

"""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


lowercase_			=  logging.get_logger(__name__)

lowercase_			=  {
    "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						(							a_			):







    """simple docstring"""





    def __init__(				self			:	Union[str, Any],lowercase_			:	Dict=None,lowercase_			:	Dict=None,*lowercase_			:	Optional[Any],**lowercase_			:	Optional[int]		)->   Optional[int]:





        '''simple docstring'''







        super().__init__(*lowercase_,**lowercase_		)

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

        A__   				=       data_args
        A__   				=       self.config.tgt_vocab_size if isinstance(self.config,lowercase_		) 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:
            A__   				=       torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id		)
        else:
            # dynamically import label_smoothed_nll_loss
            from utils import label_smoothed_nll_loss

            A__   				=       label_smoothed_nll_loss


    def 						snake_case__					(				self			:	Tuple,lowercase_			:	str		)->   List[str]:





        '''simple docstring'''







        if self.optimizer is None:
            A__   				=       ['bias', 'LayerNorm.weight']
            A__   				=       [
                {
                    '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,
                },
            ]
            A__   				=       Adafactor if self.args.adafactor else AdamW
            if self.args.adafactor:
                A__   				=       Adafactor
                A__   				=       {'scale_parameter': False, 'relative_step': False}
            else:
                A__   				=       AdamW
                A__   				=       {
                    'betas': (self.args.adam_betaa, self.args.adam_betaa),
                    'eps': self.args.adam_epsilon,
                }
            A__   				=       self.args.learning_rate
            if self.sharded_ddp:
                A__   				=       OSS(
                    params=lowercase_,optim=lowercase_,**lowercase_,)
            else:
                A__   				=       optimizer_cls(lowercase_,**lowercase_		)

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


    def 						snake_case__					(				self			:	List[Any],lowercase_			:	Dict		)->   Optional[Any]:





        '''simple docstring'''







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


    def 						snake_case__					(				self			:	List[str]		)->   Optional[torch.utils.data.Sampler]:





        '''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 						snake_case__					(				self			:	Any,lowercase_			:	Optional[Any],lowercase_			:	List[str],lowercase_			:	str		)->   Dict:





        '''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
                A__   				=       model(**lowercase_,use_cache=lowercase_		)[0]
                A__   				=       self.loss_fn(logits.view(-1,logits.shape[-1]		),labels.view(-1		)		)
            else:
                # compute usual loss via models
                A__				,						A__   				=       model(**lowercase_,labels=lowercase_,use_cache=lowercase_		)[:2]
        else:
            # compute label smoothed loss
            A__   				=       model(**lowercase_,use_cache=lowercase_		)[0]
            A__   				=       torch.nn.functional.log_softmax(lowercase_,dim=-1		)
            A__				,						A__   				=       self.loss_fn(lowercase_,lowercase_,self.args.label_smoothing,ignore_index=self.config.pad_token_id		)
        return loss, logits


    def 						snake_case__					(				self			:	List[Any],lowercase_			:	List[str],lowercase_			:	Optional[Any]		)->   Dict:





        '''simple docstring'''







        A__   				=       inputs.pop('labels'		)
        A__				,						A__   				=       self._compute_loss(lowercase_,lowercase_,lowercase_		)
        return loss


    def 						snake_case__					(				self			:	str,lowercase_			:	List[str],lowercase_			:	Tuple,lowercase_			:	List[Any],lowercase_			:	str = None,)->   Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:





        '''simple docstring'''







        A__   				=       self._prepare_inputs(lowercase_		)

        A__   				=       {
            '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:
            A__   				=       self.model.generate(
                inputs['input_ids'],attention_mask=inputs['attention_mask'],**lowercase_,)
            # in case the batch is shorter than max length, the output should be padded
            if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
                A__   				=       self._pad_tensors_to_max_len(lowercase_,gen_kwargs['max_length']		)

        A__   				=       inputs.pop('labels'		)
        with torch.no_grad():
            # compute loss on predict data
            A__				,						A__   				=       self._compute_loss(lowercase_,lowercase_,lowercase_		)

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

        A__   				=       generated_tokens if self.args.predict_with_generate else logits

        if labels.shape[-1] < gen_kwargs["max_length"]:
            A__   				=       self._pad_tensors_to_max_len(lowercase_,gen_kwargs['max_length']		)

        return (loss, logits, labels)


    def 						snake_case__					(				self			:	List[str],lowercase_			:	List[str],lowercase_			:	List[Any]		)->   Union[str, Any]:





        '''simple docstring'''







        A__   				=       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}'		)

