

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
UpperCamelCase__		   =    '''stas/tiny-wmt19-en-ru'''

class 						__snake_case						(							snake_case__ ,  unittest.TestCase     ):
       """simple docstring"""







       __SCREAMING_SNAKE_CASE													=       FSMTTokenizer
       __SCREAMING_SNAKE_CASE													=       False


       def  a		(		self      )			->       List[str]:






              """simple docstring"""


              super().setUp()

              # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
              __snake_case					  =       [
                  """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>""",
              ]
              __snake_case					  =       dict(zip(_UpperCamelCase			,   range(len(_UpperCamelCase      )      )      )      )
              __snake_case					  =       ["""l o 123""", """lo w 1456""", """e r</w> 1789""", """"""]

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

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


       @cached_property
       def  a		(		self      )			->       Dict:






              """simple docstring"""


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


       @cached_property
       def  a		(		self      )			->       str:






              """simple docstring"""


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


       def  a		(		self      )			->       Union[str, Any]:






              """simple docstring"""


              __snake_case					  =       FSMTTokenizer.from_pretrained(_UpperCamelCase      )
              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  a		(		self      )			->       Union[str, Any]:






              """simple docstring"""


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

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

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


       @slow
       def  a		(		self      )			->       Optional[int]:






              """simple docstring"""


              __snake_case					  =       self.tokenizer_ru_en

              __snake_case					  =       tokenizer.encode("""sequence builders"""			,   add_special_tokens=_UpperCamelCase      )
              __snake_case					  =       tokenizer.encode("""multi-sequence build"""			,   add_special_tokens=_UpperCamelCase      )

              __snake_case					  =       tokenizer.build_inputs_with_special_tokens(_UpperCamelCase      )
              __snake_case					  =       tokenizer.build_inputs_with_special_tokens(_UpperCamelCase			,   _UpperCamelCase      )

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


       @slow
       def  a		(		self      )			->       int:






              """simple docstring"""


              __snake_case					  =       self.tokenizer_en_ru
              __snake_case					  =       self.tokenizer_ru_en

              __snake_case					  =       [
                  [
                      """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:
                     __snake_case					  =       tokenizer_enc.encode(_UpperCamelCase			,   return_tensors=_UpperCamelCase      )
                     self.assertListEqual(_UpperCamelCase			,   _UpperCamelCase      )

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


       @slow
       def  a		(		self      )			->       Any:






              """simple docstring"""


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


       @unittest.skip("""FSMTConfig.__init__  requires non-optional args"""      )
       def  a		(		self      )			->       List[Any]:






              """simple docstring"""


              pass






       @unittest.skip("""FSMTConfig.__init__  requires non-optional args"""      )
       def  a		(		self      )			->       Optional[int]:






              """simple docstring"""


              pass




import datasets

from .evaluate import (
    apply_no_ans_threshold,
    find_all_best_thresh,
    get_raw_scores,
    make_eval_dict,
    make_qid_to_has_ans,
    merge_eval,
)


UpperCamelCase__		   =    '''\
@inproceedings{Rajpurkar2016SQuAD10,
  title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},
  author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},
  booktitle={EMNLP},
  year={2016}
}
'''

UpperCamelCase__		   =    '''
This metric wrap the official scoring script for version 2 of the Stanford Question
Answering Dataset (SQuAD).

Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by
crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,
from the corresponding reading passage, or the question might be unanswerable.

SQuAD2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions
written adversarially by crowdworkers to look similar to answerable ones.
To do well on SQuAD2.0, systems must not only answer questions when possible, but also
determine when no answer is supported by the paragraph and abstain from answering.
'''

UpperCamelCase__		   =    '''
Computes SQuAD v2 scores (F1 and EM).
Args:
    predictions: List of triple for question-answers to score with the following elements:
        - the question-answer \'id\' field as given in the references (see below)
        - the text of the answer
        - the probability that the question has no answer
    references: List of question-answers dictionaries with the following key-values:
            - \'id\': id of the question-answer pair (see above),
            - \'answers\': a list of Dict {\'text\': text of the answer as a string}
    no_answer_threshold: float
        Probability threshold to decide that a question has no answer.
Returns:
    \'exact\': Exact match (the normalized answer exactly match the gold answer)
    \'f1\': The F-score of predicted tokens versus the gold answer
    \'total\': Number of score considered
    \'HasAns_exact\': Exact match (the normalized answer exactly match the gold answer)
    \'HasAns_f1\': The F-score of predicted tokens versus the gold answer
    \'HasAns_total\': Number of score considered
    \'NoAns_exact\': Exact match (the normalized answer exactly match the gold answer)
    \'NoAns_f1\': The F-score of predicted tokens versus the gold answer
    \'NoAns_total\': Number of score considered
    \'best_exact\': Best exact match (with varying threshold)
    \'best_exact_thresh\': No-answer probability threshold associated to the best exact match
    \'best_f1\': Best F1 (with varying threshold)
    \'best_f1_thresh\': No-answer probability threshold associated to the best F1
Examples:

