





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



def 				lowerCAmelCase_ (							A_    ,A_):
    UpperCamelCase__:	str					=     "\n".join(A_)
    Path(A_).open("w").writelines(A_)



def 				lowerCAmelCase_ (							A_):
    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        _a    (    UpperCamelCase__):

      """simple docstring"""


      @parameterized.expand(
          [
              MBART_TINY,
              MARIAN_TINY,
              T5_TINY,
              BART_TINY,
              PEGASUS_XSUM,
          ]	,    )
      @slow
      def 			UpperCAmelCase_       (		self:			Union[str, Any]	,    __lowerCamelCase:			Optional[int]					):
          '''simple docstring'''


          UpperCamelCase__:	List[Any]					=     AutoTokenizer.from_pretrained(__lowerCamelCase					)
          UpperCamelCase__:	int					=     make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)
          UpperCamelCase__:	Optional[int]					=     max(len(tokenizer.encode(__lowerCamelCase					)					) for a in ARTICLES					)
          UpperCamelCase__:	Union[str, Any]					=     max(len(tokenizer.encode(__lowerCamelCase					)					) for a in SUMMARIES					)
          UpperCamelCase__:	Optional[Any]					=     4
          UpperCamelCase__:	Union[str, Any]					=     8
          assert max_len_target > max_src_len  # Will be truncated
          assert max_len_source > max_src_len  # Will be truncated
          UpperCamelCase__				, UpperCamelCase__:	Tuple					=     "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__:	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
              UpperCamelCase__:	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 			UpperCAmelCase_       (		self:			Tuple	,    __lowerCamelCase:			str					):
          '''simple docstring'''


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


          UpperCamelCase__:	int					=     AutoTokenizer.from_pretrained("facebook/mbart-large-cc25"					)

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


          if not FAIRSEQ_AVAILABLE:
              return
          UpperCamelCase__				, UpperCamelCase__				, UpperCamelCase__:	Dict					=     self._get_dataset(max_len=64					)
          UpperCamelCase__:	Tuple					=     64
          UpperCamelCase__:	str					=     ds.make_dynamic_sampler(__lowerCamelCase	,    required_batch_size_multiple=__lowerCamelCase					)
          UpperCamelCase__:	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
          UpperCamelCase__:	List[Any]					=     DataLoader(__lowerCamelCase	,    batch_sampler=__lowerCamelCase	,    collate_fn=ds.collate_fn	,    num_workers=2					)
          UpperCamelCase__:	int					=     []
          UpperCamelCase__:	Any					=     []
          for batch in data_loader:
              UpperCamelCase__:	Tuple					=     batch["input_ids"].shape
              UpperCamelCase__:	Any					=     src_shape[0]
              assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
              UpperCamelCase__:	List[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 			UpperCAmelCase_       (		self:			List[str]					):
          '''simple docstring'''


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

          UpperCamelCase__:	Union[str, Any]					=     DataLoader(__lowerCamelCase	,    batch_size=__lowerCamelCase	,    collate_fn=ds.collate_fn	,    num_workers=2					)
          UpperCamelCase__:	int					=     DataLoader(__lowerCamelCase	,    batch_size=__lowerCamelCase	,    collate_fn=ds.collate_fn	,    num_workers=2	,    sampler=__lowerCamelCase					)

          UpperCamelCase__:	Optional[int]					=     tokenizer.pad_token_id

          def count_pad_tokens(__lowerCamelCase:			Tuple	,    __lowerCamelCase:			Dict="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 			UpperCAmelCase_       (		self:			Any	,    __lowerCamelCase:			Union[str, Any]=1000	,    __lowerCamelCase:			Optional[Any]=128					):
          '''simple docstring'''


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

          UpperCamelCase__:	Tuple					=     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 			UpperCAmelCase_       (		self:			Tuple					):
          '''simple docstring'''


          UpperCamelCase__				, UpperCamelCase__				, UpperCamelCase__:	Optional[Any]					=     self._get_dataset()
          UpperCamelCase__:	Union[str, Any]					=     set(DistributedSortishSampler(__lowerCamelCase	,    256	,    num_replicas=2	,    rank=0	,    add_extra_examples=__lowerCamelCase					)					)
          UpperCamelCase__:	List[Any]					=     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 			UpperCAmelCase_       (		self:			List[Any]	,    __lowerCamelCase:			List[str]					):
          '''simple docstring'''


