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,
)


SCREAMING_SNAKE_CASE__		:					Any						=					logging.getLogger(__name__)

@dataclass(frozen=UpperCamelCase_						)
class       snake_case					:
  lowercase_			=     42
  lowercase_			=     42
  lowercase_			=     None
  lowercase_			=     None
  lowercase_			=     None




@dataclass(frozen=UpperCamelCase_						)
class       snake_case					:
  lowercase_			=     42
  lowercase_			=     None
  lowercase_			=     None
  lowercase_			=     None
  lowercase_			=     None


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

      class       snake_case					(      UpperCamelCase_						):
        lowercase_			=     42




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




             """simple docstring"""
             SCREAMING_SNAKE_CASE__				:				Tuple			=		hans_processors[task]()

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

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

                       SCREAMING_SNAKE_CASE__				:				List[Any]			=		(
                           processor.get_dev_examples(a_				) if evaluate else processor.get_train_examples(a_				)
                       )

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




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




             """simple docstring"""
             return len(self.features				)




        def __getitem__(      self     :	str     , a_     :	List[str]				)->       InputFeatures:




             """simple docstring"""
             return self.features[i]




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




             """simple docstring"""
             return self.label_list


if is_tf_available():
      import tensorflow as tf

      class       snake_case					:
        lowercase_			=     42




        def __init__(      self     :	Optional[int]     , a_     :	str     , a_     :	PreTrainedTokenizer     , a_     :	str     , a_     :	Optional[int] = 128     , a_     :	List[str]=False     , a_     :	bool = False     , )->       int:




             """simple docstring"""
             SCREAMING_SNAKE_CASE__				:				Optional[int]			=		hans_processors[task]()
             SCREAMING_SNAKE_CASE__				:				Optional[int]			=		processor.get_labels()
             if tokenizer.__class__ in (
                 RobertaTokenizer,
                 RobertaTokenizerFast,
                 XLMRobertaTokenizer,
                 BartTokenizer,
                 BartTokenizerFast,
             ):
                  # HACK(label indices are swapped in RoBERTa pretrained model)
                  SCREAMING_SNAKE_CASE__			,							SCREAMING_SNAKE_CASE__				:				Any			=		label_list[2], label_list[1]
             SCREAMING_SNAKE_CASE__				:				Dict			=		label_list

             SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		processor.get_dev_examples(a_				) if evaluate else processor.get_train_examples(a_				)
             SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		hans_convert_examples_to_features(a_     , a_     , a_     , a_				)

             def gen():
                  for ex_index, ex in tqdm.tqdm(enumerate(self.features				)     , desc='convert examples to features'				):
                       if ex_index % 1_0000 == 0:
                            logger.info('Writing example %d of %d' % (ex_index, len(a_				))				)

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

             SCREAMING_SNAKE_CASE__				:				str			=		tf.data.Dataset.from_generator(
                 a_     , (
                     {
                         '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 	__lowercase(      self     :	Optional[Any]				)->       List[Any]:




             """simple docstring"""
             return self.dataset




        def __len__(      self     :	Dict				)->       Dict:




             """simple docstring"""
             return len(self.features				)




        def __getitem__(      self     :	Optional[Any]     , a_     :	str				)->       InputFeatures:




             """simple docstring"""
             return self.features[i]




        def 	__lowercase(      self     :	List[Any]				)->       Tuple:




             """simple docstring"""
             return self.label_list

class       snake_case					(      UpperCamelCase_						):




  def 	__lowercase(      self     :	Optional[Any]     , a_     :	Dict				)->       Tuple:




       """simple docstring"""
       return self._create_examples(self._read_tsv(os.path.join(a_     , 'heuristics_train_set.txt'				)				)     , 'train'				)




  def 	__lowercase(      self     :	Dict     , a_     :	str				)->       Optional[int]:




       """simple docstring"""
       return self._create_examples(self._read_tsv(os.path.join(a_     , 'heuristics_evaluation_set.txt'				)				)     , 'dev'				)




  def 	__lowercase(      self     :	int				)->       Any:




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




  def 	__lowercase(      self     :	Any     , a_     :	Optional[Any]     , a_     :	int				)->       Tuple:




       """simple docstring"""
       SCREAMING_SNAKE_CASE__				:				List[str]			=		[]
       for i, line in enumerate(a_				):
            if i == 0:
                 continue
            SCREAMING_SNAKE_CASE__				:				List[str]			=		'%s-%s' % (set_type, line[0])
            SCREAMING_SNAKE_CASE__				:				Any			=		line[5]
            SCREAMING_SNAKE_CASE__				:				Optional[int]			=		line[6]
            SCREAMING_SNAKE_CASE__				:				int			=		line[7][2:] if line[7].startswith('ex'				) else line[7]
            SCREAMING_SNAKE_CASE__				:				Any			=		line[0]
            examples.append(InputExample(guid=a_     , text_a=a_     , text_b=a_     , label=a_     , pairID=a_				)				)
       return examples







def     _a				(      lowercase__  :			List[InputExample]     ,  lowercase__  :			List[str]     ,  lowercase__  :			int     ,  lowercase__  :			PreTrainedTokenizer     ,  ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				List[str]			=		{label: i for i, label in enumerate(lowercase__ )}

