import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors


@require_tokenizers
class      lowerCamelCase    (__lowerCamelCase     ,					unittest.TestCase    ):
  """simple docstring"""







  lowerCamelCase__   =  MvpTokenizer
  lowerCamelCase__   =  MvpTokenizerFast
  lowerCamelCase__   =  True
  lowerCamelCase__   =  filter_roberta_detectors







  def       __A   (							self       :				Optional[Any]      )     ->		Union[str, Any]:
        super().setUp()
        SCREAMING_SNAKE_CASE_      =     [
            '''l''',
            '''o''',
            '''w''',
            '''e''',
            '''r''',
            '''s''',
            '''t''',
            '''i''',
            '''d''',
            '''n''',
            '''\u0120''',
            '''\u0120l''',
            '''\u0120n''',
            '''\u0120lo''',
            '''\u0120low''',
            '''er''',
            '''\u0120lowest''',
            '''\u0120newer''',
            '''\u0120wider''',
            '''<unk>''',
        ]
        SCREAMING_SNAKE_CASE_      =     dict(zip(__magic_name__     ,   range(len(__magic_name__      )      )      )      )
        SCREAMING_SNAKE_CASE_      =     ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
        SCREAMING_SNAKE_CASE_      =     {'''unk_token''': '''<unk>'''}

        SCREAMING_SNAKE_CASE_      =     os.path.join(self.tmpdirname     ,   VOCAB_FILES_NAMES["vocab_file"]      )
        SCREAMING_SNAKE_CASE_      =     os.path.join(self.tmpdirname     ,   VOCAB_FILES_NAMES["merges_file"]      )
        with open(self.vocab_file     ,   "w"     ,   encoding="utf-8"      ) as fp:
              fp.write(json.dumps(__magic_name__      ) + "\n"      )
        with open(self.merges_file     ,   "w"     ,   encoding="utf-8"      ) as fp:
              fp.write("\n".join(__magic_name__      )      )







  def       __A   (							self       :				Any     ,   **__magic_name__       :				Optional[int]      )     ->		List[str]:
        kwargs.update(self.special_tokens_map      )
        return self.tokenizer_class.from_pretrained(self.tmpdirname     ,   **__magic_name__      )







  def       __A   (							self       :				Tuple     ,   **__magic_name__       :				List[str]      )     ->		Any:
        kwargs.update(self.special_tokens_map      )
        return self.rust_tokenizer_class.from_pretrained(self.tmpdirname     ,   **__magic_name__      )







  def       __A   (							self       :				List[Any]     ,   __magic_name__       :				int      )     ->		Optional[Any]:
        return "lower newer", "lower newer"







  @cached_property
  def       __A   (							self       :				Union[str, Any]      )     ->		List[str]:
        return MvpTokenizer.from_pretrained("RUCAIBox/mvp"      )







  @cached_property
  def       __A   (							self       :				List[str]      )     ->		Optional[int]:
        return MvpTokenizerFast.from_pretrained("RUCAIBox/mvp"      )







  @require_torch
  def       __A   (							self       :				Union[str, Any]      )     ->		int:
        SCREAMING_SNAKE_CASE_      =     ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
        SCREAMING_SNAKE_CASE_      =     [0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]

        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              SCREAMING_SNAKE_CASE_      =     tokenizer(__magic_name__     ,   max_length=len(__magic_name__      )     ,   padding=__magic_name__     ,   return_tensors="pt"      )
              self.assertIsInstance(__magic_name__     ,   __magic_name__      )

              self.assertEqual((2, 9)     ,   batch.input_ids.shape      )
              self.assertEqual((2, 9)     ,   batch.attention_mask.shape      )
              SCREAMING_SNAKE_CASE_      =     batch.input_ids.tolist()[0]
              self.assertListEqual(__magic_name__     ,   __magic_name__      )
              # Test that special tokens are reset







  @require_torch
  def       __A   (							self       :				Union[str, Any]      )     ->		Union[str, Any]:
        SCREAMING_SNAKE_CASE_      =     ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              SCREAMING_SNAKE_CASE_      =     tokenizer(__magic_name__     ,   padding=__magic_name__     ,   return_tensors="pt"      )
              # check if input_ids are returned and no labels
              self.assertIn("input_ids"     ,   __magic_name__      )
              self.assertIn("attention_mask"     ,   __magic_name__      )
              self.assertNotIn("labels"     ,   __magic_name__      )
              self.assertNotIn("decoder_attention_mask"     ,   __magic_name__      )







  @require_torch
  def       __A   (							self       :				Tuple      )     ->		Union[str, Any]:
        SCREAMING_SNAKE_CASE_      =     [
            '''Summary of the text.''',
            '''Another summary.''',
        ]
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              SCREAMING_SNAKE_CASE_      =     tokenizer(text_target=__magic_name__     ,   max_length=32     ,   padding="max_length"     ,   return_tensors="pt"      )
              self.assertEqual(32     ,   targets["input_ids"].shape[1]      )







