


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







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Path  ,   __A			:      list    )     ->		List[Any]:

       """simple docstring"""

       a_ :		Tuple							=  '\n'.join(__A    )
       Path(__A    ).open('w'    ).writelines(__A    )







def 	SCREAMING_SNAKE_CASE_						(      __A			:      List[Any]    )     ->		Tuple:

       """simple docstring"""

       for split in ["train", "val", "test"]:
              _dump_articles(os.path.join(__A  ,   F"""{split}.source"""    )  ,   __A    )
              _dump_articles(os.path.join(__A  ,   F"""{split}.target"""    )  ,   __A    )
       return tmp_dir
class 			SCREAMING_SNAKE_CASE__  (    lowercase__      ):
      @parameterized.expand(
          [
              MBART_TINY,
              MARIAN_TINY,
              T5_TINY,
              BART_TINY,
              PEGASUS_XSUM,
          ]					,							)
      @slow
      def 	SCREAMING_SNAKE_CASE							(	self    :	str					,							SCREAMING_SNAKE_CASE__    :	Union[str, Any]					)		->      str:
             a_ :		Tuple							=  AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__					)
             a_ :		List[str]							=  make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)
             a_ :		Tuple							=  max(len(tokenizer.encode(SCREAMING_SNAKE_CASE__					)					) for a in ARTICLES					)
             a_ :		Tuple							=  max(len(tokenizer.encode(SCREAMING_SNAKE_CASE__					)					) for a in SUMMARIES					)
             a_ :		Optional[Any]							=  4
             a_ :		List[str]							=  8
             assert max_len_target > max_src_len  # Will be truncated
             assert max_len_source > max_src_len  # Will be truncated
             a_							,		a_ :		Tuple							=  'ro_RO', 'de_DE'  # ignored for all but mbart, but never causes error.
             a_ :		Optional[int]							=  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__					,							)
             a_ :		List[str]							=  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
                    a_ :		Tuple							=  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    :	Optional[int]					,							SCREAMING_SNAKE_CASE__    :	List[Any]					)		->      Optional[Any]:
             a_ :		Optional[int]							=  AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__					)
             a_ :		Union[str, Any]							=  make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)
             a_ :		List[Any]							=  max(len(tokenizer.encode(SCREAMING_SNAKE_CASE__					)					) for a in ARTICLES					)
             a_ :		Dict							=  max(len(tokenizer.encode(SCREAMING_SNAKE_CASE__					)					) for a in SUMMARIES					)
             a_ :		List[Any]							=  4
             a_ :		str							=  LegacySeqaSeqDataset(
                 SCREAMING_SNAKE_CASE__					,							data_dir=SCREAMING_SNAKE_CASE__					,							type_path='train'					,							max_source_length=2_0					,							max_target_length=SCREAMING_SNAKE_CASE__					,							)
             a_ :		Tuple							=  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 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    :	Dict					)		->      Any:
             a_ :		Union[str, Any]							=  AutoTokenizer.from_pretrained('facebook/mbart-large-cc25'					)

             a_ :		List[Any]							=  Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)					)
             a_ :		Tuple							=  tmp_dir.joinpath('train.source'					).open().readlines()
             a_ :		str							=  Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)					)
             pack_data_dir(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					,							1_2_8					,							SCREAMING_SNAKE_CASE__					)
             a_ :		str							=  {x.name for x in tmp_dir.iterdir()}
             a_ :		List[Any]							=  {x.name for x in save_dir.iterdir()}
             a_ :		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(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 	SCREAMING_SNAKE_CASE							(	self    :	Optional[int]					)		->      Dict:
             if not FAIRSEQ_AVAILABLE:
                    return
             a_							,		a_							,		a_ :		Any							=  self._get_dataset(max_len=6_4					)
             a_ :		int							=  6_4
             a_ :		Any							=  ds.make_dynamic_sampler(SCREAMING_SNAKE_CASE__					,							required_batch_size_multiple=SCREAMING_SNAKE_CASE__					)
             a_ :		Tuple							=  [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
             a_ :		Optional[int]							=  DataLoader(SCREAMING_SNAKE_CASE__					,							batch_sampler=SCREAMING_SNAKE_CASE__					,							collate_fn=ds.collate_fn					,							num_workers=2					)
             a_ :		Any							=  []
             a_ :		List[Any]							=  []
             for batch in data_loader:
                    a_ :		Union[str, Any]							=  batch['input_ids'].shape
                    a_ :		Dict							=  src_shape[0]
                    assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
                    a_ :		Optional[int]							=  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 	SCREAMING_SNAKE_CASE							(	self    :	Dict					)		->      List[Any]:
             a_							,		a_							,		a_ :		Dict							=  self._get_dataset(max_len=5_1_2					)
             a_ :		List[Any]							=  2
             a_ :		Dict							=  ds.make_sortish_sampler(SCREAMING_SNAKE_CASE__					,							shuffle=SCREAMING_SNAKE_CASE__					)

