"""simple docstring"""






from typing import Any, Dict, List, Optional, Tuple, Union

import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler

from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configuration_fsmt import FSMTConfig
from transformers.optimization import (
    Adafactor,
    AdamW,
    get_constant_schedule,
    get_constant_schedule_with_warmup,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode
from transformers.utils import is_torch_tpu_available


if is_fairscale_available():
       from fairscale.optim import OSS


__lowerCAmelCase					: List[str]        =logging.get_logger(__name__)

__lowerCAmelCase					: List[Any]        ={
    """linear""": get_linear_schedule_with_warmup,
    """cosine""": get_cosine_schedule_with_warmup,
    """cosine_w_restarts""": get_cosine_with_hard_restarts_schedule_with_warmup,
    """polynomial""": get_polynomial_decay_schedule_with_warmup,
    """constant""": get_constant_schedule,
    """constant_w_warmup""": get_constant_schedule_with_warmup,
}





class 				_A		( __lowerCamelCase			):

      def __init__(		self	, __lowerCAmelCase=None	, __lowerCAmelCase=None	, *__lowerCAmelCase	, **__lowerCAmelCase		):







            """simple docstring"""



            super().__init__(*SCREAMING_SNAKE_CASE_	, **SCREAMING_SNAKE_CASE_		)

            if config is None:
                  assert isinstance(self.model	, SCREAMING_SNAKE_CASE_		), (
                      "If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
                      f' {self.model.__class__}'
                  )
                  lowercase														=			self.model.config
            else:
                  lowercase														=			config

            lowercase														=			data_args
            lowercase														=			self.config.tgt_vocab_size if isinstance(self.config	, SCREAMING_SNAKE_CASE_		) else self.config.vocab_size

            if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
                  assert self.config.pad_token_id is not None, (
                      "Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss"
                      " calculation or doing label smoothing."
                  )

            if self.config.pad_token_id is None and self.config.eos_token_id is not None:
                  logger.warning(
                      f'The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for'
                      """ padding.."""		)

            if self.args.label_smoothing == 0:
                  lowercase														=			torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id		)
            else:
                  # dynamically import label_smoothed_nll_loss
                  from utils import label_smoothed_nll_loss

                  lowercase														=			label_smoothed_nll_loss

      def        A__ (		self	, __lowerCAmelCase		):







            """simple docstring"""



            if self.optimizer is None:
                  lowercase														=			["""bias""", """LayerNorm.weight"""]
                  lowercase														=			[
                      {
                          """params""": [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay		)],
                          """weight_decay""": self.args.weight_decay,
                      },
                      {
                          """params""": [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay		)],
                          """weight_decay""": 0.0,
                      },
                  ]
                  lowercase														=			Adafactor if self.args.adafactor else AdamW
                  if self.args.adafactor:
                        lowercase														=			Adafactor
                        lowercase														=			{"""scale_parameter""": False, """relative_step""": False}
                  else:
                        lowercase														=			AdamW
                        lowercase														=			{
                            """betas""": (self.args.adam_betaa, self.args.adam_betaa),
                            """eps""": self.args.adam_epsilon,
                        }
                  lowercase														=			self.args.learning_rate
                  if self.sharded_ddp:
                        lowercase														=			OSS(
                            params=SCREAMING_SNAKE_CASE_	, optim=SCREAMING_SNAKE_CASE_	, **SCREAMING_SNAKE_CASE_	, )
                  else:
                        lowercase														=			optimizer_cls(SCREAMING_SNAKE_CASE_	, **SCREAMING_SNAKE_CASE_		)

            if self.lr_scheduler is None:
                  lowercase														=			self._get_lr_scheduler(SCREAMING_SNAKE_CASE_		)
            else:  # ignoring --lr_scheduler
                  logger.warning("""scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored."""		)

      def        A__ (		self	, __lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			arg_to_scheduler[self.args.lr_scheduler]
            if self.args.lr_scheduler == "constant":
                  lowercase														=			schedule_func(self.optimizer		)
            elif self.args.lr_scheduler == "constant_w_warmup":
                  lowercase														=			schedule_func(self.optimizer	, num_warmup_steps=self.args.warmup_steps		)
            else:
                  lowercase														=			schedule_func(
                      self.optimizer	, num_warmup_steps=self.args.warmup_steps	, num_training_steps=SCREAMING_SNAKE_CASE_		)
            return scheduler

      def        A__ (		self		):







            """simple docstring"""



            if isinstance(self.train_dataset	, torch.utils.data.IterableDataset		):
                  return None
            elif is_torch_tpu_available():
                  return get_tpu_sampler(self.train_dataset		)
            else:
                  if self.args.sortish_sampler:
                        self.train_dataset.make_sortish_sampler(
                            self.args.per_device_train_batch_size	, distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED)	, )

                  return (
                      RandomSampler(self.train_dataset		)
                      if self.args.local_rank == -1
                      else DistributedSampler(self.train_dataset		)
                  )

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase		):







