
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,
)


__magic_name__  							=	logging.getLogger(__name__)




@dataclass(frozen=__UpperCamelCase			)
class        _SCREAMING_SNAKE_CASE	:
      _A    :		str
      _A    :		str
      _A    :		Optional[str]     						=  None
      _A    :		Optional[str]     						=  None
      _A    :		Optional[str]     						=  None





@dataclass(frozen=__UpperCamelCase			)
class        _SCREAMING_SNAKE_CASE	:
      _A    :		List[int]
      _A    :		Optional[List[int]]     						=  None
      _A    :		Optional[List[int]]     						=  None
      _A    :		Optional[Union[int, float]]     						=  None
      _A    :		Optional[int]     						=  None


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




     class        _SCREAMING_SNAKE_CASE	(   __UpperCamelCase			):
           _A    :		List[InputFeatures]
           def __init__(      self		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase = None		,						lowerCamelCase=False		,						lowerCamelCase = False		,						):
               snake_case__    =					hans_processors[task]()

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

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

                       snake_case__    =					(
                           processor.get_dev_examples(lowerCamelCase			) if evaluate else processor.get_train_examples(lowerCamelCase			)
                       )

                       logger.info("Training examples: %s"		,						len(lowerCamelCase			)			)
                       snake_case__    =					hans_convert_examples_to_features(lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase			)
                       logger.info("Saving features into cached file %s"		,						lowerCamelCase			)
                       torch.save(self.features		,						lowerCamelCase			)
           def __len__(      self			):
               return len(self.features			)
           def __getitem__(      self		,						lowerCamelCase			):
               return self.features[i]



           def A_      (      self			):
               return self.label_list


if is_tf_available():
     import tensorflow as tf




     class        _SCREAMING_SNAKE_CASE	:
           _A    :		List[InputFeatures]
           def __init__(      self		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase = 1_28		,						lowerCamelCase=False		,						lowerCamelCase = False		,						):
               snake_case__    =					hans_processors[task]()
               snake_case__    =					processor.get_labels()
               if tokenizer.__class__ in (
                   RobertaTokenizer,
                   RobertaTokenizerFast,
                   XLMRobertaTokenizer,
                   BartTokenizer,
                   BartTokenizerFast,
               ):
                   # HACK(label indices are swapped in RoBERTa pretrained model)
                   snake_case__    ,       snake_case__    =					label_list[2], label_list[1]
               snake_case__    =					label_list

               snake_case__    =					processor.get_dev_examples(lowerCamelCase			) if evaluate else processor.get_train_examples(lowerCamelCase			)
               snake_case__    =					hans_convert_examples_to_features(lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase		,						lowerCamelCase			)

               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(lowerCamelCase			))			)

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

               snake_case__    =					tf.data.Dataset.from_generator(
                   lowerCamelCase		,						(
                       {
                           "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 A_      (      self			):
               return self.dataset
           def __len__(      self			):
               return len(self.features			)
           def __getitem__(      self		,						lowerCamelCase			):
               return self.features[i]



           def A_      (      self			):
               return self.label_list




class        _SCREAMING_SNAKE_CASE	(   __UpperCamelCase			):
      def A_      (      self		,						lowerCamelCase			):
          return self._create_examples(self._read_tsv(os.path.join(lowerCamelCase		,						"heuristics_train_set.txt"			)			)		,						"train"			)
      def A_      (      self		,						lowerCamelCase			):
          return self._create_examples(self._read_tsv(os.path.join(lowerCamelCase		,						"heuristics_evaluation_set.txt"			)			)		,						"dev"			)
      def A_      (      self			):
          return ["contradiction", "entailment", "neutral"]



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





def 						SCREAMING_SNAKE_CASE__     (   __lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase     ,		__lowerCAmelCase     ,		):
    snake_case__    =					{label: i for i, label in enumerate(__lowerCAmelCase				)}

    snake_case__    =					[]
    for ex_index, example in tqdm.tqdm(enumerate(__lowerCAmelCase				)     ,		desc="convert examples to features"				):
        if ex_index % 10_000 == 0:
            logger.info("Writing example %d" % (ex_index)				)

        snake_case__    =					tokenizer(
            example.text_a     ,		example.text_b     ,		add_special_tokens=__lowerCAmelCase     ,		max_length=__lowerCAmelCase     ,		padding="max_length"     ,		truncation=__lowerCAmelCase     ,		return_overflowing_tokens=__lowerCAmelCase     ,		)

