




import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union

import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn

from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForPreTraining,
    is_apex_available,
    trainer_utils,
)
from transformers.models.wavaveca.modeling_wavaveca import _compute_mask_indices


if is_apex_available():
	from apex import amp

if version.parse(version.parse(torch.__version__).base_version) >= version.parse("1.6"):
	_lowerCamelCase      :  List[Any]      						=						True
	from torch.cuda.amp import autocast


_lowerCamelCase      :  int      						=						logging.getLogger(__name__)
@dataclass
class        __snake_case  :
				lowerCAmelCase__				=     field(
				    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}		)
				lowerCAmelCase__				=     field(
				    default=_a						,	metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}						,	)
				lowerCAmelCase__				=     field(
				    default=_a						,	metadata={"help": "Whether to freeze the feature extractor layers of the model."}		)
				lowerCAmelCase__				=     field(
				    default=_a						,	metadata={"help": "Whether to log verbose messages or not."}						,	)
				lowerCAmelCase__				=     field(
				    default=2.0						,	metadata={"help": "Maximum temperature for gumbel softmax."}		)
				lowerCAmelCase__				=     field(
				    default=0.5						,	metadata={"help": "Minimum temperature for gumbel softmax."}		)
				lowerCAmelCase__				=     field(
				    default=0.9_9_9_9_9_5						,	metadata={"help": "Decay of gumbel temperature during training."}		)






def 							_UpperCAmelCase  (UpperCamelCase_ : ModelArguments      ,    UpperCamelCase_ : TrainingArguments       ):
				'''simple docstring'''
				logging.basicConfig(
				    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""      ,    datefmt="""%m/%d/%Y %H:%M:%S"""      ,    handlers=[logging.StreamHandler(sys.stdout       )]      ,    )
				_lowerCAmelCase    :    Union[str, Any]										=      logging.WARNING
				if model_args.verbose_logging:
								_lowerCAmelCase    :    str										=      logging.DEBUG
				elif trainer_utils.is_main_process(training_args.local_rank       ):
								_lowerCAmelCase    :    List[Any]										=      logging.INFO
				logger.setLevel(UpperCamelCase_       )
@dataclass
class        __snake_case  :
				lowerCAmelCase__				=     field(
				    default=_a						,	metadata={"help": "The name of the dataset to use (via the datasets library)."}		)
				lowerCAmelCase__				=     field(
				    default=_a						,	metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}		)
				lowerCAmelCase__				=     field(
				    default="train"						,	metadata={
				        "help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
				    }						,	)
				lowerCAmelCase__				=     field(
				    default="validation"						,	metadata={
				        "help": (
				            "The name of the validation data set split to use (via the datasets library). Defaults to 'validation'"
				        )
				    }						,	)
				lowerCAmelCase__				=     field(
				    default="file"						,	metadata={"help": "Column in the dataset that contains speech file path. Defaults to 'file'"}						,	)
				lowerCAmelCase__				=     field(
				    default=_a						,	metadata={"help": "Overwrite the cached preprocessed datasets or not."}		)
				lowerCAmelCase__				=     field(
				    default=1						,	metadata={
				        "help": "The percentage of the train set used as validation set in case there's no validation split"
				    }						,	)
				lowerCAmelCase__				=     field(
				    default=_a						,	metadata={"help": "The number of processes to use for the preprocessing."}						,	)
				lowerCAmelCase__				=     field(
				    default=2_0.0						,	metadata={"help": "Filter audio files that are longer than `max_duration_in_seconds` seconds"}		)






@dataclass
class        __snake_case  :
				lowerCAmelCase__				=     42
				lowerCAmelCase__				=     42
				lowerCAmelCase__				=     "longest"
				lowerCAmelCase__				=     None
				lowerCAmelCase__				=     None



				def __call__(      self      :		Optional[Any]		,	_UpperCAmelCase      :		List[Dict[str, Union[List[int], torch.Tensor]]]      )  ->    Dict[str, torch.Tensor]:





								'''simple docstring'''




								_lowerCAmelCase    :    Optional[int]										=      self.feature_extractor.pad(
								    _UpperCAmelCase		,	max_length=self.max_length		,	padding=self.padding		,	pad_to_multiple_of=self.pad_to_multiple_of		,	return_tensors="""pt"""		,	)
								_lowerCAmelCase    :    Dict										=      self.model._get_feat_extract_output_lengths(batch["""input_values"""].shape[-1]      )

