


"""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


a__    :							List[Any]            =					logging.get_logger(__name__)

a__    :							Optional[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 			UpperCamelCase_     (			_lowerCAmelCase):

	"""simple docstring"""





	def __init__(			self      :    Union[str, Any]      ,      UpperCAmelCase__      :    str=None      ,      UpperCAmelCase__      :    Tuple=None      ,      *UpperCAmelCase__      :    List[str]      ,      **UpperCAmelCase__      :    Dict       )	->     Dict:
						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__}"""
											)
											__SCREAMING_SNAKE_CASE						       =    self.model.config
						else:
											__SCREAMING_SNAKE_CASE						       =    config

						__SCREAMING_SNAKE_CASE						       =    data_args
						__SCREAMING_SNAKE_CASE						       =    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:
											__SCREAMING_SNAKE_CASE						       =    torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id       )
						else:
											# dynamically import label_smoothed_nll_loss
											from utils import label_smoothed_nll_loss

											__SCREAMING_SNAKE_CASE						       =    label_smoothed_nll_loss




	def   UpperCAmelCase_				(			self      :    Any      ,      UpperCAmelCase__      :    int       )	->     int:
						if self.optimizer is None:
											__SCREAMING_SNAKE_CASE						       =    ["bias", "LayerNorm.weight"]
											__SCREAMING_SNAKE_CASE						       =    [
											    {
											        "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,
											    },
											]
											__SCREAMING_SNAKE_CASE						       =    Adafactor if self.args.adafactor else AdamW
											if self.args.adafactor:
																__SCREAMING_SNAKE_CASE						       =    Adafactor
																__SCREAMING_SNAKE_CASE						       =    {"scale_parameter": False, "relative_step": False}
											else:
																__SCREAMING_SNAKE_CASE						       =    AdamW
																__SCREAMING_SNAKE_CASE						       =    {
																    "betas": (self.args.adam_betaa, self.args.adam_betaa),
																    "eps": self.args.adam_epsilon,
																}
											__SCREAMING_SNAKE_CASE						       =    self.args.learning_rate
											if self.sharded_ddp:
																__SCREAMING_SNAKE_CASE						       =    OSS(
																    params=_lowercase      ,      optim=_lowercase      ,      **_lowercase      ,      )
											else:
																__SCREAMING_SNAKE_CASE						       =    optimizer_cls(_lowercase      ,      **_lowercase       )

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




	def   UpperCAmelCase_				(			self      :    int      ,      UpperCAmelCase__      :    List[Any]       )	->     Tuple:
						__SCREAMING_SNAKE_CASE						       =    arg_to_scheduler[self.args.lr_scheduler]
						if self.args.lr_scheduler == "constant":
											__SCREAMING_SNAKE_CASE						       =    schedule_func(self.optimizer       )
						elif self.args.lr_scheduler == "constant_w_warmup":
											__SCREAMING_SNAKE_CASE						       =    schedule_func(self.optimizer      ,      num_warmup_steps=self.args.warmup_steps       )
						else:
											__SCREAMING_SNAKE_CASE						       =    schedule_func(
											    self.optimizer      ,      num_warmup_steps=self.args.warmup_steps      ,      num_training_steps=_lowercase       )
						return scheduler




	def   UpperCAmelCase_				(			self      :    Union[str, Any]       )	->     Any:
						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   UpperCAmelCase_				(			self      :    Optional[int]      ,      UpperCAmelCase__      :    str      ,      UpperCAmelCase__      :    Optional[Any]      ,      UpperCAmelCase__      :    Any       )	->     List[Any]:
						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
																__SCREAMING_SNAKE_CASE						       =    model(**_lowercase      ,      use_cache=_lowercase       )[0]
																__SCREAMING_SNAKE_CASE						       =    self.loss_fn(logits.view(-1      ,      logits.shape[-1]       )      ,      labels.view(-1       )       )
											else:
																# compute usual loss via models
																__SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE						       =    model(**_lowercase      ,      labels=_lowercase      ,      use_cache=_lowercase       )[:2]
						else:
											# compute label smoothed loss
											__SCREAMING_SNAKE_CASE						       =    model(**_lowercase      ,      use_cache=_lowercase       )[0]
											__SCREAMING_SNAKE_CASE						       =    torch.nn.functional.log_softmax(_lowercase      ,      dim=-1       )
											__SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE						       =    self.loss_fn(_lowercase      ,      _lowercase      ,      self.args.label_smoothing      ,      ignore_index=self.config.pad_token_id       )
						return loss, logits




	def   UpperCAmelCase_				(			self      :    Tuple      ,      UpperCAmelCase__      :    List[str]      ,      UpperCAmelCase__      :    int       )	->     Dict:
						__SCREAMING_SNAKE_CASE						       =    inputs.pop("labels"       )
						__SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE						       =    self._compute_loss(_lowercase      ,      _lowercase      ,      _lowercase       )
						return loss




	def   UpperCAmelCase_				(			self      :    Any      ,      UpperCAmelCase__      :    nn.Module      ,      UpperCAmelCase__      :    Dict[str, Union[torch.Tensor, Any]]      ,      UpperCAmelCase__      :    bool      ,      UpperCAmelCase__      :    Optional[List[str]] = None      ,      )	->     str:
						__SCREAMING_SNAKE_CASE						       =    self._prepare_inputs(_lowercase       )

