





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





def      _lowerCAmelCase		(		lowercase      ,			lowercase=0      ,			lowercase=None			)      ->      Optional[Any]:
			return [" ".join(x.split(""" """			)[begin:end]			) for x in answers if len(x.split(""" """			)			) > 1]





def      _lowerCAmelCase		(		lowercase      ,			lowercase=False			)      ->      Union[str, 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"
						__lowerCAmelCase      		=       (
						    given_answers + get_sub_answers(lowercase      ,			begin=1			) + get_sub_answers(lowercase      ,			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(lowercase			)





def      _lowerCAmelCase		(		lowercase      ,			lowercase      ,			lowercase=1      ,			lowercase=100			)      ->      str:
			__lowerCAmelCase		, __lowerCAmelCase      		=       jax.lax.top_k(lowercase      ,			lowercase			)
			__lowerCAmelCase		, __lowerCAmelCase      		=       jax.lax.top_k(lowercase      ,			lowercase			)

			__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(lowercase			).item()

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





def      _lowerCAmelCase		(		lowercase			)      ->      Union[str, Any]:
			__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(lowercase			)			) 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(lowercase			)			)

			__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(lowercase			)			) if not html[i]]			)
						if new_answer not in long_targets:
									long_targets.append(lowercase			)

			__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      _lowerCAmelCase		(		)      ->      Optional[int]:
			__lowerCAmelCase      		=       load_from_disk("""natural-questions-validation"""			)
			__lowerCAmelCase      		=       dataset.map(lowercase			).remove_columns(["""annotations""", """document""", """id"""]			)
			print(lowercase			)

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

			__lowerCAmelCase      		=       """vasudevgupta/flax-bigbird-natural-questions"""
			__lowerCAmelCase      		=       FlaxBigBirdForNaturalQuestions.from_pretrained(lowercase			)
			__lowerCAmelCase      		=       BigBirdTokenizerFast.from_pretrained(lowercase			)

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

			def evaluate(lowercase			):
						# 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=4096      ,			padding="""max_length"""      ,			truncation=lowercase      ,			)

						__lowerCAmelCase		, __lowerCAmelCase		, __lowerCAmelCase      		=       forward(**lowercase			)

						__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 1024
						__lowerCAmelCase		, __lowerCAmelCase      		=       get_best_valid_start_end_idx(
						    start_scores[0]      ,			end_scores[0]      ,			top_k=8      ,			max_size=lowercase			)

						__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=lowercase			)
						__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(lowercase			) > 0

						return example

			__lowerCAmelCase      		=       short_validation_dataset.map(lowercase			)

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


if __name__ == "__main__":
	main()








'''simple docstring'''







import os
import sys
from pathlib import Path

import torch

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, require_torch_multi_gpu
from utils import load_json


_a      :				Optional[Any]											=					torch.cuda.is_available()
_a      :				Dict											=					[""" Sam ate lunch today.""", """Sams lunch ingredients."""]
_a      :				str											=					["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
_a      :				Optional[Any]											=					{
    """max_tokens_per_batch""": None,
    """supervise_forward""": True,
    """normalize_hidden""": True,
    """label_smoothing""": 0.2,
    """eval_max_gen_length""": None,
    """eval_beams""": 1,
    """val_metric""": """loss""",
    """save_top_k""": 1,
    """adafactor""": True,
    """early_stopping_patience""": 2,
    """logger_name""": """default""",
    """length_penalty""": 0.5,
    """cache_dir""": """""",
    """task""": """summarization""",
    """num_workers""": 2,
    """alpha_hid""": 0,
    """freeze_embeds""": True,
    """enc_only""": False,
    """tgt_suffix""": """""",
    """resume_from_checkpoint""": None,
    """sortish_sampler""": True,
    """student_decoder_layers""": 1,
    """val_check_interval""": 1.0,
    """output_dir""": """""",
    """fp16""": False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
    """no_teacher""": False,
    """fp16_opt_level""": """O1""",
    """gpus""": 1 if CUDA_AVAILABLE else 0,
    """n_tpu_cores""": 0,
    """max_grad_norm""": 1.0,
    """do_train""": True,
    """do_predict""": True,
    """accumulate_grad_batches""": 1,
    """server_ip""": """""",
    """server_port""": """""",
    """seed""": 4_2,
    """model_name_or_path""": """sshleifer/bart-tiny-random""",
    """config_name""": """""",
    """tokenizer_name""": """facebook/bart-large""",
    """do_lower_case""": False,
    """learning_rate""": 0.3,
    """lr_scheduler""": """linear""",
    """weight_decay""": 0.0,
    """adam_epsilon""": 1E-08,
    """warmup_steps""": 0,
    """max_epochs""": 1,
    """train_batch_size""": 2,
    """eval_batch_size""": 2,
    """max_source_length""": 1_2,
    """max_target_length""": 1_2,
    """val_max_target_length""": 1_2,
    """test_max_target_length""": 1_2,
    """fast_dev_run""": False,
    """no_cache""": False,
    """n_train""": -1,
    """n_val""": -1,
    """n_test""": -1,
    """student_encoder_layers""": 1,
    """freeze_encoder""": False,
    """auto_scale_batch_size""": False,
    """overwrite_output_dir""": False,
    """student""": None,
}





