


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
snake_case			:      Any 							=   {
    'translation': ['bleu'],
    'summarization': ROUGE_KEYS,
}


def 				SCREAMING_SNAKE_CASE (     UpperCAmelCase__							):

				"""simple docstring"""






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


def 				SCREAMING_SNAKE_CASE (     ):

				"""simple docstring"""






				_SCREAMING_SNAKE_CASE      = sys.argv[0]

				_SCREAMING_SNAKE_CASE      = 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.'
				    )   ,)
				_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = parser.parse_known_args()
				# we share some of the args
				args_main.extend(['--task', args.task]							)
				_SCREAMING_SNAKE_CASE      = [prog] + args_main

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

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

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

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

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

				_SCREAMING_SNAKE_CASE      = 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]							)							)

				_SCREAMING_SNAKE_CASE      = results_sorted[0]
				for score in task_score_names[task]:
								del best[score]
				_SCREAMING_SNAKE_CASE      = [f'''--{k} {v}''' for k, v in best.items()]
				_SCREAMING_SNAKE_CASE      = ['--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()




import json
import os
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

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


if TYPE_CHECKING:
							from transformers.pipelines.conversational import Conversation

snake_case			:      Any 							=   logging.get_logger(__name__)

snake_case			:      str 							=   {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt'}

snake_case			:      Optional[int] 							=   {
    'vocab_file': {
        'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json',
        'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json',
        'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json',
        'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json',
        'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json',
        'microsoft/deberta-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt',
        'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt',
        'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt',
        'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt',
        'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt',
        'microsoft/deberta-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt'
        ),
    },
}

snake_case			:      Tuple 							=   {
    'microsoft/deberta-base': 5_12,
    'microsoft/deberta-large': 5_12,
    'microsoft/deberta-xlarge': 5_12,
    'microsoft/deberta-base-mnli': 5_12,
    'microsoft/deberta-large-mnli': 5_12,
    'microsoft/deberta-xlarge-mnli': 5_12,
}

snake_case			:      Any 							=   {
    'microsoft/deberta-base': {'do_lower_case': False},
    'microsoft/deberta-large': {'do_lower_case': False},
}


def 				SCREAMING_SNAKE_CASE (     ):

				"""simple docstring"""






				_SCREAMING_SNAKE_CASE      = (
				    list(range(ord('!'							)   ,ord('~'							) + 1							)							) + list(range(ord('¡'							)   ,ord('¬'							) + 1							)							) + list(range(ord('®'							)   ,ord('ÿ'							) + 1							)							)
				)
				_SCREAMING_SNAKE_CASE      = bs[:]
				_SCREAMING_SNAKE_CASE      = 0
				for b in range(2**8							):
								if b not in bs:
												bs.append(UpperCAmelCase__							)
												cs.append(2**8 + n							)
												n += 1
				_SCREAMING_SNAKE_CASE      = [chr(UpperCAmelCase__							) for n in cs]
				return dict(zip(UpperCAmelCase__   ,UpperCAmelCase__							)							)


def 				SCREAMING_SNAKE_CASE (     UpperCAmelCase__							):

				"""simple docstring"""






				_SCREAMING_SNAKE_CASE      = set()
				_SCREAMING_SNAKE_CASE      = word[0]
				for char in word[1:]:
								pairs.add((prev_char, char)							)
								_SCREAMING_SNAKE_CASE      = char
				return pairs



class    __lowercase  (       UpperCamelCase							):

							"""simple docstring"""


							SCREAMING_SNAKE_CASE   :					int        =						VOCAB_FILES_NAMES
							SCREAMING_SNAKE_CASE   :					List[Any]        =						PRETRAINED_VOCAB_FILES_MAP
							SCREAMING_SNAKE_CASE   :					Any        =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							SCREAMING_SNAKE_CASE   :					Optional[Any]        =						["input_ids", "attention_mask", "token_type_ids"]





