



'''simple docstring'''







import os
from dataclasses import replace

import jax
import wandb
from bigbird_flax import Args, DataCollator, FlaxBigBirdForNaturalQuestions, Trainer, build_tx, train_step, val_step
from datasets import load_dataset
from flax import jax_utils

from transformers import BigBirdTokenizerFast


if __name__ == "__main__":
      print('#################### AVAILABLE DEVICES ####################')
      print(jax.devices())
      print('###########################################################')

      # setup for wandb sweep
      lowercase__     :  int    					=			Args()
      lowercase__     :  int    					=			wandb.init(project='bigbird-natural-questions', config=args.__dict__)
      lowercase__     :  int    					=			dict(logger.config)
      del wandb_args["batch_size"]
      lowercase__     :  Any    					=			replace(args, **wandb_args)
      lowercase__     :  List[Any]    					=			args.base_dir + '-' + wandb.run.id
      lowercase__     :  Optional[Any]    					=			replace(args, base_dir=base_dir)
      print(args)

      lowercase__     :  Any    					=			load_dataset('json', data_files=args.tr_data_path)['train']
      lowercase__     :  Tuple    					=			load_dataset('json', data_files=args.val_data_path)['train']

      # drop extra batch for now
      lowercase__     :  Union[str, Any]    					=			range(len(tr_dataset) - len(tr_dataset) % args.batch_size)
      lowercase__     :  Union[str, Any]    					=			tr_dataset.shuffle().select(indices)
      lowercase__     :  str    					=			range(len(val_dataset) - len(val_dataset) % args.batch_size)
      lowercase__     :  int    					=			val_dataset.shuffle().select(indices)

      if os.environ.get('TRAIN_ON_SMALL', 'false') == "true":
            lowercase__     :  List[Any]    					=			tr_dataset.shuffle().select(range(8_00_00))
            lowercase__     :  str    					=			val_dataset.shuffle().select(range(80_00))

      print(tr_dataset)
      print(val_dataset)

      lowercase__     :  str    					=			FlaxBigBirdForNaturalQuestions.from_pretrained(
          args.model_id, block_size=args.block_size, num_random_blocks=args.num_random_blocks
      )
      lowercase__     :  int    					=			BigBirdTokenizerFast.from_pretrained(args.model_id)
      lowercase__     :  Union[str, Any]    					=			DataCollator(pad_id=tokenizer.pad_token_id, max_length=40_96)

      lowercase__     :  Any    					=			{
          'lr': args.lr,
          'init_lr': args.init_lr,
          'warmup_steps': args.warmup_steps,
          'num_train_steps': args.max_epochs * (len(tr_dataset) // args.batch_size),
          'weight_decay': args.weight_decay,
      }
      lowercase__					,  lowercase__     :  Optional[Any]    					=			build_tx(**tx_args)

      lowercase__     :  Union[str, Any]    					=			Trainer(
          args=args,
          data_collator=data_collator,
          model_save_fn=model.save_pretrained,
          train_step_fn=train_step,
          val_step_fn=val_step,
          logger=logger,
          scheduler_fn=lr,
      )

      lowercase__     :  Tuple    					=			None
      lowercase__     :  List[Any]    					=			trainer.create_state(model, tx, num_train_steps=tx_args['num_train_steps'], ckpt_dir=ckpt_dir)
      try:
            trainer.train(state, tr_dataset, val_dataset)
      except KeyboardInterrupt:
            print('Oooops; TRAINING STOPPED UNFORTUNATELY')

      print('SAVING WEIGHTS IN `final-weights`')
      lowercase__     :  Union[str, Any]    					=			jax_utils.unreplicate(state.params)
      model.save_pretrained(os.path.join(args.base_dir, 'final-weights'), params=params)









'''simple docstring'''







import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig





def 						snake_case__ (lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_	):
    UpperCamelCase__      =			TaConfig.from_pretrained(lowerCAmelCase_	)
    UpperCamelCase__      =			FlaxTaForConditionalGeneration(config=lowerCAmelCase_	)
    UpperCamelCase__      =			checkpoints.load_tax_checkpoint(lowerCAmelCase_	)

    UpperCamelCase__      =			"wi_0" in tax_model["target"]["encoder"]["layers_0"]["mlp"]

    # Encoder
    for layer_index in range(config.num_layers	):
        UpperCamelCase__      =			F'layers_{str(lowerCAmelCase_	)}'

        # Self-Attention
        UpperCamelCase__      =			tax_model["target"]["encoder"][layer_name]["attention"]["key"]["kernel"]
        UpperCamelCase__      =			tax_model["target"]["encoder"][layer_name]["attention"]["out"]["kernel"]
        UpperCamelCase__      =			tax_model["target"]["encoder"][layer_name]["attention"]["query"]["kernel"]
        UpperCamelCase__      =			tax_model["target"]["encoder"][layer_name]["attention"]["value"]["kernel"]

        # Layer Normalization
        UpperCamelCase__      =			tax_model["target"]["encoder"][layer_name]["pre_attention_layer_norm"]["scale"]

        if split_mlp_wi:
            UpperCamelCase__      =			tax_model["target"]["encoder"][layer_name]["mlp"]["wi_0"]["kernel"]
            UpperCamelCase__      =			tax_model["target"]["encoder"][layer_name]["mlp"]["wi_1"]["kernel"]
        else:
            UpperCamelCase__      =			tax_model["target"]["encoder"][layer_name]["mlp"]["wi"]["kernel"]

        UpperCamelCase__      =			tax_model["target"]["encoder"][layer_name]["mlp"]["wo"]["kernel"]

