

from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

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


if TYPE_CHECKING:
  from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


_UpperCAmelCase    	=			logging.get_logger(__name__)

_UpperCAmelCase    	=			{
    "microsoft/deberta-base": "https://huggingface.co/microsoft/deberta-base/resolve/main/config.json",
    "microsoft/deberta-large": "https://huggingface.co/microsoft/deberta-large/resolve/main/config.json",
    "microsoft/deberta-xlarge": "https://huggingface.co/microsoft/deberta-xlarge/resolve/main/config.json",
    "microsoft/deberta-base-mnli": "https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/config.json",
    "microsoft/deberta-large-mnli": "https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/config.json",
    "microsoft/deberta-xlarge-mnli": "https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/config.json",
}



class        _UpperCAmelCase (   __lowercase			):
  '''simple docstring'''







  SCREAMING_SNAKE_CASE							:				Union[str, Any]					      =				'''deberta'''







  def __init__( self	: Dict		,						UpperCamelCase__	: List[str]=50265		,						UpperCamelCase__	: List[Any]=768		,						UpperCamelCase__	: Tuple=12		,						UpperCamelCase__	: List[Any]=12		,						UpperCamelCase__	: str=3072		,						UpperCamelCase__	: Tuple="gelu"		,						UpperCamelCase__	: Dict=0.1		,						UpperCamelCase__	: List[str]=0.1		,						UpperCamelCase__	: str=512		,						UpperCamelCase__	: str=0		,						UpperCamelCase__	: Any=0.02		,						UpperCamelCase__	: Optional[int]=1e-7		,						UpperCamelCase__	: str=False		,						UpperCamelCase__	: Optional[Any]=-1		,						UpperCamelCase__	: Tuple=0		,						UpperCamelCase__	: Union[str, Any]=True		,						UpperCamelCase__	: int=None		,						UpperCamelCase__	: Any=0		,						UpperCamelCase__	: Optional[int]="gelu"		,						**UpperCamelCase__	: Any		,						):
        super().__init__(**UpperCamelCase__ )

        A    =				hidden_size
        A    =				num_hidden_layers
        A    =				num_attention_heads
        A    =				intermediate_size
        A    =				hidden_act
        A    =				hidden_dropout_prob
        A    =				attention_probs_dropout_prob
        A    =				max_position_embeddings
        A    =				type_vocab_size
        A    =				initializer_range
        A    =				relative_attention
        A    =				max_relative_positions
        A    =				pad_token_id
        A    =				position_biased_input

        # Backwards compatibility
        if type(UpperCamelCase__ ) == str:
              A    =				[x.strip() for x in pos_att_type.lower().split('|' )]

        A    =				pos_att_type
        A    =				vocab_size
        A    =				layer_norm_eps

        A    =				kwargs.get('pooler_hidden_size'		,						UpperCamelCase__ )
        A    =				pooler_dropout
        A    =				pooler_hidden_act



class        _UpperCAmelCase (   __lowercase			):
  '''simple docstring'''







  @property
  def 	UpperCamelCase       ( self	: str ):
        if self.task == "multiple-choice":
              A    =				{0: 'batch', 1: 'choice', 2: 'sequence'}
        else:
              A    =				{0: 'batch', 1: 'sequence'}
        if self._config.type_vocab_size > 0:
              return OrderedDict(
                  [('input_ids', dynamic_axis), ('attention_mask', dynamic_axis), ('token_type_ids', dynamic_axis)] )
        else:
              return OrderedDict([('input_ids', dynamic_axis), ('attention_mask', dynamic_axis)] )







  @property
  def 	UpperCamelCase       ( self	: int ):
        return 12







  def 	UpperCamelCase       ( self	: str		,						UpperCamelCase__	: Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]		,						UpperCamelCase__	: int = -1		,						UpperCamelCase__	: int = -1		,						UpperCamelCase__	: int = -1		,						UpperCamelCase__	: bool = False		,						UpperCamelCase__	: Optional["TensorType"] = None		,						UpperCamelCase__	: int = 3		,						UpperCamelCase__	: int = 40		,						UpperCamelCase__	: int = 40		,						UpperCamelCase__	: "PreTrainedTokenizerBase" = None		,						):
        A    =				super().generate_dummy_inputs(preprocessor=UpperCamelCase__		,						framework=UpperCamelCase__ )
        if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
              del dummy_inputs["token_type_ids"]
        return dummy_inputs






import json
import os
import unittest

from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
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        _UpperCAmelCase (   __lowercase     ,				unittest.TestCase			):
  '''simple docstring'''