def      _lowerCAmelCase		(		lowercase      ,			lowercase			)      ->      Any:
			__lowerCAmelCase      		=       """\n""".join(lowercase			)
			Path(lowercase			).open("""w"""			).writelines(lowercase			)





def      _lowerCAmelCase		(		lowercase			)      ->      Optional[int]:
			for split in ["train", "val", "test"]:
						_dump_articles(os.path.join(lowercase      ,			f'{split}.source'			)      ,			lowercase			)
						_dump_articles(os.path.join(lowercase      ,			f'{split}.target'			)      ,			lowercase			)
			return tmp_dir







class 					_UpperCAmelCase							(		lowerCAmelCase_     ):
		@classmethod
		def 						lowerCamelCase__    (						cls    ):



					'''simple docstring'''

					return cls
		@require_torch_multi_gpu
		def 						lowerCamelCase__    (						self    ):



					'''simple docstring'''

					__lowerCAmelCase      		=       {
					    """no_teacher""": True,
					    """freeze_encoder""": True,
					    """gpus""": 2,
					    """overwrite_output_dir""": True,
					    """sortish_sampler""": True,
					}
					self._test_distiller_cli_fork(__SCREAMING_SNAKE_CASE,check_contents=__SCREAMING_SNAKE_CASE    )







		def 						lowerCamelCase__    (						self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=True    ):



					'''simple docstring'''

					__lowerCAmelCase      		=       {
					    """label_smoothing""": 0.0,
					    """early_stopping_patience""": -1,
					    """train_batch_size""": 1,
					    """eval_batch_size""": 2,
					    """max_epochs""": 2,
					    """alpha_mlm""": 0.2,
					    """alpha_ce""": 0.8,
					    """do_predict""": True,
					    """model_name_or_path""": """sshleifer/tinier_bart""",
					    """teacher""": CHEAP_ARGS["""model_name_or_path"""],
					    """val_check_interval""": 0.5,
					}
					default_updates.update(__SCREAMING_SNAKE_CASE    )
					__lowerCAmelCase      		=       CHEAP_ARGS.copy()
					__lowerCAmelCase      		=       make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()    )
					__lowerCAmelCase      		=       self.get_auto_remove_tmp_dir()
					args_d.update(data_dir=__SCREAMING_SNAKE_CASE,output_dir=__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE    )

					def convert(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    ):
								if k in ["tgt_suffix", "server_ip", "server_port", "out", "n_tpu_cores"]:
											return ""
								if v is False or v is None:
											return ""
								if v is True:  # or len(str(v))==0:
											return f'--{k}'
								return f'--{k}={v}'

					__lowerCAmelCase      		=       [x for x in (convert(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE    ) for k, v in args_d.items()) if len(__SCREAMING_SNAKE_CASE    )]
					__lowerCAmelCase      		=       [sys.executable, f'{self.test_file_dir}/distillation.py'] + cli_args
					execute_subprocess_async(__SCREAMING_SNAKE_CASE,env=self.get_env()    )

					__lowerCAmelCase      		=       os.listdir(__SCREAMING_SNAKE_CASE    )
					__lowerCAmelCase      		=       {os.path.basename(__SCREAMING_SNAKE_CASE    ) for p in contents}
					__lowerCAmelCase      		=       [p for p in contents if p.endswith("""ckpt"""    )]
					assert len(__SCREAMING_SNAKE_CASE    ) > 0

					self.assertIn("""test_generations.txt""",__SCREAMING_SNAKE_CASE    )
					self.assertIn("""test_results.txt""",__SCREAMING_SNAKE_CASE    )

					# get the following from the module, (we don't have access to `model` here)
					__lowerCAmelCase      		=       os.path.join(__SCREAMING_SNAKE_CASE,"""metrics.json"""    )
					__lowerCAmelCase      		=       """rouge2"""

					__lowerCAmelCase      		=       load_json(__SCREAMING_SNAKE_CASE    )
					# {'test': [{'test_avg_loss': 10.63731575012207, 'test_avg_rouge1': 0.0, 'test_avg_rouge2': 0.0, 'test_avg_rougeL': 0.0, 'test_avg_gen_time': 0.1822289228439331, 'test_avg_gen_len': 142.0, 'step_count': 1}]}
					print(__SCREAMING_SNAKE_CASE    )
					__lowerCAmelCase      		=       metrics["""val"""][-1]
					self.assertGreaterEqual(last_step_stats["""val_avg_gen_time"""],0.01    )
					self.assertIsInstance(last_step_stats[f'val_avg_{val_metric}'],__SCREAMING_SNAKE_CASE    )
					self.assertEqual(len(metrics["""test"""]    ),1    )
					__lowerCAmelCase      		=       int(args_d["""max_epochs"""] * (1 / args_d["""val_check_interval"""]) / 2 + 1    )
					self.assertEqual(len(metrics["""val"""]    ),__SCREAMING_SNAKE_CASE    )

