






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


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


def 				__lowerCamelCase     (							__lowerCAmelCase							:					Path					,							__lowerCAmelCase							:					list							)     ->  List[Any]:
   __UpperCamelCase	:			Any					=					"""\n""".join(__lowerCAmelCase							)
   Path(__lowerCAmelCase							).open("""w"""							).writelines(__lowerCAmelCase							)


def 				__lowerCamelCase     (							__lowerCAmelCase							:					Optional[Any]							)     ->  Dict:
   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 		_A						( UpperCAmelCase_ ):

  @parameterized.expand(
      [
          MBART_TINY,
          MARIAN_TINY,
          T5_TINY,
          BART_TINY,
          PEGASUS_XSUM,
      ]   ,				)
  @slow
  def    a					(    self						:	Tuple   ,				lowerCamelCase__						:	Optional[Any]			):
     """simple docstring"""

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

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

     __UpperCamelCase	:			Optional[int]					=					AutoTokenizer.from_pretrained("""facebook/mbart-large-cc25"""			)

     __UpperCamelCase	:			List[Any]					=					Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()			)			)
     __UpperCamelCase	:			str					=					tmp_dir.joinpath("""train.source"""			).open().readlines()
     __UpperCamelCase	:			Any					=					Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()			)			)
     pack_data_dir(lowerCamelCase__   ,				lowerCamelCase__   ,				1_28   ,				lowerCamelCase__			)
     __UpperCamelCase	:			Union[str, Any]					=					{x.name for x in tmp_dir.iterdir()}
     __UpperCamelCase	:			Tuple					=					{x.name for x in save_dir.iterdir()}
     __UpperCamelCase	:			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(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    a					(    self						:	List[str]			):
     """simple docstring"""

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

     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase	:			Optional[Any]					=					self._get_dataset(max_len=5_12			)
     __UpperCamelCase	:			Union[str, Any]					=					2
     __UpperCamelCase	:			Optional[Any]					=					ds.make_sortish_sampler(lowerCamelCase__   ,				shuffle=lowerCamelCase__			)

     __UpperCamelCase	:			str					=					DataLoader(lowerCamelCase__   ,				batch_size=lowerCamelCase__   ,				collate_fn=ds.collate_fn   ,				num_workers=2			)
     __UpperCamelCase	:			Tuple					=					DataLoader(lowerCamelCase__   ,				batch_size=lowerCamelCase__   ,				collate_fn=ds.collate_fn   ,				num_workers=2   ,				sampler=lowerCamelCase__			)

     __UpperCamelCase	:			Dict					=					tokenizer.pad_token_id

     def count_pad_tokens(lowerCamelCase__						:	Optional[int]   ,				lowerCamelCase__						:	Tuple="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    a					(    self						:	List[Any]   ,				lowerCamelCase__						:	Any=10_00   ,				lowerCamelCase__						:	List[Any]=1_28			):
     """simple docstring"""

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

     __UpperCamelCase	:			int					=					AutoTokenizer.from_pretrained(lowerCamelCase__			)
     __UpperCamelCase	:			Tuple					=					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    a					(    self						:	List[Any]			):
     """simple docstring"""

     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase	:			Dict					=					self._get_dataset()
     __UpperCamelCase	:			Optional[int]					=					set(DistributedSortishSampler(lowerCamelCase__   ,				2_56   ,				num_replicas=2   ,				rank=0   ,				add_extra_examples=lowerCamelCase__			)			)
     __UpperCamelCase	:			Tuple					=					set(DistributedSortishSampler(lowerCamelCase__   ,				2_56   ,				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    a					(    self						:	Dict   ,				lowerCamelCase__						:	Optional[Any]			):
     """simple docstring"""

     __UpperCamelCase	:			List[str]					=					AutoTokenizer.from_pretrained(lowerCamelCase__   ,				use_fast=lowerCamelCase__			)
     if tok_name == MBART_TINY:
        __UpperCamelCase	:			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   ,				src_lang="""EN"""   ,				tgt_lang="""FR"""   ,				)
        __UpperCamelCase	:			int					=					train_dataset.dataset_kwargs
        assert "src_lang" in kwargs and "tgt_lang" in kwargs
     else:
        __UpperCamelCase	:			List[Any]					=					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   ,				)
        __UpperCamelCase	:			str					=					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 __future__ import annotations







class 		_A						:

  def __init__(    self						:	List[str]   ,				lowerCamelCase__						:	Any=None			):
     """simple docstring"""

     __UpperCamelCase	:			Union[str, Any]					=					data
     __UpperCamelCase	:			Union[str, Any]					=					None

  def __repr__(    self						:	int			):
     """simple docstring"""

     __UpperCamelCase	:			Union[str, Any]					=					[]
     __UpperCamelCase	:			Optional[int]					=					self
     while temp:
        string_rep.append(f'{temp.data}'			)
        __UpperCamelCase	:			Dict					=					temp.next
     return "->".join(lowerCamelCase__			)







def 				__lowerCamelCase     (							__lowerCAmelCase							:					list							)     ->  Tuple:
   if not elements_list:
      raise Exception("""The Elements List is empty"""							)

   __UpperCamelCase	:			Dict					=					Node(elements_list[0]							)
   for i in range(1					,							len(__lowerCAmelCase							)							):
      __UpperCamelCase	:			List[str]					=					Node(elements_list[i]							)
      __UpperCamelCase	:			Optional[int]					=					current.next
   return head


def 				__lowerCamelCase     (							__lowerCAmelCase							:					Node							)     ->  None:
   if head_node is not None and isinstance(__lowerCAmelCase					,							__lowerCAmelCase							):
      print_reverse(head_node.next							)
      print(head_node.data							)


def 				__lowerCamelCase     (							)     ->  Union[str, Any]:
   from doctest import testmod

   testmod()

   __UpperCamelCase	:			Any					=					make_linked_list([14, 52, 14, 12, 43]							)
   print("""Linked List:"""							)
   print(__lowerCAmelCase							)
   print("""Elements in Reverse:"""							)
   print_reverse(__lowerCAmelCase							)


if __name__ == "__main__":
       main()



