
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


SCREAMING_SNAKE_CASE							:int							 =				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							:int							 =				{
    '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      (  snake_case_  ):




	'''simple docstring'''





	def __init__(       self					: str							,A					: Any=None							,A					: Optional[int]=None							,*A					: Dict							,**A					: Union[str, Any] ):
					super().__init__(*A							,**A )

					if config is None:
									assert isinstance(self.model							,A ), (
									    "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							,A ) 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 				UpperCamelCase_  (       self					: Optional[int]							,A					: Union[str, Any] ):
					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=A							,optim=A							,**A							,)
									else:
													__A   			= optimizer_cls(A							,**A )

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






	def 				UpperCamelCase_  (       self					: Union[str, Any]							,A					: Tuple ):
					__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=A )
					return scheduler






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






	def 				UpperCamelCase_  (       self					: List[str]							,A					: Optional[int]							,A					: int ):
					__A   			= inputs.pop("labels" )
					__A			,							__A   			= self._compute_loss(A							,A							,A )
					return loss






	def 				UpperCamelCase_  (       self					: int							,A					: str							,A					: List[Any]							,A					: List[Any]							,A					: str = None							,):
					__A   			= self._prepare_inputs(A )

					__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"]							,**A							,)
									# 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(A							,gen_kwargs["max_length"] )

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

					__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(A							,gen_kwargs["max_length"] )

					return (loss, logits, labels)







	def 				UpperCamelCase_  (       self					: Union[str, Any]							,A					: Optional[Any]							,A					: Tuple ):
					# If PAD token is not defined at least EOS token has to be defined
					__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







'''simple docstring'''

from unittest import TestCase

from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset


class 					a_     (				snake_case_       ):

    '''simple docstring'''


    def 			snake_case_(		self       )		->   Tuple:
        return [
            {"col_1": 3, "col_2": "a"},
            {"col_1": 2, "col_2": "b"},
            {"col_1": 1, "col_2": "c"},
            {"col_1": 0, "col_2": "d"},
        ]


    def 			snake_case_(		self       )		->   Optional[int]:
        _SCREAMING_SNAKE_CASE									=			{"""col_1""": [3, 2, 1, 0], """col_2""": ["""a""", """b""", """c""", """d"""]}
        return Dataset.from_dict(A       )


    def 			snake_case_(		self       )		->   str:
        _SCREAMING_SNAKE_CASE									=			self._create_example_records()
        _SCREAMING_SNAKE_CASE									=			Dataset.from_list(A       )
        self.assertListEqual(dset.column_names ,		["""col_1""", """col_2"""]       )
        for i, r in enumerate(A       ):
            self.assertDictEqual(A ,		example_records[i]       )


    def 			snake_case_(		self       )		->   str:
        _SCREAMING_SNAKE_CASE									=			self._create_example_records()
        _SCREAMING_SNAKE_CASE									=			Dataset.from_list(A       )
        _SCREAMING_SNAKE_CASE									=			Dataset.from_dict({k: [r[k] for r in example_records] for k in example_records[0]}       )
        self.assertEqual(dset.info ,		dset_from_dict.info       )


    def 			snake_case_(		self       )		->   Union[str, Any]:  # checks what happens with missing columns
        _SCREAMING_SNAKE_CASE									=			[{"""col_1""": 1}, {"""col_2""": """x"""}]
        _SCREAMING_SNAKE_CASE									=			Dataset.from_list(A       )
        self.assertDictEqual(dset[0] ,		{"""col_1""": 1}       )
        self.assertDictEqual(dset[1] ,		{"""col_1""": None}       )  # NB: first record is used for columns


    def 			snake_case_(		self       )		->   Optional[Any]:  # checks if the type can be inferred from the second record
        _SCREAMING_SNAKE_CASE									=			[{"""col_1""": []}, {"""col_1""": [1, 2]}]
        _SCREAMING_SNAKE_CASE									=			Dataset.from_list(A       )
        self.assertEqual(dset.info.features["""col_1"""] ,		Sequence(Value("""int64"""       )       )       )





    def 			snake_case_(		self       )		->   str:
        _SCREAMING_SNAKE_CASE									=			Dataset.from_list([]       )
        self.assertEqual(len(A       ) ,		0       )
        self.assertListEqual(dset.column_names ,		[]       )



