





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


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






def       _A  (      lowerCamelCase							, lowerCamelCase   ):
   a__	:						Union[str, Any]      		=  "\n".join(lowerCamelCase   )
   Path(lowerCamelCase   ).open("w"   ).writelines(lowerCamelCase   )






def       _A  (      lowerCamelCase   ):
   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   __lowerCAmelCase			(					_UpperCamelCase  ):





   @parameterized.expand(
       [
           MBART_TINY,
           MARIAN_TINY,
           T5_TINY,
           BART_TINY,
           PEGASUS_XSUM,
       ]					,			)
   @slow
   def 	_snake_case ( self					,			snake_case      )				->  Dict:
      """simple docstring"""





      a__	:						Optional[int]      		=  AutoTokenizer.from_pretrained(snake_case      )
      a__	:						str      		=  make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )
      a__	:						int      		=  max(len(tokenizer.encode(snake_case      )      ) for a in ARTICLES      )
      a__	:						List[Any]      		=  max(len(tokenizer.encode(snake_case      )      ) for a in SUMMARIES      )
      a__	:						Tuple      		=  4
      a__	:						Optional[Any]      		=  8
      assert max_len_target > max_src_len  # Will be truncated
      assert max_len_source > max_src_len  # Will be truncated
      a__	:						Union[str, Any]      		=  "ro_RO", "de_DE"  # ignored for all but mbart, but never causes error.
      a__	:						Tuple      		=  SeqaSeqDataset(
          snake_case					,			data_dir=snake_case					,			type_path="train"					,			max_source_length=snake_case					,			max_target_length=snake_case					,			src_lang=snake_case					,			tgt_lang=snake_case					,			)
      a__	:						int      		=  DataLoader(snake_case					,			batch_size=2					,			collate_fn=train_dataset.collate_fn      )
      for batch in dataloader:
         assert isinstance(snake_case					,			snake_case      )
         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
         a__	:						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 	_snake_case ( self					,			snake_case      )				->  List[str]:
      """simple docstring"""





      a__	:						Optional[Any]      		=  AutoTokenizer.from_pretrained(snake_case      )
      a__	:						Union[str, Any]      		=  make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )
      a__	:						List[Any]      		=  max(len(tokenizer.encode(snake_case      )      ) for a in ARTICLES      )
      a__	:						List[str]      		=  max(len(tokenizer.encode(snake_case      )      ) for a in SUMMARIES      )
      a__	:						Optional[int]      		=  4
      a__	:						Union[str, Any]      		=  LegacySeqaSeqDataset(
          snake_case					,			data_dir=snake_case					,			type_path="train"					,			max_source_length=20					,			max_target_length=snake_case					,			)
      a__	:						Union[str, Any]      		=  DataLoader(snake_case					,			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 	_snake_case ( self      )				->  Dict:
      """simple docstring"""





      a__	:						List[str]      		=  AutoTokenizer.from_pretrained("facebook/mbart-large-cc25"      )

      a__	:						str      		=  Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )      )
      a__	:						Tuple      		=  tmp_dir.joinpath("train.source"      ).open().readlines()
      a__	:						int      		=  Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()      )      )
      pack_data_dir(snake_case					,			snake_case					,			128					,			snake_case      )
      a__	:						str      		=  {x.name for x in tmp_dir.iterdir()}
      a__	:						Dict      		=  {x.name for x in save_dir.iterdir()}
      a__	:						Dict      		=  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(snake_case      ) < len(snake_case      )
      assert len(snake_case      ) == 1
      assert len(packed_examples[0]      ) == sum(len(snake_case      ) for x in orig_examples      )
      assert orig_paths == new_paths





   @pytest.mark.skipif(not FAIRSEQ_AVAILABLE					,			reason="This test requires fairseq"      )
   def 	_snake_case ( self      )				->  Any:
      """simple docstring"""





      if not FAIRSEQ_AVAILABLE:
         return
      a__	:						Optional[int]      		=  self._get_dataset(max_len=64      )
      a__	:						List[Any]      		=  64
      a__	:						Optional[int]      		=  ds.make_dynamic_sampler(snake_case					,			required_batch_size_multiple=snake_case      )
      a__	:						Union[str, Any]      		=  [len(snake_case      ) for x in batch_sampler]
      assert len(set(snake_case      )      ) > 1  # it's not dynamic batch size if every batch is the same length
      assert sum(snake_case      ) == len(snake_case      )  # no dropped or added examples
      a__	:						Tuple      		=  DataLoader(snake_case					,			batch_sampler=snake_case					,			collate_fn=ds.collate_fn					,			num_workers=2      )
      a__	:						List[Any]      		=  []
      a__	:						int      		=  []
      for batch in data_loader:
         a__	:						Tuple      		=  batch["input_ids"].shape
         a__	:						Tuple      		=  src_shape[0]
         assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
         a__	:						Any      		=  np.product(batch["input_ids"].shape      )
         num_src_per_batch.append(snake_case      )
         if num_src_tokens > (max_tokens * 1.1):
            failures.append(snake_case      )
      assert num_src_per_batch[0] == max(snake_case      )
      if failures:
         raise AssertionError(F"""too many tokens in {len(snake_case      )} batches"""      )





   def 	_snake_case ( self      )				->  str:
      """simple docstring"""





      a__	:						Any      		=  self._get_dataset(max_len=512      )
      a__	:						Dict      		=  2
      a__	:						Optional[int]      		=  ds.make_sortish_sampler(snake_case					,			shuffle=snake_case      )

      a__	:						int      		=  DataLoader(snake_case					,			batch_size=snake_case					,			collate_fn=ds.collate_fn					,			num_workers=2      )
      a__	:						Optional[Any]      		=  DataLoader(snake_case					,			batch_size=snake_case					,			collate_fn=ds.collate_fn					,			num_workers=2					,			sampler=snake_case      )

      a__	:						Optional[Any]      		=  tokenizer.pad_token_id

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

      assert sum(count_pad_tokens(snake_case					,			k="labels"      )      ) < sum(count_pad_tokens(snake_case					,			k="labels"      )      )
      assert sum(count_pad_tokens(snake_case      )      ) < sum(count_pad_tokens(snake_case      )      )
      assert len(snake_case      ) == len(snake_case      )





   def 	_snake_case ( self					,			snake_case=1_000					,			snake_case=128      )				->  Tuple:
      """simple docstring"""





