






import os
from typing import List, Union

import tensorflow as tf
from tensorflow_text import BertTokenizer as BertTokenizerLayer
from tensorflow_text import FastBertTokenizer, ShrinkLongestTrimmer, case_fold_utfa, combine_segments, pad_model_inputs

from .tokenization_bert import BertTokenizer

class        _SCREAMING_SNAKE_CASE			(     tf.keras.layers.Layer ):





	def __init__(						self							:   str					,    __lowerCamelCase							:   Tuple					,    __lowerCamelCase							:   Tuple					,    __lowerCamelCase							:   Tuple = None					,    __lowerCamelCase							:   List[str] = None					,    __lowerCamelCase							:   Optional[Any] = None					,    __lowerCamelCase							:   int = "longest"					,    __lowerCamelCase							:   int = True					,    __lowerCamelCase							:   int = 512					,    __lowerCamelCase							:   List[str] = None					,    __lowerCamelCase							:   int = True					,    __lowerCamelCase							:   Optional[Any] = True					,    __lowerCamelCase							:   Optional[int] = True					,    **__lowerCamelCase							:   List[str]					,    ):
		super().__init__()
		if use_fast_bert_tokenizer:
			UpperCamelCase	:Any		=      FastBertTokenizer(
			    __snake_case					,    token_out_type=tf.intaa					,    lower_case_nfd_strip_accents=__snake_case					,    **__snake_case      )
		else:
			UpperCamelCase	:Dict		=      tf.lookup.StaticVocabularyTable(
			    tf.lookup.KeyValueTensorInitializer(
			        keys=__snake_case					,    key_dtype=tf.string					,    values=tf.range(tf.size(__snake_case					,    out_type=tf.intaa      )					,    dtype=tf.intaa      )					,    value_dtype=tf.intaa					,    )					,    num_oov_buckets=1					,    )
			UpperCamelCase	:Any		=      BertTokenizerLayer(
			    __snake_case					,    token_out_type=tf.intaa					,    lower_case=__snake_case					,    **__snake_case      )

		UpperCamelCase	:Tuple		=      vocab_list
		UpperCamelCase	:Optional[int]		=      do_lower_case
		UpperCamelCase	:Union[str, Any]		=      cls_token_id or vocab_list.index("""[CLS]"""      )
		UpperCamelCase	:Optional[int]		=      sep_token_id or vocab_list.index("""[SEP]"""      )
		UpperCamelCase	:Any		=      pad_token_id or vocab_list.index("""[PAD]"""      )
		UpperCamelCase	:List[Any]		=      ShrinkLongestTrimmer(max_length - 3					,    axis=1      )  # Allow room for special tokens
		UpperCamelCase	:Tuple		=      max_length
		UpperCamelCase	:Dict		=      padding
		UpperCamelCase	:int		=      truncation
		UpperCamelCase	:Optional[int]		=      pad_to_multiple_of
		UpperCamelCase	:List[str]		=      return_token_type_ids
		UpperCamelCase	:int		=      return_attention_mask





	@classmethod
	def 		_A					(						cls							:   Union[str, Any]					,    __lowerCamelCase							:   Any					,    **__lowerCamelCase							:   Optional[Any]      ):  # noqa: F821
		UpperCamelCase	:int		=      kwargs.pop("""do_lower_case"""					,    __snake_case      )
		UpperCamelCase	:List[str]		=      tokenizer.do_lower_case if do_lower_case is None else do_lower_case
		UpperCamelCase	:Any		=      kwargs.pop("""cls_token_id"""					,    __snake_case      )
		UpperCamelCase	:Tuple		=      tokenizer.cls_token_id if cls_token_id is None else cls_token_id
		UpperCamelCase	:str		=      kwargs.pop("""sep_token_id"""					,    __snake_case      )
		UpperCamelCase	:Optional[Any]		=      tokenizer.sep_token_id if sep_token_id is None else sep_token_id
		UpperCamelCase	:List[str]		=      kwargs.pop("""pad_token_id"""					,    __snake_case      )
		UpperCamelCase	:Any		=      tokenizer.pad_token_id if pad_token_id is None else pad_token_id

		UpperCamelCase	:List[str]		=      tokenizer.get_vocab()
		UpperCamelCase	:Union[str, Any]		=      sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()]					,    key=lambda __lowerCamelCase      : x[1]      )
		UpperCamelCase	:Optional[Any]		=      [entry[0] for entry in vocab]
		return cls(
		    vocab_list=__snake_case					,    do_lower_case=__snake_case					,    cls_token_id=__snake_case					,    sep_token_id=__snake_case					,    pad_token_id=__snake_case					,    **__snake_case					,    )





	@classmethod
	def 		_A					(						cls							:   Tuple					,    __lowerCamelCase							:   Optional[Any]					,    *__lowerCamelCase							:   Union[str, Any]					,    **__lowerCamelCase							:   int      ):
		try:
			UpperCamelCase	:Tuple		=      BertTokenizer.from_pretrained(__snake_case					,    *__snake_case					,    **__snake_case      )
		except:  # noqa: E722
			from .tokenization_bert_fast import BertTokenizerFast

