






"""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						:						List[str]					   =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE						:						Optional[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       snake_case_       (    _lowerCamelCase       ):



	"""simple docstring"""

	def __init__(      self  ,					__a=None  ,					__a=None  ,					*__a  ,					**__a						):


					"""simple docstring"""

					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  ,					__a						):


					"""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=__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  ,					__a						):


					"""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=__a						)
					return scheduler


	def       _UpperCAmelCase						(      self						):


					"""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       _UpperCAmelCase						(      self  ,					__a  ,					__a  ,					__a						):


					"""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(**__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  ,					__a  ,					__a						):


					"""simple docstring"""

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


	def       _UpperCAmelCase						(      self  ,					__a  ,					__a  ,					__a  ,					__a = None  ,					):


					"""simple docstring"""

					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  ,					__a  ,					__a						):


					"""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










"""simple docstring"""






# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from pathlib import Path

import torch

from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter


SCREAMING_SNAKE_CASE						:						Dict					   =       '''Create a default config file for Accelerate with only a few flags set.'''



def  __lowerCamelCase					(					lowerCAmelCase__="no"       ,lowerCAmelCase__ = default_json_config_file       ,lowerCAmelCase__ = False				):
				A__							 =       Path(lowerCAmelCase__				)
				path.parent.mkdir(parents=lowerCAmelCase__       ,exist_ok=lowerCAmelCase__				)
				if path.exists():
								print(
								    f'''Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`.'''				)
								return False
				A__							 =       mixed_precision.lower()
				if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
								raise ValueError(
								    f'''`mixed_precision` should be one of \'no\', \'fp16\', \'bf16\', or \'fp8\'. Received {mixed_precision}'''				)
				A__							 =       {
				    'compute_environment': 'LOCAL_MACHINE',
				    'mixed_precision': mixed_precision,
				}
				if torch.cuda.is_available():
								A__							 =       torch.cuda.device_count()
								A__							 =       num_gpus
								A__							 =       False
								if num_gpus > 1:
												A__							 =       'MULTI_GPU'
								else:
												A__							 =       'NO'
				elif is_xpu_available() and use_xpu:
								A__							 =       torch.xpu.device_count()
								A__							 =       num_xpus
								A__							 =       False
								if num_xpus > 1:
												A__							 =       'MULTI_XPU'
								else:
												A__							 =       'NO'
				elif is_npu_available():
								A__							 =       torch.npu.device_count()
								A__							 =       num_npus
								A__							 =       False
								if num_npus > 1:
												A__							 =       'MULTI_NPU'
								else:
												A__							 =       'NO'
				else:
								A__							 =       0
								A__							 =       True
								A__							 =       1
								A__							 =       'NO'
				A__							 =       ClusterConfig(**lowerCAmelCase__				)
				config.to_json_file(lowerCAmelCase__				)
				return path



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__				):
				A__							 =       parser.add_parser('default'       ,parents=lowerCAmelCase__       ,help=lowerCAmelCase__       ,formatter_class=lowerCAmelCase__				)
				parser.add_argument(
				    '--config_file'       ,default=lowerCAmelCase__       ,help=(
				        'The path to use to store the config file. Will default to a file named default_config.yaml in the cache '
				        'location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '
				        'such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '
				        'with \'huggingface\'.'
				    )       ,dest='save_location'       ,)

				parser.add_argument(
				    '--mixed_precision'       ,choices=['no', 'fp16', 'bf16']       ,type=lowerCAmelCase__       ,help='Whether or not to use mixed precision training. '
				    'Choose between FP16 and BF16 (bfloat16) training. '
				    'BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later.'       ,default='no'       ,)
				parser.set_defaults(func=lowerCAmelCase__				)
				return parser







def  __lowerCamelCase					(					lowerCAmelCase__				):
				A__							 =       write_basic_config(args.mixed_precision       ,args.save_location				)
				if config_file:
								print(f'''accelerate configuration saved at {config_file}'''				)


