





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






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    Path    ,       _lowercase						:    list					) ->Optional[Any]:






	'''simple docstring'''






	a      :		str	=				"\n".join(_lowercase					)
	Path(_lowercase					).open("w"					).writelines(_lowercase					)






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    Tuple					) ->Optional[Any]:






	'''simple docstring'''






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







class        __UpperCamelCase				(						a__						):






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

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

								a      :		Union[str, Any]	=				DataLoader(lowerCAmelCase__      ,					batch_size=lowerCAmelCase__      ,					collate_fn=ds.collate_fn      ,					num_workers=2      )
								a      :		Any	=				DataLoader(lowerCAmelCase__      ,					batch_size=lowerCAmelCase__      ,					collate_fn=ds.collate_fn      ,					num_workers=2      ,					sampler=lowerCAmelCase__      )

								a      :		List[Any]	=				tokenizer.pad_token_id

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

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




from unittest.mock import Mock, patch

from file_transfer.send_file import send_file






@patch("socket.socket"					)
@patch("builtins.open"					)
def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    List[Any]    ,       _lowercase						:    int					) ->str:






	'''simple docstring'''






	a      :		Optional[Any]	=				Mock()
	a      :		Dict	=				conn, Mock()
	a      :		Union[str, Any]	=				iter([1, None]					)
	a      :		Optional[int]	=				lambda _lowercase					: next(_lowercase					)

	# ===== invoke =====
	send_file(filename="mytext.txt"    ,       testing=_lowercase					)

	# ===== ensurance =====
	sock.assert_called_once()
	sock.return_value.bind.assert_called_once()
	sock.return_value.listen.assert_called_once()
	sock.return_value.accept.assert_called_once()
	conn.recv.assert_called_once()

	file.return_value.__enter__.assert_called_once()
	file.return_value.__enter__.return_value.read.assert_called()

	conn.send.assert_called_once()
	conn.close.assert_called_once()
	sock.return_value.shutdown.assert_called_once()
	sock.return_value.close.assert_called_once()


