

import os
from shutil import copyfile
from typing import Optional, Tuple

from ...file_utils import is_sentencepiece_available
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if is_sentencepiece_available():
  from .tokenization_deberta_va import DebertaVaTokenizer
else:
  __UpperCAmelCase     					=  None

__UpperCAmelCase     					=  logging.get_logger(__name__)

__UpperCAmelCase     					=  {'vocab_file': 'spm.model', 'tokenizer_file': 'tokenizer.json'}

__UpperCAmelCase     					=  {
    'vocab_file': {
        'microsoft/deberta-v2-xlarge': 'https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/spm.model',
        'microsoft/deberta-v2-xxlarge': 'https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/spm.model',
        'microsoft/deberta-v2-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/spm.model'
        ),
        'microsoft/deberta-v2-xxlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/spm.model'
        ),
    }
}

__UpperCAmelCase     					=  {
    'microsoft/deberta-v2-xlarge': 5_1_2,
    'microsoft/deberta-v2-xxlarge': 5_1_2,
    'microsoft/deberta-v2-xlarge-mnli': 5_1_2,
    'microsoft/deberta-v2-xxlarge-mnli': 5_1_2,
}

__UpperCAmelCase     					=  {
    'microsoft/deberta-v2-xlarge': {'do_lower_case': False},
    'microsoft/deberta-v2-xxlarge': {'do_lower_case': False},
    'microsoft/deberta-v2-xlarge-mnli': {'do_lower_case': False},
    'microsoft/deberta-v2-xxlarge-mnli': {'do_lower_case': False},
}





class A__     (			A			):



 """simple docstring"""







 _lowercase  :       List[str]  			=    VOCAB_FILES_NAMES
 _lowercase  :       Union[str, Any]  			=    PRETRAINED_VOCAB_FILES_MAP
 _lowercase  :       int  			=    PRETRAINED_INIT_CONFIGURATION
 _lowercase  :       List[str]  			=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _lowercase  :       Optional[Any]  			=    DebertaVaTokenizer

 def __init__(		self			:						Optional[Any]  ,    A_			:						Dict=None  ,    A_			:						Union[str, Any]=None  ,    A_			:						Optional[int]=False  ,    A_			:						Optional[int]=False  ,    A_			:						Optional[Any]="[CLS]"  ,    A_			:						Optional[int]="[SEP]"  ,    A_			:						List[Any]="[UNK]"  ,    A_			:						Tuple="[SEP]"  ,    A_			:						int="[PAD]"  ,    A_			:						Union[str, Any]="[CLS]"  ,    A_			:						List[str]="[MASK]"  ,    **A_			:						str  ,    ):






      '''simple docstring'''




      super().__init__(
          A_  ,    tokenizer_file=A_  ,    do_lower_case=A_  ,    bos_token=A_  ,    eos_token=A_  ,    unk_token=A_  ,    sep_token=A_  ,    pad_token=A_  ,    cls_token=A_  ,    mask_token=A_  ,    split_by_punct=A_  ,    **A_  ,    )

      _lowerCAmelCase :       int	   =     do_lower_case
      _lowerCAmelCase :       Tuple	   =     split_by_punct
      _lowerCAmelCase :       Union[str, Any]	   =     vocab_file
      _lowerCAmelCase :       Dict	   =     False if not self.vocab_file else True

 def  __magic_name__							(		self			:						Dict  ,    A_			:						List[Any]  ,    A_			:						Tuple=None							):






      '''simple docstring'''




      if token_ids_a is None:
           return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
      _lowerCAmelCase :       Union[str, Any]	   =     [self.cls_token_id]
      _lowerCAmelCase :       Any	   =     [self.sep_token_id]
      return cls + token_ids_a + sep + token_ids_a + sep

 def  __magic_name__							(		self			:						Optional[int]  ,    A_			:						Optional[Any]  ,    A_			:						List[str]=None  ,    A_			:						List[str]=False							):






      '''simple docstring'''




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

      if token_ids_a is not None:
           return [1] + ([0] * len(A_							)) + [1] + ([0] * len(A_							)) + [1]
      return [1] + ([0] * len(A_							)) + [1]

 def  __magic_name__							(		self			:						List[str]  ,    A_			:						List[str]  ,    A_			:						int=None							):






      '''simple docstring'''




      _lowerCAmelCase :       Tuple	   =     [self.sep_token_id]
      _lowerCAmelCase :       Any	   =     [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							) * [0] + len(token_ids_a + sep							) * [1]


 def  __magic_name__							(		self			:						str  ,    A_			:						str  ,    A_			:						Optional[str] = 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(A_							):
           logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''							)
           return
      _lowerCAmelCase :       Optional[Any]	   =     os.path.join(
          A_  ,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]							)

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

      return (out_vocab_file,)









import argparse
import itertools
import operator
import sys
from collections import OrderedDict

from run_eval import datetime_now, run_generate

from utils import ROUGE_KEYS


# A table of supported tasks and the list of scores in the order of importance to be sorted by.
# To add a new task, simply list the score names that `run_eval.run_generate()` returns
__UpperCAmelCase     					=  {
    'translation': ['bleu'],
    'summarization': ROUGE_KEYS,
}







def 						_snake_case		(     SCREAMING_SNAKE_CASE					)							->   Optional[Any]:

     """simple docstring"""







