"""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


__lowerCAmelCase    :       Union[str, Any]					     ="""bert-base-cased"""
__lowerCAmelCase    :       Any					     ="""google/pegasus-xsum"""
__lowerCAmelCase    :       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    :       Optional[int]					     ="""patrickvonplaten/t5-tiny-random"""
__lowerCAmelCase    :       Optional[Any]					     ="""sshleifer/bart-tiny-random"""
__lowerCAmelCase    :       Dict					     ="""sshleifer/tiny-mbart"""
__lowerCAmelCase    :       List[Any]					     ="""sshleifer/tiny-marian-en-de"""


def 			UpperCAmelCase__       (				lowerCAmelCase__     :Path					,		lowerCAmelCase__     :list		) ->    Dict:
      '''simple docstring'''


      lowercase														=	"""\n""".join(lowerCAmelCase__		)
      Path(lowerCAmelCase__		).open("""w"""		).writelines(lowerCAmelCase__		)


def 			UpperCAmelCase__       (				lowerCAmelCase__     :str		) ->    Union[str, Any]:
      '''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  _A						(       lowerCAmelCase	):

      @parameterized.expand(
          [
              MBART_TINY,
              MARIAN_TINY,
              T5_TINY,
              BART_TINY,
              PEGASUS_XSUM,
          ]		,       )
      @slow
      def 				A__       (						self		,       __lowerCAmelCase						):







            """simple docstring"""



            lowercase														=	AutoTokenizer.from_pretrained(__lowerCAmelCase						)
            lowercase														=	make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()						)
            lowercase														=	max(len(tokenizer.encode(__lowerCAmelCase						)						) for a in ARTICLES						)
            lowercase														=	max(len(tokenizer.encode(__lowerCAmelCase						)						) 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(
                __lowerCAmelCase		,       data_dir=__lowerCAmelCase		,       type_path="""train"""		,       max_source_length=__lowerCAmelCase		,       max_target_length=__lowerCAmelCase		,       src_lang=__lowerCAmelCase		,       tgt_lang=__lowerCAmelCase		,       )
            lowercase														=	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
                  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 				A__       (						self		,       __lowerCAmelCase						):







            """simple docstring"""



            lowercase														=	AutoTokenizer.from_pretrained(__lowerCAmelCase						)
            lowercase														=	make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()						)
            lowercase														=	max(len(tokenizer.encode(__lowerCAmelCase						)						) for a in ARTICLES						)
            lowercase														=	max(len(tokenizer.encode(__lowerCAmelCase						)						) for a in SUMMARIES						)
            lowercase														=	4
            lowercase														=	LegacySeqaSeqDataset(
                __lowerCAmelCase		,       data_dir=__lowerCAmelCase		,       type_path="""train"""		,       max_source_length=20		,       max_target_length=__lowerCAmelCase		,       )
            lowercase														=	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 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 				A__       (						self						):







            """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(__lowerCAmelCase		,       __lowerCAmelCase		,       128		,       __lowerCAmelCase						)
            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(__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 				A__       (						self						):







            """simple docstring"""



            if not FAIRSEQ_AVAILABLE:
                  return
            lowercase							,							lowercase							,							lowercase														=	self._get_dataset(max_len=64						)
            lowercase														=	64
            lowercase														=	ds.make_dynamic_sampler(__lowerCAmelCase		,       required_batch_size_multiple=__lowerCAmelCase						)
            lowercase														=	[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
            lowercase														=	DataLoader(__lowerCAmelCase		,       batch_sampler=__lowerCAmelCase		,       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(__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 				A__       (						self						):







            """simple docstring"""



            lowercase							,							lowercase							,							lowercase														=	self._get_dataset(max_len=512						)
            lowercase														=	2
            lowercase														=	ds.make_sortish_sampler(__lowerCAmelCase		,       shuffle=__lowerCAmelCase						)

            lowercase														=	DataLoader(__lowerCAmelCase		,       batch_size=__lowerCAmelCase		,       collate_fn=ds.collate_fn		,       num_workers=2						)
            lowercase														=	DataLoader(__lowerCAmelCase		,       batch_size=__lowerCAmelCase		,       collate_fn=ds.collate_fn		,       num_workers=2		,       sampler=__lowerCAmelCase						)

            lowercase														=	tokenizer.pad_token_id

            def count_pad_tokens(__lowerCAmelCase		,       __lowerCAmelCase="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 				A__       (						self		,       __lowerCAmelCase=1000		,       __lowerCAmelCase=128						):







            """simple docstring"""



            if os.getenv("""USE_REAL_DATA"""		,       __lowerCAmelCase						):
                  lowercase														=	"""examples/seq2seq/wmt_en_ro"""
                  lowercase														=	max_len * 2 * 64
                  if not Path(__lowerCAmelCase						).joinpath("""train.len"""						).exists():
                        save_len_file(__lowerCAmelCase		,       __lowerCAmelCase						)
            else:
                  lowercase														=	"""examples/seq2seq/test_data/wmt_en_ro"""
                  lowercase														=	max_len * 4
                  save_len_file(__lowerCAmelCase		,       __lowerCAmelCase						)

            lowercase														=	AutoTokenizer.from_pretrained(__lowerCAmelCase						)
            lowercase														=	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 				A__       (						self						):







