
"""simple docstring"""



import argparse
import csv
import logging
import os
import random

import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange

from transformers import (
    CONFIG_NAME,
    WEIGHTS_NAME,
    AdamW,
    OpenAIGPTDoubleHeadsModel,
    OpenAIGPTTokenizer,
    get_linear_schedule_with_warmup,
)


logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)
UpperCAmelCase__          = logging.getLogger(__name__)
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Tuple ,					__lowerCamelCase   :       List[Any]		)     ->					Optional[int]:
  _snake_case					     =       np.argmax(__lowerCamelCase ,					axis=1		)
  return np.sum(outputs == labels		)
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Optional[Any]		)     ->					int:
  with open(__lowerCamelCase ,					encoding='''utf_8'''		) as f:
    _snake_case					     =       csv.reader(__lowerCamelCase		)
    _snake_case					     =       []
    next(__lowerCamelCase		)  # skip the first line
    for line in tqdm(__lowerCamelCase		):
      output.append((''' '''.join(line[1:5]		), line[5], line[6], int(line[-1]		) - 1)		)
  return output
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Any ,					__lowerCamelCase   :       Dict ,					__lowerCamelCase   :       Dict ,					__lowerCamelCase   :       List[str] ,					__lowerCamelCase   :       Optional[int] ,					__lowerCamelCase   :       Optional[int]		)     ->					Any:
  _snake_case					     =       []
  for dataset in encoded_datasets:
    _snake_case					     =       len(__lowerCamelCase		)
    _snake_case					     =       np.zeros((n_batch, 2, input_len) ,					dtype=np.intaa		)
    _snake_case					     =       np.zeros((n_batch, 2) ,					dtype=np.intaa		)
    _snake_case					     =       np.full((n_batch, 2, input_len) ,					fill_value=-1_00 ,					dtype=np.intaa		)
    _snake_case					     =       np.zeros((n_batch,) ,					dtype=np.intaa		)
    for (
        i,
        (story, conta, conta, mc_label),
    ) in enumerate(__lowerCamelCase		):
      _snake_case					     =       [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
      _snake_case					     =       [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
      _snake_case					     =       with_conta
      _snake_case					     =       with_conta
      _snake_case					     =       len(__lowerCamelCase		) - 1
      _snake_case					     =       len(__lowerCamelCase		) - 1
      _snake_case					     =       with_conta
      _snake_case					     =       with_conta
      _snake_case					     =       mc_label
    _snake_case					     =       (input_ids, mc_token_ids, lm_labels, mc_labels)
    tensor_datasets.append(tuple(torch.tensor(__lowerCamelCase		) for t in all_inputs		)		)
  return tensor_datasets
def 					_UpperCAmelCase      ( )     ->					Optional[Any]:
  _snake_case					     =       argparse.ArgumentParser()
  parser.add_argument('''--model_name''' ,					type=__lowerCamelCase ,					default='''openai-gpt''' ,					help='''pretrained model name'''		)
  parser.add_argument('''--do_train''' ,					action='''store_true''' ,					help='''Whether to run training.'''		)
  parser.add_argument('''--do_eval''' ,					action='''store_true''' ,					help='''Whether to run eval on the dev set.'''		)
  parser.add_argument(
      '''--output_dir''' ,					default=__lowerCamelCase ,					type=__lowerCamelCase ,					required=__lowerCamelCase ,					help='''The output directory where the model predictions and checkpoints will be written.''' ,					)
  parser.add_argument('''--train_dataset''' ,					type=__lowerCamelCase ,					default=''''''		)
  parser.add_argument('''--eval_dataset''' ,					type=__lowerCamelCase ,					default=''''''		)
  parser.add_argument('''--seed''' ,					type=__lowerCamelCase ,					default=42		)
  parser.add_argument('''--num_train_epochs''' ,					type=__lowerCamelCase ,					default=3		)
  parser.add_argument('''--train_batch_size''' ,					type=__lowerCamelCase ,					default=8		)
  parser.add_argument('''--eval_batch_size''' ,					type=__lowerCamelCase ,					default=16		)
  parser.add_argument('''--adam_epsilon''' ,					default=1E-8 ,					type=__lowerCamelCase ,					help='''Epsilon for Adam optimizer.'''		)
  parser.add_argument('''--max_grad_norm''' ,					type=__lowerCamelCase ,					default=1		)
  parser.add_argument(
      '''--max_steps''' ,					default=-1 ,					type=__lowerCamelCase ,					help=(
          '''If > 0: set total number of training                         steps to perform. Override num_train_epochs.'''
      ) ,					)
  parser.add_argument(
      '''--gradient_accumulation_steps''' ,					type=__lowerCamelCase ,					default=1 ,					help='''Number of updates steps to accumulate before                        performing a backward/update pass.''' ,					)
  parser.add_argument('''--learning_rate''' ,					type=__lowerCamelCase ,					default=6.2_5E-5		)
  parser.add_argument('''--warmup_steps''' ,					default=0 ,					type=__lowerCamelCase ,					help='''Linear warmup over warmup_steps.'''		)
  parser.add_argument('''--lr_schedule''' ,					type=__lowerCamelCase ,					default='''warmup_linear'''		)
  parser.add_argument('''--weight_decay''' ,					type=__lowerCamelCase ,					default=0.01		)
  parser.add_argument('''--lm_coef''' ,					type=__lowerCamelCase ,					default=0.9		)
  parser.add_argument('''--n_valid''' ,					type=__lowerCamelCase ,					default=3_74		)

