





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


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase				)    ->  Any:
    lowerCamelCase__					:   List[str]      				=	'\n'.join(_UpperCAmelCase				)
    Path(_UpperCAmelCase				).open('w'				).writelines(_UpperCAmelCase				)


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase				)    ->  List[Any]:
    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      lowerCAmelCase							(      __UpperCamelCase    ):
 @parameterized.expand(
     [
         MBART_TINY,
         MARIAN_TINY,
         T5_TINY,
         BART_TINY,
         PEGASUS_XSUM,
     ]    ,  )
 @slow
 def   A_			(   self						:   List[str]    ,  UpperCAmelCase						:   int			)    ->			Tuple:
     lowerCamelCase__					:   Tuple      				=	AutoTokenizer.from_pretrained(UpperCAmelCase			)
     lowerCamelCase__					:   int      				=	make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()			)
     lowerCamelCase__					:   Dict      				=	max(len(tokenizer.encode(UpperCAmelCase			)			) for a in ARTICLES			)
     lowerCamelCase__					:   int      				=	max(len(tokenizer.encode(UpperCAmelCase			)			) for a in SUMMARIES			)
     lowerCamelCase__					:   List[Any]      				=	4
     lowerCamelCase__					:   Union[str, Any]      				=	8
     assert max_len_target > max_src_len  # Will be truncated
     assert max_len_source > max_src_len  # Will be truncated
     lowerCamelCase__				,							lowerCamelCase__					:   Dict      				=	'ro_RO', 'de_DE'  # ignored for all but mbart, but never causes error.
     lowerCamelCase__					:   Dict      				=	SeqaSeqDataset(
         UpperCAmelCase    ,  data_dir=UpperCAmelCase    ,  type_path='train'    ,  max_source_length=UpperCAmelCase    ,  max_target_length=UpperCAmelCase    ,  src_lang=UpperCAmelCase    ,  tgt_lang=UpperCAmelCase    ,  )
     lowerCamelCase__					:   List[Any]      				=	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
         lowerCamelCase__					:   Union[str, Any]      				=	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						:   Any    ,  UpperCAmelCase						:   List[str]			)    ->			str:
     lowerCamelCase__					:   Optional[Any]      				=	AutoTokenizer.from_pretrained(UpperCAmelCase			)
     lowerCamelCase__					:   Optional[Any]      				=	make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()			)
     lowerCamelCase__					:   List[str]      				=	max(len(tokenizer.encode(UpperCAmelCase			)			) for a in ARTICLES			)
     lowerCamelCase__					:   str      				=	max(len(tokenizer.encode(UpperCAmelCase			)			) for a in SUMMARIES			)
     lowerCamelCase__					:   int      				=	4
     lowerCamelCase__					:   Union[str, Any]      				=	LegacySeqaSeqDataset(
         UpperCAmelCase    ,  data_dir=UpperCAmelCase    ,  type_path='train'    ,  max_source_length=20    ,  max_target_length=UpperCAmelCase    ,  )
     lowerCamelCase__					:   Dict      				=	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   A_			(   self						:   Optional[Any]			)    ->			Any:
     lowerCamelCase__					:   Union[str, Any]      				=	AutoTokenizer.from_pretrained('facebook/mbart-large-cc25'			)

     lowerCamelCase__					:   Optional[int]      				=	Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()			)			)
     lowerCamelCase__					:   Optional[int]      				=	tmp_dir.joinpath('train.source'			).open().readlines()
     lowerCamelCase__					:   int      				=	Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()			)			)
     pack_data_dir(UpperCAmelCase    ,  UpperCAmelCase    ,  128    ,  UpperCAmelCase			)
     lowerCamelCase__					:   Union[str, Any]      				=	{x.name for x in tmp_dir.iterdir()}
     lowerCamelCase__					:   Optional[Any]      				=	{x.name for x in save_dir.iterdir()}
     lowerCamelCase__					:   Tuple      				=	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   A_			(   self						:   Any			)    ->			Union[str, Any]:
     if not FAIRSEQ_AVAILABLE:
         return
     lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__					:   str      				=	self._get_dataset(max_len=64			)
     lowerCamelCase__					:   Optional[int]      				=	64
     lowerCamelCase__					:   Any      				=	ds.make_dynamic_sampler(UpperCAmelCase    ,  required_batch_size_multiple=UpperCAmelCase			)
     lowerCamelCase__					:   List[Any]      				=	[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
     lowerCamelCase__					:   Optional[Any]      				=	DataLoader(UpperCAmelCase    ,  batch_sampler=UpperCAmelCase    ,  collate_fn=ds.collate_fn    ,  num_workers=2			)
     lowerCamelCase__					:   Any      				=	[]
     lowerCamelCase__					:   Tuple      				=	[]
     for batch in data_loader:
         lowerCamelCase__					:   Dict      				=	batch['input_ids'].shape
         lowerCamelCase__					:   Union[str, Any]      				=	src_shape[0]
         assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
         lowerCamelCase__					:   Tuple      				=	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   A_			(   self						:   Optional[int]			)    ->			Optional[int]:
     lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__					:   List[str]      				=	self._get_dataset(max_len=512			)
     lowerCamelCase__					:   int      				=	2
     lowerCamelCase__					:   Union[str, Any]      				=	ds.make_sortish_sampler(UpperCAmelCase    ,  shuffle=UpperCAmelCase			)

