


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

def    lowercase_	(	_A    :					Path					,      _A    :					list		):

      """simple docstring"""




      lowerCamelCase__	:       Optional[Any]					=						"\n".join(_A		)
      Path(_A		).open("w"		).writelines(_A		)

def    lowercase_	(	_A    :					Union[str, Any]		):

      """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				(							lowercase__):


       """simple docstring"""




       @parameterized.expand(
           [
               MBART_TINY,
               MARIAN_TINY,
               T5_TINY,
               BART_TINY,
               PEGASUS_XSUM,
           ]      ,   )
       @slow
       def 		lowerCAmelCase  (     self							:    Optional[Any]      ,   __lowerCamelCase							:    int ):


             '''simple docstring'''



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


             '''simple docstring'''



             lowerCamelCase__	:       Dict					=						AutoTokenizer.from_pretrained(__lowerCamelCase )
             lowerCamelCase__	:       str					=						make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )
             lowerCamelCase__	:       Tuple					=						max(len(tokenizer.encode(__lowerCamelCase ) ) for a in ARTICLES )
             lowerCamelCase__	:       Any					=						max(len(tokenizer.encode(__lowerCamelCase ) ) for a in SUMMARIES )
             lowerCamelCase__	:       Optional[Any]					=						4
             lowerCamelCase__	:       int					=						LegacySeqaSeqDataset(
                 __lowerCamelCase      ,   data_dir=__lowerCamelCase      ,   type_path="train"      ,   max_source_length=20      ,   max_target_length=__lowerCamelCase      ,   )
             lowerCamelCase__	:       Tuple					=						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 		lowerCAmelCase  (     self							:    int ):


             '''simple docstring'''



             lowerCamelCase__	:       str					=						AutoTokenizer.from_pretrained("facebook/mbart-large-cc25" )

             lowerCamelCase__	:       Optional[Any]					=						Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) )
             lowerCamelCase__	:       int					=						tmp_dir.joinpath("train.source" ).open().readlines()
             lowerCamelCase__	:       str					=						Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) )
             pack_data_dir(__lowerCamelCase      ,   __lowerCamelCase      ,   128      ,   __lowerCamelCase )
             lowerCamelCase__	:       Optional[int]					=						{x.name for x in tmp_dir.iterdir()}
             lowerCamelCase__	:       List[Any]					=						{x.name for x in save_dir.iterdir()}
             lowerCamelCase__	:       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 		lowerCAmelCase  (     self							:    Optional[int] ):


             '''simple docstring'''



             if not FAIRSEQ_AVAILABLE:
                   return
             lowerCamelCase__					,     lowerCamelCase__					,     lowerCamelCase__	:       Any					=						self._get_dataset(max_len=64 )
             lowerCamelCase__	:       int					=						64
             lowerCamelCase__	:       int					=						ds.make_dynamic_sampler(__lowerCamelCase      ,   required_batch_size_multiple=__lowerCamelCase )
             lowerCamelCase__	:       int					=						[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__	:       int					=						DataLoader(__lowerCamelCase      ,   batch_sampler=__lowerCamelCase      ,   collate_fn=ds.collate_fn      ,   num_workers=2 )
             lowerCamelCase__	:       Dict					=						[]
             lowerCamelCase__	:       Optional[int]					=						[]
             for batch in data_loader:
                   lowerCamelCase__	:       int					=						batch["input_ids"].shape
                   lowerCamelCase__	:       Dict					=						src_shape[0]
                   assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
                   lowerCamelCase__	:       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 		lowerCAmelCase  (     self							:    int ):


