


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

from tokenizers import processors

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
from ...utils.versions import require_version


if TYPE_CHECKING:
  from transformers.pipelines.conversational import Conversation

require_version('''tokenizers>=0.13.3''')

if is_sentencepiece_available():
  from .tokenization_llama import LlamaTokenizer
else:
  UpperCAmelCase__        =				None

UpperCAmelCase__        =				logging.get_logger(__name__)
UpperCAmelCase__        =				{'''vocab_file''': '''tokenizer.model''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCAmelCase__      ,UpperCAmelCase__        =				'''[INST]''', '''[/INST]'''
UpperCAmelCase__      ,UpperCAmelCase__        =				'''<<SYS>>\n''', '''\n<</SYS>>\n\n'''

# fmt: off
UpperCAmelCase__        =				'''You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your\
answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\
that your responses are socially unbiased and positive in nature.

If a question does not make any sense, or is not factually coherent, explain why instead of answering something not\
correct. If you don\'t know the answer to a question, please don\'t share false information.'''







class  lowerCamelCase__  (			_lowercase):
     SCREAMING_SNAKE_CASE__      =     VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE__      =     LlamaTokenizer
     SCREAMING_SNAKE_CASE__      =     '''left'''
     SCREAMING_SNAKE_CASE__      =     ['''input_ids''', '''attention_mask''']





     def __init__(self						,	UpperCAmelCase=None						,	UpperCAmelCase=None						,	UpperCAmelCase=False						,	UpperCAmelCase="<unk>"						,	UpperCAmelCase="<s>"						,	UpperCAmelCase="</s>"						,	UpperCAmelCase=True						,	UpperCAmelCase=False						,	**UpperCAmelCase						,	)	->  int:
      super().__init__(
          vocab_file=__snake_case						,	tokenizer_file=__snake_case						,	clean_up_tokenization_spaces=__snake_case						,	unk_token=__snake_case						,	bos_token=__snake_case						,	eos_token=__snake_case						,	**__snake_case						,	)
      _lowercase				 =add_bos_token
      _lowercase				 =add_eos_token
      self.update_post_processor()

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





     def 	__A   (self			)	->  Optional[int]:
      _lowercase				 =self.bos_token
      _lowercase				 =self.bos_token_id

      _lowercase				 =self.eos_token
      _lowercase				 =self.eos_token_id

      _lowercase				 =f"{(bos+':0 ') * self.add_bos_token}$A:0{(' '+eos+':0') * self.add_eos_token}"
      _lowercase				 =f"{single}{(' '+bos+':1') * self.add_bos_token} $B:1{(' '+eos+':1') * self.add_eos_token}"

      _lowercase				 =[]
      if self.add_bos_token:
       special_tokens.append((bos, bos_token_id)			)
      if self.add_eos_token:
       special_tokens.append((eos, eos_token_id)			)
      _lowercase				 =processors.TemplateProcessing(
          single=__snake_case						,	pair=__snake_case						,	special_tokens=__snake_case			)





     @property
     def 	__A   (self			)	->  Any:
      return self._add_eos_token





     @property
     def 	__A   (self			)	->  List[str]:
      return self._add_bos_token





     @add_eos_token.setter
     def 	__A   (self						,	UpperCAmelCase			)	->  Union[str, Any]:
      _lowercase				 =value
      self.update_post_processor()





     @add_bos_token.setter
     def 	__A   (self						,	UpperCAmelCase			)	->  str:
      _lowercase				 =value
      self.update_post_processor()





     def 	__A   (self						,	UpperCAmelCase						,	UpperCAmelCase = None			)	->  Union[str, Any]:
      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(__snake_case			):
       logger.error(f"Vocabulary path ({save_directory}) should be a directory"			)
       return
      _lowercase				 =os.path.join(
          __snake_case						,	(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']			)

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

      return (out_vocab_file,)





     def 	__A   (self						,	UpperCAmelCase			)	->  int:
      _lowercase				 =list(conversation.iter_texts()			)
      if not all([is_user for is_user, msg in dialogue[::2]]			) or not all(
          [not is_user for is_user, msg in dialogue[1::2]]			):
       raise ValueError(
           '''The model only supports \'user\' and \'assistant\' roles, starting with user and alternating (u/a/u/a/u...)'''			)