    >>> predictions = [{\'prediction_text\': \'1976\', \'id\': \'56e10a3be3433e1400422b22\', \'no_answer_probability\': 0.}]
    >>> references = [{\'answers\': {\'answer_start\': [97], \'text\': [\'1976\']}, \'id\': \'56e10a3be3433e1400422b22\'}]
    >>> squad_v2_metric = datasets.load_metric("squad_v2")
    >>> results = squad_v2_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'exact\': 100.0, \'f1\': 100.0, \'total\': 1, \'HasAns_exact\': 100.0, \'HasAns_f1\': 100.0, \'HasAns_total\': 1, \'best_exact\': 100.0, \'best_exact_thresh\': 0.0, \'best_f1\': 100.0, \'best_f1_thresh\': 0.0}
'''

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION ,  _KWARGS_DESCRIPTION     )
class 						__snake_case						(							datasets.Metric     ):
       """simple docstring"""







       def  a		(		self      )			->       Tuple:






              """simple docstring"""


              return datasets.MetricInfo(
                  description=_DESCRIPTION			,   citation=_CITATION			,   inputs_description=_KWARGS_DESCRIPTION			,   features=datasets.Features(
                      {
                          """predictions""": {
                              """id""": datasets.Value("""string"""      ),
                              """prediction_text""": datasets.Value("""string"""      ),
                              """no_answer_probability""": datasets.Value("""float32"""      ),
                          },
                          """references""": {
                              """id""": datasets.Value("""string"""      ),
                              """answers""": datasets.features.Sequence(
                                  {"""text""": datasets.Value("""string"""      ), """answer_start""": datasets.Value("""int32"""      )}      ),
                          },
                      }      )			,   codebase_urls=["""https://rajpurkar.github.io/SQuAD-explorer/"""]			,   reference_urls=["""https://rajpurkar.github.io/SQuAD-explorer/"""]			,   )






       def  a		(		self			,   _UpperCamelCase			,   _UpperCamelCase			,   _UpperCamelCase=1.0      )			->       Dict:






              """simple docstring"""


              __snake_case					  =       {p["""id"""]: p["""no_answer_probability"""] for p in predictions}
              __snake_case					  =       [{"""paragraphs""": [{"""qas""": references}]}]
              __snake_case					  =       {p["""id"""]: p["""prediction_text"""] for p in predictions}

              __snake_case					  =       make_qid_to_has_ans(_UpperCamelCase      )  # maps qid to True/False
              __snake_case					  =       [k for k, v in qid_to_has_ans.items() if v]
              __snake_case					  =       [k for k, v in qid_to_has_ans.items() if not v]

              __snake_case  ,						__snake_case					  =       get_raw_scores(_UpperCamelCase			,   _UpperCamelCase      )
              __snake_case					  =       apply_no_ans_threshold(_UpperCamelCase			,   _UpperCamelCase			,   _UpperCamelCase			,   _UpperCamelCase      )
              __snake_case					  =       apply_no_ans_threshold(_UpperCamelCase			,   _UpperCamelCase			,   _UpperCamelCase			,   _UpperCamelCase      )
              __snake_case					  =       make_eval_dict(_UpperCamelCase			,   _UpperCamelCase      )

              if has_ans_qids:
                     __snake_case					  =       make_eval_dict(_UpperCamelCase			,   _UpperCamelCase			,   qid_list=_UpperCamelCase      )
                     merge_eval(_UpperCamelCase			,   _UpperCamelCase			,   """HasAns"""      )
              if no_ans_qids:
                     __snake_case					  =       make_eval_dict(_UpperCamelCase			,   _UpperCamelCase			,   qid_list=_UpperCamelCase      )
                     merge_eval(_UpperCamelCase			,   _UpperCamelCase			,   """NoAns"""      )
              find_all_best_thresh(_UpperCamelCase			,   _UpperCamelCase			,   _UpperCamelCase			,   _UpperCamelCase			,   _UpperCamelCase			,   _UpperCamelCase      )
              return dict(_UpperCamelCase      )

