





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

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    Path  ,     _UpperCAmelCase					:    list			):
 lowerCAmelCase    				= '\n'.join(_UpperCAmelCase			)
 Path(_UpperCAmelCase			).open('w'			).writelines(_UpperCAmelCase			)

def 		_SCREAMING_SNAKE_CASE			(_UpperCAmelCase					:    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							(   a__  ):



 @parameterized.expand(
     [
         MBART_TINY,
         MARIAN_TINY,
         T5_TINY,
         BART_TINY,
         PEGASUS_XSUM,
     ]				,       )
 @slow
 def    UpperCamelCase__  (     self				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= AutoTokenizer.from_pretrained(_snake_case     )
  lowerCAmelCase    				= make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     )
  lowerCAmelCase    				= max(len(tokenizer.encode(_snake_case     )     ) for a in ARTICLES     )
  lowerCAmelCase    				= max(len(tokenizer.encode(_snake_case     )     ) for a in SUMMARIES     )
  lowerCAmelCase    				= 4
  lowerCAmelCase    				= 8
  assert max_len_target > max_src_len  # Will be truncated
  assert max_len_source > max_src_len  # Will be truncated
  lowerCAmelCase				,lowerCAmelCase    				= 'ro_RO', 'de_DE'  # ignored for all but mbart, but never causes error.
  lowerCAmelCase    				= 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				,       )
  lowerCAmelCase    				= 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
   lowerCAmelCase    				= 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				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= AutoTokenizer.from_pretrained(_snake_case     )
  lowerCAmelCase    				= make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     )
  lowerCAmelCase    				= max(len(tokenizer.encode(_snake_case     )     ) for a in ARTICLES     )
  lowerCAmelCase    				= max(len(tokenizer.encode(_snake_case     )     ) for a in SUMMARIES     )
  lowerCAmelCase    				= 4
  lowerCAmelCase    				= LegacySeqaSeqDataset(
      _snake_case				,       data_dir=_snake_case				,       type_path='train'				,       max_source_length=20				,       max_target_length=_snake_case				,       )
  lowerCAmelCase    				= 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    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= AutoTokenizer.from_pretrained('facebook/mbart-large-cc25'     )

  lowerCAmelCase    				= Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     )     )
  lowerCAmelCase    				= tmp_dir.joinpath('train.source'     ).open().readlines()
  lowerCAmelCase    				= Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     )     )
  pack_data_dir(_snake_case				,       _snake_case				,       1_28				,       _snake_case     )
  lowerCAmelCase    				= {x.name for x in tmp_dir.iterdir()}
  lowerCAmelCase    				= {x.name for x in save_dir.iterdir()}
  lowerCAmelCase    				= 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    UpperCamelCase__  (     self     ):







  """simple docstring"""






  if not FAIRSEQ_AVAILABLE:
   return
  lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase    				= self._get_dataset(max_len=64     )
  lowerCAmelCase    				= 64
  lowerCAmelCase    				= ds.make_dynamic_sampler(_snake_case				,       required_batch_size_multiple=_snake_case     )
  lowerCAmelCase    				= [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
  lowerCAmelCase    				= DataLoader(_snake_case				,       batch_sampler=_snake_case				,       collate_fn=ds.collate_fn				,       num_workers=2     )
  lowerCAmelCase    				= []
  lowerCAmelCase    				= []
  for batch in data_loader:
   lowerCAmelCase    				= batch['input_ids'].shape
   lowerCAmelCase    				= src_shape[0]
   assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
   lowerCAmelCase    				= 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    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase    				= self._get_dataset(max_len=5_12     )
  lowerCAmelCase    				= 2
  lowerCAmelCase    				= ds.make_sortish_sampler(_snake_case				,       shuffle=_snake_case     )

  lowerCAmelCase    				= DataLoader(_snake_case				,       batch_size=_snake_case				,       collate_fn=ds.collate_fn				,       num_workers=2     )
  lowerCAmelCase    				= DataLoader(_snake_case				,       batch_size=_snake_case				,       collate_fn=ds.collate_fn				,       num_workers=2				,       sampler=_snake_case     )

  lowerCAmelCase    				= tokenizer.pad_token_id

  def count_pad_tokens(_snake_case				,       _snake_case="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    UpperCamelCase__  (     self				,       _snake_case=10_00				,       _snake_case=1_28     ):







