






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


UpperCAmelCase__				   =		"bert-base-cased"
UpperCAmelCase__				   =		"google/pegasus-xsum"
UpperCAmelCase__				   =		[" Sam ate lunch today.", "Sams lunch ingredients."]
UpperCAmelCase__				   =		["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
UpperCAmelCase__				   =		"patrickvonplaten/t5-tiny-random"
UpperCAmelCase__				   =		"sshleifer/bart-tiny-random"
UpperCAmelCase__				   =		"sshleifer/tiny-mbart"
UpperCAmelCase__				   =		"sshleifer/tiny-marian-en-de"
def 						_a    (      a :Path	,							a :list )							->			Optional[int]:
 a		  =      '''\n'''.join(a )
 Path(a ).open('''w''' ).writelines(a )
def 						_a    (      a :Dict )							->			Dict:
 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      :						int ,					__UpperCAmelCase      :						List[str]  )    ->Tuple:




       """simple docstring"""


       a		  =      AutoTokenizer.from_pretrained(__UpperCAmelCase  )
       a		  =      make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()  )
       a		  =      max(len(tokenizer.encode(__UpperCAmelCase  )  ) for a in ARTICLES  )
       a		  =      max(len(tokenizer.encode(__UpperCAmelCase  )  ) for a in SUMMARIES  )
       a		  =      4
       a		  =      8
       assert max_len_target > max_src_len  # Will be truncated
       assert max_len_source > max_src_len  # Will be truncated
       a  ,     a		  =      '''ro_RO''', '''de_DE'''  # ignored for all but mbart, but never causes error.
       a		  =      SeqaSeqDataset(
           __UpperCAmelCase ,					data_dir=__UpperCAmelCase ,					type_path='''train''' ,					max_source_length=__UpperCAmelCase ,					max_target_length=__UpperCAmelCase ,					src_lang=__UpperCAmelCase ,					tgt_lang=__UpperCAmelCase ,					)
       a		  =      DataLoader(__UpperCAmelCase ,					batch_size=2 ,					collate_fn=train_dataset.collate_fn  )
       for batch in dataloader:
        assert isinstance(__UpperCAmelCase ,					__UpperCAmelCase  )
        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
        a		  =      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      :						int ,					__UpperCAmelCase      :						str  )    ->Optional[int]:




       """simple docstring"""


       a		  =      AutoTokenizer.from_pretrained(__UpperCAmelCase  )
       a		  =      make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()  )
       a		  =      max(len(tokenizer.encode(__UpperCAmelCase  )  ) for a in ARTICLES  )
       a		  =      max(len(tokenizer.encode(__UpperCAmelCase  )  ) for a in SUMMARIES  )
       a		  =      4
       a		  =      LegacySeqaSeqDataset(
           __UpperCAmelCase ,					data_dir=__UpperCAmelCase ,					type_path='''train''' ,					max_source_length=20 ,					max_target_length=__UpperCAmelCase ,					)
       a		  =      DataLoader(__UpperCAmelCase ,					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      :						Tuple  )    ->Tuple:




       """simple docstring"""


       a		  =      AutoTokenizer.from_pretrained('''facebook/mbart-large-cc25'''  )

       a		  =      Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()  )  )
       a		  =      tmp_dir.joinpath('''train.source'''  ).open().readlines()
       a		  =      Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()  )  )
       pack_data_dir(__UpperCAmelCase ,					__UpperCAmelCase ,					128 ,					__UpperCAmelCase  )
       a		  =      {x.name for x in tmp_dir.iterdir()}
       a		  =      {x.name for x in save_dir.iterdir()}
       a		  =      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(__UpperCAmelCase  ) < len(__UpperCAmelCase  )
       assert len(__UpperCAmelCase  ) == 1
       assert len(packed_examples[0]  ) == sum(len(__UpperCAmelCase  ) for x in orig_examples  )
       assert orig_paths == new_paths







      @pytest.mark.skipif(not FAIRSEQ_AVAILABLE ,					reason='''This test requires fairseq'''  )
      def       __lowerCAmelCase					(      self      :						str  )    ->Optional[Any]:




       """simple docstring"""


       if not FAIRSEQ_AVAILABLE:
        return
       a  ,     a  ,     a		  =      self._get_dataset(max_len=64  )
       a		  =      64
       a		  =      ds.make_dynamic_sampler(__UpperCAmelCase ,					required_batch_size_multiple=__UpperCAmelCase  )
       a		  =      [len(__UpperCAmelCase  ) for x in batch_sampler]
       assert len(set(__UpperCAmelCase  )  ) > 1  # it's not dynamic batch size if every batch is the same length
       assert sum(__UpperCAmelCase  ) == len(__UpperCAmelCase  )  # no dropped or added examples
       a		  =      DataLoader(__UpperCAmelCase ,					batch_sampler=__UpperCAmelCase ,					collate_fn=ds.collate_fn ,					num_workers=2  )
       a		  =      []
       a		  =      []
       for batch in data_loader:
        a		  =      batch['''input_ids'''].shape
        a		  =      src_shape[0]
        assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
        a		  =      np.product(batch['''input_ids'''].shape  )
        num_src_per_batch.append(__UpperCAmelCase  )
        if num_src_tokens > (max_tokens * 1.1):
         failures.append(__UpperCAmelCase  )
       assert num_src_per_batch[0] == max(__UpperCAmelCase  )
       if failures:
        raise AssertionError(F"""too many tokens in {len(__UpperCAmelCase  )} batches"""  )







      def       __lowerCAmelCase					(      self      :						Any  )    ->List[str]:




       """simple docstring"""


       a  ,     a  ,     a		  =      self._get_dataset(max_len=512  )
       a		  =      2
       a		  =      ds.make_sortish_sampler(__UpperCAmelCase ,					shuffle=__UpperCAmelCase  )

       a		  =      DataLoader(__UpperCAmelCase ,					batch_size=__UpperCAmelCase ,					collate_fn=ds.collate_fn ,					num_workers=2  )
       a		  =      DataLoader(__UpperCAmelCase ,					batch_size=__UpperCAmelCase ,					collate_fn=ds.collate_fn ,					num_workers=2 ,					sampler=__UpperCAmelCase  )

       a		  =      tokenizer.pad_token_id

       def count_pad_tokens(__UpperCAmelCase      :						Any ,					__UpperCAmelCase      :						Dict="input_ids"  ):
        return [batch[k].eq(__UpperCAmelCase  ).sum().item() for batch in data_loader]

       assert sum(count_pad_tokens(__UpperCAmelCase ,					k='''labels'''  )  ) < sum(count_pad_tokens(__UpperCAmelCase ,					k='''labels'''  )  )
       assert sum(count_pad_tokens(__UpperCAmelCase  )  ) < sum(count_pad_tokens(__UpperCAmelCase  )  )
       assert len(__UpperCAmelCase  ) == len(__UpperCAmelCase  )







      def       __lowerCAmelCase					(      self      :						Tuple ,					__UpperCAmelCase      :						Tuple=1_000 ,					__UpperCAmelCase      :						Dict=128  )    ->Any:




       """simple docstring"""


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

       a		  =      AutoTokenizer.from_pretrained(__UpperCAmelCase  )
       a		  =      SeqaSeqDataset(
           __UpperCAmelCase ,					data_dir=__UpperCAmelCase ,					type_path='''train''' ,					max_source_length=__UpperCAmelCase ,					max_target_length=__UpperCAmelCase ,					n_obs=__UpperCAmelCase ,					)
       return ds, max_tokens, tokenizer







      def       __lowerCAmelCase					(      self      :						Optional[int]  )    ->Optional[Any]:




       """simple docstring"""


       a  ,     a  ,     a		  =      self._get_dataset()
       a		  =      set(DistributedSortishSampler(__UpperCAmelCase ,					256 ,					num_replicas=2 ,					rank=0 ,					add_extra_examples=__UpperCAmelCase  )  )
       a		  =      set(DistributedSortishSampler(__UpperCAmelCase ,					256 ,					num_replicas=2 ,					rank=1 ,					add_extra_examples=__UpperCAmelCase  )  )
       assert idsa.intersection(__UpperCAmelCase  ) == set()







