

import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union

import tqdm
from filelock import FileLock

from transformers import (
    BartTokenizer,
    BartTokenizerFast,
    DataProcessor,
    PreTrainedTokenizer,
    RobertaTokenizer,
    RobertaTokenizerFast,
    XLMRobertaTokenizer,
    is_tf_available,
    is_torch_available,
)


lowercase_			=  logging.getLogger(__name__)


@dataclass(frozen=_UpperCAmelCase			)
class 			A						:







    """simple docstring"""





    lowerCamelCase		     =							42
    lowerCamelCase		     =							42
    lowerCamelCase		     =							None
    lowerCamelCase		     =							None
    lowerCamelCase		     =							None




@dataclass(frozen=_UpperCAmelCase			)
class 			A						:







    """simple docstring"""





    lowerCamelCase		     =							42
    lowerCamelCase		     =							None
    lowerCamelCase		     =							None
    lowerCamelCase		     =							None
    lowerCamelCase		     =							None


if is_torch_available():
     import torch
     from torch.utils.data import Dataset


     class 			A						(							_UpperCAmelCase			):







         """simple docstring"""





         lowerCamelCase		     =							42


         def __init__(				self			:	Union[str, Any],lowercase_			:	str,lowercase_			:	PreTrainedTokenizer,lowercase_			:	str,lowercase_			:	Optional[int] = None,lowercase_			:	Dict=False,lowercase_			:	bool = False,)->   Dict:





             '''simple docstring'''







             A__   				=       hans_processors[task]()

             A__   				=       os.path.join(
                 lowercase_,'cached_{}_{}_{}_{}'.format(
                     'dev' if evaluate else 'train',tokenizer.__class__.__name__,str(lowercase_		),lowercase_,),)
             A__   				=       processor.get_labels()
             if tokenizer.__class__ in (
                 RobertaTokenizer,
                 RobertaTokenizerFast,
                 XLMRobertaTokenizer,
                 BartTokenizer,
                 BartTokenizerFast,
             ):
                 # HACK(label indices are swapped in RoBERTa pretrained model)
                 A__				,						A__   				=       label_list[2], label_list[1]
             A__   				=       label_list

             # Make sure only the first process in distributed training processes the dataset,
             # and the others will use the cache.
             A__   				=       cached_features_file + '.lock'
             with FileLock(lowercase_		):
                 if os.path.exists(lowercase_		) and not overwrite_cache:
                     logger.info(F'Loading features from cached file {cached_features_file}'		)
                     A__   				=       torch.load(lowercase_		)
                 else:
                     logger.info(F'Creating features from dataset file at {data_dir}'		)

                     A__   				=       (
                         processor.get_dev_examples(lowercase_		) if evaluate else processor.get_train_examples(lowercase_		)
                     )

                     logger.info('Training examples: %s',len(lowercase_		)		)
                     A__   				=       hans_convert_examples_to_features(lowercase_,lowercase_,lowercase_,lowercase_		)
                     logger.info('Saving features into cached file %s',lowercase_		)
                     torch.save(self.features,lowercase_		)


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





             '''simple docstring'''







             return len(self.features		)


         def __getitem__(				self			:	Optional[Any],lowercase_			:	Dict		)->   InputFeatures:





             '''simple docstring'''







             return self.features[i]


         def 						snake_case__					(				self			:	List[Any]		)->   Optional[Any]:





             '''simple docstring'''







             return self.label_list


if is_tf_available():
     import tensorflow as tf


     class 			A						:







         """simple docstring"""





         lowerCamelCase		     =							42


         def __init__(				self			:	int,lowercase_			:	str,lowercase_			:	PreTrainedTokenizer,lowercase_			:	str,lowercase_			:	Optional[int] = 1_2_8,lowercase_			:	Tuple=False,lowercase_			:	bool = False,)->   str:





             '''simple docstring'''







             A__   				=       hans_processors[task]()
             A__   				=       processor.get_labels()
             if tokenizer.__class__ in (
                 RobertaTokenizer,
                 RobertaTokenizerFast,
                 XLMRobertaTokenizer,
                 BartTokenizer,
                 BartTokenizerFast,
             ):
                 # HACK(label indices are swapped in RoBERTa pretrained model)
                 A__				,						A__   				=       label_list[2], label_list[1]
             A__   				=       label_list

