



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







def 						__UpperCamelCase				(							_lowercase,				_lowercase					) ->			Union[str, Any]:
	_lowercase					:       str       =		'\n'.join(_lowercase					)
	Path(_lowercase					).open('w'					).writelines(_lowercase					)







def 						__UpperCamelCase				(							_lowercase					) ->			List[Any]:
	for split in ["train", "val", "test"]:
		_dump_articles(os.path.join(_lowercase,				f'''{split}.source'''					),				_lowercase					)
		_dump_articles(os.path.join(_lowercase,				f'''{split}.target'''					),				_lowercase					)
	return tmp_dir






class 		lowerCamelCase__      (						A     ):




					'''simple docstring'''



					@parameterized.expand(
					    [
					        MBART_TINY,
					        MARIAN_TINY,
					        T5_TINY,
					        BART_TINY,
					        PEGASUS_XSUM,
					    ]     ,  )
					@slow
					def 						__UpperCAmelCase     (  self				:	Tuple     ,  UpperCamelCase_				:	Union[str, Any]					)  ->       List[str]:



						'''simple docstring'''





						_lowercase					:       Optional[Any]       =		AutoTokenizer.from_pretrained(UpperCamelCase_					)
						_lowercase					:       Tuple       =		make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)
						_lowercase					:       List[Any]       =		max(len(tokenizer.encode(UpperCamelCase_					)					) for a in ARTICLES					)
						_lowercase					:       Dict       =		max(len(tokenizer.encode(UpperCamelCase_					)					) for a in SUMMARIES					)
						_lowercase					:       int       =		4
						_lowercase					:       Union[str, Any]       =		8
						assert max_len_target > max_src_len  # Will be truncated
						assert max_len_source > max_src_len  # Will be truncated
						_lowercase  ,    _lowercase					:       str       =		'ro_RO', 'de_DE'  # ignored for all but mbart, but never causes error.
						_lowercase					:       Dict       =		SeqaSeqDataset(
						    UpperCamelCase_     ,  data_dir=UpperCamelCase_     ,  type_path='train'     ,  max_source_length=UpperCamelCase_     ,  max_target_length=UpperCamelCase_     ,  src_lang=UpperCamelCase_     ,  tgt_lang=UpperCamelCase_     ,  )
						_lowercase					:       List[str]       =		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
							_lowercase					:       List[Any]       =		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 						__UpperCAmelCase     (  self				:	int     ,  UpperCamelCase_				:	Union[str, Any]					)  ->       Union[str, Any]:



						'''simple docstring'''





						_lowercase					:       List[Any]       =		AutoTokenizer.from_pretrained(UpperCamelCase_					)
						_lowercase					:       Dict       =		make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)
						_lowercase					:       str       =		max(len(tokenizer.encode(UpperCamelCase_					)					) for a in ARTICLES					)
						_lowercase					:       List[Any]       =		max(len(tokenizer.encode(UpperCamelCase_					)					) for a in SUMMARIES					)
						_lowercase					:       List[Any]       =		4
						_lowercase					:       Optional[int]       =		LegacySeqaSeqDataset(
						    UpperCamelCase_     ,  data_dir=UpperCamelCase_     ,  type_path='train'     ,  max_source_length=20     ,  max_target_length=UpperCamelCase_     ,  )
						_lowercase					:       Any       =		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 						__UpperCAmelCase     (  self				:	Optional[int]					)  ->       Optional[Any]:



						'''simple docstring'''





						_lowercase					:       Tuple       =		AutoTokenizer.from_pretrained('facebook/mbart-large-cc25'					)

						_lowercase					:       int       =		Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)					)
						_lowercase					:       Dict       =		tmp_dir.joinpath('train.source'					).open().readlines()
						_lowercase					:       Optional[Any]       =		Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)					)
						pack_data_dir(UpperCamelCase_     ,  UpperCamelCase_     ,  128     ,  UpperCamelCase_					)
						_lowercase					:       Dict       =		{x.name for x in tmp_dir.iterdir()}
						_lowercase					:       Optional[int]       =		{x.name for x in save_dir.iterdir()}
						_lowercase					:       List[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 						__UpperCAmelCase     (  self				:	Any					)  ->       Any:



						'''simple docstring'''





						if not FAIRSEQ_AVAILABLE:
							return
						_lowercase  ,    _lowercase  ,    _lowercase					:       int       =		self._get_dataset(max_len=64					)
						_lowercase					:       List[Any]       =		64
						_lowercase					:       List[Any]       =		ds.make_dynamic_sampler(UpperCamelCase_     ,  required_batch_size_multiple=UpperCamelCase_					)
						_lowercase					:       Union[str, Any]       =		[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
						_lowercase					:       List[Any]       =		DataLoader(UpperCamelCase_     ,  batch_sampler=UpperCamelCase_     ,  collate_fn=ds.collate_fn     ,  num_workers=2					)
						_lowercase					:       Optional[int]       =		[]
						_lowercase					:       Optional[Any]       =		[]
						for batch in data_loader:
							_lowercase					:       str       =		batch['input_ids'].shape
							_lowercase					:       str       =		src_shape[0]
							assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
							_lowercase					:       Optional[Any]       =		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 						__UpperCAmelCase     (  self				:	Dict					)  ->       int:



						'''simple docstring'''





						_lowercase  ,    _lowercase  ,    _lowercase					:       str       =		self._get_dataset(max_len=512					)
						_lowercase					:       Any       =		2
						_lowercase					:       List[Any]       =		ds.make_sortish_sampler(UpperCamelCase_     ,  shuffle=UpperCamelCase_					)

						_lowercase					:       Union[str, Any]       =		DataLoader(UpperCamelCase_     ,  batch_size=UpperCamelCase_     ,  collate_fn=ds.collate_fn     ,  num_workers=2					)
						_lowercase					:       int       =		DataLoader(UpperCamelCase_     ,  batch_size=UpperCamelCase_     ,  collate_fn=ds.collate_fn     ,  num_workers=2     ,  sampler=UpperCamelCase_					)

						_lowercase					:       List[Any]       =		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 						__UpperCAmelCase     (  self				:	Any     ,  UpperCamelCase_				:	Optional[Any]=1000     ,  UpperCamelCase_				:	str=128					)  ->       Union[str, Any]:



						'''simple docstring'''





						if os.getenv('USE_REAL_DATA'     ,  UpperCamelCase_					):
							_lowercase					:       List[str]       =		'examples/seq2seq/wmt_en_ro'
							_lowercase					:       Any       =		max_len * 2 * 64
							if not Path(UpperCamelCase_					).joinpath('train.len'					).exists():
								save_len_file(UpperCamelCase_     ,  UpperCamelCase_					)
						else:
							_lowercase					:       Optional[int]       =		'examples/seq2seq/test_data/wmt_en_ro'
							_lowercase					:       List[str]       =		max_len * 4
							save_len_file(UpperCamelCase_     ,  UpperCamelCase_					)

						_lowercase					:       Dict       =		AutoTokenizer.from_pretrained(UpperCamelCase_					)
						_lowercase					:       List[str]       =		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 						__UpperCAmelCase     (  self				:	int					)  ->       int:



						'''simple docstring'''





						_lowercase  ,    _lowercase  ,    _lowercase					:       List[str]       =		self._get_dataset()
						_lowercase					:       List[str]       =		set(DistributedSortishSampler(UpperCamelCase_     ,  256     ,  num_replicas=2     ,  rank=0     ,  add_extra_examples=UpperCamelCase_					)					)
						_lowercase					:       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 						__UpperCAmelCase     (  self				:	Any     ,  UpperCamelCase_				:	List[str]					)  ->       Dict:



						'''simple docstring'''





						_lowercase					:       Tuple       =		AutoTokenizer.from_pretrained(UpperCamelCase_     ,  use_fast=UpperCamelCase_					)
						if tok_name == MBART_TINY:
							_lowercase					:       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'     ,  )
							_lowercase					:       int       =		train_dataset.dataset_kwargs
							assert "src_lang" in kwargs and "tgt_lang" in kwargs
						else:
							_lowercase					:       str       =		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     ,  )
							_lowercase					:       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





'''simple docstring'''

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A				:    Dict     						=logging.get_logger(__name__)

_A				:    Dict     						={
    # See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}



class 		lowerCamelCase__      (						A     ):




					'''simple docstring'''



					A_						    =						"""megatron-bert"""




					def __init__(  self				:	int     ,  UpperCamelCase_				:	int=2_9056     ,  UpperCamelCase_				:	Optional[int]=1024     ,  UpperCamelCase_				:	Optional[Any]=24     ,  UpperCamelCase_				:	List[Any]=16     ,  UpperCamelCase_				:	Optional[int]=4096     ,  UpperCamelCase_				:	Optional[Any]="gelu"     ,  UpperCamelCase_				:	List[Any]=0.1     ,  UpperCamelCase_				:	Union[str, Any]=0.1     ,  UpperCamelCase_				:	int=512     ,  UpperCamelCase_				:	Dict=2     ,  UpperCamelCase_				:	Union[str, Any]=0.02     ,  UpperCamelCase_				:	Any=1E-12     ,  UpperCamelCase_				:	Tuple=0     ,  UpperCamelCase_				:	Optional[int]="absolute"     ,  UpperCamelCase_				:	Optional[Any]=True     ,  **UpperCamelCase_				:	Any     ,  )  ->       List[Any]:



						'''simple docstring'''





						super().__init__(pad_token_id=UpperCamelCase_     ,  **UpperCamelCase_					)

						_lowercase					:       Dict       =		vocab_size
						_lowercase					:       Any       =		hidden_size
						_lowercase					:       Union[str, Any]       =		num_hidden_layers
						_lowercase					:       Dict       =		num_attention_heads
						_lowercase					:       Dict       =		hidden_act
						_lowercase					:       Optional[Any]       =		intermediate_size
						_lowercase					:       Optional[int]       =		hidden_dropout_prob
						_lowercase					:       Optional[Any]       =		attention_probs_dropout_prob
						_lowercase					:       Any       =		max_position_embeddings
						_lowercase					:       str       =		type_vocab_size
						_lowercase					:       Optional[Any]       =		initializer_range
						_lowercase					:       List[str]       =		layer_norm_eps
						_lowercase					:       List[Any]       =		position_embedding_type
						_lowercase					:       Optional[Any]       =		use_cache
