




import logging

import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader

from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, set_seed
class       a__   ( snake_case__				):






						def __init__(       self		,      _A		,      _A		,      _A=1_0_2_4		,      _A=1_0_2_4		,      _A=3.6      ):




											"""simple docstring"""



											__lowerCAmelCase           =	tokenizer
											__lowerCAmelCase           =	tokenizer.bos_token_id
											__lowerCAmelCase           =	dataset
											__lowerCAmelCase           =	seq_length
											__lowerCAmelCase           =	seq_length * chars_per_token * num_of_sequences






						def __iter__(       self      ):




											"""simple docstring"""



											__lowerCAmelCase           =	iter(self.dataset      )
											__lowerCAmelCase           =	True
											while more_examples:
																__lowerCAmelCase       ,							__lowerCAmelCase           =	[], 0
																while True:
																					if buffer_len >= self.input_characters:
																										break
																					try:
																										buffer.append(next(_A      )["content"]      )
																										buffer_len += len(buffer[-1]      )
																					except StopIteration:
																										__lowerCAmelCase           =	False
																										break
																__lowerCAmelCase           =	tokenizer(_A		,      truncation=_A      )["input_ids"]
																__lowerCAmelCase           =	[]
																for tokenized_input in tokenized_inputs:
																					all_token_ids.extend(tokenized_input + [self.concat_token_id]      )
																for i in range(0		,      len(_A      )		,      self.seq_length      ):
																					__lowerCAmelCase           =	all_token_ids[i : i + self.seq_length]
																					if len(_A      ) == self.seq_length:
																										yield torch.tensor(_A      )







def      _a				(						SCREAMING_SNAKE_CASE_							:    str					):
					__lowerCAmelCase           =	{"streaming": True}
					__lowerCAmelCase           =	load_dataset(args.dataset_name				,				split="train"				,				**SCREAMING_SNAKE_CASE_					)
					__lowerCAmelCase           =	ConstantLengthDataset(SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_				,				seq_length=args.seq_length					)
					__lowerCAmelCase           =	DataLoader(SCREAMING_SNAKE_CASE_				,				batch_size=args.batch_size					)
					return eval_dataloader







def      _a				(						SCREAMING_SNAKE_CASE_							:    Dict					):
					model.eval()
					__lowerCAmelCase           =	[]
					for step, batch in enumerate(SCREAMING_SNAKE_CASE_					):
										with torch.no_grad():
															__lowerCAmelCase           =	model(SCREAMING_SNAKE_CASE_				,				labels=SCREAMING_SNAKE_CASE_					)
										__lowerCAmelCase           =	outputs.loss.repeat(args.batch_size					)
										losses.append(accelerator.gather(SCREAMING_SNAKE_CASE_					)					)

										if args.max_eval_steps > 0 and step >= args.max_eval_steps:
															break
					__lowerCAmelCase           =	torch.mean(torch.cat(SCREAMING_SNAKE_CASE_					)					)
					try:
										__lowerCAmelCase           =	torch.exp(SCREAMING_SNAKE_CASE_					)
					except OverflowError:
										__lowerCAmelCase           =	float("inf"					)
					return loss.item(), perplexity.item()


# Setup Accelerator
UpperCamelCase__         =      Accelerator()

# Parse configuration
UpperCamelCase__         =      HfArgumentParser(EvaluationArguments)
UpperCamelCase__         =      parser.parse_args()
set_seed(args.seed)

# Logging
UpperCamelCase__         =      logging.getLogger(__name__)
logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)

# Load model and tokenizer
UpperCamelCase__         =      AutoModelForCausalLM.from_pretrained(args.model_ckpt)
UpperCamelCase__         =      AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
UpperCamelCase__         =      create_dataloader(args)

# Prepare everything with our `accelerator`.
UpperCamelCase__      ,     UpperCamelCase__         =      accelerator.prepare(model, eval_dataloader)

# Evaluate and save the last checkpoint
logger.info("""Evaluating and saving model after training""")
UpperCamelCase__      ,     UpperCamelCase__         =      evaluate(args)


logger.info(f'''loss/eval: {eval_loss}, perplexity: {perplexity}''')










import argparse
import os

import torch

from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint







def      _a				(						SCREAMING_SNAKE_CASE_							:    Any					):
					# encoder.embeddings are double copied in original FLAVA
					return sum(param.float().sum() if "encoder.embeddings" not in key else 0 for key, param in state_dict.items()					)







def      _a				(						SCREAMING_SNAKE_CASE_							:    Dict				,				SCREAMING_SNAKE_CASE_							:    Dict					):
					__lowerCAmelCase           =	{}

					for key, value in state_dict.items():
										if "text_encoder.embeddings" in key or "image_encoder.embeddings" in key:
															continue