  @require_torch
  def       __A   (							self       :				Optional[Any]      )     ->		int:
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              SCREAMING_SNAKE_CASE_      =     tokenizer(
                  ["I am a small frog" * 1_024, "I am a small frog"]     ,   padding=__magic_name__     ,   truncation=__magic_name__     ,   return_tensors="pt"      )
              self.assertIsInstance(__magic_name__     ,   __magic_name__      )
              self.assertEqual(batch.input_ids.shape     ,   (2, 1_024)      )







  @require_torch
  def       __A   (							self       :				Optional[int]      )     ->		Optional[int]:
        SCREAMING_SNAKE_CASE_      =     ['''A long paragraph for summarization.''']
        SCREAMING_SNAKE_CASE_      =     [
            '''Summary of the text.''',
        ]
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              SCREAMING_SNAKE_CASE_      =     tokenizer(__magic_name__     ,   text_target=__magic_name__     ,   return_tensors="pt"      )
              SCREAMING_SNAKE_CASE_      =     inputs['''input_ids''']
              SCREAMING_SNAKE_CASE_      =     inputs['''labels''']
              self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()      )
              self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()      )
              self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()      )
              self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()      )







  def       __A   (							self       :				Tuple      )     ->		str:
        pass







  def       __A   (							self       :				str      )     ->		List[Any]:
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''      ):
                    SCREAMING_SNAKE_CASE_      =     self.rust_tokenizer_class.from_pretrained(__magic_name__     ,   **__magic_name__      )
                    SCREAMING_SNAKE_CASE_      =     self.tokenizer_class.from_pretrained(__magic_name__     ,   **__magic_name__      )
                    SCREAMING_SNAKE_CASE_      =     '''A, <mask> AllenNLP sentence.'''
                    SCREAMING_SNAKE_CASE_      =     tokenizer_r.encode_plus(__magic_name__     ,   add_special_tokens=__magic_name__     ,   return_token_type_ids=__magic_name__      )
                    SCREAMING_SNAKE_CASE_      =     tokenizer_p.encode_plus(__magic_name__     ,   add_special_tokens=__magic_name__     ,   return_token_type_ids=__magic_name__      )

                    # token_type_ids should put 0 everywhere
                    self.assertEqual(sum(tokens_r["token_type_ids"]      )     ,   sum(tokens_p["token_type_ids"]      )      )

                    # attention_mask should put 1 everywhere, so sum over length should be 1
                    self.assertEqual(
                        sum(tokens_r["attention_mask"]      ) / len(tokens_r["attention_mask"]      )     ,   sum(tokens_p["attention_mask"]      ) / len(tokens_p["attention_mask"]      )     ,   )

                    SCREAMING_SNAKE_CASE_      =     tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]      )
                    SCREAMING_SNAKE_CASE_      =     tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"]      )

                    # Rust correctly handles the space before the mask while python doesnt
                    self.assertSequenceEqual(tokens_p["input_ids"]     ,   [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]      )
                    self.assertSequenceEqual(tokens_r["input_ids"]     ,   [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]      )

                    self.assertSequenceEqual(
                        __magic_name__     ,   ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]      )
                    self.assertSequenceEqual(
                        __magic_name__     ,   ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]      )









import re
from pathlib import Path
from unittest import TestCase

import pytest






@pytest.mark.integration
class     lowercase__       (	__lowerCamelCase				):

   '''simple docstring'''


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






       """simple docstring"""

       with open(__magic_name__,		encoding='''utf-8'''							) as input_file:
           UpperCamelCase__							:				Tuple          =      re.compile(R'''(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)'''							)
           UpperCamelCase__							:				str          =      input_file.read()
           UpperCamelCase__							:				List[Any]          =      regexp.search(__magic_name__							)

       return match




   def       UpperCamelCase__  (    self,		__magic_name__							) ->      Any:






       """simple docstring"""

       with open(__magic_name__,		encoding='''utf-8'''							) as input_file:
           UpperCamelCase__							:				Dict          =      re.compile(R'''#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()''',		re.DOTALL							)
           UpperCamelCase__							:				Any          =      input_file.read()
           # use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
           UpperCamelCase__							:				Tuple          =      regexp.finditer(__magic_name__							)

       UpperCamelCase__							:				Dict          =      [match for match in matches if match is not None and match.group(1							) is not None]
       return matches[0] if matches else None




   def       UpperCamelCase__  (    self							) ->      List[Any]:






       """simple docstring"""
       UpperCamelCase__							:				int          =      Path('''./datasets'''							)
       UpperCamelCase__							:				Any          =      list(dataset_paths.absolute().glob('''**/*.py'''							)							)

       for dataset in dataset_files:
           if self._no_encoding_on_file_open(str(__magic_name__							)							):
               raise AssertionError(f"open(...) must use utf-8 encoding in {dataset}"							)




   def       UpperCamelCase__  (    self							) ->      Dict:






       """simple docstring"""
       UpperCamelCase__							:				Optional[int]          =      Path('''./datasets'''							)
       UpperCamelCase__							:				Optional[Any]          =      list(dataset_paths.absolute().glob('''**/*.py'''							)							)

       for dataset in dataset_files:
           if self._no_print_statements(str(__magic_name__							)							):
               raise AssertionError(f"print statement found in {dataset}. Use datasets.logger/logging instead."							)

