





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


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


def 							SCREAMING_SNAKE_CASE__  (      __a				,	__a				):
	snake_case_						:     List[Any]     =		'\n'.join(__a				)
	Path(__a				).open('w'				).writelines(__a				)


def 							SCREAMING_SNAKE_CASE__  (      __a				):
	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 SCREAMING_SNAKE_CASE_  (   snake_case_   ):






						@parameterized.expand(
						    [
						        MBART_TINY,
						        MARIAN_TINY,
						        T5_TINY,
						        BART_TINY,
						        PEGASUS_XSUM,
						    ]						,				)
						@slow
						def UpperCAmelCase_			( self : List[str]						,				_A : Any		)							->      Optional[int]:







							"""simple docstring"""


							snake_case_						:     int     =		AutoTokenizer.from_pretrained(_A		)
							snake_case_						:     Any     =		make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)
							snake_case_						:     Dict     =		max(len(tokenizer.encode(_A		)		) for a in ARTICLES		)
							snake_case_						:     str     =		max(len(tokenizer.encode(_A		)		) for a in SUMMARIES		)
							snake_case_						:     str     =		4
							snake_case_						:     List[str]     =		8
							assert max_len_target > max_src_len  # Will be truncated
							assert max_len_source > max_src_len  # Will be truncated
							snake_case_  ,snake_case_						:     Optional[int]     =		'ro_RO', 'de_DE'  # ignored for all but mbart, but never causes error.
							snake_case_						:     Union[str, Any]     =		SeqaSeqDataset(
							    _A						,				data_dir=_A						,				type_path='train'						,				max_source_length=_A						,				max_target_length=_A						,				src_lang=_A						,				tgt_lang=_A						,				)
							snake_case_						:     Optional[Any]     =		DataLoader(_A						,				batch_size=2						,				collate_fn=train_dataset.collate_fn		)
							for batch in dataloader:
								assert isinstance(_A						,				_A		)
								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
								snake_case_						:     Union[str, 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 : Any						,				_A : List[str]		)							->      Any:







							"""simple docstring"""


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







							"""simple docstring"""


							snake_case_						:     Dict     =		AutoTokenizer.from_pretrained('facebook/mbart-large-cc25'		)

							snake_case_						:     List[Any]     =		Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)		)
							snake_case_						:     Optional[Any]     =		tmp_dir.joinpath('train.source'		).open().readlines()
							snake_case_						:     Optional[int]     =		Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)		)
							pack_data_dir(_A						,				_A						,				128						,				_A		)
							snake_case_						:     Optional[int]     =		{x.name for x in tmp_dir.iterdir()}
							snake_case_						:     Optional[Any]     =		{x.name for x in save_dir.iterdir()}
							snake_case_						:     List[str]     =		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(_A		) < len(_A		)
							assert len(_A		) == 1
							assert len(packed_examples[0]		) == sum(len(_A		) for x in orig_examples		)
							assert orig_paths == new_paths






						@pytest.mark.skipif(not FAIRSEQ_AVAILABLE						,				reason='This test requires fairseq'		)
						def UpperCAmelCase_			( self : Union[str, Any]		)							->      Any:







							"""simple docstring"""


							if not FAIRSEQ_AVAILABLE:
								return
							snake_case_  ,snake_case_  ,snake_case_						:     str     =		self._get_dataset(max_len=64		)
							snake_case_						:     Optional[int]     =		64
							snake_case_						:     List[Any]     =		ds.make_dynamic_sampler(_A						,				required_batch_size_multiple=_A		)
							snake_case_						:     Optional[int]     =		[len(_A		) for x in batch_sampler]
							assert len(set(_A		)		) > 1  # it's not dynamic batch size if every batch is the same length
							assert sum(_A		) == len(_A		)  # no dropped or added examples
							snake_case_						:     List[str]     =		DataLoader(_A						,				batch_sampler=_A						,				collate_fn=ds.collate_fn						,				num_workers=2		)
							snake_case_						:     List[Any]     =		[]
							snake_case_						:     Optional[int]     =		[]
							for batch in data_loader:
								snake_case_						:     str     =		batch['input_ids'].shape
								snake_case_						:     Tuple     =		src_shape[0]
								assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
								snake_case_						:     Optional[int]     =		np.product(batch['input_ids'].shape		)
								num_src_per_batch.append(_A		)
								if num_src_tokens > (max_tokens * 1.1):
									failures.append(_A		)
							assert num_src_per_batch[0] == max(_A		)
							if failures:
								raise AssertionError(F"""too many tokens in {len(_A		)} batches"""		)






						def UpperCAmelCase_			( self : int		)							->      Tuple:







							"""simple docstring"""


							snake_case_  ,snake_case_  ,snake_case_						:     Optional[Any]     =		self._get_dataset(max_len=512		)
							snake_case_						:     Dict     =		2
							snake_case_						:     Dict     =		ds.make_sortish_sampler(_A						,				shuffle=_A		)

							snake_case_						:     Dict     =		DataLoader(_A						,				batch_size=_A						,				collate_fn=ds.collate_fn						,				num_workers=2		)
							snake_case_						:     Union[str, Any]     =		DataLoader(_A						,				batch_size=_A						,				collate_fn=ds.collate_fn						,				num_workers=2						,				sampler=_A		)

							snake_case_						:     List[Any]     =		tokenizer.pad_token_id

							def count_pad_tokens(_A : Union[str, Any]						,				_A : int="input_ids"		):
								return [batch[k].eq(_A		).sum().item() for batch in data_loader]

							assert sum(count_pad_tokens(_A						,				k='labels'		)		) < sum(count_pad_tokens(_A						,				k='labels'		)		)
							assert sum(count_pad_tokens(_A		)		) < sum(count_pad_tokens(_A		)		)
							assert len(_A		) == len(_A		)






						def UpperCAmelCase_			( self : List[Any]						,				_A : Optional[int]=1000						,				_A : List[str]=128		)							->      int:







							"""simple docstring"""


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

							snake_case_						:     List[str]     =		AutoTokenizer.from_pretrained(_A		)
							snake_case_						:     str     =		SeqaSeqDataset(
							    _A						,				data_dir=_A						,				type_path='train'						,				max_source_length=_A						,				max_target_length=_A						,				n_obs=_A						,				)
							return ds, max_tokens, tokenizer






						def UpperCAmelCase_			( self : int		)							->      str:







							"""simple docstring"""


							snake_case_  ,snake_case_  ,snake_case_						:     Optional[int]     =		self._get_dataset()
							snake_case_						:     List[str]     =		set(DistributedSortishSampler(_A						,				256						,				num_replicas=2						,				rank=0						,				add_extra_examples=_A		)		)
							snake_case_						:     List[str]     =		set(DistributedSortishSampler(_A						,				256						,				num_replicas=2						,				rank=1						,				add_extra_examples=_A		)		)
							assert idsa.intersection(_A		) == set()






						@parameterized.expand(
						    [
						        MBART_TINY,
						        MARIAN_TINY,
						        T5_TINY,
						        BART_TINY,
						        PEGASUS_XSUM,
						    ]						,				)
						def UpperCAmelCase_			( self : int						,				_A : int		)							->      Tuple:







							"""simple docstring"""


							snake_case_						:     List[Any]     =		AutoTokenizer.from_pretrained(_A						,				use_fast=_A		)
							if tok_name == MBART_TINY:
								snake_case_						:     Optional[int]     =		SeqaSeqDataset(
								    _A						,				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'						,				)
								snake_case_						:     Dict     =		train_dataset.dataset_kwargs
								assert "src_lang" in kwargs and "tgt_lang" in kwargs
							else:
								snake_case_						:     Optional[int]     =		SeqaSeqDataset(
								    _A						,				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						,				)
								snake_case_						:     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(_A		) == 1 if tok_name == BART_TINY else len(_A		) == 0








# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
#   python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results

import re
import subprocess
import sys


_SCREAMING_SNAKE_CASE 		=						subprocess.check_output("""git merge-base main HEAD""".split()).decode("""utf-8""")
_SCREAMING_SNAKE_CASE 		=						(
    subprocess.check_output(F'''git diff --diff-filter=d --name-only {fork_point_sha}'''.split()).decode("""utf-8""").split()
)

_SCREAMING_SNAKE_CASE 		=						"""|""".join(sys.argv[1:])
_SCREAMING_SNAKE_CASE 		=						re.compile(RF'''^({joined_dirs}).*?\.py$''')

_SCREAMING_SNAKE_CASE 		=						[x for x in modified_files if regex.match(x)]
print(""" """.join(relevant_modified_files), end="""""")

