





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     A_      (	lowercase_   ,		lowercase_	)      ->    Union[str, Any]:
       _snake_case   :				Any  					=    '''\n'''.join(_UpperCAmelCase	)
       Path(_UpperCAmelCase	).open('''w'''	).writelines(_UpperCAmelCase	)




def     A_      (	lowercase_	)      ->    List[str]:


       for split in ["train", "val", "test"]:
              _dump_articles(os.path.join(_UpperCAmelCase   ,		f'''{split}.source'''	)   ,		_UpperCAmelCase	)
              _dump_articles(os.path.join(_UpperCAmelCase   ,		f'''{split}.target'''	)   ,		_UpperCAmelCase	)
       return tmp_dir

class 					A     (_SCREAMING_SNAKE_CASE					):



      @parameterized.expand(
          [
              MBART_TINY,
              MARIAN_TINY,
              T5_TINY,
              BART_TINY,
              PEGASUS_XSUM,
          ]  ,	)
      @slow
      def  __a			(       self  ,	lowercase_       )   -> Any:



             '''simple docstring'''

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



             '''simple docstring'''

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



             '''simple docstring'''

             _snake_case   :				str  					=    AutoTokenizer.from_pretrained('''facebook/mbart-large-cc25'''       )

             _snake_case   :				List[str]  					=    Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )       )
             _snake_case   :				int  					=    tmp_dir.joinpath('''train.source'''       ).open().readlines()
             _snake_case   :				List[str]  					=    Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )       )
             pack_data_dir(__snake_case  ,	__snake_case  ,	128  ,	__snake_case       )
             _snake_case   :				Optional[Any]  					=    {x.name for x in tmp_dir.iterdir()}
             _snake_case   :				List[Any]  					=    {x.name for x in save_dir.iterdir()}
             _snake_case   :				str  					=    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(__snake_case       ) < len(__snake_case       )
             assert len(__snake_case       ) == 1
             assert len(packed_examples[0]       ) == sum(len(__snake_case       ) for x in orig_examples       )
             assert orig_paths == new_paths



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



             '''simple docstring'''

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



      def  __a			(       self       )   -> Optional[int]:



             '''simple docstring'''

             _snake_case					,     _snake_case					,     _snake_case   :				List[Any]  					=    self._get_dataset(max_len=512       )
             _snake_case   :				Any  					=    2
             _snake_case   :				Union[str, Any]  					=    ds.make_sortish_sampler(__snake_case  ,	shuffle=__snake_case       )

             _snake_case   :				Optional[int]  					=    DataLoader(__snake_case  ,	batch_size=__snake_case  ,	collate_fn=ds.collate_fn  ,	num_workers=2       )
             _snake_case   :				str  					=    DataLoader(__snake_case  ,	batch_size=__snake_case  ,	collate_fn=ds.collate_fn  ,	num_workers=2  ,	sampler=__snake_case       )

             _snake_case   :				int  					=    tokenizer.pad_token_id

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

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



      def  __a			(       self  ,	lowercase_=1000  ,	lowercase_=128       )   -> List[Any]:



             '''simple docstring'''

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

             _snake_case   :				Optional[int]  					=    AutoTokenizer.from_pretrained(__snake_case       )
             _snake_case   :				Optional[Any]  					=    SeqaSeqDataset(
                 __snake_case  ,	data_dir=__snake_case  ,	type_path='''train'''  ,	max_source_length=__snake_case  ,	max_target_length=__snake_case  ,	n_obs=__snake_case  ,	)
             return ds, max_tokens, tokenizer



      def  __a			(       self       )   -> Tuple:



             '''simple docstring'''

             _snake_case					,     _snake_case					,     _snake_case   :				Tuple  					=    self._get_dataset()
             _snake_case   :				int  					=    set(DistributedSortishSampler(__snake_case  ,	256  ,	num_replicas=2  ,	rank=0  ,	add_extra_examples=__snake_case       )       )
             _snake_case   :				List[Any]  					=    set(DistributedSortishSampler(__snake_case  ,	256  ,	num_replicas=2  ,	rank=1  ,	add_extra_examples=__snake_case       )       )
             assert idsa.intersection(__snake_case       ) == set()