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

        snake_case__    =					int(example.pairID				)

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

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

    return features


__magic_name__  							=	{
    '''hans''': 3,
}





__magic_name__  							=	{
    '''hans''': HansProcessor,
}





import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch


if is_vision_available():
     from transformers import TvltImageProcessor

if is_speech_available():
     from transformers import TvltFeatureExtractor

from transformers import TvltProcessor




@require_torch
class        _SCREAMING_SNAKE_CASE	(   unittest.TestCase			):
      def A_      (      self			):
          snake_case__    =					"ZinengTang/tvlt-base"
          snake_case__    =					tempfile.mkdtemp()
      def A_      (      self		,						**lowerCamelCase			):
          return TvltImageProcessor.from_pretrained(self.checkpoint		,						**lowerCamelCase			)
      def A_      (      self		,						**lowerCamelCase			):
          return TvltFeatureExtractor.from_pretrained(self.checkpoint		,						**lowerCamelCase			)
      def A_      (      self			):
          shutil.rmtree(self.tmpdirname			)
      def A_      (      self			):
          snake_case__    =					self.get_image_processor()
          snake_case__    =					self.get_feature_extractor()

          snake_case__    =					TvltProcessor(image_processor=lowerCamelCase		,						feature_extractor=lowerCamelCase			)
          processor.save_pretrained(self.tmpdirname			)
          snake_case__    =					TvltProcessor.from_pretrained(self.tmpdirname			)

          self.assertIsInstance(processor.feature_extractor		,						lowerCamelCase			)
          self.assertIsInstance(processor.image_processor		,						lowerCamelCase			)
      def A_      (      self			):
          snake_case__    =					self.get_image_processor()
          snake_case__    =					self.get_feature_extractor()

          snake_case__    =					TvltProcessor(image_processor=lowerCamelCase		,						feature_extractor=lowerCamelCase			)

          snake_case__    =					np.ones([1_20_00]			)

          snake_case__    =					feature_extractor(lowerCamelCase		,						return_tensors="np"			)
          snake_case__    =					processor(audio=lowerCamelCase		,						return_tensors="np"			)

          for key in audio_dict.keys():
              self.assertAlmostEqual(audio_dict[key].sum()		,						input_processor[key].sum()		,						delta=1e-2			)
      def A_      (      self			):
          snake_case__    =					self.get_image_processor()
          snake_case__    =					self.get_feature_extractor()

          snake_case__    =					TvltProcessor(image_processor=lowerCamelCase		,						feature_extractor=lowerCamelCase			)

          snake_case__    =					np.ones([3, 2_24, 2_24]			)

          snake_case__    =					image_processor(lowerCamelCase		,						return_tensors="np"			)
          snake_case__    =					processor(images=lowerCamelCase		,						return_tensors="np"			)

          for key in image_dict.keys():
              self.assertAlmostEqual(image_dict[key].sum()		,						input_processor[key].sum()		,						delta=1e-2			)
      def A_      (      self			):
          snake_case__    =					self.get_image_processor()
          snake_case__    =					self.get_feature_extractor()

          snake_case__    =					TvltProcessor(image_processor=lowerCamelCase		,						feature_extractor=lowerCamelCase			)

          snake_case__    =					np.ones([1_20_00]			)
          snake_case__    =					np.ones([3, 2_24, 2_24]			)

          snake_case__    =					processor(audio=lowerCamelCase		,						images=lowerCamelCase			)

          self.assertListEqual(list(inputs.keys()			)		,						["audio_values", "audio_mask", "pixel_values", "pixel_mask"]			)

          # test if it raises when no input is passed
          with pytest.raises(lowerCamelCase			):
              processor()



      def A_      (      self			):
          snake_case__    =					self.get_image_processor()
          snake_case__    =					self.get_feature_extractor()

          snake_case__    =					TvltProcessor(image_processor=lowerCamelCase		,						feature_extractor=lowerCamelCase			)

          self.assertListEqual(
              processor.model_input_names		,						image_processor.model_input_names + feature_extractor.model_input_names		,						msg="`processor` and `image_processor`+`feature_extractor` model input names do not match"		,						)



