
'''simple docstring'''





import json
import os
from functools import lru_cache
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

UpperCAmelCase            = logging.get_logger(__name__)

UpperCAmelCase            = {
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
}

UpperCAmelCase            = {
    "vocab_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/vocab.json",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/vocab.json",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/vocab.json",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/vocab.json",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/vocab.json",
    },
    "merges_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/merges.txt",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/merges.txt",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/merges.txt",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/merges.txt",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/merges.txt",
    },
}

UpperCAmelCase            = {
    "gpt2": 1_024,
    "gpt2-medium": 1_024,
    "gpt2-large": 1_024,
    "gpt2-xl": 1_024,
    "distilgpt2": 1_024,
}

@lru_cache()
def   A			(							):
						snake_case							:  Dict			=     (
						    list(range(ord('''!'''							)     ,				ord('''~'''							) + 1							)							) + list(range(ord('''¡'''							)     ,				ord('''¬'''							) + 1							)							) + list(range(ord('''®'''							)     ,				ord('''ÿ'''							) + 1							)							)
						)
						snake_case							:  Tuple			=     bs[:]
						snake_case							:  int			=     0
						for b in range(2**8							):
												if b not in bs:
																		bs.append(A_							)
																		cs.append(2**8 + n							)
																		n += 1
						snake_case							:  Union[str, Any]			=     [chr(A_							) for n in cs]
						return dict(zip(A_     ,				A_							)							)

def   A			(							A_				:       Any							):
						snake_case							:  List[str]			=     set()
						snake_case							:  int			=     word[0]
						for char in word[1:]:
												pairs.add((prev_char, char)							)
												snake_case							:  List[str]			=     char
						return pairs





class     a (					__magic_name__     ):
				_snake_case		     =			VOCAB_FILES_NAMES
				_snake_case		     =			PRETRAINED_VOCAB_FILES_MAP
				_snake_case		     =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				_snake_case		     =			['''input_ids''', '''attention_mask''']




				def __init__(				self						:					Any,     SCREAMING_SNAKE_CASE_						:					Optional[Any],     SCREAMING_SNAKE_CASE_						:					str,     SCREAMING_SNAKE_CASE_						:					Tuple="replace",     SCREAMING_SNAKE_CASE_						:					Dict="<|endoftext|>",     SCREAMING_SNAKE_CASE_						:					str="<|endoftext|>",     SCREAMING_SNAKE_CASE_						:					Any="<|endoftext|>",     SCREAMING_SNAKE_CASE_						:					List[str]=None,     SCREAMING_SNAKE_CASE_						:					Union[str, Any]=False,     SCREAMING_SNAKE_CASE_						:					Tuple=False,     **SCREAMING_SNAKE_CASE_						:					str,     ):
										snake_case							:  Dict			=     AddedToken(SCREAMING_SNAKE_CASE_,     lstrip=SCREAMING_SNAKE_CASE_,     rstrip=SCREAMING_SNAKE_CASE_   ) if isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   ) else bos_token
										snake_case							:  Union[str, Any]			=     AddedToken(SCREAMING_SNAKE_CASE_,     lstrip=SCREAMING_SNAKE_CASE_,     rstrip=SCREAMING_SNAKE_CASE_   ) if isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   ) else eos_token
										snake_case							:  List[Any]			=     AddedToken(SCREAMING_SNAKE_CASE_,     lstrip=SCREAMING_SNAKE_CASE_,     rstrip=SCREAMING_SNAKE_CASE_   ) if isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   ) else unk_token
										snake_case							:  Tuple			=     AddedToken(SCREAMING_SNAKE_CASE_,     lstrip=SCREAMING_SNAKE_CASE_,     rstrip=SCREAMING_SNAKE_CASE_   ) if isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   ) else pad_token
										super().__init__(
										    errors=SCREAMING_SNAKE_CASE_,     unk_token=SCREAMING_SNAKE_CASE_,     bos_token=SCREAMING_SNAKE_CASE_,     eos_token=SCREAMING_SNAKE_CASE_,     pad_token=SCREAMING_SNAKE_CASE_,     add_prefix_space=SCREAMING_SNAKE_CASE_,     add_bos_token=SCREAMING_SNAKE_CASE_,     **SCREAMING_SNAKE_CASE_,     )
										snake_case							:  int			=     add_bos_token