      @parameterized.expand(
          [
              MBART_TINY,
              MARIAN_TINY,
              T5_TINY,
              BART_TINY,
              PEGASUS_XSUM,
          ]  ,	)
      def  __a			(       self  ,	lowercase_       )   -> int:



             '''simple docstring'''

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













# Author: OMKAR PATHAK, Nwachukwu Chidiebere

# Use a Python dictionary to construct the graph.
from __future__ import annotations

from pprint import pformat
from typing import Generic, TypeVar

a__      :     Optional[int]         =		TypeVar('T')





class UpperCAmelCase_				( Generic[T] ):


  def __init__(							self ,__snake_case = True ):



       """simple docstring"""





       A_          =       {}  # dictionary of lists
       A_          =       directed


  def 						__UpperCAmelCase     (							self ,__snake_case ,__snake_case ):



       """simple docstring"""





       if not self.directed:  # For undirected graphs
            # if both source vertex and destination vertex are both present in the
            # adjacency list, add destination vertex to source vertex list of adjacent
            # vertices and add source vertex to destination vertex list of adjacent
            # vertices.
            if source_vertex in self.adj_list and destination_vertex in self.adj_list:
                 self.adj_list[source_vertex].append(__snake_case )
                 self.adj_list[destination_vertex].append(__snake_case )
            # if only source vertex is present in adjacency list, add destination vertex
            # to source vertex list of adjacent vertices, then create a new vertex with
            # destination vertex as key and assign a list containing the source vertex
            # as it's first adjacent vertex.
            elif source_vertex in self.adj_list:
                 self.adj_list[source_vertex].append(__snake_case )
                 A_          =       [source_vertex]
            # if only destination vertex is present in adjacency list, add source vertex
            # to destination vertex list of adjacent vertices, then create a new vertex
            # with source vertex as key and assign a list containing the source vertex
            # as it's first adjacent vertex.
            elif destination_vertex in self.adj_list:
                 self.adj_list[destination_vertex].append(__snake_case )
                 A_          =       [destination_vertex]
            # if both source vertex and destination vertex are not present in adjacency
            # list, create a new vertex with source vertex as key and assign a list
            # containing the destination vertex as it's first adjacent vertex also
            # create a new vertex with destination vertex as key and assign a list
            # containing the source vertex as it's first adjacent vertex.
            else:
                 A_          =       [destination_vertex]
                 A_          =       [source_vertex]
       else:  # For directed graphs
            # if both source vertex and destination vertex are present in adjacency
            # list, add destination vertex to source vertex list of adjacent vertices.
            if source_vertex in self.adj_list and destination_vertex in self.adj_list:
                 self.adj_list[source_vertex].append(__snake_case )
            # if only source vertex is present in adjacency list, add destination
            # vertex to source vertex list of adjacent vertices and create a new vertex
            # with destination vertex as key, which has no adjacent vertex
            elif source_vertex in self.adj_list:
                 self.adj_list[source_vertex].append(__snake_case )
                 A_          =       []
            # if only destination vertex is present in adjacency list, create a new
            # vertex with source vertex as key and assign a list containing destination
            # vertex as first adjacent vertex
            elif destination_vertex in self.adj_list:
                 A_          =       [destination_vertex]
            # if both source vertex and destination vertex are not present in adjacency
            # list, create a new vertex with source vertex as key and a list containing
            # destination vertex as it's first adjacent vertex. Then create a new vertex
            # with destination vertex as key, which has no adjacent vertex
            else:
                 A_          =       [destination_vertex]
                 A_          =       []

       return self




  def __repr__(							self ):



       """simple docstring"""





       return pformat(self.adj_list )



