

"""simple docstring"""





import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset

from ..utils.generic import ModelOutput




class  _lowerCAmelCase  (     lowercase       ):







 """simple docstring"""







 def __init__(      self							:      List[str], UpperCAmelCase__							:      Union[str, Any], UpperCAmelCase__							:      Any, UpperCAmelCase__							:      Dict     ):
    __lowercase								=						dataset
    __lowercase								=						process
    __lowercase								=						params


 def __len__(      self							:      str     ):
    return len(self.dataset     )



 def __getitem__(      self							:      List[Any], UpperCAmelCase__							:      int     ):
    __lowercase								=						self.dataset[i]
    __lowercase								=						self.process(UpperCAmelCase__, **self.params     )
    return processed







class  _lowerCAmelCase  (     lowercase       ):







 """simple docstring"""







 def __init__(      self							:      Optional[Any], UpperCAmelCase__							:      Dict, UpperCAmelCase__							:      Any, UpperCAmelCase__							:      Union[str, Any], UpperCAmelCase__							:      List[Any]=None     ):
    __lowercase								=						loader
    __lowercase								=						infer
    __lowercase								=						params
    if loader_batch_size == 1:
       # Let's spare some time by deactivating altogether
       __lowercase								=						None
    __lowercase								=						loader_batch_size

    # Internal bookkeeping
    __lowercase								=						None
    __lowercase								=						None


 def __len__(      self							:      str     ):
    return len(self.loader     )


 def __iter__(      self							:      List[str]     ):
    __lowercase								=						iter(self.loader     )
    return self


 def  _lowercase			(      self							:      Union[str, Any]     ):
    if isinstance(self._loader_batch_data, torch.Tensor     ):
       # Batch data is simple tensor, just fetch the slice
       __lowercase								=						self._loader_batch_data[self._loader_batch_index]
    else:
       # Batch data is assumed to be BaseModelOutput (or dict)
       __lowercase								=						{}
       for k, element in self._loader_batch_data.items():
          if isinstance(UpperCAmelCase__, UpperCAmelCase__     ):
             # Convert ModelOutput to tuple first
             __lowercase								=						element.to_tuple()
             if isinstance(element[0], torch.Tensor     ):
                __lowercase								=						tuple(el[self._loader_batch_index].unsqueeze(0     ) for el in element     )
             elif isinstance(element[0], np.ndarray     ):
                __lowercase								=						tuple(np.expand_dims(el[self._loader_batch_index], 0     ) for el in element     )
             continue
          if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(UpperCAmelCase__, UpperCAmelCase__     ):
             # Those are stored as lists of tensors so need specific unbatching.
             if isinstance(element[0], torch.Tensor     ):
                __lowercase								=						tuple(el[self._loader_batch_index].unsqueeze(0     ) for el in element     )
             elif isinstance(element[0], np.ndarray     ):
                __lowercase								=						tuple(np.expand_dims(el[self._loader_batch_index], 0     ) for el in element     )
             continue
          if element is None:
             # This can happen for optional data that get passed around
             __lowercase								=						None
          elif isinstance(element[self._loader_batch_index], torch.Tensor     ):
             # Take correct batch data, but make it looked like batch_size=1
             # For compatibility with other methods within transformers

             __lowercase								=						element[self._loader_batch_index].unsqueeze(0     )
          elif isinstance(element[self._loader_batch_index], np.ndarray     ):
             # Take correct batch data, but make it looked like batch_size=1
             # For compatibility with other methods within transformers
             __lowercase								=						np.expand_dims(element[self._loader_batch_index], 0     )
          else:
             # This is typically a list, so no need to `unsqueeze`.
             __lowercase								=						element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
       __lowercase								=						self._loader_batch_data.__class__(UpperCAmelCase__     )
    self._loader_batch_index += 1
    return result



 def  _lowercase			(      self							:      Tuple     ):
    if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
       # We are currently unrolling a batch so we just need to return
       # the current item within a batch
       return self.loader_batch_item()

    # We're out of items within a batch
    __lowercase								=						next(self.iterator     )
    __lowercase								=						self.infer(UpperCAmelCase__, **self.params     )
    # We now have a batch of "inferred things".
    if self.loader_batch_size is not None:
       # Try to infer the size of the batch
       if isinstance(UpperCAmelCase__, torch.Tensor     ):
          __lowercase								=						processed
       else:
          __lowercase								=						list(processed.keys()     )[0]
          __lowercase								=						processed[key]
       if isinstance(UpperCAmelCase__, UpperCAmelCase__     ):
          __lowercase								=						len(UpperCAmelCase__     )
       else:
          __lowercase								=						first_tensor.shape[0]
       if 0 < observed_batch_size < self.loader_batch_size:
          # could be last batch so we can't unroll as many
          # elements.
          __lowercase								=						observed_batch_size
       # Setting internal index to unwrap the batch
       __lowercase								=						processed
       __lowercase								=						0
       return self.loader_batch_item()
    else:
       # We're not unrolling batches
       return processed







class  _lowerCAmelCase  (     lowercase       ):







