



import argparse
import collections
import json
import os
import re
import string
import sys

import numpy as np


a_    :  Dict						  =		re.compile(R'\b(a|an|the)\b', re.UNICODE)

a_    :  Optional[int]						  =		None

def      __lowercase(						):


      """simple docstring"""



      lowerCamelCase             =		argparse.ArgumentParser("Official evaluation script for SQuAD version 2.0."			)
      parser.add_argument("data_file" , metavar="data.json" , help="Input data JSON file."			)
      parser.add_argument("pred_file" , metavar="pred.json" , help="Model predictions."			)
      parser.add_argument(
          "--out-file" , "-o" , metavar="eval.json" , help="Write accuracy metrics to file (default is stdout)."			)
      parser.add_argument(
          "--na-prob-file" , "-n" , metavar="na_prob.json" , help="Model estimates of probability of no answer."			)
      parser.add_argument(
          "--na-prob-thresh" , "-t" , type=UpperCAmelCase__ , default=1.0 , help="Predict \"\" if no-answer probability exceeds this (default = 1.0)." , )
      parser.add_argument(
          "--out-image-dir" , "-p" , metavar="out_images" , default=UpperCAmelCase__ , help="Save precision-recall curves to directory."			)
      parser.add_argument("--verbose" , "-v" , action="store_true"			)
      if len(sys.argv			) == 1:
            parser.print_help()
            sys.exit(1			)
      return parser.parse_args()

def      __lowercase(						UpperCAmelCase__			):


      """simple docstring"""



      lowerCamelCase             =		{}
      for article in dataset:
            for p in article["paragraphs"]:
                  for qa in p["qas"]:
                        lowerCamelCase             =		bool(qa["answers"]["text"]			)
      return qid_to_has_ans

def      __lowercase(						UpperCAmelCase__			):


      """simple docstring"""



      def remove_articles(UpperCAmelCase__			):
            return ARTICLES_REGEX.sub(" " , UpperCAmelCase__			)

      def white_space_fix(UpperCAmelCase__			):
            return " ".join(text.split()			)

      def remove_punc(UpperCAmelCase__			):
            lowerCamelCase             =		set(string.punctuation			)
            return "".join(ch for ch in text if ch not in exclude			)

      def lower(UpperCAmelCase__			):
            return text.lower()

      return white_space_fix(remove_articles(remove_punc(lower(UpperCAmelCase__			)			)			)			)

def      __lowercase(						UpperCAmelCase__			):


      """simple docstring"""



      if not s:
            return []
      return normalize_answer(UpperCAmelCase__			).split()

def      __lowercase(						UpperCAmelCase__ , UpperCAmelCase__			):


      """simple docstring"""



      return int(normalize_answer(UpperCAmelCase__			) == normalize_answer(UpperCAmelCase__			)			)

def      __lowercase(						UpperCAmelCase__ , UpperCAmelCase__			):


      """simple docstring"""



      lowerCamelCase             =		get_tokens(UpperCAmelCase__			)
      lowerCamelCase             =		get_tokens(UpperCAmelCase__			)
      lowerCamelCase             =		collections.Counter(UpperCAmelCase__			) & collections.Counter(UpperCAmelCase__			)
      lowerCamelCase             =		sum(common.values()			)
      if len(UpperCAmelCase__			) == 0 or len(UpperCAmelCase__			) == 0:
            # If either is no-answer, then F1 is 1 if they agree, 0 otherwise
            return int(gold_toks == pred_toks			)
      if num_same == 0:
            return 0
      lowerCamelCase             =		1.0 * num_same / len(UpperCAmelCase__			)
      lowerCamelCase             =		1.0 * num_same / len(UpperCAmelCase__			)
      lowerCamelCase             =		(2 * precision * recall) / (precision + recall)
      return fa

def      __lowercase(						UpperCAmelCase__ , UpperCAmelCase__			):


