


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

import datasets
import numpy as np
import torch
import torchaudio
from packaging import version
from torch import nn

import transformers
from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaProcessor,
    is_apex_available,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


if is_apex_available():
			from apex import amp


if version.parse(version.parse(torch.__version__).base_version) >= version.parse("1.6"):
			_snake_case  :	Dict			      =       True
			from torch.cuda.amp import autocast

_snake_case  :	List[str]			      =       logging.getLogger(__name__)







def        lowerCAmelCase_  (      __lowerCamelCase=None							,			__lowerCamelCase=None			):
							return field(default_factory=lambda: default							,			metadata=__lowerCamelCase			)

@dataclass
class        a	:




							"""simple docstring"""


							__UpperCAmelCase					:							str  						=						field(
							    metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}   )
							__UpperCAmelCase					:							Optional[str]  						=						field(
							    default=_lowerCAmelCase  ,       metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}  ,       )
							__UpperCAmelCase					:							Optional[bool]  						=						field(
							    default=_lowerCAmelCase  ,       metadata={"help": "Whether to freeze the feature extractor layers of the model."}   )
							__UpperCAmelCase					:							Optional[float]  						=						field(
							    default=0.1  ,       metadata={"help": "The dropout ratio for the attention probabilities."}   )
							__UpperCAmelCase					:							Optional[float]  						=						field(
							    default=0.1  ,       metadata={"help": "The dropout ratio for activations inside the fully connected layer."}   )
							__UpperCAmelCase					:							Optional[float]  						=						field(
							    default=0.1  ,       metadata={
							        "help": "The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler."
							    }  ,       )
							__UpperCAmelCase					:							Optional[float]  						=						field(
							    default=0.1  ,       metadata={"help": "The dropout probabilitiy for all 1D convolutional layers in feature extractor."}  ,       )
							__UpperCAmelCase					:							Optional[float]  						=						field(
							    default=0.05  ,       metadata={
							        "help": (
							            "Propability of each feature vector along the time axis to be chosen as the start of the vector"
							            "span to be masked. Approximately ``mask_time_prob * sequence_length // mask_time_length`` feature"
							            "vectors will be masked along the time axis. This is only relevant if ``apply_spec_augment is True``."
							        )
							    }  ,       )
							__UpperCAmelCase					:							Optional[float]  						=						field(default=0.0  ,       metadata={"help": "The LayerDrop probability."}   )

@dataclass
class        a	:




							"""simple docstring"""


							__UpperCAmelCase					:							Optional[str]  						=						field(
							    default=_lowerCAmelCase  ,       metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}   )
							__UpperCAmelCase					:							Optional[str]  						=						field(
							    default="train+validation"  ,       metadata={
							        "help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
							    }  ,       )
							__UpperCAmelCase					:							bool  						=						field(
							    default=_lowerCAmelCase  ,       metadata={"help": "Overwrite the cached preprocessed datasets or not."}   )
							__UpperCAmelCase					:							Optional[int]  						=						field(
							    default=_lowerCAmelCase  ,       metadata={"help": "The number of processes to use for the preprocessing."}  ,       )
							__UpperCAmelCase					:							Optional[int]  						=						field(
							    default=_lowerCAmelCase  ,       metadata={
							        "help": (
							            "For debugging purposes or quicker training, truncate the number of training examples to this "
							            "value if set."
							        )
							    }  ,       )
							__UpperCAmelCase					:							Optional[int]  						=						field(
							    default=_lowerCAmelCase  ,       metadata={
							        "help": (
							            "For debugging purposes or quicker training, truncate the number of validation examples to this "
							            "value if set."
							        )
							    }  ,       )
							__UpperCAmelCase					:							List[str]  						=						list_field(
							    default=[",", "?", ".", "!", "-", ";", ":", "\"\"", "%", "'", "\"", "�"]  ,       metadata={"help": "A list of characters to remove from the transcripts."}  ,       )

