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


def  _UpperCamelCase	(						_A					,      _A      )    ->  Dict:





    """simple docstring"""

    _UpperCAmelCase          =   """\n""".join(_A      )
    Path(_A      ).open("""w"""      ).writelines(_A      )


def  _UpperCamelCase	(						_A      )    ->  str:





    """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 		a_						( _UpperCAmelCase	):







    @parameterized.expand(
        [
            MBART_TINY,
            MARIAN_TINY,
            T5_TINY,
            BART_TINY,
            PEGASUS_XSUM,
        ]     ,					)
    @slow
    def      _snake_case					(			self			:       List[Any]     ,					__UpperCamelCase			:       Any     )		->Any:






        '''simple docstring'''






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






        '''simple docstring'''






        _UpperCAmelCase          =   AutoTokenizer.from_pretrained(__UpperCamelCase     )
        _UpperCAmelCase          =   make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     )
        _UpperCAmelCase          =   max(len(tokenizer.encode(__UpperCamelCase     )     ) for a in ARTICLES     )
        _UpperCAmelCase          =   max(len(tokenizer.encode(__UpperCamelCase     )     ) for a in SUMMARIES     )
        _UpperCAmelCase          =   4
        _UpperCAmelCase          =   LegacySeqaSeqDataset(
            __UpperCamelCase     ,					data_dir=__UpperCamelCase     ,					type_path="""train"""     ,					max_source_length=20     ,					max_target_length=__UpperCamelCase     ,					)
        _UpperCAmelCase          =   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      _snake_case					(			self			:       List[Any]     )		->Union[str, Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   AutoTokenizer.from_pretrained("""facebook/mbart-large-cc25"""     )

        _UpperCAmelCase          =   Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     )     )
        _UpperCAmelCase          =   tmp_dir.joinpath("""train.source"""     ).open().readlines()
        _UpperCAmelCase          =   Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     )     )
        pack_data_dir(__UpperCamelCase     ,					__UpperCamelCase     ,					1_28     ,					__UpperCamelCase     )
        _UpperCAmelCase          =   {x.name for x in tmp_dir.iterdir()}
        _UpperCAmelCase          =   {x.name for x in save_dir.iterdir()}
        _UpperCAmelCase          =   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      _snake_case					(			self			:       Dict     )		->Optional[int]:






        '''simple docstring'''






        if not FAIRSEQ_AVAILABLE:
            return
        _UpperCAmelCase    ,_UpperCAmelCase    ,_UpperCAmelCase          =   self._get_dataset(max_len=64     )
        _UpperCAmelCase          =   64
        _UpperCAmelCase          =   ds.make_dynamic_sampler(__UpperCamelCase     ,					required_batch_size_multiple=__UpperCamelCase     )
        _UpperCAmelCase          =   [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
        _UpperCAmelCase          =   DataLoader(__UpperCamelCase     ,					batch_sampler=__UpperCamelCase     ,					collate_fn=ds.collate_fn     ,					num_workers=2     )
        _UpperCAmelCase          =   []
        _UpperCAmelCase          =   []
        for batch in data_loader:
            _UpperCAmelCase          =   batch["""input_ids"""].shape
            _UpperCAmelCase          =   src_shape[0]
            assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
            _UpperCAmelCase          =   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      _snake_case					(			self			:       Any     )		->Union[str, Any]:






        '''simple docstring'''






        _UpperCAmelCase    ,_UpperCAmelCase    ,_UpperCAmelCase          =   self._get_dataset(max_len=5_12     )
        _UpperCAmelCase          =   2
        _UpperCAmelCase          =   ds.make_sortish_sampler(__UpperCamelCase     ,					shuffle=__UpperCamelCase     )

        _UpperCAmelCase          =   DataLoader(__UpperCamelCase     ,					batch_size=__UpperCamelCase     ,					collate_fn=ds.collate_fn     ,					num_workers=2     )
        _UpperCAmelCase          =   DataLoader(__UpperCamelCase     ,					batch_size=__UpperCamelCase     ,					collate_fn=ds.collate_fn     ,					num_workers=2     ,					sampler=__UpperCamelCase     )

        _UpperCAmelCase          =   tokenizer.pad_token_id

        def count_pad_tokens(__UpperCamelCase			:       Union[str, 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      _snake_case					(			self			:       int     ,					__UpperCamelCase			:       Tuple=10_00     ,					__UpperCamelCase			:       List[Any]=1_28     )		->str:






        '''simple docstring'''






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

        _UpperCAmelCase          =   AutoTokenizer.from_pretrained(__UpperCamelCase     )
        _UpperCAmelCase          =   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      _snake_case					(			self			:       List[str]     )		->str:






        '''simple docstring'''






        _UpperCAmelCase    ,_UpperCAmelCase    ,_UpperCAmelCase          =   self._get_dataset()
        _UpperCAmelCase          =   set(DistributedSortishSampler(__UpperCamelCase     ,					2_56     ,					num_replicas=2     ,					rank=0     ,					add_extra_examples=__UpperCamelCase     )     )
        _UpperCAmelCase          =   set(DistributedSortishSampler(__UpperCamelCase     ,					2_56     ,					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      _snake_case					(			self			:       Dict     ,					__UpperCamelCase			:       Tuple     )		->List[str]:






        '''simple docstring'''






        _UpperCAmelCase          =   AutoTokenizer.from_pretrained(__UpperCamelCase     ,					use_fast=__UpperCamelCase     )
        if tok_name == MBART_TINY:
            _UpperCAmelCase          =   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"""     ,					)
            _UpperCAmelCase          =   train_dataset.dataset_kwargs
            assert "src_lang" in kwargs and "tgt_lang" in kwargs
        else:
            _UpperCAmelCase          =   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     ,					)
            _UpperCAmelCase          =   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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a					:	Tuple							=		{
    '''configuration_pegasus_x''': ['''PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PegasusXConfig'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a					:	List[str]							=		[
     '''PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''PegasusXForConditionalGeneration''',
     '''PegasusXModel''',
     '''PegasusXPreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_pegasus_x import PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusXConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_pegasus_x import (
      PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST,
      PegasusXForConditionalGeneration,
      PegasusXModel,
      PegasusXPreTrainedModel,
  )


else:
 import sys

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