								_lowerCAmelCase    :    int										=      batch["""input_values"""].shape[0]

								# make sure that no loss is computed on padded inputs
								if batch["attention_mask"] is not None:
												# compute real output lengths according to convolution formula
												_lowerCAmelCase    :    List[Any]										=      self.model._get_feat_extract_output_lengths(batch["""attention_mask"""].sum(-1      )      ).to(
												    torch.long      )

												_lowerCAmelCase    :    Optional[Any]										=      torch.zeros(
												    (batch_size, mask_indices_seq_length)		,	dtype=torch.long		,	device=batch["""input_values"""].device      )

												# these two operations makes sure that all values
												# before the output lengths indices are attended to
												_lowerCAmelCase    :    Optional[int]										=      1
												_lowerCAmelCase    :    Optional[Any]										=      attention_mask.flip([-1]      ).cumsum(-1      ).flip([-1]      ).bool()

								# sample randomly masked indices
								_lowerCAmelCase    :    Union[str, Any]										=      _compute_mask_indices(
								    (batch_size, mask_indices_seq_length)		,	self.model.config.mask_time_prob		,	self.model.config.mask_time_length		,	attention_mask=_UpperCAmelCase		,	min_masks=2		,	)

								return batch






class        __snake_case  (_a		):



				def __init__(      self      :		Dict		,	*_UpperCAmelCase      :		Optional[Any]		,	_UpperCAmelCase      :		Union[str, Any]=1		,	_UpperCAmelCase      :		List[str]=0		,	_UpperCAmelCase      :		List[Any]=1.0		,	**_UpperCAmelCase      :		Union[str, Any]      )  ->    Tuple:





								'''simple docstring'''




								super().__init__(*_UpperCAmelCase		,	**_UpperCAmelCase      )
								_lowerCAmelCase    :    Tuple										=      0
								_lowerCAmelCase    :    int										=      max_gumbel_temp
								_lowerCAmelCase    :    List[str]										=      min_gumbel_temp
								_lowerCAmelCase    :    Any										=      gumbel_temp_decay



				def       SCREAMING_SNAKE_CASE       (      self      :		Dict		,	_UpperCAmelCase      :		nn.Module		,	_UpperCAmelCase      :		Dict[str, Union[torch.Tensor, Any]]      )  ->    torch.Tensor:





								'''simple docstring'''




								model.train()
								_lowerCAmelCase    :    Optional[int]										=      self._prepare_inputs(_UpperCAmelCase      )

								if self.use_amp:
												with autocast():
																_lowerCAmelCase    :    Optional[int]										=      self.compute_loss(_UpperCAmelCase		,	_UpperCAmelCase      )
								else:
												_lowerCAmelCase    :    Optional[Any]										=      self.compute_loss(_UpperCAmelCase		,	_UpperCAmelCase      )

								if self.args.n_gpu > 1 or self.deepspeed:
												if model.module.config.ctc_loss_reduction == "mean":
																_lowerCAmelCase    :    Tuple										=      loss.mean()
												elif model.module.config.ctc_loss_reduction == "sum":
																_lowerCAmelCase    :    Optional[Any]										=      loss.sum() / (inputs["""mask_time_indices"""]).sum()
												else:
																raise ValueError(f"{model.config.ctc_loss_reduction} is not valid. Choose one of ['mean', 'sum']"      )

								if self.args.gradient_accumulation_steps > 1:
												_lowerCAmelCase    :    Dict										=      loss / self.args.gradient_accumulation_steps

								if self.use_amp:
												self.scaler.scale(_UpperCAmelCase      ).backward()
								elif self.use_apex:
												with amp.scale_loss(_UpperCAmelCase		,	self.optimizer      ) as scaled_loss:
																scaled_loss.backward()
								elif self.deepspeed:
												self.deepspeed.backward(_UpperCAmelCase      )
								else:
												loss.backward()

								self.num_update_step += 1
								# make sure gumbel softmax temperature is decayed
								if self.args.n_gpu > 1 or self.deepspeed:
												model.module.set_gumbel_temperature(
												    max(self.max_gumbel_temp * self.gumbel_temp_decay**self.num_update_step		,	self.min_gumbel_temp      )      )
								else:
												model.set_gumbel_temperature(
												    max(self.max_gumbel_temp * self.gumbel_temp_decay**self.num_update_step		,	self.min_gumbel_temp      )      )

								return loss.detach()






def 							_UpperCAmelCase  ():
				'''simple docstring'''
				# See all possible arguments in src/transformers/training_args.py
				# or by passing the --help flag to this script.
				# We now keep distinct sets of args, for a cleaner separation of concerns.

