=			LoRALayer(module.v_proj				, rank=16)

        # Step 3: dummy batch
      a						:		Union[str, Any]      							=			self.tokenizer("Test batch "				, return_tensors="pt").to(0)

      # Step 4: Check if the gradient is not None
      with torch.cuda.amp.autocast():
        a						:		Union[str, Any]      							=			model.forward(**__UpperCAmelCase)
        out.logits.norm().backward()

      for module in model.modules():
        if isinstance(__UpperCAmelCase				, __UpperCAmelCase):
          self.assertTrue(module.adapter[1].weight.grad is not None)
          self.assertTrue(module.adapter[1].weight.grad.norm().item() > 0)
        elif isinstance(__UpperCAmelCase				, nn.Embedding):
          self.assertTrue(module.weight.grad is None)






class 						_A  (  _lowerCAmelCase  ):







    """simple docstring"""




    UpperCAmelCase       :							List[Any]	=       """gpt2-xl"""
    UpperCAmelCase       :							Any	=       3.3191_8548_5415_2187













'''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 )


