





'''simple docstring'''




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 			SCREAMING_SNAKE_CASE   (a__ ,		unittest.TestCase							):
     lowerCAmelCase		      =	MvpTokenizer
     lowerCAmelCase		      =	MvpTokenizerFast
     lowerCAmelCase		      =	True
     lowerCAmelCase		      =	filter_roberta_detectors


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






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

          __A						:			Any								=	os.path.join(self.tmpdirname						,     VOCAB_FILES_NAMES['vocab_file'])
          __A						:			Optional[Any]								=	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(_UpperCAmelCase) + '\n')
          with open(self.merges_file						,     'w'						,     encoding='utf-8') as fp:
               fp.write('\n'.join(_UpperCAmelCase))


     def 							SCREAMING_SNAKE_CASE  (						self						,     **_UpperCAmelCase):

          '''simple docstring'''






          kwargs.update(self.special_tokens_map)
          return self.tokenizer_class.from_pretrained(self.tmpdirname						,     **_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self						,     **_UpperCAmelCase):

          '''simple docstring'''






          kwargs.update(self.special_tokens_map)
          return self.rust_tokenizer_class.from_pretrained(self.tmpdirname						,     **_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase):

          '''simple docstring'''






          return "lower newer", "lower newer"


     @cached_property
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          return MvpTokenizer.from_pretrained('RUCAIBox/mvp')


     @cached_property
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          return MvpTokenizerFast.from_pretrained('RUCAIBox/mvp')


     @require_torch
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Any								=	['A long paragraph for summarization.', 'Another paragraph for summarization.']
          __A						:			Any								=	[0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
               __A						:			Any								=	tokenizer(_UpperCAmelCase						,     max_length=len(_UpperCAmelCase)						,     padding=_UpperCAmelCase						,     return_tensors='pt')
               self.assertIsInstance(_UpperCAmelCase						,     _UpperCAmelCase)

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


     @require_torch
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






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


     @require_torch
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			int								=	[
              'Summary of the text.',
              'Another summary.',
          ]
          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
               __A						:			Union[str, Any]								=	tokenizer(text_target=_UpperCAmelCase						,     max_length=32						,     padding='max_length'						,     return_tensors='pt')
               self.assertEqual(32						,     targets['input_ids'].shape[1])


     @require_torch
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
               __A						:			List[Any]								=	tokenizer(
                   ['I am a small frog' * 1024, 'I am a small frog']						,     padding=_UpperCAmelCase						,     truncation=_UpperCAmelCase						,     return_tensors='pt')
               self.assertIsInstance(_UpperCAmelCase						,     _UpperCAmelCase)
               self.assertEqual(batch.input_ids.shape						,     (2, 1024))


     @require_torch
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Optional[int]								=	['A long paragraph for summarization.']
          __A						:			Any								=	[
              'Summary of the text.',
          ]
          for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
               __A						:			List[str]								=	tokenizer(_UpperCAmelCase						,     text_target=_UpperCAmelCase						,     return_tensors='pt')
               __A						:			Union[str, Any]								=	inputs['input_ids']
               __A						:			int								=	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 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          pass


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






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

                    # 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'])						,     )

                    __A						:			List[str]								=	tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids'])
                    __A						:			List[str]								=	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, 5_0264, 3823, 487, 2_1992, 3645, 4, 2])
                    self.assertSequenceEqual(tokens_r['input_ids']						,     [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2])

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






'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase__   :				Optional[Any]      =   logging.get_logger(__name__)

lowercase__   :				str      =   {
    '''andreasmadsen/efficient_mlm_m0.40''': (
        '''https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json'''
    ),
}


class 			SCREAMING_SNAKE_CASE   (a__							):
     lowerCAmelCase		      =	'''roberta-prelayernorm'''


     def __init__(						self						,     _UpperCAmelCase=5_0265						,     _UpperCAmelCase=768						,     _UpperCAmelCase=12						,     _UpperCAmelCase=12						,     _UpperCAmelCase=3072						,     _UpperCAmelCase="gelu"						,     _UpperCAmelCase=0.1						,     _UpperCAmelCase=0.1						,     _UpperCAmelCase=512						,     _UpperCAmelCase=2						,     _UpperCAmelCase=0.02						,     _UpperCAmelCase=1e-1_2						,     _UpperCAmelCase=1						,     _UpperCAmelCase=0						,     _UpperCAmelCase=2						,     _UpperCAmelCase="absolute"						,     _UpperCAmelCase=True						,     _UpperCAmelCase=None						,     **_UpperCAmelCase						,     ):

          '''simple docstring'''






          super().__init__(pad_token_id=_UpperCAmelCase						,     bos_token_id=_UpperCAmelCase						,     eos_token_id=_UpperCAmelCase						,     **_UpperCAmelCase)

          __A						:			Union[str, Any]								=	vocab_size
          __A						:			List[str]								=	hidden_size
          __A						:			Tuple								=	num_hidden_layers
          __A						:			List[str]								=	num_attention_heads
          __A						:			Tuple								=	hidden_act
          __A						:			Optional[Any]								=	intermediate_size
          __A						:			List[Any]								=	hidden_dropout_prob
          __A						:			Union[str, Any]								=	attention_probs_dropout_prob
          __A						:			Dict								=	max_position_embeddings
          __A						:			Any								=	type_vocab_size
          __A						:			Optional[int]								=	initializer_range
          __A						:			List[str]								=	layer_norm_eps
          __A						:			Optional[int]								=	position_embedding_type
          __A						:			Dict								=	use_cache
          __A						:			List[str]								=	classifier_dropout




class 			SCREAMING_SNAKE_CASE   (a__							):


     @property
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          if self.task == "multiple-choice":
               __A						:			List[str]								=	{0: 'batch', 1: 'choice', 2: 'sequence'}
          else:
               __A						:			Tuple								=	{0: 'batch', 1: 'sequence'}
          return OrderedDict(
              [
                  ('input_ids', dynamic_axis),
                  ('attention_mask', dynamic_axis),
              ])