





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


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


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase )			->     Optional[Any]:
    lowerCamelCase__    :      Union[str, Any]					 =		'\n'.join(__lowerCamelCase )
    Path(__lowerCamelCase ).open('w' ).writelines(__lowerCamelCase )


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

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

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

     lowerCamelCase__    :      Tuple					 =		tokenizer.pad_token_id

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

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

import importlib.metadata
import operator
import re
import sys
from typing import Optional

from packaging import version


lowercase_					= {
    """<""": operator.lt,
    """<=""": operator.le,
    """==""": operator.eq,
    """!=""": operator.ne,
    """>=""": operator.ge,
    """>""": operator.gt,
}





def lowerCamelCase						(     __lowerCamelCase :    Tuple					,       __lowerCamelCase :    Optional[Any]					,       __lowerCamelCase :    Dict					,       __lowerCamelCase :    List[Any]					,       __lowerCamelCase :    Optional[int]					,       __lowerCamelCase :    Optional[Any]    )						->Tuple:
    if got_ver is None or want_ver is None:
        raise ValueError(
            F'Unable to compare versions for {requirement}: need={want_ver} found={got_ver}. This is unusual. Consider'
            F' reinstalling {pkg}.'    )
    if not ops[op](version.parse(__lowerCamelCase    )					,       version.parse(__lowerCamelCase    )    ):
        raise ImportError(
            F'{requirement} is required for a normal functioning of this module, but found {pkg}=={got_ver}.{hint}'    )





def lowerCamelCase						(     __lowerCamelCase :    str					,       __lowerCamelCase :    Optional[str] = None    )						->None:
    _SCREAMING_SNAKE_CASE									=			F'\n{hint}' if hint is not None else """"""

    # non-versioned check
    if re.match(R"""^[\w_\-\d]+$"""					,       __lowerCamelCase    ):
        _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			requirement, None, None
    else:
        _SCREAMING_SNAKE_CASE									=			re.findall(R"""^([^!=<>\s]+)([\s!=<>]{1,2}.+)"""					,       __lowerCamelCase    )
        if not match:
            raise ValueError(
                """requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23, but"""
                F' got {requirement}'    )
        _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			match[0]
        _SCREAMING_SNAKE_CASE									=			want_full.split(""","""    )  # there could be multiple requirements
        _SCREAMING_SNAKE_CASE									=			{}
        for w in want_range:
            _SCREAMING_SNAKE_CASE									=			re.findall(R"""^([\s!=<>]{1,2})(.+)"""					,       __lowerCamelCase    )
            if not match:
                raise ValueError(
                    """requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23,"""
                    F' but got {requirement}'    )
            _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			match[0]
            _SCREAMING_SNAKE_CASE									=			want_ver
            if op not in ops:
                raise ValueError(F'{requirement}: need one of {list(ops.keys()    )}, but got {op}'    )

    # special case
    if pkg == "python":
        _SCREAMING_SNAKE_CASE									=			""".""".join([str(__lowerCamelCase    ) for x in sys.version_info[:3]]    )
        for op, want_ver in wanted.items():
            _compare_versions(__lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase    )
        return

    # check if any version is installed
    try:
        _SCREAMING_SNAKE_CASE									=			importlib.metadata.version(__lowerCamelCase    )
    except importlib.metadata.PackageNotFoundError:
        raise importlib.metadata.PackageNotFoundError(
            F'The \'{requirement}\' distribution was not found and is required by this application. {hint}'    )

    # check that the right version is installed if version number or a range was provided
    if want_ver is not None:
        for op, want_ver in wanted.items():
            _compare_versions(__lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase    )






def lowerCamelCase						(     __lowerCamelCase :    Union[str, Any]    )						->str:
    _SCREAMING_SNAKE_CASE									=			"""Try: pip install transformers -U or pip install -e '.[dev]' if you're working with git main"""
    return require_version(__lowerCamelCase					,       __lowerCamelCase    )