@dataclass
class        a	:




							"""simple docstring"""


							__UpperCAmelCase					:							WavaVecaProcessor
							__UpperCAmelCase					:							Union[bool, str]  						=						True
							__UpperCAmelCase					:							Optional[int]  						=						None
							__UpperCAmelCase					:							Optional[int]  						=						None
							__UpperCAmelCase					:							Optional[int]  						=						None
							__UpperCAmelCase					:							Optional[int]  						=						None

							def __call__(							self :						Dict					,			lowerCamelCase :						List[Dict[str, Union[List[int], torch.Tensor]]]       )			->					Dict[str, torch.Tensor]:
														# split inputs and labels since they have to be of different lenghts and need
														# different padding methods
														__snake_case	:    Union[str, Any]											=     [{"input_values": feature["input_values"]} for feature in features]
														__snake_case	:    Optional[Any]											=     [{"input_ids": feature["labels"]} for feature in features]

														__snake_case	:    Tuple											=     self.processor.pad(
														    lowerCamelCase					,			padding=self.padding					,			max_length=self.max_length					,			pad_to_multiple_of=self.pad_to_multiple_of					,			return_tensors="pt"					,			)
														__snake_case	:    Optional[int]											=     self.processor.pad(
														    labels=lowerCamelCase					,			padding=self.padding					,			max_length=self.max_length_labels					,			pad_to_multiple_of=self.pad_to_multiple_of_labels					,			return_tensors="pt"					,			)

														# replace padding with -100 to ignore loss correctly
														__snake_case	:    str											=     labels_batch["input_ids"].masked_fill(labels_batch.attention_mask.ne(1       )					,			-100       )

														__snake_case	:    Dict											=     labels

														return batch

class        a	(_lowerCAmelCase   ):




							"""simple docstring"""


							def 					__snake_case      (							self :						Dict					,			lowerCamelCase :						nn.Module					,			lowerCamelCase :						Dict[str, Union[torch.Tensor, Any]]       )			->					torch.Tensor:
														model.train()
														__snake_case	:    List[str]											=     self._prepare_inputs(lowerCamelCase       )

														if self.use_amp:
																					with autocast():
																												__snake_case	:    List[str]											=     self.compute_loss(lowerCamelCase					,			lowerCamelCase       )
														else:
																					__snake_case	:    List[Any]											=     self.compute_loss(lowerCamelCase					,			lowerCamelCase       )

														if self.args.n_gpu > 1:
																					if model.module.config.ctc_loss_reduction == "mean":
																												__snake_case	:    Dict											=     loss.mean()
																					elif model.module.config.ctc_loss_reduction == "sum":
																												__snake_case	:    List[Any]											=     loss.sum() / (inputs["labels"] >= 0).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:
																					__snake_case	:    List[Any]											=     loss / self.args.gradient_accumulation_steps

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

														return loss.detach()







def        lowerCAmelCase_  (      ):
							# 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.

							__snake_case	:    Any											=     HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)			)
							if len(sys.argv			) == 2 and sys.argv[1].endswith(".json"			):
														# If we pass only one argument to the script and it's the path to a json file,
														# let's parse it to get our arguments.
														__snake_case				,    __snake_case				,    __snake_case	:    Dict											=     parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]			)			)
							else:
														__snake_case				,    __snake_case				,    __snake_case	:    Any											=     parser.parse_args_into_dataclasses()

							# Detecting last checkpoint.
							__snake_case	:    List[str]											=     None
							if os.path.isdir(training_args.output_dir			) and training_args.do_train and not training_args.overwrite_output_dir:
														__snake_case	:    List[Any]											=     get_last_checkpoint(training_args.output_dir			)
														if last_checkpoint is None and len(os.listdir(training_args.output_dir			)			) > 0:
																					raise ValueError(
																					    F'Output directory ({training_args.output_dir}) already exists and is not empty. '
																					    "Use --overwrite_output_dir to overcome."			)
														elif last_checkpoint is not None:
																					logger.info(
																					    F'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
																					    "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."			)

