





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



def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase     ):
   '''simple docstring'''


   A_     :				List[str]			      =	"""\n""".join(_lowerCAmelCase     )
   Path(_lowerCAmelCase     ).open("""w"""     ).writelines(_lowerCAmelCase     )



def     _lowerCAmelCase		(  _lowerCAmelCase     ):
   '''simple docstring'''


   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    _UpperCAmelCase							(		_lowerCamelCase     ):

      '''simple docstring'''





      @parameterized.expand(
          [
              MBART_TINY,
              MARIAN_TINY,
              T5_TINY,
              BART_TINY,
              PEGASUS_XSUM,
          ]      ,       )
      @slow
      def 		_lowerCamelCase      (		self      ,       a__       ):
         A_     :				List[Any]			      =	AutoTokenizer.from_pretrained(a__       )
         A_     :				str			      =	make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )
         A_     :				int			      =	max(len(tokenizer.encode(a__       )       ) for a in ARTICLES       )
         A_     :				List[Any]			      =	max(len(tokenizer.encode(a__       )       ) for a in SUMMARIES       )
         A_     :				str			      =	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_     :				Optional[int]			      =	"""ro_RO""", """de_DE"""  # ignored for all but mbart, but never causes error.
         A_     :				Dict			      =	SeqaSeqDataset(
             a__      ,       data_dir=a__      ,       type_path="""train"""      ,       max_source_length=a__      ,       max_target_length=a__      ,       src_lang=a__      ,       tgt_lang=a__      ,       )
         A_     :				int			      =	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_     :				Optional[int]			      =	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 		_lowerCamelCase      (		self      ,       a__       ):
         A_     :				Optional[int]			      =	AutoTokenizer.from_pretrained(a__       )
         A_     :				Dict			      =	make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )
         A_     :				int			      =	max(len(tokenizer.encode(a__       )       ) for a in ARTICLES       )
         A_     :				Union[str, Any]			      =	max(len(tokenizer.encode(a__       )       ) for a in SUMMARIES       )
         A_     :				Union[str, Any]			      =	4
         A_     :				Union[str, Any]			      =	LegacySeqaSeqDataset(
             a__      ,       data_dir=a__      ,       type_path="""train"""      ,       max_source_length=20      ,       max_target_length=a__      ,       )
         A_     :				Union[str, Any]			      =	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 		_lowerCamelCase      (		self       ):
         A_     :				Tuple			      =	AutoTokenizer.from_pretrained("""facebook/mbart-large-cc25"""       )

         A_     :				str			      =	Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )       )
         A_     :				List[Any]			      =	tmp_dir.joinpath("""train.source"""       ).open().readlines()
         A_     :				Dict			      =	Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )       )
         pack_data_dir(a__      ,       a__      ,       128      ,       a__       )
         A_     :				Optional[Any]			      =	{x.name for x in tmp_dir.iterdir()}
         A_     :				int			      =	{x.name for x in save_dir.iterdir()}
         A_     :				Dict			      =	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 		_lowerCamelCase      (		self       ):
         if not FAIRSEQ_AVAILABLE:
            return
         A_     :				List[Any]			      =	self._get_dataset(max_len=64       )
         A_     :				Tuple			      =	64
         A_     :				Optional[Any]			      =	ds.make_dynamic_sampler(a__      ,       required_batch_size_multiple=a__       )
         A_     :				Tuple			      =	[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_     :				List[str]			      =	DataLoader(a__      ,       batch_sampler=a__      ,       collate_fn=ds.collate_fn      ,       num_workers=2       )
         A_     :				str			      =	[]
         A_     :				int			      =	[]
         for batch in data_loader:
            A_     :				Tuple			      =	batch["""input_ids"""].shape
            A_     :				Any			      =	src_shape[0]
            assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
            A_     :				List[Any]			      =	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 		_lowerCamelCase      (		self       ):
         A_     :				Union[str, Any]			      =	self._get_dataset(max_len=512       )
         A_     :				List[Any]			      =	2
         A_     :				Optional[Any]			      =	ds.make_sortish_sampler(a__      ,       shuffle=a__       )

