


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


def lowerCAmelCase_		(				UpperCamelCase__	: Path				,   UpperCamelCase__	: list ):






 """simple docstring"""







 __lowercase									=	"""\n""".join(UpperCamelCase__ )
 Path(UpperCamelCase__ ).open("""w""" ).writelines(UpperCamelCase__ )


def lowerCAmelCase_		(				UpperCamelCase__	: Optional[int] ):






 """simple docstring"""







 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__						(		_a    ):
      @parameterized.expand(
          [
              MBART_TINY,
              MARIAN_TINY,
              T5_TINY,
              BART_TINY,
              PEGASUS_XSUM,
          ]      ,       )
      @slow
      def SCREAMING_SNAKE_CASE_	(		self	:	Tuple      ,       A_	:	Optional[int]							):






       '''simple docstring'''


       __lowercase									=	AutoTokenizer.from_pretrained(A_							)
       __lowercase									=	make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)
       __lowercase									=	max(len(tokenizer.encode(A_							)							) for a in ARTICLES							)
       __lowercase									=	max(len(tokenizer.encode(A_							)							) for a in SUMMARIES							)
       __lowercase									=	4
       __lowercase									=	8
       assert max_len_target > max_src_len  # Will be truncated
       assert max_len_source > max_src_len  # Will be truncated
       __lowercase							,		__lowercase									=	"""ro_RO""", """de_DE"""  # ignored for all but mbart, but never causes error.
       __lowercase									=	SeqaSeqDataset(
           A_      ,       data_dir=A_      ,       type_path="""train"""      ,       max_source_length=A_      ,       max_target_length=A_      ,       src_lang=A_      ,       tgt_lang=A_      ,       )
       __lowercase									=	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
        __lowercase									=	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	:	Any      ,       A_	:	Union[str, Any]							):






       '''simple docstring'''


       __lowercase									=	AutoTokenizer.from_pretrained(A_							)
       __lowercase									=	make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)
       __lowercase									=	max(len(tokenizer.encode(A_							)							) for a in ARTICLES							)
       __lowercase									=	max(len(tokenizer.encode(A_							)							) for a in SUMMARIES							)
       __lowercase									=	4
       __lowercase									=	LegacySeqaSeqDataset(
           A_      ,       data_dir=A_      ,       type_path="""train"""      ,       max_source_length=2_0      ,       max_target_length=A_      ,       )
       __lowercase									=	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 2_0 >= 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	:	Any							):






       '''simple docstring'''


       __lowercase									=	AutoTokenizer.from_pretrained("""facebook/mbart-large-cc25"""							)

       __lowercase									=	Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)							)
       __lowercase									=	tmp_dir.joinpath("""train.source"""							).open().readlines()
       __lowercase									=	Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)							)
       pack_data_dir(A_      ,       A_      ,       1_2_8      ,       A_							)
       __lowercase									=	{x.name for x in tmp_dir.iterdir()}
       __lowercase									=	{x.name for x in save_dir.iterdir()}
       __lowercase									=	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	:	List[str]							):






       '''simple docstring'''


       if not FAIRSEQ_AVAILABLE:
        return
       __lowercase							,		__lowercase							,		__lowercase									=	self._get_dataset(max_len=6_4							)
       __lowercase									=	6_4
       __lowercase									=	ds.make_dynamic_sampler(A_      ,       required_batch_size_multiple=A_							)
       __lowercase									=	[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
       __lowercase									=	DataLoader(A_      ,       batch_sampler=A_      ,       collate_fn=ds.collate_fn      ,       num_workers=2							)
       __lowercase									=	[]
       __lowercase									=	[]
       for batch in data_loader:
        __lowercase									=	batch["""input_ids"""].shape
        __lowercase									=	src_shape[0]
        assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
        __lowercase									=	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	:	List[Any]							):






       '''simple docstring'''


       __lowercase							,		__lowercase							,		__lowercase									=	self._get_dataset(max_len=5_1_2							)
       __lowercase									=	2
       __lowercase									=	ds.make_sortish_sampler(A_      ,       shuffle=A_							)

       __lowercase									=	DataLoader(A_      ,       batch_size=A_      ,       collate_fn=ds.collate_fn      ,       num_workers=2							)
       __lowercase									=	DataLoader(A_      ,       batch_size=A_      ,       collate_fn=ds.collate_fn      ,       num_workers=2      ,       sampler=A_							)

       __lowercase									=	tokenizer.pad_token_id

       def count_pad_tokens(A_	:	Union[str, Any]      ,       A_	:	str="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	:	Any      ,       A_	:	Union[str, Any]=1_0_0_0      ,       A_	:	Optional[Any]=1_2_8							):






       '''simple docstring'''


       if os.getenv("""USE_REAL_DATA"""      ,       A_							):
        __lowercase									=	"""examples/seq2seq/wmt_en_ro"""
        __lowercase									=	max_len * 2 * 6_4
        if not Path(A_							).joinpath("""train.len"""							).exists():
         save_len_file(A_      ,       A_							)
       else:
        __lowercase									=	"""examples/seq2seq/test_data/wmt_en_ro"""
        __lowercase									=	max_len * 4
        save_len_file(A_      ,       A_							)

       __lowercase									=	AutoTokenizer.from_pretrained(A_							)
       __lowercase									=	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	:	int							):






       '''simple docstring'''


       __lowercase							,		__lowercase							,		__lowercase									=	self._get_dataset()
       __lowercase									=	set(DistributedSortishSampler(A_      ,       2_5_6      ,       num_replicas=2      ,       rank=0      ,       add_extra_examples=A_							)							)
       __lowercase									=	set(DistributedSortishSampler(A_      ,       2_5_6      ,       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	:	Any      ,       A_	:	Optional[int]							):






       '''simple docstring'''


       __lowercase									=	AutoTokenizer.from_pretrained(A_      ,       use_fast=A_							)
       if tok_name == MBART_TINY:
        __lowercase									=	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"""      ,       )
        __lowercase									=	train_dataset.dataset_kwargs
        assert "src_lang" in kwargs and "tgt_lang" in kwargs
       else:
        __lowercase									=	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      ,       )
        __lowercase									=	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_torch_available, is_vision_available


UpperCAmelCase__  							={"configuration_van": ["VAN_PRETRAINED_CONFIG_ARCHIVE_MAP", "VanConfig"]}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCAmelCase__  							=[
           "VAN_PRETRAINED_MODEL_ARCHIVE_LIST",
           "VanForImageClassification",
           "VanModel",
           "VanPreTrainedModel",
       ]

if TYPE_CHECKING:
       from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_van import (
                  VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
                  VanForImageClassification,
                  VanModel,
                  VanPreTrainedModel,
              )

else:
       import sys

       UpperCAmelCase__  							=_LazyModule(__name__, globals()["__file__"], _import_structure)