    # Setup logging
							logging.basicConfig(
							    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"							,			datefmt="%m/%d/%Y %H:%M:%S"							,			handlers=[logging.StreamHandler(sys.stdout			)]							,			)
							logger.setLevel(logging.INFO if is_main_process(training_args.local_rank			) else logging.WARN			)

							# Log on each process the small summary:
							logger.warning(
							    F'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'
							    + F'distributed training: {bool(training_args.local_rank != -1			)}, 16-bits training: {training_args.fpaa}'			)
							# Set the verbosity to info of the Transformers logger (on main process only):
							if is_main_process(training_args.local_rank			):
														transformers.utils.logging.set_verbosity_info()
							logger.info("Training/evaluation parameters %s"							,			__lowerCamelCase			)

							# Set seed before initializing model.
							set_seed(training_args.seed			)

							# Get the datasets:
							__snake_case	:    List[str]											=     datasets.load_dataset(
							    "common_voice"							,			data_args.dataset_config_name							,			split=data_args.train_split_name			)
							__snake_case	:    str											=     datasets.load_dataset("common_voice"							,			data_args.dataset_config_name							,			split="test"			)

							# Create and save tokenizer
							__snake_case	:    int											=     F'[{"".join(data_args.chars_to_ignore			)}]'

							def remove_special_characters(__lowerCamelCase			):
														__snake_case	:    Optional[Any]											=     re.sub(__lowerCamelCase							,			""							,			batch["sentence"]			).lower() + " "
														return batch

							__snake_case	:    Tuple											=     train_dataset.map(__lowerCamelCase							,			remove_columns=["sentence"]			)
							__snake_case	:    List[Any]											=     eval_dataset.map(__lowerCamelCase							,			remove_columns=["sentence"]			)

							def extract_all_chars(__lowerCamelCase			):
														__snake_case	:    List[Any]											=     " ".join(batch["text"]			)
														__snake_case	:    str											=     list(set(__lowerCamelCase			)			)
														return {"vocab": [vocab], "all_text": [all_text]}

							__snake_case	:    Any											=     train_dataset.map(
							    __lowerCamelCase							,			batched=__lowerCamelCase							,			batch_size=-1							,			keep_in_memory=__lowerCamelCase							,			remove_columns=train_dataset.column_names							,			)
							__snake_case	:    Union[str, Any]											=     train_dataset.map(
							    __lowerCamelCase							,			batched=__lowerCamelCase							,			batch_size=-1							,			keep_in_memory=__lowerCamelCase							,			remove_columns=eval_dataset.column_names							,			)

							__snake_case	:    Dict											=     list(set(vocab_train["vocab"][0]			) | set(vocab_test["vocab"][0]			)			)
							__snake_case	:    List[Any]											=     {v: k for k, v in enumerate(__lowerCamelCase			)}
							__snake_case	:    List[str]											=     vocab_dict[" "]
							del vocab_dict[" "]
							__snake_case	:    Optional[int]											=     len(__lowerCamelCase			)
							__snake_case	:    Optional[Any]											=     len(__lowerCamelCase			)

							with open("vocab.json"							,			"w"			) as vocab_file:
														json.dump(__lowerCamelCase							,			__lowerCamelCase			)

