




'''simple docstring'''
import json
import os
import unittest

from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES, FSMTTokenizer
from transformers.testing_utils import slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


# using a different tiny model than the one used for default params defined in init to ensure proper testing
A				: List[str]					      =			'stas/tiny-wmt19-en-ru'






class 			SCREAMING_SNAKE_CASE(							__snake_case  , unittest.TestCase	):
       snake_case_					:	Optional[Any]       =   FSMTTokenizer
       snake_case_					:	Optional[Any]       =   False

       def        snake_case__     (       self	)       ->   Dict:

           """simple docstring"""



           super().setUp()

           # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
           __lowercase     							=		[
               """l""",
               """o""",
               """w""",
               """e""",
               """r""",
               """s""",
               """t""",
               """i""",
               """d""",
               """n""",
               """w</w>""",
               """r</w>""",
               """t</w>""",
               """lo""",
               """low""",
               """er</w>""",
               """low</w>""",
               """lowest</w>""",
               """newer</w>""",
               """wider</w>""",
               """<unk>""",
           ]
           __lowercase     							=		dict(zip(__A   ,						range(len(__A	)	)	)	)
           __lowercase     							=		["""l o 123""", """lo w 1456""", """e r</w> 1789""", """"""]

           __lowercase     							=		["""en""", """ru"""]
           __lowercase     							=		{
               """langs""": self.langs,
               """src_vocab_size""": 10,
               """tgt_vocab_size""": 20,
           }

           __lowercase     							=		os.path.join(self.tmpdirname   ,						VOCAB_FILES_NAMES["""src_vocab_file"""]	)
           __lowercase     							=		os.path.join(self.tmpdirname   ,						VOCAB_FILES_NAMES["""tgt_vocab_file"""]	)
           __lowercase     							=		os.path.join(self.tmpdirname   ,						"""tokenizer_config.json"""	)
           __lowercase     							=		os.path.join(self.tmpdirname   ,						VOCAB_FILES_NAMES["""merges_file"""]	)
           with open(self.src_vocab_file   ,						"""w"""	) as fp:
               fp.write(json.dumps(__A	)	)
           with open(self.tgt_vocab_file   ,						"""w"""	) as fp:
               fp.write(json.dumps(__A	)	)
           with open(self.merges_file   ,						"""w"""	) as fp:
               fp.write("""\n""".join(__A	)	)
           with open(__A   ,						"""w"""	) as fp:
               fp.write(json.dumps(__A	)	)

       @cached_property
       def        snake_case__     (       self	)       ->   List[str]:

           """simple docstring"""



           return FSMTTokenizer.from_pretrained("""facebook/wmt19-ru-en"""	)

       @cached_property
       def        snake_case__     (       self	)       ->   Tuple:

           """simple docstring"""



           return FSMTTokenizer.from_pretrained("""facebook/wmt19-en-ru"""	)

       def        snake_case__     (       self	)       ->   int:

           """simple docstring"""



           __lowercase     							=		FSMTTokenizer.from_pretrained(__A	)
           self.assertListEqual([tokenizer.src_lang, tokenizer.tgt_lang]   ,						["""en""", """ru"""]	)
           self.assertEqual(tokenizer.src_vocab_size   ,						21	)
           self.assertEqual(tokenizer.tgt_vocab_size   ,						21	)

       def        snake_case__     (       self	)       ->   int:

           """simple docstring"""



           __lowercase     							=		FSMTTokenizer(self.langs   ,						self.src_vocab_file   ,						self.tgt_vocab_file   ,						self.merges_file	)

           __lowercase     							=		"""lower"""
           __lowercase     							=		["""low""", """er</w>"""]
           __lowercase     							=		tokenizer.tokenize(__A	)
           self.assertListEqual(__A   ,						__A	)

           __lowercase     							=		tokens + ["""<unk>"""]
           __lowercase     							=		[14, 15, 20]
           self.assertListEqual(tokenizer.convert_tokens_to_ids(__A	)   ,						__A	)

       @slow
       def        snake_case__     (       self	)       ->   Any:

           """simple docstring"""



           __lowercase     							=		self.tokenizer_ru_en

           __lowercase     							=		tokenizer.encode("""sequence builders"""   ,						add_special_tokens=__A	)
           __lowercase     							=		tokenizer.encode("""multi-sequence build"""   ,						add_special_tokens=__A	)

           __lowercase     							=		tokenizer.build_inputs_with_special_tokens(__A	)
           __lowercase     							=		tokenizer.build_inputs_with_special_tokens(__A   ,						__A	)

           assert encoded_sentence == text + [2]
           assert encoded_pair == text + [2] + text_a + [2]

       @slow
       def        snake_case__     (       self	)       ->   str:

           """simple docstring"""



           __lowercase     							=		self.tokenizer_en_ru
           __lowercase     							=		self.tokenizer_ru_en

           __lowercase     							=		[
               [
                   """Here\'s a little song I wrote. Don\'t worry, be happy.""",
                   [2470, 39, 11, 2349, 7222, 70, 5979, 7, 8450, 1050, 1_3160, 5, 26, 6445, 7, 2],
               ],
               ["""This is it. No more. I\'m done!""", [132, 21, 37, 7, 1434, 86, 7, 70, 6476, 1305, 427, 2]],
           ]

           # if data needs to be recreated or added, run:
           # import torch
           # model = torch.hub.load("pytorch/fairseq", "transformer.wmt19.en-ru", checkpoint_file="model4.pt", tokenizer="moses", bpe="fastbpe")
           # for src_text, _ in targets: print(f"""[\n"{src_text}",\n {model.encode(src_text).tolist()}\n],""")

           for src_text, tgt_input_ids in targets:
               __lowercase     							=		tokenizer_enc.encode(__A   ,						return_tensors=__A	)
               self.assertListEqual(__A   ,						__A	)

               # and decode backward, using the reversed languages model
               __lowercase     							=		tokenizer_dec.decode(__A   ,						skip_special_tokens=__A	)
               self.assertEqual(__A   ,						__A	)

       @slow
       def        snake_case__     (       self	)       ->   Dict:

           """simple docstring"""



           __lowercase     							=		FSMTTokenizer.from_pretrained("""facebook/wmt19-ru-en"""   ,						do_lower_case=__A	)
           __lowercase     							=		tokenizer.tokenize("""USA is United States of America"""	)
           __lowercase     							=		["""us""", """a</w>""", """is</w>""", """un""", """i""", """ted</w>""", """st""", """ates</w>""", """of</w>""", """am""", """er""", """ica</w>"""]
           self.assertListEqual(__A   ,						__A	)

       @unittest.skip("""FSMTConfig.__init__  requires non-optional args"""	)
       def        snake_case__     (       self	)       ->   Union[str, Any]:

           """simple docstring"""



           pass




       @unittest.skip("""FSMTConfig.__init__  requires non-optional args"""	)
       def        snake_case__     (       self	)       ->   Any:

           """simple docstring"""



           pass












"""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()


