




'''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


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





							'''simple docstring'''


							_a			  =       "\n".join(_lowerCamelCase   )
							Path(_lowerCamelCase   ).open('''w'''   ).writelines(_lowerCamelCase   )
def 	snake_case_					(UpperCamelCase :						Union[str, Any]   ):





							'''simple docstring'''


							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     A					(	_A					):







							@parameterized.expand(
							    [
							        MBART_TINY,
							        MARIAN_TINY,
							        T5_TINY,
							        BART_TINY,
							        PEGASUS_XSUM,
							    ]							,    )
							@slow
							def      __lowerCAmelCase    (						self :						str							,    lowerCAmelCase_ :						Optional[int]      )    ->							Tuple:


														"""simple docstring"""


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


														"""simple docstring"""


														_a			  =       AutoTokenizer.from_pretrained(__lowerCamelCase      )
														_a			  =       make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )
														_a			  =       max(len(tokenizer.encode(__lowerCamelCase      )      ) for a in ARTICLES      )
														_a			  =       max(len(tokenizer.encode(__lowerCamelCase      )      ) for a in SUMMARIES      )
														_a			  =       4
														_a			  =       LegacySeqaSeqDataset(
														    __lowerCamelCase							,    data_dir=__lowerCamelCase							,    type_path='''train'''							,    max_source_length=20							,    max_target_length=__lowerCamelCase							,    )
														_a			  =       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      __lowerCAmelCase    (						self :						List[str]      )    ->							List[str]:


														"""simple docstring"""


														_a			  =       AutoTokenizer.from_pretrained('''facebook/mbart-large-cc25'''      )

														_a			  =       Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )      )
														_a			  =       tmp_dir.joinpath('''train.source'''      ).open().readlines()
														_a			  =       Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )      )
														pack_data_dir(__lowerCamelCase							,    __lowerCamelCase							,    1_28							,    __lowerCamelCase      )
														_a			  =       {x.name for x in tmp_dir.iterdir()}
														_a			  =       {x.name for x in save_dir.iterdir()}
														_a			  =       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      __lowerCAmelCase    (						self :						int      )    ->							Any:


														"""simple docstring"""


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


														"""simple docstring"""


														_a			  =       self._get_dataset(max_len=5_12      )
														_a			  =       2
														_a			  =       ds.make_sortish_sampler(__lowerCamelCase							,    shuffle=__lowerCamelCase      )

														_a			  =       DataLoader(__lowerCamelCase							,    batch_size=__lowerCamelCase							,    collate_fn=ds.collate_fn							,    num_workers=2      )
														_a			  =       DataLoader(__lowerCamelCase							,    batch_size=__lowerCamelCase							,    collate_fn=ds.collate_fn							,    num_workers=2							,    sampler=__lowerCamelCase      )

														_a			  =       tokenizer.pad_token_id

														def count_pad_tokens(lowerCAmelCase_ :						List[str]							,    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      __lowerCAmelCase    (						self :						List[str]							,    lowerCAmelCase_ :						Any=10_00							,    lowerCAmelCase_ :						List[Any]=1_28      )    ->							Any:


														"""simple docstring"""


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

														_a			  =       AutoTokenizer.from_pretrained(__lowerCamelCase      )
														_a			  =       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      __lowerCAmelCase    (						self :						Any      )    ->							Any:


														"""simple docstring"""


														_a			  =       self._get_dataset()
														_a			  =       set(DistributedSortishSampler(__lowerCamelCase							,    2_56							,    num_replicas=2							,    rank=0							,    add_extra_examples=__lowerCamelCase      )      )
														_a			  =       set(DistributedSortishSampler(__lowerCamelCase							,    2_56							,    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      __lowerCAmelCase    (						self :						Dict							,    lowerCAmelCase_ :						List[str]      )    ->							Optional[Any]:


														"""simple docstring"""


														_a			  =       AutoTokenizer.from_pretrained(__lowerCamelCase							,    use_fast=__lowerCamelCase      )
														if tok_name == MBART_TINY:
																					_a			  =       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'''							,    )
																					_a			  =       train_dataset.dataset_kwargs
																					assert "src_lang" in kwargs and "tgt_lang" in kwargs
														else:
																					_a			  =       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							,    )
																					_a			  =       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'''


import os

import pytest

from transformers.dynamic_module_utils import get_imports


_snake_case   :   List[str]     							=	'\nimport os\n'

_snake_case   :   Dict     							=	'\ndef foo():\n    import os\n    return False\n'

_snake_case   :   List[Any]     							=	'\ndef foo():\n    def bar():\n        if True:\n            import os\n        return False\n    return bar()\n'

_snake_case   :   Dict     							=	'\nimport os\n\ntry:\n    import bar\nexcept ImportError:\n    raise ValueError()\n'

_snake_case   :   Optional[Any]     							=	'\nimport os\n\ndef foo():\n    try:\n        import bar\n    except ImportError:\n        raise ValueError()\n'

_snake_case   :   Optional[Any]     							=	'\nimport os\n\ntry:\n    import bar\nexcept (ImportError, AttributeError):\n    raise ValueError()\n'

_snake_case   :   Any     							=	'\nimport os\n\ntry:\n    import bar\nexcept ImportError as e:\n    raise ValueError()\n'

_snake_case   :   List[str]     							=	'\nimport os\n\ntry:\n    import bar\nexcept:\n    raise ValueError()\n'

_snake_case   :   Dict     							=	'\nimport os\n\ntry:\n    import bar\n    import baz\nexcept ImportError:\n    raise ValueError()\n'

_snake_case   :   Any     							=	'\nimport os\n\ntry:\n    import bar\n    import baz\nexcept ImportError:\n    x = 1\n    raise ValueError()\n'

_snake_case   :   Union[str, Any]     							=	[
    TOP_LEVEL_IMPORT,
    IMPORT_IN_FUNCTION,
    DEEPLY_NESTED_IMPORT,
    TOP_LEVEL_TRY_IMPORT,
    GENERIC_EXCEPT_IMPORT,
    MULTILINE_TRY_IMPORT,
    MULTILINE_BOTH_IMPORT,
    MULTIPLE_EXCEPTS_IMPORT,
    EXCEPT_AS_IMPORT,
    TRY_IMPORT_IN_FUNCTION,
]
@pytest.mark.parametrize('''case'''				,		UpperCamelCase   )
def 	snake_case_					(UpperCamelCase :						str				,		UpperCamelCase :						List[Any]   ):





							'''simple docstring'''


							_a			  =       os.path.join(UpperCamelCase				,		'''test_file.py'''   )
							with open(UpperCamelCase				,		'''w'''   ) as _tmp_file:
														_tmp_file.write(UpperCamelCase   )

							_a			  =       get_imports(UpperCamelCase   )
							assert parsed_imports == ["os"]