  SCREAMING_SNAKE_CASE							:				List[Any]					      =				BartTokenizer
  SCREAMING_SNAKE_CASE							:				List[str]					      =				BartTokenizerFast
  SCREAMING_SNAKE_CASE							:				Any					      =				True
  SCREAMING_SNAKE_CASE							:				Union[str, Any]					      =				filter_roberta_detectors







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

        A    =				os.path.join(self.tmpdirname		,						VOCAB_FILES_NAMES['vocab_file'] )
        A    =				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 	UpperCamelCase       ( self	: Dict		,						**UpperCamelCase__	: List[Any] ):
        kwargs.update(self.special_tokens_map )
        return self.tokenizer_class.from_pretrained(self.tmpdirname		,						**UpperCamelCase__ )







  def 	UpperCamelCase       ( self	: Any		,						**UpperCamelCase__	: Union[str, Any] ):
        kwargs.update(self.special_tokens_map )
        return self.rust_tokenizer_class.from_pretrained(self.tmpdirname		,						**UpperCamelCase__ )







  def 	UpperCamelCase       ( self	: int		,						UpperCamelCase__	: str ):
        return "lower newer", "lower newer"







  @cached_property
  def 	UpperCamelCase       ( self	: Optional[Any] ):
        return BartTokenizer.from_pretrained('facebook/bart-large' )







  @cached_property
  def 	UpperCamelCase       ( self	: str ):
        return BartTokenizerFast.from_pretrained('facebook/bart-large' )







  @require_torch
  def 	UpperCamelCase       ( self	: Dict ):
        A    =				['A long paragraph for summarization.', 'Another paragraph for summarization.']
        A    =				[0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              A    =				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    =				batch.input_ids.tolist()[0]
              self.assertListEqual(UpperCamelCase__		,						UpperCamelCase__ )
              # Test that special tokens are reset







  @require_torch
  def 	UpperCamelCase       ( self	: Tuple ):
        A    =				['A long paragraph for summarization.', 'Another paragraph for summarization.']
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              A    =				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 	UpperCamelCase       ( self	: Dict ):
        A    =				[
            'Summary of the text.',
            'Another summary.',
        ]
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              A    =				tokenizer(text_target=UpperCamelCase__		,						max_length=32		,						padding='max_length'		,						return_tensors='pt' )
              self.assertEqual(32		,						targets['input_ids'].shape[1] )







  @require_torch
  def 	UpperCamelCase       ( self	: int ):
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              A    =				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 	UpperCamelCase       ( self	: Optional[Any] ):
        A    =				['A long paragraph for summarization.']
        A    =				[
            'Summary of the text.',
        ]
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
              A    =				tokenizer(UpperCamelCase__		,						return_tensors='pt' )
              A    =				tokenizer(text_target=UpperCamelCase__		,						return_tensors='pt' )
              A    =				inputs['input_ids']
              A    =				targets['input_ids']
              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 	UpperCamelCase       ( self	: List[str] ):
        pass







  def 	UpperCamelCase       ( self	: Tuple ):
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
              with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
                    A    =				self.rust_tokenizer_class.from_pretrained(UpperCamelCase__		,						**UpperCamelCase__ )
                    A    =				self.tokenizer_class.from_pretrained(UpperCamelCase__		,						**UpperCamelCase__ )
                    A    =				'A, <mask> AllenNLP sentence.'
                    A    =				tokenizer_r.encode_plus(UpperCamelCase__		,						add_special_tokens=UpperCamelCase__		,						return_token_type_ids=UpperCamelCase__ )
                    A    =				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    =				tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids'] )
                    A    =				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, 50264, 3823, 487, 21992, 3645, 4, 2] )
                    self.assertSequenceEqual(tokens_r['input_ids']		,						[0, 250, 6, 50264, 3823, 487, 21992, 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>'] )



