


"""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


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







    """simple docstring"""
    a					='''\n'''.join(lowercase		)
    Path(lowercase		).open('''w'''		).writelines(lowercase		)
def _A						(						lowercase		):







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






class 				__A    (	_SCREAMING_SNAKE_CASE      ):






 """simple docstring"""




 @parameterized.expand(
     [
         MBART_TINY,
         MARIAN_TINY,
         T5_TINY,
         BART_TINY,
         PEGASUS_XSUM,
     ]			,   )
 @slow
 def 				SCREAMING_SNAKE_CASE							(		self			,   __A						) ->					List[Any]:
     a					=AutoTokenizer.from_pretrained(__A						)
     a					=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()						)
     a					=max(len(tokenizer.encode(__A						)						) for a in ARTICLES						)
     a					=max(len(tokenizer.encode(__A						)						) for a in SUMMARIES						)
     a					=4
     a					=8
     assert max_len_target > max_src_len  # Will be truncated
     assert max_len_source > max_src_len  # Will be truncated
     a		,				a					='''ro_RO''', '''de_DE'''  # ignored for all but mbart, but never causes error.
     a					=SeqaSeqDataset(
         __A			,   data_dir=__A			,   type_path='''train'''			,   max_source_length=__A			,   max_target_length=__A			,   src_lang=__A			,   tgt_lang=__A			,   )
     a					=DataLoader(__A			,   batch_size=2			,   collate_fn=train_dataset.collate_fn						)
     for batch in dataloader:
         assert isinstance(__A			,   __A						)
         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
         a					=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 				SCREAMING_SNAKE_CASE							(		self			,   __A						) ->					int:
     a					=AutoTokenizer.from_pretrained(__A						)
     a					=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()						)
     a					=max(len(tokenizer.encode(__A						)						) for a in ARTICLES						)
     a					=max(len(tokenizer.encode(__A						)						) for a in SUMMARIES						)
     a					=4
     a					=LegacySeqaSeqDataset(
         __A			,   data_dir=__A			,   type_path='''train'''			,   max_source_length=20			,   max_target_length=__A			,   )
     a					=DataLoader(__A			,   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 				SCREAMING_SNAKE_CASE							(		self						) ->					int:
     a					=AutoTokenizer.from_pretrained('''facebook/mbart-large-cc25'''						)

     a					=Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()						)						)
     a					=tmp_dir.joinpath('''train.source'''						).open().readlines()
     a					=Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()						)						)
     pack_data_dir(__A			,   __A			,   128			,   __A						)
     a					={x.name for x in tmp_dir.iterdir()}
     a					={x.name for x in save_dir.iterdir()}
     a					=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(__A						) < len(__A						)
     assert len(__A						) == 1
     assert len(packed_examples[0]						) == sum(len(__A						) for x in orig_examples						)
     assert orig_paths == new_paths


 @pytest.mark.skipif(not FAIRSEQ_AVAILABLE			,   reason='''This test requires fairseq'''						)
 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Optional[Any]:
     if not FAIRSEQ_AVAILABLE:
         return
     a		,				a		,				a					=self._get_dataset(max_len=64						)
     a					=64
     a					=ds.make_dynamic_sampler(__A			,   required_batch_size_multiple=__A						)
     a					=[len(__A						) for x in batch_sampler]
     assert len(set(__A						)						) > 1  # it's not dynamic batch size if every batch is the same length
     assert sum(__A						) == len(__A						)  # no dropped or added examples
     a					=DataLoader(__A			,   batch_sampler=__A			,   collate_fn=ds.collate_fn			,   num_workers=2						)
     a					=[]
     a					=[]
     for batch in data_loader:
         a					=batch['''input_ids'''].shape
         a					=src_shape[0]
         assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
         a					=np.product(batch['''input_ids'''].shape						)
         num_src_per_batch.append(__A						)
         if num_src_tokens > (max_tokens * 1.1):
             failures.append(__A						)
     assert num_src_per_batch[0] == max(__A						)
     if failures:
         raise AssertionError(f'''too many tokens in {len(__A						)} batches'''						)


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					Tuple:
     a		,				a		,				a					=self._get_dataset(max_len=512						)
     a					=2
     a					=ds.make_sortish_sampler(__A			,   shuffle=__A						)

     a					=DataLoader(__A			,   batch_size=__A			,   collate_fn=ds.collate_fn			,   num_workers=2						)
     a					=DataLoader(__A			,   batch_size=__A			,   collate_fn=ds.collate_fn			,   num_workers=2			,   sampler=__A						)

     a					=tokenizer.pad_token_id

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

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


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A=1000			,   __A=128						) ->					List[str]:
     if os.getenv('''USE_REAL_DATA'''			,   __A						):
         a					='''examples/seq2seq/wmt_en_ro'''
         a					=max_len * 2 * 64
         if not Path(__A						).joinpath('''train.len'''						).exists():
             save_len_file(__A			,   __A						)
     else:
         a					='''examples/seq2seq/test_data/wmt_en_ro'''
         a					=max_len * 4
         save_len_file(__A			,   __A						)

     a					=AutoTokenizer.from_pretrained(__A						)
     a					=SeqaSeqDataset(
         __A			,   data_dir=__A			,   type_path='''train'''			,   max_source_length=__A			,   max_target_length=__A			,   n_obs=__A			,   )
     return ds, max_tokens, tokenizer


 def 				SCREAMING_SNAKE_CASE							(		self						) ->					str:
     a		,				a		,				a					=self._get_dataset()
     a					=set(DistributedSortishSampler(__A			,   256			,   num_replicas=2			,   rank=0			,   add_extra_examples=__A						)						)
     a					=set(DistributedSortishSampler(__A			,   256			,   num_replicas=2			,   rank=1			,   add_extra_examples=__A						)						)
     assert idsa.intersection(__A						) == set()





 @parameterized.expand(
     [
         MBART_TINY,
         MARIAN_TINY,
         T5_TINY,
         BART_TINY,
         PEGASUS_XSUM,
     ]			,   )
 def 				SCREAMING_SNAKE_CASE							(		self			,   __A						) ->					Any:
     a					=AutoTokenizer.from_pretrained(__A			,   use_fast=__A						)
     if tok_name == MBART_TINY:
         a					=SeqaSeqDataset(
             __A			,   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'''			,   )
         a					=train_dataset.dataset_kwargs
         assert "src_lang" in kwargs and "tgt_lang" in kwargs
     else:
         a					=SeqaSeqDataset(
             __A			,   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			,   )
         a					=train_dataset.dataset_kwargs
         assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
         assert len(__A						) == 1 if tok_name == BART_TINY else len(__A						) == 0



"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


lowerCamelCase_       :       Union[str, Any]      						=  {"""processing_layoutxlm""": ["""LayoutXLMProcessor"""]}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase_       :       Optional[int]      						=  ["""LayoutXLMTokenizer"""]

try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCamelCase_       :       Dict      						=  ["""LayoutXLMTokenizerFast"""]

if TYPE_CHECKING:
       from .processing_layoutxlm import LayoutXLMProcessor

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_layoutxlm import LayoutXLMTokenizer

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_layoutxlm_fast import LayoutXLMTokenizerFast

else:
       import sys

       lowerCamelCase_       :       Union[str, Any]      						=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)