



'''simple docstring'''




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


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



def    __lowerCamelCase						(      _lowercase			,       _lowercase	)   ->    List[Any]:
							UpperCAmelCase  :	str							  =	"\n".join(_lowerCamelCase	)
							Path(_lowerCamelCase	).open("""w"""	).writelines(_lowerCamelCase	)



def    __lowerCamelCase						(      _lowercase	)   ->    str:
							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     UpperCamelCase_				(							_A    ):





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

								UpperCAmelCase  :	Optional[Any]							  =	Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)					)
								UpperCAmelCase  :	Optional[int]							  =	tmp_dir.joinpath("""train.source"""					).open().readlines()
								UpperCAmelCase  :	Union[str, Any]							  =	Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)					)
								pack_data_dir(__lowerCamelCase	,	__lowerCamelCase	,	128	,	__lowerCamelCase					)
								UpperCAmelCase  :	List[str]							  =	{x.name for x in tmp_dir.iterdir()}
								UpperCAmelCase  :	Tuple							  =	{x.name for x in save_dir.iterdir()}
								UpperCAmelCase  :	List[str]							  =	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 				_lowercase(						self					)    ->    Any:
								if not FAIRSEQ_AVAILABLE:
															return
								UpperCAmelCase  :	Any							  =	self._get_dataset(max_len=64					)
								UpperCAmelCase  :	List[Any]							  =	64
								UpperCAmelCase  :	str							  =	ds.make_dynamic_sampler(__lowerCamelCase	,	required_batch_size_multiple=__lowerCamelCase					)
								UpperCAmelCase  :	Optional[Any]							  =	[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
								UpperCAmelCase  :	Any							  =	DataLoader(__lowerCamelCase	,	batch_sampler=__lowerCamelCase	,	collate_fn=ds.collate_fn	,	num_workers=2					)
								UpperCAmelCase  :	List[Any]							  =	[]
								UpperCAmelCase  :	Union[str, Any]							  =	[]
								for batch in data_loader:
															UpperCAmelCase  :	Union[str, Any]							  =	batch["input_ids"].shape
															UpperCAmelCase  :	str							  =	src_shape[0]
															assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
															UpperCAmelCase  :	int							  =	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 				_lowercase(						self					)    ->    str:
								UpperCAmelCase  :	Dict							  =	self._get_dataset(max_len=512					)
								UpperCAmelCase  :	List[str]							  =	2
								UpperCAmelCase  :	List[str]							  =	ds.make_sortish_sampler(__lowerCamelCase	,	shuffle=__lowerCamelCase					)

								UpperCAmelCase  :	Any							  =	DataLoader(__lowerCamelCase	,	batch_size=__lowerCamelCase	,	collate_fn=ds.collate_fn	,	num_workers=2					)
								UpperCAmelCase  :	List[str]							  =	DataLoader(__lowerCamelCase	,	batch_size=__lowerCamelCase	,	collate_fn=ds.collate_fn	,	num_workers=2	,	sampler=__lowerCamelCase					)

								UpperCAmelCase  :	Optional[Any]							  =	tokenizer.pad_token_id

								def count_pad_tokens(A	,	A="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 				_lowercase(						self	,	A=1000	,	A=128					)    ->    Any:
								if os.getenv("""USE_REAL_DATA"""	,	__lowerCamelCase					):
															UpperCAmelCase  :	List[Any]							  =	"examples/seq2seq/wmt_en_ro"
															UpperCAmelCase  :	Dict							  =	max_len * 2 * 64
															if not Path(__lowerCamelCase					).joinpath("""train.len"""					).exists():
																						save_len_file(__lowerCamelCase	,	__lowerCamelCase					)
								else:
															UpperCAmelCase  :	Tuple							  =	"examples/seq2seq/test_data/wmt_en_ro"
															UpperCAmelCase  :	List[str]							  =	max_len * 4
															save_len_file(__lowerCamelCase	,	__lowerCamelCase					)

								UpperCAmelCase  :	Any							  =	AutoTokenizer.from_pretrained(__lowerCamelCase					)
								UpperCAmelCase  :	Tuple							  =	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 				_lowercase(						self					)    ->    Any:
								UpperCAmelCase  :	Any							  =	self._get_dataset()
								UpperCAmelCase  :	Dict							  =	set(DistributedSortishSampler(__lowerCamelCase	,	256	,	num_replicas=2	,	rank=0	,	add_extra_examples=__lowerCamelCase					)					)
								UpperCAmelCase  :	Union[str, Any]							  =	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 				_lowercase(						self	,	A					)    ->    Optional[Any]:
								UpperCAmelCase  :	List[str]							  =	AutoTokenizer.from_pretrained(__lowerCamelCase	,	use_fast=__lowerCamelCase					)
								if tok_name == MBART_TINY:
															UpperCAmelCase  :	str							  =	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"""	,	)
															UpperCAmelCase  :	int							  =	train_dataset.dataset_kwargs
															assert "src_lang" in kwargs and "tgt_lang" in kwargs
								else:
															UpperCAmelCase  :	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	,	)
															UpperCAmelCase  :	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






'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


a     :    Any      						=					{
    """configuration_nezha""": ["""NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """NezhaConfig"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a     :    Any      						=					[
							    """NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """NezhaForNextSentencePrediction""",
							    """NezhaForMaskedLM""",
							    """NezhaForPreTraining""",
							    """NezhaForMultipleChoice""",
							    """NezhaForQuestionAnswering""",
							    """NezhaForSequenceClassification""",
							    """NezhaForTokenClassification""",
							    """NezhaModel""",
							    """NezhaPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_nezha import (
														    NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    NezhaForMaskedLM,
														    NezhaForMultipleChoice,
														    NezhaForNextSentencePrediction,
														    NezhaForPreTraining,
														    NezhaForQuestionAnswering,
														    NezhaForSequenceClassification,
														    NezhaForTokenClassification,
														    NezhaModel,
														    NezhaPreTrainedModel,
														)


else:
							import sys

							a     :    Union[str, Any]      						=					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