 """simple docstring"""







 def __init__(      self							:      Union[str, Any], UpperCAmelCase__							:      Any, UpperCAmelCase__							:      List[str], UpperCAmelCase__							:      int, UpperCAmelCase__							:      str=None     ):
    super().__init__(UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__     )


 def __iter__(      self							:      str     ):
    __lowercase								=						iter(self.loader     )
    __lowercase								=						None
    return self



 def  _lowercase			(      self							:      int     ):
    if self.subiterator is None:
       __lowercase								=						self.infer(next(self.iterator     ), **self.params     )
    try:
       # Try to return next item
       __lowercase								=						next(self.subiterator     )
    except StopIteration:
       # When a preprocess iterator ends, we can start lookig at the next item
       # ChunkIterator will keep feeding until ALL elements of iterator
       # all have created their subiterator and have been iterating against.
       #
       # Another way to look at it, is we're basically flattening lists of lists
       # into a single list, but with generators
       __lowercase								=						self.infer(next(self.iterator     ), **self.params     )
       __lowercase								=						next(self.subiterator     )
    return processed







class  _lowerCAmelCase  (     lowercase       ):







 """simple docstring"""







 def __iter__(      self							:      int     ):
    __lowercase								=						iter(self.loader     )
    return self



 def  _lowercase			(      self							:      List[str]     ):
    # Extremely similar to PipelineIterator in its unpacking mechanism
    # BUT, we have an extra required item which is the presence of `is_last`
    # That is because everything is flattened by `PipelineChunkIterator` we
    # need to keep track of how to regroup here in the original `process`
    # boundaries so that `process` and `postprocess` see the same data.

    # This iterator accumulates items (possibly while unbatching) until it
    # its a `is_last` and then just passes it on to the caller.
    __lowercase								=						False
    __lowercase								=						[]
    if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
       while self._loader_batch_index < self.loader_batch_size:
          __lowercase								=						self.loader_batch_item()
          __lowercase								=						item.pop("is_last"     )
          accumulator.append(UpperCAmelCase__     )
          if is_last:
             return accumulator

    while not is_last:
       __lowercase								=						self.infer(next(self.iterator     ), **self.params     )
       if self.loader_batch_size is not None:
          if isinstance(UpperCAmelCase__, torch.Tensor     ):
             __lowercase								=						processed
          else:
             __lowercase								=						list(processed.keys()     )[0]
             __lowercase								=						processed[key]
          if isinstance(UpperCAmelCase__, UpperCAmelCase__     ):
             __lowercase								=						len(UpperCAmelCase__     )
          else:
             __lowercase								=						first_tensor.shape[0]
          if 0 < observed_batch_size < self.loader_batch_size:
             # could be last batch so we can't unroll as many
             # elements.
             __lowercase								=						observed_batch_size
          __lowercase								=						processed
          __lowercase								=						0
          while self._loader_batch_index < self.loader_batch_size:
             __lowercase								=						self.loader_batch_item()
             __lowercase								=						item.pop("is_last"     )
             accumulator.append(UpperCAmelCase__     )
             if is_last:
                return accumulator
       else:
          __lowercase								=						processed
          __lowercase								=						item.pop("is_last"     )
          accumulator.append(UpperCAmelCase__     )
    return accumulator







class  _lowerCAmelCase  (     lowercase       ):







 """simple docstring"""







 def __init__(      self							:      List[Any], UpperCAmelCase__							:      Dataset, UpperCAmelCase__							:      str     ):
    __lowercase								=						dataset
    __lowercase								=						key


 def __len__(      self							:      Optional[Any]     ):
    return len(self.dataset     )



 def __getitem__(      self							:      Union[str, Any], UpperCAmelCase__							:      Any     ):
    return self.dataset[i][self.key]







class  _lowerCAmelCase  (     lowercase       ):







 """simple docstring"""







 def __init__(      self							:      str, UpperCAmelCase__							:      Dataset, UpperCAmelCase__							:      str, UpperCAmelCase__							:      str     ):
    __lowercase								=						dataset
    __lowercase								=						keya
    __lowercase								=						keya


 def __len__(      self							:      Optional[int]     ):
    return len(self.dataset     )



 def __getitem__(      self							:      Dict, UpperCAmelCase__							:      Tuple     ):
    return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}







"""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_					:	Path,      UpperCamelCase_					:	list)     ->						Optional[Any]:
   '''simple docstring'''


   __lowercase								=						"\n".join(UpperCamelCase_)
   Path(UpperCamelCase_).open("w").writelines(UpperCamelCase_)




def   _A  (  UpperCamelCase_					:	Union[str, Any])     ->						int:
   '''simple docstring'''


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




class  _lowerCAmelCase  (     lowercase       ):







 """simple docstring"""