			UpperCamelCase	:Optional[Any]		=      BertTokenizerFast.from_pretrained(__snake_case					,    *__snake_case					,    **__snake_case      )
		return cls.from_tokenizer(__snake_case					,    **__snake_case      )





	def 		_A					(						self							:   Optional[Any]					,    __lowerCamelCase							:   Union[str, Any]      ):
		if self.do_lower_case:
			UpperCamelCase	:Dict		=      case_fold_utfa(__snake_case      )
		UpperCamelCase	:Optional[Any]		=      self.tf_tokenizer.tokenize(__snake_case      )
		return tokens.merge_dims(1					,    -1      )





	def 		_A					(						self							:   List[str]					,    __lowerCamelCase							:   List[str]					,    __lowerCamelCase							:   List[Any]=None					,    __lowerCamelCase							:   List[str]=None					,    __lowerCamelCase							:   Tuple=None					,    __lowerCamelCase							:   str=None					,    __lowerCamelCase							:   Optional[Any]=None					,    __lowerCamelCase							:   Any=None					,    __lowerCamelCase							:   Union[str, Any]=None					,    ):
		if padding is None:
			UpperCamelCase	:Dict		=      self.padding
		if padding not in ("longest", "max_length"):
			raise ValueError("""Padding must be either \'longest\' or \'max_length\'!"""      )
		if max_length is not None and text_pair is not None:
			# Because we have to instantiate a Trimmer to do it properly
			raise ValueError("""max_length cannot be overridden at call time when truncating paired texts!"""      )
		if max_length is None:
			UpperCamelCase	:Tuple		=      self.max_length
		if truncation is None:
			UpperCamelCase	:Dict		=      self.truncation
		if pad_to_multiple_of is None:
			UpperCamelCase	:Dict		=      self.pad_to_multiple_of
		if return_token_type_ids is None:
			UpperCamelCase	:Optional[Any]		=      self.return_token_type_ids
		if return_attention_mask is None:
			UpperCamelCase	:Dict		=      self.return_attention_mask
		if not isinstance(__snake_case					,    tf.Tensor      ):
			UpperCamelCase	:List[str]		=      tf.convert_to_tensor(__snake_case      )
		if text_pair is not None and not isinstance(__snake_case					,    tf.Tensor      ):
			UpperCamelCase	:int		=      tf.convert_to_tensor(__snake_case      )
		if text_pair is not None:
			if text.shape.rank > 1:
				raise ValueError("""text argument should not be multidimensional when a text pair is supplied!"""      )
			if text_pair.shape.rank > 1:
				raise ValueError("""text_pair should not be multidimensional!"""      )
		if text.shape.rank == 2:
			UpperCamelCase	:Optional[Any]		=      text[:, 0], text[:, 1]
		UpperCamelCase	:List[Any]		=      self.unpaired_tokenize(__snake_case      )
		if text_pair is None:  # Unpaired text
			if truncation:
				UpperCamelCase	:Any		=      text[:, : max_length - 2]  # Allow room for special tokens
			UpperCamelCase	:int		=      combine_segments(
			    (text,)					,    start_of_sequence_id=self.cls_token_id					,    end_of_segment_id=self.sep_token_id      )
		else:  # Paired text
			UpperCamelCase	:Tuple		=      self.unpaired_tokenize(__snake_case      )
			if truncation:
				UpperCamelCase	:Dict		=      self.paired_trimmer.trim([text, text_pair]      )
			UpperCamelCase	:Optional[Any]		=      combine_segments(
			    (text, text_pair)					,    start_of_sequence_id=self.cls_token_id					,    end_of_segment_id=self.sep_token_id      )
		if padding == "longest":
			UpperCamelCase	:str		=      input_ids.bounding_shape(axis=1      )
			if pad_to_multiple_of is not None:
				# No ceiling division in tensorflow, so we negate floordiv instead
				UpperCamelCase	:Optional[int]		=      pad_to_multiple_of * (-tf.math.floordiv(-pad_length					,    __snake_case      ))
		else:
			UpperCamelCase	:List[Any]		=      max_length

		UpperCamelCase	:List[Any]		=      pad_model_inputs(__snake_case					,    max_seq_length=__snake_case					,    pad_value=self.pad_token_id      )
		UpperCamelCase	:Dict		=      {'input_ids': input_ids}
		if return_attention_mask:
			UpperCamelCase	:str		=      attention_mask
		if return_token_type_ids:
			UpperCamelCase	:Any		=      pad_model_inputs(
			    __snake_case					,    max_seq_length=__snake_case					,    pad_value=self.pad_token_id      )
			UpperCamelCase	:str		=      token_type_ids
		return output





	def 		_A					(						self							:   List[str]      ):
		return {
		    "vocab_list": self.vocab_list,
		    "do_lower_case": self.do_lower_case,
		    "cls_token_id": self.cls_token_id,
		    "sep_token_id": self.sep_token_id,
		    "pad_token_id": self.pad_token_id,
		}



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()




