code: 
  
  import logging
  import os
  import time
  from argparse import Namespace
  from pathlib import Path
  
  import datasets
  import torch
  from accelerate import Accelerator, DistributedType
  from accelerate.utils import ProjectConfiguration
  from arguments import TrainingArguments
  from datasets import load_dataset
  from huggingface_hub import Repository
  from torch.optim import AdamW
  from torch.utils.data import IterableDataset
  from torch.utils.data.dataloader import DataLoader
  from torch.utils.data.datapipes.iter.combinatorics import ShufflerIterDataPipe
  
  import transformers
  from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, get_scheduler, set_seed
  
  class __UpperCamelCase    (							_a		):
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					def __init__(							self							,			lowerCamelCase__							,			lowerCamelCase__							,			lowerCamelCase__=False							,			lowerCamelCase__=1_0_2_4							,			lowerCamelCase__=1_0_2_4							,			lowerCamelCase__=3.6							,			lowerCamelCase__=False							,			):
  						UpperCAmelCase__:  List[str]		       =   tokenizer
  						UpperCAmelCase__:  Dict		       =   tokenizer.bos_token_id
  						UpperCAmelCase__:  Optional[Any]		       =   dataset
  						UpperCAmelCase__:  Union[str, Any]		       =   seq_length
  						UpperCAmelCase__:  List[Any]		       =   0
  						UpperCAmelCase__:  Dict		       =   infinite
  						UpperCAmelCase__:  int		       =   0
  						UpperCAmelCase__:  str		       =   tokenized
  
  						if self.tokenized:
  							UpperCAmelCase__:  Tuple		       =   seq_length * num_of_sequences
  							UpperCAmelCase__:  Optional[Any]		       =   "input_ids"
  						else:
  							UpperCAmelCase__:  Union[str, Any]		       =   seq_length * chars_per_token * num_of_sequences
  							UpperCAmelCase__:  Optional[int]		       =   "content"
  
  
  
  
  
  					def __iter__(							self	):
  						UpperCAmelCase__:  int		       =   iter(self.dataset	)
  						UpperCAmelCase__:  Optional[Any]		       =   True
  						while more_examples:
  							UpperCAmelCase__       ,	UpperCAmelCase__:  List[str]		       =   [], 0
  							while True:
  								if buffer_len >= self.max_buffer_size:
  									break
  								try:
  									buffer.append(next(lowerCamelCase__	)[self.content_field]	)
  									buffer_len += len(buffer[-1]	)
  								except StopIteration:
  									if self.infinite:
  										UpperCAmelCase__:  Tuple		       =   iter(self.dataset	)
  										self.epoch += 1
  										logger.info(F"Dataset epoch: {self.epoch}"	)
  									else:
  										UpperCAmelCase__:  Any		       =   False
  										break
  							if self.tokenized:
  								UpperCAmelCase__:  Union[str, Any]		       =   buffer
  							else:
  								UpperCAmelCase__:  List[str]		       =   self.tokenizer(lowerCamelCase__							,			truncation=lowerCamelCase__	)["input_ids"]
  							UpperCAmelCase__:  Union[str, Any]		       =   []
  							for tokenized_input in tokenized_inputs:
  								all_token_ids.extend(tokenized_input + [self.concat_token_id]	)
  							for i in range(0							,			len(lowerCamelCase__	)							,			self.seq_length	):
  								UpperCAmelCase__:  Tuple		       =   all_token_ids[i : i + self.seq_length]
  								if len(lowerCamelCase__	) == self.seq_length:
  									self.current_size += 1
  									yield torch.tensor(lowerCamelCase__	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self							,			lowerCamelCase__=1_0_0_0	):
  						return ShufflerIterDataPipe(self							,			buffer_size=lowerCamelCase__	)
  
