






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







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase					,    __lowerCAmelCase			) ->	List[Any]:
     UpperCamelCase__			:   Dict       				=  "\n".join(__lowerCAmelCase			)
     Path(__lowerCAmelCase			).open("w"			).writelines(__lowerCAmelCase			)







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase			) ->	Tuple:
     for split in ["train", "val", "test"]:
          _dump_articles(os.path.join(__lowerCAmelCase					,    f'{split}.source'			)					,    __lowerCAmelCase			)
          _dump_articles(os.path.join(__lowerCAmelCase					,    f'{split}.target'			)					,    __lowerCAmelCase			)
     return tmp_dir





class 			__a  (    A__       ):







 @parameterized.expand(
     [
         MBART_TINY,
         MARIAN_TINY,
         T5_TINY,
         BART_TINY,
         PEGASUS_XSUM,
     ]							,   )
 @slow
 def 					__lowercase					(		self					:   Tuple							,   SCREAMING_SNAKE_CASE					:   Optional[Any]						):


      '''simple docstring'''






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


      '''simple docstring'''






      UpperCamelCase__			:   List[str]       				=  AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Tuple       				=  make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()						)
      UpperCamelCase__			:   Optional[int]       				=  max(len(tokenizer.encode(SCREAMING_SNAKE_CASE						)						) for a in ARTICLES						)
      UpperCamelCase__			:   int       				=  max(len(tokenizer.encode(SCREAMING_SNAKE_CASE						)						) for a in SUMMARIES						)
      UpperCamelCase__			:   int       				=  4
      UpperCamelCase__			:   Dict       				=  LegacySeqaSeqDataset(
          SCREAMING_SNAKE_CASE							,   data_dir=SCREAMING_SNAKE_CASE							,   type_path="train"							,   max_source_length=20							,   max_target_length=SCREAMING_SNAKE_CASE							,   )
      UpperCamelCase__			:   Optional[Any]       				=  DataLoader(SCREAMING_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 					__lowercase					(		self					:   Any						):


      '''simple docstring'''






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

      UpperCamelCase__			:   List[Any]       				=  Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()						)						)
      UpperCamelCase__			:   str       				=  tmp_dir.joinpath("train.source"						).open().readlines()
      UpperCamelCase__			:   Tuple       				=  Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()						)						)
      pack_data_dir(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE							,   1_28							,   SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Optional[Any]       				=  {x.name for x in tmp_dir.iterdir()}
      UpperCamelCase__			:   str       				=  {x.name for x in save_dir.iterdir()}
      UpperCamelCase__			:   List[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(SCREAMING_SNAKE_CASE						) < len(SCREAMING_SNAKE_CASE						)
      assert len(SCREAMING_SNAKE_CASE						) == 1
      assert len(packed_examples[0]						) == sum(len(SCREAMING_SNAKE_CASE						) for x in orig_examples						)
      assert orig_paths == new_paths







 @pytest.mark.skipif(not FAIRSEQ_AVAILABLE							,   reason="This test requires fairseq"						)
 def 					__lowercase					(		self					:   List[Any]						):


      '''simple docstring'''






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







 def 					__lowercase					(		self					:   Any						):


      '''simple docstring'''






      UpperCamelCase__				,     UpperCamelCase__				,     UpperCamelCase__			:   Union[str, Any]       				=  self._get_dataset(max_len=5_12						)
      UpperCamelCase__			:   List[Any]       				=  2
      UpperCamelCase__			:   List[Any]       				=  ds.make_sortish_sampler(SCREAMING_SNAKE_CASE							,   shuffle=SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   Dict       				=  DataLoader(SCREAMING_SNAKE_CASE							,   batch_size=SCREAMING_SNAKE_CASE							,   collate_fn=ds.collate_fn							,   num_workers=2						)
      UpperCamelCase__			:   int       				=  DataLoader(SCREAMING_SNAKE_CASE							,   batch_size=SCREAMING_SNAKE_CASE							,   collate_fn=ds.collate_fn							,   num_workers=2							,   sampler=SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   Union[str, Any]       				=  tokenizer.pad_token_id

      def count_pad_tokens(SCREAMING_SNAKE_CASE					:   Union[str, Any]							,   SCREAMING_SNAKE_CASE					:   List[str]="input_ids"						):
           return [batch[k].eq(SCREAMING_SNAKE_CASE						).sum().item() for batch in data_loader]