        # Layer Normalization
        UpperCamelCase__      =			tax_model["target"]["encoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

        # Assigning
        UpperCamelCase__      =			tax_attention_key
        UpperCamelCase__      =			tax_attention_out
        UpperCamelCase__      =			tax_attention_query
        UpperCamelCase__      =			tax_attention_value

        UpperCamelCase__      =			tax_attention_layer_norm

        if split_mlp_wi:
            UpperCamelCase__      =			tax_mlp_wi_a
            UpperCamelCase__      =			tax_mlp_wi_a
        else:
            UpperCamelCase__      =			tax_mlp_wi

        UpperCamelCase__      =			tax_mlp_wo
        UpperCamelCase__      =			tax_mlp_layer_norm

    # Only for layer 0:
    UpperCamelCase__      =			tax_model["target"]["encoder"]["relpos_bias"]["rel_embedding"].T
    UpperCamelCase__      =			tax_encoder_rel_embedding

    # Assigning
    UpperCamelCase__      =			tax_model["target"]["encoder"]["encoder_norm"]["scale"]
    UpperCamelCase__      =			tax_encoder_norm

    # Decoder
    for layer_index in range(config.num_decoder_layers	):
        UpperCamelCase__      =			F'layers_{str(lowerCAmelCase_	)}'

        # Self-Attention
        UpperCamelCase__      =			tax_model["target"]["decoder"][layer_name]["self_attention"]["key"]["kernel"]
        UpperCamelCase__      =			tax_model["target"]["decoder"][layer_name]["self_attention"]["out"]["kernel"]
        UpperCamelCase__      =			tax_model["target"]["decoder"][layer_name]["self_attention"]["query"]["kernel"]
        UpperCamelCase__      =			tax_model["target"]["decoder"][layer_name]["self_attention"]["value"]["kernel"]

        # Layer Normalization
        UpperCamelCase__      =			tax_model["target"]["decoder"][layer_name]["pre_self_attention_layer_norm"][
            "scale"
        ]

        # Encoder-Decoder-Attention
        UpperCamelCase__      =			tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["key"][
            "kernel"
        ]
        UpperCamelCase__      =			tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["out"][
            "kernel"
        ]
        UpperCamelCase__      =			tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["query"][
            "kernel"
        ]
        UpperCamelCase__      =			tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["value"][
            "kernel"
        ]

        # Layer Normalization
        UpperCamelCase__      =			tax_model["target"]["decoder"][layer_name]["pre_cross_attention_layer_norm"]["scale"]

        # MLP
        if split_mlp_wi:
            UpperCamelCase__      =			tax_model["target"]["decoder"][layer_name]["mlp"]["wi_0"]["kernel"]
            UpperCamelCase__      =			tax_model["target"]["decoder"][layer_name]["mlp"]["wi_1"]["kernel"]
        else:
            UpperCamelCase__      =			tax_model["target"]["decoder"][layer_name]["mlp"]["wi"]["kernel"]

        UpperCamelCase__      =			tax_model["target"]["decoder"][layer_name]["mlp"]["wo"]["kernel"]

        # Layer Normalization
        UpperCamelCase__      =			tax_model["target"]["decoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

        # Assigning
        UpperCamelCase__      =			tax_attention_key
        UpperCamelCase__      =			tax_attention_out
        UpperCamelCase__      =			tax_attention_query
        UpperCamelCase__      =			tax_attention_value

        UpperCamelCase__      =			tax_pre_attention_layer_norm

        UpperCamelCase__      =			tax_enc_dec_attention_key
        UpperCamelCase__      =			tax_enc_dec_attention_out
        UpperCamelCase__      =			tax_enc_dec_attention_query
        UpperCamelCase__      =			tax_enc_dec_attention_value

        UpperCamelCase__      =			tax_cross_layer_norm

        if split_mlp_wi:
            UpperCamelCase__      =			tax_mlp_wi_a
            UpperCamelCase__      =			tax_mlp_wi_a
        else:
            UpperCamelCase__      =			tax_mlp_wi

        UpperCamelCase__      =			tax_mlp_wo

        UpperCamelCase__      =			txa_mlp_layer_norm

    # Decoder Normalization
    UpperCamelCase__      =			tax_model["target"]["decoder"]["decoder_norm"]["scale"]
    UpperCamelCase__      =			txa_decoder_norm

    # Only for layer 0:
    UpperCamelCase__      =			tax_model["target"]["decoder"]["relpos_bias"]["rel_embedding"].T
    UpperCamelCase__      =			tax_decoder_rel_embedding

    # Token Embeddings
    UpperCamelCase__      =			tax_model["target"]["token_embedder"]["embedding"]
    UpperCamelCase__      =			txa_token_embeddings

    # LM Head (only in v1.1 checkpoints)
    if "logits_dense" in tax_model["target"]["decoder"]:
        UpperCamelCase__      =			tax_model["target"]["decoder"]["logits_dense"]["kernel"]

    flax_model.save_pretrained(lowerCAmelCase_	)
    print("T5X Model was sucessfully converted!"	)


if __name__ == "__main__":
      lowercase__     :  Any    					=			argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '--t5x_checkpoint_path', default=None, type=str, required=True, help='Path the TX5 checkpoint.'
      )
      parser.add_argument('--config_name', default=None, type=str, required=True, help='Config name of T5 model.')
      parser.add_argument(
          '--flax_dump_folder_path', default=None, type=str, required=True, help='Path to the output FLAX model.'
      )
      lowercase__     :  int    					=			parser.parse_args()
      convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)