  parser.add_argument('''--server_ip''' ,					type=__lowerCamelCase ,					default='''''' ,					help='''Can be used for distant debugging.'''		)
  parser.add_argument('''--server_port''' ,					type=__lowerCamelCase ,					default='''''' ,					help='''Can be used for distant debugging.'''		)
  _snake_case					     =       parser.parse_args()
  print(__lowerCamelCase		)

  if args.server_ip and args.server_port:
    # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
    import ptvsd

    print('''Waiting for debugger attach'''		)
    ptvsd.enable_attach(address=(args.server_ip, args.server_port) ,					redirect_output=__lowerCamelCase		)
    ptvsd.wait_for_attach()

  random.seed(args.seed		)
  np.random.seed(args.seed		)
  torch.manual_seed(args.seed		)
  torch.cuda.manual_seed_all(args.seed		)

  _snake_case					     =       torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''		)
  _snake_case					     =       torch.cuda.device_count()
  logger.info('''device: {}, n_gpu {}'''.format(__lowerCamelCase ,					__lowerCamelCase		)		)

  if not args.do_train and not args.do_eval:
    raise ValueError('''At least one of `do_train` or `do_eval` must be True.'''		)

  if not os.path.exists(args.output_dir		):
    os.makedirs(args.output_dir		)

  # Load tokenizer and model
  # This loading functions also add new tokens and embeddings called `special tokens`
  # These new embeddings will be fine-tuned on the RocStories dataset
  _snake_case					     =       ['''_start_''', '''_delimiter_''', '''_classify_''']
  _snake_case					     =       OpenAIGPTTokenizer.from_pretrained(args.model_name		)
  tokenizer.add_tokens(__lowerCamelCase		)
  _snake_case					     =       tokenizer.convert_tokens_to_ids(__lowerCamelCase		)
  _snake_case					     =       OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name		)
  model.resize_token_embeddings(len(__lowerCamelCase		)		)
  model.to(__lowerCamelCase		)

  # Load and encode the datasets
  def tokenize_and_encode(__lowerCamelCase   :       Union[str, Any]		):
    if isinstance(__lowerCamelCase ,					__lowerCamelCase		):
      return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(__lowerCamelCase		)		)
    elif isinstance(__lowerCamelCase ,					__lowerCamelCase		):
      return obj
    return [tokenize_and_encode(__lowerCamelCase		) for o in obj]

  logger.info('''Encoding dataset...'''		)
  _snake_case					     =       load_rocstories_dataset(args.train_dataset		)
  _snake_case					     =       load_rocstories_dataset(args.eval_dataset		)
  _snake_case					     =       (train_dataset, eval_dataset)
  _snake_case					     =       tokenize_and_encode(__lowerCamelCase		)