				_lowerCAmelCase    :    int										=      HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)       )

				_lowerCAmelCase					,    _lowerCAmelCase					,    _lowerCAmelCase    :    List[str]										=      parser.parse_args_into_dataclasses()
				configure_logger(UpperCamelCase_      ,    UpperCamelCase_       )

				# Downloading and loading a dataset from the hub.
				_lowerCAmelCase    :    Optional[int]										=      load_dataset(data_args.dataset_name      ,    data_args.dataset_config_name      ,    cache_dir=model_args.cache_dir       )

				if "validation" not in datasets.keys():
								# make sure only "validation" and "train" keys remain"
								_lowerCAmelCase    :    List[str]										=      DatasetDict()
								_lowerCAmelCase    :    List[str]										=      load_dataset(
								    data_args.dataset_name      ,    data_args.dataset_config_name      ,    split=F"{data_args.train_split_name}[:{data_args.validation_split_percentage}%]"      ,    cache_dir=model_args.cache_dir      ,    )
								_lowerCAmelCase    :    Union[str, Any]										=      load_dataset(
								    data_args.dataset_name      ,    data_args.dataset_config_name      ,    split=F"{data_args.train_split_name}[{data_args.validation_split_percentage}%:]"      ,    cache_dir=model_args.cache_dir      ,    )
				else:
								# make sure only "validation" and "train" keys remain"
								_lowerCAmelCase    :    Optional[int]										=      DatasetDict()
								_lowerCAmelCase    :    Any										=      load_dataset(
								    data_args.dataset_name      ,    data_args.dataset_config_name      ,    split="""validation"""      ,    cache_dir=model_args.cache_dir      ,    )
								_lowerCAmelCase    :    List[str]										=      load_dataset(
								    data_args.dataset_name      ,    data_args.dataset_config_name      ,    split=F"{data_args.train_split_name}"      ,    cache_dir=model_args.cache_dir      ,    )

				# only normalized-inputs-training is supported
				_lowerCAmelCase    :    Dict										=      WavaVecaFeatureExtractor.from_pretrained(
				    model_args.model_name_or_path      ,    cache_dir=model_args.cache_dir      ,    do_normalize=UpperCamelCase_       )

				def prepare_dataset(UpperCamelCase_ : Union[str, Any]       ):
								# check that all files have the correct sampling rate
								_lowerCAmelCase					,    _lowerCAmelCase    :    Tuple										=      librosa.load(batch[data_args.speech_file_column]      ,    sr=feature_extractor.sampling_rate       )
								return batch

				# load audio files into numpy arrays
				_lowerCAmelCase    :    Any										=      datasets.map(
				    UpperCamelCase_      ,    num_proc=data_args.preprocessing_num_workers      ,    remove_columns=datasets["""train"""].column_names       )

				# filter audio files that are too long
				_lowerCAmelCase    :    Union[str, Any]										=      vectorized_datasets.filter(
				    lambda UpperCamelCase_       : len(data["""speech"""]       ) < int(data_args.max_duration_in_seconds * feature_extractor.sampling_rate       )       )

				def normalize(UpperCamelCase_ : Optional[int]       ):
								return feature_extractor(batch["""speech"""]      ,    sampling_rate=feature_extractor.sampling_rate       )

				# normalize and transform to `BatchFeatures`
				_lowerCAmelCase    :    List[str]										=      vectorized_datasets.map(
				    UpperCamelCase_      ,    batched=UpperCamelCase_      ,    num_proc=data_args.preprocessing_num_workers      ,    load_from_cache_file=not data_args.overwrite_cache      ,    remove_columns=vectorized_datasets["""train"""].column_names      ,    )

