






"""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


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





def 				lowercase__		(					lowerCamelCase,						lowerCamelCase       ):
				_SCREAMING_SNAKE_CASE   :		int       		=    '\n'.join(lowerCamelCase       )
				Path(lowerCamelCase       ).open('w'       ).writelines(lowerCamelCase       )





def 				lowercase__		(					lowerCamelCase       ):
				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						(    __UpperCAmelCase       ):







						@parameterized.expand(
						    [
						        MBART_TINY,
						        MARIAN_TINY,
						        T5_TINY,
						        BART_TINY,
						        PEGASUS_XSUM,
						    ]   ,				)
						@slow
						def 			A      (    self   ,				lowerCAmelCase_							)		->							int:
										_SCREAMING_SNAKE_CASE   :		Dict       		=    AutoTokenizer.from_pretrained(lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Dict       		=    make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)
										_SCREAMING_SNAKE_CASE   :		Any       		=    max(len(tokenizer.encode(lowerCAmelCase_							)							) for a in ARTICLES							)
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    max(len(tokenizer.encode(lowerCAmelCase_							)							) for a in SUMMARIES							)
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    4
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    8
										assert max_len_target > max_src_len  # Will be truncated
										assert max_len_source > max_src_len  # Will be truncated
										_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		Tuple       		=    'ro_RO', 'de_DE'  # ignored for all but mbart, but never causes error.
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    SeqaSeqDataset(
										    lowerCAmelCase_   ,				data_dir=lowerCAmelCase_   ,				type_path='train'   ,				max_source_length=lowerCAmelCase_   ,				max_target_length=lowerCAmelCase_   ,				src_lang=lowerCAmelCase_   ,				tgt_lang=lowerCAmelCase_   ,				)
										_SCREAMING_SNAKE_CASE   :		Optional[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
														_SCREAMING_SNAKE_CASE   :		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   ,				lowerCAmelCase_							)		->							Dict:
										_SCREAMING_SNAKE_CASE   :		Any       		=    AutoTokenizer.from_pretrained(lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)
										_SCREAMING_SNAKE_CASE   :		str       		=    max(len(tokenizer.encode(lowerCAmelCase_							)							) for a in ARTICLES							)
										_SCREAMING_SNAKE_CASE   :		Dict       		=    max(len(tokenizer.encode(lowerCAmelCase_							)							) for a in SUMMARIES							)
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    4
										_SCREAMING_SNAKE_CASE   :		int       		=    LegacySeqaSeqDataset(
										    lowerCAmelCase_   ,				data_dir=lowerCAmelCase_   ,				type_path='train'   ,				max_source_length=2_0   ,				max_target_length=lowerCAmelCase_   ,				)
										_SCREAMING_SNAKE_CASE   :		Any       		=    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 2_0 >= 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							)		->							Tuple:
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    AutoTokenizer.from_pretrained('facebook/mbart-large-cc25'							)

										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)							)
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    tmp_dir.joinpath('train.source'							).open().readlines()
										_SCREAMING_SNAKE_CASE   :		str       		=    Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)							)
										pack_data_dir(lowerCAmelCase_   ,				lowerCAmelCase_   ,				1_2_8   ,				lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Any       		=    {x.name for x in tmp_dir.iterdir()}
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    {x.name for x in save_dir.iterdir()}
										_SCREAMING_SNAKE_CASE   :		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(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							)		->							Union[str, Any]:
										if not FAIRSEQ_AVAILABLE:
														return
										_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		Tuple       		=    self._get_dataset(max_len=6_4							)
										_SCREAMING_SNAKE_CASE   :		int       		=    6_4
										_SCREAMING_SNAKE_CASE   :		int       		=    ds.make_dynamic_sampler(lowerCAmelCase_   ,				required_batch_size_multiple=lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    [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
										_SCREAMING_SNAKE_CASE   :		Any       		=    DataLoader(lowerCAmelCase_   ,				batch_sampler=lowerCAmelCase_   ,				collate_fn=ds.collate_fn   ,				num_workers=2							)
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    []
										_SCREAMING_SNAKE_CASE   :		int       		=    []
										for batch in data_loader:
														_SCREAMING_SNAKE_CASE   :		Dict       		=    batch['input_ids'].shape
														_SCREAMING_SNAKE_CASE   :		Dict       		=    src_shape[0]
														assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
														_SCREAMING_SNAKE_CASE   :		List[str]       		=    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							)		->							Union[str, Any]:
										_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		Any       		=    self._get_dataset(max_len=5_1_2							)
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    2
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    ds.make_sortish_sampler(lowerCAmelCase_   ,				shuffle=lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		str       		=    DataLoader(lowerCAmelCase_   ,				batch_size=lowerCAmelCase_   ,				collate_fn=ds.collate_fn   ,				num_workers=2							)
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    DataLoader(lowerCAmelCase_   ,				batch_size=lowerCAmelCase_   ,				collate_fn=ds.collate_fn   ,				num_workers=2   ,				sampler=lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		str       		=    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_=1_0_0_0   ,				lowerCAmelCase_=1_2_8							)		->							str:
										if os.getenv('USE_REAL_DATA'   ,				lowerCAmelCase_							):
														_SCREAMING_SNAKE_CASE   :		int       		=    'examples/seq2seq/wmt_en_ro'
														_SCREAMING_SNAKE_CASE   :		Dict       		=    max_len * 2 * 6_4
														if not Path(lowerCAmelCase_							).joinpath('train.len'							).exists():
																		save_len_file(lowerCAmelCase_   ,				lowerCAmelCase_							)
										else:
														_SCREAMING_SNAKE_CASE   :		Tuple       		=    'examples/seq2seq/test_data/wmt_en_ro'
														_SCREAMING_SNAKE_CASE   :		str       		=    max_len * 4
														save_len_file(lowerCAmelCase_   ,				lowerCAmelCase_							)

										_SCREAMING_SNAKE_CASE   :		int       		=    AutoTokenizer.from_pretrained(lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		str       		=    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							)		->							List[Any]:
										_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		List[Any]       		=    self._get_dataset()
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    set(DistributedSortishSampler(lowerCAmelCase_   ,				2_5_6   ,				num_replicas=2   ,				rank=0   ,				add_extra_examples=lowerCAmelCase_							)							)
										_SCREAMING_SNAKE_CASE   :		Dict       		=    set(DistributedSortishSampler(lowerCAmelCase_   ,				2_5_6   ,				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_							)		->							Any:
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    AutoTokenizer.from_pretrained(lowerCAmelCase_   ,				use_fast=lowerCAmelCase_							)
										if tok_name == MBART_TINY:
														_SCREAMING_SNAKE_CASE   :		int       		=    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'   ,				)
														_SCREAMING_SNAKE_CASE   :		Any       		=    train_dataset.dataset_kwargs
														assert "src_lang" in kwargs and "tgt_lang" in kwargs
										else:
														_SCREAMING_SNAKE_CASE   :		List[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   ,				)
														_SCREAMING_SNAKE_CASE   :		Union[str, 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(lowerCAmelCase_							) == 1 if tok_name == BART_TINY else len(lowerCAmelCase_							) == 0









"""simple docstring"""
lowerCAmelCase__    						=			'\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'

lowerCAmelCase__    						=			[{'type': 'code', 'content': INSTALL_CONTENT}]
lowerCAmelCase__    						=			{
    '{processor_class}': 'FakeProcessorClass',
    '{model_class}': 'FakeModelClass',
    '{object_class}': 'FakeObjectClass',
}