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

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




				@property
				def 						__snake_case				(				self						:					Tuple   ):
										return len(self.encoder   )




				def 						__snake_case				(				self						:					List[Any]   ):
										return dict(self.encoder,     **self.added_tokens_encoder   )




				def 						__snake_case				(				self						:					int,     SCREAMING_SNAKE_CASE_						:					Tuple   ):
										if token in self.cache:
																return self.cache[token]
										snake_case							:  List[Any]			=     tuple(SCREAMING_SNAKE_CASE_   )
										snake_case							:  Optional[Any]			=     get_pairs(SCREAMING_SNAKE_CASE_   )

										if not pairs:
																return token

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

																						if word[i] == first and i < len(SCREAMING_SNAKE_CASE_   ) - 1 and word[i + 1] == second:
																												new_word.append(first + second   )
																												i += 2
																						else:
																												new_word.append(word[i]   )
																												i += 1
																snake_case							:  Tuple			=     tuple(SCREAMING_SNAKE_CASE_   )
																snake_case							:  Tuple			=     new_word
																if len(SCREAMING_SNAKE_CASE_   ) == 1:
																						break
																else:
																						snake_case							:  List[str]			=     get_pairs(SCREAMING_SNAKE_CASE_   )
										snake_case							:  Union[str, Any]			=     ''' '''.join(SCREAMING_SNAKE_CASE_   )
										snake_case							:  List[Any]			=     word
										return word




				def 						__snake_case				(				self						:					Union[str, Any],     SCREAMING_SNAKE_CASE_						:					Optional[int],     SCREAMING_SNAKE_CASE_						:					Optional[Any]=None   ):
										if self.add_bos_token:
																snake_case							:  int			=     [self.bos_token_id]
										else:
																snake_case							:  Dict			=     []

										snake_case							:  List[str]			=     bos_token_ids + token_ids_a

										if token_ids_a is None:
																return output

										return output + bos_token_ids + token_ids_a




				def 						__snake_case				(				self						:					Dict,     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 not self.add_bos_token:
																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_   ))
										return [1] + ([0] * len(SCREAMING_SNAKE_CASE_   )) + [1] + ([0] * len(SCREAMING_SNAKE_CASE_   ))




				def 						__snake_case				(				self						:					str,     SCREAMING_SNAKE_CASE_						:					List[Any]   ):
										snake_case							:  Optional[int]			=     []
										for token in re.findall(self.pat,     SCREAMING_SNAKE_CASE_   ):
																snake_case							:  Dict			=     ''''''.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(SCREAMING_SNAKE_CASE_   ).split(''' '''   )   )
										return bpe_tokens




				def 						__snake_case				(				self						:					Optional[Any],     SCREAMING_SNAKE_CASE_						:					List[str]   ):
										return self.encoder.get(SCREAMING_SNAKE_CASE_,     self.encoder.get(self.unk_token   )   )




				def 						__snake_case				(				self						:					List[str],     SCREAMING_SNAKE_CASE_						:					Tuple   ):
										return self.decoder.get(SCREAMING_SNAKE_CASE_   )




				def 						__snake_case				(				self						:					List[Any],     SCREAMING_SNAKE_CASE_						:					Dict   ):
										snake_case							:  Union[str, Any]			=     ''''''.join(SCREAMING_SNAKE_CASE_   )
										snake_case							:  Any			=     bytearray([self.byte_decoder[c] for c in text]   ).decode('''utf-8''',     errors=self.errors   )
										return text




				def 						__snake_case				(				self						:					Dict,     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
										snake_case							:  int			=     os.path.join(
										    SCREAMING_SNAKE_CASE_,     (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']   )
										snake_case							:  List[Any]			=     os.path.join(
										    SCREAMING_SNAKE_CASE_,     (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']   )

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

										snake_case							:  Optional[int]			=     0
										with open(SCREAMING_SNAKE_CASE_,     '''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 SCREAMING_SNAKE_CASE_   : 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!'''   )
																												snake_case							:  Union[str, Any]			=     token_index
																						writer.write(''' '''.join(SCREAMING_SNAKE_CASE_   ) + '''\n'''   )
																						index += 1

										return vocab_file, merge_file




				def 						__snake_case				(				self						:					Optional[int],     SCREAMING_SNAKE_CASE_						:					Tuple,     SCREAMING_SNAKE_CASE_						:					Dict=False,     **SCREAMING_SNAKE_CASE_						:					Optional[int]   ):
										snake_case							:  Any			=     kwargs.pop('''add_prefix_space''',     self.add_prefix_space   )
										if is_split_into_words or add_prefix_space:
																snake_case							:  Dict			=     ''' ''' + text
										return (text, kwargs)




				def 						__snake_case				(				self						:					Union[str, Any],     SCREAMING_SNAKE_CASE_						:					"Conversation"   ):
										snake_case							:  List[str]			=     []
										for is_user, text in conversation.iter_texts():
																input_ids.extend(self.encode(SCREAMING_SNAKE_CASE_,     add_special_tokens=SCREAMING_SNAKE_CASE_   ) + [self.eos_token_id]   )
										if len(SCREAMING_SNAKE_CASE_   ) > self.model_max_length:
																snake_case							:  str			=     input_ids[-self.model_max_length :]
										return input_ids




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()




