






'''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


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        _a(     UpperCamelCase__ :  Tuple,		UpperCamelCase__ :  Union[str, Any]							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__   : Optional[int]													="""\n""".join(lowerCAmelCase_							)
      Path(lowerCAmelCase_							).open('''w'''							).writelines(lowerCAmelCase_							)





def        _a(     UpperCamelCase__ :  Optional[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    __SCREAMING_SNAKE_CASE					(	_lowerCamelCase ):




 @parameterized.expand(
     [
         MBART_TINY,
         MARIAN_TINY,
         T5_TINY,
         BART_TINY,
         PEGASUS_XSUM,
     ]    ,					)
 @slow
 def    __magic_name__		(					self		:							Optional[Any]    ,					__lowercase		:							List[str]					)		->  List[Any]:
       SCREAMING_SNAKE_CASE__   : str													=AutoTokenizer.from_pretrained(a_					)
       SCREAMING_SNAKE_CASE__   : Any													=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)
       SCREAMING_SNAKE_CASE__   : Union[str, Any]													=max(len(tokenizer.encode(a_					)					) for a in ARTICLES					)
       SCREAMING_SNAKE_CASE__   : Tuple													=max(len(tokenizer.encode(a_					)					) for a in SUMMARIES					)
       SCREAMING_SNAKE_CASE__   : Any													=4
       SCREAMING_SNAKE_CASE__   : Optional[int]													=8
       assert max_len_target > max_src_len  # Will be truncated
       assert max_len_source > max_src_len  # Will be truncated
       SCREAMING_SNAKE_CASE__   : str													="""ro_RO""", """de_DE"""  # ignored for all but mbart, but never causes error.
       SCREAMING_SNAKE_CASE__   : Dict													=SeqaSeqDataset(
           a_    ,					data_dir=a_    ,					type_path='''train'''    ,					max_source_length=a_    ,					max_target_length=a_    ,					src_lang=a_    ,					tgt_lang=a_    ,					)
       SCREAMING_SNAKE_CASE__   : Tuple													=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
             SCREAMING_SNAKE_CASE__   : 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    __magic_name__		(					self		:							Union[str, Any]    ,					__lowercase		:							int					)		->  str:
       SCREAMING_SNAKE_CASE__   : Any													=AutoTokenizer.from_pretrained(a_					)
       SCREAMING_SNAKE_CASE__   : Optional[Any]													=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)
       SCREAMING_SNAKE_CASE__   : List[str]													=max(len(tokenizer.encode(a_					)					) for a in ARTICLES					)
       SCREAMING_SNAKE_CASE__   : Tuple													=max(len(tokenizer.encode(a_					)					) for a in SUMMARIES					)
       SCREAMING_SNAKE_CASE__   : Tuple													=4
       SCREAMING_SNAKE_CASE__   : List[Any]													=LegacySeqaSeqDataset(
           a_    ,					data_dir=a_    ,					type_path='''train'''    ,					max_source_length=20    ,					max_target_length=a_    ,					)
       SCREAMING_SNAKE_CASE__   : 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    __magic_name__		(					self		:							List[Any]					)		->  Tuple:
       SCREAMING_SNAKE_CASE__   : str													=AutoTokenizer.from_pretrained('''facebook/mbart-large-cc25'''					)

       SCREAMING_SNAKE_CASE__   : List[str]													=Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)					)
       SCREAMING_SNAKE_CASE__   : Dict													=tmp_dir.joinpath('''train.source'''					).open().readlines()
       SCREAMING_SNAKE_CASE__   : Optional[Any]													=Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)					)
       pack_data_dir(a_    ,					a_    ,					1_28    ,					a_					)
       SCREAMING_SNAKE_CASE__   : List[str]													={x.name for x in tmp_dir.iterdir()}
       SCREAMING_SNAKE_CASE__   : Tuple													={x.name for x in save_dir.iterdir()}
       SCREAMING_SNAKE_CASE__   : List[str]													=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    __magic_name__		(					self		:							Optional[Any]					)		->  int:
       if not FAIRSEQ_AVAILABLE:
             return
       SCREAMING_SNAKE_CASE__   : str													=self._get_dataset(max_len=64					)
       SCREAMING_SNAKE_CASE__   : Any													=64
       SCREAMING_SNAKE_CASE__   : List[str]													=ds.make_dynamic_sampler(a_    ,					required_batch_size_multiple=a_					)
       SCREAMING_SNAKE_CASE__   : Dict													=[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
       SCREAMING_SNAKE_CASE__   : Union[str, Any]													=DataLoader(a_    ,					batch_sampler=a_    ,					collate_fn=ds.collate_fn    ,					num_workers=2					)
       SCREAMING_SNAKE_CASE__   : List[str]													=[]
       SCREAMING_SNAKE_CASE__   : List[Any]													=[]
       for batch in data_loader:
             SCREAMING_SNAKE_CASE__   : Optional[Any]													=batch["""input_ids"""].shape
             SCREAMING_SNAKE_CASE__   : Tuple													=src_shape[0]
             assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
             SCREAMING_SNAKE_CASE__   : Tuple													=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    __magic_name__		(					self		:							Any					)		->  Optional[int]:
       SCREAMING_SNAKE_CASE__   : Dict													=self._get_dataset(max_len=5_12					)
       SCREAMING_SNAKE_CASE__   : Optional[Any]													=2
       SCREAMING_SNAKE_CASE__   : Optional[Any]													=ds.make_sortish_sampler(a_    ,					shuffle=a_					)

