




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







def 			lowerCamelCase_		(       UpperCamelCase__				:      Path ,     UpperCamelCase__				:      list      )     ->       List[str]:





				"""simple docstring"""


				__lowerCamelCase		       =    '\n'.join(UpperCamelCase__      )
				Path(UpperCamelCase__      ).open('w'      ).writelines(UpperCamelCase__      )







def 			lowerCamelCase_		(       UpperCamelCase__				:      str      )     ->       Optional[int]:





				"""simple docstring"""


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







class     __lowerCAmelCase (       __magic_name__ ):

			"""simple docstring"""







			@parameterized.expand(
			    [
			        MBART_TINY,
			        MARIAN_TINY,
			        T5_TINY,
			        BART_TINY,
			        PEGASUS_XSUM,
			    ]			,						)
			@slow
			def       lowercase_    (      self			,						lowerCamelCase__							)    ->      Any:



							'''simple docstring'''
							__lowerCamelCase		       =    AutoTokenizer.from_pretrained(lowerCamelCase__							)
							__lowerCamelCase		       =    make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)
							__lowerCamelCase		       =    max(len(tokenizer.encode(lowerCamelCase__							)							) for a in ARTICLES							)
							__lowerCamelCase		       =    max(len(tokenizer.encode(lowerCamelCase__							)							) for a in SUMMARIES							)
							__lowerCamelCase		       =    4
							__lowerCamelCase		       =    8
							assert max_len_target > max_src_len  # Will be truncated
							assert max_len_source > max_src_len  # Will be truncated
							__lowerCamelCase       ,						__lowerCamelCase		       =    'ro_RO', 'de_DE'  # ignored for all but mbart, but never causes error.
							__lowerCamelCase		       =    SeqaSeqDataset(
							    lowerCamelCase__			,						data_dir=lowerCamelCase__			,						type_path='train'			,						max_source_length=lowerCamelCase__			,						max_target_length=lowerCamelCase__			,						src_lang=lowerCamelCase__			,						tgt_lang=lowerCamelCase__			,						)
							__lowerCamelCase		       =    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
											__lowerCamelCase		       =    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       lowercase_    (      self			,						lowerCamelCase__							)    ->      Tuple:



							'''simple docstring'''
							__lowerCamelCase		       =    AutoTokenizer.from_pretrained(lowerCamelCase__							)
							__lowerCamelCase		       =    make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)
							__lowerCamelCase		       =    max(len(tokenizer.encode(lowerCamelCase__							)							) for a in ARTICLES							)
							__lowerCamelCase		       =    max(len(tokenizer.encode(lowerCamelCase__							)							) for a in SUMMARIES							)
							__lowerCamelCase		       =    4
							__lowerCamelCase		       =    LegacySeqaSeqDataset(
							    lowerCamelCase__			,						data_dir=lowerCamelCase__			,						type_path='train'			,						max_source_length=20			,						max_target_length=lowerCamelCase__			,						)
							__lowerCamelCase		       =    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       lowercase_    (      self							)    ->      Optional[int]:



							'''simple docstring'''
							__lowerCamelCase		       =    AutoTokenizer.from_pretrained('facebook/mbart-large-cc25'							)

							__lowerCamelCase		       =    Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)							)
							__lowerCamelCase		       =    tmp_dir.joinpath('train.source'							).open().readlines()
							__lowerCamelCase		       =    Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)							)
							pack_data_dir(lowerCamelCase__			,						lowerCamelCase__			,						128			,						lowerCamelCase__							)
							__lowerCamelCase		       =    {x.name for x in tmp_dir.iterdir()}
							__lowerCamelCase		       =    {x.name for x in save_dir.iterdir()}
							__lowerCamelCase		       =    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       lowercase_    (      self							)    ->      Dict:



							'''simple docstring'''
							if not FAIRSEQ_AVAILABLE:
											return
							__lowerCamelCase       ,						__lowerCamelCase       ,						__lowerCamelCase		       =    self._get_dataset(max_len=64							)
							__lowerCamelCase		       =    64
							__lowerCamelCase		       =    ds.make_dynamic_sampler(lowerCamelCase__			,						required_batch_size_multiple=lowerCamelCase__							)
							__lowerCamelCase		       =    [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
							__lowerCamelCase		       =    DataLoader(lowerCamelCase__			,						batch_sampler=lowerCamelCase__			,						collate_fn=ds.collate_fn			,						num_workers=2							)
							__lowerCamelCase		       =    []
							__lowerCamelCase		       =    []
							for batch in data_loader:
											__lowerCamelCase		       =    batch['input_ids'].shape
											__lowerCamelCase		       =    src_shape[0]
											assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
											__lowerCamelCase		       =    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       lowercase_    (      self							)    ->      Union[str, Any]:



							'''simple docstring'''
							__lowerCamelCase       ,						__lowerCamelCase       ,						__lowerCamelCase		       =    self._get_dataset(max_len=512							)
							__lowerCamelCase		       =    2
							__lowerCamelCase		       =    ds.make_sortish_sampler(lowerCamelCase__			,						shuffle=lowerCamelCase__							)

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

							__lowerCamelCase		       =    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       lowercase_    (      self			,						lowerCamelCase__=1_000			,						lowerCamelCase__=128							)    ->      Dict:



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

							__lowerCamelCase		       =    AutoTokenizer.from_pretrained(lowerCamelCase__							)
							__lowerCamelCase		       =    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       lowercase_    (      self							)    ->      Tuple:



							'''simple docstring'''
							__lowerCamelCase       ,						__lowerCamelCase       ,						__lowerCamelCase		       =    self._get_dataset()
							__lowerCamelCase		       =    set(DistributedSortishSampler(lowerCamelCase__			,						256			,						num_replicas=2			,						rank=0			,						add_extra_examples=lowerCamelCase__							)							)
							__lowerCamelCase		       =    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       lowercase_    (      self			,						lowerCamelCase__							)    ->      Union[str, Any]:



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




class      A__							:






				def __init__(						self					:      Dict    ,     a					:      list[int]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Union[str, Any]         =		len(a   )
							lowerCAmelCase__      :      Optional[Any]         =		[0] * len_array

							if len_array > 0:
										lowerCAmelCase__      :      List[Any]         =		array[0]

							for i in range(1    ,     a   ):
										lowerCAmelCase__      :      Optional[int]         =		self.prefix_sum[i - 1] + array[i]






				def 							_lowerCamelCase		(						self					:      int    ,     a					:      int    ,     a					:      int   ):






							'''simple docstring'''







							if start == 0:
										return self.prefix_sum[end]

							return self.prefix_sum[end] - self.prefix_sum[start - 1]






				def 							_lowerCamelCase		(						self					:      Optional[Any]    ,     a					:      int   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Any         =		{0}
							for sum_item in self.prefix_sum:
										if sum_item - target_sum in sums:
													return True

										sums.add(a   )

							return False


if __name__ == "__main__":
					import doctest

					doctest.testmod()