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 :	List[Any]							     =		logging.getLogger(__name__)
@dataclass(frozen=a_ )
class    A_ :
  _SCREAMING_SNAKE_CASE    				=			42
  _SCREAMING_SNAKE_CASE    				=			42
  _SCREAMING_SNAKE_CASE    				=			None
  _SCREAMING_SNAKE_CASE    				=			None
  _SCREAMING_SNAKE_CASE    				=			None
@dataclass(frozen=a_ )
class    A_ :
  _SCREAMING_SNAKE_CASE    				=			42
  _SCREAMING_SNAKE_CASE    				=			None
  _SCREAMING_SNAKE_CASE    				=			None
  _SCREAMING_SNAKE_CASE    				=			None
  _SCREAMING_SNAKE_CASE    				=			None


if is_torch_available():
  import torch
  from torch.utils.data import Dataset
  class    A_ (       a_ ):
    _SCREAMING_SNAKE_CASE    				=			42

    def __init__(		self :      Union[str, Any]     ,							__SCREAMING_SNAKE_CASE :      str     ,							__SCREAMING_SNAKE_CASE :      PreTrainedTokenizer     ,							__SCREAMING_SNAKE_CASE :      str     ,							__SCREAMING_SNAKE_CASE :      Optional[int] = None     ,							__SCREAMING_SNAKE_CASE :      List[str]=False     ,							__SCREAMING_SNAKE_CASE :      bool = False     ,							):
     __a       =  hans_processors[task]()

     __a       =  os.path.join(
         __SCREAMING_SNAKE_CASE     ,							"cached_{}_{}_{}_{}".format(
             "dev" if evaluate else "train"     ,							tokenizer.__class__.__name__     ,							str(__SCREAMING_SNAKE_CASE       )     ,							__SCREAMING_SNAKE_CASE     ,							)     ,							)
     __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(__SCREAMING_SNAKE_CASE       ):
      if os.path.exists(__SCREAMING_SNAKE_CASE       ) and not overwrite_cache:
       logger.info(f"""Loading features from cached file {cached_features_file}"""       )
       __a       =  torch.load(__SCREAMING_SNAKE_CASE       )
      else:
       logger.info(f"""Creating features from dataset file at {data_dir}"""       )

       __a       =  (
           processor.get_dev_examples(__SCREAMING_SNAKE_CASE       ) if evaluate else processor.get_train_examples(__SCREAMING_SNAKE_CASE       )
       )

       logger.info("Training examples: %s"     ,							len(__SCREAMING_SNAKE_CASE       )       )
       __a       =  hans_convert_examples_to_features(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )
       logger.info("Saving features into cached file %s"     ,							__SCREAMING_SNAKE_CASE       )
       torch.save(self.features     ,							__SCREAMING_SNAKE_CASE       )

    def __len__(		self :      int       ):
     return len(self.features       )

    def __getitem__(		self :      Any     ,							__SCREAMING_SNAKE_CASE :      Dict       ):
     return self.features[i]

    def 		_UpperCAmelCase   (		self :      Union[str, Any]       ):
     return self.label_list


if is_tf_available():
  import tensorflow as tf
  class    A_ :
    _SCREAMING_SNAKE_CASE    				=			42

    def __init__(		self :      Optional[Any]     ,							__SCREAMING_SNAKE_CASE :      str     ,							__SCREAMING_SNAKE_CASE :      PreTrainedTokenizer     ,							__SCREAMING_SNAKE_CASE :      str     ,							__SCREAMING_SNAKE_CASE :      Optional[int] = 1_28     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=False     ,							__SCREAMING_SNAKE_CASE :      bool = False     ,							):
     __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(__SCREAMING_SNAKE_CASE       ) if evaluate else processor.get_train_examples(__SCREAMING_SNAKE_CASE       )
     __a       =  hans_convert_examples_to_features(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )

     def gen():
      for ex_index, ex in tqdm.tqdm(enumerate(self.features       )     ,							desc="convert examples to features"       ):
       if ex_index % 1_00_00 == 0:
        logger.info("Writing example %d of %d" % (ex_index, len(__SCREAMING_SNAKE_CASE       ))       )

       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(
         __SCREAMING_SNAKE_CASE     ,							(
             {
                 "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 		_UpperCAmelCase   (		self :      Union[str, Any]       ):
     return self.dataset

    def __len__(		self :      str       ):
     return len(self.features       )

    def __getitem__(		self :      List[Any]     ,							__SCREAMING_SNAKE_CASE :      int       ):
     return self.features[i]

    def 		_UpperCAmelCase   (		self :      Union[str, Any]       ):
     return self.label_list
class    A_ (       a_ ):

  def 		_UpperCAmelCase   (		self :      str     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]       ):
   return self._create_examples(self._read_tsv(os.path.join(__SCREAMING_SNAKE_CASE     ,							"heuristics_train_set.txt"       )       )     ,							"train"       )

  def 		_UpperCAmelCase   (		self :      str     ,							__SCREAMING_SNAKE_CASE :      Any       ):
   return self._create_examples(self._read_tsv(os.path.join(__SCREAMING_SNAKE_CASE     ,							"heuristics_evaluation_set.txt"       )       )     ,							"dev"       )

  def 		_UpperCAmelCase   (		self :      Dict       ):
   return ["contradiction", "entailment", "neutral"]

  def 		_UpperCAmelCase   (		self :      Any     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      List[str]       ):
   __a       =  []
   for i, line in enumerate(__SCREAMING_SNAKE_CASE       ):
    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=__SCREAMING_SNAKE_CASE     ,							text_a=__SCREAMING_SNAKE_CASE     ,							text_b=__SCREAMING_SNAKE_CASE     ,							label=__SCREAMING_SNAKE_CASE     ,							pairID=__SCREAMING_SNAKE_CASE       )       )
   return examples



def   __A		(			_A							,				_A							,				_A							,				_A							,				):



 """simple docstring"""


 __a       =  {label: i for i, label in enumerate(_A					)}

 __a       =  []
 for ex_index, example in tqdm.tqdm(enumerate(_A					)							,				desc="convert examples to features"					):
  if ex_index % 1_0000 == 0:
   logger.info("Writing example %d" % (ex_index)					)

  __a       =  tokenizer(
      example.text_a							,				example.text_b							,				add_special_tokens=_A							,				max_length=_A							,				padding="max_length"							,				truncation=_A							,				return_overflowing_tokens=_A							,				)

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

  __a       =  int(example.pairID					)

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

 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 :	List[str]							     =		{
    """hans""": 3,
}






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



from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
  if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
  from .pipeline_unclip import UnCLIPPipeline
  from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
  from .text_proj import UnCLIPTextProjModel