             '''simple docstring'''



             lowerCamelCase__					,     lowerCamelCase__					,     lowerCamelCase__	:       Any					=						self._get_dataset(max_len=512 )
             lowerCamelCase__	:       Optional[Any]					=						2
             lowerCamelCase__	:       Any					=						ds.make_sortish_sampler(__lowerCamelCase      ,   shuffle=__lowerCamelCase )

             lowerCamelCase__	:       Optional[Any]					=						DataLoader(__lowerCamelCase      ,   batch_size=__lowerCamelCase      ,   collate_fn=ds.collate_fn      ,   num_workers=2 )
             lowerCamelCase__	:       List[Any]					=						DataLoader(__lowerCamelCase      ,   batch_size=__lowerCamelCase      ,   collate_fn=ds.collate_fn      ,   num_workers=2      ,   sampler=__lowerCamelCase )

             lowerCamelCase__	:       Dict					=						tokenizer.pad_token_id

             def count_pad_tokens(__lowerCamelCase							:    int      ,   __lowerCamelCase							:    List[str]="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							:    str      ,   __lowerCamelCase							:    Union[str, Any]=1000      ,   __lowerCamelCase							:    Union[str, Any]=128 ):


             '''simple docstring'''



             if os.getenv("USE_REAL_DATA"      ,   __lowerCamelCase ):
                   lowerCamelCase__	:       Optional[int]					=						"examples/seq2seq/wmt_en_ro"
                   lowerCamelCase__	:       Optional[int]					=						max_len * 2 * 64
                   if not Path(__lowerCamelCase ).joinpath("train.len" ).exists():
                         save_len_file(__lowerCamelCase      ,   __lowerCamelCase )
             else:
                   lowerCamelCase__	:       Dict					=						"examples/seq2seq/test_data/wmt_en_ro"
                   lowerCamelCase__	:       List[Any]					=						max_len * 4
                   save_len_file(__lowerCamelCase      ,   __lowerCamelCase )

             lowerCamelCase__	:       str					=						AutoTokenizer.from_pretrained(__lowerCamelCase )
             lowerCamelCase__	:       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							:    Optional[Any] ):


             '''simple docstring'''



             lowerCamelCase__					,     lowerCamelCase__					,     lowerCamelCase__	:       Union[str, Any]					=						self._get_dataset()
             lowerCamelCase__	:       str					=						set(DistributedSortishSampler(__lowerCamelCase      ,   256      ,   num_replicas=2      ,   rank=0      ,   add_extra_examples=__lowerCamelCase ) )
             lowerCamelCase__	:       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 		lowerCAmelCase  (     self							:    int      ,   __lowerCamelCase							:    str ):


             '''simple docstring'''



             lowerCamelCase__	:       List[Any]					=						AutoTokenizer.from_pretrained(__lowerCamelCase      ,   use_fast=__lowerCamelCase )
             if tok_name == MBART_TINY:
                   lowerCamelCase__	:       Tuple					=						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__	:       List[Any]					=						train_dataset.dataset_kwargs
                   assert "src_lang" in kwargs and "tgt_lang" in kwargs
             else:
                   lowerCamelCase__	:       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      ,   )
                   lowerCamelCase__	:       Optional[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






import os

def    lowercase_	(	_A    :					str = "input.txt"		):

      """simple docstring"""




      with open(os.path.join(os.path.dirname(_A		)					,      _A		)		) as input_file:
            lowerCamelCase__	:       List[Any]					=						[
                [int(_A		) for element in line.split(","		)]
                for line in input_file.readlines()
            ]

      lowerCamelCase__	:       Optional[Any]					=						len(_A		)
      lowerCamelCase__	:       Union[str, Any]					=						len(matrix[0]		)

      lowerCamelCase__	:       Union[str, Any]					=						[[-1 for _ in range(_A		)] for _ in range(_A		)]
      for i in range(_A		):
            lowerCamelCase__	:       Optional[Any]					=						matrix[i][0]

      for j in range(1					,      _A		):
            for i in range(_A		):
                  lowerCamelCase__	:       int					=						minimal_path_sums[i][j - 1] + matrix[i][j]

            for i in range(1					,      _A		):
                  lowerCamelCase__	:       Tuple					=						min(
                      minimal_path_sums[i][j]					,      minimal_path_sums[i - 1][j] + matrix[i][j]		)

            for i in range(rows - 2					,      -1					,      -1		):
                  lowerCamelCase__	:       str					=						min(
                      minimal_path_sums[i][j]					,      minimal_path_sums[i + 1][j] + matrix[i][j]		)

      return min(minimal_path_sums_row[-1] for minimal_path_sums_row in minimal_path_sums		)


if __name__ == "__main__":
    print(f'{solution() = }')