      """simple docstring"""



      lowerCamelCase             =		{}
      lowerCamelCase             =		{}
      for article in dataset:
            for p in article["paragraphs"]:
                  for qa in p["qas"]:
                        lowerCamelCase             =		qa["id"]
                        lowerCamelCase             =		[t for t in qa["answers"]["text"] if normalize_answer(UpperCAmelCase__			)]
                        if not gold_answers:
                              # For unanswerable questions, only correct answer is empty string
                              lowerCamelCase             =		[""]
                        if qid not in preds:
                              print(F"""Missing prediction for {qid}"""			)
                              continue
                        lowerCamelCase             =		preds[qid]
                        # Take max over all gold answers
                        lowerCamelCase             =		max(compute_exact(UpperCAmelCase__ , UpperCAmelCase__			) for a in gold_answers			)
                        lowerCamelCase             =		max(compute_fa(UpperCAmelCase__ , UpperCAmelCase__			) for a in gold_answers			)
      return exact_scores, fa_scores

def      __lowercase(						UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__			):


      """simple docstring"""



      lowerCamelCase             =		{}
      for qid, s in scores.items():
            lowerCamelCase             =		na_probs[qid] > na_prob_thresh
            if pred_na:
                  lowerCamelCase             =		float(not qid_to_has_ans[qid]			)
            else:
                  lowerCamelCase             =		s
      return new_scores

def      __lowercase(						UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__=None			):


      """simple docstring"""



      if not qid_list:
            lowerCamelCase             =		len(UpperCAmelCase__			)
            return collections.OrderedDict(
                [
                    ("exact", 1_0_0.0 * sum(exact_scores.values()			) / total),
                    ("f1", 1_0_0.0 * sum(fa_scores.values()			) / total),
                    ("total", total),
                ]			)
      else:
            lowerCamelCase             =		len(UpperCAmelCase__			)
            return collections.OrderedDict(
                [
                    ("exact", 1_0_0.0 * sum(exact_scores[k] for k in qid_list			) / total),
                    ("f1", 1_0_0.0 * sum(fa_scores[k] for k in qid_list			) / total),
                    ("total", total),
                ]			)

def      __lowercase(						UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__			):


      """simple docstring"""



      for k in new_eval:
            lowerCamelCase             =		new_eval[k]

def      __lowercase(						UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__			):


      """simple docstring"""



      plt.step(UpperCAmelCase__ , UpperCAmelCase__ , color="b" , alpha=0.2 , where="post"			)
      plt.fill_between(UpperCAmelCase__ , UpperCAmelCase__ , step="post" , alpha=0.2 , color="b"			)
      plt.xlabel("Recall"			)
      plt.ylabel("Precision"			)
      plt.xlim([0.0, 1.0_5]			)
      plt.ylim([0.0, 1.0_5]			)
      plt.title(UpperCAmelCase__			)
      plt.savefig(UpperCAmelCase__			)
      plt.clf()

def      __lowercase(						UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__=None , UpperCAmelCase__=None			):


      """simple docstring"""



      lowerCamelCase             =		sorted(UpperCAmelCase__ , key=lambda UpperCAmelCase__			: na_probs[k]			)
      lowerCamelCase             =		0.0
      lowerCamelCase             =		1.0
      lowerCamelCase             =		0.0
      lowerCamelCase             =		[1.0]
      lowerCamelCase             =		[0.0]
      lowerCamelCase             =		0.0
      for i, qid in enumerate(UpperCAmelCase__			):
            if qid_to_has_ans[qid]:
                  true_pos += scores[qid]
            lowerCamelCase             =		true_pos / float(i + 1			)
            lowerCamelCase             =		true_pos / float(UpperCAmelCase__			)
            if i == len(UpperCAmelCase__			) - 1 or na_probs[qid] != na_probs[qid_list[i + 1]]:
                  # i.e., if we can put a threshold after this point
                  avg_prec += cur_p * (cur_r - recalls[-1])
                  precisions.append(UpperCAmelCase__			)
                  recalls.append(UpperCAmelCase__			)
      if out_image:
            plot_pr_curve(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__			)
      return {"ap": 1_0_0.0 * avg_prec}

def      __lowercase(						UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__			):