  # Compute the max input length for the Transformer
  _snake_case					     =       model.config.n_positions // 2 - 2
  _snake_case					     =       max(
      len(story[:max_length]		) + max(len(conta[:max_length]		) ,					len(conta[:max_length]		)		) + 3
      for dataset in encoded_datasets
      for story, conta, conta, _ in dataset		)
  _snake_case					     =       min(__lowerCamelCase ,					model.config.n_positions		)  # Max size of input for the pre-trained model

  # Prepare inputs tensors and dataloaders
  _snake_case					     =       pre_process_datasets(__lowerCamelCase ,					__lowerCamelCase ,					__lowerCamelCase ,					*__lowerCamelCase		)
  _snake_case     ,					_snake_case					     =       tensor_datasets[0], tensor_datasets[1]

  _snake_case					     =       TensorDataset(*__lowerCamelCase		)
  _snake_case					     =       RandomSampler(__lowerCamelCase		)
  _snake_case					     =       DataLoader(__lowerCamelCase ,					sampler=__lowerCamelCase ,					batch_size=args.train_batch_size		)

  _snake_case					     =       TensorDataset(*__lowerCamelCase		)
  _snake_case					     =       SequentialSampler(__lowerCamelCase		)
  _snake_case					     =       DataLoader(__lowerCamelCase ,					sampler=__lowerCamelCase ,					batch_size=args.eval_batch_size		)

  # Prepare optimizer
  if args.do_train:
    if args.max_steps > 0:
      _snake_case					     =       args.max_steps
      _snake_case					     =       args.max_steps // (len(__lowerCamelCase		) // args.gradient_accumulation_steps) + 1
    else:
      _snake_case					     =       len(__lowerCamelCase		) // args.gradient_accumulation_steps * args.num_train_epochs

    _snake_case					     =       list(model.named_parameters()		)
    _snake_case					     =       ['''bias''', '''LayerNorm.bias''', '''LayerNorm.weight''']
    _snake_case					     =       [
        {
            '''params''': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay		)],
            '''weight_decay''': args.weight_decay,
        },
        {'''params''': [p for n, p in param_optimizer if any(nd in n for nd in no_decay		)], '''weight_decay''': 0.0},
    ]
    _snake_case					     =       AdamW(__lowerCamelCase ,					lr=args.learning_rate ,					eps=args.adam_epsilon		)
    _snake_case					     =       get_linear_schedule_with_warmup(
        __lowerCamelCase ,					num_warmup_steps=args.warmup_steps ,					num_training_steps=__lowerCamelCase		)

  if args.do_train:
    _snake_case     ,					_snake_case     ,					_snake_case					     =       0, 0, None
    model.train()
    for _ in trange(int(args.num_train_epochs		) ,					desc='''Epoch'''		):
      _snake_case					     =       0
      _snake_case					     =       0
      _snake_case					     =       tqdm(__lowerCamelCase ,					desc='''Training'''		)
      for step, batch in enumerate(__lowerCamelCase		):
        _snake_case					     =       tuple(t.to(__lowerCamelCase		) for t in batch		)
        _snake_case     ,					_snake_case     ,					_snake_case     ,					_snake_case					     =       batch
        _snake_case					     =       model(__lowerCamelCase ,					mc_token_ids=__lowerCamelCase ,					lm_labels=__lowerCamelCase ,					mc_labels=__lowerCamelCase		)
        _snake_case					     =       args.lm_coef * losses[0] + losses[1]
        loss.backward()
        optimizer.step()
        scheduler.step()
        optimizer.zero_grad()
        tr_loss += loss.item()
        _snake_case					     =       (
            loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
        )
        nb_tr_steps += 1
        _snake_case					     =       '''Training loss: {:.2e} lr: {:.2e}'''.format(__lowerCamelCase ,					scheduler.get_lr()[0]		)

