

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


__a     =			"""bert-base-cased"""
__a     =			"""google/pegasus-xsum"""
__a     =			[""" Sam ate lunch today.""", """Sams lunch ingredients."""]
__a     =			["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
__a     =			"""patrickvonplaten/t5-tiny-random"""
__a     =			"""sshleifer/bart-tiny-random"""
__a     =			"""sshleifer/tiny-mbart"""
__a     =			"""sshleifer/tiny-marian-en-de"""
def   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_   )						->str:
   UpperCAmelCase							=     """\n""".join(lowerCAmelCase_   )
   Path(lowerCAmelCase_   ).open("""w"""   ).writelines(lowerCAmelCase_   )
def   _UpperCamelCase				(						lowerCAmelCase_   )						->str:
   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   __lowercase				(  __snake_case  ):





      @parameterized.expand(
          [
              MBART_TINY,
              MARIAN_TINY,
              T5_TINY,
              BART_TINY,
              PEGASUS_XSUM,
          ]					,       )
      @slow
      def        _lowercase				(		self						:					Optional[int]					,       __lowerCamelCase						:					Any       )    ->   List[str]:






         """simple docstring"""







         UpperCAmelCase							=     AutoTokenizer.from_pretrained(__lowerCamelCase       )
         UpperCAmelCase							=     make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )
         UpperCAmelCase							=     max(len(tokenizer.encode(__lowerCamelCase       )       ) for a in ARTICLES       )
         UpperCAmelCase							=     max(len(tokenizer.encode(__lowerCamelCase       )       ) for a in SUMMARIES       )
         UpperCAmelCase							=     4
         UpperCAmelCase							=     8
         assert max_len_target > max_src_len  # Will be truncated
         assert max_len_source > max_src_len  # Will be truncated
         UpperCAmelCase				,  UpperCAmelCase							=     """ro_RO""", """de_DE"""  # ignored for all but mbart, but never causes error.
         UpperCAmelCase							=     SeqaSeqDataset(
             __lowerCamelCase					,       data_dir=__lowerCamelCase					,       type_path="""train"""					,       max_source_length=__lowerCamelCase					,       max_target_length=__lowerCamelCase					,       src_lang=__lowerCamelCase					,       tgt_lang=__lowerCamelCase					,       )
         UpperCAmelCase							=     DataLoader(__lowerCamelCase					,       batch_size=2					,       collate_fn=train_dataset.collate_fn       )
         for batch in dataloader:
            assert isinstance(__lowerCamelCase					,       __lowerCamelCase       )
            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							=     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        _lowercase				(		self						:					Optional[int]					,       __lowerCamelCase						:					List[str]       )    ->   List[Any]:






         """simple docstring"""







         UpperCAmelCase							=     AutoTokenizer.from_pretrained(__lowerCamelCase       )
         UpperCAmelCase							=     make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )
         UpperCAmelCase							=     max(len(tokenizer.encode(__lowerCamelCase       )       ) for a in ARTICLES       )
         UpperCAmelCase							=     max(len(tokenizer.encode(__lowerCamelCase       )       ) for a in SUMMARIES       )
         UpperCAmelCase							=     4
         UpperCAmelCase							=     LegacySeqaSeqDataset(
             __lowerCamelCase					,       data_dir=__lowerCamelCase					,       type_path="""train"""					,       max_source_length=2_0					,       max_target_length=__lowerCamelCase					,       )
         UpperCAmelCase							=     DataLoader(__lowerCamelCase					,       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        _lowercase				(		self						:					Dict       )    ->   Dict:






         """simple docstring"""







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

         UpperCAmelCase							=     Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )       )
         UpperCAmelCase							=     tmp_dir.joinpath("""train.source"""       ).open().readlines()
         UpperCAmelCase							=     Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )       )
         pack_data_dir(__lowerCamelCase					,       __lowerCamelCase					,       1_2_8					,       __lowerCamelCase       )
         UpperCAmelCase							=     {x.name for x in tmp_dir.iterdir()}
         UpperCAmelCase							=     {x.name for x in save_dir.iterdir()}
         UpperCAmelCase							=     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(__lowerCamelCase       ) < len(__lowerCamelCase       )
         assert len(__lowerCamelCase       ) == 1
         assert len(packed_examples[0]       ) == sum(len(__lowerCamelCase       ) for x in orig_examples       )
         assert orig_paths == new_paths





      @pytest.mark.skipif(not FAIRSEQ_AVAILABLE					,       reason="""This test requires fairseq"""       )
      def        _lowercase				(		self						:					Tuple       )    ->   str:






         """simple docstring"""







         if not FAIRSEQ_AVAILABLE:
            return
         UpperCAmelCase				,  UpperCAmelCase				,  UpperCAmelCase							=     self._get_dataset(max_len=6_4       )
         UpperCAmelCase							=     6_4
         UpperCAmelCase							=     ds.make_dynamic_sampler(__lowerCamelCase					,       required_batch_size_multiple=__lowerCamelCase       )
         UpperCAmelCase							=     [len(__lowerCamelCase       ) for x in batch_sampler]
         assert len(set(__lowerCamelCase       )       ) > 1  # it's not dynamic batch size if every batch is the same length
         assert sum(__lowerCamelCase       ) == len(__lowerCamelCase       )  # no dropped or added examples
         UpperCAmelCase							=     DataLoader(__lowerCamelCase					,       batch_sampler=__lowerCamelCase					,       collate_fn=ds.collate_fn					,       num_workers=2       )
         UpperCAmelCase							=     []
         UpperCAmelCase							=     []
         for batch in data_loader:
            UpperCAmelCase							=     batch["""input_ids"""].shape
            UpperCAmelCase							=     src_shape[0]
            assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
            UpperCAmelCase							=     np.product(batch["""input_ids"""].shape       )
            num_src_per_batch.append(__lowerCamelCase       )
            if num_src_tokens > (max_tokens * 1.1):
               failures.append(__lowerCamelCase       )
         assert num_src_per_batch[0] == max(__lowerCamelCase       )
         if failures:
            raise AssertionError(F"""too many tokens in {len(__lowerCamelCase       )} batches"""       )





      def        _lowercase				(		self						:					Optional[Any]       )    ->   int:






         """simple docstring"""







         UpperCAmelCase				,  UpperCAmelCase				,  UpperCAmelCase							=     self._get_dataset(max_len=5_1_2       )
         UpperCAmelCase							=     2
         UpperCAmelCase							=     ds.make_sortish_sampler(__lowerCamelCase					,       shuffle=__lowerCamelCase       )

         UpperCAmelCase							=     DataLoader(__lowerCamelCase					,       batch_size=__lowerCamelCase					,       collate_fn=ds.collate_fn					,       num_workers=2       )
         UpperCAmelCase							=     DataLoader(__lowerCamelCase					,       batch_size=__lowerCamelCase					,       collate_fn=ds.collate_fn					,       num_workers=2					,       sampler=__lowerCamelCase       )

         UpperCAmelCase							=     tokenizer.pad_token_id

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

         assert sum(count_pad_tokens(__lowerCamelCase					,       k="""labels"""       )       ) < sum(count_pad_tokens(__lowerCamelCase					,       k="""labels"""       )       )
         assert sum(count_pad_tokens(__lowerCamelCase       )       ) < sum(count_pad_tokens(__lowerCamelCase       )       )
         assert len(__lowerCamelCase       ) == len(__lowerCamelCase       )





      def        _lowercase				(		self						:					Tuple					,       __lowerCamelCase						:					int=1_0_0_0					,       __lowerCamelCase						:					Dict=1_2_8       )    ->   Optional[Any]:






         """simple docstring"""







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

         UpperCAmelCase							=     AutoTokenizer.from_pretrained(__lowerCamelCase       )
         UpperCAmelCase							=     SeqaSeqDataset(
             __lowerCamelCase					,       data_dir=__lowerCamelCase					,       type_path="""train"""					,       max_source_length=__lowerCamelCase					,       max_target_length=__lowerCamelCase					,       n_obs=__lowerCamelCase					,       )
         return ds, max_tokens, tokenizer





      def        _lowercase				(		self						:					str       )    ->   List[str]:






         """simple docstring"""







         UpperCAmelCase				,  UpperCAmelCase				,  UpperCAmelCase							=     self._get_dataset()
         UpperCAmelCase							=     set(DistributedSortishSampler(__lowerCamelCase					,       2_5_6					,       num_replicas=2					,       rank=0					,       add_extra_examples=__lowerCamelCase       )       )
         UpperCAmelCase							=     set(DistributedSortishSampler(__lowerCamelCase					,       2_5_6					,       num_replicas=2					,       rank=1					,       add_extra_examples=__lowerCamelCase       )       )
         assert idsa.intersection(__lowerCamelCase       ) == set()





      @parameterized.expand(
          [
              MBART_TINY,
              MARIAN_TINY,
              T5_TINY,
              BART_TINY,
              PEGASUS_XSUM,
          ]					,       )
      def        _lowercase				(		self						:					Tuple					,       __lowerCamelCase						:					List[Any]       )    ->   Optional[Any]:






         """simple docstring"""







         UpperCAmelCase							=     AutoTokenizer.from_pretrained(__lowerCamelCase					,       use_fast=__lowerCamelCase       )
         if tok_name == MBART_TINY:
            UpperCAmelCase							=     SeqaSeqDataset(
                __lowerCamelCase					,       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							=     train_dataset.dataset_kwargs
            assert "src_lang" in kwargs and "tgt_lang" in kwargs
         else:
            UpperCAmelCase							=     SeqaSeqDataset(
                __lowerCamelCase					,       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							=     train_dataset.dataset_kwargs
            assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
            assert len(__lowerCamelCase       ) == 1 if tok_name == BART_TINY else len(__lowerCamelCase       ) == 0







import argparse


__a     =			"""docs/source/_static/js/custom.js"""
def   _UpperCamelCase				(						lowerCAmelCase_   )						->Optional[Any]:
   with open(lowerCAmelCase_  ,						encoding="""utf-8"""  ,						newline="""\n"""   ) as f:
      UpperCAmelCase							=     f.readlines()
   UpperCAmelCase							=     0

   # First let's put the right version
   while not lines[index].startswith("""const stableVersion ="""   ):
      index += 1
   UpperCAmelCase							=     F"""const stableVersion = \"v{version}\"\n"""

   # Then update the dictionary
   while not lines[index].startswith("""const versionMapping = {"""   ):
      index += 1

   # We go until the end
   while not lines[index].startswith("""}"""   ):
      index += 1
   # We add the new version at the end
   lines[index - 1] += F"""    \"v{version}\": \"v{version}\",\n"""

   with open(lowerCAmelCase_  ,						"""w"""  ,						encoding="""utf-8"""  ,						newline="""\n"""   ) as f:
      f.writelines(lowerCAmelCase_   )


if __name__ == "__main__":
 __a     =			argparse.ArgumentParser()
 parser.add_argument("""--version""", help="""Release version.""")
 __a     =			parser.parse_args()
 update_custom_js(args.version)



