






"""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


lowerCamelCase_       			=   logging.get_logger(__name__)

lowerCamelCase_       			=   {
    '''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_ (__A  ):





	def __init__(   self      :				Optional[Any]			,			lowerCAmelCase_      :				Any=None			,			lowerCAmelCase_      :				int=None			,			*lowerCAmelCase_      :				List[Any]			,			**lowerCAmelCase_      :				Union[str, Any]		)		->    List[str]:
				super().__init__(*lowerCAmelCase_			,			**lowerCAmelCase_		)

				if config is None:
							assert isinstance(self.model			,			lowerCAmelCase_		), (
							    "If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
							    f""" {self.model.__class__}"""
							)
							UpperCAmelCase_  :				Tuple      =      self.model.config
				else:
							UpperCAmelCase_  :				Optional[Any]      =      config

				UpperCAmelCase_  :				Dict      =      data_args
				UpperCAmelCase_  :				Union[str, Any]      =      self.config.tgt_vocab_size if isinstance(self.config			,			lowerCAmelCase_		) 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_  :				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_  :				str      =      label_smoothed_nll_loss





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

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





	def   _SCREAMING_SNAKE_CASE   (   self      :				Union[str, Any]			,			lowerCAmelCase_      :				Any		)		->    Optional[int]:
				UpperCAmelCase_  :				Dict      =      arg_to_scheduler[self.args.lr_scheduler]
				if self.args.lr_scheduler == "constant":
							UpperCAmelCase_  :				List[Any]      =      schedule_func(self.optimizer		)
				elif self.args.lr_scheduler == "constant_w_warmup":
							UpperCAmelCase_  :				List[str]      =      schedule_func(self.optimizer			,			num_warmup_steps=self.args.warmup_steps		)
				else:
							UpperCAmelCase_  :				List[str]      =      schedule_func(
							    self.optimizer			,			num_warmup_steps=self.args.warmup_steps			,			num_training_steps=lowerCAmelCase_		)
				return scheduler





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





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





	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[Any]			,			lowerCAmelCase_      :				nn.Module			,			lowerCAmelCase_      :				Dict[str, Union[torch.Tensor, Any]]			,			lowerCAmelCase_      :				bool			,			lowerCAmelCase_      :				Optional[List[str]] = None			,			)		->    Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:
				UpperCAmelCase_  :				List[Any]      =      self._prepare_inputs(lowerCAmelCase_		)

				UpperCAmelCase_  :				Any      =      {
				    "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"]			,			**lowerCAmelCase_			,			)
							# in case the batch is shorter than max length, the output should be padded
							if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
										UpperCAmelCase_  :				List[Any]      =      self._pad_tensors_to_max_len(lowerCAmelCase_			,			gen_kwargs["max_length"]		)

				UpperCAmelCase_  :				Optional[int]      =      inputs.pop("labels"		)
				with torch.no_grad():
							# compute loss on predict data
							UpperCAmelCase_  :				str      =      self._compute_loss(lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_		)

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

				UpperCAmelCase_  :				Optional[int]      =      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(lowerCAmelCase_			,			gen_kwargs["max_length"]		)

				return (loss, logits, labels)





	def   _SCREAMING_SNAKE_CASE   (   self      :				str			,			lowerCAmelCase_      :				Optional[Any]			,			lowerCAmelCase_      :				List[str]		)		->    str:
				# If PAD token is not defined at least EOS token has to be defined
				UpperCAmelCase_  :				Optional[int]      =      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_  :				Optional[Any]      =      pad_token_id * torch.ones(
				    (tensor.shape[0], max_length)			,			dtype=tensor.dtype			,			device=tensor.device		)
				UpperCAmelCase_  :				str      =      tensor
				return padded_tensor











"""simple docstring"""
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"

from pathlib import Path
import json
import tempfile

from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES

lowerCamelCase_       			=   '''tiny-wmt19-en-ru'''

# Build

# borrowed from a test
lowerCamelCase_       			=   [
    '''l''',
    '''o''',
    '''w''',
    '''e''',
    '''r''',
    '''s''',
    '''t''',
    '''i''',
    '''d''',
    '''n''',
    '''w</w>''',
    '''r</w>''',
    '''t</w>''',
    '''lo''',
    '''low''',
    '''er</w>''',
    '''low</w>''',
    '''lowest</w>''',
    '''newer</w>''',
    '''wider</w>''',
    '''<unk>''',
]
lowerCamelCase_       			=   dict(zip(vocab, range(len(vocab))))
lowerCamelCase_       			=   ['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

with tempfile.TemporaryDirectory() as tmpdirname:
			lowerCamelCase_       			=   Path(tmpdirname)
			lowerCamelCase_       			=   build_dir / VOCAB_FILES_NAMES['''src_vocab_file''']
			lowerCamelCase_       			=   build_dir / VOCAB_FILES_NAMES['''tgt_vocab_file''']
			lowerCamelCase_       			=   build_dir / VOCAB_FILES_NAMES['''merges_file''']
			with open(src_vocab_file, '''w''') as fp:
						fp.write(json.dumps(vocab_tokens))
			with open(tgt_vocab_file, '''w''') as fp:
						fp.write(json.dumps(vocab_tokens))
			with open(merges_file, '''w''') as fp:
						fp.write('''\n'''.join(merges))

			lowerCamelCase_       			=   FSMTTokenizer(
			    langs=['''en''', '''ru'''],
			    src_vocab_size=len(vocab),
			    tgt_vocab_size=len(vocab),
			    src_vocab_file=src_vocab_file,
			    tgt_vocab_file=tgt_vocab_file,
			    merges_file=merges_file,
			)

lowerCamelCase_       			=   FSMTConfig(
    langs=['''ru''', '''en'''],
    src_vocab_size=1000,
    tgt_vocab_size=1000,
    d_model=4,
    encoder_layers=1,
    decoder_layers=1,
    encoder_ffn_dim=4,
    decoder_ffn_dim=4,
    encoder_attention_heads=1,
    decoder_attention_heads=1,
)

lowerCamelCase_       			=   FSMTForConditionalGeneration(config)
print(f'num of params {tiny_model.num_parameters()}')

# Test
lowerCamelCase_       			=   tokenizer(['''Making tiny model'''], return_tensors='''pt''')
lowerCamelCase_       			=   tiny_model(**batch)

print('''test output:''', len(outputs.logits[0]))

# Save
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(f'Generated {mname_tiny}')

# Upload
# transformers-cli upload tiny-wmt19-en-ru