				# pretraining is only supported for "newer" stable layer norm architecture
				# apply_spec_augment has to be True, mask_feature_prob has to be 0.0
				_lowerCAmelCase    :    Any										=      WavaVecaConfig.from_pretrained(
				    model_args.model_name_or_path      ,    cache_dir=model_args.cache_dir      ,    gradient_checkpointing=training_args.gradient_checkpointing      ,    )

				if not config.do_stable_layer_norm or config.feat_extract_norm != "layer":
								raise ValueError(
								    """PreTraining is only supported for ``config.do_stable_layer_norm=True`` and"""
								    """ ``config.feat_extract_norm='layer'"""       )

				_lowerCAmelCase    :    Any										=      WavaVecaForPreTraining(UpperCamelCase_       )

				_lowerCAmelCase    :    Optional[int]										=      DataCollatorForWavaVecaPretraining(model=UpperCamelCase_      ,    feature_extractor=UpperCamelCase_       )

				_lowerCAmelCase    :    Any										=      WavaVecaPreTrainer(
				    model=UpperCamelCase_      ,    data_collator=UpperCamelCase_      ,    args=UpperCamelCase_      ,    train_dataset=vectorized_datasets["""train"""]      ,    eval_dataset=vectorized_datasets["""validation"""]      ,    tokenizer=UpperCamelCase_      ,    max_gumbel_temp=model_args.max_gumbel_temperature      ,    min_gumbel_temp=model_args.min_gumbel_temperature      ,    gumbel_temp_decay=model_args.gumbel_temperature_decay      ,    )
				trainer.train()


if __name__ == "__main__":
	main()







import os
from pathlib import Path

import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader

from transformers import AutoTokenizer
from transformers.models.mbart.modeling_mbart import shift_tokens_right
from transformers.testing_utils import TestCasePlus, slow
from utils import FAIRSEQ_AVAILABLE, DistributedSortishSampler, LegacySeqaSeqDataset, SeqaSeqDataset


_lowerCamelCase      :  int      						=						"bert-base-cased"
_lowerCamelCase      :  Union[str, Any]      						=						"google/pegasus-xsum"
_lowerCamelCase      :  Optional[int]      						=						[" Sam ate lunch today.", "Sams lunch ingredients."]
_lowerCamelCase      :  Dict      						=						["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
_lowerCamelCase      :  Any      						=						"patrickvonplaten/t5-tiny-random"
_lowerCamelCase      :  Dict      						=						"sshleifer/bart-tiny-random"
_lowerCamelCase      :  int      						=						"sshleifer/tiny-mbart"
_lowerCamelCase      :  List[Any]      						=						"sshleifer/tiny-marian-en-de"




def 							_UpperCAmelCase  (UpperCamelCase_ : Path      ,    UpperCamelCase_ : list       ):
				'''simple docstring'''
				_lowerCAmelCase    :    Union[str, Any]										=      """\n""".join(UpperCamelCase_       )
				Path(UpperCamelCase_       ).open("""w"""       ).writelines(UpperCamelCase_       )




def 							_UpperCAmelCase  (UpperCamelCase_ : str       ):
				'''simple docstring'''
				for split in ["train", "val", "test"]:
								_dump_articles(os.path.join(UpperCamelCase_      ,    F"{split}.source"       )      ,    UpperCamelCase_       )
								_dump_articles(os.path.join(UpperCamelCase_      ,    F"{split}.target"       )      ,    UpperCamelCase_       )
				return tmp_dir





class        __snake_case  (_a		):



				@parameterized.expand(
				    [
				        MBART_TINY,
				        MARIAN_TINY,
				        T5_TINY,
				        BART_TINY,
				        PEGASUS_XSUM,
				    ]		,	)
				@slow
				def       SCREAMING_SNAKE_CASE       (      self      :		Tuple		,	_UpperCAmelCase      :		List[str]      )  ->    int:





								'''simple docstring'''