  """simple docstring"""






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

  lowerCAmelCase    				= AutoTokenizer.from_pretrained(_snake_case     )
  lowerCAmelCase    				= 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    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase    				= self._get_dataset()
  lowerCAmelCase    				= set(DistributedSortishSampler(_snake_case				,       2_56				,       num_replicas=2				,       rank=0				,       add_extra_examples=_snake_case     )     )
  lowerCAmelCase    				= set(DistributedSortishSampler(_snake_case				,       2_56				,       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    UpperCamelCase__  (     self				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= AutoTokenizer.from_pretrained(_snake_case				,       use_fast=_snake_case     )
  if tok_name == MBART_TINY:
   lowerCAmelCase    				= 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'				,       )
   lowerCAmelCase    				= train_dataset.dataset_kwargs
   assert "src_lang" in kwargs and "tgt_lang" in kwargs
  else:
   lowerCAmelCase    				= 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				,       )
   lowerCAmelCase    				= 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











'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase_		:     Dict          =		{
    '''configuration_roformer''': ['''ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoFormerConfig''', '''RoFormerOnnxConfig'''],
    '''tokenization_roformer''': ['''RoFormerTokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCamelCase_		:     Union[str, Any]          =		['''RoFormerTokenizerFast''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCamelCase_		:     Tuple          =		[
       '''ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''RoFormerForCausalLM''',
       '''RoFormerForMaskedLM''',
       '''RoFormerForMultipleChoice''',
       '''RoFormerForQuestionAnswering''',
       '''RoFormerForSequenceClassification''',
       '''RoFormerForTokenClassification''',
       '''RoFormerLayer''',
       '''RoFormerModel''',
       '''RoFormerPreTrainedModel''',
       '''load_tf_weights_in_roformer''',
   ]


try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCamelCase_		:     str          =		[
       '''TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFRoFormerForCausalLM''',
       '''TFRoFormerForMaskedLM''',
       '''TFRoFormerForMultipleChoice''',
       '''TFRoFormerForQuestionAnswering''',
       '''TFRoFormerForSequenceClassification''',
       '''TFRoFormerForTokenClassification''',
       '''TFRoFormerLayer''',
       '''TFRoFormerModel''',
       '''TFRoFormerPreTrainedModel''',
   ]


try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCamelCase_		:     Tuple          =		[
       '''FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''FlaxRoFormerForMaskedLM''',
       '''FlaxRoFormerForMultipleChoice''',
       '''FlaxRoFormerForQuestionAnswering''',
       '''FlaxRoFormerForSequenceClassification''',
       '''FlaxRoFormerForTokenClassification''',
       '''FlaxRoFormerModel''',
       '''FlaxRoFormerPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
   from .tokenization_roformer import RoFormerTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_roformer_fast import RoFormerTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_roformer import (
          ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          RoFormerForCausalLM,
          RoFormerForMaskedLM,
          RoFormerForMultipleChoice,
          RoFormerForQuestionAnswering,
          RoFormerForSequenceClassification,
          RoFormerForTokenClassification,
          RoFormerLayer,
          RoFormerModel,
          RoFormerPreTrainedModel,
          load_tf_weights_in_roformer,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_roformer import (
          TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFRoFormerForCausalLM,
          TFRoFormerForMaskedLM,
          TFRoFormerForMultipleChoice,
          TFRoFormerForQuestionAnswering,
          TFRoFormerForSequenceClassification,
          TFRoFormerForTokenClassification,
          TFRoFormerLayer,
          TFRoFormerModel,
          TFRoFormerPreTrainedModel,
      )

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_roformer import (
          FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          FlaxRoFormerForMaskedLM,
          FlaxRoFormerForMultipleChoice,
          FlaxRoFormerForQuestionAnswering,
          FlaxRoFormerForSequenceClassification,
          FlaxRoFormerForTokenClassification,
          FlaxRoFormerModel,
          FlaxRoFormerPreTrainedModel,
      )


else:
   import sys

   UpperCamelCase_		:     Optional[int]          =		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