      """simple docstring"""



      if out_image_dir and not os.path.exists(UpperCAmelCase__			):
            os.makedirs(UpperCAmelCase__			)
      lowerCamelCase             =		sum(1 for v in qid_to_has_ans.values() if v			)
      if num_true_pos == 0:
            return
      lowerCamelCase             =		make_precision_recall_eval(
          UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , out_image=os.path.join(UpperCAmelCase__ , "pr_exact.png"			) , title="Precision-Recall curve for Exact Match score" , )
      lowerCamelCase             =		make_precision_recall_eval(
          UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , out_image=os.path.join(UpperCAmelCase__ , "pr_f1.png"			) , title="Precision-Recall curve for F1 score" , )
      lowerCamelCase             =		{k: float(UpperCAmelCase__			) for k, v in qid_to_has_ans.items()}
      lowerCamelCase             =		make_precision_recall_eval(
          UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , out_image=os.path.join(UpperCAmelCase__ , "pr_oracle.png"			) , title="Oracle Precision-Recall curve (binary task of HasAns vs. NoAns)" , )
      merge_eval(UpperCAmelCase__ , UpperCAmelCase__ , "pr_exact"			)
      merge_eval(UpperCAmelCase__ , UpperCAmelCase__ , "pr_f1"			)
      merge_eval(UpperCAmelCase__ , UpperCAmelCase__ , "pr_oracle"			)

def      __lowercase(						UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__			):


      """simple docstring"""



      if not qid_list:
            return
      lowerCamelCase             =		[na_probs[k] for k in qid_list]
      lowerCamelCase             =		np.ones_like(UpperCAmelCase__			) / float(len(UpperCAmelCase__			)			)
      plt.hist(UpperCAmelCase__ , weights=UpperCAmelCase__ , bins=20 , range=(0.0, 1.0)			)
      plt.xlabel("Model probability of no-answer"			)
      plt.ylabel("Proportion of dataset"			)
      plt.title(F"""Histogram of no-answer probability: {name}"""			)
      plt.savefig(os.path.join(UpperCAmelCase__ , F"""na_prob_hist_{name}.png"""			)			)
      plt.clf()

def      __lowercase(						UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__			):


      """simple docstring"""



      lowerCamelCase             =		sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k]			)
      lowerCamelCase             =		num_no_ans
      lowerCamelCase             =		cur_score
      lowerCamelCase             =		0.0
      lowerCamelCase             =		sorted(UpperCAmelCase__ , key=lambda UpperCAmelCase__			: na_probs[k]			)
      for i, qid in enumerate(UpperCAmelCase__			):
            if qid not in scores:
                  continue
            if qid_to_has_ans[qid]:
                  lowerCamelCase             =		scores[qid]
            else:
                  if preds[qid]:
                        lowerCamelCase             =		-1
                  else:
                        lowerCamelCase             =		0
            cur_score += diff
            if cur_score > best_score:
                  lowerCamelCase             =		cur_score
                  lowerCamelCase             =		na_probs[qid]
      return 1_0_0.0 * best_score / len(UpperCAmelCase__			), best_thresh

def      __lowercase(						UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__			):


      """simple docstring"""



      lowerCamelCase      ,				lowerCamelCase             =		find_best_thresh(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__			)
      lowerCamelCase      ,				lowerCamelCase             =		find_best_thresh(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__			)
      lowerCamelCase             =		best_exact
      lowerCamelCase             =		exact_thresh
      lowerCamelCase             =		best_fa
      lowerCamelCase             =		fa_thresh

def      __lowercase(						):


      """simple docstring"""