							def __init__(    self				, A_				, A_				, A_="replace"				, A_="[CLS]"				, A_="[SEP]"				, A_="[SEP]"				, A_="[CLS]"				, A_="[UNK]"				, A_="[PAD]"				, A_="[MASK]"				, A_=False				, A_=False				, **A_				, )->	List[str]:
											_SCREAMING_SNAKE_CASE      = AddedToken(A_				, lstrip=A_				, rstrip=A_	) if isinstance(A_				, A_	) else bos_token
											_SCREAMING_SNAKE_CASE      = AddedToken(A_				, lstrip=A_				, rstrip=A_	) if isinstance(A_				, A_	) else eos_token
											_SCREAMING_SNAKE_CASE      = AddedToken(A_				, lstrip=A_				, rstrip=A_	) if isinstance(A_				, A_	) else sep_token
											_SCREAMING_SNAKE_CASE      = AddedToken(A_				, lstrip=A_				, rstrip=A_	) if isinstance(A_				, A_	) else cls_token
											_SCREAMING_SNAKE_CASE      = AddedToken(A_				, lstrip=A_				, rstrip=A_	) if isinstance(A_				, A_	) else unk_token
											_SCREAMING_SNAKE_CASE      = AddedToken(A_				, lstrip=A_				, rstrip=A_	) if isinstance(A_				, A_	) else pad_token

											# Mask token behave like a normal word, i.e. include the space before it
											_SCREAMING_SNAKE_CASE      = AddedToken(A_				, lstrip=A_				, rstrip=A_	) if isinstance(A_				, A_	) else mask_token

											super().__init__(
											    errors=A_				, bos_token=A_				, eos_token=A_				, unk_token=A_				, sep_token=A_				, cls_token=A_				, pad_token=A_				, mask_token=A_				, add_prefix_space=A_				, add_bos_token=A_				, **A_				, )
											_SCREAMING_SNAKE_CASE      = add_bos_token

											with open(A_				, encoding='utf-8'	) as vocab_handle:
															_SCREAMING_SNAKE_CASE      = json.load(A_	)
											_SCREAMING_SNAKE_CASE      = {v: k for k, v in self.encoder.items()}
											_SCREAMING_SNAKE_CASE      = errors  # how to handle errors in decoding
											_SCREAMING_SNAKE_CASE      = bytes_to_unicode()
											_SCREAMING_SNAKE_CASE      = {v: k for k, v in self.byte_encoder.items()}
											with open(A_				, encoding='utf-8'	) as merges_handle:
															_SCREAMING_SNAKE_CASE      = merges_handle.read().split('\n'	)[1:-1]
											_SCREAMING_SNAKE_CASE      = [tuple(merge.split()	) for merge in bpe_merges]
											_SCREAMING_SNAKE_CASE      = dict(zip(A_				, range(len(A_	)	)	)	)
											_SCREAMING_SNAKE_CASE      = {}
											_SCREAMING_SNAKE_CASE      = add_prefix_space

											# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
											_SCREAMING_SNAKE_CASE      = re.compile(r'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'	)





							@property
							# Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer.vocab_size
							def 		__magic_name__			(    self	)->	int:
											return len(self.encoder	)





							def 		__magic_name__			(    self	)->	Tuple:
											return dict(self.encoder				, **self.added_tokens_encoder	)





							def 		__magic_name__			(    self				, A_	)->	Optional[int]:
											if token in self.cache:
															return self.cache[token]
											_SCREAMING_SNAKE_CASE      = tuple(A_	)
											_SCREAMING_SNAKE_CASE      = get_pairs(A_	)

											if not pairs:
															return token

											while True:
															_SCREAMING_SNAKE_CASE      = min(A_				, key=lambda A_	: self.bpe_ranks.get(A_				, float('inf'	)	)	)
															if bigram not in self.bpe_ranks:
																			break
															_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = bigram
															_SCREAMING_SNAKE_CASE      = []
															_SCREAMING_SNAKE_CASE      = 0
															while i < len(A_	):
																			try:
																							_SCREAMING_SNAKE_CASE      = word.index(A_				, A_	)
																			except ValueError:
																							new_word.extend(word[i:]	)
																							break
																			else:
																							new_word.extend(word[i:j]	)
																							_SCREAMING_SNAKE_CASE      = j

