





'''simple docstring'''




from __future__ import annotations


class 			SCREAMING_SNAKE_CASE   :


     def __init__(						self						,     _UpperCAmelCase						,     _UpperCAmelCase):

          '''simple docstring'''






          __A						:			Dict								=	text, pattern
          __A						:			int								=	len(_a), len(_a)


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase):

          '''simple docstring'''






          for i in range(self.patLen - 1						,     -1						,     -1):
               if char == self.pattern[i]:
                    return i
          return -1


     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase):

          '''simple docstring'''






          for i in range(self.patLen - 1						,     -1						,     -1):
               if self.pattern[i] != self.text[current_pos + i]:
                    return current_pos + i
          return -1


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			List[str]								=	[]
          for i in range(self.textLen - self.patLen + 1):
               __A						:			Optional[int]								=	self.mismatch_in_text(_a)
               if mismatch_index == -1:
                    positions.append(_a)
               else:
                    __A						:			List[Any]								=	self.match_in_pattern(self.text[mismatch_index])
                    __A						:			Optional[Any]								=	(
                        mismatch_index - match_index
                    )  # shifting index lgtm [py/multiple-definition]
          return positions




lowercase__   :				Tuple 		=   "ABAABA"
lowercase__   :				List[str] 		=   "AB"
lowercase__   :				Dict 		=   BoyerMooreSearch(text, pattern)
lowercase__   :				Dict 		=   bms.bad_character_heuristic()

if len(positions) == 0:
  print('''No match found''')
else:
  print('''Pattern found in following positions: ''')
  print(positions)






'''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


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






def 	_lowerCAmelCase      (  __snake_case  :			Path			,							__snake_case  :			list      )					->					str:
     __A						:			Any								=	'\n'.join(__snake_case      )
     Path(__snake_case      ).open('w'      ).writelines(__snake_case      )






def 	_lowerCAmelCase      (  __snake_case  :			Optional[int]      )					->					Tuple:
     for split in ["train", "val", "test"]:
          _dump_articles(os.path.join(__snake_case			,							f'{split}.source'      )			,							__snake_case      )
          _dump_articles(os.path.join(__snake_case			,							f'{split}.target'      )			,							__snake_case      )
     return tmp_dir







class 			SCREAMING_SNAKE_CASE   (a__							):


     @parameterized.expand(
         [
             MBART_TINY,
             MARIAN_TINY,
             T5_TINY,
             BART_TINY,
             PEGASUS_XSUM,
         ]						,     )
     @slow
     def 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase):

          '''simple docstring'''






          __A						:			str								=	AutoTokenizer.from_pretrained(_UpperCAmelCase)
          __A						:			int								=	make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
          __A						:			int								=	max(len(tokenizer.encode(_UpperCAmelCase)) for a in ARTICLES)
          __A						:			str								=	max(len(tokenizer.encode(_UpperCAmelCase)) for a in SUMMARIES)
          __A						:			Dict								=	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			,__A						:			Any								=	'ro_RO', 'de_DE'  # ignored for all but mbart, but never causes error.
          __A						:			List[str]								=	SeqaSeqDataset(
              _UpperCAmelCase						,     data_dir=_UpperCAmelCase						,     type_path='train'						,     max_source_length=_UpperCAmelCase						,     max_target_length=_UpperCAmelCase						,     src_lang=_UpperCAmelCase						,     tgt_lang=_UpperCAmelCase						,     )
          __A						:			Any								=	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
               __A						:			Optional[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 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase):

          '''simple docstring'''






          __A						:			str								=	AutoTokenizer.from_pretrained(_UpperCAmelCase)
          __A						:			Optional[int]								=	make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
          __A						:			Tuple								=	max(len(tokenizer.encode(_UpperCAmelCase)) for a in ARTICLES)
          __A						:			Any								=	max(len(tokenizer.encode(_UpperCAmelCase)) for a in SUMMARIES)
          __A						:			Optional[int]								=	4
          __A						:			Any								=	LegacySeqaSeqDataset(
              _UpperCAmelCase						,     data_dir=_UpperCAmelCase						,     type_path='train'						,     max_source_length=20						,     max_target_length=_UpperCAmelCase						,     )
          __A						:			Union[str, Any]								=	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 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Dict								=	AutoTokenizer.from_pretrained('facebook/mbart-large-cc25')