  
  
  def  _A  (			SCREAMING_SNAKE_CASE			):
  	UpperCAmelCase__:  List[str]		       =   args.model_ckpt.split("/"			)[-1]
  	UpperCAmelCase__:  str		       =   logging.getLogger(__name__			)
  	UpperCAmelCase__:  int		       =   Path(args.save_dir			) / "log/"
  	log_dir.mkdir(exist_ok=SCREAMING_SNAKE_CASE			)
  	UpperCAmelCase__:  List[Any]		       =   f"debug_{accelerator.process_index}.log"
  	logging.basicConfig(
  	    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"  ,datefmt="%m/%d/%Y %H:%M:%S"  ,level=logging.INFO  ,handlers=[logging.FileHandler(log_dir / filename			), logging.StreamHandler()]  ,)
  	if accelerator.is_main_process:  # we only want to setup logging once
  		accelerator.init_trackers(SCREAMING_SNAKE_CASE  ,vars(SCREAMING_SNAKE_CASE			)			)
  		UpperCAmelCase__:  Tuple		       =   accelerator.trackers[0].run.name
  		logger.setLevel(logging.INFO			)
  		datasets.utils.logging.set_verbosity_info()
  		transformers.utils.logging.set_verbosity_info()
  	else:
  		UpperCAmelCase__:  List[Any]		       =   ""
  		logger.setLevel(logging.ERROR			)
  		datasets.utils.logging.set_verbosity_error()
  		transformers.utils.logging.set_verbosity_error()
  	return logger, run_name
  
  
  
  def  _A  (			SCREAMING_SNAKE_CASE			):
  	UpperCAmelCase__:  Optional[int]		       =   {"streaming": True}
  	UpperCAmelCase__:  Dict		       =   load_dataset(args.dataset_name_train  ,split="train"  ,**SCREAMING_SNAKE_CASE			)
  	UpperCAmelCase__:  Optional[int]		       =   train_data.shuffle(buffer_size=args.shuffle_buffer  ,seed=args.seed			)
  	UpperCAmelCase__:  Dict		       =   load_dataset(args.dataset_name_valid  ,split="train"  ,**SCREAMING_SNAKE_CASE			)
  	UpperCAmelCase__:  List[Any]		       =   ConstantLengthDataset(
  	    SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,infinite=SCREAMING_SNAKE_CASE  ,seq_length=args.seq_length  ,tokenized=args.tokenized			)
  	UpperCAmelCase__:  Tuple		       =   ConstantLengthDataset(
  	    SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,infinite=SCREAMING_SNAKE_CASE  ,seq_length=args.seq_length  ,tokenized=args.tokenized			)
  	UpperCAmelCase__:  Any		       =   train_dataset.shuffle(buffer_size=args.shuffle_buffer			)
  	UpperCAmelCase__:  Any		       =   DataLoader(SCREAMING_SNAKE_CASE  ,batch_size=args.train_batch_size  ,shuffle=SCREAMING_SNAKE_CASE			)
  	UpperCAmelCase__:  Union[str, Any]		       =   DataLoader(SCREAMING_SNAKE_CASE  ,batch_size=args.valid_batch_size			)
  	return train_dataloader, eval_dataloader
  
  
  
  def  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]			):
  	UpperCAmelCase__       ,	UpperCAmelCase__:  Union[str, Any]		       =   [], []
  	for n, p in model.named_parameters():
  		if any(nd in n for nd in no_decay			):
  			params_without_wd.append(SCREAMING_SNAKE_CASE			)
  		else:
  			params_with_wd.append(SCREAMING_SNAKE_CASE			)
  	return [
  	    {"params": params_with_wd, "weight_decay": args.weight_decay},
  	    {"params": params_without_wd, "weight_decay": 0.0},
  	]
  
  
  
  def  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			):
  	logger.info(f"Step {step}: {metrics}"			)
  	if accelerator.is_main_process:
  		accelerator.log(SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			)
  