										__lowerCAmelCase           =	key.replace("heads.cmd.mim_head.cls.predictions"				,				"mmm_image_head"					)
										__lowerCAmelCase           =	key.replace("heads.cmd.mlm_head.cls.predictions"				,				"mmm_text_head"					)
										__lowerCAmelCase           =	key.replace("heads.cmd.itm_head.cls"				,				"itm_head"					)
										__lowerCAmelCase           =	key.replace("heads.cmd.itm_head.pooler"				,				"itm_head.pooler"					)
										__lowerCAmelCase           =	key.replace("heads.cmd.clip_head.logit_scale"				,				"flava.logit_scale"					)
										__lowerCAmelCase           =	key.replace("heads.fairseq_mlm.cls.predictions"				,				"mlm_head"					)
										__lowerCAmelCase           =	key.replace("heads.imagenet.mim_head.cls.predictions"				,				"mim_head"					)
										__lowerCAmelCase           =	key.replace("mm_text_projection"				,				"flava.text_to_mm_projection"					)
										__lowerCAmelCase           =	key.replace("mm_image_projection"				,				"flava.image_to_mm_projection"					)
										__lowerCAmelCase           =	key.replace("image_encoder.module"				,				"flava.image_model"					)
										__lowerCAmelCase           =	key.replace("text_encoder.module"				,				"flava.text_model"					)
										__lowerCAmelCase           =	key.replace("mm_encoder.module.encoder.cls_token"				,				"flava.multimodal_model.cls_token"					)
										__lowerCAmelCase           =	key.replace("mm_encoder.module"				,				"flava.multimodal_model"					)
										__lowerCAmelCase           =	key.replace("text_projection"				,				"flava.text_projection"					)
										__lowerCAmelCase           =	key.replace("image_projection"				,				"flava.image_projection"					)

										__lowerCAmelCase           =	value.float()

					for key, value in codebook_state_dict.items():
										__lowerCAmelCase           =	value

					return upgrade







@torch.no_grad()
def      _a				(						SCREAMING_SNAKE_CASE_							:    List[Any]				,				SCREAMING_SNAKE_CASE_							:    Optional[int]				,				SCREAMING_SNAKE_CASE_							:    List[str]				,				SCREAMING_SNAKE_CASE_							:    int=None					):
					if config_path is not None:
										__lowerCAmelCase           =	FlavaConfig.from_pretrained(SCREAMING_SNAKE_CASE_					)
					else:
										__lowerCAmelCase           =	FlavaConfig()

					__lowerCAmelCase           =	FlavaForPreTraining(SCREAMING_SNAKE_CASE_					).eval()

					__lowerCAmelCase           =	convert_dalle_checkpoint(SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_				,				save_checkpoint=SCREAMING_SNAKE_CASE_					)

					if os.path.exists(SCREAMING_SNAKE_CASE_					):
										__lowerCAmelCase           =	torch.load(SCREAMING_SNAKE_CASE_				,				map_location="cpu"					)
					else:
										__lowerCAmelCase           =	torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE_				,				map_location="cpu"					)

					__lowerCAmelCase           =	upgrade_state_dict(SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_					)
					hf_model.load_state_dict(SCREAMING_SNAKE_CASE_					)
					__lowerCAmelCase           =	hf_model.state_dict()
					__lowerCAmelCase           =	count_parameters(SCREAMING_SNAKE_CASE_					)
					__lowerCAmelCase           =	count_parameters(SCREAMING_SNAKE_CASE_					) + count_parameters(SCREAMING_SNAKE_CASE_					)

					assert torch.allclose(SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_				,				atol=1E-3					)

					hf_model.save_pretrained(SCREAMING_SNAKE_CASE_					)


if __name__ == "__main__":
					UpperCamelCase__         =      argparse.ArgumentParser()
					parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
					parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to flava checkpoint""")
					parser.add_argument("""--codebook_path""", default=None, type=str, help="""Path to flava codebook checkpoint""")
					parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
					UpperCamelCase__         =      parser.parse_args()

					convert_flava_checkpoint(args.checkpoint_path, args.codebook_path, args.pytorch_dump_folder_path, args.config_path)