            """simple docstring"""



            if self.args.label_smoothing == 0:
                  if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
                        # force training to ignore pad token
                        lowercase														=			model(**SCREAMING_SNAKE_CASE_	, use_cache=SCREAMING_SNAKE_CASE_		)[0]
                        lowercase														=			self.loss_fn(logits.view(-1	, logits.shape[-1]		)	, labels.view(-1		)		)
                  else:
                        # compute usual loss via models
                        lowercase							,							lowercase														=			model(**SCREAMING_SNAKE_CASE_	, labels=SCREAMING_SNAKE_CASE_	, use_cache=SCREAMING_SNAKE_CASE_		)[:2]
            else:
                  # compute label smoothed loss
                  lowercase														=			model(**SCREAMING_SNAKE_CASE_	, use_cache=SCREAMING_SNAKE_CASE_		)[0]
                  lowercase														=			torch.nn.functional.log_softmax(SCREAMING_SNAKE_CASE_	, dim=-1		)
                  lowercase							,							lowercase														=			self.loss_fn(SCREAMING_SNAKE_CASE_	, SCREAMING_SNAKE_CASE_	, self.args.label_smoothing	, ignore_index=self.config.pad_token_id		)
            return loss, logits

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			inputs.pop("""labels"""		)
            lowercase							,							lowercase														=			self._compute_loss(SCREAMING_SNAKE_CASE_	, SCREAMING_SNAKE_CASE_	, SCREAMING_SNAKE_CASE_		)
            return loss

      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase	, __lowerCAmelCase = None	, ):







            """simple docstring"""



            lowercase														=			self._prepare_inputs(SCREAMING_SNAKE_CASE_		)

            lowercase														=			{
                """max_length""": self.data_args.val_max_target_length
                if self.data_args is not None
                else self.config.max_length,
                """num_beams""": self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
            }

            if self.args.predict_with_generate and not self.args.prediction_loss_only:
                  lowercase														=			self.model.generate(
                      inputs["""input_ids"""]	, attention_mask=inputs["""attention_mask"""]	, **SCREAMING_SNAKE_CASE_	, )
                  # in case the batch is shorter than max length, the output should be padded
                  if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
                        lowercase														=			self._pad_tensors_to_max_len(SCREAMING_SNAKE_CASE_	, gen_kwargs["""max_length"""]		)

            lowercase														=			inputs.pop("""labels"""		)
            with torch.no_grad():
                  # compute loss on predict data
                  lowercase							,							lowercase														=			self._compute_loss(SCREAMING_SNAKE_CASE_	, SCREAMING_SNAKE_CASE_	, SCREAMING_SNAKE_CASE_		)

            lowercase														=			loss.mean().detach()
            if self.args.prediction_loss_only:
                  return (loss, None, None)

            lowercase														=			generated_tokens if self.args.predict_with_generate else logits

            if labels.shape[-1] < gen_kwargs["max_length"]:
                  lowercase														=			self._pad_tensors_to_max_len(SCREAMING_SNAKE_CASE_	, gen_kwargs["""max_length"""]		)

            return (loss, logits, labels)







      def        A__ (		self	, __lowerCAmelCase	, __lowerCAmelCase		):







            """simple docstring"""



            lowercase														=			self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id

            if pad_token_id is None:
                  raise ValueError(
                      """Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be"""
                      f' padded to `max_length`={max_length}'		)

            lowercase														=			pad_token_id * torch.ones(
                (tensor.shape[0], max_length)	, dtype=tensor.dtype	, device=tensor.device		)
            lowercase														=			tensor
            return padded_tensor




"""simple docstring"""






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     UpperCAmelCase__ (       lowerCAmelCase__  :str	)				->					Optional[int]:
      '''simple docstring'''


      return sum(param.float().sum() if """encoder.embeddings""" not in key else 0 for key, param in state_dict.items()	)


def     UpperCAmelCase__ (       lowerCAmelCase__  :Any      ,	lowerCAmelCase__  :Union[str, Any]	)				->					str:
      '''simple docstring'''


      lowercase														=			{}

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

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

            lowercase														=			value.float()

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

      return upgrade


@torch.no_grad()
def     UpperCAmelCase__ (       lowerCAmelCase__  :Union[str, Any]      ,	lowerCAmelCase__  :Dict      ,	lowerCAmelCase__  :Optional[int]      ,	lowerCAmelCase__  :str=None	)				->					int:
      '''simple docstring'''


      if config_path is not None:
            lowercase														=			FlavaConfig.from_pretrained(lowerCAmelCase__	)
      else:
            lowercase														=			FlavaConfig()

      lowercase														=			FlavaForPreTraining(lowerCAmelCase__	).eval()

      lowercase														=			convert_dalle_checkpoint(lowerCAmelCase__      ,	lowerCAmelCase__      ,	save_checkpoint=lowerCAmelCase__	)

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

      lowercase														=			upgrade_state_dict(lowerCAmelCase__      ,	lowerCAmelCase__	)
      hf_model.load_state_dict(lowerCAmelCase__	)
      lowercase														=			hf_model.state_dict()
      lowercase														=			count_parameters(lowerCAmelCase__	)
      lowercase														=			count_parameters(lowerCAmelCase__	) + count_parameters(lowerCAmelCase__	)

      assert torch.allclose(lowerCAmelCase__      ,	lowerCAmelCase__      ,	atol=1e-3	)

      hf_model.save_pretrained(lowerCAmelCase__	)


if __name__ == "__main__":
       __lowerCAmelCase					: Tuple        =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""")
       __lowerCAmelCase					: List[str]        =parser.parse_args()

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