  
  
  def  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			):
  	# TFLOPs formula (from Equation 3 in Section 5.1 of https://arxiv.org/pdf/2104.04473.pdf).
  	UpperCAmelCase__:  Tuple		       =   accelerator.unwrap_model(SCREAMING_SNAKE_CASE			).config
  	UpperCAmelCase__:  Optional[Any]		       =   4 if args.gradient_checkpointing else 3
  	UpperCAmelCase__:  str		       =   args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
  	UpperCAmelCase__:  List[Any]		       =   2_4 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
  	UpperCAmelCase__:  Any		       =   factor * (
  	    1.0
  	    + (args.seq_length / (6.0 * config_model.n_embd))
  	    + (tokenizer.vocab_size / (16.0 * config_model.n_layer * config_model.n_embd))
  	)
  	UpperCAmelCase__:  Dict		       =   flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (1_0**1_2))
  	return tflops
  
  
  
  def  _A  (			SCREAMING_SNAKE_CASE			):
  	model.eval()
  	UpperCAmelCase__:  Optional[Any]		       =   []
  	for step, batch in enumerate(SCREAMING_SNAKE_CASE			):
  		with torch.no_grad():
  			UpperCAmelCase__:  Union[str, Any]		       =   model(SCREAMING_SNAKE_CASE  ,labels=SCREAMING_SNAKE_CASE			)
  		UpperCAmelCase__:  str		       =   outputs.loss.repeat(args.valid_batch_size			)
  		losses.append(accelerator.gather(SCREAMING_SNAKE_CASE			)			)
  		if args.max_eval_steps > 0 and step >= args.max_eval_steps:
  			break
  	UpperCAmelCase__:  List[Any]		       =   torch.cat(SCREAMING_SNAKE_CASE			)
  	UpperCAmelCase__:  Any		       =   losses[: eval_dataloader.dataset.current_size].mean()
  	try:
  		UpperCAmelCase__:  Any		       =   torch.exp(SCREAMING_SNAKE_CASE			)
  	except OverflowError:
  		UpperCAmelCase__:  Optional[Any]		       =   float("inf"			)
  	return loss.item(), perplexity.item()
  
  
  # Settings
  _lowerCAmelCase     :			str    =HfArgumentParser(TrainingArguments)
  _lowerCAmelCase     :			str    =parser.parse_args()
  
  # Accelerator
  _lowerCAmelCase     :			Any    =ProjectConfiguration(project_dir=args.save_dir, logging_dir="""log""")
  _lowerCAmelCase     :			Tuple    =Accelerator(log_with=["""wandb""", """tensorboard"""], project_config=config)
  _lowerCAmelCase     :			List[str]    ={str(k): str(v) for k, v in accelerator.state.__dict__.items()}
  
  _lowerCAmelCase     :			Optional[Any]    =Namespace(**vars(args), **acc_state)
  _lowerCAmelCase     :			Any    =accelerator.state.num_processes * args.train_batch_size
  set_seed(args.seed)
  
  # Clone model repository
  if accelerator.is_main_process:
  					_lowerCAmelCase     :			str    =Repository(args.save_dir, clone_from=args.model_ckpt)
  
  # Logging
  _lowerCAmelCase   ,  _lowerCAmelCase     :			List[str]    =setup_logging(args)
  logger.info(accelerator.state)
  
  # Checkout new branch on repo
  if accelerator.is_main_process:
  					hf_repo.git_checkout(run_name, create_branch_ok=True)
  
  # Load model and tokenizer
  _lowerCAmelCase     :			Optional[Any]    =AutoModelForCausalLM.from_pretrained(args.save_dir)
  if args.gradient_checkpointing:
  					model.gradient_checkpointing_enable()
  _lowerCAmelCase     :			List[str]    =AutoTokenizer.from_pretrained(args.save_dir)
  