      _lowercase				 =[]
      if len(conversation.past_user_inputs			) > 0:
       if not conversation.past_user_inputs[0].startswith(__snake_case			) or E_SYS not in conversation.past_user_inputs[0]:
        _lowercase				 =(
            B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
        )
      elif not dialogue[0][1].startswith(__snake_case			) or E_SYS not in dialogue[0][1]:
       _lowercase				 =(dialogue[0][0], B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + dialogue[0][1])

      dialog_tokens += sum(
          [
              [self.bos_token_id]
              + self.encode(
                  f"{B_INST} {(prompt[1]).strip()} {E_INST} {(answer[1]).strip()} "						,	add_special_tokens=__snake_case			)
              + [self.eos_token_id]
              for prompt, answer in zip(dialogue[::2]						,	dialogue[1::2]			)
          ]						,	[]						,	)
      if not (dialogue[-1][0]):
       raise ValueError(f"Last message must be from user, got {dialogue[-1]['role']}"			)
      dialog_tokens += [self.bos_token_id] + self.encode(
          f"{B_INST} {(dialogue[-1][1]).strip()} {E_INST}"						,	add_special_tokens=__snake_case			)
      return dialog_tokens




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
lowerCAmelCase__             =		{
    """translation""": ["""bleu"""],
    """summarization""": ROUGE_KEYS,
}






def        lowerCamelCase_  (				UpperCAmelCase_				:  Optional[int]						)    ->					Any:




	'''simple docstring'''




	_UpperCamelCase :		str								=    search.split()
	_UpperCamelCase :		str								=    dict((g.split('='						) for g in groups)						)
	_UpperCamelCase :		List[Any]								=    list(entries.keys()						)
	_UpperCamelCase :		Dict								=    [[F'''--{k} {v}''' for v in vs.split(':'						)] for k, vs in entries.items()]
	_UpperCamelCase :		Dict								=    [list(UpperCAmelCase_						) for x in itertools.product(*UpperCAmelCase_						)]
	return matrix, entry_names






def        lowerCamelCase_  (				)    ->					int:




	'''simple docstring'''




	_UpperCamelCase :		Optional[Any]								=    sys.argv[0]

	_UpperCamelCase :		Tuple								=    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=UpperCAmelCase_				,							required=UpperCAmelCase_				,							help='param space to search, e.g. "num_beams=5:10 length_penalty=0.8:1.0:1.2"'				,							)
	parser.add_argument(
	    '--bs'				,							type=UpperCAmelCase_				,							default=8				,							required=UpperCAmelCase_				,							help='initial batch size (may get reduced if it\'s too big)'						)
	parser.add_argument('--task'				,							type=UpperCAmelCase_				,							help='used for task_specific_params + metrics'						)
	parser.add_argument(
	    '--info'				,							nargs='?'				,							type=UpperCAmelCase_				,							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.'
	    )				,							)
	_UpperCamelCase						,		_UpperCamelCase :		str								=    parser.parse_known_args()
	# we share some of the args
	args_main.extend(['--task', args.task]						)
	_UpperCamelCase :		str								=    [prog] + args_main

	# to support variations like translation_en_to_de"
	_UpperCamelCase :		Tuple								=    'translation' if 'translation' in args.task else 'summarization'

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

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

		_UpperCamelCase :		Any								=    run_generate(verbose=UpperCAmelCase_						)
		# make sure scores are first in the table
		_UpperCamelCase :		Optional[int]								=    OrderedDict()
		for score in task_score_names[task]:
			_UpperCamelCase :		Tuple								=    scores[score]
		result.update(UpperCAmelCase_						)
		results.append(UpperCAmelCase_						)

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

	_UpperCamelCase :		Optional[int]								=    sorted(UpperCAmelCase_				,							key=operator.itemgetter(*task_score_names[task]						)				,							reverse=UpperCAmelCase_						)
	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]						)						)

	_UpperCamelCase :		Optional[int]								=    results_sorted[0]
	for score in task_score_names[task]:
		del best[score]
	_UpperCamelCase :		str								=    [F'''--{k} {v}''' for k, v in best.items()]
	_UpperCamelCase :		Optional[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()