    # Save a trained model
  if args.do_train:
    # Save a trained model, configuration and tokenizer
    _snake_case					     =       model.module if hasattr(__lowerCamelCase ,					'''module'''		) else model  # Only save the model itself

    # If we save using the predefined names, we can load using `from_pretrained`
    _snake_case					     =       os.path.join(args.output_dir ,					__lowerCamelCase		)
    _snake_case					     =       os.path.join(args.output_dir ,					__lowerCamelCase		)

    torch.save(model_to_save.state_dict() ,					__lowerCamelCase		)
    model_to_save.config.to_json_file(__lowerCamelCase		)
    tokenizer.save_vocabulary(args.output_dir		)

    # Load a trained model and vocabulary that you have fine-tuned
    _snake_case					     =       OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir		)
    _snake_case					     =       OpenAIGPTTokenizer.from_pretrained(args.output_dir		)
    model.to(__lowerCamelCase		)

  if args.do_eval:
    model.eval()
    _snake_case     ,					_snake_case					     =       0, 0
    _snake_case     ,					_snake_case					     =       0, 0
    for batch in tqdm(__lowerCamelCase ,					desc='''Evaluating'''		):
      _snake_case					     =       tuple(t.to(__lowerCamelCase		) for t in batch		)
      _snake_case     ,					_snake_case     ,					_snake_case     ,					_snake_case					     =       batch
      with torch.no_grad():
        _snake_case     ,					_snake_case     ,					_snake_case     ,					_snake_case					     =       model(
            __lowerCamelCase ,					mc_token_ids=__lowerCamelCase ,					lm_labels=__lowerCamelCase ,					mc_labels=__lowerCamelCase		)

      _snake_case					     =       mc_logits.detach().cpu().numpy()
      _snake_case					     =       mc_labels.to('''cpu'''		).numpy()
      _snake_case					     =       accuracy(__lowerCamelCase ,					__lowerCamelCase		)

      eval_loss += mc_loss.mean().item()
      eval_accuracy += tmp_eval_accuracy

      nb_eval_examples += input_ids.size(0		)
      nb_eval_steps += 1

    _snake_case					     =       eval_loss / nb_eval_steps
    _snake_case					     =       eval_accuracy / nb_eval_examples
    _snake_case					     =       tr_loss / nb_tr_steps if args.do_train else None
    _snake_case					     =       {'''eval_loss''': eval_loss, '''eval_accuracy''': eval_accuracy, '''train_loss''': train_loss}

    _snake_case					     =       os.path.join(args.output_dir ,					'''eval_results.txt'''		)
    with open(__lowerCamelCase ,					'''w'''		) as writer:
      logger.info('''***** Eval results *****'''		)
      for key in sorted(result.keys()		):
        logger.info('''  %s = %s''' ,					__lowerCamelCase ,					str(result[key]		)		)
        writer.write('''%s = %s\n''' % (key, str(result[key]		))		)


if __name__ == "__main__":
 main()





"""simple docstring"""



import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
 from PIL import Image

 from transformers import CLIPImageProcessor, CLIPProcessor

@require_vision
class       lowerCAmelCase__				(						unittest.TestCase	):







     def      lowercase	(							self			:			Any			):
       _snake_case					     =       tempfile.mkdtemp()

       # fmt: off
       _snake_case					     =       ['''l''', '''o''', '''w''', '''e''', '''r''', '''s''', '''t''', '''i''', '''d''', '''n''', '''lo''', '''l</w>''', '''w</w>''', '''r</w>''', '''t</w>''', '''low</w>''', '''er</w>''', '''lowest</w>''', '''newer</w>''', '''wider''', '''<unk>''', '''<|startoftext|>''', '''<|endoftext|>''']
       # fmt: on
       _snake_case					     =       dict(zip(_lowerCamelCase    ,      range(len(_lowerCamelCase			)			)			)			)
       _snake_case					     =       ['''#version: 0.2''', '''l o''', '''lo w</w>''', '''e r</w>''', '''''']
       _snake_case					     =       {'''unk_token''': '''<unk>'''}