 @parameterized.expand(
     [
         MBART_TINY,
         MARIAN_TINY,
         T5_TINY,
         BART_TINY,
         PEGASUS_XSUM,
     ], )
 @slow
 def  _lowercase			(      self							:      List[Any], UpperCAmelCase__							:      Optional[Any]     ):
    __lowercase								=						AutoTokenizer.from_pretrained(UpperCAmelCase__     )
    __lowercase								=						make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     )
    __lowercase								=						max(len(tokenizer.encode(UpperCAmelCase__     )     ) for a in ARTICLES     )
    __lowercase								=						max(len(tokenizer.encode(UpperCAmelCase__     )     ) 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(
        UpperCAmelCase__, data_dir=UpperCAmelCase__, type_path="train", max_source_length=UpperCAmelCase__, max_target_length=UpperCAmelCase__, src_lang=UpperCAmelCase__, tgt_lang=UpperCAmelCase__, )
    __lowercase								=						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
       __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  _lowercase			(      self							:      Dict, UpperCAmelCase__							:      int     ):
    __lowercase								=						AutoTokenizer.from_pretrained(UpperCAmelCase__     )
    __lowercase								=						make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     )
    __lowercase								=						max(len(tokenizer.encode(UpperCAmelCase__     )     ) for a in ARTICLES     )
    __lowercase								=						max(len(tokenizer.encode(UpperCAmelCase__     )     ) for a in SUMMARIES     )
    __lowercase								=						4
    __lowercase								=						LegacySeqaSeqDataset(
        UpperCAmelCase__, data_dir=UpperCAmelCase__, type_path="train", max_source_length=2_0, max_target_length=UpperCAmelCase__, )
    __lowercase								=						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 2_0 >= 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  _lowercase			(      self							:      str     ):
    __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(UpperCAmelCase__, UpperCAmelCase__, 1_2_8, UpperCAmelCase__     )
    __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(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  _lowercase			(      self							:      List[str]     ):
    if not FAIRSEQ_AVAILABLE:
       return
    __lowercase		,__lowercase		,__lowercase								=						self._get_dataset(max_len=6_4     )
    __lowercase								=						6_4
    __lowercase								=						ds.make_dynamic_sampler(UpperCAmelCase__, required_batch_size_multiple=UpperCAmelCase__     )
    __lowercase								=						[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
    __lowercase								=						DataLoader(UpperCAmelCase__, batch_sampler=UpperCAmelCase__, 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(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  _lowercase			(      self							:      Any     ):
    __lowercase		,__lowercase		,__lowercase								=						self._get_dataset(max_len=5_1_2     )
    __lowercase								=						2
    __lowercase								=						ds.make_sortish_sampler(UpperCAmelCase__, shuffle=UpperCAmelCase__     )

    __lowercase								=						DataLoader(UpperCAmelCase__, batch_size=UpperCAmelCase__, collate_fn=ds.collate_fn, num_workers=2     )
    __lowercase								=						DataLoader(UpperCAmelCase__, batch_size=UpperCAmelCase__, collate_fn=ds.collate_fn, num_workers=2, sampler=UpperCAmelCase__     )

    __lowercase								=						tokenizer.pad_token_id

    def count_pad_tokens(UpperCAmelCase__							:      Any, UpperCAmelCase__							:      Union[str, Any]="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  _lowercase			(      self							:      List[Any], UpperCAmelCase__							:      int=1_0_0_0, UpperCAmelCase__							:      str=1_2_8     ):
    if os.getenv("USE_REAL_DATA", UpperCAmelCase__     ):
       __lowercase								=						"examples/seq2seq/wmt_en_ro"
       __lowercase								=						max_len * 2 * 6_4
       if not Path(UpperCAmelCase__     ).joinpath("train.len"     ).exists():
          save_len_file(UpperCAmelCase__, UpperCAmelCase__     )
    else:
       __lowercase								=						"examples/seq2seq/test_data/wmt_en_ro"
       __lowercase								=						max_len * 4
       save_len_file(UpperCAmelCase__, UpperCAmelCase__     )

    __lowercase								=						AutoTokenizer.from_pretrained(UpperCAmelCase__     )
    __lowercase								=						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  _lowercase			(      self							:      Union[str, Any]     ):
    __lowercase		,__lowercase		,__lowercase								=						self._get_dataset()
    __lowercase								=						set(DistributedSortishSampler(UpperCAmelCase__, 2_5_6, num_replicas=2, rank=0, add_extra_examples=UpperCAmelCase__     )     )
    __lowercase								=						set(DistributedSortishSampler(UpperCAmelCase__, 2_5_6, 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  _lowercase			(      self							:      int, UpperCAmelCase__							:      Optional[Any]     ):
    __lowercase								=						AutoTokenizer.from_pretrained(UpperCAmelCase__, use_fast=UpperCAmelCase__     )
    if tok_name == MBART_TINY:
       __lowercase								=						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", )
       __lowercase								=						train_dataset.dataset_kwargs
       assert "src_lang" in kwargs and "tgt_lang" in kwargs
    else:
       __lowercase								=						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, )
       __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(UpperCAmelCase__     ) == 1 if tok_name == BART_TINY else len(UpperCAmelCase__     ) == 0




