


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

def    lowercase_	(	_A    :					Path					,      _A    :					list		):

 """simple docstring"""




 lowerCamelCase__	:       Optional[Any]					=						"\n".join(_A		)
 Path(_A		).open("w"		).writelines(_A		)

def    lowercase_	(	_A    :					Union[str, Any]		):

 """simple docstring"""




 for split in ["train", "val", "test"]:
  _dump_articles(os.path.join(_A					,      F"{split}.source"		)					,      _A		)
  _dump_articles(os.path.join(_A					,      F"{split}.target"		)					,      _A		)
 return tmp_dir







class     _lowercase				(							lowercase__):


       """simple docstring"""




       @parameterized.expand(
           [
               MBART_TINY,
               MARIAN_TINY,
               T5_TINY,
               BART_TINY,
               PEGASUS_XSUM,
           ]      ,   )
       @slow
       def 		lowerCAmelCase  (     self							:    Optional[Any]      ,   __lowerCamelCase							:    int ):


        '''simple docstring'''



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


        '''simple docstring'''



        lowerCamelCase__	:       Dict					=						AutoTokenizer.from_pretrained(__lowerCamelCase )
        lowerCamelCase__	:       str					=						make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() )
        lowerCamelCase__	:       Tuple					=						max(len(tokenizer.encode(__lowerCamelCase ) ) for a in ARTICLES )
        lowerCamelCase__	:       Any					=						max(len(tokenizer.encode(__lowerCamelCase ) ) for a in SUMMARIES )
        lowerCamelCase__	:       Optional[Any]					=						4
        lowerCamelCase__	:       int					=						LegacySeqaSeqDataset(
            __lowerCamelCase      ,   data_dir=__lowerCamelCase      ,   type_path="train"      ,   max_source_length=20      ,   max_target_length=__lowerCamelCase      ,   )
        lowerCamelCase__	:       Tuple					=						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 		lowerCAmelCase  (     self							:    int ):


        '''simple docstring'''



        lowerCamelCase__	:       str					=						AutoTokenizer.from_pretrained("facebook/mbart-large-cc25" )

        lowerCamelCase__	:       Optional[Any]					=						Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) )
        lowerCamelCase__	:       int					=						tmp_dir.joinpath("train.source" ).open().readlines()
        lowerCamelCase__	:       str					=						Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir() ) )
        pack_data_dir(__lowerCamelCase      ,   __lowerCamelCase      ,   128      ,   __lowerCamelCase )
        lowerCamelCase__	:       Optional[int]					=						{x.name for x in tmp_dir.iterdir()}
        lowerCamelCase__	:       List[Any]					=						{x.name for x in save_dir.iterdir()}
        lowerCamelCase__	:       Union[str, Any]					=						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 		lowerCAmelCase  (     self							:    Optional[int] ):


        '''simple docstring'''



        if not FAIRSEQ_AVAILABLE:
         return
        lowerCamelCase__	:       Any					=						self._get_dataset(max_len=64 )
        lowerCamelCase__	:       int					=						64
        lowerCamelCase__	:       int					=						ds.make_dynamic_sampler(__lowerCamelCase      ,   required_batch_size_multiple=__lowerCamelCase )
        lowerCamelCase__	:       int					=						[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
        lowerCamelCase__	:       int					=						DataLoader(__lowerCamelCase      ,   batch_sampler=__lowerCamelCase      ,   collate_fn=ds.collate_fn      ,   num_workers=2 )
        lowerCamelCase__	:       Dict					=						[]
        lowerCamelCase__	:       Optional[int]					=						[]
        for batch in data_loader:
         lowerCamelCase__	:       int					=						batch["input_ids"].shape
         lowerCamelCase__	:       Dict					=						src_shape[0]
         assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
         lowerCamelCase__	:       Any					=						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 		lowerCAmelCase  (     self							:    int ):


