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  :					Tuple          =  """stas/tiny-wmt19-en-ru"""




class        _UpperCamelCase				(						_UpperCAmelCase						,unittest.TestCase			):


			"""simple docstring"""






			__a  :			str      			=						FSMTTokenizer
			__a  :			Optional[int]      			=						False





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Tuple:

							'''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(lowerCAmelCase__				,       range(len(lowerCAmelCase__						)						)						)						)
							__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(lowerCAmelCase__						)						)
							with open(self.tgt_vocab_file				,       '''w'''						) as fp:
											fp.write(json.dumps(lowerCAmelCase__						)						)
							with open(self.merges_file				,       '''w'''						) as fp:
											fp.write('''\n'''.join(lowerCAmelCase__						)						)
							with open(lowerCAmelCase__				,       '''w'''						) as fp:
											fp.write(json.dumps(lowerCAmelCase__						)						)





			@cached_property
			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Any:

							'''simple docstring'''


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





			@cached_property
			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> List[Any]:

							'''simple docstring'''


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





			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Tuple:

							'''simple docstring'''


							__lowercase			      =							FSMTTokenizer.from_pretrained(lowerCAmelCase__						)
							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 	_SCREAMING_SNAKE_CASE      (       self						)					-> Optional[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(lowerCAmelCase__						)
							self.assertListEqual(lowerCAmelCase__				,       lowerCAmelCase__						)

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





			@slow
			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Union[str, Any]:

							'''simple docstring'''


							__lowercase			      =							self.tokenizer_ru_en

							__lowercase			      =							tokenizer.encode('''sequence builders'''				,       add_special_tokens=lowerCAmelCase__						)
							__lowercase			      =							tokenizer.encode('''multi-sequence build'''				,       add_special_tokens=lowerCAmelCase__						)

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

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





			@slow
			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> List[Any]:

							'''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.''',
							        [24_70, 39, 11, 23_49, 72_22, 70, 59_79, 7, 84_50, 10_50, 1_31_60, 5, 26, 64_45, 7, 2],
							    ],
							    ['''This is it. No more. I\'m done!''', [1_32, 21, 37, 7, 14_34, 86, 7, 70, 64_76, 13_05, 4_27, 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(lowerCAmelCase__				,       return_tensors=lowerCAmelCase__						)
											self.assertListEqual(lowerCAmelCase__				,       lowerCAmelCase__						)

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





			@slow
			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Optional[Any]:

							'''simple docstring'''


							__lowercase			      =							FSMTTokenizer.from_pretrained('''facebook/wmt19-ru-en'''				,       do_lower_case=lowerCAmelCase__						)
							__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(lowerCAmelCase__				,       lowerCAmelCase__						)





			@unittest.skip('''FSMTConfig.__init__  requires non-optional args'''						)
			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> Dict:

							'''simple docstring'''


							pass





			@unittest.skip('''FSMTConfig.__init__  requires non-optional args'''						)
			def 	_SCREAMING_SNAKE_CASE      (       self						)					-> str:

							'''simple docstring'''


							pass
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  :					List[Any]          =  set("""""".join(["""‘""", """’""", """´""", """`""", """.""", """,""", """-""", """\""""]))





def     UpperCAmelCase						(   lowercase							,      lowercase=0							,      lowercase=None							):







				"""simple docstring"""



				return [" ".join(x.split(''' '''							)[begin:end]							) for x in answers if len(x.split(''' '''							)							) > 1]





def     UpperCAmelCase						(   lowercase							,      lowercase=False							):







				"""simple docstring"""



				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(lowercase							,      begin=1							) + get_sub_answers(lowercase							,      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(lowercase							)





def     UpperCAmelCase						(   lowercase							,      lowercase							,      lowercase=1							,      lowercase=100							):







				"""simple docstring"""



				__lowercase      ,					__lowercase			      =							jax.lax.top_k(lowercase							,      lowercase							)
				__lowercase      ,					__lowercase			      =							jax.lax.top_k(lowercase							,      lowercase							)

				__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(lowercase							).item()

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





def     UpperCAmelCase						(   lowercase							):







				"""simple docstring"""



				__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(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",
												}

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

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

				__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     UpperCAmelCase						(   ):







				"""simple docstring"""



				__lowercase			      =							load_from_disk('''natural-questions-validation'''							)
				__lowercase			      =							dataset.map(lowercase							).remove_columns(['''annotations''', '''document''', '''id''']							)
				print(lowercase							)

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

				__lowercase			      =							'''vasudevgupta/flax-bigbird-natural-questions'''
				__lowercase			      =							FlaxBigBirdForNaturalQuestions.from_pretrained(lowercase							)
				__lowercase			      =							BigBirdTokenizerFast.from_pretrained(lowercase							)

				@jax.jit
				def forward(*lowercase							,      **lowercase							):
								__lowercase      ,					__lowercase      ,					__lowercase			      =							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
								__lowercase			      =							tokenizer(
								    example['''question''']							,      example['''context''']							,      return_tensors='''np'''							,      max_length=4096							,      padding='''max_length'''							,      truncation=lowercase							,      )

								__lowercase      ,					__lowercase      ,					__lowercase			      =							forward(**lowercase							)

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

								__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=lowercase							)
								__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(lowercase							) > 0

								return example

				__lowercase			      =							short_validation_dataset.map(lowercase							)

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


if __name__ == "__main__":
		main()