





'''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						: List[str]		=   'google/pegasus-xsum'
__lowerCamelCase						: Any		=   [' Sam ate lunch today.', 'Sams lunch ingredients.']
__lowerCamelCase						: List[str]		=   ['A very interesting story about what I ate for lunch.', 'Avocado, celery, turkey, coffee']
__lowerCamelCase						: int		=   'patrickvonplaten/t5-tiny-random'
__lowerCamelCase						: List[Any]		=   'sshleifer/bart-tiny-random'
__lowerCamelCase						: Optional[int]		=   'sshleifer/tiny-mbart'
__lowerCamelCase						: Union[str, Any]		=   'sshleifer/tiny-marian-en-de'


def 					_a   (__SCREAMING_SNAKE_CASE  ,      __SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

  _UpperCamelCase       			='''\n'''.join(__SCREAMING_SNAKE_CASE   )
  Path(__SCREAMING_SNAKE_CASE   ).open('''w'''   ).writelines(__SCREAMING_SNAKE_CASE   )


def 					_a   (__SCREAMING_SNAKE_CASE   ):


  """simple docstring"""

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







class 		UpperCAmelCase   (				lowercase_):


  """simple docstring"""







  @parameterized.expand(
      [
          MBART_TINY,
          MARIAN_TINY,
          T5_TINY,
          BART_TINY,
          PEGASUS_XSUM,
      ]							,	)
  @slow
  def   UpperCamelCase__      (				self    :					Any							,	UpperCamelCase__    :					List[str]  )     ->						Optional[int]:
    _UpperCamelCase       			=AutoTokenizer.from_pretrained(UpperCamelCase__  )
    _UpperCamelCase       			=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()  )
    _UpperCamelCase       			=max(len(tokenizer.encode(UpperCamelCase__  )  ) for a in ARTICLES  )
    _UpperCamelCase       			=max(len(tokenizer.encode(UpperCamelCase__  )  ) 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(
        UpperCamelCase__							,	data_dir=UpperCamelCase__							,	type_path='''train'''							,	max_source_length=UpperCamelCase__							,	max_target_length=UpperCamelCase__							,	src_lang=UpperCamelCase__							,	tgt_lang=UpperCamelCase__							,	)
    _UpperCamelCase       			=DataLoader(UpperCamelCase__							,	batch_size=2							,	collate_fn=train_dataset.collate_fn  )
    for batch in dataloader:
      assert isinstance(UpperCamelCase__							,	UpperCamelCase__  )
      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   UpperCamelCase__      (				self    :					Optional[int]							,	UpperCamelCase__    :					int  )     ->						List[Any]:
    _UpperCamelCase       			=AutoTokenizer.from_pretrained(UpperCamelCase__  )
    _UpperCamelCase       			=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()  )
    _UpperCamelCase       			=max(len(tokenizer.encode(UpperCamelCase__  )  ) for a in ARTICLES  )
    _UpperCamelCase       			=max(len(tokenizer.encode(UpperCamelCase__  )  ) for a in SUMMARIES  )
    _UpperCamelCase       			=4
    _UpperCamelCase       			=LegacySeqaSeqDataset(
        UpperCamelCase__							,	data_dir=UpperCamelCase__							,	type_path='''train'''							,	max_source_length=20							,	max_target_length=UpperCamelCase__							,	)
    _UpperCamelCase       			=DataLoader(UpperCamelCase__							,	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   UpperCamelCase__      (				self    :					Dict  )     ->						str:
    _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(UpperCamelCase__							,	UpperCamelCase__							,	128							,	UpperCamelCase__  )
    _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(UpperCamelCase__  ) < len(UpperCamelCase__  )
    assert len(UpperCamelCase__  ) == 1
    assert len(packed_examples[0]  ) == sum(len(UpperCamelCase__  ) for x in orig_examples  )
    assert orig_paths == new_paths


  @pytest.mark.skipif(not FAIRSEQ_AVAILABLE							,	reason='''This test requires fairseq'''  )
  def   UpperCamelCase__      (				self    :					Optional[int]  )     ->						Union[str, Any]:
    if not FAIRSEQ_AVAILABLE:
      return
    _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase       			=self._get_dataset(max_len=64  )
    _UpperCamelCase       			=64
    _UpperCamelCase       			=ds.make_dynamic_sampler(UpperCamelCase__							,	required_batch_size_multiple=UpperCamelCase__  )
    _UpperCamelCase       			=[len(UpperCamelCase__  ) for x in batch_sampler]
    assert len(set(UpperCamelCase__  )  ) > 1  # it's not dynamic batch size if every batch is the same length
    assert sum(UpperCamelCase__  ) == len(UpperCamelCase__  )  # no dropped or added examples
    _UpperCamelCase       			=DataLoader(UpperCamelCase__							,	batch_sampler=UpperCamelCase__							,	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(UpperCamelCase__  )
      if num_src_tokens > (max_tokens * 1.1):
        failures.append(UpperCamelCase__  )
    assert num_src_per_batch[0] == max(UpperCamelCase__  )
    if failures:
      raise AssertionError(F'''too many tokens in {len(UpperCamelCase__  )} batches'''  )


  def   UpperCamelCase__      (				self    :					int  )     ->						str:
    _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase       			=self._get_dataset(max_len=512  )
    _UpperCamelCase       			=2
    _UpperCamelCase       			=ds.make_sortish_sampler(UpperCamelCase__							,	shuffle=UpperCamelCase__  )