             A__   				=       processor.get_dev_examples(lowercase_		) if evaluate else processor.get_train_examples(lowercase_		)
             A__   				=       hans_convert_examples_to_features(lowercase_,lowercase_,lowercase_,lowercase_		)

             def gen():
                 for ex_index, ex in tqdm.tqdm(enumerate(self.features		),desc='convert examples to features'		):
                     if ex_index % 1_0_0_0_0 == 0:
                         logger.info('Writing example %d of %d' % (ex_index, len(lowercase_		))		)

                     yield (
                         {
                             "example_id": 0,
                             "input_ids": ex.input_ids,
                             "attention_mask": ex.attention_mask,
                             "token_type_ids": ex.token_type_ids,
                         },
                         ex.label,
                     )

             A__   				=       tf.data.Dataset.from_generator(
                 lowercase_,(
                     {
                         'example_id': tf.intaa,
                         'input_ids': tf.intaa,
                         'attention_mask': tf.intaa,
                         'token_type_ids': tf.intaa,
                     },
                     tf.intaa,
                 ),(
                     {
                         'example_id': tf.TensorShape([]		),
                         'input_ids': tf.TensorShape([None, None]		),
                         'attention_mask': tf.TensorShape([None, None]		),
                         'token_type_ids': tf.TensorShape([None, None]		),
                     },
                     tf.TensorShape([]		),
                 ),)


         def 						snake_case__					(				self			:	int		)->   Tuple:





             '''simple docstring'''







             return self.dataset


         def __len__(				self			:	Optional[int]		)->   List[str]:





             '''simple docstring'''







             return len(self.features		)


         def __getitem__(				self			:	Tuple,lowercase_			:	int		)->   InputFeatures:





             '''simple docstring'''







             return self.features[i]


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





             '''simple docstring'''







             return self.label_list


class 			A						(							_UpperCAmelCase			):







    """simple docstring"""





    def 						snake_case__					(				self			:	Union[str, Any],lowercase_			:	Tuple		)->   Optional[Any]:





        '''simple docstring'''







        return self._create_examples(self._read_tsv(os.path.join(lowercase_,'heuristics_train_set.txt'		)		),'train'		)


    def 						snake_case__					(				self			:	Dict,lowercase_			:	Union[str, Any]		)->   Tuple:





        '''simple docstring'''







        return self._create_examples(self._read_tsv(os.path.join(lowercase_,'heuristics_evaluation_set.txt'		)		),'dev'		)


    def 						snake_case__					(				self			:	List[str]		)->   Any:





        '''simple docstring'''







        return ["contradiction", "entailment", "neutral"]


    def 						snake_case__					(				self			:	str,lowercase_			:	Any,lowercase_			:	List[str]		)->   str:





        '''simple docstring'''







        A__   				=       []
        for i, line in enumerate(lowercase_		):
            if i == 0:
                continue
            A__   				=       '%s-%s' % (set_type, line[0])
            A__   				=       line[5]
            A__   				=       line[6]
            A__   				=       line[7][2:] if line[7].startswith('ex'		) else line[7]
            A__   				=       line[0]
            examples.append(InputExample(guid=lowercase_,text_a=lowercase_,text_b=lowercase_,label=lowercase_,pairID=lowercase_		)		)
        return examples




def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     List[InputExample]   ,      SCREAMING_SNAKE_CASE__      :     List[str]   ,      SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     PreTrainedTokenizer   ,      )							->				List[Any]:





    '''simple docstring'''






    A__   				=       {label: i for i, label in enumerate(SCREAMING_SNAKE_CASE__				)}

    A__   				=       []
    for ex_index, example in tqdm.tqdm(enumerate(SCREAMING_SNAKE_CASE__				)   ,      desc='convert examples to features'				):
        if ex_index % 10000 == 0:
            logger.info('Writing example %d' % (ex_index)				)