     _lowerCAmelCase :       Dict	   =     search.split()
     _lowerCAmelCase :       int	   =     dict((g.split("="					) for g in groups)					)
     _lowerCAmelCase :       Dict	   =     list(entries.keys()					)
     _lowerCAmelCase :       Tuple	   =     [[f'''--{k} {v}''' for v in vs.split(":"					)] for k, vs in entries.items()]
     _lowerCAmelCase :       Dict	   =     [list(SCREAMING_SNAKE_CASE					) for x in itertools.product(*SCREAMING_SNAKE_CASE					)]
     return matrix, entry_names







def 						_snake_case		(     )							->   Tuple:

     """simple docstring"""







     _lowerCAmelCase :       List[str]	   =     sys.argv[0]

     _lowerCAmelCase :       Any	   =     argparse.ArgumentParser(
         usage=(
             "\n\nImportant: this script accepts all arguments `run_eval.py` accepts and then a few extra, therefore"
             " refer to `run_eval.py -h` for the complete list."
         )					)
     parser.add_argument(
         "--search"		,				type=SCREAMING_SNAKE_CASE		,				required=SCREAMING_SNAKE_CASE		,				help="param space to search, e.g. \"num_beams=5:10 length_penalty=0.8:1.0:1.2\""		,				)
     parser.add_argument(
         "--bs"		,				type=SCREAMING_SNAKE_CASE		,				default=8		,				required=SCREAMING_SNAKE_CASE		,				help="initial batch size (may get reduced if it's too big)"					)
     parser.add_argument("--task"		,				type=SCREAMING_SNAKE_CASE		,				help="used for task_specific_params + metrics"					)
     parser.add_argument(
         "--info"		,				nargs="?"		,				type=SCREAMING_SNAKE_CASE		,				const=datetime_now()		,				help=(
             "add custom notes to be printed before the results table. If no value is passed, the current datetime"
             " string will be used."
         )		,				)
     _lowerCAmelCase   ,      _lowerCAmelCase :       List[str]	   =     parser.parse_known_args()
     # we share some of the args
     args_main.extend(["--task", args.task]					)
     _lowerCAmelCase :       str	   =     [prog] + args_main

     # to support variations like translation_en_to_de"
     _lowerCAmelCase :       Any	   =     "translation" if "translation" in args.task else "summarization"

     _lowerCAmelCase   ,      _lowerCAmelCase :       int	   =     parse_search_arg(args.search					)
     _lowerCAmelCase :       Optional[Any]	   =     task_score_names[task]  # score cols first
     _lowerCAmelCase :       int	   =     {col: len(str(SCREAMING_SNAKE_CASE					)					) for col in col_names}
     _lowerCAmelCase :       Optional[int]	   =     []
     for r in matrix:
          _lowerCAmelCase :       List[str]	   =     dict((x.replace("--"		,				""					).split() for x in r)					)
          _lowerCAmelCase :       Any	   =     " ".join(SCREAMING_SNAKE_CASE					).split()
          args_exp.extend(["--bs", str(args.bs					)]					)  # in case we need to reduce its size due to CUDA OOM
          _lowerCAmelCase :       List[str]	   =     args_normal + args_exp

          # XXX: need to trap CUDA OOM and lower args.bs if that happens and retry

          _lowerCAmelCase :       Any	   =     run_generate(verbose=SCREAMING_SNAKE_CASE					)
          # make sure scores are first in the table
          _lowerCAmelCase :       Any	   =     OrderedDict()
          for score in task_score_names[task]:
               _lowerCAmelCase :       Dict	   =     scores[score]
          result.update(SCREAMING_SNAKE_CASE					)
          results.append(SCREAMING_SNAKE_CASE					)

          # find widest entries
          for k, v in result.items():
               _lowerCAmelCase :       Any	   =     len(str(SCREAMING_SNAKE_CASE					)					)
               if l > col_widths[k]:
                    _lowerCAmelCase :       str	   =     l

     _lowerCAmelCase :       Optional[Any]	   =     sorted(SCREAMING_SNAKE_CASE		,				key=operator.itemgetter(*task_score_names[task]					)		,				reverse=SCREAMING_SNAKE_CASE					)
     print(" | ".join([f'''{col:{col_widths[col]}}''' for col in col_names]					)					)
     print(" | ".join([f'''{'-'*col_widths[col]}''' for col in col_names]					)					)
     for row in results_sorted:
          print(" | ".join([f'''{row[col]:{col_widths[col]}}''' for col in col_names]					)					)

     _lowerCAmelCase :       Optional[int]	   =     results_sorted[0]
     for score in task_score_names[task]:
          del best[score]
     _lowerCAmelCase :       Tuple	   =     [f'''--{k} {v}''' for k, v in best.items()]
     _lowerCAmelCase :       Union[str, Any]	   =     ["--bs", str(args.bs					)]
     if args.info:
          print(f'''\nInfo: {args.info}'''					)
     print("\nBest score args:"					)
     print(" ".join(args_main + best_args + dyn_args					)					)

     return results_sorted


if __name__ == "__main__":
  # Usage:
  # [normal-run_eval_search.py cmd plus] \
  # --search="num_beams=1:5:10 length_penalty=0.8:1:1.2 early_stopping=true:false"
  #
  # Example:
  # PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval_search.py $MODEL_NAME \
  # $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target \
  # --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation \
  # --search="num_beams=1:5:10 length_penalty=0.8:1:1.2 early_stopping=true:false"
  run_search()