							# Load pretrained model and tokenizer
							#
							# Distributed training:
							# The .from_pretrained methods guarantee that only one local process can concurrently
							# download model & vocab.
							__snake_case	:    str											=     WavaVecaCTCTokenizer(
							    "vocab.json"							,			unk_token="[UNK]"							,			pad_token="[PAD]"							,			word_delimiter_token="|"							,			)
							__snake_case	:    Optional[Any]											=     WavaVecaFeatureExtractor(
							    feature_size=1							,			sampling_rate=1_6_0_0_0							,			padding_value=0.0							,			do_normalize=__lowerCamelCase							,			return_attention_mask=__lowerCamelCase			)
							__snake_case	:    List[Any]											=     WavaVecaProcessor(feature_extractor=__lowerCamelCase							,			tokenizer=__lowerCamelCase			)
							__snake_case	:    Any											=     WavaVecaForCTC.from_pretrained(
							    model_args.model_name_or_path							,			cache_dir=model_args.cache_dir							,			activation_dropout=model_args.activation_dropout							,			attention_dropout=model_args.attention_dropout							,			hidden_dropout=model_args.hidden_dropout							,			feat_proj_dropout=model_args.feat_proj_dropout							,			mask_time_prob=model_args.mask_time_prob							,			gradient_checkpointing=training_args.gradient_checkpointing							,			layerdrop=model_args.layerdrop							,			ctc_loss_reduction="mean"							,			pad_token_id=processor.tokenizer.pad_token_id							,			vocab_size=len(processor.tokenizer			)							,			)

							if data_args.max_train_samples is not None:
														__snake_case	:    Tuple											=     min(len(__lowerCamelCase			)							,			data_args.max_train_samples			)
														__snake_case	:    str											=     train_dataset.select(range(__lowerCamelCase			)			)

							if data_args.max_val_samples is not None:
														__snake_case	:    str											=     eval_dataset.select(range(data_args.max_val_samples			)			)

							__snake_case	:    str											=     torchaudio.transforms.Resample(4_8_0_0_0							,			1_6_0_0_0			)

							# Preprocessing the datasets.
							# We need to read the aduio files as arrays and tokenize the targets.
							def speech_file_to_array_fn(__lowerCamelCase			):
														__snake_case				,    __snake_case	:    str											=     torchaudio.load(batch["path"]			)
														__snake_case	:    Optional[int]											=     resampler(__lowerCamelCase			).squeeze().numpy()
														__snake_case	:    Tuple											=     1_6_0_0_0
														__snake_case	:    Dict											=     batch["text"]
														return batch

							__snake_case	:    Any											=     train_dataset.map(
							    __lowerCamelCase							,			remove_columns=train_dataset.column_names							,			num_proc=data_args.preprocessing_num_workers							,			)
							__snake_case	:    Any											=     eval_dataset.map(
							    __lowerCamelCase							,			remove_columns=eval_dataset.column_names							,			num_proc=data_args.preprocessing_num_workers							,			)

							def prepare_dataset(__lowerCamelCase			):
														# check that all files have the correct sampling rate
														assert (
														    len(set(batch["sampling_rate"]			)			) == 1
														), F'Make sure all inputs have the same sampling rate of {processor.feature_extractor.sampling_rate}.'

														__snake_case	:    Tuple											=     processor(
														    audio=batch["speech"]							,			text=batch["target_text"]							,			sampling_rate=batch["sampling_rate"][0]			)
														batch.update(__lowerCamelCase			)
														return batch

							__snake_case	:    Union[str, Any]											=     train_dataset.map(
							    __lowerCamelCase							,			remove_columns=train_dataset.column_names							,			batch_size=training_args.per_device_train_batch_size							,			batched=__lowerCamelCase							,			num_proc=data_args.preprocessing_num_workers							,			)
							__snake_case	:    Dict											=     eval_dataset.map(
							    __lowerCamelCase							,			remove_columns=eval_dataset.column_names							,			batch_size=training_args.per_device_train_batch_size							,			batched=__lowerCamelCase							,			num_proc=data_args.preprocessing_num_workers							,			)

							# Metric
							__snake_case	:    Union[str, Any]											=     datasets.load_metric("wer"			)

							def compute_metrics(__lowerCamelCase			):
														__snake_case	:    Union[str, Any]											=     pred.predictions
														__snake_case	:    Optional[int]											=     np.argmax(__lowerCamelCase							,			axis=-1			)