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




import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     List[Any]   ,      SCREAMING_SNAKE_CASE__      :     List[str]   ,      SCREAMING_SNAKE_CASE__      :     Optional[int]   ,      SCREAMING_SNAKE_CASE__      :     Optional[int]="attention"				)							->				Union[str, Any]:





    '''simple docstring'''






    A__   				=       params[f'{prefix}/layers_{i}/{layer_name}/key/kernel']
    A__   				=       params[f'{prefix}/layers_{i}/{layer_name}/out/kernel']
    A__   				=       params[f'{prefix}/layers_{i}/{layer_name}/query/kernel']
    A__   				=       params[f'{prefix}/layers_{i}/{layer_name}/value/kernel']
    return k, o, q, v
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Any   ,      SCREAMING_SNAKE_CASE__      :     Tuple   ,      SCREAMING_SNAKE_CASE__      :     Optional[Any]   ,      SCREAMING_SNAKE_CASE__      :     Dict=False				)							->				str:





    '''simple docstring'''






    if split_mlp_wi:
        A__   				=       params[f'{prefix}/layers_{i}/mlp/wi_0/kernel']
        A__   				=       params[f'{prefix}/layers_{i}/mlp/wi_1/kernel']
        A__   				=       (wi_a, wi_a)
    else:
        A__   				=       params[f'{prefix}/layers_{i}/mlp/wi/kernel']

    A__   				=       params[f'{prefix}/layers_{i}/mlp/wo/kernel']
    return wi, wo
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Optional[Any]   ,      SCREAMING_SNAKE_CASE__      :     Tuple   ,      SCREAMING_SNAKE_CASE__      :     Dict   ,      SCREAMING_SNAKE_CASE__      :     int				)							->				str:





    '''simple docstring'''






    return params[f'{prefix}/layers_{i}/{layer_name}/scale']
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     dict   ,      *, SCREAMING_SNAKE_CASE__      :     int   ,      SCREAMING_SNAKE_CASE__      :     bool				)							->				int:





    '''simple docstring'''






    A__   				=       traverse_util.flatten_dict(variables['target']				)
    A__   				=       {'/'.join(SCREAMING_SNAKE_CASE__				): v for k, v in old.items()}

    # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
    A__   				=       'encoder/layers_0/mlp/wi_0/kernel' in old
    print('Split MLP:'   ,      SCREAMING_SNAKE_CASE__				)

    A__   				=       collections.OrderedDict()

    # Shared embeddings.
    A__   				=       old['token_embedder/embedding']

    # Encoder.
    for i in range(SCREAMING_SNAKE_CASE__				):
        # Block i, layer 0 (Self Attention).
        A__   				=       tax_layer_norm_lookup(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      'encoder'   ,      'pre_attention_layer_norm'				)
        A__				,						A__				,						A__				,						A__   				=       tax_attention_lookup(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      'encoder'   ,      'attention'				)
        A__   				=       layer_norm
        A__   				=       k.T
        A__   				=       o.T
        A__   				=       q.T
        A__   				=       v.T

        # Block i, layer 1 (MLP).
        A__   				=       tax_layer_norm_lookup(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      'encoder'   ,      'pre_mlp_layer_norm'				)
        A__				,						A__   				=       tax_mlp_lookup(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      'encoder'   ,      SCREAMING_SNAKE_CASE__				)
        A__   				=       layer_norm
        if split_mlp_wi:
            A__   				=       wi[0].T
            A__   				=       wi[1].T
        else:
            A__   				=       wi.T
        A__   				=       wo.T

    A__   				=       old[
        'encoder/relpos_bias/rel_embedding'
    ].T
    A__   				=       old['encoder/encoder_norm/scale']

    if not is_encoder_only:
        # Decoder.
        for i in range(SCREAMING_SNAKE_CASE__				):
            # Block i, layer 0 (Self Attention).
            A__   				=       tax_layer_norm_lookup(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      'decoder'   ,      'pre_self_attention_layer_norm'				)
            A__				,						A__				,						A__				,						A__   				=       tax_attention_lookup(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      'decoder'   ,      'self_attention'				)
            A__   				=       layer_norm
            A__   				=       k.T
            A__   				=       o.T
            A__   				=       q.T
            A__   				=       v.T

            # Block i, layer 1 (Cross Attention).
            A__   				=       tax_layer_norm_lookup(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      'decoder'   ,      'pre_cross_attention_layer_norm'				)
            A__				,						A__				,						A__				,						A__   				=       tax_attention_lookup(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      'decoder'   ,      'encoder_decoder_attention'				)
            A__   				=       layer_norm
            A__   				=       k.T
            A__   				=       o.T
            A__   				=       q.T
            A__   				=       v.T

