






"""simple docstring"""







from collections import Counter
import datasets
import transformers
from transformers.convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS

from transformers.utils import logging

logging.set_verbosity_info()

__a							:						Any   							=			{
    name: (getattr(transformers, name), getattr(transformers, name + 'Fast')) for name in SLOW_TO_FAST_CONVERTERS
}

__a							:						Union[str, Any]   							=			datasets.load_dataset('xnli', split='test+validation')

__a							:						Tuple   							=			0
__a							:						Tuple   							=			0
__a							:						int   							=			0
__a							:						str   							=			0





def 							SCREAMING_SNAKE_CASE							(   lowerCamelCase_			,  lowerCamelCase_			,  lowerCamelCase_			,  lowerCamelCase_):
					if spm_diff == list(reversed(lowerCamelCase_)):
										# AAA -> AA+A vs A+AA case.
										return True
					elif len(lowerCamelCase_) == len(lowerCamelCase_) and fast.decode(lowerCamelCase_) == fast.decode(lowerCamelCase_):
										# Second order OK
										# Barrich -> Barr + ich vs Bar + rich
										return True
					a__ 							=	slow.encode(slow.decode(lowerCamelCase_))
					a__ 							=	fast.encode(fast.decode(lowerCamelCase_))
					if spm_reencoded != spm_diff and spm_reencoded == tok_reencoded:
										# Type 3 error.
										# Snehagatha ->
										#       Sne, h, aga, th, a
										#       Sne, ha, gat, ha
										# Encoding the wrong with sp does not even recover what spm gave us
										# It fits tokenizer however...
										return True
					return False





def 							SCREAMING_SNAKE_CASE							(   lowerCamelCase_			,  lowerCamelCase_			,  lowerCamelCase_):
					a__ 							=	fast.encode_plus(lowerCamelCase_)[0]
					a__ 							=	enc.offsets
					a__							,a__ 							=	offsets[idx], offsets[idx - 1]
					if curr is not None and line[curr[0] : curr[1]] == "\u200f":
										return True
					if prev is not None and line[prev[0] : prev[1]] == "\u200f":
										return True





def 							SCREAMING_SNAKE_CASE							(   lowerCamelCase_			,  lowerCamelCase_			,  lowerCamelCase_			,  lowerCamelCase_			,  lowerCamelCase_):
					# Encoding can be the same with same result AAA -> A + AA vs AA + A
					# We can check that we use at least exactly the same number of tokens.
					for i, (spm_id, tok_id) in enumerate(zip(lowerCamelCase_			,  lowerCamelCase_)):
										if spm_id != tok_id:
															break
					a__ 							=	i
					for i, (spm_id, tok_id) in enumerate(zip(reversed(lowerCamelCase_)			,  reversed(lowerCamelCase_))):
										if spm_id != tok_id:
															break
					a__ 							=	len(lowerCamelCase_) - i

					a__ 							=	spm_ids[first:last]
					a__ 							=	tok_ids[first:last]

					if check_diff(lowerCamelCase_			,  lowerCamelCase_			,  lowerCamelCase_			,  lowerCamelCase_):
										return True

					if check_LTR_mark(lowerCamelCase_			,  lowerCamelCase_			,  lowerCamelCase_):
										return True

					if last - first > 5:
										# We might have twice a single problem, attempt to subdivide the disjointed tokens into smaller problems
										a__ 							=	Counter(spm_ids[first:last])
										a__ 							=	Counter(tok_ids[first:last])

										a__ 							=	{spm_ for (spm_, si) in spms.items() if toks.get(spm_			,  0) == si}
										a__ 							=	3
										for i in range(last - first - min_width):
															if all(spm_ids[first + i + j] in removable_tokens for j in range(lowerCamelCase_)):
																				a__ 							=	[
																				    k
																				    for k in range(last - first - min_width)
																				    if tok_ids[first + k : first + k + min_width] == spm_ids[first + i : first + i + min_width]
																				]
																				for j in possible_matches:
																									if check_diff(spm_ids[first : first + i]			,  tok_ids[first : first + j]			,  lowerCamelCase_			,  lowerCamelCase_) and check_details(
																									    lowerCamelCase_			,  spm_ids[first + i : last]			,  tok_ids[first + j : last]			,  lowerCamelCase_			,  lowerCamelCase_			,  ):
																														return True

					print(f'Spm: {[fast.decode([spm_ids[i]]) for i in range(lowerCamelCase_			,  lowerCamelCase_)]}')
					try:
										print(f'Tok: {[fast.decode([tok_ids[i]]) for i in range(lowerCamelCase_			,  lowerCamelCase_)]}')
					except Exception:
										pass

					a__ 							=	fast.decode(spm_ids[:first])
					a__ 							=	fast.decode(spm_ids[last:])
					a__ 							=	fast.decode(spm_ids[first:last])
					print()
					print(lowerCamelCase_)
					return False





def 							SCREAMING_SNAKE_CASE							(   lowerCamelCase_			,  lowerCamelCase_			,  lowerCamelCase_):
					global perfect
					global imperfect
					global wrong
					global total

					a__ 							=	slow.encode(lowerCamelCase_)
					a__ 							=	fast.encode(lowerCamelCase_)

					a__ 							=	False
					total += 1

					if slow_ids != fast_ids:
										if check_details(lowerCamelCase_			,  lowerCamelCase_			,  lowerCamelCase_			,  lowerCamelCase_			,  lowerCamelCase_):
															a__ 							=	True
															imperfect += 1
										else:
															wrong += 1
					else:
										perfect += 1

					if total % 10000 == 0:
										print(f'({perfect} / {imperfect} / {wrong} ----- {perfect + imperfect + wrong})')

					if skip_assert:
										return

					assert (
					    slow_ids == fast_ids
					), f'line {text} : \n\n{slow_ids}\n{fast_ids}\n\n{slow.tokenize(lowerCamelCase_)}\n{fast.tokenize(lowerCamelCase_)}'





def 							SCREAMING_SNAKE_CASE							(   lowerCamelCase_			,  lowerCamelCase_):
					global batch_total
					for i in range(len(lowerCamelCase_)):
										# premise, all languages
										for text in dataset[i]["premise"].values():
															test_string(lowerCamelCase_			,  lowerCamelCase_			,  lowerCamelCase_)