								_lowerCAmelCase    :    Union[str, Any]										=      AutoTokenizer.from_pretrained(_UpperCAmelCase      )
								_lowerCAmelCase    :    Optional[Any]										=      make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )
								_lowerCAmelCase    :    Any										=      max(len(tokenizer.encode(_UpperCAmelCase      )      ) for a in ARTICLES      )
								_lowerCAmelCase    :    Dict										=      max(len(tokenizer.encode(_UpperCAmelCase      )      ) for a in SUMMARIES      )
								_lowerCAmelCase    :    List[str]										=      4
								_lowerCAmelCase    :    List[Any]										=      8
								assert max_len_target > max_src_len  # Will be truncated
								assert max_len_source > max_src_len  # Will be truncated
								_lowerCAmelCase					,    _lowerCAmelCase    :    Any										=      """ro_RO""", """de_DE"""  # ignored for all but mbart, but never causes error.
								_lowerCAmelCase    :    Tuple										=      SeqaSeqDataset(
								    _UpperCAmelCase		,	data_dir=_UpperCAmelCase		,	type_path="""train"""		,	max_source_length=_UpperCAmelCase		,	max_target_length=_UpperCAmelCase		,	src_lang=_UpperCAmelCase		,	tgt_lang=_UpperCAmelCase		,	)
								_lowerCAmelCase    :    int										=      DataLoader(_UpperCAmelCase		,	batch_size=2		,	collate_fn=train_dataset.collate_fn      )
								for batch in dataloader:
												assert isinstance(_UpperCAmelCase		,	_UpperCAmelCase      )
												assert batch["attention_mask"].shape == batch["input_ids"].shape
												# show that articles were trimmed.
												assert batch["input_ids"].shape[1] == max_src_len
												# show that targets are the same len
												assert batch["labels"].shape[1] == max_tgt_len
												if tok_name != MBART_TINY:
																continue
												# check language codes in correct place
												_lowerCAmelCase    :    Dict										=      shift_tokens_right(batch["""labels"""]		,	tokenizer.pad_token_id      )
												assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
												assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
												assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
												assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]

												break  # No need to test every batch



				@parameterized.expand([BART_TINY, BERT_BASE_CASED]      )
				def       SCREAMING_SNAKE_CASE       (      self      :		Tuple		,	_UpperCAmelCase      :		List[str]      )  ->    Dict:





								'''simple docstring'''




								_lowerCAmelCase    :    Dict										=      AutoTokenizer.from_pretrained(_UpperCAmelCase      )
								_lowerCAmelCase    :    str										=      make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )
								_lowerCAmelCase    :    Optional[int]										=      max(len(tokenizer.encode(_UpperCAmelCase      )      ) for a in ARTICLES      )
								_lowerCAmelCase    :    str										=      max(len(tokenizer.encode(_UpperCAmelCase      )      ) for a in SUMMARIES      )
								_lowerCAmelCase    :    Union[str, Any]										=      4
								_lowerCAmelCase    :    Dict										=      LegacySeqaSeqDataset(
								    _UpperCAmelCase		,	data_dir=_UpperCAmelCase		,	type_path="""train"""		,	max_source_length=20		,	max_target_length=_UpperCAmelCase		,	)
								_lowerCAmelCase    :    str										=      DataLoader(_UpperCAmelCase		,	batch_size=2		,	collate_fn=train_dataset.collate_fn      )
								for batch in dataloader:
												assert batch["attention_mask"].shape == batch["input_ids"].shape
												# show that articles were trimmed.
												assert batch["input_ids"].shape[1] == max_len_source
												assert 20 >= batch["input_ids"].shape[1]  # trimmed significantly
												# show that targets were truncated
												assert batch["labels"].shape[1] == trunc_target  # Truncated
												assert max_len_target > trunc_target  # Truncated
												break  # No need to test every batch



				def       SCREAMING_SNAKE_CASE       (      self      :		Optional[int]      )  ->    str:





								'''simple docstring'''




								_lowerCAmelCase    :    int										=      AutoTokenizer.from_pretrained("""facebook/mbart-large-cc25"""      )