          UpperCamelCase__:	Optional[Any]					=     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__:	Any					=     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__:	Optional[int]					=     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 __future__ import annotations

from typing import Any




class        _a    :

      """simple docstring"""


      def __init__(		self:			Optional[int]	,    __lowerCamelCase:			int					):
          '''simple docstring'''


          UpperCamelCase__:	Optional[Any]					=     num_of_nodes
          UpperCamelCase__:	list[list[int]]					=     []
          UpperCamelCase__:	dict[int, int]					=     {}






      def 			UpperCAmelCase_       (		self:			Any	,    __lowerCamelCase:			int	,    __lowerCamelCase:			int	,    __lowerCamelCase:			int					):
          '''simple docstring'''


          self.m_edges.append([u_node, v_node, weight]					)






      def 			UpperCAmelCase_       (		self:			Dict	,    __lowerCamelCase:			int					):
          '''simple docstring'''


          if self.m_component[u_node] == u_node:
              return u_node
          return self.find_component(self.m_component[u_node]					)






      def 			UpperCAmelCase_       (		self:			str	,    __lowerCamelCase:			int					):
          '''simple docstring'''


          if self.m_component[u_node] != u_node:
              for k in self.m_component:
                  UpperCamelCase__:	Dict					=     self.find_component(__lowerCamelCase					)






      def 			UpperCAmelCase_       (		self:			str	,    __lowerCamelCase:			list[int]	,    __lowerCamelCase:			int	,    __lowerCamelCase:			int					):
          '''simple docstring'''


          if component_size[u_node] <= component_size[v_node]:
              UpperCamelCase__:	List[str]					=     v_node
              component_size[v_node] += component_size[u_node]
              self.set_component(__lowerCamelCase					)

          elif component_size[u_node] >= component_size[v_node]:
              UpperCamelCase__:	Any					=     self.find_component(__lowerCamelCase					)
              component_size[u_node] += component_size[v_node]
              self.set_component(__lowerCamelCase					)






      def 			UpperCAmelCase_       (		self:			Tuple					):
          '''simple docstring'''


          UpperCamelCase__:	Optional[int]					=     []
          UpperCamelCase__:	Optional[int]					=     0

          UpperCamelCase__:	list[Any]					=     [-1] * self.m_num_of_nodes

          # A list of components (initialized to all of the nodes)
          for node in range(self.m_num_of_nodes					):
              self.m_component.update({node: node}					)
              component_size.append(1					)

          UpperCamelCase__:	Optional[int]					=     self.m_num_of_nodes

          while num_of_components > 1:
              for edge in self.m_edges:
                  UpperCamelCase__				, UpperCamelCase__				, UpperCamelCase__:	List[str]					=     edge

                  UpperCamelCase__:	List[Any]					=     self.m_component[u]
                  UpperCamelCase__:	Tuple					=     self.m_component[v]

                  if u_component != v_component:

                      for component in (u_component, v_component):
                          if (
                              minimum_weight_edge[component] == -1
                              or minimum_weight_edge[component][2] > w
                          ):
                              UpperCamelCase__:	Any					=     [u, v, w]

              for edge in minimum_weight_edge:
                  if isinstance(__lowerCamelCase	,    __lowerCamelCase					):
                      UpperCamelCase__				, UpperCamelCase__				, UpperCamelCase__:	List[str]					=     edge

                      UpperCamelCase__:	Tuple					=     self.m_component[u]
                      UpperCamelCase__:	Optional[Any]					=     self.m_component[v]

                      if u_component != v_component:
                          mst_weight += w
                          self.union(__lowerCamelCase	,    __lowerCamelCase	,    __lowerCamelCase					)
                          print(F"Added edge [{u} - {v}]\nAdded weight: {w}\n"					)
                          num_of_components -= 1

              UpperCamelCase__:	List[Any]					=     [-1] * self.m_num_of_nodes
          print(F"The total weight of the minimal spanning tree is: {mst_weight}"					)



def 				lowerCAmelCase_ (							):
    pass


if __name__ == "__main__":
   import doctest

   doctest.testmod()