       _snake_case					     =       os.path.join(self.tmpdirname    ,      VOCAB_FILES_NAMES['''vocab_file''']			)
       _snake_case					     =       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(_lowerCamelCase			) + '''\n'''			)
       with open(self.merges_file    ,      '''w'''    ,      encoding='''utf-8'''			) as fp:
         fp.write('''\n'''.join(_lowerCamelCase			)			)

       _snake_case					     =       {
           '''do_resize''': True,
           '''size''': 20,
           '''do_center_crop''': True,
           '''crop_size''': 18,
           '''do_normalize''': True,
           '''image_mean''': [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
           '''image_std''': [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
       }
       _snake_case					     =       os.path.join(self.tmpdirname    ,      _lowerCamelCase			)
       with open(self.image_processor_file    ,      '''w'''    ,      encoding='''utf-8'''			) as fp:
         json.dump(_lowerCamelCase    ,      _lowerCamelCase			)







     def      lowercase	(							self			:			Tuple    ,      **_lowerCamelCase			:			Any			):
       return CLIPTokenizer.from_pretrained(self.tmpdirname    ,      **_lowerCamelCase			)







     def      lowercase	(							self			:			str    ,      **_lowerCamelCase			:			Any			):
       return CLIPTokenizerFast.from_pretrained(self.tmpdirname    ,      **_lowerCamelCase			)







     def      lowercase	(							self			:			int    ,      **_lowerCamelCase			:			Optional[int]			):
       return CLIPImageProcessor.from_pretrained(self.tmpdirname    ,      **_lowerCamelCase			)







     def      lowercase	(							self			:			Union[str, Any]			):
       shutil.rmtree(self.tmpdirname			)







     def      lowercase	(							self			:			Any			):
       _snake_case					     =       [np.random.randint(255    ,      size=(3, 30, 400)    ,      dtype=np.uinta			)]

       _snake_case					     =       [Image.fromarray(np.moveaxis(_lowerCamelCase    ,      0    ,      -1			)			) for x in image_inputs]

       return image_inputs







     def      lowercase	(							self			:			Optional[Any]			):
       _snake_case					     =       self.get_tokenizer()
       _snake_case					     =       self.get_rust_tokenizer()
       _snake_case					     =       self.get_image_processor()

       _snake_case					     =       CLIPProcessor(tokenizer=_lowerCamelCase    ,      image_processor=_lowerCamelCase			)
       processor_slow.save_pretrained(self.tmpdirname			)
       _snake_case					     =       CLIPProcessor.from_pretrained(self.tmpdirname    ,      use_fast=_lowerCamelCase			)

       _snake_case					     =       CLIPProcessor(tokenizer=_lowerCamelCase    ,      image_processor=_lowerCamelCase			)
       processor_fast.save_pretrained(self.tmpdirname			)
       _snake_case					     =       CLIPProcessor.from_pretrained(self.tmpdirname			)

       self.assertEqual(processor_slow.tokenizer.get_vocab()    ,      tokenizer_slow.get_vocab()			)
       self.assertEqual(processor_fast.tokenizer.get_vocab()    ,      tokenizer_fast.get_vocab()			)
       self.assertEqual(tokenizer_slow.get_vocab()    ,      tokenizer_fast.get_vocab()			)
       self.assertIsInstance(processor_slow.tokenizer    ,      _lowerCamelCase			)
       self.assertIsInstance(processor_fast.tokenizer    ,      _lowerCamelCase			)

       self.assertEqual(processor_slow.image_processor.to_json_string()    ,      image_processor.to_json_string()			)
       self.assertEqual(processor_fast.image_processor.to_json_string()    ,      image_processor.to_json_string()			)
       self.assertIsInstance(processor_slow.image_processor    ,      _lowerCamelCase			)
       self.assertIsInstance(processor_fast.image_processor    ,      _lowerCamelCase			)







     def      lowercase	(							self			:			List[Any]			):
       _snake_case					     =       CLIPProcessor(tokenizer=self.get_tokenizer()    ,      image_processor=self.get_image_processor()			)
       processor.save_pretrained(self.tmpdirname			)

