




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

from transformers import BigBirdTokenizerFast


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


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


def 				_a       (				lowerCAmelCase						,	lowerCAmelCase=False   )->  str:



		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"
				SCREAMING_SNAKE_CASE_							=       (
				    given_answers + get_sub_answers(__A						,	begin=1   ) + get_sub_answers(__A						,	end=-1   )
				)
		SCREAMING_SNAKE_CASE_							=       []
		for answer in given_answers:
				SCREAMING_SNAKE_CASE_							=       answer.replace('_'						,	' '   ).lower()
				SCREAMING_SNAKE_CASE_							=       ''.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 				_a       (				lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase=1						,	lowerCAmelCase=100   )->  Optional[Any]:
		SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       jax.lax.top_k(__A						,	__A   )
		SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       jax.lax.top_k(__A						,	__A   )

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

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


def 				_a       (				lowerCAmelCase   )->  List[Any]:
		SCREAMING_SNAKE_CASE_							=       sample['question']['text']
		SCREAMING_SNAKE_CASE_							=       sample['document']['tokens']['token']
		SCREAMING_SNAKE_CASE_							=       sample['document']['tokens']['is_html']
		SCREAMING_SNAKE_CASE_							=       sample['annotations']['long_answer']
		SCREAMING_SNAKE_CASE_							=       sample['annotations']['short_answers']

		SCREAMING_SNAKE_CASE_							=       ' '.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",
						}

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

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

		SCREAMING_SNAKE_CASE_							=       '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 				_a       (				)->  Union[str, Any]:
		SCREAMING_SNAKE_CASE_							=       load_from_disk('natural-questions-validation'   )
		SCREAMING_SNAKE_CASE_							=       dataset.map(__A   ).remove_columns(['annotations', 'document', 'id']   )
		print(__A   )

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

		SCREAMING_SNAKE_CASE_							=       'vasudevgupta/flax-bigbird-natural-questions'
		SCREAMING_SNAKE_CASE_							=       FlaxBigBirdForNaturalQuestions.from_pretrained(__A   )
		SCREAMING_SNAKE_CASE_							=       BigBirdTokenizerFast.from_pretrained(__A   )

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

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

				SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_			,							SCREAMING_SNAKE_CASE_							=       forward(**__A   )

				SCREAMING_SNAKE_CASE_							=       CATEGORY_MAPPING[category.item()]

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

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

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

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

				SCREAMING_SNAKE_CASE_							=       expand_to_aliases(example['targets']						,	make_sub_answers=__A   )
				SCREAMING_SNAKE_CASE_							=       expand_to_aliases(example['output']   )

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

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

				return example

		SCREAMING_SNAKE_CASE_							=       short_validation_dataset.map(__A   )

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


if __name__ == "__main__":
						main()





import collections

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
						import torch


SCREAMING_SNAKE_CASE:    Any											=   logging.get_logger(__name__)



class       lowercase_		:



						def __init__( self  :      Union[str, Any]				,       snake_case__  :      List[str]				,       snake_case__  :      Dict=21				,       snake_case__  :      List[str]=[]				,       snake_case__  :      List[str]=None     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       model
								SCREAMING_SNAKE_CASE_							=       trace_batch_nums
								SCREAMING_SNAKE_CASE_							=       abort_after_batch_num

								# keep a LIFO buffer of frames to dump as soon as inf/nan is encountered to give context to the problem emergence
								SCREAMING_SNAKE_CASE_							=       collections.deque([]				,       snake_case__     )
								SCREAMING_SNAKE_CASE_							=       []
								SCREAMING_SNAKE_CASE_							=       0
								SCREAMING_SNAKE_CASE_							=       0
								SCREAMING_SNAKE_CASE_							=       False
								SCREAMING_SNAKE_CASE_							=       '                 '

								self.analyse_model()

								self.register_forward_hook()



						def   __a				( self  :      Union[str, Any]				,       snake_case__  :      Optional[int]=None     ):

								"""simple docstring"""

								if frame is not None:
										self.expand_frame(snake_case__     )
								self.frames.append('\n'.join(self.frame     )     )
								SCREAMING_SNAKE_CASE_							=       []  # start a new frame



						def   __a				( self  :      Union[str, Any]				,       snake_case__  :      Any     ):

								"""simple docstring"""

								self.frame.append(snake_case__     )



						def   __a				( self  :      Optional[int]     ):

								"""simple docstring"""

								print('\n'.join(self.frames     )     )
								SCREAMING_SNAKE_CASE_							=       []



						def   __a				( self  :      Dict     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       []



						def   __a				( self  :      int     ):

								"""simple docstring"""

								print(f'''\nDetected inf/nan during batch_number={self.batch_number}'''     )
								print(f'''Last {len(self.frames     )} forward frames:'''     )
								print(f'''{"abs min":8} {"abs max":8} metadata'''     )
								print('\n'.join(self.frames     )     )
								print('\n\n'     )
								SCREAMING_SNAKE_CASE_							=       []



						def   __a				( self  :      str     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       {m: name for name, m in self.model.named_modules()}
								# self.longest_module_name = max(len(v) for v in self.module_names.values())



						def   __a				( self  :      List[str]				,       snake_case__  :      List[str]				,       snake_case__  :      Union[str, Any]     ):

								"""simple docstring"""

								if torch.is_tensor(snake_case__     ):
										self.expand_frame(get_abs_min_max(snake_case__				,       snake_case__     )     )
										if detect_overflow(snake_case__				,       snake_case__     ):
												SCREAMING_SNAKE_CASE_							=       True
								elif var is None:
										self.expand_frame(f'''{"None":>17} {ctx}'''     )
								else:
										self.expand_frame(f'''{"not a tensor":>17} {ctx}'''     )



						def   __a				( self  :      str     ):