      assert sum(count_pad_tokens(SCREAMING_SNAKE_CASE							,   k="labels"						)						) < sum(count_pad_tokens(SCREAMING_SNAKE_CASE							,   k="labels"						)						)
      assert sum(count_pad_tokens(SCREAMING_SNAKE_CASE						)						) < sum(count_pad_tokens(SCREAMING_SNAKE_CASE						)						)
      assert len(SCREAMING_SNAKE_CASE						) == len(SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   List[str]							,   SCREAMING_SNAKE_CASE					:   Optional[Any]=10_00							,   SCREAMING_SNAKE_CASE					:   Tuple=1_28						):


      '''simple docstring'''






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

      UpperCamelCase__			:   Tuple       				=  AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Union[str, Any]       				=  SeqaSeqDataset(
          SCREAMING_SNAKE_CASE							,   data_dir=SCREAMING_SNAKE_CASE							,   type_path="train"							,   max_source_length=SCREAMING_SNAKE_CASE							,   max_target_length=SCREAMING_SNAKE_CASE							,   n_obs=SCREAMING_SNAKE_CASE							,   )
      return ds, max_tokens, tokenizer







 def 					__lowercase					(		self					:   Optional[Any]						):


      '''simple docstring'''






      UpperCamelCase__				,     UpperCamelCase__				,     UpperCamelCase__			:   Any       				=  self._get_dataset()
      UpperCamelCase__			:   Optional[int]       				=  set(DistributedSortishSampler(SCREAMING_SNAKE_CASE							,   2_56							,   num_replicas=2							,   rank=0							,   add_extra_examples=SCREAMING_SNAKE_CASE						)						)
      UpperCamelCase__			:   str       				=  set(DistributedSortishSampler(SCREAMING_SNAKE_CASE							,   2_56							,   num_replicas=2							,   rank=1							,   add_extra_examples=SCREAMING_SNAKE_CASE						)						)
      assert idsa.intersection(SCREAMING_SNAKE_CASE						) == set()







 @parameterized.expand(
     [
         MBART_TINY,
         MARIAN_TINY,
         T5_TINY,
         BART_TINY,
         PEGASUS_XSUM,
     ]							,   )
 def 					__lowercase					(		self					:   Dict							,   SCREAMING_SNAKE_CASE					:   int						):


      '''simple docstring'''






      UpperCamelCase__			:   int       				=  AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE							,   use_fast=SCREAMING_SNAKE_CASE						)
      if tok_name == MBART_TINY:
           UpperCamelCase__			:   Tuple       				=  SeqaSeqDataset(
               SCREAMING_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"							,   )
           UpperCamelCase__			:   List[str]       				=  train_dataset.dataset_kwargs
           assert "src_lang" in kwargs and "tgt_lang" in kwargs
      else:
           UpperCamelCase__			:   List[Any]       				=  SeqaSeqDataset(
               SCREAMING_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							,   )
           UpperCamelCase__			:   List[str]       				=  train_dataset.dataset_kwargs
           assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
           assert len(SCREAMING_SNAKE_CASE						) == 1 if tok_name == BART_TINY else len(SCREAMING_SNAKE_CASE						) == 0







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase	:							Tuple				={
    '''configuration_luke''': ['''LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LukeConfig'''],
    '''tokenization_luke''': ['''LukeTokenizer'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase	:							Union[str, Any]				=[
     '''LUKE_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''LukeForEntityClassification''',
     '''LukeForEntityPairClassification''',
     '''LukeForEntitySpanClassification''',
     '''LukeForMultipleChoice''',
     '''LukeForQuestionAnswering''',
     '''LukeForSequenceClassification''',
     '''LukeForTokenClassification''',
     '''LukeForMaskedLM''',
     '''LukeModel''',
     '''LukePreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
 from .tokenization_luke import LukeTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_luke import (
      LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
      LukeForEntityClassification,
      LukeForEntityPairClassification,
      LukeForEntitySpanClassification,
      LukeForMaskedLM,
      LukeForMultipleChoice,
      LukeForQuestionAnswering,
      LukeForSequenceClassification,
      LukeForTokenClassification,
      LukeModel,
      LukePreTrainedModel,
  )

else:
 import sys

 lowerCamelCase	:							Dict				=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)