       _snake_case					     =       self.get_tokenizer(bos_token='''(BOS)'''    ,      eos_token='''(EOS)'''			)
       _snake_case					     =       self.get_image_processor(do_normalize=_lowerCamelCase    ,      padding_value=1.0			)

       _snake_case					     =       CLIPProcessor.from_pretrained(
           self.tmpdirname    ,      bos_token='''(BOS)'''    ,      eos_token='''(EOS)'''    ,      do_normalize=_lowerCamelCase    ,      padding_value=1.0			)

       self.assertEqual(processor.tokenizer.get_vocab()    ,      tokenizer_add_kwargs.get_vocab()			)
       self.assertIsInstance(processor.tokenizer    ,      _lowerCamelCase			)

       self.assertEqual(processor.image_processor.to_json_string()    ,      image_processor_add_kwargs.to_json_string()			)
       self.assertIsInstance(processor.image_processor    ,      _lowerCamelCase			)







     def      lowercase	(							self			:			int			):
       _snake_case					     =       self.get_image_processor()
       _snake_case					     =       self.get_tokenizer()

       _snake_case					     =       CLIPProcessor(tokenizer=_lowerCamelCase    ,      image_processor=_lowerCamelCase			)

       _snake_case					     =       self.prepare_image_inputs()

       _snake_case					     =       image_processor(_lowerCamelCase    ,      return_tensors='''np'''			)
       _snake_case					     =       processor(images=_lowerCamelCase    ,      return_tensors='''np'''			)

       for key in input_image_proc.keys():
         self.assertAlmostEqual(input_image_proc[key].sum()    ,      input_processor[key].sum()    ,      delta=1e-2			)







     def      lowercase	(							self			:			Any			):
       _snake_case					     =       self.get_image_processor()
       _snake_case					     =       self.get_tokenizer()

       _snake_case					     =       CLIPProcessor(tokenizer=_lowerCamelCase    ,      image_processor=_lowerCamelCase			)

       _snake_case					     =       '''lower newer'''

       _snake_case					     =       processor(text=_lowerCamelCase			)

       _snake_case					     =       tokenizer(_lowerCamelCase			)

       for key in encoded_tok.keys():
         self.assertListEqual(encoded_tok[key]    ,      encoded_processor[key]			)







     def      lowercase	(							self			:			Any			):
       _snake_case					     =       self.get_image_processor()
       _snake_case					     =       self.get_tokenizer()

       _snake_case					     =       CLIPProcessor(tokenizer=_lowerCamelCase    ,      image_processor=_lowerCamelCase			)

       _snake_case					     =       '''lower newer'''
       _snake_case					     =       self.prepare_image_inputs()

       _snake_case					     =       processor(text=_lowerCamelCase    ,      images=_lowerCamelCase			)

       self.assertListEqual(list(inputs.keys()			)    ,      ['''input_ids''', '''attention_mask''', '''pixel_values''']			)

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







     def      lowercase	(							self			:			List[str]			):
       _snake_case					     =       self.get_image_processor()
       _snake_case					     =       self.get_tokenizer()

       _snake_case					     =       CLIPProcessor(tokenizer=_lowerCamelCase    ,      image_processor=_lowerCamelCase			)

       _snake_case					     =       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

       _snake_case					     =       processor.batch_decode(_lowerCamelCase			)
       _snake_case					     =       tokenizer.batch_decode(_lowerCamelCase			)

       self.assertListEqual(_lowerCamelCase    ,      _lowerCamelCase			)







     def      lowercase	(							self			:			List[Any]			):
       _snake_case					     =       self.get_image_processor()
       _snake_case					     =       self.get_tokenizer()

       _snake_case					     =       CLIPProcessor(tokenizer=_lowerCamelCase    ,      image_processor=_lowerCamelCase			)

       _snake_case					     =       '''lower newer'''
       _snake_case					     =       self.prepare_image_inputs()

       _snake_case					     =       processor(text=_lowerCamelCase    ,      images=_lowerCamelCase			)

       self.assertListEqual(list(inputs.keys()			)    ,      processor.model_input_names			)