  # Load dataset and dataloader
  _lowerCAmelCase   ,  _lowerCAmelCase     :			str    =create_dataloaders(args)
  
  # Prepare the optimizer and learning rate scheduler
  _lowerCAmelCase     :			List[Any]    =AdamW(get_grouped_params(model, args), lr=args.learning_rate)
  _lowerCAmelCase     :			int    =get_scheduler(
      name=args.lr_scheduler_type,
      optimizer=optimizer,
      num_warmup_steps=args.num_warmup_steps,
      num_training_steps=args.max_train_steps,
  )
  accelerator.register_for_checkpointing(lr_scheduler)
  
  
  
  def  _A  (			):
  	return optimizer.param_groups[0]["lr"]
  
  
  # Prepare everything with our `accelerator`.
  _lowerCAmelCase   ,  _lowerCAmelCase   ,  _lowerCAmelCase   ,  _lowerCAmelCase     :			Optional[Any]    =accelerator.prepare(
      model, optimizer, train_dataloader, eval_dataloader
  )
  
  # load in the weights and states from a previous save
  if args.resume_from_checkpoint:
  					if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
  										accelerator.print(F"Resumed from checkpoint: {args.resume_from_checkpoint}")
  										accelerator.load_state(args.resume_from_checkpoint)
  										_lowerCAmelCase     :			str    =os.path.basename(args.resume_from_checkpoint)
  					else:
  										# Get the most recent checkpoint
  										_lowerCAmelCase     :			List[Any]    =[f.name for f in os.scandir(args.save_dir) if f.is_dir() and """step""" in str(f)]
  										dirs.sort(key=os.path.getctime)
  										_lowerCAmelCase     :			Union[str, Any]    =dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
  					# Extract the step of the checkpoint to continue from there
  					_lowerCAmelCase     :			List[str]    =os.path.splitext(path)[0]
  					_lowerCAmelCase     :			Tuple    =int(training_difference.replace("""step_""", """"""))
  
  # Train model
  model.train()
  _lowerCAmelCase     :			Any    =0
  _lowerCAmelCase     :			Dict    =time.time()
  _lowerCAmelCase     :			Optional[Any]    =0
  for step, batch in enumerate(train_dataloader, start=1):
  					if args.resume_from_checkpoint and step < resume_step:
  										continue  # we need to skip steps until we reach the resumed step
  					_lowerCAmelCase     :			Optional[int]    =model(batch, labels=batch, use_cache=False).loss
  					_lowerCAmelCase     :			Any    =accelerator.gather(loss.repeat(args.train_batch_size)).mean()
  					loss_tracking += avg_loss.item() / args.gradient_accumulation_steps
  					log_metrics(step, {"""samples""": step * samples_per_step, """loss_per_step/train""": loss.item()})
  					_lowerCAmelCase     :			Tuple    =loss / args.gradient_accumulation_steps
  					if step % args.gradient_accumulation_steps != 0:
  										# Prevent backward from doing gradient all_reduce in every step
  										if accelerator.distributed_type == DistributedType.MULTI_GPU:
  															with model.no_sync():
  																				accelerator.backward(loss)
  										else:
  															accelerator.backward(loss)
  					else:
  										_lowerCAmelCase     :			List[Any]    =get_lr()
  										accelerator.backward(loss)
  										accelerator.clip_grad_norm_(model.parameters(), 1.0)
  										optimizer.step()
  										lr_scheduler.step()
  										optimizer.zero_grad()
  										_lowerCAmelCase     :			Union[str, Any]    =time.time() - t_start
  										_lowerCAmelCase     :			Dict    =compute_tflops(elapsed_time, accelerator, args)
  										log_metrics(
  										    step,
  										    {
  										        """steps""": completed_steps,
  										        """loss/train""": loss_tracking,
  										        """lr""": lr,
  										        """tflops""": tflops,
  										        """time_per_iteration""": elapsed_time,
  										    },
  										)
  										_lowerCAmelCase     :			int    =time.time()
  										_lowerCAmelCase     :			int    =0
  										completed_steps += 1
  					if step % args.save_checkpoint_steps == 0:
  										logger.info("""Evaluating and saving model checkpoint""")
  										_lowerCAmelCase   ,  _lowerCAmelCase     :			List[Any]    =evaluate(args)
  										log_metrics(step, {"""loss/eval""": eval_loss, """perplexity""": perplexity})
  										accelerator.wait_for_everyone()
  										_lowerCAmelCase     :			Optional[int]    =os.path.join(args.save_dir, F"step_{step}")
  										accelerator.save_state(save_dir)
  										if accelerator.is_main_process:
  															hf_repo.push_to_hub(commit_message=F"step {step}")
  										model.train()
  					if completed_steps >= args.max_train_steps:
  										break
  