						__SCREAMING_SNAKE_CASE						       =    {
						    "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:
											__SCREAMING_SNAKE_CASE						       =    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"]:
																__SCREAMING_SNAKE_CASE						       =    self._pad_tensors_to_max_len(_lowercase      ,      gen_kwargs["max_length"]       )

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

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

						__SCREAMING_SNAKE_CASE						       =    generated_tokens if self.args.predict_with_generate else logits

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

						return (loss, logits, labels)




	def   UpperCAmelCase_				(			self      :    Union[str, Any]      ,      UpperCAmelCase__      :    Union[str, Any]      ,      UpperCAmelCase__      :    Any       )	->     Union[str, Any]:
						# If PAD token is not defined at least EOS token has to be defined
						__SCREAMING_SNAKE_CASE						       =    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}"""       )

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




"""simple docstring"""







import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


a__    :							Union[str, Any]            =					logging.get_logger(__name__)




def        UpperCAmelCase__	(lowerCAmelCase_       ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						       =    R"\w+[.]\d+"
					__SCREAMING_SNAKE_CASE						       =    re.findall(lowerCAmelCase_  ,						lowerCAmelCase_       )
					for pat in pats:
										__SCREAMING_SNAKE_CASE						       =    key.replace(lowerCAmelCase_  ,						"_".join(pat.split("."       )       )       )
					return key




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_       ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						       =    pt_tuple_key[:-1] + ("scale",)
					if (
					    any("norm" in str_ for str_ in pt_tuple_key       )
					    and (pt_tuple_key[-1] == "bias")
					    and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
					    and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
					):
										__SCREAMING_SNAKE_CASE						       =    pt_tuple_key[:-1] + ("scale",)
										return renamed_pt_tuple_key, pt_tensor
					elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
										__SCREAMING_SNAKE_CASE						       =    pt_tuple_key[:-1] + ("scale",)
										return renamed_pt_tuple_key, pt_tensor

					# embedding
					if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
										__SCREAMING_SNAKE_CASE						       =    pt_tuple_key[:-1] + ("embedding",)
										return renamed_pt_tuple_key, pt_tensor

					# conv layer
					__SCREAMING_SNAKE_CASE						       =    pt_tuple_key[:-1] + ("kernel",)
					if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
										__SCREAMING_SNAKE_CASE						       =    pt_tensor.transpose(2  ,						3  ,						1  ,						0       )
										return renamed_pt_tuple_key, pt_tensor

					# linear layer
					__SCREAMING_SNAKE_CASE						       =    pt_tuple_key[:-1] + ("kernel",)
					if pt_tuple_key[-1] == "weight":
										__SCREAMING_SNAKE_CASE						       =    pt_tensor.T
										return renamed_pt_tuple_key, pt_tensor

					# old PyTorch layer norm weight
					__SCREAMING_SNAKE_CASE						       =    pt_tuple_key[:-1] + ("weight",)
					if pt_tuple_key[-1] == "gamma":
										return renamed_pt_tuple_key, pt_tensor

					# old PyTorch layer norm bias
					__SCREAMING_SNAKE_CASE						       =    pt_tuple_key[:-1] + ("bias",)
					if pt_tuple_key[-1] == "beta":
										return renamed_pt_tuple_key, pt_tensor

					return pt_tuple_key, pt_tensor




def        UpperCAmelCase__	(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_=42       ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						       =    {k: v.numpy() for k, v in pt_state_dict.items()}

					# Step 2: Since the model is stateless, get random Flax params
					__SCREAMING_SNAKE_CASE						       =    flax_model.init_weights(PRNGKey(lowerCAmelCase_       )       )

					__SCREAMING_SNAKE_CASE						       =    flatten_dict(lowerCAmelCase_       )
					__SCREAMING_SNAKE_CASE						       =    {}

					# Need to change some parameters name to match Flax names
					for pt_key, pt_tensor in pt_state_dict.items():
										__SCREAMING_SNAKE_CASE						       =    rename_key(lowerCAmelCase_       )
										__SCREAMING_SNAKE_CASE						       =    tuple(renamed_pt_key.split("."       )       )

										# Correctly rename weight parameters
										__SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE						       =    rename_key_and_reshape_tensor(lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_       )

										if flax_key in random_flax_state_dict:
															if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
																				raise ValueError(
																				    f"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
																				    f"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."""       )

        # also add unexpected weight so that warning is thrown
										__SCREAMING_SNAKE_CASE						       =    jnp.asarray(lowerCAmelCase_       )

					return unflatten_dict(lowerCAmelCase_       )