														__snake_case	:    Dict											=     processor.tokenizer.pad_token_id

														__snake_case	:    Optional[Any]											=     processor.batch_decode(__lowerCamelCase			)
														# we do not want to group tokens when computing the metrics
														__snake_case	:    List[str]											=     processor.batch_decode(pred.label_ids							,			group_tokens=__lowerCamelCase			)

														__snake_case	:    Dict											=     wer_metric.compute(predictions=__lowerCamelCase							,			references=__lowerCamelCase			)

														return {"wer": wer}

							if model_args.freeze_feature_extractor:
														model.freeze_feature_extractor()

							# Data collator
							__snake_case	:    int											=     DataCollatorCTCWithPadding(processor=__lowerCamelCase							,			padding=__lowerCamelCase			)

							# Initialize our Trainer
							__snake_case	:    str											=     CTCTrainer(
							    model=__lowerCamelCase							,			data_collator=__lowerCamelCase							,			args=__lowerCamelCase							,			compute_metrics=__lowerCamelCase							,			train_dataset=train_dataset if training_args.do_train else None							,			eval_dataset=eval_dataset if training_args.do_eval else None							,			tokenizer=processor.feature_extractor							,			)

							# Training
							if training_args.do_train:
														if last_checkpoint is not None:
																					__snake_case	:    str											=     last_checkpoint
														elif os.path.isdir(model_args.model_name_or_path			):
																					__snake_case	:    List[str]											=     model_args.model_name_or_path
														else:
																					__snake_case	:    Optional[Any]											=     None

														# Save the feature_extractor and the tokenizer
														if is_main_process(training_args.local_rank			):
																					processor.save_pretrained(training_args.output_dir			)

														__snake_case	:    Any											=     trainer.train(resume_from_checkpoint=__lowerCamelCase			)
														trainer.save_model()

														__snake_case	:    int											=     train_result.metrics
														__snake_case	:    Dict											=     (
														    data_args.max_train_samples if data_args.max_train_samples is not None else len(__lowerCamelCase			)
														)
														__snake_case	:    Union[str, Any]											=     min(__lowerCamelCase							,			len(__lowerCamelCase			)			)

														trainer.log_metrics("train"							,			__lowerCamelCase			)
														trainer.save_metrics("train"							,			__lowerCamelCase			)
														trainer.save_state()

							# Evaluation
							__snake_case	:    Any											=     {}
							if training_args.do_eval:
														logger.info("*** Evaluate ***"			)
														__snake_case	:    Optional[int]											=     trainer.evaluate()
														__snake_case	:    Tuple											=     data_args.max_val_samples if data_args.max_val_samples is not None else len(__lowerCamelCase			)
														__snake_case	:    int											=     min(__lowerCamelCase							,			len(__lowerCamelCase			)			)

														trainer.log_metrics("eval"							,			__lowerCamelCase			)
														trainer.save_metrics("eval"							,			__lowerCamelCase			)

							return results


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


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







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase			):
							__snake_case	:    Union[str, Any]											=     "\n".join(__lowerCamelCase			)
							Path(__lowerCamelCase			).open("w"			).writelines(__lowerCamelCase			)







def        lowerCAmelCase_  (      __lowerCamelCase			):
							for split in ["train", "val", "test"]:
														_dump_articles(os.path.join(__lowerCamelCase							,			F'{split}.source'			)							,			__lowerCamelCase			)
														_dump_articles(os.path.join(__lowerCamelCase							,			F'{split}.target'			)							,			__lowerCamelCase			)
							return tmp_dir

class        a	(_lowerCAmelCase   ):




							"""simple docstring"""