        A__   				=       tokenizer(
            example.text_a   ,      example.text_b   ,      add_special_tokens=SCREAMING_SNAKE_CASE__   ,      max_length=SCREAMING_SNAKE_CASE__   ,      padding='max_length'   ,      truncation=SCREAMING_SNAKE_CASE__   ,      return_overflowing_tokens=SCREAMING_SNAKE_CASE__   ,      )

        A__   				=       label_map[example.label] if example.label in label_map else 0

        A__   				=       int(example.pairID				)

        features.append(InputFeatures(**SCREAMING_SNAKE_CASE__   ,      label=SCREAMING_SNAKE_CASE__   ,      pairID=SCREAMING_SNAKE_CASE__				)				)

    for i, example in enumerate(examples[:5]				):
        logger.info('*** Example ***'				)
        logger.info(f'guid: {example}'				)
        logger.info(f'features: {features[i]}'				)

    return features


lowercase_			=  {
    "hans": 3,
}

lowercase_			=  {
    "hans": HansProcessor,
}




import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


lowercase_			=  logging.get_logger(__name__)

lowercase_			=  {
    "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/config.json",
    # See all BART models at https://huggingface.co/models?filter=bart
}


class 			A						(							_UpperCAmelCase			):







    """simple docstring"""





    lowerCamelCase		     =							'bart'
    lowerCamelCase		     =							['past_key_values']
    lowerCamelCase		     =							{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}


    def __init__(				self			:	Tuple,lowercase_			:	Optional[int]=5_0_2_6_5,lowercase_			:	List[str]=1_0_2_4,lowercase_			:	Any=1_2,lowercase_			:	Optional[Any]=4_0_9_6,lowercase_			:	str=1_6,lowercase_			:	int=1_2,lowercase_			:	Optional[Any]=4_0_9_6,lowercase_			:	Any=1_6,lowercase_			:	Any=0.0,lowercase_			:	str=0.0,lowercase_			:	Optional[Any]="gelu",lowercase_			:	List[str]=1_0_2_4,lowercase_			:	List[Any]=0.1,lowercase_			:	Union[str, Any]=0.0,lowercase_			:	Optional[int]=0.0,lowercase_			:	List[Any]=0.02,lowercase_			:	int=0.0,lowercase_			:	Optional[Any]=False,lowercase_			:	List[Any]=True,lowercase_			:	Union[str, Any]=3,lowercase_			:	int=1,lowercase_			:	int=0,lowercase_			:	List[str]=2,lowercase_			:	Optional[int]=True,lowercase_			:	Tuple=2,lowercase_			:	List[str]=2,**lowercase_			:	Dict,)->   List[Any]:





        '''simple docstring'''







        A__   				=       vocab_size
        A__   				=       max_position_embeddings
        A__   				=       d_model
        A__   				=       encoder_ffn_dim
        A__   				=       encoder_layers
        A__   				=       encoder_attention_heads
        A__   				=       decoder_ffn_dim
        A__   				=       decoder_layers
        A__   				=       decoder_attention_heads
        A__   				=       dropout
        A__   				=       attention_dropout
        A__   				=       activation_dropout
        A__   				=       activation_function
        A__   				=       init_std
        A__   				=       encoder_layerdrop
        A__   				=       decoder_layerdrop
        A__   				=       classifier_dropout
        A__   				=       use_cache
        A__   				=       encoder_layers
        A__   				=       scale_embedding  # scale factor will be sqrt(d_model) if True

        super().__init__(
            num_labels=lowercase_,pad_token_id=lowercase_,bos_token_id=lowercase_,eos_token_id=lowercase_,is_encoder_decoder=lowercase_,decoder_start_token_id=lowercase_,forced_eos_token_id=lowercase_,**lowercase_,)

        # ensure backward compatibility for BART CNN models
        if self.forced_bos_token_id is None and kwargs.get('force_bos_token_to_be_generated',lowercase_		):
            A__   				=       self.bos_token_id
            warnings.warn(
                F'Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '
                'The config can simply be saved and uploaded again to be fixed.'		)




class 			A						(							_UpperCAmelCase			):







    """simple docstring"""





    @property
    def 						snake_case__					(				self			:	Dict		)->   Mapping[str, Mapping[int, str]]:





        '''simple docstring'''







        if self.task in ["default", "seq2seq-lm"]:
            A__   				=       OrderedDict(
                [
                    ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                    ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                ]		)

            if self.use_past:
                A__   				=       {0: 'batch'}
                A__   				=       {0: 'batch', 1: 'past_decoder_sequence + sequence'}
            else:
                A__   				=       {0: 'batch', 1: 'decoder_sequence'}
                A__   				=       {0: 'batch', 1: 'decoder_sequence'}

            if self.use_past:
                self.fill_with_past_key_values_(lowercase_,direction='inputs'		)
        elif self.task == "causal-lm":
            # TODO: figure this case out.
            A__   				=       OrderedDict(
                [
                    ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                    ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                ]		)
            if self.use_past:
                A__				,						A__   				=       self.num_layers
                for i in range(lowercase_		):
                    A__   				=       {0: 'batch', 2: 'past_sequence + sequence'}
                    A__   				=       {0: 'batch', 2: 'past_sequence + sequence'}
        else:
            A__   				=       OrderedDict(
                [
                    ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
                    ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
                    ('decoder_input_ids', {0: 'batch', 1: 'decoder_sequence'}),
                    ('decoder_attention_mask', {0: 'batch', 1: 'decoder_sequence'}),
                ]		)

        return common_inputs


    @property
    def 						snake_case__					(				self			:	Optional[Any]		)->   Mapping[str, Mapping[int, str]]:





        '''simple docstring'''







        if self.task in ["default", "seq2seq-lm"]:
            A__   				=       super().outputs
        else:
            A__   				=       super(lowercase_,self		).outputs
            if self.use_past:
                A__				,						A__   				=       self.num_layers
                for i in range(lowercase_		):
                    A__   				=       {0: 'batch', 2: 'past_sequence + sequence'}
                    A__   				=       {0: 'batch', 2: 'past_sequence + sequence'}
        return common_outputs


    def 						snake_case__					(				self			:	Tuple,lowercase_			:	PreTrainedTokenizer,lowercase_			:	int = -1,lowercase_			:	int = -1,lowercase_			:	bool = False,lowercase_			:	Optional[TensorType] = None,)->   Mapping[str, Any]:





        '''simple docstring'''







        A__   				=       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            lowercase_,lowercase_,lowercase_,lowercase_,lowercase_		)

        # Generate decoder inputs
        A__   				=       seq_length if not self.use_past else 1
        A__   				=       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            lowercase_,lowercase_,lowercase_,lowercase_,lowercase_		)
        A__   				=       {F'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
        A__   				=       dict(**lowercase_,**lowercase_		)

        if self.use_past:
            if not is_torch_available():
                raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'		)
            else:
                import torch
            A__				,						A__   				=       common_inputs['input_ids'].shape
            A__   				=       common_inputs['decoder_input_ids'].shape[1]
            A__				,						A__   				=       self.num_attention_heads
            A__   				=       (
                batch,
                num_encoder_attention_heads,
                encoder_seq_length,
                self._config.hidden_size // num_encoder_attention_heads,
            )
            A__   				=       decoder_seq_length + 3
            A__   				=       (
                batch,
                num_decoder_attention_heads,
                decoder_past_length,
                self._config.hidden_size // num_decoder_attention_heads,
            )

            A__   				=       torch.cat(
                [common_inputs['decoder_attention_mask'], torch.ones(lowercase_,lowercase_		)],dim=1		)

            A__   				=       []
            # If the number of encoder and decoder layers are present in the model configuration, both are considered
            A__				,						A__   				=       self.num_layers
            A__   				=       min(lowercase_,lowercase_		)
            A__   				=       max(lowercase_,lowercase_		) - min_num_layers
            A__   				=       'encoder' if num_encoder_layers > num_decoder_layers else 'decoder'

            for _ in range(lowercase_		):
                common_inputs["past_key_values"].append(
                    (
                        torch.zeros(lowercase_		),
                        torch.zeros(lowercase_		),
                        torch.zeros(lowercase_		),
                        torch.zeros(lowercase_		),
                    )		)
            # TODO: test this.
            A__   				=       encoder_shape if remaining_side_name == 'encoder' else decoder_shape
            for _ in range(lowercase_,lowercase_		):
                common_inputs["past_key_values"].append((torch.zeros(lowercase_		), torch.zeros(lowercase_		))		)
        return common_inputs


    def 						snake_case__					(				self			:	List[str],lowercase_			:	PreTrainedTokenizer,lowercase_			:	int = -1,lowercase_			:	int = -1,lowercase_			:	bool = False,lowercase_			:	Optional[TensorType] = None,)->   Mapping[str, Any]:





        '''simple docstring'''







        A__   				=       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            lowercase_,lowercase_,lowercase_,lowercase_,lowercase_		)

        if self.use_past:
            if not is_torch_available():
                raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'		)
            else:
                import torch
            A__				,						A__   				=       common_inputs['input_ids'].shape
            # Not using the same length for past_key_values
            A__   				=       seqlen + 2
            A__				,						A__   				=       self.num_layers
            A__				,						A__   				=       self.num_attention_heads
            A__   				=       (
                batch,
                num_encoder_attention_heads,
                past_key_values_length,
                self._config.hidden_size // num_encoder_attention_heads,
            )

            A__   				=       common_inputs['attention_mask'].dtype
            A__   				=       torch.cat(
                [common_inputs['attention_mask'], torch.ones(lowercase_,lowercase_,dtype=lowercase_		)],dim=1		)
            A__   				=       [
                (torch.zeros(lowercase_		), torch.zeros(lowercase_		)) for _ in range(lowercase_		)
            ]
        return common_inputs


    def 						snake_case__					(				self			:	Union[str, Any],lowercase_			:	PreTrainedTokenizer,lowercase_			:	int = -1,lowercase_			:	int = -1,lowercase_			:	bool = False,lowercase_			:	Optional[TensorType] = None,)->   Mapping[str, Any]:





        '''simple docstring'''







        A__   				=       compute_effective_axis_dimension(
            lowercase_,fixed_dimension=OnnxConfig.default_fixed_batch,num_token_to_add=0		)

        # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
        A__   				=       tokenizer.num_special_tokens_to_add(lowercase_		)
        A__   				=       compute_effective_axis_dimension(
            lowercase_,fixed_dimension=OnnxConfig.default_fixed_sequence,num_token_to_add=lowercase_		)

        # Generate dummy inputs according to compute batch and sequence
        A__   				=       [' '.join([tokenizer.unk_token]		) * seq_length] * batch_size
        A__   				=       dict(tokenizer(lowercase_,return_tensors=lowercase_		)		)
        return common_inputs


    def 						snake_case__					(				self			:	Union[str, Any],lowercase_			:	PreTrainedTokenizer,lowercase_			:	int = -1,lowercase_			:	int = -1,lowercase_			:	bool = False,lowercase_			:	Optional[TensorType] = None,)->   Mapping[str, Any]:





        '''simple docstring'''







        if self.task in ["default", "seq2seq-lm"]:
            A__   				=       self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                lowercase_,batch_size=lowercase_,seq_length=lowercase_,is_pair=lowercase_,framework=lowercase_		)

        elif self.task == "causal-lm":
            A__   				=       self._generate_dummy_inputs_for_causal_lm(
                lowercase_,batch_size=lowercase_,seq_length=lowercase_,is_pair=lowercase_,framework=lowercase_		)
        else:
            A__   				=       self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                lowercase_,batch_size=lowercase_,seq_length=lowercase_,is_pair=lowercase_,framework=lowercase_		)

        return common_inputs


    def 						snake_case__					(				self			:	int,lowercase_			:	Tuple,lowercase_			:	int,lowercase_			:	int,lowercase_			:	str		)->   str:





        '''simple docstring'''







        if self.task in ["default", "seq2seq-lm"]:
            A__   				=       super()._flatten_past_key_values_(lowercase_,lowercase_,lowercase_,lowercase_		)
        else:
            A__   				=       super(lowercase_,self		)._flatten_past_key_values_(
                lowercase_,lowercase_,lowercase_,lowercase_		)