    _UpperCamelCase       			=DataLoader(UpperCamelCase__							,	batch_size=UpperCamelCase__							,	collate_fn=ds.collate_fn							,	num_workers=2  )
    _UpperCamelCase       			=DataLoader(UpperCamelCase__							,	batch_size=UpperCamelCase__							,	collate_fn=ds.collate_fn							,	num_workers=2							,	sampler=UpperCamelCase__  )

    _UpperCamelCase       			=tokenizer.pad_token_id

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

    assert sum(count_pad_tokens(UpperCamelCase__							,	k='''labels'''  )  ) < sum(count_pad_tokens(UpperCamelCase__							,	k='''labels'''  )  )
    assert sum(count_pad_tokens(UpperCamelCase__  )  ) < sum(count_pad_tokens(UpperCamelCase__  )  )
    assert len(UpperCamelCase__  ) == len(UpperCamelCase__  )


  def   UpperCamelCase__      (				self    :					Optional[int]							,	UpperCamelCase__    :					List[str]=1000							,	UpperCamelCase__    :					Tuple=128  )     ->						Tuple:
    if os.getenv('''USE_REAL_DATA'''							,	UpperCamelCase__  ):
      _UpperCamelCase       			='''examples/seq2seq/wmt_en_ro'''
      _UpperCamelCase       			=max_len * 2 * 64
      if not Path(UpperCamelCase__  ).joinpath('''train.len'''  ).exists():
        save_len_file(UpperCamelCase__							,	UpperCamelCase__  )
    else:
      _UpperCamelCase       			='''examples/seq2seq/test_data/wmt_en_ro'''
      _UpperCamelCase       			=max_len * 4
      save_len_file(UpperCamelCase__							,	UpperCamelCase__  )

    _UpperCamelCase       			=AutoTokenizer.from_pretrained(UpperCamelCase__  )
    _UpperCamelCase       			=SeqaSeqDataset(
        UpperCamelCase__							,	data_dir=UpperCamelCase__							,	type_path='''train'''							,	max_source_length=UpperCamelCase__							,	max_target_length=UpperCamelCase__							,	n_obs=UpperCamelCase__							,	)
    return ds, max_tokens, tokenizer


  def   UpperCamelCase__      (				self    :					int  )     ->						List[Any]:
    _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase       			=self._get_dataset()
    _UpperCamelCase       			=set(DistributedSortishSampler(UpperCamelCase__							,	256							,	num_replicas=2							,	rank=0							,	add_extra_examples=UpperCamelCase__  )  )
    _UpperCamelCase       			=set(DistributedSortishSampler(UpperCamelCase__							,	256							,	num_replicas=2							,	rank=1							,	add_extra_examples=UpperCamelCase__  )  )
    assert idsa.intersection(UpperCamelCase__  ) == set()




  @parameterized.expand(
      [
          MBART_TINY,
          MARIAN_TINY,
          T5_TINY,
          BART_TINY,
          PEGASUS_XSUM,
      ]							,	)
  def   UpperCamelCase__      (				self    :					Dict							,	UpperCamelCase__    :					Dict  )     ->						int:
    _UpperCamelCase       			=AutoTokenizer.from_pretrained(UpperCamelCase__							,	use_fast=UpperCamelCase__  )
    if tok_name == MBART_TINY:
      _UpperCamelCase       			=SeqaSeqDataset(
          UpperCamelCase__							,	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(
          UpperCamelCase__							,	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(UpperCamelCase__  ) == 1 if tok_name == BART_TINY else len(UpperCamelCase__  ) == 0













'''simple docstring'''






from typing import TYPE_CHECKING

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


__lowerCamelCase						: str		=   {
    'configuration_speech_to_text': ['SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Speech2TextConfig'],
    'processing_speech_to_text': ['Speech2TextProcessor'],
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowerCamelCase						: List[str]		=   ['Speech2TextTokenizer']

try:
      if not is_speech_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowerCamelCase						: str		=   ['Speech2TextFeatureExtractor']

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowerCamelCase						: Optional[Any]		=   [
          'TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
          'TFSpeech2TextForConditionalGeneration',
          'TFSpeech2TextModel',
          'TFSpeech2TextPreTrainedModel',
      ]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowerCamelCase						: Union[str, Any]		=   [
          'SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
          'Speech2TextForConditionalGeneration',
          'Speech2TextModel',
          'Speech2TextPreTrainedModel',
      ]


if TYPE_CHECKING:
      from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
      from .processing_speech_to_text import SpeechaTextProcessor

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_speech_to_text import SpeechaTextTokenizer

      try:
            if not is_speech_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_speech_to_text import (
                TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFSpeechaTextForConditionalGeneration,
                TFSpeechaTextModel,
                TFSpeechaTextPreTrainedModel,
            )

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_speech_to_text import (
                SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
                SpeechaTextForConditionalGeneration,
                SpeechaTextModel,
                SpeechaTextPreTrainedModel,
            )

else:
      import sys

      __lowerCamelCase						: Optional[int]		=   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