							@parameterized.expand(
							    [
							        MBART_TINY,
							        MARIAN_TINY,
							        T5_TINY,
							        BART_TINY,
							        PEGASUS_XSUM,
							    ]					,			)
							@slow
							def 					__snake_case      (							self :						List[Any]					,			lowerCamelCase :						int       )			->					Union[str, Any]:
														__snake_case	:    Optional[Any]											=     AutoTokenizer.from_pretrained(lowerCamelCase       )
														__snake_case	:    List[Any]											=     make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )
														__snake_case	:    Optional[int]											=     max(len(tokenizer.encode(lowerCamelCase       )       ) for a in ARTICLES       )
														__snake_case	:    Optional[int]											=     max(len(tokenizer.encode(lowerCamelCase       )       ) for a in SUMMARIES       )
														__snake_case	:    str											=     4
														__snake_case	:    Optional[int]											=     8
														assert max_len_target > max_src_len  # Will be truncated
														assert max_len_source > max_src_len  # Will be truncated
														__snake_case				,    __snake_case	:    Optional[int]											=     "ro_RO", "de_DE"  # ignored for all but mbart, but never causes error.
														__snake_case	:    List[str]											=     SeqaSeqDataset(
														    lowerCamelCase					,			data_dir=lowerCamelCase					,			type_path="train"					,			max_source_length=lowerCamelCase					,			max_target_length=lowerCamelCase					,			src_lang=lowerCamelCase					,			tgt_lang=lowerCamelCase					,			)
														__snake_case	:    str											=     DataLoader(lowerCamelCase					,			batch_size=2					,			collate_fn=train_dataset.collate_fn       )
														for batch in dataloader:
																					assert isinstance(lowerCamelCase					,			lowerCamelCase       )
																					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
																					__snake_case	:    int											=     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 					__snake_case      (							self :						Union[str, Any]					,			lowerCamelCase :						List[Any]       )			->					Optional[Any]:
														__snake_case	:    Any											=     AutoTokenizer.from_pretrained(lowerCamelCase       )
														__snake_case	:    List[str]											=     make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )
														__snake_case	:    Union[str, Any]											=     max(len(tokenizer.encode(lowerCamelCase       )       ) for a in ARTICLES       )
														__snake_case	:    Tuple											=     max(len(tokenizer.encode(lowerCamelCase       )       ) for a in SUMMARIES       )
														__snake_case	:    List[str]											=     4
														__snake_case	:    List[str]											=     LegacySeqaSeqDataset(
														    lowerCamelCase					,			data_dir=lowerCamelCase					,			type_path="train"					,			max_source_length=20					,			max_target_length=lowerCamelCase					,			)
														__snake_case	:    int											=     DataLoader(lowerCamelCase					,			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 					__snake_case      (							self :						List[str]       )			->					int:
														__snake_case	:    str											=     AutoTokenizer.from_pretrained("facebook/mbart-large-cc25"       )

														__snake_case	:    int											=     Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )       )
														__snake_case	:    str											=     tmp_dir.joinpath("train.source"       ).open().readlines()
														__snake_case	:    Any											=     Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )       )
														pack_data_dir(lowerCamelCase					,			lowerCamelCase					,			128					,			lowerCamelCase       )
														__snake_case	:    str											=     {x.name for x in tmp_dir.iterdir()}
														__snake_case	:    Optional[int]											=     {x.name for x in save_dir.iterdir()}
														__snake_case	:    Optional[int]											=     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(lowerCamelCase       ) < len(lowerCamelCase       )
														assert len(lowerCamelCase       ) == 1
														assert len(packed_examples[0]       ) == sum(len(lowerCamelCase       ) for x in orig_examples       )
														assert orig_paths == new_paths

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

							def 					__snake_case      (							self :						int       )			->					Any:
														__snake_case				,    __snake_case				,    __snake_case	:    Union[str, Any]											=     self._get_dataset(max_len=512       )
														__snake_case	:    Union[str, Any]											=     2
														__snake_case	:    List[str]											=     ds.make_sortish_sampler(lowerCamelCase					,			shuffle=lowerCamelCase       )

