'''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=  '''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  _UpperCamelCase (     _a	:	Path				,     _a	:	list			):




    """simple docstring"""



    __UpperCamelCase	:	str		    =  '\n'.join(_a			)
    Path(_a			).open('w'			).writelines(_a			)







def  _UpperCamelCase (     _a	:	List[str]			):




    """simple docstring"""



    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 			__lowercase		(						_lowerCamelCase						):







     """simple docstring"""






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

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

         __UpperCamelCase	:	List[str]		    =  DataLoader(_lowerCamelCase     ,			batch_size=_lowerCamelCase     ,			collate_fn=ds.collate_fn     ,			num_workers=2    )
         __UpperCamelCase	:	List[Any]		    =  DataLoader(_lowerCamelCase     ,			batch_size=_lowerCamelCase     ,			collate_fn=ds.collate_fn     ,			num_workers=2     ,			sampler=_lowerCamelCase    )

         __UpperCamelCase	:	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 lowerCAmelCase     (		self     ,			_lowerCamelCase=1_0_0_0     ,			_lowerCamelCase=1_2_8    ):
         if os.getenv('USE_REAL_DATA'     ,			_lowerCamelCase    ):
             __UpperCamelCase	:	Dict		    =  'examples/seq2seq/wmt_en_ro'
             __UpperCamelCase	:	List[Any]		    =  max_len * 2 * 6_4
             if not Path(_lowerCamelCase    ).joinpath('train.len'    ).exists():
                 save_len_file(_lowerCamelCase     ,			_lowerCamelCase    )
         else:
             __UpperCamelCase	:	Optional[Any]		    =  'examples/seq2seq/test_data/wmt_en_ro'
             __UpperCamelCase	:	str		    =  max_len * 4
             save_len_file(_lowerCamelCase     ,			_lowerCamelCase    )

         __UpperCamelCase	:	List[Any]		    =  AutoTokenizer.from_pretrained(_lowerCamelCase    )
         __UpperCamelCase	:	int		    =  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 lowerCAmelCase     (		self    ):
         __UpperCamelCase    ,					__UpperCamelCase    ,					__UpperCamelCase	:	str		    =  self._get_dataset()
         __UpperCamelCase	:	List[str]		    =  set(DistributedSortishSampler(_lowerCamelCase     ,			2_5_6     ,			num_replicas=2     ,			rank=0     ,			add_extra_examples=_lowerCamelCase    )    )
         __UpperCamelCase	:	List[Any]		    =  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 lowerCAmelCase     (		self     ,			_lowerCamelCase    ):
         __UpperCamelCase	:	Dict		    =  AutoTokenizer.from_pretrained(_lowerCamelCase     ,			use_fast=_lowerCamelCase    )
         if tok_name == MBART_TINY:
             __UpperCamelCase	:	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'     ,			)
             __UpperCamelCase	:	Tuple		    =  train_dataset.dataset_kwargs
             assert "src_lang" in kwargs and "tgt_lang" in kwargs
         else:
             __UpperCamelCase	:	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     ,			)
             __UpperCamelCase	:	Dict		    =  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 timeit import timeit

a=  {
    '''MALAYALAM''': True,
    '''String''': False,
    '''rotor''': True,
    '''level''': True,
    '''A''': True,
    '''BB''': True,
    '''ABC''': False,
    '''amanaplanacanalpanama''': True,  # "a man a plan a canal panama"
}
# Ensure our test data is valid
assert all((key == key[::-1]) is value for key, value in test_data.items())







def  _UpperCamelCase (     _a	:	str			):




    """simple docstring"""



    __UpperCamelCase	:	int		    =  0
    __UpperCamelCase	:	str		    =  len(_a			) - 1
    while start_i < end_i:
        if s[start_i] == s[end_i]:
            start_i += 1
            end_i -= 1
        else:
            return False
    return True







def  _UpperCamelCase (     _a	:	str			):




    """simple docstring"""



    __UpperCamelCase	:	Optional[Any]		    =  len(_a			) // 2
    __UpperCamelCase	:	Tuple		    =  len(_a			)

    # We need to traverse till half of the length of string
    # as we can get access of the i'th last element from
    # i'th index.
    # eg: [0,1,2,3,4,5] => 4th index can be accessed
    # with the help of 1st index (i==n-i-1)
    # where n is length of string
    return all(s[i] == s[n - i - 1] for i in range(_a			)			)







def  _UpperCamelCase (     _a	:	str			):




    """simple docstring"""



    if len(_a			) <= 2:
        return True
    if s[0] == s[len(_a			) - 1]:
        return is_palindrome_recursive(s[1:-1]			)
    else:
        return False







def  _UpperCamelCase (     _a	:	str			):




    """simple docstring"""



    return s == s[::-1]







def  _UpperCamelCase (     _a	:	str			):




    """simple docstring"""



    __UpperCamelCase	:	int		    =  f"""all({name}(key) is value for key, value in test_data.items())"""
    __UpperCamelCase	:	Union[str, Any]		    =  f"""from __main__ import test_data, {name}"""
    __UpperCamelCase	:	Any		    =  5_0_0_0_0_0
    __UpperCamelCase	:	Tuple		    =  timeit(stmt=_a				,     setup=_a				,     number=_a			)
    print(f"""{name:<35} finished {number:,} runs in {result:.5f} seconds"""			)


if __name__ == "__main__":
  for key, value in test_data.items():
    assert is_palindrome(key) is is_palindrome_recursive(key)
    assert is_palindrome(key) is is_palindrome_slice(key)
    print(F"""{key:21} {value}""")
  print('''a man a plan a canal panama''')

  # finished 500,000 runs in 0.46793 seconds
  benchmark_function('''is_palindrome_slice''')
  # finished 500,000 runs in 0.85234 seconds
  benchmark_function('''is_palindrome''')
  # finished 500,000 runs in 1.32028 seconds
  benchmark_function('''is_palindrome_recursive''')
  # finished 500,000 runs in 2.08679 seconds
  benchmark_function('''is_palindrome_traversal''')