								_lowerCAmelCase    :    List[str]										=      Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )      )
								_lowerCAmelCase    :    List[Any]										=      tmp_dir.joinpath("""train.source"""      ).open().readlines()
								_lowerCAmelCase    :    Tuple										=      Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )      )
								pack_data_dir(_UpperCAmelCase		,	_UpperCAmelCase		,	128		,	_UpperCAmelCase      )
								_lowerCAmelCase    :    List[str]										=      {x.name for x in tmp_dir.iterdir()}
								_lowerCAmelCase    :    List[Any]										=      {x.name for x in save_dir.iterdir()}
								_lowerCAmelCase    :    Optional[Any]										=      save_dir.joinpath("""train.source"""      ).open().readlines()
								# orig: [' Sam ate lunch today.\n', 'Sams lunch ingredients.']
								# desired_packed: [' Sam ate lunch today.\n Sams lunch ingredients.']
								assert len(_UpperCAmelCase      ) < len(_UpperCAmelCase      )
								assert len(_UpperCAmelCase      ) == 1
								assert len(packed_examples[0]      ) == sum(len(_UpperCAmelCase      ) for x in orig_examples      )
								assert orig_paths == new_paths



				@pytest.mark.skipif(not FAIRSEQ_AVAILABLE		,	reason="""This test requires fairseq"""      )
				def       SCREAMING_SNAKE_CASE       (      self      :		Any      )  ->    str:





								'''simple docstring'''




								if not FAIRSEQ_AVAILABLE:
												return
								_lowerCAmelCase					,    _lowerCAmelCase					,    _lowerCAmelCase    :    int										=      self._get_dataset(max_len=64      )
								_lowerCAmelCase    :    Optional[Any]										=      64
								_lowerCAmelCase    :    Union[str, Any]										=      ds.make_dynamic_sampler(_UpperCAmelCase		,	required_batch_size_multiple=_UpperCAmelCase      )
								_lowerCAmelCase    :    Tuple										=      [len(_UpperCAmelCase      ) for x in batch_sampler]
								assert len(set(_UpperCAmelCase      )      ) > 1  # it's not dynamic batch size if every batch is the same length
								assert sum(_UpperCAmelCase      ) == len(_UpperCAmelCase      )  # no dropped or added examples
								_lowerCAmelCase    :    List[Any]										=      DataLoader(_UpperCAmelCase		,	batch_sampler=_UpperCAmelCase		,	collate_fn=ds.collate_fn		,	num_workers=2      )
								_lowerCAmelCase    :    List[Any]										=      []
								_lowerCAmelCase    :    Tuple										=      []
								for batch in data_loader:
												_lowerCAmelCase    :    Dict										=      batch["""input_ids"""].shape
												_lowerCAmelCase    :    Optional[int]										=      src_shape[0]
												assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
												_lowerCAmelCase    :    Dict										=      np.product(batch["""input_ids"""].shape      )
												num_src_per_batch.append(_UpperCAmelCase      )
												if num_src_tokens > (max_tokens * 1.1):
																failures.append(_UpperCAmelCase      )
								assert num_src_per_batch[0] == max(_UpperCAmelCase      )
								if failures:
												raise AssertionError(f"too many tokens in {len(_UpperCAmelCase      )} batches"      )



				def       SCREAMING_SNAKE_CASE       (      self      :		List[Any]      )  ->    str:





								'''simple docstring'''




								_lowerCAmelCase					,    _lowerCAmelCase					,    _lowerCAmelCase    :    Union[str, Any]										=      self._get_dataset(max_len=512      )
								_lowerCAmelCase    :    List[Any]										=      2
								_lowerCAmelCase    :    Tuple										=      ds.make_sortish_sampler(_UpperCAmelCase		,	shuffle=_UpperCAmelCase      )

								_lowerCAmelCase    :    Dict										=      DataLoader(_UpperCAmelCase		,	batch_size=_UpperCAmelCase		,	collate_fn=ds.collate_fn		,	num_workers=2      )
								_lowerCAmelCase    :    Dict										=      DataLoader(_UpperCAmelCase		,	batch_size=_UpperCAmelCase		,	collate_fn=ds.collate_fn		,	num_workers=2		,	sampler=_UpperCAmelCase      )

