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

import sentencepiece as spm

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


SCREAMING_SNAKE_CASE			:						int				  =		logging.get_logger(__name__)

SCREAMING_SNAKE_CASE			:						Any				  =		"""▁"""

SCREAMING_SNAKE_CASE			:						Optional[int]				  =		{"""vocab_file""": """sentencepiece.bpe.model"""}

SCREAMING_SNAKE_CASE			:						Union[str, Any]				  =		{
    """vocab_file""": {
        """xlm-roberta-base""": """https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model""",
        """xlm-roberta-large""": """https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model""",
        """xlm-roberta-large-finetuned-conll02-dutch""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll02-spanish""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll03-english""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll03-german""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model"""
        ),
    }
}

SCREAMING_SNAKE_CASE			:						Optional[Any]				  =		{
    """xlm-roberta-base""": 512,
    """xlm-roberta-large""": 512,
    """xlm-roberta-large-finetuned-conll02-dutch""": 512,
    """xlm-roberta-large-finetuned-conll02-spanish""": 512,
    """xlm-roberta-large-finetuned-conll03-english""": 512,
    """xlm-roberta-large-finetuned-conll03-german""": 512,
}
class  A_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      VOCAB_FILES_NAMES
  _SCREAMING_SNAKE_CASE     					=      PRETRAINED_VOCAB_FILES_MAP
  _SCREAMING_SNAKE_CASE     					=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _SCREAMING_SNAKE_CASE     					=      ["""input_ids""", """attention_mask"""]

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

   __a							=	{} if sp_model_kwargs is None else sp_model_kwargs

   super().__init__(
       bos_token=__SCREAMING_SNAKE_CASE  ,				eos_token=__SCREAMING_SNAKE_CASE  ,				unk_token=__SCREAMING_SNAKE_CASE  ,				sep_token=__SCREAMING_SNAKE_CASE  ,				cls_token=__SCREAMING_SNAKE_CASE  ,				pad_token=__SCREAMING_SNAKE_CASE  ,				mask_token=__SCREAMING_SNAKE_CASE  ,				sp_model_kwargs=self.sp_model_kwargs  ,				**__SCREAMING_SNAKE_CASE  ,				)

   __a							=	spm.SentencePieceProcessor(**self.sp_model_kwargs							)
   self.sp_model.Load(str(__SCREAMING_SNAKE_CASE							)							)
   __a							=	vocab_file

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

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

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

   __a							=	len(self.sp_model							) + self.fairseq_offset
   __a							=	{v: k for k, v in self.fairseq_tokens_to_ids.items()}

  def __getstate__(		self			:   Dict							):
   __a							=	self.__dict__.copy()
   __a							=	None
   __a							=	self.sp_model.serialized_model_proto()
   return state

  def __setstate__(		self			:   int  ,				__SCREAMING_SNAKE_CASE			:   Any							):
   __a							=	d

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

   __a							=	spm.SentencePieceProcessor(**self.sp_model_kwargs							)
   self.sp_model.LoadFromSerializedProto(self.sp_model_proto							)

  def 		_UpperCAmelCase (		self			:   List[str]  ,				__SCREAMING_SNAKE_CASE			:   List[int]  ,				__SCREAMING_SNAKE_CASE			:   Optional[List[int]] = None							):

   if token_ids_a is None:
    return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
   __a							=	[self.cls_token_id]
   __a							=	[self.sep_token_id]
   return cls + token_ids_a + sep + sep + token_ids_a + sep

  def 		_UpperCAmelCase (		self			:   List[str]  ,				__SCREAMING_SNAKE_CASE			:   List[int]  ,				__SCREAMING_SNAKE_CASE			:   Optional[List[int]] = None  ,				__SCREAMING_SNAKE_CASE			:   bool = False							):

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

   if token_ids_a is None:
    return [1] + ([0] * len(__SCREAMING_SNAKE_CASE							)) + [1]
   return [1] + ([0] * len(__SCREAMING_SNAKE_CASE							)) + [1, 1] + ([0] * len(__SCREAMING_SNAKE_CASE							)) + [1]

  def 		_UpperCAmelCase (		self			:   Union[str, Any]  ,				__SCREAMING_SNAKE_CASE			:   List[int]  ,				__SCREAMING_SNAKE_CASE			:   Optional[List[int]] = None							):
   __a							=	[self.sep_token_id]
   __a							=	[self.cls_token_id]

   if token_ids_a is None:
    return len(cls + token_ids_a + sep							) * [0]
   return len(cls + token_ids_a + sep + sep + token_ids_a + sep							) * [0]

  @property
  def 		_UpperCAmelCase (		self			:   List[Any]							):
   return len(self.sp_model							) + self.fairseq_offset + 1  # Add the <mask> token

  def 		_UpperCAmelCase (		self			:   List[Any]							):
   __a							=	{self.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE							): i for i in range(self.vocab_size							)}
   vocab.update(self.added_tokens_encoder							)
   return vocab

  def 		_UpperCAmelCase (		self			:   int  ,				__SCREAMING_SNAKE_CASE			:   str							):
   return self.sp_model.encode(__SCREAMING_SNAKE_CASE  ,				out_type=__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   List[Any]  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]							):
   if token in self.fairseq_tokens_to_ids:
    return self.fairseq_tokens_to_ids[token]
   __a							=	self.sp_model.PieceToId(__SCREAMING_SNAKE_CASE							)