								"""simple docstring"""

								self.expand_frame(f'''\n\n{self.prefix} *** Starting batch number={self.batch_number} ***'''     )
								self.expand_frame(f'''{"abs min":8} {"abs max":8} metadata'''     )



						def   __a				( self  :      List[str]     ):

								"""simple docstring"""

								self.expand_frame(f'''{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n'''     )



						def   __a				( self  :      Optional[int]				,       snake_case__  :      int				,       snake_case__  :      List[Any]				,       snake_case__  :      str     ):

								"""simple docstring"""

								self.expand_frame(f'''{self.prefix} {self.module_names[module]} {module.__class__.__name__}'''     )

								# params
								for name, p in module.named_parameters(recurse=snake_case__     ):
										self.analyse_variable(snake_case__				,       snake_case__     )

								# inputs
								if isinstance(snake_case__				,       snake_case__     ):
										for i, x in enumerate(snake_case__     ):
												self.analyse_variable(snake_case__				,       f'''input[{i}]'''     )
								else:
										self.analyse_variable(snake_case__				,       'input'     )

								# outputs
								if isinstance(snake_case__				,       snake_case__     ):
										for i, x in enumerate(snake_case__     ):
												# possibly a tuple of tuples
												if isinstance(snake_case__				,       snake_case__     ):
														for j, y in enumerate(snake_case__     ):
																self.analyse_variable(snake_case__				,       f'''output[{i}][{j}]'''     )
												else:
														self.analyse_variable(snake_case__				,       f'''output[{i}]'''     )
								else:
										self.analyse_variable(snake_case__				,       'output'     )

								self.save_frame()



						def   __a				( self  :      Tuple     ):

								"""simple docstring"""

								self.model.apply(self._register_forward_hook     )



						def   __a				( self  :      int				,       snake_case__  :      Optional[int]     ):

								"""simple docstring"""

								module.register_forward_hook(self.forward_hook     )






						def   __a				( self  :      Optional[Any]				,       snake_case__  :      str				,       snake_case__  :      Optional[Any]				,       snake_case__  :      Dict     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       False

								SCREAMING_SNAKE_CASE_							=       True if self.batch_number in self.trace_batch_nums else False
								if trace_mode:
										self.reset_saved_frames()

								if self.total_calls == 0:
										self.batch_start_frame()
								self.total_calls += 1

								# count batch numbers - the very first forward hook of the batch will be called when the
								# batch completes - i.e. it gets called very last - we know this batch has finished
								if module == self.model:
										self.batch_number += 1
										SCREAMING_SNAKE_CASE_							=       True

								self.create_frame(snake_case__				,       snake_case__				,       snake_case__     )

								# if last_frame_of_batch:
								#     self.batch_end_frame()

								if trace_mode:
										self.trace_frames()

								if last_frame_of_batch:
										self.batch_start_frame()

								if self.detected_overflow and not trace_mode:
										self.dump_saved_frames()

										# now we can abort, as it's pointless to continue running
										raise ValueError(
										    'DebugUnderflowOverflow: inf/nan detected, aborting as there is no point running further. '
										    'Please scroll up above this traceback to see the activation values prior to this event.'     )

								# abort after certain batch if requested to do so
								if self.abort_after_batch_num is not None and self.batch_number > self.abort_after_batch_num:
										raise ValueError(
										    f'''DebugUnderflowOverflow: aborting after {self.batch_number} batches due to'''
										    f''' `abort_after_batch_num={self.abort_after_batch_num}` arg'''     )


def 				_a       (				lowerCAmelCase						,	lowerCAmelCase   )->  Union[str, Any]:
		SCREAMING_SNAKE_CASE_							=       var.abs()
		return F'''{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}'''


def 				_a       (				lowerCAmelCase						,	lowerCAmelCase   )->  int:
		SCREAMING_SNAKE_CASE_							=       False
		if torch.isnan(lowerCAmelCase   ).any().item():
				SCREAMING_SNAKE_CASE_							=       True
				print(F'''{ctx} has nans'''   )
		if torch.isinf(lowerCAmelCase   ).any().item():
				SCREAMING_SNAKE_CASE_							=       True
				print(F'''{ctx} has infs'''   )

		# if needed to monitor large elements can enable the following
		if 0:  # and detected:
				SCREAMING_SNAKE_CASE_							=       var[torch.ge(var.abs()						,	100   )]
				if naaa.numel() > 0:
						print(F'''{ctx}:  n100={naaa.numel()}'''   )
				SCREAMING_SNAKE_CASE_							=       var[torch.ge(var.abs()						,	1000   )]
				if naaaa.numel() > 0:
						print(F'''{ctx}: n1000={naaaa.numel()}'''   )
				SCREAMING_SNAKE_CASE_							=       var[torch.ge(var.abs()						,	10000   )]
				if naaaaa.numel() > 0:
						print(F'''{ctx}: n10000={naaaaa.numel()}'''   )

		if 0:
				print(F'''min={var.min():9.2e} max={var.max():9.2e}'''   )

		if 0:
				print(F'''min={var.min():9.2e} max={var.max():9.2e} var={var.var():9.2e} mean={var.mean():9.2e} ({ctx})'''   )

		return detected




class       lowercase_		(SCREAMING_SNAKE_CASE__   ):
						lowerCAmelCase__ 			="underflow_overflow"
						lowerCAmelCase__ 			="tpu_metrics_debug"