  # Evaluate and save the last checkpoint
  logger.info("""Evaluating and saving model after training""")
  _lowerCAmelCase   ,  _lowerCAmelCase     :			Dict    =evaluate(args)
  log_metrics(step, {"""loss/eval""": eval_loss, """perplexity""": perplexity})
  accelerator.wait_for_everyone()
  _lowerCAmelCase     :			List[Any]    =accelerator.unwrap_model(model)
  unwrapped_model.save_pretrained(args.save_dir, save_function=accelerator.save)
  _lowerCAmelCase     :			Union[str, Any]    =os.path.join(args.save_dir, F"step_{step}")
  accelerator.save_state(save_dir)
  if accelerator.is_main_process:
  					hf_repo.push_to_hub(commit_message="""final model""")
style_context: 
  
  import json
  import os
  import random
  import shutil
  import tempfile
  import unittest
  
  import numpy as np
  import pytest
  
  from transformers import BertTokenizer, BertTokenizerFast
  from transformers.models.bert.tokenization_bert 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 FlavaImageProcessor, FlavaProcessor
  					from transformers.models.flava.image_processing_flava import (
  					    FLAVA_CODEBOOK_MEAN,
  					    FLAVA_CODEBOOK_STD,
  					    FLAVA_IMAGE_MEAN,
  					    FLAVA_IMAGE_STD,
  					)
  
  @require_vision
  class __UpperCamelCase    (							unittest.TestCase		):
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  Optional[int]		       =   tempfile.mkdtemp()
  
  						# fmt: off
  						UpperCAmelCase__:  Optional[Any]		       =   ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "want", "##want", "##ed", "wa", "un", "runn", "##ing", ",", "low", "lowest"]
  						# fmt: on
  						UpperCAmelCase__:  str		       =   os.path.join(self.tmpdirname							,			VOCAB_FILES_NAMES["vocab_file"]	)
  
  						with open(self.vocab_file							,			"w"							,			encoding="utf-8"	) as fp:
  							fp.write("".join([x + "\n" for x in vocab_tokens]	)	)
  
  						UpperCAmelCase__:  Tuple		       =   {
  						    "image_mean": FLAVA_IMAGE_MEAN,
  						    "image_std": FLAVA_IMAGE_STD,
  						    "do_normalize": True,
  						    "do_resize": True,
  						    "size": 2_2_4,
  						    "do_center_crop": True,
  						    "crop_size": 2_2_4,
  						    "input_size_patches": 1_4,
  						    "total_mask_patches": 7_5,
  						    "mask_group_max_patches": None,
  						    "mask_group_min_patches": 1_6,
  						    "mask_group_min_aspect_ratio": 0.3,
  						    "mask_group_max_aspect_ratio": None,
  						    "codebook_do_resize": True,
  						    "codebook_size": 1_1_2,
  						    "codebook_do_center_crop": True,
  						    "codebook_crop_size": 1_1_2,
  						    "codebook_do_map_pixels": True,
  						    "codebook_do_normalize": True,
  						    "codebook_image_mean": FLAVA_CODEBOOK_MEAN,
  						    "codebook_image_std": FLAVA_CODEBOOK_STD,
  						}
  