      with open(OPTS.data_file			) as f:
            lowerCamelCase             =		json.load(UpperCAmelCase__			)
            lowerCamelCase             =		dataset_json["data"]
      with open(OPTS.pred_file			) as f:
            lowerCamelCase             =		json.load(UpperCAmelCase__			)
      if OPTS.na_prob_file:
            with open(OPTS.na_prob_file			) as f:
                  lowerCamelCase             =		json.load(UpperCAmelCase__			)
      else:
            lowerCamelCase             =		{k: 0.0 for k in preds}
      lowerCamelCase             =		make_qid_to_has_ans(UpperCAmelCase__			)  # maps qid to True/False
      lowerCamelCase             =		[k for k, v in qid_to_has_ans.items() if v]
      lowerCamelCase             =		[k for k, v in qid_to_has_ans.items() if not v]
      lowerCamelCase      ,				lowerCamelCase             =		get_raw_scores(UpperCAmelCase__ , UpperCAmelCase__			)
      lowerCamelCase             =		apply_no_ans_threshold(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , OPTS.na_prob_thresh			)
      lowerCamelCase             =		apply_no_ans_threshold(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , OPTS.na_prob_thresh			)
      lowerCamelCase             =		make_eval_dict(UpperCAmelCase__ , UpperCAmelCase__			)
      if has_ans_qids:
            lowerCamelCase             =		make_eval_dict(UpperCAmelCase__ , UpperCAmelCase__ , qid_list=UpperCAmelCase__			)
            merge_eval(UpperCAmelCase__ , UpperCAmelCase__ , "HasAns"			)
      if no_ans_qids:
            lowerCamelCase             =		make_eval_dict(UpperCAmelCase__ , UpperCAmelCase__ , qid_list=UpperCAmelCase__			)
            merge_eval(UpperCAmelCase__ , UpperCAmelCase__ , "NoAns"			)
      if OPTS.na_prob_file:
            find_all_best_thresh(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__			)
      if OPTS.na_prob_file and OPTS.out_image_dir:
            run_precision_recall_analysis(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , OPTS.out_image_dir			)
            histogram_na_prob(UpperCAmelCase__ , UpperCAmelCase__ , OPTS.out_image_dir , "hasAns"			)
            histogram_na_prob(UpperCAmelCase__ , UpperCAmelCase__ , OPTS.out_image_dir , "noAns"			)
      if OPTS.out_file:
            with open(OPTS.out_file , "w"			) as f:
                  json.dump(UpperCAmelCase__ , UpperCAmelCase__			)
      else:
            print(json.dumps(UpperCAmelCase__ , indent=2			)			)


if __name__ == "__main__":
     a_    :  Union[str, Any]						  =		parse_args()
     if OPTS.out_image_dir:
          import matplotlib

          matplotlib.use('Agg')
          import matplotlib.pyplot as plt
     main()




import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)

from ...test_tokenization_common import TokenizerTesterMixin


a_    :  Tuple						  =		get_tests_dir('fixtures/test_sentencepiece.model')

if is_torch_available():
     from transformers.models.mbart.modeling_mbart import shift_tokens_right

a_    :  Union[str, Any]						  =		2_5_0_0_0_4
a_    :  int						  =		2_5_0_0_2_0





@require_sentencepiece
@require_tokenizers
class  lowerCamelCase__	(    UpperCAmelCase_							,     unittest.TestCase):
     """simple docstring"""



     _A      =      MBartaaTokenizer
     _A      =      MBartaaTokenizerFast
     _A      =      True
     _A      =      True







     def     _a   (self     ):






           '''simple docstring'''



           super().setUp()

           # We have a SentencePiece fixture for testing
           lowerCamelCase             =		MBartaaTokenizer(__a					,      src_lang="en_XX"					,      tgt_lang="ro_RO"					,      keep_accents=__a     )
           tokenizer.save_pretrained(self.tmpdirname     )







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		"<s>"
           lowerCamelCase             =		0

           self.assertEqual(self.get_tokenizer()._convert_token_to_id(__a     )					,      __a     )
           self.assertEqual(self.get_tokenizer()._convert_id_to_token(__a     )					,      __a     )







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		list(self.get_tokenizer().get_vocab().keys()     )

           self.assertEqual(vocab_keys[0]					,      "<s>"     )
           self.assertEqual(vocab_keys[1]					,      "<pad>"     )
           self.assertEqual(vocab_keys[-1]					,      "<mask>"     )
           self.assertEqual(len(__a     )					,      10_54     )







     def     _a   (self     ):






           '''simple docstring'''



           self.assertEqual(self.get_tokenizer().vocab_size					,      10_54     )







