





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



def 						A_							(	_lowerCAmelCase				,  _lowerCAmelCase	)		->					Dict:
 UpperCamelCase      :   List[Any]			      =  "\n".join(_lowerCAmelCase	)
 Path(_lowerCAmelCase	).open("w"	).writelines(_lowerCAmelCase	)



def 						A_							(	_lowerCAmelCase	)		->					Optional[int]:
 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__  (			__snake_case     ):





      @parameterized.expand(
          [
              MBART_TINY,
              MARIAN_TINY,
              T5_TINY,
              BART_TINY,
              PEGASUS_XSUM,
          ]			,				)
      @slow
      def 			__UpperCamelCase(					self			,				A_  ):
       '''simple docstring'''




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




       UpperCamelCase      :   Dict			      =  AutoTokenizer.from_pretrained(A_  )
       UpperCamelCase      :   Tuple			      =  make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()  )
       UpperCamelCase      :   Optional[int]			      =  max(len(tokenizer.encode(A_  )  ) for a in ARTICLES  )
       UpperCamelCase      :   Any			      =  max(len(tokenizer.encode(A_  )  ) for a in SUMMARIES  )
       UpperCamelCase      :   Optional[int]			      =  4
       UpperCamelCase      :   Dict			      =  LegacySeqaSeqDataset(
           A_			,				data_dir=A_			,				type_path="train"			,				max_source_length=20			,				max_target_length=A_			,				)
       UpperCamelCase      :   Optional[int]			      =  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 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      :   Dict			      =  AutoTokenizer.from_pretrained("facebook/mbart-large-cc25"  )

       UpperCamelCase      :   str			      =  Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()  )  )
       UpperCamelCase      :   Dict			      =  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(A_			,				A_			,				128			,				A_  )
       UpperCamelCase      :   Tuple			      =  {x.name for x in tmp_dir.iterdir()}
       UpperCamelCase      :   List[Any]			      =  {x.name for x in save_dir.iterdir()}
       UpperCamelCase      :   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(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 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       if not FAIRSEQ_AVAILABLE:
        return
       UpperCamelCase      ,       UpperCamelCase      ,       UpperCamelCase      :   Tuple			      =  self._get_dataset(max_len=64  )
       UpperCamelCase      :   Optional[int]			      =  64
       UpperCamelCase      :   Tuple			      =  ds.make_dynamic_sampler(A_			,				required_batch_size_multiple=A_  )
       UpperCamelCase      :   List[Any]			      =  [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
       UpperCamelCase      :   int			      =  DataLoader(A_			,				batch_sampler=A_			,				collate_fn=ds.collate_fn			,				num_workers=2  )
       UpperCamelCase      :   str			      =  []
       UpperCamelCase      :   str			      =  []
       for batch in data_loader:
        UpperCamelCase      :   int			      =  batch["input_ids"].shape
        UpperCamelCase      :   List[str]			      =  src_shape[0]
        assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
        UpperCamelCase      :   List[str]			      =  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 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      ,       UpperCamelCase      ,       UpperCamelCase      :   Any			      =  self._get_dataset(max_len=512  )
       UpperCamelCase      :   List[Any]			      =  2
       UpperCamelCase      :   str			      =  ds.make_sortish_sampler(A_			,				shuffle=A_  )

       UpperCamelCase      :   Dict			      =  DataLoader(A_			,				batch_size=A_			,				collate_fn=ds.collate_fn			,				num_workers=2  )
       UpperCamelCase      :   Dict			      =  DataLoader(A_			,				batch_size=A_			,				collate_fn=ds.collate_fn			,				num_workers=2			,				sampler=A_  )

       UpperCamelCase      :   int			      =  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 			__UpperCamelCase(					self			,				A_=1000			,				A_=128  ):
       '''simple docstring'''




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

       UpperCamelCase      :   List[str]			      =  AutoTokenizer.from_pretrained(A_  )
       UpperCamelCase      :   Any			      =  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 			__UpperCamelCase(					self  ):
       '''simple docstring'''




       UpperCamelCase      ,       UpperCamelCase      ,       UpperCamelCase      :   List[str]			      =  self._get_dataset()
       UpperCamelCase      :   List[str]			      =  set(DistributedSortishSampler(A_			,				256			,				num_replicas=2			,				rank=0			,				add_extra_examples=A_  )  )
       UpperCamelCase      :   int			      =  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 			__UpperCamelCase(					self			,				A_  ):
       '''simple docstring'''




       UpperCamelCase      :   Tuple			      =  AutoTokenizer.from_pretrained(A_			,				use_fast=A_  )
       if tok_name == MBART_TINY:
        UpperCamelCase      :   str			      =  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"			,				)
        UpperCamelCase      :   List[str]			      =  train_dataset.dataset_kwargs
        assert "src_lang" in kwargs and "tgt_lang" in kwargs
       else:
        UpperCamelCase      :   int			      =  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			,				)
        UpperCamelCase      :   Any			      =  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







from typing import TYPE_CHECKING

from ...utils import _LazyModule


__lowerCamelCase			:	str					    =	{"""tokenization_byt5""": ["""ByT5Tokenizer"""]}


if TYPE_CHECKING:
    from .tokenization_byta import ByTaTokenizer
else:
    import sys

    __lowerCamelCase			:	List[str]					    =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
