

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

from ...tokenization_utils_fast import AddedToken, PreTrainedTokenizerFast
from ...utils import logging


lowerCamelCase   					=  logging.get_logger(__name__)

lowerCamelCase   					=  {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

lowerCamelCase   					=  {
    '''vocab_file''': {
        '''TsinghuaAI/CPM-Generate''': '''https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model''',
    },
    '''tokenizer_file''': {
        '''TsinghuaAI/CPM-Generate''': '''https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/tokenizer.json''',
    },
}





class      snake_case_      (					_a				):


							"""simple docstring"""





							def __init__(			self       ,    _A=None       ,    _A=None       ,    _A=False       ,    _A=True       ,    _A=False       ,    _A="<s>"       ,    _A="</s>"       ,    _A="<unk>"       ,    _A="<sep>"       ,    _A="<pad>"       ,    _A="<cls>"       ,    _A="<mask>"       ,    _A=["<eop>", "<eod>"]       ,    **_A       ,    ):
												__lowerCAmelCase       =				AddedToken(_A       ,    lstrip=_A       ,    rstrip=_A				) if isinstance(_A       ,    _A				) else mask_token

												super().__init__(
												    vocab_file=_A       ,    tokenizer_file=_A       ,    do_lower_case=_A       ,    remove_space=_A       ,    keep_accents=_A       ,    bos_token=_A       ,    eos_token=_A       ,    unk_token=_A       ,    sep_token=_A       ,    pad_token=_A       ,    cls_token=_A       ,    mask_token=_A       ,    additional_special_tokens=_A       ,    **_A       ,    )

												__lowerCAmelCase       =				3
												__lowerCAmelCase       =				do_lower_case
												__lowerCAmelCase       =				remove_space
												__lowerCAmelCase       =				keep_accents
												__lowerCAmelCase       =				vocab_file
												__lowerCAmelCase       =				False if not self.vocab_file else True

												try:
																	import jieba
												except ModuleNotFoundError as error:
																	raise error.__class__(
																	    'You need to install jieba to use CpmTokenizer or CpmTokenizerFast. '
																	    'See https://pypi.org/project/jieba/ for installation.'				)
												__lowerCAmelCase       =				jieba
												__lowerCAmelCase       =				str.maketrans(' \n'       ,    '\u2582\u2583'				)




							def 							A__	(			self       ,    _A       ,    _A = None				):
												__lowerCAmelCase       =				[self.sep_token_id]
												__lowerCAmelCase       =				[self.cls_token_id]
												if token_ids_a is None:
																	return token_ids_a + sep + cls
												return token_ids_a + sep + token_ids_a + sep + cls




							def 							A__	(			self       ,    _A       ,    _A = None				):
												__lowerCAmelCase       =				[self.sep_token_id]
												__lowerCAmelCase       =				[2]

												if token_ids_a is None:
																	return len(token_ids_a + sep				) * [0] + cls_segment_id
												return len(token_ids_a + sep				) * [0] + len(token_ids_a + sep				) * [1] + cls_segment_id




							def 							A__	(			self       ,    _A       ,    _A = None				):
												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       =				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,)




							def 							A__	(			self       ,    _A       ,    *_A       ,    **_A				):
												__lowerCAmelCase       =				[
												    ' '.join([x.translate(self.translator				) for x in self.jieba.cut(_A       ,    cut_all=_A				)]				)
												    for text in batch_text_or_text_pairs
												]
												return super()._batch_encode_plus(_A       ,    *_A       ,    **_A				)




							def 							A__	(			self       ,    *_A       ,    **_A				):
												__lowerCAmelCase       =				super()._decode(*_A       ,    **_A				)
												__lowerCAmelCase       =				text.replace(' '       ,    ''				).replace('\u2582'       ,    ' '				).replace('\u2583'       ,    '\n'				)
												return text









import jax
import jax.numpy as jnp
from bigbird_flax import FlaxBigBirdForNaturalQuestions
from datasets import load_from_disk

from transformers import BigBirdTokenizerFast


lowerCamelCase   					=  {0: '''null''', 1: '''short''', 2: '''long''', 3: '''yes''', 4: '''no'''}
lowerCamelCase   					=  set(''''''.join(['''‘''', '''’''', '''´''', '''`''', '''.''', ''',''', '''-''', '''"''']))


def 							__lowercase     (	UpperCAmelCase__		,       UpperCAmelCase__=0		,       UpperCAmelCase__=None   ):

