


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        snake_case_					(  snake_case      ,							snake_case					)  ->    Tuple:
       lowercase__:       List[str]        =       '\n'.join(snake_case					)
       Path(snake_case					).open('w'					).writelines(snake_case					)




def        snake_case_					(  snake_case					)  ->    List[Any]:
       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   __a      (   __UpperCamelCase				):
 @parameterized.expand(
     [
         MBART_TINY,
         MARIAN_TINY,
         T5_TINY,
         BART_TINY,
         PEGASUS_XSUM,
     ]    ,			)
 @slow
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__			) ->    Any:




        '''simple docstring'''
        lowercase__:       Tuple        =       AutoTokenizer.from_pretrained(lowerCAmelCase__			)
        lowercase__:       List[str]        =       make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()			)
        lowercase__:       List[Any]        =       max(len(tokenizer.encode(lowerCAmelCase__			)			) for a in ARTICLES			)
        lowercase__:       Union[str, Any]        =       max(len(tokenizer.encode(lowerCAmelCase__			)			) for a in SUMMARIES			)
        lowercase__:       Dict        =       4
        lowercase__:       Optional[int]        =       8
        assert max_len_target > max_src_len  # Will be truncated
        assert max_len_source > max_src_len  # Will be truncated
        lowercase__      ,						lowercase__:       int        =       'ro_RO', 'de_DE'  # ignored for all but mbart, but never causes error.
        lowercase__:       Tuple        =       SeqaSeqDataset(
            lowerCAmelCase__    ,			data_dir=lowerCAmelCase__    ,			type_path='train'    ,			max_source_length=lowerCAmelCase__    ,			max_target_length=lowerCAmelCase__    ,			src_lang=lowerCAmelCase__    ,			tgt_lang=lowerCAmelCase__    ,			)
        lowercase__:       Tuple        =       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
               lowercase__:       str        =       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 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__			) ->    List[Any]:




        '''simple docstring'''
        lowercase__:       str        =       AutoTokenizer.from_pretrained(lowerCAmelCase__			)
        lowercase__:       Dict        =       make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()			)
        lowercase__:       Tuple        =       max(len(tokenizer.encode(lowerCAmelCase__			)			) for a in ARTICLES			)
        lowercase__:       Optional[int]        =       max(len(tokenizer.encode(lowerCAmelCase__			)			) for a in SUMMARIES			)
        lowercase__:       Dict        =       4
        lowercase__:       List[Any]        =       LegacySeqaSeqDataset(
            lowerCAmelCase__    ,			data_dir=lowerCAmelCase__    ,			type_path='train'    ,			max_source_length=20    ,			max_target_length=lowerCAmelCase__    ,			)
        lowercase__:       Optional[int]        =       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 		SCREAMING_SNAKE_CASE__       (       self			) ->    Union[str, Any]:




        '''simple docstring'''
        lowercase__:       str        =       AutoTokenizer.from_pretrained('facebook/mbart-large-cc25'			)

        lowercase__:       Tuple        =       Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()			)			)
        lowercase__:       Tuple        =       tmp_dir.joinpath('train.source'			).open().readlines()
        lowercase__:       Union[str, Any]        =       Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()			)			)
        pack_data_dir(lowerCAmelCase__    ,			lowerCAmelCase__    ,			128    ,			lowerCAmelCase__			)
        lowercase__:       Tuple        =       {x.name for x in tmp_dir.iterdir()}
        lowercase__:       Dict        =       {x.name for x in save_dir.iterdir()}
        lowercase__:       Union[str, 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 		SCREAMING_SNAKE_CASE__       (       self			) ->    Tuple:




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




        '''simple docstring'''
        lowercase__      ,						lowercase__      ,						lowercase__:       Any        =       self._get_dataset(max_len=512			)
        lowercase__:       Dict        =       2
        lowercase__:       Optional[int]        =       ds.make_sortish_sampler(lowerCAmelCase__    ,			shuffle=lowerCAmelCase__			)

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

        lowercase__:       Optional[int]        =       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 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__=1_000    ,			lowerCAmelCase__=128			) ->    Any:




        '''simple docstring'''
        if os.getenv('USE_REAL_DATA'    ,			lowerCAmelCase__			):
               lowercase__:       List[Any]        =       'examples/seq2seq/wmt_en_ro'
               lowercase__:       Optional[Any]        =       max_len * 2 * 64
               if not Path(lowerCAmelCase__			).joinpath('train.len'			).exists():
                      save_len_file(lowerCAmelCase__    ,			lowerCAmelCase__			)
        else:
               lowercase__:       List[str]        =       'examples/seq2seq/test_data/wmt_en_ro'
               lowercase__:       int        =       max_len * 4
               save_len_file(lowerCAmelCase__    ,			lowerCAmelCase__			)

        lowercase__:       str        =       AutoTokenizer.from_pretrained(lowerCAmelCase__			)
        lowercase__:       List[Any]        =       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 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[str]:




        '''simple docstring'''
        lowercase__      ,						lowercase__      ,						lowercase__:       Optional[int]        =       self._get_dataset()
        lowercase__:       int        =       set(DistributedSortishSampler(lowerCAmelCase__    ,			256    ,			num_replicas=2    ,			rank=0    ,			add_extra_examples=lowerCAmelCase__			)			)
        lowercase__:       Tuple        =       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 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__			) ->    Tuple:




        '''simple docstring'''
        lowercase__:       Tuple        =       AutoTokenizer.from_pretrained(lowerCAmelCase__    ,			use_fast=lowerCAmelCase__			)
        if tok_name == MBART_TINY:
               lowercase__:       Dict        =       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'    ,			)
               lowercase__:       str        =       train_dataset.dataset_kwargs
               assert "src_lang" in kwargs and "tgt_lang" in kwargs
        else:
               lowercase__:       List[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    ,			)
               lowercase__:       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






from math import pow




def        snake_case_					(  snake_case      ,							snake_case      ,							snake_case      ,							snake_case      ,							snake_case      ,							)  ->    tuple[int, int]:
       if current_sum == needed_sum:
              # If the sum of the powers is equal to needed_sum, then we have a solution.
              solutions_count += 1
              return current_sum, solutions_count

       lowercase__:       int        =       int(pow(snake_case      ,							snake_case					)					)
       if current_sum + i_to_n <= needed_sum:
              # If the sum of the powers is less than needed_sum, then continue adding powers.
              current_sum += i_to_n
              lowercase__      ,						lowercase__:       Any        =       backtrack(
                  snake_case      ,							snake_case      ,							current_number + 1      ,							snake_case      ,							snake_case					)
              current_sum -= i_to_n
       if i_to_n < needed_sum:
              # If the power of i is less than needed_sum, then try with the next power.
              lowercase__      ,						lowercase__:       Optional[int]        =       backtrack(
                  snake_case      ,							snake_case      ,							current_number + 1      ,							snake_case      ,							snake_case					)
       return current_sum, solutions_count




def        snake_case_					(  snake_case      ,							snake_case					)  ->    int:
       if not (1 <= needed_sum <= 10_00 and 2 <= power <= 10):
              raise ValueError(
                  'Invalid input\n'
                  'needed_sum must be between 1 and 1000, power between 2 and 10.'					)

       return backtrack(snake_case      ,							snake_case      ,							1      ,							0      ,							0					)[1]  # Return the solutions_count


if __name__ == "__main__":
 import doctest

 doctest.testmod()