         A_     :				List[Any]			      =	DataLoader(a__      ,       batch_size=a__      ,       collate_fn=ds.collate_fn      ,       num_workers=2       )
         A_     :				List[str]			      =	DataLoader(a__      ,       batch_size=a__      ,       collate_fn=ds.collate_fn      ,       num_workers=2      ,       sampler=a__       )

         A_     :				Optional[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 		_lowerCamelCase      (		self      ,       a__=1000      ,       a__=128       ):
         if os.getenv("""USE_REAL_DATA"""      ,       a__       ):
            A_     :				int			      =	"""examples/seq2seq/wmt_en_ro"""
            A_     :				Dict			      =	max_len * 2 * 64
            if not Path(a__       ).joinpath("""train.len"""       ).exists():
               save_len_file(a__      ,       a__       )
         else:
            A_     :				Optional[int]			      =	"""examples/seq2seq/test_data/wmt_en_ro"""
            A_     :				str			      =	max_len * 4
            save_len_file(a__      ,       a__       )

         A_     :				List[str]			      =	AutoTokenizer.from_pretrained(a__       )
         A_     :				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 		_lowerCamelCase      (		self       ):
         A_     :				Optional[int]			      =	self._get_dataset()
         A_     :				List[str]			      =	set(DistributedSortishSampler(a__      ,       256      ,       num_replicas=2      ,       rank=0      ,       add_extra_examples=a__       )       )
         A_     :				str			      =	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 		_lowerCamelCase      (		self      ,       a__       ):
         A_     :				int			      =	AutoTokenizer.from_pretrained(a__      ,       use_fast=a__       )
         if tok_name == MBART_TINY:
            A_     :				Union[str, Any]			      =	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_     :				Any			      =	train_dataset.dataset_kwargs
            assert "src_lang" in kwargs and "tgt_lang" in kwargs
         else:
            A_     :				List[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      ,       )
            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(a__       ) == 1 if tok_name == BART_TINY else len(a__       ) == 0













from typing import List

from .keymap import KEYMAP, get_character



def     _lowerCAmelCase		(  _lowerCAmelCase     ):
   '''simple docstring'''


   def decorator(_lowerCAmelCase     ):
      A_     :				List[Any]			      =	getattr(_lowerCAmelCase  ,"""handle_key"""  ,[]     )
      handle += [key]
      setattr(_lowerCAmelCase  ,"""handle_key"""  ,_lowerCAmelCase     )
      return func

   return decorator



def     _lowerCAmelCase		(  *_lowerCAmelCase     ):
   '''simple docstring'''


   def decorator(_lowerCAmelCase     ):
      A_     :				Tuple			      =	getattr(_lowerCAmelCase  ,"""handle_key"""  ,[]     )
      handle += keys
      setattr(_lowerCAmelCase  ,"""handle_key"""  ,_lowerCAmelCase     )
      return func

   return decorator

class    _UpperCAmelCase							(		_lowerCamelCase     ):
      def __new__(		cls      ,       a__      ,       a__      ,       a__       ):
         A_     :				Any			      =	super().__new__(cls      ,       a__      ,       a__      ,       a__       )
         if not hasattr(a__      ,       """key_handler"""       ):
            setattr(a__      ,       """key_handler"""      ,       {}       )
         setattr(a__      ,       """handle_input"""      ,       KeyHandler.handle_input       )

         for value in attrs.values():
            A_     :				Tuple			      =	getattr(a__      ,       """handle_key"""      ,       []       )
            for key in handled_keys:
               A_     :				Optional[Any]			      =	value
         return new_cls






      @staticmethod
      def 		_lowerCamelCase      (		cls       ):
         A_     :				List[str]			      =	get_character()
         if char != KEYMAP["undefined"]:
            A_     :				str			      =	ord(a__       )
         A_     :				List[str]			      =	cls.key_handler.get(a__       )
         if handler:
            A_     :				Optional[int]			      =	char
            return handler(cls       )
         else:
            return None






def     _lowerCAmelCase		(  cls     ):
   '''simple docstring'''


   return KeyHandler(cls.__name__  ,cls.__bases__  ,cls.__dict__.copy()     )