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		MBartaaTokenizer(__a					,      src_lang="en_XX"					,      tgt_lang="ro_RO"					,      keep_accents=__a     )

           lowerCamelCase             =		tokenizer.tokenize("This is a test"     )
           self.assertListEqual(__a					,      ["▁This", "▁is", "▁a", "▁t", "est"]     )

           self.assertListEqual(
               tokenizer.convert_tokens_to_ids(__a     )					,      [value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]]					,      )

           lowerCamelCase             =		tokenizer.tokenize("I was born in 92000, and this is falsé."     )
           self.assertListEqual(
               __a					,      [SPIECE_UNDERLINE + "I", SPIECE_UNDERLINE + "was", SPIECE_UNDERLINE + "b", "or", "n", SPIECE_UNDERLINE + "in", SPIECE_UNDERLINE + "", "9", "2", "0", "0", "0", ",", SPIECE_UNDERLINE + "and", SPIECE_UNDERLINE + "this", SPIECE_UNDERLINE + "is", SPIECE_UNDERLINE + "f", "al", "s", "é", "."]					,      )
           lowerCamelCase             =		tokenizer.convert_tokens_to_ids(__a     )
           self.assertListEqual(
               __a					,      [
                   value + tokenizer.fairseq_offset
                   for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
               ]					,      )

           lowerCamelCase             =		tokenizer.convert_ids_to_tokens(__a     )
           self.assertListEqual(
               __a					,      [SPIECE_UNDERLINE + "I", SPIECE_UNDERLINE + "was", SPIECE_UNDERLINE + "b", "or", "n", SPIECE_UNDERLINE + "in", SPIECE_UNDERLINE + "", "<unk>", "2", "0", "0", "0", ",", SPIECE_UNDERLINE + "and", SPIECE_UNDERLINE + "this", SPIECE_UNDERLINE + "is", SPIECE_UNDERLINE + "f", "al", "s", "<unk>", "."]					,      )







     @slow
     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		{"input_ids": [[25_00_04, 1_10_62, 8_27_72, 7, 15, 8_27_72, 5_38, 5_15_29, 2_37, 1_71_98, 12_90, 2_06, 9, 21_51_75, 13_14, 1_36, 1_71_98, 12_90, 2_06, 9, 5_63_59, 42, 12_20_09, 9, 1_64_66, 16, 8_73_44, 45_37, 9, 47_17, 7_83_81, 6, 15_99_58, 7, 15, 2_44_80, 6_18, 4, 5_27, 2_26_93, 54_28, 4, 27_77, 2_44_80, 98_74, 4, 4_35_23, 5_94, 4, 8_03, 1_83_92, 3_31_89, 18, 4, 4_35_23, 2_44_47, 1_23_99, 1_00, 2_49_55, 8_36_58, 96_26, 14_40_57, 15, 8_39, 2_23_35, 16, 1_36, 2_49_55, 8_36_58, 8_34_79, 15, 3_91_02, 7_24, 16, 6_78, 6_45, 27_89, 13_28, 45_89, 42, 12_20_09, 11_57_74, 23, 8_05, 13_28, 4_68_76, 7, 1_36, 5_38_94, 19_40, 4_22_27, 4_11_59, 1_77_21, 8_23, 4_25, 4, 2_75_12, 9_87_22, 2_06, 1_36, 55_31, 49_70, 9_19, 1_73_36, 5, 2], [25_00_04, 2_00_80, 6_18, 83, 8_27_75, 47, 4_79, 9, 15_17, 73, 5_38_94, 3_33, 8_05_81, 11_01_17, 1_88_11, 52_56, 12_95, 51, 15_25_26, 2_97, 79_86, 3_90, 12_44_16, 5_38, 3_54_31, 2_14, 98, 1_50_44, 2_57_37, 1_36, 71_08, 4_37_01, 23, 7_56, 13_53_55, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [25_00_04, 5_81, 6_37_73, 11_94_55, 6, 14_77_97, 8_82_03, 7, 6_45, 70, 21, 32_85, 1_02_69, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
           # fmt: on

           self.tokenizer_integration_test_util(
               expected_encoding=__a					,      model_name="facebook/mbart-large-50"					,      revision="d3913889c59cd5c9e456b269c376325eabad57e2"					,      )







     def     _a   (self     ):






           '''simple docstring'''



           if not self.test_slow_tokenizer:
                 # as we don't have a slow version, we can't compare the outputs between slow and fast versions
                 return

           lowerCamelCase             =		(self.rust_tokenizer_class, "hf-internal-testing/tiny-random-mbart50", {})
           for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""     ):
                       lowerCamelCase             =		self.rust_tokenizer_class.from_pretrained(__a					,      **__a     )
                       lowerCamelCase             =		self.tokenizer_class.from_pretrained(__a					,      **__a     )

                       lowerCamelCase             =		tempfile.mkdtemp()

                       lowerCamelCase             =		tokenizer_r.save_pretrained(__a     )
                       lowerCamelCase             =		tokenizer_p.save_pretrained(__a     )