        '''simple docstring'''



        lowerCamelCase__	:       Any					=						self._get_dataset(max_len=512 )
        lowerCamelCase__	:       Optional[Any]					=						2
        lowerCamelCase__	:       Any					=						ds.make_sortish_sampler(__lowerCamelCase      ,   shuffle=__lowerCamelCase )

        lowerCamelCase__	:       Optional[Any]					=						DataLoader(__lowerCamelCase      ,   batch_size=__lowerCamelCase      ,   collate_fn=ds.collate_fn      ,   num_workers=2 )
        lowerCamelCase__	:       List[Any]					=						DataLoader(__lowerCamelCase      ,   batch_size=__lowerCamelCase      ,   collate_fn=ds.collate_fn      ,   num_workers=2      ,   sampler=__lowerCamelCase )

        lowerCamelCase__	:       Dict					=						tokenizer.pad_token_id

        def count_pad_tokens(__lowerCamelCase							:    int      ,   __lowerCamelCase							:    List[str]="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 		lowerCAmelCase  (     self							:    str      ,   __lowerCamelCase							:    Union[str, Any]=1000      ,   __lowerCamelCase							:    Union[str, Any]=128 ):


        '''simple docstring'''



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

        lowerCamelCase__	:       str					=						AutoTokenizer.from_pretrained(__lowerCamelCase )
        lowerCamelCase__	:       int					=						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 		lowerCAmelCase  (     self							:    Optional[Any] ):


        '''simple docstring'''



        lowerCamelCase__	:       Union[str, Any]					=						self._get_dataset()
        lowerCamelCase__	:       str					=						set(DistributedSortishSampler(__lowerCamelCase      ,   256      ,   num_replicas=2      ,   rank=0      ,   add_extra_examples=__lowerCamelCase ) )
        lowerCamelCase__	:       Tuple					=						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 		lowerCAmelCase  (     self							:    int      ,   __lowerCamelCase							:    str ):


        '''simple docstring'''



        lowerCamelCase__	:       List[Any]					=						AutoTokenizer.from_pretrained(__lowerCamelCase      ,   use_fast=__lowerCamelCase )
        if tok_name == MBART_TINY:
         lowerCamelCase__	:       Tuple					=						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"      ,   )
         lowerCamelCase__	:       List[Any]					=						train_dataset.dataset_kwargs
         assert "src_lang" in kwargs and "tgt_lang" in kwargs
        else:
         lowerCamelCase__	:       List[str]					=						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      ,   )
         lowerCamelCase__	:       Optional[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(__lowerCamelCase ) == 1 if tok_name == BART_TINY else len(__lowerCamelCase ) == 0






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_torch_available,
)


A   :					Optional[int]	=      {
    "configuration_speecht5": [
        "SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP",
        "SpeechT5Config",
        "SpeechT5HifiGanConfig",
    ],
    "feature_extraction_speecht5": ["SpeechT5FeatureExtractor"],
    "processing_speecht5": ["SpeechT5Processor"],
}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A   :					List[Any]	=      ["SpeechT5Tokenizer"]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    A   :					List[str]	=      [
        "SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST",
        "SpeechT5ForSpeechToText",
        "SpeechT5ForSpeechToSpeech",
        "SpeechT5ForTextToSpeech",
        "SpeechT5Model",
        "SpeechT5PreTrainedModel",
        "SpeechT5HifiGan",
    ]

if TYPE_CHECKING:
    from .configuration_speechta import (
        SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
        SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
        SpeechTaConfig,
        SpeechTaHifiGanConfig,
    )
    from .feature_extraction_speechta import SpeechTaFeatureExtractor
    from .processing_speechta import SpeechTaProcessor

    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_speechta import SpeechTaTokenizer

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_speechta import (
            SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
            SpeechTaForSpeechToSpeech,
            SpeechTaForSpeechToText,
            SpeechTaForTextToSpeech,
            SpeechTaHifiGan,
            SpeechTaModel,
            SpeechTaPreTrainedModel,
        )

else:
    import sys

    A   :					Any	=      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