            """simple docstring"""



            lowercase							,							lowercase							,							lowercase														=	self._get_dataset()
            lowercase														=	set(DistributedSortishSampler(__lowerCAmelCase		,       256		,       num_replicas=2		,       rank=0		,       add_extra_examples=__lowerCAmelCase						)						)
            lowercase														=	set(DistributedSortishSampler(__lowerCAmelCase		,       256		,       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 				A__       (						self		,       __lowerCAmelCase						):







            """simple docstring"""



            lowercase														=	AutoTokenizer.from_pretrained(__lowerCAmelCase		,       use_fast=__lowerCAmelCase						)
            if tok_name == MBART_TINY:
                  lowercase														=	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"""		,       )
                  lowercase														=	train_dataset.dataset_kwargs
                  assert "src_lang" in kwargs and "tgt_lang" in kwargs
            else:
                  lowercase														=	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		,       )
                  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(__lowerCAmelCase						) == 1 if tok_name == BART_TINY else len(__lowerCAmelCase						) == 0




"""simple docstring"""






import logging
import random

import ray

from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex


__lowerCAmelCase    :       int					     =logging.getLogger(__name__)





class  _A						:

      def __init__(						self						):







            """simple docstring"""



            lowercase														=	False

      def 				A__       (						self		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase						):







            """simple docstring"""



            if not self.initialized:
                  lowercase														=	RagRetriever(
                      __lowerCAmelCase		,       question_encoder_tokenizer=__lowerCAmelCase		,       generator_tokenizer=__lowerCAmelCase		,       index=__lowerCAmelCase		,       init_retrieval=__lowerCAmelCase		,       )
                  lowercase														=	True

      def 				A__       (						self						):







            """simple docstring"""



            self.retriever.index.init_index()







      def 				A__       (						self		,       __lowerCAmelCase		,       __lowerCAmelCase						):







            """simple docstring"""



            lowercase							,							lowercase														=	self.retriever._main_retrieve(__lowerCAmelCase		,       __lowerCAmelCase						)
            return doc_ids, retrieved_doc_embeds






class  _A						(       lowerCAmelCase	):

      def __init__(						self		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase=None						):







            """simple docstring"""



            if index is not None and index.is_initialized() and len(__lowerCAmelCase						) > 0:
                  raise ValueError(
                      """When using Ray for distributed fine-tuning, """
                      """you'll need to provide the paths instead, """
                      """as the dataset and the index are loaded """
                      """separately. More info in examples/rag/use_own_knowledge_dataset.py """						)
            super().__init__(
                __lowerCAmelCase		,       question_encoder_tokenizer=__lowerCAmelCase		,       generator_tokenizer=__lowerCAmelCase		,       index=__lowerCAmelCase		,       init_retrieval=__lowerCAmelCase		,       )
            lowercase														=	retrieval_workers
            if len(self.retrieval_workers						) > 0:
                  ray.get(
                      [
                          worker.create_rag_retriever.remote(__lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase						)
                          for worker in self.retrieval_workers
                      ]						)

      def 				A__       (						self						):







            """simple docstring"""



            logger.info("""initializing retrieval"""						)

            if len(self.retrieval_workers						) > 0:
                  ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers]						)
            else:
                  # Non-distributed training. Load index into this same process.
                  self.index.init_index()

      def 				A__       (						self		,       __lowerCAmelCase		,       __lowerCAmelCase						):







            """simple docstring"""



            if len(self.retrieval_workers						) > 0:
                  # Select a random retrieval actor.
                  lowercase														=	self.retrieval_workers[random.randint(0		,       len(self.retrieval_workers						) - 1						)]
                  lowercase							,							lowercase														=	ray.get(random_worker.retrieve.remote(__lowerCAmelCase		,       __lowerCAmelCase						)						)
            else:
                  lowercase							,							lowercase														=	self._main_retrieve(__lowerCAmelCase		,       __lowerCAmelCase						)
            return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(__lowerCAmelCase						)

      @classmethod
      def 				A__       (						cls		,       __lowerCAmelCase		,       __lowerCAmelCase=None		,       **__lowerCAmelCase						):







            """simple docstring"""



            return super(__lowerCAmelCase		,       cls						).get_tokenizers(__lowerCAmelCase		,       __lowerCAmelCase		,       **__lowerCAmelCase						)







      @classmethod
      def 				A__       (						cls		,       __lowerCAmelCase		,       __lowerCAmelCase		,       __lowerCAmelCase=None		,       **__lowerCAmelCase						):







            """simple docstring"""



            lowercase														=	kwargs.pop("""config"""		,       __lowerCAmelCase						) or RagConfig.from_pretrained(__lowerCAmelCase		,       **__lowerCAmelCase						)
            lowercase														=	RagTokenizer.from_pretrained(__lowerCAmelCase		,       config=__lowerCAmelCase						)
            lowercase														=	rag_tokenizer.question_encoder
            lowercase														=	rag_tokenizer.generator
            if indexed_dataset is not None:
                  lowercase														=	"""custom"""
                  lowercase														=	CustomHFIndex(config.retrieval_vector_size		,       __lowerCAmelCase						)
            else:
                  lowercase														=	cls._build_index(__lowerCAmelCase						)
            return cls(
                __lowerCAmelCase		,       question_encoder_tokenizer=__lowerCAmelCase		,       generator_tokenizer=__lowerCAmelCase		,       retrieval_workers=__lowerCAmelCase		,       index=__lowerCAmelCase		,       )