     SCREAMING_SNAKE_CASE__				:				Dict			=		[]
     for ex_index, example in tqdm.tqdm(enumerate(lowercase__ )     ,  desc='convert examples to features' ):
          if ex_index % 1_00_00 == 0:
               logger.info('Writing example %d' % (ex_index) )

          SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		tokenizer(
              example.text_a     ,  example.text_b     ,  add_special_tokens=lowercase__     ,  max_length=lowercase__     ,  padding='max_length'     ,  truncation=lowercase__     ,  return_overflowing_tokens=lowercase__     ,  )

          SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		label_map[example.label] if example.label in label_map else 0

          SCREAMING_SNAKE_CASE__				:				Any			=		int(example.pairID )

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

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

     return features


SCREAMING_SNAKE_CASE__		:					str						=					{
    "hans": 3,
}







SCREAMING_SNAKE_CASE__		:					Tuple						=					{
    "hans": HansProcessor,
}




import os
import socket
from contextlib import contextmanager

import torch

from ..commands.config.default import write_basic_config  # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import convert_model
from .versions import is_torch_version


if is_deepspeed_available():
      from deepspeed import DeepSpeedEngine

if is_tpu_available(check_device=False):
      import torch_xla.core.xla_model as xm







def     _a				(      lowercase__  :			int ):







     '''simple docstring'''


     if is_torch_version('<'     ,  '2.0.0' ) or not hasattr(lowercase__     ,  '_dynamo' ):
          return False
     return isinstance(lowercase__     ,  torch._dynamo.eval_frame.OptimizedModule )







def     _a				(      lowercase__  :			Optional[Any]     ,  lowercase__  :			bool = True ):







     '''simple docstring'''


     SCREAMING_SNAKE_CASE__				:				Union[str, Any]			=		(torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)

     SCREAMING_SNAKE_CASE__				:				Dict			=		is_compiled_module(lowercase__ )
     if is_compiled:
          SCREAMING_SNAKE_CASE__				:				Tuple			=		model
          SCREAMING_SNAKE_CASE__				:				int			=		model._orig_mod

     if is_deepspeed_available():
          options += (DeepSpeedEngine,)

     while isinstance(lowercase__     ,  lowercase__ ):
          SCREAMING_SNAKE_CASE__				:				Any			=		model.module

     if not keep_fpaa_wrapper:
          SCREAMING_SNAKE_CASE__				:				List[Any]			=		getattr(lowercase__     ,  'forward' )
          SCREAMING_SNAKE_CASE__				:				str			=		model.__dict__.pop('_original_forward'     ,  lowercase__ )
          if original_forward is not None:
               while hasattr(lowercase__     ,  '__wrapped__' ):
                    SCREAMING_SNAKE_CASE__				:				Dict			=		forward.__wrapped__
                    if forward == original_forward:
                         break
               SCREAMING_SNAKE_CASE__				:				Dict			=		forward
          if getattr(lowercase__     ,  '_converted_to_transformer_engine'     ,  lowercase__ ):
               convert_model(lowercase__     ,  to_transformer_engine=lowercase__ )

     if is_compiled:
          SCREAMING_SNAKE_CASE__				:				List[Any]			=		model
          SCREAMING_SNAKE_CASE__				:				Optional[Any]			=		compiled_model

     return model







def     _a				(      ):







     '''simple docstring'''


     PartialState().wait_for_everyone()







def     _a				(      lowercase__  :			str     ,  lowercase__  :			Optional[Any] ):







     '''simple docstring'''


     if PartialState().distributed_type == DistributedType.TPU:
          xm.save(lowercase__     ,  lowercase__ )
     elif PartialState().local_process_index == 0:
          torch.save(lowercase__     ,  lowercase__ )







@contextmanager
def     _a				(      **lowercase__  :			str ):







     '''simple docstring'''


     for key, value in kwargs.items():
          SCREAMING_SNAKE_CASE__				:				int			=		str(lowercase__ )

     yield

     for key in kwargs:
          if key.upper() in os.environ:
               del os.environ[key.upper()]







def     _a				(      lowercase__  :			Optional[Any] ):







     '''simple docstring'''


     if not hasattr(lowercase__     ,  '__qualname__' ) and not hasattr(lowercase__     ,  '__name__' ):
          SCREAMING_SNAKE_CASE__				:				Any			=		getattr(lowercase__     ,  '__class__'     ,  lowercase__ )
     if hasattr(lowercase__     ,  '__qualname__' ):
          return obj.__qualname__
     if hasattr(lowercase__     ,  '__name__' ):
          return obj.__name__
     return str(lowercase__ )







def     _a				(      lowercase__  :			List[str]     ,  lowercase__  :			List[Any] ):







     '''simple docstring'''


     for key, value in source.items():
          if isinstance(lowercase__     ,  lowercase__ ):
               SCREAMING_SNAKE_CASE__				:				List[str]			=		destination.setdefault(lowercase__     ,  {} )
               merge_dicts(lowercase__     ,  lowercase__ )
          else:
               SCREAMING_SNAKE_CASE__				:				List[Any]			=		value

     return destination







def     _a				(      lowercase__  :			int = None ):







     '''simple docstring'''


     if port is None:
          SCREAMING_SNAKE_CASE__				:				int			=		2_95_00
     with socket.socket(socket.AF_INET     ,  socket.SOCK_STREAM ) as s:
          return s.connect_ex(('localhost', port) ) == 0



