




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







def 		lowercase__   (		__A:	Path							,__A:	list				):





					'''simple docstring'''






					__magic_name__							:	int    							=							'''\n'''.join(__A				)
					Path(__A				).open('''w'''				).writelines(__A				)







def 		lowercase__   (		__A:	Tuple				):





					'''simple docstring'''






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


class    lowerCamelCase    (		_lowerCamelCase						):






			'''simple docstring'''





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

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

								__magic_name__							:	Any    							=							DataLoader(lowerCamelCase_       ,						batch_size=lowerCamelCase_       ,						collate_fn=ds.collate_fn       ,						num_workers=2  )
								__magic_name__							:	Dict    							=							DataLoader(lowerCamelCase_       ,						batch_size=lowerCamelCase_       ,						collate_fn=ds.collate_fn       ,						num_workers=2       ,						sampler=lowerCamelCase_  )

								__magic_name__							:	Dict    							=							tokenizer.pad_token_id

								def count_pad_tokens(lowerCamelCase_			:   List[Any]       ,						lowerCamelCase_			:   Dict="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 							UpperCAmelCase__       ( self			:   Optional[Any]       ,						lowerCamelCase_			:   Dict=1000       ,						lowerCamelCase_			:   Dict=128  )						->   Dict:
								if os.getenv('''USE_REAL_DATA'''       ,						lowerCamelCase_  ):
													__magic_name__							:	Any    							=							'''examples/seq2seq/wmt_en_ro'''
													__magic_name__							:	List[Any]    							=							max_len * 2 * 64
													if not Path(lowerCamelCase_  ).joinpath('''train.len'''  ).exists():
																		save_len_file(lowerCamelCase_       ,						lowerCamelCase_  )
								else:
													__magic_name__							:	Any    							=							'''examples/seq2seq/test_data/wmt_en_ro'''
													__magic_name__							:	Optional[Any]    							=							max_len * 4
													save_len_file(lowerCamelCase_       ,						lowerCamelCase_  )

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









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


__lowerCamelCase   :						List[str]				=			{
    '''configuration_roberta_prelayernorm''': [
        '''ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''RobertaPreLayerNormConfig''',
        '''RobertaPreLayerNormOnnxConfig''',
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase   :						Dict				=			[
	    '''ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''RobertaPreLayerNormForCausalLM''',
	    '''RobertaPreLayerNormForMaskedLM''',
	    '''RobertaPreLayerNormForMultipleChoice''',
	    '''RobertaPreLayerNormForQuestionAnswering''',
	    '''RobertaPreLayerNormForSequenceClassification''',
	    '''RobertaPreLayerNormForTokenClassification''',
	    '''RobertaPreLayerNormModel''',
	    '''RobertaPreLayerNormPreTrainedModel''',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase   :						Optional[Any]				=			[
	    '''TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TFRobertaPreLayerNormForCausalLM''',
	    '''TFRobertaPreLayerNormForMaskedLM''',
	    '''TFRobertaPreLayerNormForMultipleChoice''',
	    '''TFRobertaPreLayerNormForQuestionAnswering''',
	    '''TFRobertaPreLayerNormForSequenceClassification''',
	    '''TFRobertaPreLayerNormForTokenClassification''',
	    '''TFRobertaPreLayerNormMainLayer''',
	    '''TFRobertaPreLayerNormModel''',
	    '''TFRobertaPreLayerNormPreTrainedModel''',
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase   :						Optional[Any]				=			[
	    '''FlaxRobertaPreLayerNormForCausalLM''',
	    '''FlaxRobertaPreLayerNormForMaskedLM''',
	    '''FlaxRobertaPreLayerNormForMultipleChoice''',
	    '''FlaxRobertaPreLayerNormForQuestionAnswering''',
	    '''FlaxRobertaPreLayerNormForSequenceClassification''',
	    '''FlaxRobertaPreLayerNormForTokenClassification''',
	    '''FlaxRobertaPreLayerNormModel''',
	    '''FlaxRobertaPreLayerNormPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_roberta_prelayernorm import (
	    ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    RobertaPreLayerNormConfig,
	    RobertaPreLayerNormOnnxConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_roberta_prelayernorm import (
		    ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    RobertaPreLayerNormForCausalLM,
		    RobertaPreLayerNormForMaskedLM,
		    RobertaPreLayerNormForMultipleChoice,
		    RobertaPreLayerNormForQuestionAnswering,
		    RobertaPreLayerNormForSequenceClassification,
		    RobertaPreLayerNormForTokenClassification,
		    RobertaPreLayerNormModel,
		    RobertaPreLayerNormPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_roberta_prelayernorm import (
		    TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFRobertaPreLayerNormForCausalLM,
		    TFRobertaPreLayerNormForMaskedLM,
		    TFRobertaPreLayerNormForMultipleChoice,
		    TFRobertaPreLayerNormForQuestionAnswering,
		    TFRobertaPreLayerNormForSequenceClassification,
		    TFRobertaPreLayerNormForTokenClassification,
		    TFRobertaPreLayerNormMainLayer,
		    TFRobertaPreLayerNormModel,
		    TFRobertaPreLayerNormPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_roberta_prelayernorm import (
		    FlaxRobertaPreLayerNormForCausalLM,
		    FlaxRobertaPreLayerNormForMaskedLM,
		    FlaxRobertaPreLayerNormForMultipleChoice,
		    FlaxRobertaPreLayerNormForQuestionAnswering,
		    FlaxRobertaPreLayerNormForSequenceClassification,
		    FlaxRobertaPreLayerNormForTokenClassification,
		    FlaxRobertaPreLayerNormModel,
		    FlaxRobertaPreLayerNormPreTrainedModel,
		)

else:
	import sys

	__lowerCamelCase   :						Dict				=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