       SCREAMING_SNAKE_CASE__   : Optional[int]													=DataLoader(a_    ,					batch_size=a_    ,					collate_fn=ds.collate_fn    ,					num_workers=2					)
       SCREAMING_SNAKE_CASE__   : str													=DataLoader(a_    ,					batch_size=a_    ,					collate_fn=ds.collate_fn    ,					num_workers=2    ,					sampler=a_					)

       SCREAMING_SNAKE_CASE__   : Union[str, Any]													=tokenizer.pad_token_id

       def count_pad_tokens(__lowercase		:							str    ,					__lowercase		:							str="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    __magic_name__		(					self		:							Optional[int]    ,					__lowercase		:							str=10_00    ,					__lowercase		:							Tuple=1_28					)		->  List[str]:
       if os.getenv('''USE_REAL_DATA'''    ,					a_					):
             SCREAMING_SNAKE_CASE__   : List[Any]													="""examples/seq2seq/wmt_en_ro"""
             SCREAMING_SNAKE_CASE__   : int													=max_len * 2 * 64
             if not Path(a_					).joinpath('''train.len'''					).exists():
                   save_len_file(a_    ,					a_					)
       else:
             SCREAMING_SNAKE_CASE__   : Any													="""examples/seq2seq/test_data/wmt_en_ro"""
             SCREAMING_SNAKE_CASE__   : Tuple													=max_len * 4
             save_len_file(a_    ,					a_					)

       SCREAMING_SNAKE_CASE__   : List[Any]													=AutoTokenizer.from_pretrained(a_					)
       SCREAMING_SNAKE_CASE__   : Tuple													=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    __magic_name__		(					self		:							Optional[int]					)		->  List[str]:
       SCREAMING_SNAKE_CASE__   : Optional[Any]													=self._get_dataset()
       SCREAMING_SNAKE_CASE__   : Any													=set(DistributedSortishSampler(a_    ,					2_56    ,					num_replicas=2    ,					rank=0    ,					add_extra_examples=a_					)					)
       SCREAMING_SNAKE_CASE__   : Dict													=set(DistributedSortishSampler(a_    ,					2_56    ,					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    __magic_name__		(					self		:							int    ,					__lowercase		:							Dict					)		->  Any:
       SCREAMING_SNAKE_CASE__   : str													=AutoTokenizer.from_pretrained(a_    ,					use_fast=a_					)
       if tok_name == MBART_TINY:
             SCREAMING_SNAKE_CASE__   : 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'''    ,					)
             SCREAMING_SNAKE_CASE__   : Dict													=train_dataset.dataset_kwargs
             assert "src_lang" in kwargs and "tgt_lang" in kwargs
       else:
             SCREAMING_SNAKE_CASE__   : 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    ,					)
             SCREAMING_SNAKE_CASE__   : Union[str, 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




'''simple docstring'''






import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list



@require_torchaudio
@require_sentencepiece
class 							lowercase       (  unittest.TestCase				):


 """simple docstring"""


 def _snake_case (							self							)		->   Optional[Any]:
        _UpperCAmelCase  :						str			     = """laion/clap-htsat-unfused"""
        _UpperCAmelCase  :						int			     = tempfile.mkdtemp()







 def _snake_case (							self  ,**a_							)		->   str:
        return RobertaTokenizer.from_pretrained(self.checkpoint  ,**a_							)







 def _snake_case (							self  ,**a_							)		->   Tuple:
        return ClapFeatureExtractor.from_pretrained(self.checkpoint  ,**a_							)







 def _snake_case (							self							)		->   int:
        shutil.rmtree(self.tmpdirname							)







 def _snake_case (							self							)		->   Optional[int]:
        _UpperCAmelCase  :						str			     = self.get_tokenizer()
        _UpperCAmelCase  :						Any			     = self.get_feature_extractor()