					"""simple docstring"""






					return [" ".join(x.split(' '   )[begin:end]   ) for x in answers if len(x.split(' '   )   ) > 1]


def 							__lowercase     (	UpperCAmelCase__		,       UpperCAmelCase__=False   ):

					"""simple docstring"""






					if make_sub_answers:
										# if answers are longer than one word, make sure a predictions is correct if it coresponds to the complete 1: or :-1 sub word
										# *e.g.* if the correct answer contains a prefix such as "the", or "a"
										__lowerCAmelCase       =				(
										    given_answers + get_sub_answers(UpperCAmelCase__		,       begin=1   ) + get_sub_answers(UpperCAmelCase__		,       end=-1   )
										)
					__lowerCAmelCase       =				[]
					for answer in given_answers:
										__lowerCAmelCase       =				answer.replace('_'		,       ' '   ).lower()
										__lowerCAmelCase       =				''.join(c if c not in PUNCTUATION_SET_TO_EXCLUDE else ' ' for c in alias   )
										answers.append(' '.join(alias.split()   ).strip()   )
					return set(UpperCAmelCase__   )


def 							__lowercase     (	UpperCAmelCase__		,       UpperCAmelCase__		,       UpperCAmelCase__=1		,       UpperCAmelCase__=100   ):

					"""simple docstring"""






					__lowerCAmelCase,						__lowerCAmelCase       =				jax.lax.top_k(UpperCAmelCase__		,       UpperCAmelCase__   )
					__lowerCAmelCase,						__lowerCAmelCase       =				jax.lax.top_k(UpperCAmelCase__		,       UpperCAmelCase__   )

					__lowerCAmelCase       =				best_end_idx[:, None] - best_start_idx[None, :]
					__lowerCAmelCase       =				jnp.logical_or(widths < 0		,       widths > max_size   )
					__lowerCAmelCase       =				(best_end_scores[:, None] + best_start_scores[None, :]) - (1E8 * mask)
					__lowerCAmelCase       =				jnp.argmax(UpperCAmelCase__   ).item()

					return best_start_idx[best_score % top_k], best_end_idx[best_score // top_k]


def 							__lowercase     (	UpperCAmelCase__   ):

					"""simple docstring"""






					__lowerCAmelCase       =				sample['question']['text']
					__lowerCAmelCase       =				sample['document']['tokens']['token']
					__lowerCAmelCase       =				sample['document']['tokens']['is_html']
					__lowerCAmelCase       =				sample['annotations']['long_answer']
					__lowerCAmelCase       =				sample['annotations']['short_answers']

					__lowerCAmelCase       =				' '.join([context[i] for i in range(len(UpperCAmelCase__   )   ) if not is_html[i]]   )

					# 0 - No ; 1 - Yes
					for answer in sample["annotations"]["yes_no_answer"]:
										if answer == 0 or answer == 1:
															return {
															    "question": question,
															    "context": context_string,
															    "short": [],
															    "long": [],
															    "category": "no" if answer == 0 else "yes",
															}

					__lowerCAmelCase       =				[]
					for s in short_answers:
										short_targets.extend(s['text']   )
					__lowerCAmelCase       =				list(set(UpperCAmelCase__   )   )

					__lowerCAmelCase       =				[]
					for s in long_answers:
										if s["start_token"] == -1:
															continue
										__lowerCAmelCase       =				context[s['start_token'] : s['end_token']]
										__lowerCAmelCase       =				is_html[s['start_token'] : s['end_token']]
										__lowerCAmelCase       =				' '.join([answer[i] for i in range(len(UpperCAmelCase__   )   ) if not html[i]]   )
										if new_answer not in long_targets:
															long_targets.append(UpperCAmelCase__   )

					__lowerCAmelCase       =				'long_short' if len(short_targets + long_targets   ) > 0 else 'null'

					return {
					    "question": question,
					    "context": context_string,
					    "short": short_targets,
					    "long": long_targets,
					    "category": category,
					}


def 							__lowercase     (	):