             a_ :		List[Any]							=  DataLoader(SCREAMING_SNAKE_CASE__					,							batch_size=SCREAMING_SNAKE_CASE__					,							collate_fn=ds.collate_fn					,							num_workers=2					)
             a_ :		Dict							=  DataLoader(SCREAMING_SNAKE_CASE__					,							batch_size=SCREAMING_SNAKE_CASE__					,							collate_fn=ds.collate_fn					,							num_workers=2					,							sampler=SCREAMING_SNAKE_CASE__					)

             a_ :		Dict							=  tokenizer.pad_token_id

             def count_pad_tokens(SCREAMING_SNAKE_CASE__    :	int					,							SCREAMING_SNAKE_CASE__    :	Union[str, Any]="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 	SCREAMING_SNAKE_CASE							(	self    :	List[Any]					,							SCREAMING_SNAKE_CASE__    :	List[Any]=1_0_0_0					,							SCREAMING_SNAKE_CASE__    :	List[Any]=1_2_8					)		->      Dict:
             if os.getenv('USE_REAL_DATA'					,							SCREAMING_SNAKE_CASE__					):
                    a_ :		List[Any]							=  'examples/seq2seq/wmt_en_ro'
                    a_ :		Optional[int]							=  max_len * 2 * 6_4
                    if not Path(SCREAMING_SNAKE_CASE__					).joinpath('train.len'					).exists():
                           save_len_file(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					)
             else:
                    a_ :		int							=  'examples/seq2seq/test_data/wmt_en_ro'
                    a_ :		List[Any]							=  max_len * 4
                    save_len_file(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					)

             a_ :		Optional[int]							=  AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__					)
             a_ :		Optional[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 	SCREAMING_SNAKE_CASE							(	self    :	str					)		->      Optional[Any]:
             a_							,		a_							,		a_ :		Dict							=  self._get_dataset()
             a_ :		Optional[int]							=  set(DistributedSortishSampler(SCREAMING_SNAKE_CASE__					,							2_5_6					,							num_replicas=2					,							rank=0					,							add_extra_examples=SCREAMING_SNAKE_CASE__					)					)
             a_ :		Union[str, Any]							=  set(DistributedSortishSampler(SCREAMING_SNAKE_CASE__					,							2_5_6					,							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 	SCREAMING_SNAKE_CASE							(	self    :	int					,							SCREAMING_SNAKE_CASE__    :	int					)		->      Optional[Any]:
             a_ :		int							=  AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__					,							use_fast=SCREAMING_SNAKE_CASE__					)
             if tok_name == MBART_TINY:
                    a_ :		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					,							src_lang='EN'					,							tgt_lang='FR'					,							)
                    a_ :		List[Any]							=  train_dataset.dataset_kwargs
                    assert "src_lang" in kwargs and "tgt_lang" in kwargs
             else:
                    a_ :		Union[str, 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					,							)
                    a_ :		Tuple							=  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


UpperCAmelCase_				:			Dict   =	{'configuration_vit_msn': ['VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTMSNConfig']}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase_				:			int   =	[
       'VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST',
       'ViTMSNModel',
       'ViTMSNForImageClassification',
       'ViTMSNPreTrainedModel',
   ]

if TYPE_CHECKING:
   from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_vit_msn import (
          VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST,
          ViTMSNForImageClassification,
          ViTMSNModel,
          ViTMSNPreTrainedModel,
      )

else:
   import sys

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