        _UpperCAmelCase  :						int			     = ClapProcessor(tokenizer=a_  ,feature_extractor=a_							)

        processor.save_pretrained(self.tmpdirname							)
        _UpperCAmelCase  :						List[Any]			     = ClapProcessor.from_pretrained(self.tmpdirname							)

        self.assertEqual(processor.tokenizer.get_vocab()  ,tokenizer.get_vocab()							)
        self.assertIsInstance(processor.tokenizer  ,a_							)

        self.assertEqual(processor.feature_extractor.to_json_string()  ,feature_extractor.to_json_string()							)
        self.assertIsInstance(processor.feature_extractor  ,a_							)







 def _snake_case (							self							)		->   List[Any]:
        _UpperCAmelCase  :						int			     = ClapProcessor(tokenizer=self.get_tokenizer()  ,feature_extractor=self.get_feature_extractor()							)
        processor.save_pretrained(self.tmpdirname							)

        _UpperCAmelCase  :						List[str]			     = self.get_tokenizer(bos_token="""(BOS)"""  ,eos_token="""(EOS)"""							)
        _UpperCAmelCase  :						List[Any]			     = self.get_feature_extractor(do_normalize=a_  ,padding_value=1.0							)

        _UpperCAmelCase  :						Optional[Any]			     = ClapProcessor.from_pretrained(
            self.tmpdirname  ,bos_token="""(BOS)"""  ,eos_token="""(EOS)"""  ,do_normalize=a_  ,padding_value=1.0							)

        self.assertEqual(processor.tokenizer.get_vocab()  ,tokenizer_add_kwargs.get_vocab()							)
        self.assertIsInstance(processor.tokenizer  ,a_							)

        self.assertEqual(processor.feature_extractor.to_json_string()  ,feature_extractor_add_kwargs.to_json_string()							)
        self.assertIsInstance(processor.feature_extractor  ,a_							)







 def _snake_case (							self							)		->   str:
        _UpperCAmelCase  :						Tuple			     = self.get_feature_extractor()
        _UpperCAmelCase  :						Dict			     = self.get_tokenizer()

        _UpperCAmelCase  :						str			     = ClapProcessor(tokenizer=a_  ,feature_extractor=a_							)

        _UpperCAmelCase  :						Tuple			     = floats_list((3, 1_000)							)

        _UpperCAmelCase  :						int			     = feature_extractor(a_  ,return_tensors="""np"""							)
        _UpperCAmelCase  :						Union[str, Any]			     = processor(audios=a_  ,return_tensors="""np"""							)

        for key in input_feat_extract.keys():
               self.assertAlmostEqual(input_feat_extract[key].sum()  ,input_processor[key].sum()  ,delta=1E-2							)







 def _snake_case (							self							)		->   Tuple:
        _UpperCAmelCase  :						List[Any]			     = self.get_feature_extractor()
        _UpperCAmelCase  :						Any			     = self.get_tokenizer()

        _UpperCAmelCase  :						Optional[int]			     = ClapProcessor(tokenizer=a_  ,feature_extractor=a_							)

        _UpperCAmelCase  :						Union[str, Any]			     = """This is a test string"""

        _UpperCAmelCase  :						Optional[Any]			     = processor(text=a_							)

        _UpperCAmelCase  :						Any			     = tokenizer(a_							)

        for key in encoded_tok.keys():
               self.assertListEqual(encoded_tok[key]  ,encoded_processor[key]							)







 def _snake_case (							self							)		->   List[Any]:
        _UpperCAmelCase  :						str			     = self.get_feature_extractor()
        _UpperCAmelCase  :						List[str]			     = self.get_tokenizer()

        _UpperCAmelCase  :						Any			     = ClapProcessor(tokenizer=a_  ,feature_extractor=a_							)

        _UpperCAmelCase  :						List[str]			     = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        _UpperCAmelCase  :						Dict			     = processor.batch_decode(a_							)
        _UpperCAmelCase  :						Any			     = tokenizer.batch_decode(a_							)

        self.assertListEqual(a_  ,a_							)







 def _snake_case (							self							)		->   Dict:
        _UpperCAmelCase  :						List[str]			     = self.get_feature_extractor()
        _UpperCAmelCase  :						int			     = self.get_tokenizer()

        _UpperCAmelCase  :						Dict			     = ClapProcessor(tokenizer=a_  ,feature_extractor=a_							)

        self.assertListEqual(
            processor.model_input_names[2:]  ,feature_extractor.model_input_names  ,msg="""`processor` and `feature_extractor` model input names do not match"""  ,)