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

      a__	:						str      		=  AutoTokenizer.from_pretrained(snake_case      )
      a__	:						List[Any]      		=  SeqaSeqDataset(
          snake_case					,			data_dir=snake_case					,			type_path="train"					,			max_source_length=snake_case					,			max_target_length=snake_case					,			n_obs=snake_case					,			)
      return ds, max_tokens, tokenizer





   def 	_snake_case ( self      )				->  Tuple:
      """simple docstring"""





      a__	:						str      		=  self._get_dataset()
      a__	:						Union[str, Any]      		=  set(DistributedSortishSampler(snake_case					,			256					,			num_replicas=2					,			rank=0					,			add_extra_examples=snake_case      )      )
      a__	:						int      		=  set(DistributedSortishSampler(snake_case					,			256					,			num_replicas=2					,			rank=1					,			add_extra_examples=snake_case      )      )
      assert idsa.intersection(snake_case      ) == set()





   @parameterized.expand(
       [
           MBART_TINY,
           MARIAN_TINY,
           T5_TINY,
           BART_TINY,
           PEGASUS_XSUM,
       ]					,			)
   def 	_snake_case ( self					,			snake_case      )				->  Any:
      """simple docstring"""





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










import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer


SCREAMING_SNAKE_CASE__       :			Optional[Any]			      =						{"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

SCREAMING_SNAKE_CASE__       :			List[Any]			      =						{
    """vocab_file""": {
        """unc-nlp/lxmert-base-uncased""": """https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/vocab.txt""",
    },
    """tokenizer_file""": {
        """unc-nlp/lxmert-base-uncased""": (
            """https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/tokenizer.json"""
        ),
    },
}

SCREAMING_SNAKE_CASE__       :			Any			      =						{
    """unc-nlp/lxmert-base-uncased""": 5_1_2,
}

SCREAMING_SNAKE_CASE__       :			Optional[int]			      =						{
    """unc-nlp/lxmert-base-uncased""": {"""do_lower_case""": True},
}







class   __lowerCAmelCase			(					_UpperCamelCase  ):
   _UpperCamelCase :						List[str]     		=				VOCAB_FILES_NAMES
   _UpperCamelCase :						str     		=				PRETRAINED_VOCAB_FILES_MAP
   _UpperCamelCase :						List[str]     		=				PRETRAINED_INIT_CONFIGURATION
   _UpperCamelCase :						Optional[int]     		=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _UpperCamelCase :						Optional[Any]     		=				LxmertTokenizer





   def __init__( self					,			snake_case=None					,			snake_case=None					,			snake_case=True					,			snake_case="[UNK]"					,			snake_case="[SEP]"					,			snake_case="[PAD]"					,			snake_case="[CLS]"					,			snake_case="[MASK]"					,			snake_case=True					,			snake_case=None					,			**snake_case					,			)				->  Any:
      """simple docstring"""





      super().__init__(
          snake_case					,			tokenizer_file=snake_case					,			do_lower_case=snake_case					,			unk_token=snake_case					,			sep_token=snake_case					,			pad_token=snake_case					,			cls_token=snake_case					,			mask_token=snake_case					,			tokenize_chinese_chars=snake_case					,			strip_accents=snake_case					,			**snake_case					,			)

      a__	:						Optional[Any]      		=  json.loads(self.backend_tokenizer.normalizer.__getstate__()      )
      if (
          normalizer_state.get("lowercase"					,			snake_case      ) != do_lower_case
          or normalizer_state.get("strip_accents"					,			snake_case      ) != strip_accents
          or normalizer_state.get("handle_chinese_chars"					,			snake_case      ) != tokenize_chinese_chars
      ):
         a__	:						str      		=  getattr(snake_case					,			normalizer_state.pop("type"      )      )
         a__	:						Tuple      		=  do_lower_case
         a__	:						Union[str, Any]      		=  strip_accents
         a__	:						str      		=  tokenize_chinese_chars
         a__	:						List[str]      		=  normalizer_class(**snake_case      )

      a__	:						str      		=  do_lower_case





   def 	_snake_case ( self					,			snake_case					,			snake_case=None      )				->  List[str]:
      """simple docstring"""





      a__	:						Optional[int]      		=  [self.cls_token_id] + token_ids_a + [self.sep_token_id]

      if token_ids_a:
         output += token_ids_a + [self.sep_token_id]

      return output





   def 	_snake_case ( self					,			snake_case					,			snake_case = None      )				->  List[int]:
      """simple docstring"""





      a__	:						Tuple      		=  [self.sep_token_id]
      a__	:						str      		=  [self.cls_token_id]
      if token_ids_a is None:
         return len(cls + token_ids_a + sep      ) * [0]
      return len(cls + token_ids_a + sep      ) * [0] + len(token_ids_a + sep      ) * [1]





   def 	_snake_case ( self					,			snake_case					,			snake_case = None      )				->  Tuple[str]:
      """simple docstring"""





      a__	:						Optional[int]      		=  self._tokenizer.model.save(snake_case					,			name=snake_case      )
      return tuple(snake_case      )