      @parameterized.expand(
          [
              MBART_TINY,
              MARIAN_TINY,
              T5_TINY,
              BART_TINY,
              PEGASUS_XSUM,
          ] ,					)
      def       __lowerCAmelCase					(      self      :						List[str] ,					__UpperCAmelCase      :						str  )    ->Union[str, Any]:




       """simple docstring"""


       a		  =      AutoTokenizer.from_pretrained(__UpperCAmelCase ,					use_fast=__UpperCAmelCase  )
       if tok_name == MBART_TINY:
        a		  =      SeqaSeqDataset(
            __UpperCAmelCase ,					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''' ,					)
        a		  =      train_dataset.dataset_kwargs
        assert "src_lang" in kwargs and "tgt_lang" in kwargs
       else:
        a		  =      SeqaSeqDataset(
            __UpperCAmelCase ,					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 ,					)
        a		  =      train_dataset.dataset_kwargs
        assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
        assert len(__UpperCAmelCase  ) == 1 if tok_name == BART_TINY else len(__UpperCAmelCase  ) == 0










from __future__ import annotations

UpperCAmelCase__				   =		list[list[int]]

# assigning initial values to the grid
UpperCAmelCase__				   =		[
    [3, 0, 6, 5, 0, 8, 4, 0, 0],
    [5, 2, 0, 0, 0, 0, 0, 0, 0],
    [0, 8, 7, 0, 0, 0, 0, 3, 1],
    [0, 0, 3, 0, 1, 0, 0, 8, 0],
    [9, 0, 0, 8, 6, 3, 0, 0, 5],
    [0, 5, 0, 0, 9, 0, 6, 0, 0],
    [1, 3, 0, 0, 0, 0, 2, 5, 0],
    [0, 0, 0, 0, 0, 0, 0, 7, 4],
    [0, 0, 5, 2, 0, 6, 3, 0, 0],
]

# a grid with no solution
UpperCAmelCase__				   =		[
    [5, 0, 6, 5, 0, 8, 4, 0, 3],
    [5, 2, 0, 0, 0, 0, 0, 0, 2],
    [1, 8, 7, 0, 0, 0, 0, 3, 1],
    [0, 0, 3, 0, 1, 0, 0, 8, 0],
    [9, 0, 0, 8, 6, 3, 0, 0, 5],
    [0, 5, 0, 0, 9, 0, 6, 0, 0],
    [1, 3, 0, 0, 0, 0, 2, 5, 0],
    [0, 0, 0, 0, 0, 0, 0, 7, 4],
    [0, 0, 5, 2, 0, 6, 3, 0, 0],
]
def 						_a    (      a :Matrix	,							a :int	,							a :int	,							a :int )							->			bool:
 for i in range(9 ):
  if grid[row][i] == n or grid[i][column] == n:
   return False

 for i in range(3 ):
  for j in range(3 ):
   if grid[(row - row % 3) + i][(column - column % 3) + j] == n:
    return False

 return True
def 						_a    (      a :Matrix )							->			tuple[int, int] | None:
 for i in range(9 ):
  for j in range(9 ):
   if grid[i][j] == 0:
    return i, j
 return None
def 						_a    (      a :Matrix )							->			Matrix | None:
 if location := find_empty_location(a ):
  a  ,     a		  =      location
 else:
  # If the location is ``None``, then the grid is solved.
  return grid

 for digit in range(1	,							10 ):
  if is_safe(a	,							a	,							a	,							a ):
   a		  =      digit

   if sudoku(a ) is not None:
    return grid

   a		  =      0

 return None
def 						_a    (      a :Matrix )							->			None:
 for row in grid:
  for cell in row:
   print(a	,							end=''' ''' )
  print()


if __name__ == "__main__":
     # make a copy of grid so that you can compare with the unmodified grid
     for example_grid in (initial_grid, no_solution):
          print("\nExample grid:\n" + "=" * 20)
          print_solution(example_grid)
          print("\nExample grid solution:")
          UpperCAmelCase__				   =		sudoku(example_grid)
          if solution is not None:
               print_solution(solution)
          else:
               print("Cannot find a solution.")


