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 	__lowerCamelCase							(				__a    : str      ,							__a    : Optional[Any]=0      ,							__a    : Optional[int]=None		)      ->       Optional[Any]:
	return [" ".join(x.split(" "		)[begin:end]		) for x in answers if len(x.split(" "		)		) > 1]

def 	__lowerCamelCase							(				__a    : Optional[Any]      ,							__a    : Any=False		)      ->       Any:
	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"
		_lowercase   =(
		    given_answers + get_sub_answers(__a      ,							begin=1		) + get_sub_answers(__a      ,							end=-1		)
		)
	_lowercase   =[]
	for answer in given_answers:
		_lowercase   =answer.replace("_"      ,							" "		).lower()
		_lowercase   ="".join(c if c not in PUNCTUATION_SET_TO_EXCLUDE else " " for c in alias		)
		answers.append(" ".join(alias.split()		).strip()		)
	return set(__a		)

def 	__lowerCamelCase							(				__a    : Any      ,							__a    : Tuple      ,							__a    : Dict=1      ,							__a    : str=100		)      ->       Dict:
	_lowercase   ,  _lowercase   =jax.lax.top_k(__a      ,							__a		)
	_lowercase   ,  _lowercase   =jax.lax.top_k(__a      ,							__a		)

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

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

def 	__lowerCamelCase							(				__a    : Optional[int]		)      ->       str:
	_lowercase   =sample["question"]["text"]
	_lowercase   =sample["document"]["tokens"]["token"]
	_lowercase   =sample["document"]["tokens"]["is_html"]
	_lowercase   =sample["annotations"]["long_answer"]
	_lowercase   =sample["annotations"]["short_answers"]

	_lowercase   =" ".join([context[i] for i in range(len(__a		)		) 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",
			}

	_lowercase   =[]
	for s in short_answers:
		short_targets.extend(s["text"]		)
	_lowercase   =list(set(__a		)		)

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

	_lowercase   ="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 	__lowerCamelCase							(				)      ->       Optional[int]:
	_lowercase   =load_from_disk("natural-questions-validation"		)
	_lowercase   =dataset.map(__a		).remove_columns(["annotations", "document", "id"]		)
	print(__a		)

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

	_lowercase   ="vasudevgupta/flax-bigbird-natural-questions"
	_lowercase   =FlaxBigBirdForNaturalQuestions.from_pretrained(__a		)
	_lowercase   =BigBirdTokenizerFast.from_pretrained(__a		)

	@jax.jit
	def forward(*__a    : Tuple      ,							**__a    : int		):
		_lowercase   ,  _lowercase   ,  _lowercase   =model(*__a      ,							**__a		)
		return start_logits, end_logits, jnp.argmax(__a      ,							axis=-1		)

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

		_lowercase   ,  _lowercase   ,  _lowercase   =forward(**__a		)

		_lowercase   =CATEGORY_MAPPING[category.item()]

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

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

		_lowercase   =38 if predicted_category == "short" else 1_024
		_lowercase   ,  _lowercase   =get_best_valid_start_end_idx(
		    start_scores[0]      ,							end_scores[0]      ,							top_k=8      ,							max_size=__a		)

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

		_lowercase   =expand_to_aliases(example["targets"]      ,							make_sub_answers=__a		)
		_lowercase   =expand_to_aliases(example["output"]		)

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

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

		return example

	_lowercase   =short_validation_dataset.map(__a		)

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


if __name__ == "__main__":
						main()





'''simple docstring'''




import argparse
import logging
import sys
import time

import tensorflow as tf
from datasets import load_dataset

from transformers import AutoTokenizer, TFAutoModelForSequenceClassification


if __name__ == "__main__":
  __snake_case  : Any		       =    argparse.ArgumentParser()

  # Hyperparameters sent by the client are passed as command-line arguments to the script.
  parser.add_argument('--epochs', type=int, default=1)
  parser.add_argument('--per_device_train_batch_size', type=int, default=16)
  parser.add_argument('--per_device_eval_batch_size', type=int, default=8)
  parser.add_argument('--model_name_or_path', type=str)
  parser.add_argument('--learning_rate', type=str, default=5E-5)
  parser.add_argument('--do_train', type=bool, default=True)
  parser.add_argument('--do_eval', type=bool, default=True)
  parser.add_argument('--output_dir', type=str)

  __snake_case       ,       __snake_case  : Dict		       =    parser.parse_known_args()

  # overwrite batch size until we have tf_glue.py
  __snake_case  : Any		       =    16
  __snake_case  : Optional[Any]		       =    16

  # Set up logging
  __snake_case  : Union[str, Any]		       =    logging.getLogger(__name__)

  logging.basicConfig(
      level=logging.getLevelName('INFO'),
      handlers=[logging.StreamHandler(sys.stdout)],
      format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
  )

  # Load model and tokenizer
  __snake_case  : Optional[Any]		       =    TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
  __snake_case  : Optional[Any]		       =    AutoTokenizer.from_pretrained(args.model_name_or_path)

  # Load dataset
  __snake_case       ,       __snake_case  : Optional[int]		       =    load_dataset('imdb', split=['train', 'test'])
  __snake_case  : List[str]		       =    train_dataset.shuffle().select(range(5_000))  # smaller the size for train dataset to 5k
  __snake_case  : List[str]		       =    test_dataset.shuffle().select(range(500))  # smaller the size for test dataset to 500

  # Preprocess train dataset
  __snake_case  : int		       =    train_dataset.map(
      lambda e: tokenizer(e['text'], truncation=True, padding='max_length'), batched=True
  )
  train_dataset.set_format(type='tensorflow', columns=['input_ids', 'attention_mask', 'label'])

  __snake_case  : Dict		       =    {
      x: train_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
      for x in ['input_ids', 'attention_mask']
  }
  __snake_case  : str		       =    tf.data.Dataset.from_tensor_slices((train_features, train_dataset['label'])).batch(
      args.per_device_train_batch_size
  )

  # Preprocess test dataset
  __snake_case  : Tuple		       =    test_dataset.map(
      lambda e: tokenizer(e['text'], truncation=True, padding='max_length'), batched=True
  )
  test_dataset.set_format(type='tensorflow', columns=['input_ids', 'attention_mask', 'label'])

  __snake_case  : Optional[int]		       =    {
      x: test_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
      for x in ['input_ids', 'attention_mask']
  }
  __snake_case  : List[str]		       =    tf.data.Dataset.from_tensor_slices((test_features, test_dataset['label'])).batch(
      args.per_device_eval_batch_size
  )

  # fine optimizer and loss
  __snake_case  : Optional[int]		       =    tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
  __snake_case  : Union[str, Any]		       =    tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
  __snake_case  : int		       =    [tf.keras.metrics.SparseCategoricalAccuracy()]
  model.compile(optimizer=optimizer, loss=loss, metrics=metrics)

  __snake_case  : Any		       =    time.time()
  __snake_case  : int		       =    model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
  __snake_case  : Union[str, Any]		       =    time.time() - start_train_time

  logger.info('*** Train ***')
  logger.info(F"""train_runtime = {end_train_time}""")
  for key, value in train_results.history.items():
    logger.info(F"""  {key} = {value}""")