                       # Checks it save with the same files + the tokenizer.json file for the fast one
                       self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files     )     )
                       lowerCamelCase             =		tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f     )
                       self.assertSequenceEqual(__a					,      __a     )

                       # Checks everything loads correctly in the same way
                       lowerCamelCase             =		tokenizer_r.from_pretrained(__a     )
                       lowerCamelCase             =		tokenizer_p.from_pretrained(__a     )

                       # Check special tokens are set accordingly on Rust and Python
                       for key in tokenizer_pp.special_tokens_map:
                             self.assertTrue(hasattr(__a					,      __a     )     )
                             # self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
                             # self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

                       shutil.rmtree(__a     )

                       # Save tokenizer rust, legacy_format=True
                       lowerCamelCase             =		tempfile.mkdtemp()

                       lowerCamelCase             =		tokenizer_r.save_pretrained(__a					,      legacy_format=__a     )
                       lowerCamelCase             =		tokenizer_p.save_pretrained(__a     )

                       # Checks it save with the same files
                       self.assertSequenceEqual(__a					,      __a     )

                       # Checks everything loads correctly in the same way
                       lowerCamelCase             =		tokenizer_r.from_pretrained(__a     )
                       lowerCamelCase             =		tokenizer_p.from_pretrained(__a     )

                       # Check special tokens are set accordingly on Rust and Python
                       for key in tokenizer_pp.special_tokens_map:
                             self.assertTrue(hasattr(__a					,      __a     )     )

                       shutil.rmtree(__a     )

                       # Save tokenizer rust, legacy_format=False
                       lowerCamelCase             =		tempfile.mkdtemp()

                       lowerCamelCase             =		tokenizer_r.save_pretrained(__a					,      legacy_format=__a     )
                       lowerCamelCase             =		tokenizer_p.save_pretrained(__a     )

                       # Checks it saved the tokenizer.json file
                       self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files     )     )

                       # Checks everything loads correctly in the same way
                       lowerCamelCase             =		tokenizer_r.from_pretrained(__a     )
                       lowerCamelCase             =		tokenizer_p.from_pretrained(__a     )

                       # Check special tokens are set accordingly on Rust and Python
                       for key in tokenizer_pp.special_tokens_map:
                             self.assertTrue(hasattr(__a					,      __a     )     )

                       shutil.rmtree(__a     )





@require_torch
@require_sentencepiece
@require_tokenizers
class  lowerCamelCase__	(    unittest.TestCase):
     """simple docstring"""



     _A      =      'facebook/mbart-large-50-one-to-many-mmt'
     _A      =      [
         ' UN Chief Says There Is No Military Solution in Syria',
         ' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.',
     ]
     _A      =      [
         'Şeful ONU declară că nu există o soluţie militară în Siria',
         'Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'
         ' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'
         ' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.',
     ]
     _A      =      [EN_CODE, 82_74, 12_78_73, 2_59_16, 7, 86_22, 20_71, 4_38, 6_74_85, 53, 18_78_95, 23, 5_17_12, 2]







     @classmethod
     def     _a   (cls     ):






           '''simple docstring'''



           lowerCamelCase             =		MBartaaTokenizer.from_pretrained(
               cls.checkpoint_name					,      src_lang="en_XX"					,      tgt_lang="ro_RO"     )
           lowerCamelCase             =		1
           return cls







     def     _a   (self     ):






           '''simple docstring'''



           self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ar_AR"]					,      25_00_01     )
           self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["en_EN"]					,      25_00_04     )
           self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ro_RO"]					,      25_00_20     )
           self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["mr_IN"]					,      25_00_38     )







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		self.tokenizer.batch_encode_plus(self.src_text     ).input_ids[0]
           self.assertListEqual(self.expected_src_tokens					,      __a     )







     def     _a   (self     ):