														__snake_case	:    Dict											=     DataLoader(lowerCamelCase					,			batch_size=lowerCamelCase					,			collate_fn=ds.collate_fn					,			num_workers=2       )
														__snake_case	:    List[Any]											=     DataLoader(lowerCamelCase					,			batch_size=lowerCamelCase					,			collate_fn=ds.collate_fn					,			num_workers=2					,			sampler=lowerCamelCase       )

														__snake_case	:    List[Any]											=     tokenizer.pad_token_id

														def count_pad_tokens(lowerCamelCase :						List[Any]					,			lowerCamelCase :						Optional[int]="input_ids"       ):
																					return [batch[k].eq(lowerCamelCase       ).sum().item() for batch in data_loader]

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

							def 					__snake_case      (							self :						Any					,			lowerCamelCase :						List[Any]=1000					,			lowerCamelCase :						Union[str, Any]=128       )			->					Any:
														if os.getenv("USE_REAL_DATA"					,			lowerCamelCase       ):
																					__snake_case	:    int											=     "examples/seq2seq/wmt_en_ro"
																					__snake_case	:    Union[str, Any]											=     max_len * 2 * 64
																					if not Path(lowerCamelCase       ).joinpath("train.len"       ).exists():
																												save_len_file(lowerCamelCase					,			lowerCamelCase       )
														else:
																					__snake_case	:    List[str]											=     "examples/seq2seq/test_data/wmt_en_ro"
																					__snake_case	:    List[Any]											=     max_len * 4
																					save_len_file(lowerCamelCase					,			lowerCamelCase       )

														__snake_case	:    Dict											=     AutoTokenizer.from_pretrained(lowerCamelCase       )
														__snake_case	:    int											=     SeqaSeqDataset(
														    lowerCamelCase					,			data_dir=lowerCamelCase					,			type_path="train"					,			max_source_length=lowerCamelCase					,			max_target_length=lowerCamelCase					,			n_obs=lowerCamelCase					,			)
														return ds, max_tokens, tokenizer

							def 					__snake_case      (							self :						Tuple       )			->					Dict:
														__snake_case				,    __snake_case				,    __snake_case	:    Any											=     self._get_dataset()
														__snake_case	:    Optional[Any]											=     set(DistributedSortishSampler(lowerCamelCase					,			256					,			num_replicas=2					,			rank=0					,			add_extra_examples=lowerCamelCase       )       )
														__snake_case	:    int											=     set(DistributedSortishSampler(lowerCamelCase					,			256					,			num_replicas=2					,			rank=1					,			add_extra_examples=lowerCamelCase       )       )
														assert idsa.intersection(lowerCamelCase       ) == set()






							@parameterized.expand(
							    [
							        MBART_TINY,
							        MARIAN_TINY,
							        T5_TINY,
							        BART_TINY,
							        PEGASUS_XSUM,
							    ]					,			)
							def 					__snake_case      (							self :						List[str]					,			lowerCamelCase :						Union[str, Any]       )			->					str:
														__snake_case	:    Tuple											=     AutoTokenizer.from_pretrained(lowerCamelCase					,			use_fast=lowerCamelCase       )
														if tok_name == MBART_TINY:
																					__snake_case	:    Dict											=     SeqaSeqDataset(
																					    lowerCamelCase					,			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"					,			)
																					__snake_case	:    Any											=     train_dataset.dataset_kwargs
																					assert "src_lang" in kwargs and "tgt_lang" in kwargs
														else:
																					__snake_case	:    Union[str, Any]											=     SeqaSeqDataset(
																					    lowerCamelCase					,			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					,			)
																					__snake_case	:    Optional[int]											=     train_dataset.dataset_kwargs
																					assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
																					assert len(lowerCamelCase       ) == 1 if tok_name == BART_TINY else len(lowerCamelCase       ) == 0