																			if word[i] == first and i < len(A_	) - 1 and word[i + 1] == second:
																							new_word.append(first + second	)
																							i += 2
																			else:
																							new_word.append(word[i]	)
																							i += 1
															_SCREAMING_SNAKE_CASE      = tuple(A_	)
															_SCREAMING_SNAKE_CASE      = new_word
															if len(A_	) == 1:
																			break
															else:
																			_SCREAMING_SNAKE_CASE      = get_pairs(A_	)
											_SCREAMING_SNAKE_CASE      = ' '.join(A_	)
											_SCREAMING_SNAKE_CASE      = word
											return word





							def 		__magic_name__			(    self				, A_				, A_ = None	)->	List[int]:
											if token_ids_a is None:
															return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
											_SCREAMING_SNAKE_CASE      = [self.cls_token_id]
											_SCREAMING_SNAKE_CASE      = [self.sep_token_id]
											return cls + token_ids_a + sep + token_ids_a + sep





							def 		__magic_name__			(    self				, A_				, A_ = None				, A_ = False	)->	List[int]:
											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 None:
															return [1] + ([0] * len(A_	)) + [1]
											return [1] + ([0] * len(A_	)) + [1] + ([0] * len(A_	)) + [1]





							def 		__magic_name__			(    self				, A_				, A_ = None	)->	List[int]:
											_SCREAMING_SNAKE_CASE      = [self.sep_token_id]
											_SCREAMING_SNAKE_CASE      = [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				, A_	)->	Dict:
											_SCREAMING_SNAKE_CASE      = []
											for token in re.findall(self.pat				, A_	):
															_SCREAMING_SNAKE_CASE      = ''.join(
															    self.byte_encoder[b] for b in token.encode('utf-8'	)	)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
															bpe_tokens.extend(bpe_token for bpe_token in self.bpe(A_	).split(' '	)	)
											return bpe_tokens





							def 		__magic_name__			(    self				, A_	)->	Optional[Any]:
											return self.encoder.get(A_				, self.encoder.get(self.unk_token	)	)





							def 		__magic_name__			(    self				, A_	)->	Dict:
											return self.decoder.get(A_	)





							def 		__magic_name__			(    self				, A_	)->	Dict:
											_SCREAMING_SNAKE_CASE      = ''.join(A_	)
											_SCREAMING_SNAKE_CASE      = bytearray([self.byte_decoder[c] for c in text]	).decode('utf-8'				, errors=self.errors	)
											return text





							def 		__magic_name__			(    self				, A_				, A_ = None	)->	Tuple[str]:
											if not os.path.isdir(A_	):
															logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''	)
															return
											_SCREAMING_SNAKE_CASE      = os.path.join(
											    A_				, (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']	)
											_SCREAMING_SNAKE_CASE      = os.path.join(
											    A_				, (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']	)

											with open(A_				, 'w'				, encoding='utf-8'	) as f:
															f.write(json.dumps(self.encoder				, indent=2				, sort_keys=A_				, ensure_ascii=A_	) + '\n'	)

											_SCREAMING_SNAKE_CASE      = 0
											with open(A_				, 'w'				, encoding='utf-8'	) as writer:
															writer.write('#version: 0.2\n'	)
															for bpe_tokens, token_index in sorted(self.bpe_ranks.items()				, key=lambda A_	: kv[1]	):
																			if index != token_index:
																							logger.warning(
																							    F'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
																							    ' Please check that the tokenizer is not corrupted!'	)
																							_SCREAMING_SNAKE_CASE      = token_index
																			writer.write(' '.join(A_	) + '\n'	)
																			index += 1

											return vocab_file, merge_file





							def 		__magic_name__			(    self				, A_				, A_=False				, **A_	)->	str:
											_SCREAMING_SNAKE_CASE      = kwargs.pop('add_prefix_space'				, self.add_prefix_space	)
											if (is_split_into_words or add_prefix_space) and (len(A_	) > 0 and not text[0].isspace()):
															_SCREAMING_SNAKE_CASE      = ' ' + text
											return (text, kwargs)





							def 		__magic_name__			(    self				, A_	)->	List[int]:
											_SCREAMING_SNAKE_CASE      = []
											for is_user, text in conversation.iter_texts():
															input_ids.extend(self.encode(A_				, add_special_tokens=A_	) + [self.eos_token_id]	)
											if len(A_	) > self.model_max_length:
															_SCREAMING_SNAKE_CASE      = input_ids[-self.model_max_length :]
											return input_ids