  						UpperCAmelCase__:  Tuple		       =   os.path.join(self.tmpdirname							,			lowerCamelCase__	)
  						with open(self.image_processor_file							,			"w"							,			encoding="utf-8"	) as fp:
  							json.dump(lowerCamelCase__							,			lowerCamelCase__	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self							,			**lowerCamelCase__	):
  						return BertTokenizer.from_pretrained(self.tmpdirname							,			**lowerCamelCase__	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self							,			**lowerCamelCase__	):
  						return BertTokenizerFast.from_pretrained(self.tmpdirname							,			**lowerCamelCase__	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self							,			**lowerCamelCase__	):
  						return FlavaImageProcessor.from_pretrained(self.tmpdirname							,			**lowerCamelCase__	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						shutil.rmtree(self.tmpdirname	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  Dict		       =   [np.random.randint(2_5_5							,			size=(3, 3_0, 4_0_0)							,			dtype=np.uinta	)]
  
  						UpperCAmelCase__:  Any		       =   [Image.fromarray(np.moveaxis(lowerCamelCase__							,			0							,			-1	)	) for x in image_inputs]
  
  						return image_inputs
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  int		       =   self.get_tokenizer()
  						UpperCAmelCase__:  Any		       =   self.get_rust_tokenizer()
  						UpperCAmelCase__:  List[Any]		       =   self.get_image_processor()
  
  						UpperCAmelCase__:  Tuple		       =   FlavaProcessor(tokenizer=lowerCamelCase__							,			image_processor=lowerCamelCase__	)
  						processor_slow.save_pretrained(self.tmpdirname	)
  						UpperCAmelCase__:  Dict		       =   FlavaProcessor.from_pretrained(self.tmpdirname							,			use_fast=lowerCamelCase__	)
  
  						UpperCAmelCase__:  Union[str, Any]		       =   FlavaProcessor(tokenizer=lowerCamelCase__							,			image_processor=lowerCamelCase__	)
  						processor_fast.save_pretrained(self.tmpdirname	)
  						UpperCAmelCase__:  int		       =   FlavaProcessor.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       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  Any		       =   FlavaProcessor(tokenizer=self.get_tokenizer()							,			image_processor=self.get_image_processor()	)
  						processor.save_pretrained(self.tmpdirname	)
  
  						UpperCAmelCase__:  Any		       =   self.get_tokenizer(bos_token="(BOS)"							,			eos_token="(EOS)"	)
  						UpperCAmelCase__:  str		       =   self.get_image_processor(do_normalize=lowerCamelCase__							,			padding_value=1.0	)
  
  						UpperCAmelCase__:  str		       =   FlavaProcessor.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       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  int		       =   self.get_image_processor()
  						UpperCAmelCase__:  Optional[Any]		       =   self.get_tokenizer()
  
  						UpperCAmelCase__:  Optional[Any]		       =   FlavaProcessor(tokenizer=lowerCamelCase__							,			image_processor=lowerCamelCase__	)
  
  						UpperCAmelCase__:  Optional[int]		       =   self.prepare_image_inputs()
  
  						UpperCAmelCase__:  Tuple		       =   image_processor(lowerCamelCase__							,			return_tensors="np"	)
  						UpperCAmelCase__:  str		       =   processor(images=lowerCamelCase__							,			return_tensors="np"	)
  
  						for key in input_feat_extract.keys():
  							self.assertAlmostEqual(input_feat_extract[key].sum()							,			input_processor[key].sum()							,			delta=1e-2	)
  
  						# With rest of the args
  						random.seed(1_2_3_4	)
  						UpperCAmelCase__:  Optional[int]		       =   image_processor(
  						    lowerCamelCase__							,			return_image_mask=lowerCamelCase__							,			return_codebook_pixels=lowerCamelCase__							,			return_tensors="np"	)
  						random.seed(1_2_3_4	)
  						UpperCAmelCase__:  Tuple		       =   processor(
  						    images=lowerCamelCase__							,			return_image_mask=lowerCamelCase__							,			return_codebook_pixels=lowerCamelCase__							,			return_tensors="np"	)
  
  						for key in input_feat_extract.keys():
  							self.assertAlmostEqual(input_feat_extract[key].sum()							,			input_processor[key].sum()							,			delta=1e-2	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  Dict		       =   self.get_image_processor()
  						UpperCAmelCase__:  str		       =   self.get_tokenizer()
  