					"""simple docstring"""






					__lowerCAmelCase       =				load_from_disk('natural-questions-validation'   )
					__lowerCAmelCase       =				dataset.map(UpperCAmelCase__   ).remove_columns(['annotations', 'document', 'id']   )
					print(UpperCAmelCase__   )

					__lowerCAmelCase       =				dataset.filter(lambda UpperCAmelCase__   : (len(x['question']   ) + len(x['context']   )) < 4 * 4_096   )
					__lowerCAmelCase       =				short_validation_dataset.filter(lambda UpperCAmelCase__   : x["category"] != "null"   )
					short_validation_dataset

					__lowerCAmelCase       =				'vasudevgupta/flax-bigbird-natural-questions'
					__lowerCAmelCase       =				FlaxBigBirdForNaturalQuestions.from_pretrained(UpperCAmelCase__   )
					__lowerCAmelCase       =				BigBirdTokenizerFast.from_pretrained(UpperCAmelCase__   )

					@jax.jit
					def forward(*UpperCAmelCase__		,       **UpperCAmelCase__   ):
										__lowerCAmelCase,						__lowerCAmelCase,						__lowerCAmelCase       =				model(*UpperCAmelCase__		,       **UpperCAmelCase__   )
										return start_logits, end_logits, jnp.argmax(UpperCAmelCase__		,       axis=-1   )

					def evaluate(UpperCAmelCase__   ):
										# encode question and context so that they are separated by a tokenizer.sep_token and cut at max_length
										__lowerCAmelCase       =				tokenizer(
										    example['question']		,       example['context']		,       return_tensors='np'		,       max_length=4_096		,       padding='max_length'		,       truncation=UpperCAmelCase__		,       )

										__lowerCAmelCase,						__lowerCAmelCase,						__lowerCAmelCase       =				forward(**UpperCAmelCase__   )

										__lowerCAmelCase       =				CATEGORY_MAPPING[category.item()]

										__lowerCAmelCase       =				example['long'] + example['short']
										if example["category"] in ["yes", "no", "null"]:
															__lowerCAmelCase       =				[example['category']]
										__lowerCAmelCase       =				example['category'] != 'null'
										# Now target can be: "yes", "no", "null", "list of long & short answers"

										if predicted_category in ["yes", "no", "null"]:
															__lowerCAmelCase       =				[predicted_category]
															__lowerCAmelCase       =				example['output'] == example['targets']
															__lowerCAmelCase       =				predicted_category != 'null'
															return example

										__lowerCAmelCase       =				38 if predicted_category == 'short' else 1_024
										__lowerCAmelCase,						__lowerCAmelCase       =				get_best_valid_start_end_idx(
										    start_scores[0]		,       end_scores[0]		,       top_k=8		,       max_size=UpperCAmelCase__   )

										__lowerCAmelCase       =				inputs['input_ids'][0].tolist()
										__lowerCAmelCase       =				[tokenizer.decode(input_ids[start_score : end_score + 1]   )]

										__lowerCAmelCase       =				expand_to_aliases(example['targets']		,       make_sub_answers=UpperCAmelCase__   )
										__lowerCAmelCase       =				expand_to_aliases(example['output']   )

										# some preprocessing to both prediction and answer
										__lowerCAmelCase       =				{''.join(a.split()   ) for a in answers}
										__lowerCAmelCase       =				{''.join(p.split()   ) for p in predictions}
										__lowerCAmelCase       =				{s for s in predictions if s not in ['``', '\'\'', '`', '\'']}

										# if there is a common element, it's a exact match
										__lowerCAmelCase       =				len(list(answers & predictions   )   ) > 0
										__lowerCAmelCase       =				predicted_category != 'null' and len(UpperCAmelCase__   ) > 0

										return example

					__lowerCAmelCase       =				short_validation_dataset.map(UpperCAmelCase__   )

					__lowerCAmelCase       =				len(UpperCAmelCase__   )
					__lowerCAmelCase       =				len(short_validation_dataset.filter(lambda UpperCAmelCase__   : x["match"] == 1   )   )
					print('EM score:'		,       (matched / total) * 100		,       '%'   )


if __name__ == "__main__":
		main()






