'''simple docstring'''




from argparse import ArgumentParser

from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand


__SCREAMING_SNAKE_CASE       :     Any     =  logging.get_logger(__name__)  # pylint: disable=invalid-name

def 	UpperCamelCase_							(					_UpperCAmelCase  :      str    )    ->    Dict:


       """simple docstring"""







       if not path:
              return "pipe"

       for ext in PipelineDataFormat.SUPPORTED_FORMATS:
              if path.endswith(_UpperCAmelCase    ):
                     return ext

       raise Exception(
           F"""Unable to determine file format from file extension {path}. """
           F"""Please provide the format through --format {PipelineDataFormat.SUPPORTED_FORMATS}"""    )

def 	UpperCamelCase_							(					_UpperCAmelCase  :      int    )    ->    Tuple:


       """simple docstring"""







       _UpperCAmelCase     :  Optional[Any]							      =    pipeline(
           task=args.task			,	model=args.model if args.model else None			,	config=args.config			,	tokenizer=args.tokenizer			,	device=args.device			,	)
       _UpperCAmelCase     :  Optional[Any]							      =    try_infer_format_from_ext(args.input    ) if args.format == "infer" else args.format
       _UpperCAmelCase     :  List[Any]							      =    PipelineDataFormat.from_str(
           format=_UpperCAmelCase			,	output_path=args.output			,	input_path=args.input			,	column=args.column if args.column else nlp.default_input_names			,	overwrite=args.overwrite			,	)
       return RunCommand(_UpperCAmelCase			,	_UpperCAmelCase    )


class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      def __init__(       self						:				str						,					A						:				Pipeline						,					A						:				PipelineDataFormat       ):
             _UpperCAmelCase     :  Any							      =    nlp
             _UpperCAmelCase     :  List[Any]							      =    reader




      @staticmethod
      def      _A	(       A						:				ArgumentParser       ):
             _UpperCAmelCase     :  List[str]							      =    parser.add_parser("run"						,					help="Run a pipeline through the CLI"       )
             run_parser.add_argument("--task"						,					choices=get_supported_tasks()						,					help="Task to run"       )
             run_parser.add_argument("--input"						,					type=A						,					help="Path to the file to use for inference"       )
             run_parser.add_argument("--output"						,					type=A						,					help="Path to the file that will be used post to write results."       )
             run_parser.add_argument("--model"						,					type=A						,					help="Name or path to the model to instantiate."       )
             run_parser.add_argument("--config"						,					type=A						,					help="Name or path to the model's config to instantiate."       )
             run_parser.add_argument(
                 "--tokenizer"						,					type=A						,					help="Name of the tokenizer to use. (default: same as the model name)"       )
             run_parser.add_argument(
                 "--column"						,					type=A						,					help="Name of the column to use as input. (For multi columns input as QA use column1,columns2)"						,					)
             run_parser.add_argument(
                 "--format"						,					type=A						,					default="infer"						,					choices=PipelineDataFormat.SUPPORTED_FORMATS						,					help="Input format to read from"						,					)
             run_parser.add_argument(
                 "--device"						,					type=A						,					default=-1						,					help="Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)"						,					)
             run_parser.add_argument("--overwrite"						,					action="store_true"						,					help="Allow overwriting the output file."       )
             run_parser.set_defaults(func=A       )





      def      _A	(       self						:				int       ):
             _UpperCAmelCase      ,  _UpperCAmelCase     :  Dict							      =    self._nlp, []

             for entry in self._reader:
                    _UpperCAmelCase     :  Optional[int]							      =    nlp(**A       ) if self._reader.is_multi_columns else nlp(A       )
                    if isinstance(A						,					A       ):
                           outputs.append(A       )
                    else:
                           outputs += output

        # Saving data
             if self._nlp.binary_output:
                    _UpperCAmelCase     :  Dict							      =    self._reader.save_binary(A       )
                    logger.warning(F"""Current pipeline requires output to be in binary format, saving at {binary_path}"""       )
             else:
                    self._reader.save(A       )


'''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


__SCREAMING_SNAKE_CASE       :     str     =  logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE       :     Union[str, 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 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      def __init__(       self						:				Any						,					A						:				Optional[int]=None						,					A						:				Tuple=None						,					*A						:				Tuple						,					**A						:				List[str]       ):
             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__}"""
                    )
                    _UpperCAmelCase     :  str							      =    self.model.config
             else:
                    _UpperCAmelCase     :  List[str]							      =    config