  						UpperCAmelCase__:  List[str]		       =   FlavaProcessor(tokenizer=lowerCamelCase__							,			image_processor=lowerCamelCase__	)
  
  						UpperCAmelCase__:  Union[str, Any]		       =   "lower newer"
  
  						UpperCAmelCase__:  Optional[Any]		       =   processor(text=lowerCamelCase__	)
  
  						UpperCAmelCase__:  Optional[Any]		       =   tokenizer(lowerCamelCase__	)
  
  						for key in encoded_tok.keys():
  							self.assertListEqual(encoded_tok[key]							,			encoded_processor[key]	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  List[Any]		       =   self.get_image_processor()
  						UpperCAmelCase__:  int		       =   self.get_tokenizer()
  
  						UpperCAmelCase__:  str		       =   FlavaProcessor(tokenizer=lowerCamelCase__							,			image_processor=lowerCamelCase__	)
  
  						UpperCAmelCase__:  Any		       =   "lower newer"
  						UpperCAmelCase__:  Any		       =   self.prepare_image_inputs()
  
  						UpperCAmelCase__:  Optional[int]		       =   processor(text=lowerCamelCase__							,			images=lowerCamelCase__	)
  
  						self.assertListEqual(list(inputs.keys()	)							,			["input_ids", "token_type_ids", "attention_mask", "pixel_values"]	)
  
  						# add extra args
  						UpperCAmelCase__:  str		       =   processor(text=lowerCamelCase__							,			images=lowerCamelCase__							,			return_codebook_pixels=lowerCamelCase__							,			return_image_mask=lowerCamelCase__	)
  
  						self.assertListEqual(
  						    list(inputs.keys()	)							,			[
  						        "input_ids",
  						        "token_type_ids",
  						        "attention_mask",
  						        "pixel_values",
  						        "codebook_pixel_values",
  						        "bool_masked_pos",
  						    ]							,			)
  
  						# test if it raises when no input is passed
  						with pytest.raises(lowerCamelCase__	):
  							processor()
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  Optional[Any]		       =   self.get_image_processor()
  						UpperCAmelCase__:  List[Any]		       =   self.get_tokenizer()
  
  						UpperCAmelCase__:  Any		       =   FlavaProcessor(tokenizer=lowerCamelCase__							,			image_processor=lowerCamelCase__	)
  
  						UpperCAmelCase__:  List[Any]		       =   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
  
  						UpperCAmelCase__:  str		       =   processor.batch_decode(lowerCamelCase__	)
  						UpperCAmelCase__:  Dict		       =   tokenizer.batch_decode(lowerCamelCase__	)
  
  						self.assertListEqual(lowerCamelCase__							,			lowerCamelCase__	)
  
  
  
  
  
  					def       _UpperCAmelCase						(							self	):
  						UpperCAmelCase__:  List[str]		       =   self.get_image_processor()
  						UpperCAmelCase__:  Dict		       =   self.get_tokenizer()
  
  						UpperCAmelCase__:  Tuple		       =   FlavaProcessor(tokenizer=lowerCamelCase__							,			image_processor=lowerCamelCase__	)
  
  						UpperCAmelCase__:  Optional[Any]		       =   "lower newer"
  						UpperCAmelCase__:  Dict		       =   self.prepare_image_inputs()
  
  						UpperCAmelCase__:  int		       =   processor(text=lowerCamelCase__							,			images=lowerCamelCase__	)
  
  						self.assertListEqual(list(inputs.keys()	)							,			processor.model_input_names	)