

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""")


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	)