            # Block i, layer 2 (MLP).
            A__   				=       tax_layer_norm_lookup(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      'decoder'   ,      'pre_mlp_layer_norm'				)
            A__				,						A__   				=       tax_mlp_lookup(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      'decoder'   ,      SCREAMING_SNAKE_CASE__				)
            A__   				=       layer_norm
            if split_mlp_wi:
                A__   				=       wi[0].T
                A__   				=       wi[1].T
            else:
                A__   				=       wi.T
            A__   				=       wo.T

        A__   				=       old['decoder/decoder_norm/scale']
        A__   				=       old[
            'decoder/relpos_bias/rel_embedding'
        ].T

        # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
        if "decoder/logits_dense/kernel" in old:
            A__   				=       old['decoder/logits_dense/kernel'].T

    return new
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Optional[Any]   ,      SCREAMING_SNAKE_CASE__      :     bool				)							->				Dict:





    '''simple docstring'''






    A__   				=       collections.OrderedDict([(k, torch.from_numpy(v.copy()				)) for (k, v) in converted_params.items()]				)

    # Add what is missing.
    if "encoder.embed_tokens.weight" not in state_dict:
        A__   				=       state_dict['shared.weight']

    if not is_encoder_only:
        if "decoder.embed_tokens.weight" not in state_dict:
            A__   				=       state_dict['shared.weight']

        if "lm_head.weight" not in state_dict:  # For old 1.0 models.
            print('Using shared word embeddings as lm_head.'				)
            A__   				=       state_dict['shared.weight']

    return state_dict
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Union[str, Any]   ,      SCREAMING_SNAKE_CASE__      :     Any   ,      SCREAMING_SNAKE_CASE__      :     Any   ,      SCREAMING_SNAKE_CASE__      :     Tuple				)							->				int:





    '''simple docstring'''






    A__   				=       checkpoints.load_tax_checkpoint(SCREAMING_SNAKE_CASE__				)
    A__   				=       convert_tax_to_pytorch(SCREAMING_SNAKE_CASE__   ,      num_layers=config.num_layers   ,      is_encoder_only=SCREAMING_SNAKE_CASE__				)
    A__   				=       make_state_dict(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)
    model.load_state_dict(SCREAMING_SNAKE_CASE__   ,      strict=SCREAMING_SNAKE_CASE__				)
def 						_snake_case(   SCREAMING_SNAKE_CASE__      :     Optional[int]   ,      SCREAMING_SNAKE_CASE__      :     List[Any]   ,      SCREAMING_SNAKE_CASE__      :     Tuple   ,      SCREAMING_SNAKE_CASE__      :     bool = False				)							->				Any:





    '''simple docstring'''






    A__   				=       TaConfig.from_json_file(SCREAMING_SNAKE_CASE__				)
    print(f'Building PyTorch model from configuration: {config}'				)
    # Non-v1.1 checkpoints could also use T5Model, but this works for all.
    # The v1.0 checkpoints will simply have an LM head that is the word embeddings.
    if is_encoder_only:
        A__   				=       TaEncoderModel(SCREAMING_SNAKE_CASE__				)
    else:
        A__   				=       TaForConditionalGeneration(SCREAMING_SNAKE_CASE__				)

    # Load weights from tf checkpoint
    load_tax_weights_in_ta(SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__   ,      SCREAMING_SNAKE_CASE__				)

    # Save pytorch-model
    print(f'Save PyTorch model to {pytorch_dump_path}'				)
    model.save_pretrained(SCREAMING_SNAKE_CASE__				)

    # Verify that we can load the checkpoint.
    model.from_pretrained(SCREAMING_SNAKE_CASE__				)
    print('Done'				)


if __name__ == "__main__":
     lowercase_			=  argparse.ArgumentParser(description="Converts a native T5X checkpoint into a PyTorch checkpoint.")
     # Required parameters
     parser.add_argument(
         "--t5x_checkpoint_path", default=None, type=str, required=True, help="Path to the T5X checkpoint."
     )
     parser.add_argument(
         "--config_file",
         default=None,
         type=str,
         required=True,
         help="The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.",
     )
     parser.add_argument(
         "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
     )
     parser.add_argument(
         "--is_encoder_only", action="store_true", help="Check if the model is encoder-decoder model", default=False
     )
     lowercase_			=  parser.parse_args()
     convert_tax_checkpoint_to_pytorch(
         args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
     )

