






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

def      __lowercase					( snake_case,  snake_case ):







				"""simple docstring"""






				__magic_name__		:int        =      '''\n'''.join(snake_case )
				Path(snake_case ).open('''w''' ).writelines(snake_case )

def      __lowercase					( snake_case ):







				"""simple docstring"""






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

class 						lowerCamelCase_				(      lowerCamelCase ):







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

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

								__magic_name__		:str        =      DataLoader(__lowerCAmelCase       ,					batch_size=__lowerCAmelCase       ,					collate_fn=ds.collate_fn       ,					num_workers=2     )
								__magic_name__		:Dict        =      DataLoader(__lowerCAmelCase       ,					batch_size=__lowerCAmelCase       ,					collate_fn=ds.collate_fn       ,					num_workers=2       ,					sampler=__lowerCAmelCase     )

								__magic_name__		:Dict        =      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     ):
								"""simple docstring"""
								if os.getenv('''USE_REAL_DATA'''       ,					__lowerCAmelCase     ):
												__magic_name__		:Any        =      '''examples/seq2seq/wmt_en_ro'''
												__magic_name__		:Dict        =      max_len * 2 * 6_4
												if not Path(__lowerCAmelCase     ).joinpath('''train.len'''     ).exists():
																save_len_file(__lowerCAmelCase       ,					__lowerCAmelCase     )
								else:
												__magic_name__		:Optional[int]        =      '''examples/seq2seq/test_data/wmt_en_ro'''
												__magic_name__		:Tuple        =      max_len * 4
												save_len_file(__lowerCAmelCase       ,					__lowerCAmelCase     )

								__magic_name__		:Optional[Any]        =      AutoTokenizer.from_pretrained(__lowerCAmelCase     )
								__magic_name__		:Dict        =      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     ):
								"""simple docstring"""
								__magic_name__     ,				__magic_name__     ,				__magic_name__		:Tuple        =      self._get_dataset()
								__magic_name__		:str        =      set(DistributedSortishSampler(__lowerCAmelCase       ,					2_5_6       ,					num_replicas=2       ,					rank=0       ,					add_extra_examples=__lowerCAmelCase     )     )
								__magic_name__		:List[str]        =      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     ):
								"""simple docstring"""
								__magic_name__		:List[Any]        =      AutoTokenizer.from_pretrained(__lowerCAmelCase       ,					use_fast=__lowerCAmelCase     )
								if tok_name == MBART_TINY:
												__magic_name__		: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'''       ,					)
												__magic_name__		:Union[str, Any]        =      train_dataset.dataset_kwargs
												assert "src_lang" in kwargs and "tgt_lang" in kwargs
								else:
												__magic_name__		:str        =      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       ,					)
												__magic_name__		: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














def      __lowercase					( snake_case,  snake_case,  snake_case,  snake_case ):







				"""simple docstring"""






				global f  # a global dp table for knapsack
				if f[i][j] < 0:
								if j < wt[i - 1]:
												__magic_name__		:Dict        =      mf_knapsack(i - 1,  snake_case,  snake_case,  snake_case )
								else:
												__magic_name__		:Optional[Any]        =      max(
												    mf_knapsack(i - 1,  snake_case,  snake_case,  snake_case ),  mf_knapsack(i - 1,  snake_case,  snake_case,  j - wt[i - 1] ) + val[i - 1],  )
								__magic_name__		:List[Any]        =      val
				return f[i][j]

def      __lowercase					( snake_case,  snake_case,  snake_case,  snake_case ):







				"""simple docstring"""






				__magic_name__		:str        =      [[0] * (w + 1) for _ in range(n + 1 )]

				for i in range(1,  n + 1 ):
								for w_ in range(1,  w + 1 ):
												if wt[i - 1] <= w_:
																__magic_name__		:List[str]        =      max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]],  dp[i - 1][w_] )
												else:
																__magic_name__		:Optional[int]        =      dp[i - 1][w_]

				return dp[n][w_], dp

def      __lowercase					( snake_case,  snake_case,  snake_case ):







				"""simple docstring"""






				if not (isinstance(snake_case,  (list, tuple) ) and isinstance(snake_case,  (list, tuple) )):
								raise ValueError(
								    '''Both the weights and values vectors must be either lists or tuples''' )

				__magic_name__		:Dict        =      len(snake_case )
				if num_items != len(snake_case ):
								__magic_name__		:str        =      (
								    '''The number of weights must be the same as the number of values.\n'''
								    f'''But got {num_items} weights and {len(snake_case )} values'''
								)
								raise ValueError(snake_case )
				for i in range(snake_case ):
								if not isinstance(wt[i],  snake_case ):
												__magic_name__		:str        =      (
												    '''All weights must be integers but got weight of '''
												    f'''type {type(wt[i] )} at index {i}'''
												)
												raise TypeError(snake_case )

				__magic_name__     ,				__magic_name__		:Tuple        =      knapsack(snake_case,  snake_case,  snake_case,  snake_case )
				__magic_name__		:set        =      set()
				_construct_solution(snake_case,  snake_case,  snake_case,  snake_case,  snake_case )

				return optimal_val, example_optional_set

def      __lowercase					( snake_case,  snake_case,  snake_case,  snake_case,  snake_case ):







				"""simple docstring"""






				if i > 0 and j > 0:
								if dp[i - 1][j] == dp[i][j]:
												_construct_solution(snake_case,  snake_case,  i - 1,  snake_case,  snake_case )
								else:
												optimal_set.add(snake_case )
												_construct_solution(snake_case,  snake_case,  i - 1,  j - wt[i - 1],  snake_case )


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__      :			Union[str, Any]							=		[3, 2, 4, 4]
					SCREAMING_SNAKE_CASE__      :			Any							=		[4, 3, 2, 3]
					SCREAMING_SNAKE_CASE__      :			List[str]							=		4
					SCREAMING_SNAKE_CASE__      :			Optional[int]							=		6
					SCREAMING_SNAKE_CASE__      :			str							=		[[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      :			List[str]							=		knapsack(w, wt, val, n)
					print(optimal_solution)
					print(mf_knapsack(n, wt, val, w))  # switched the n and w

					# testing the dynamic programming problem with example
					# the optimal subset for the above example are items 3 and 4
					SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      :			List[str]							=		knapsack_with_example_solution(w, wt, val)
					assert optimal_solution == 8
					assert optimal_subset == {3, 4}
					print("""optimal_value = """, optimal_solution)
					print("""An optimal subset corresponding to the optimal value""", optimal_subset)