           '''simple docstring'''



           self.assertIn(__a					,      self.tokenizer.all_special_ids     )
           lowerCamelCase             =		[RO_CODE, 8_84, 90_19, 96, 9, 9_16, 8_67_92, 36, 1_87_43, 1_55_96, 5, 2]
           lowerCamelCase             =		self.tokenizer.decode(__a					,      skip_special_tokens=__a     )
           lowerCamelCase             =		self.tokenizer.decode(generated_ids[1:]					,      skip_special_tokens=__a     )
           self.assertEqual(__a					,      __a     )
           self.assertNotIn(self.tokenizer.eos_token					,      __a     )







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		["this is gunna be a long sentence " * 20]
           assert isinstance(src_text[0]					,      __a     )
           lowerCamelCase             =		10
           lowerCamelCase             =		self.tokenizer(__a					,      max_length=__a					,      truncation=__a     ).input_ids[0]
           self.assertEqual(ids[0]					,      __a     )
           self.assertEqual(ids[-1]					,      2     )
           self.assertEqual(len(__a     )					,      __a     )







     def     _a   (self     ):






           '''simple docstring'''



           self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"]     )					,      [25_00_53, 25_00_01]     )







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		tempfile.mkdtemp()
           lowerCamelCase             =		self.tokenizer.fairseq_tokens_to_ids
           self.tokenizer.save_pretrained(__a     )
           lowerCamelCase             =		MBartaaTokenizer.from_pretrained(__a     )
           self.assertDictEqual(new_tok.fairseq_tokens_to_ids					,      __a     )







     @require_torch
     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		self.tokenizer(self.src_text					,      text_target=self.tgt_text					,      padding=__a					,      return_tensors="pt"     )
           lowerCamelCase             =		shift_tokens_right(batch["labels"]					,      self.tokenizer.pad_token_id     )

           # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
           assert batch.input_ids[1][0] == EN_CODE
           assert batch.input_ids[1][-1] == 2
           assert batch.labels[1][0] == RO_CODE
           assert batch.labels[1][-1] == 2
           assert batch.decoder_input_ids[1][:2].tolist() == [2, RO_CODE]







     @require_torch
     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		self.tokenizer(
               self.src_text					,      text_target=self.tgt_text					,      padding=__a					,      truncation=__a					,      max_length=len(self.expected_src_tokens     )					,      return_tensors="pt"					,      )
           lowerCamelCase             =		shift_tokens_right(batch["labels"]					,      self.tokenizer.pad_token_id     )

           self.assertIsInstance(__a					,      __a     )

           self.assertEqual((2, 14)					,      batch.input_ids.shape     )
           self.assertEqual((2, 14)					,      batch.attention_mask.shape     )
           lowerCamelCase             =		batch.input_ids.tolist()[0]
           self.assertListEqual(self.expected_src_tokens					,      __a     )
           self.assertEqual(2					,      batch.decoder_input_ids[0, 0]     )  # decoder_start_token_id
           # Test that special tokens are reset
           self.assertEqual(self.tokenizer.prefix_tokens					,      [EN_CODE]     )
           self.assertEqual(self.tokenizer.suffix_tokens					,      [self.tokenizer.eos_token_id]     )







     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		self.tokenizer(self.src_text					,      padding=__a					,      truncation=__a					,      max_length=3					,      return_tensors="pt"     )
           lowerCamelCase             =		self.tokenizer(
               text_target=self.tgt_text					,      padding=__a					,      truncation=__a					,      max_length=10					,      return_tensors="pt"     )
           lowerCamelCase             =		targets["input_ids"]
           lowerCamelCase             =		shift_tokens_right(__a					,      self.tokenizer.pad_token_id     )

           self.assertEqual(batch.input_ids.shape[1]					,      3     )
           self.assertEqual(batch.decoder_input_ids.shape[1]					,      10     )







     @require_torch
     def     _a   (self     ):






           '''simple docstring'''



           lowerCamelCase             =		self.tokenizer._build_translation_inputs(
               "A test"					,      return_tensors="pt"					,      src_lang="en_XX"					,      tgt_lang="ar_AR"     )

           self.assertEqual(
               nested_simplify(__a     )					,      {
                   # en_XX, A, test, EOS
                   "input_ids": [[25_00_04, 62, 30_34, 2]],
                   "attention_mask": [[1, 1, 1, 1]],
                   # ar_AR
                   "forced_bos_token_id": 25_00_01,
               }					,      )