          __A						:			int								=	Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()))
          __A						:			List[str]								=	tmp_dir.joinpath('train.source').open().readlines()
          __A						:			Optional[Any]								=	Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()))
          pack_data_dir(_UpperCAmelCase						,     _UpperCAmelCase						,     128						,     _UpperCAmelCase)
          __A						:			Dict								=	{x.name for x in tmp_dir.iterdir()}
          __A						:			Dict								=	{x.name for x in save_dir.iterdir()}
          __A						:			str								=	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 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          if not FAIRSEQ_AVAILABLE:
               return
          __A			,__A			,__A						:			List[Any]								=	self._get_dataset(max_len=64)
          __A						:			Union[str, Any]								=	64
          __A						:			List[Any]								=	ds.make_dynamic_sampler(_UpperCAmelCase						,     required_batch_size_multiple=_UpperCAmelCase)
          __A						:			Union[str, Any]								=	[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
          __A						:			List[Any]								=	DataLoader(_UpperCAmelCase						,     batch_sampler=_UpperCAmelCase						,     collate_fn=ds.collate_fn						,     num_workers=2)
          __A						:			Optional[int]								=	[]
          __A						:			Tuple								=	[]
          for batch in data_loader:
               __A						:			Optional[int]								=	batch['input_ids'].shape
               __A						:			Any								=	src_shape[0]
               assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
               __A						:			Tuple								=	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 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A			,__A			,__A						:			Optional[int]								=	self._get_dataset(max_len=512)
          __A						:			Optional[int]								=	2
          __A						:			Dict								=	ds.make_sortish_sampler(_UpperCAmelCase						,     shuffle=_UpperCAmelCase)

          __A						:			Tuple								=	DataLoader(_UpperCAmelCase						,     batch_size=_UpperCAmelCase						,     collate_fn=ds.collate_fn						,     num_workers=2)
          __A						:			Union[str, Any]								=	DataLoader(_UpperCAmelCase						,     batch_size=_UpperCAmelCase						,     collate_fn=ds.collate_fn						,     num_workers=2						,     sampler=_UpperCAmelCase)

          __A						:			str								=	tokenizer.pad_token_id

          def count_pad_tokens(_UpperCAmelCase						,     _UpperCAmelCase="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 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase=1000						,     _UpperCAmelCase=128):

          '''simple docstring'''






          if os.getenv('USE_REAL_DATA'						,     _UpperCAmelCase):
               __A						:			Dict								=	'examples/seq2seq/wmt_en_ro'
               __A						:			Any								=	max_len * 2 * 64
               if not Path(_UpperCAmelCase).joinpath('train.len').exists():
                    save_len_file(_UpperCAmelCase						,     _UpperCAmelCase)
          else:
               __A						:			int								=	'examples/seq2seq/test_data/wmt_en_ro'
               __A						:			Any								=	max_len * 4
               save_len_file(_UpperCAmelCase						,     _UpperCAmelCase)

          __A						:			Tuple								=	AutoTokenizer.from_pretrained(_UpperCAmelCase)
          __A						:			Optional[int]								=	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 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A			,__A			,__A						:			Tuple								=	self._get_dataset()
          __A						:			Optional[int]								=	set(DistributedSortishSampler(_UpperCAmelCase						,     256						,     num_replicas=2						,     rank=0						,     add_extra_examples=_UpperCAmelCase))
          __A						:			List[str]								=	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 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase):

          '''simple docstring'''






          __A						:			Union[str, Any]								=	AutoTokenizer.from_pretrained(_UpperCAmelCase						,     use_fast=_UpperCAmelCase)
          if tok_name == MBART_TINY:
               __A						:			Dict								=	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'						,     )
               __A						:			List[Any]								=	train_dataset.dataset_kwargs
               assert "src_lang" in kwargs and "tgt_lang" in kwargs
          else:
               __A						:			Any								=	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						,     )
               __A						:			List[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(_UpperCAmelCase) == 1 if tok_name == BART_TINY else len(_UpperCAmelCase) == 0