										# hypothesis, all languages
										for text in dataset[i]["hypothesis"]["translation"]:
															test_string(lowerCamelCase_			,  lowerCamelCase_			,  lowerCamelCase_)


if __name__ == "__main__":
	for name, (slow_class, fast_class) in TOKENIZER_CLASSES.items():
		__a							:						str   							=			list(slow_class.max_model_input_sizes.keys())
		for checkpoint in checkpoint_names:
			__a							:						Optional[Any]   							=			0
			__a							:						Union[str, Any]   							=			0
			__a							:						Tuple   							=			0
			__a							:						Dict   							=			0

			print(F'''========================== Checking {name}: {checkpoint} ==========================''')
			__a							:						int   							=			slow_class.from_pretrained(checkpoint, force_download=True)
			__a							:						List[Any]   							=			fast_class.from_pretrained(checkpoint, force_download=True)
			test_tokenizer(slow, fast)
			print(F'''Accuracy {perfect * 100 / total:.2f}''')










"""simple docstring"""







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

from transformers import BigBirdTokenizerFast


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





def 							SCREAMING_SNAKE_CASE							(   lowerCamelCase_			,  lowerCamelCase_=0			,  lowerCamelCase_=None):
					return [" ".join(x.split(''' ''')[begin:end]) for x in answers if len(x.split(''' ''')) > 1]





def 							SCREAMING_SNAKE_CASE							(   lowerCamelCase_			,  lowerCamelCase_=False):
					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"
										a__ 							=	(
										    given_answers + get_sub_answers(lowerCamelCase_			,  begin=1) + get_sub_answers(lowerCamelCase_			,  end=-1)
										)
					a__ 							=	[]
					for answer in given_answers:
										a__ 							=	answer.replace('''_'''			,  ''' ''').lower()
										a__ 							=	''''''.join(c if c not in PUNCTUATION_SET_TO_EXCLUDE else ''' ''' for c in alias)
										answers.append(''' '''.join(alias.split()).strip())
					return set(lowerCamelCase_)





def 							SCREAMING_SNAKE_CASE							(   lowerCamelCase_			,  lowerCamelCase_			,  lowerCamelCase_=1			,  lowerCamelCase_=100):
					a__							,a__ 							=	jax.lax.top_k(lowerCamelCase_			,  lowerCamelCase_)
					a__							,a__ 							=	jax.lax.top_k(lowerCamelCase_			,  lowerCamelCase_)

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

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





def 							SCREAMING_SNAKE_CASE							(   lowerCamelCase_):
					a__ 							=	sample['''question''']['''text''']
					a__ 							=	sample['''document''']['''tokens''']['''token''']
					a__ 							=	sample['''document''']['''tokens''']['''is_html''']
					a__ 							=	sample['''annotations''']['''long_answer''']
					a__ 							=	sample['''annotations''']['''short_answers''']

					a__ 							=	''' '''.join([context[i] for i in range(len(lowerCamelCase_)) 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",
															}

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

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

					a__ 							=	'''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 							SCREAMING_SNAKE_CASE							(   ):
					a__ 							=	load_from_disk('''natural-questions-validation''')
					a__ 							=	dataset.map(lowerCamelCase_).remove_columns(['''annotations''', '''document''', '''id'''])
					print(lowerCamelCase_)

					a__ 							=	dataset.filter(lambda lowerCamelCase_: (len(x['''question''']) + len(x['''context'''])) < 4 * 4096)
					a__ 							=	short_validation_dataset.filter(lambda lowerCamelCase_: x["category"] != "null")
					short_validation_dataset

					a__ 							=	'''vasudevgupta/flax-bigbird-natural-questions'''
					a__ 							=	FlaxBigBirdForNaturalQuestions.from_pretrained(lowerCamelCase_)
					a__ 							=	BigBirdTokenizerFast.from_pretrained(lowerCamelCase_)

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

					def evaluate(lowerCamelCase_):
										# encode question and context so that they are separated by a tokenizer.sep_token and cut at max_length
										a__ 							=	tokenizer(
										    example['''question''']			,  example['''context''']			,  return_tensors='''np'''			,  max_length=4096			,  padding='''max_length'''			,  truncation=lowerCamelCase_			,  )

										a__							,a__							,a__ 							=	forward(**lowerCamelCase_)

										a__ 							=	CATEGORY_MAPPING[category.item()]

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

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

										a__ 							=	38 if predicted_category == '''short''' else 1024
										a__							,a__ 							=	get_best_valid_start_end_idx(
										    start_scores[0]			,  end_scores[0]			,  top_k=8			,  max_size=lowerCamelCase_)

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

										a__ 							=	expand_to_aliases(example['''targets''']			,  make_sub_answers=lowerCamelCase_)
										a__ 							=	expand_to_aliases(example['''output'''])

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

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

										return example

					a__ 							=	short_validation_dataset.map(lowerCamelCase_)

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


if __name__ == "__main__":
	main()