             _UpperCAmelCase     :  List[Any]							      =    data_args
             _UpperCAmelCase     :  str							      =    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:
                    _UpperCAmelCase     :  Optional[Any]							      =    torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id       )
             else:
                    # dynamically import label_smoothed_nll_loss
                    from utils import label_smoothed_nll_loss

                    _UpperCAmelCase     :  Dict							      =    label_smoothed_nll_loss




      def      _A	(       self						:				Tuple						,					A						:				int       ):
             if self.optimizer is None:
                    _UpperCAmelCase     :  Tuple							      =    ["bias", "LayerNorm.weight"]
                    _UpperCAmelCase     :  str							      =    [
                        {
                            "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,
                        },
                    ]
                    _UpperCAmelCase     :  int							      =    Adafactor if self.args.adafactor else AdamW
                    if self.args.adafactor:
                           _UpperCAmelCase     :  List[str]							      =    Adafactor
                           _UpperCAmelCase     :  List[Any]							      =    {"scale_parameter": False, "relative_step": False}
                    else:
                           _UpperCAmelCase     :  List[str]							      =    AdamW
                           _UpperCAmelCase     :  List[str]							      =    {
                               "betas": (self.args.adam_betaa, self.args.adam_betaa),
                               "eps": self.args.adam_epsilon,
                           }
                    _UpperCAmelCase     :  List[Any]							      =    self.args.learning_rate
                    if self.sharded_ddp:
                           _UpperCAmelCase     :  List[Any]							      =    OSS(
                               params=A						,					optim=A						,					**A						,					)
                    else:
                           _UpperCAmelCase     :  Union[str, Any]							      =    optimizer_cls(A						,					**A       )

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




      def      _A	(       self						:				List[str]						,					A						:				Optional[int]       ):
             _UpperCAmelCase     :  List[str]							      =    arg_to_scheduler[self.args.lr_scheduler]
             if self.args.lr_scheduler == "constant":
                    _UpperCAmelCase     :  Optional[Any]							      =    schedule_func(self.optimizer       )
             elif self.args.lr_scheduler == "constant_w_warmup":
                    _UpperCAmelCase     :  str							      =    schedule_func(self.optimizer						,					num_warmup_steps=self.args.warmup_steps       )
             else:
                    _UpperCAmelCase     :  str							      =    schedule_func(
                        self.optimizer						,					num_warmup_steps=self.args.warmup_steps						,					num_training_steps=A       )
             return scheduler




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




      def      _A	(       self						:				List[str]						,					A						:				Optional[int]						,					A						:				Optional[int]       ):
             _UpperCAmelCase     :  Union[str, Any]							      =    inputs.pop("labels"       )
             _UpperCAmelCase      ,  _UpperCAmelCase     :  Optional[int]							      =    self._compute_loss(A						,					A						,					A       )
             return loss




      def      _A	(       self						:				List[str]						,					A						:				nn.Module						,					A						:				Dict[str, Union[torch.Tensor, Any]]						,					A						:				bool						,					A						:				Optional[List[str]] = None						,					):
             _UpperCAmelCase     :  List[str]							      =    self._prepare_inputs(A       )

             _UpperCAmelCase     :  Dict							      =    {
                 "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:
                    _UpperCAmelCase     :  Dict							      =    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"]:
                           _UpperCAmelCase     :  int							      =    self._pad_tensors_to_max_len(A						,					gen_kwargs["max_length"]       )

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

             _UpperCAmelCase     :  List[str]							      =    loss.mean().detach()
             if self.args.prediction_loss_only:
                    return (loss, None, None)

             _UpperCAmelCase     :  str							      =    generated_tokens if self.args.predict_with_generate else logits

             if labels.shape[-1] < gen_kwargs["max_length"]:
                    _UpperCAmelCase     :  Optional[Any]							      =    self._pad_tensors_to_max_len(A						,					gen_kwargs["max_length"]       )

             return (loss, logits, labels)





      def      _A	(       self						:				Dict						,					A						:				int						,					A						:				List[str]       ):
             # If PAD token is not defined at least EOS token has to be defined
             _UpperCAmelCase     :  Union[str, Any]							      =    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}"""       )

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