								_lowerCAmelCase    :    Dict										=      tokenizer.pad_token_id

								def count_pad_tokens(_UpperCAmelCase      :		Optional[Any]		,	_UpperCAmelCase      :		Union[str, Any]="input_ids"      ):
												return [batch[k].eq(_UpperCAmelCase      ).sum().item() for batch in data_loader]

								assert sum(count_pad_tokens(_UpperCAmelCase		,	k="""labels"""      )      ) < sum(count_pad_tokens(_UpperCAmelCase		,	k="""labels"""      )      )
								assert sum(count_pad_tokens(_UpperCAmelCase      )      ) < sum(count_pad_tokens(_UpperCAmelCase      )      )
								assert len(_UpperCAmelCase      ) == len(_UpperCAmelCase      )



				def       SCREAMING_SNAKE_CASE       (      self      :		Any		,	_UpperCAmelCase      :		str=1000		,	_UpperCAmelCase      :		Dict=128      )  ->    Dict:





								'''simple docstring'''




								if os.getenv("""USE_REAL_DATA"""		,	_UpperCAmelCase      ):
												_lowerCAmelCase    :    Optional[int]										=      """examples/seq2seq/wmt_en_ro"""
												_lowerCAmelCase    :    Optional[int]										=      max_len * 2 * 64
												if not Path(_UpperCAmelCase      ).joinpath("""train.len"""      ).exists():
																save_len_file(_UpperCAmelCase		,	_UpperCAmelCase      )
								else:
												_lowerCAmelCase    :    int										=      """examples/seq2seq/test_data/wmt_en_ro"""
												_lowerCAmelCase    :    int										=      max_len * 4
												save_len_file(_UpperCAmelCase		,	_UpperCAmelCase      )

								_lowerCAmelCase    :    str										=      AutoTokenizer.from_pretrained(_UpperCAmelCase      )
								_lowerCAmelCase    :    Dict										=      SeqaSeqDataset(
								    _UpperCAmelCase		,	data_dir=_UpperCAmelCase		,	type_path="""train"""		,	max_source_length=_UpperCAmelCase		,	max_target_length=_UpperCAmelCase		,	n_obs=_UpperCAmelCase		,	)
								return ds, max_tokens, tokenizer



				def       SCREAMING_SNAKE_CASE       (      self      :		Union[str, Any]      )  ->    Optional[Any]:





								'''simple docstring'''




								_lowerCAmelCase					,    _lowerCAmelCase					,    _lowerCAmelCase    :    Tuple										=      self._get_dataset()
								_lowerCAmelCase    :    Optional[Any]										=      set(DistributedSortishSampler(_UpperCAmelCase		,	256		,	num_replicas=2		,	rank=0		,	add_extra_examples=_UpperCAmelCase      )      )
								_lowerCAmelCase    :    Any										=      set(DistributedSortishSampler(_UpperCAmelCase		,	256		,	num_replicas=2		,	rank=1		,	add_extra_examples=_UpperCAmelCase      )      )
								assert idsa.intersection(_UpperCAmelCase      ) == set()



				@parameterized.expand(
				    [
				        MBART_TINY,
				        MARIAN_TINY,
				        T5_TINY,
				        BART_TINY,
				        PEGASUS_XSUM,
				    ]		,	)
				def       SCREAMING_SNAKE_CASE       (      self      :		str		,	_UpperCAmelCase      :		Union[str, Any]      )  ->    List[Any]:





								'''simple docstring'''




								_lowerCAmelCase    :    List[Any]										=      AutoTokenizer.from_pretrained(_UpperCAmelCase		,	use_fast=_UpperCAmelCase      )
								if tok_name == MBART_TINY:
												_lowerCAmelCase    :    Optional[Any]										=      SeqaSeqDataset(
												    _UpperCAmelCase		,	data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )		,	type_path="""train"""		,	max_source_length=4		,	max_target_length=8		,	src_lang="""EN"""		,	tgt_lang="""FR"""		,	)
												_lowerCAmelCase    :    Tuple										=      train_dataset.dataset_kwargs
												assert "src_lang" in kwargs and "tgt_lang" in kwargs
								else:
												_lowerCAmelCase    :    Dict										=      SeqaSeqDataset(
												    _UpperCAmelCase		,	data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )		,	type_path="""train"""		,	max_source_length=4		,	max_target_length=8		,	)
												_lowerCAmelCase    :    Optional[Any]										=      train_dataset.dataset_kwargs
												assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
												assert len(_UpperCAmelCase      ) == 1 if tok_name == BART_TINY else len(_UpperCAmelCase      ) == 0