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

  def 		_UpperCAmelCase (		self			:   Tuple  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]							):
   if index in self.fairseq_ids_to_tokens:
    return self.fairseq_ids_to_tokens[index]
   return self.sp_model.IdToPiece(index - self.fairseq_offset							)

  def 		_UpperCAmelCase (		self			:   List[str]  ,				__SCREAMING_SNAKE_CASE			:   List[Any]							):
   __a							=	"".join(__SCREAMING_SNAKE_CASE							).replace(__SCREAMING_SNAKE_CASE  ,				" "							).strip()
   return out_string

  def 		_UpperCAmelCase (		self			:   Optional[Any]  ,				__SCREAMING_SNAKE_CASE			:   str  ,				__SCREAMING_SNAKE_CASE			:   Optional[str] = None							):
   if not os.path.isdir(__SCREAMING_SNAKE_CASE							):
    logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""							)
    return
   __a							=	os.path.join(
       __SCREAMING_SNAKE_CASE  ,				(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]							)

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

   return (out_vocab_file,)



import json
import logging
import os
import sys
from pathlib import Path

import finetune_rag

from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    require_ray,
    require_torch_gpu,
    require_torch_multi_gpu,
)


logging.basicConfig(level=logging.DEBUG)
SCREAMING_SNAKE_CASE			:						List[Any]				  =		logging.getLogger()

SCREAMING_SNAKE_CASE			:						List[str]				  =		logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class  A_			(							a_			):

  def 		_UpperCAmelCase (		self			:   str  ,				__SCREAMING_SNAKE_CASE			:   int							):
   os.makedirs(__SCREAMING_SNAKE_CASE  ,				exist_ok=__SCREAMING_SNAKE_CASE							)
   __a							=	{"source": "What is love ?", "target": "life"}
   __a							=	{"train": 12, "val": 2, "test": 2}
   for split in ["train", "test", "val"]:
    for field in ["source", "target"]:
     __a							=	"\n".join([contents[field]] * n_lines[split]							)
     with open(os.path.join(__SCREAMING_SNAKE_CASE  ,				f"""{split}.{field}"""							)  ,				"w"							) as f:
      f.write(__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   str  ,				__SCREAMING_SNAKE_CASE			:   int  ,				__SCREAMING_SNAKE_CASE			:   str = "pytorch"							):
   __a							=	self.get_auto_remove_tmp_dir()
   __a							=	os.path.join(__SCREAMING_SNAKE_CASE  ,				"output"							)
   __a							=	os.path.join(__SCREAMING_SNAKE_CASE  ,				"data"							)
   self._create_dummy_data(data_dir=__SCREAMING_SNAKE_CASE							)

   __a							=	f"""
                --data_dir {data_dir} \
                --output_dir {output_dir} \
                --model_name_or_path facebook/rag-sequence-base \
                --model_type rag_sequence \
                --do_train \
                --do_predict \
                --n_val -1 \
                --val_check_interval 1.0 \
                --train_batch_size 2 \
                --eval_batch_size 1 \
                --max_source_length 25 \
                --max_target_length 25 \
                --val_max_target_length 25 \
                --test_max_target_length 25 \
                --label_smoothing 0.1 \
                --dropout 0.1 \
                --attention_dropout 0.1 \
                --weight_decay 0.001 \
                --adam_epsilon 1e-08 \
                --max_grad_norm 0.1 \
                --lr_scheduler polynomial \
                --learning_rate 3e-04 \
                --num_train_epochs 1 \
                --warmup_steps 4 \
                --gradient_accumulation_steps 1 \
                --distributed-port 8787 \
                --use_dummy_dataset 1 \
                --distributed_retriever {distributed_retriever} \
            """.split()

   if gpus > 0:
    testargs.append(f"""--gpus={gpus}"""							)
    if is_apex_available():
     testargs.append("--fp16"							)
   else:
    testargs.append("--gpus=0"							)
    testargs.append("--distributed_backend=ddp_cpu"							)
    testargs.append("--num_processes=2"							)

   __a							=	[sys.executable, str(Path(finetune_rag.__file__							).resolve()							)] + testargs
   execute_subprocess_async(__SCREAMING_SNAKE_CASE  ,				env=self.get_env()							)

   __a							=	os.path.join(__SCREAMING_SNAKE_CASE  ,				"metrics.json"							)
   with open(__SCREAMING_SNAKE_CASE							) as f:
    __a							=	json.load(__SCREAMING_SNAKE_CASE							)
   return result

  @require_torch_gpu
  def 		_UpperCAmelCase (		self			:   Dict							):
   __a							=	self._run_finetune(gpus=1							)
   self.assertGreaterEqual(result["test"][0]["test_avg_em"]  ,				0.2							)

  @require_torch_multi_gpu
  def 		_UpperCAmelCase (		self			:   Optional[int]							):
   __a							=	self._run_finetune(gpus=2							)
   self.assertGreaterEqual(result["test"][0]["test_avg_em"]  ,				0.2							)

  @require_torch_gpu
  @require_ray
  def 		_UpperCAmelCase (		self			:   Optional[Any]							):
   __a							=	self._run_finetune(gpus=1  ,				distributed_retriever="ray"							)
   self.assertGreaterEqual(result["test"][0]["test_avg_em"]  ,				0.2							)

  @require_torch_multi_gpu
  @require_ray
  def 		_UpperCAmelCase (		self			:   Any							):
   __a							=	self._run_finetune(gpus=1  ,				distributed_retriever="ray"							)
   self.assertGreaterEqual(result["test"][0]["test_avg_em"]  ,				0.2							)


