






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					:str      ,				a					:List[str]					)				->     int:
 a						    = '''\n'''.join(a					)
 Path(a					).open('''w'''					).writelines(a					)
def 		_a       ( a					:str					)				->     Union[str, Any]:
 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_   (      UpperCAmelCase_  ):






      '''simple docstring'''




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




       """simple docstring"""


       a						    = AutoTokenizer.from_pretrained(__lowerCamelCase    )
       a						    = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()    )
       a						    = max(len(tokenizer.encode(__lowerCamelCase    )    ) for a in ARTICLES    )
       a						    = max(len(tokenizer.encode(__lowerCamelCase    )    ) 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(
           __lowerCamelCase					,   data_dir=__lowerCamelCase					,   type_path='''train'''					,   max_source_length=__lowerCamelCase					,   max_target_length=__lowerCamelCase					,   src_lang=__lowerCamelCase					,   tgt_lang=__lowerCamelCase					,   )
       a						    = 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
        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 :		Optional[int]					,   __UpperCAmelCase :		int    )       ->List[Any]:




       """simple docstring"""


       a						    = AutoTokenizer.from_pretrained(__lowerCamelCase    )
       a						    = make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()    )
       a						    = max(len(tokenizer.encode(__lowerCamelCase    )    ) for a in ARTICLES    )
       a						    = max(len(tokenizer.encode(__lowerCamelCase    )    ) for a in SUMMARIES    )
       a						    = 4
       a						    = LegacySeqaSeqDataset(
           __lowerCamelCase					,   data_dir=__lowerCamelCase					,   type_path='''train'''					,   max_source_length=20					,   max_target_length=__lowerCamelCase					,   )
       a						    = 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 :		str    )       ->List[Any]:




       """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(__lowerCamelCase					,   __lowerCamelCase					,   128					,   __lowerCamelCase    )
       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(__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]    )       ->Optional[int]:




       """simple docstring"""


       if not FAIRSEQ_AVAILABLE:
        return
       a    ,	a    ,	a						    = self._get_dataset(max_len=64    )
       a						    = 64
       a						    = ds.make_dynamic_sampler(__lowerCamelCase					,   required_batch_size_multiple=__lowerCamelCase    )
       a						    = [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
       a						    = DataLoader(__lowerCamelCase					,   batch_sampler=__lowerCamelCase					,   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(__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    )       ->Tuple:




       """simple docstring"""


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

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

       a						    = tokenizer.pad_token_id

       def count_pad_tokens(__UpperCAmelCase :		int					,   __UpperCAmelCase :		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 :		Dict					,   __UpperCAmelCase :		Dict=1_000					,   __UpperCAmelCase :		int=128    )       ->Tuple:




       """simple docstring"""


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

       a						    = AutoTokenizer.from_pretrained(__lowerCamelCase    )
       a						    = 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 :		str    )       ->Union[str, Any]:




       """simple docstring"""


       a    ,	a    ,	a						    = self._get_dataset()
       a						    = set(DistributedSortishSampler(__lowerCamelCase					,   256					,   num_replicas=2					,   rank=0					,   add_extra_examples=__lowerCamelCase    )    )
       a						    = 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					,   __UpperCAmelCase :		Tuple    )       ->Any:




       """simple docstring"""


       a						    = AutoTokenizer.from_pretrained(__lowerCamelCase					,   use_fast=__lowerCamelCase    )
       if tok_name == MBART_TINY:
        a						    = 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'''					,   )
        a						    = train_dataset.dataset_kwargs
        assert "src_lang" in kwargs and "tgt_lang" in kwargs
       else:
        a						    = 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					,   )
        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(__lowerCamelCase    ) == 1 if tok_name == BART_TINY else len(__lowerCamelCase    ) == 0









def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case						,   snake_case							)		->    int:
		# Return True if there is node that has not iterated.
		__lowercase											=     [False] * len(snake_case							)
		__lowercase											=     []
		queue.append(snake_case							)
		__lowercase											=     True

		while queue:
				__lowercase											=     queue.pop(0							)
				for ind in range(len(graph[u]							)							):
						if visited[ind] is False and graph[u][ind] > 0:
								queue.append(snake_case							)
								__lowercase											=     True
								__lowercase											=     u

		return visited[t]



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case							)		->    Dict:
		# This array is filled by BFS and to store path
		__lowercase											=     [-1] * (len(snake_case							))
		__lowercase											=     0
		while bfs(snake_case						,   snake_case						,   snake_case						,   snake_case							):
				__lowercase											=     float('Inf'							)
				__lowercase											=     sink

				while s != source:
						# Find the minimum value in select path
						__lowercase											=     min(snake_case						,   graph[parent[s]][s]							)
						__lowercase											=     parent[s]

				max_flow += path_flow
				__lowercase											=     sink

				while v != source:
						__lowercase											=     parent[v]
						graph[u][v] -= path_flow
						graph[v][u] += path_flow
						__lowercase											=     parent[v]
		return max_flow


SCREAMING_SNAKE_CASE_   :      Any			=   [
    [0, 16, 13, 0, 0, 0],
    [0, 0, 10, 12, 0, 0],
    [0, 4, 0, 0, 14, 0],
    [0, 0, 9, 0, 0, 20],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]

SCREAMING_SNAKE_CASE_	,SCREAMING_SNAKE_CASE_   :      Optional[Any]			=   0, 5


print(ford_fulkerson(graph, source, sink))