     lowerCamelCase__					:   Optional[int]      				=	DataLoader(UpperCAmelCase    ,  batch_size=UpperCAmelCase    ,  collate_fn=ds.collate_fn    ,  num_workers=2			)
     lowerCamelCase__					:   Dict      				=	DataLoader(UpperCAmelCase    ,  batch_size=UpperCAmelCase    ,  collate_fn=ds.collate_fn    ,  num_workers=2    ,  sampler=UpperCAmelCase			)

     lowerCamelCase__					:   List[str]      				=	tokenizer.pad_token_id

     def count_pad_tokens(UpperCAmelCase						:   str    ,  UpperCAmelCase						:   List[str]="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   A_			(   self						:   List[Any]    ,  UpperCAmelCase						:   int=1000    ,  UpperCAmelCase						:   Dict=128			)    ->			List[str]:
     if os.getenv('USE_REAL_DATA'    ,  UpperCAmelCase			):
         lowerCamelCase__					:   Optional[Any]      				=	'examples/seq2seq/wmt_en_ro'
         lowerCamelCase__					:   Any      				=	max_len * 2 * 64
         if not Path(UpperCAmelCase			).joinpath('train.len'			).exists():
             save_len_file(UpperCAmelCase    ,  UpperCAmelCase			)
     else:
         lowerCamelCase__					:   Dict      				=	'examples/seq2seq/test_data/wmt_en_ro'
         lowerCamelCase__					:   Tuple      				=	max_len * 4
         save_len_file(UpperCAmelCase    ,  UpperCAmelCase			)

     lowerCamelCase__					:   List[str]      				=	AutoTokenizer.from_pretrained(UpperCAmelCase			)
     lowerCamelCase__					:   int      				=	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   A_			(   self						:   Any			)    ->			List[str]:
     lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__					:   Union[str, Any]      				=	self._get_dataset()
     lowerCamelCase__					:   Dict      				=	set(DistributedSortishSampler(UpperCAmelCase    ,  256    ,  num_replicas=2    ,  rank=0    ,  add_extra_examples=UpperCAmelCase			)			)
     lowerCamelCase__					:   Union[str, Any]      				=	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   A_			(   self						:   Tuple    ,  UpperCAmelCase						:   List[str]			)    ->			List[Any]:
     lowerCamelCase__					:   List[str]      				=	AutoTokenizer.from_pretrained(UpperCAmelCase    ,  use_fast=UpperCAmelCase			)
     if tok_name == MBART_TINY:
         lowerCamelCase__					:   Any      				=	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'    ,  )
         lowerCamelCase__					:   int      				=	train_dataset.dataset_kwargs
         assert "src_lang" in kwargs and "tgt_lang" in kwargs
     else:
         lowerCamelCase__					:   Optional[Any]      				=	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    ,  )
         lowerCamelCase__					:   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(UpperCAmelCase			) == 1 if tok_name == BART_TINY else len(UpperCAmelCase			) == 0












import enum
import shutil
import sys


_UpperCAmelCase     ,_UpperCAmelCase       : List[Any]			     =       shutil.get_terminal_size()

_UpperCAmelCase       : Union[str, Any]			     =       {"""UP""": """A""", """DOWN""": """B""", """RIGHT""": """C""", """LEFT""": """D"""}

class      lowerCAmelCase							(      enum.Enum    ):
 UpperCAmelCase__									=	0
 UpperCAmelCase__									=	1


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase=""				)    ->  int:
    sys.stdout.write(str(_UpperCAmelCase				) + end				)
    sys.stdout.flush()


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase		,			_UpperCAmelCase=""				)    ->  Union[str, Any]:
    forceWrite(F"""\u001b[{color}m{content}\u001b[0m"""		,			_UpperCAmelCase				)


def  SCREAMING_SNAKE_CASE   ( )    ->  Dict:
    forceWrite('\r'				)


def  SCREAMING_SNAKE_CASE   ( _UpperCAmelCase		,			_UpperCAmelCase				)    ->  Optional[Any]:
    forceWrite(F"""\033[{num_lines}{CURSOR_TO_CHAR[direction.upper()]}"""				)


def  SCREAMING_SNAKE_CASE   ( )    ->  int:
    forceWrite(' ' * TERMINAL_WIDTH				)
    reset_cursor()


def  SCREAMING_SNAKE_CASE   ( )    ->  Optional[Any]:
    reset_cursor()
    forceWrite('-' * TERMINAL_WIDTH				)





