

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




def  _lowercase					(    __lowerCAmelCase  ,					__lowerCAmelCase							)       ->				Tuple:
      SCREAMING_SNAKE_CASE__   :      Dict      		=	"""\n""".join(UpperCamelCase__							)
      Path(UpperCamelCase__							).open("""w"""							).writelines(UpperCamelCase__							)




def  _lowercase					(    __lowerCAmelCase							)       ->				str:





      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      __a							(UpperCAmelCase_):






     '''simple docstring'''

     @parameterized.expand(
         [
             MBART_TINY,
             MARIAN_TINY,
             T5_TINY,
             BART_TINY,
             PEGASUS_XSUM,
         ]	,    )
     @slow
     def 					_a			(						self	,    _a  )  ->      Any:


           """simple docstring"""


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


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      int      		=	AutoTokenizer.from_pretrained(_lowercase  )
           SCREAMING_SNAKE_CASE__   :      int      		=	make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()  )
           SCREAMING_SNAKE_CASE__   :      str      		=	max(len(tokenizer.encode(_lowercase  )  ) for a in ARTICLES  )
           SCREAMING_SNAKE_CASE__   :      int      		=	max(len(tokenizer.encode(_lowercase  )  ) for a in SUMMARIES  )
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	4
           SCREAMING_SNAKE_CASE__   :      List[str]      		=	LegacySeqaSeqDataset(
               _lowercase	,    data_dir=_lowercase	,    type_path="""train"""	,    max_source_length=20	,    max_target_length=_lowercase	,    )
           SCREAMING_SNAKE_CASE__   :      str      		=	DataLoader(_lowercase	,    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[Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Tuple      		=	AutoTokenizer.from_pretrained("""facebook/mbart-large-cc25"""  )

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

     @pytest.mark.skipif(not FAIRSEQ_AVAILABLE	,    reason="""This test requires fairseq"""  )
     def 					_a			(						self  )  ->      Optional[Any]:


           """simple docstring"""


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

     def 					_a			(						self  )  ->      Optional[int]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__   :      List[str]      		=	self._get_dataset(max_len=512  )
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	2
           SCREAMING_SNAKE_CASE__   :      str      		=	ds.make_sortish_sampler(_lowercase	,    shuffle=_lowercase  )

           SCREAMING_SNAKE_CASE__   :      Any      		=	DataLoader(_lowercase	,    batch_size=_lowercase	,    collate_fn=ds.collate_fn	,    num_workers=2  )
           SCREAMING_SNAKE_CASE__   :      Any      		=	DataLoader(_lowercase	,    batch_size=_lowercase	,    collate_fn=ds.collate_fn	,    num_workers=2	,    sampler=_lowercase  )

           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	tokenizer.pad_token_id

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

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

     def 					_a			(						self	,    _a=1_000	,    _a=128  )  ->      Optional[Any]:


           """simple docstring"""


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

           SCREAMING_SNAKE_CASE__   :      List[str]      		=	AutoTokenizer.from_pretrained(_lowercase  )
           SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	SeqaSeqDataset(
               _lowercase	,    data_dir=_lowercase	,    type_path="""train"""	,    max_source_length=_lowercase	,    max_target_length=_lowercase	,    n_obs=_lowercase	,    )
           return ds, max_tokens, tokenizer

     def 					_a			(						self  )  ->      List[Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__		,		SCREAMING_SNAKE_CASE__   :      Optional[Any]      		=	self._get_dataset()
           SCREAMING_SNAKE_CASE__   :      List[Any]      		=	set(DistributedSortishSampler(_lowercase	,    256	,    num_replicas=2	,    rank=0	,    add_extra_examples=_lowercase  )  )
           SCREAMING_SNAKE_CASE__   :      Any      		=	set(DistributedSortishSampler(_lowercase	,    256	,    num_replicas=2	,    rank=1	,    add_extra_examples=_lowercase  )  )
           assert idsa.intersection(_lowercase  ) == set()





     @parameterized.expand(
         [
             MBART_TINY,
             MARIAN_TINY,
             T5_TINY,
             BART_TINY,
             PEGASUS_XSUM,
         ]	,    )
     def 					_a			(						self	,    _a  )  ->      Optional[Any]:


           """simple docstring"""


           SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	AutoTokenizer.from_pretrained(_lowercase	,    use_fast=_lowercase  )
           if tok_name == MBART_TINY:
                 SCREAMING_SNAKE_CASE__   :      Dict      		=	SeqaSeqDataset(
                     _lowercase	,    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"""	,    )
                 SCREAMING_SNAKE_CASE__   :      Tuple      		=	train_dataset.dataset_kwargs
                 assert "src_lang" in kwargs and "tgt_lang" in kwargs
           else:
                 SCREAMING_SNAKE_CASE__   :      Dict      		=	SeqaSeqDataset(
                     _lowercase	,    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	,    )
                 SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	train_dataset.dataset_kwargs
                 assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
                 assert len(_lowercase  ) == 1 if tok_name == BART_TINY else len(_lowercase  ) == 0







"""simple docstring"""


import os

a					:List[str]    = {"I": 1, "V": 5, "X": 10, "L": 50, "C": 100, "D": 500, "M": 1_000}




def  _lowercase					(    __lowerCAmelCase							)       ->				int:
      SCREAMING_SNAKE_CASE__   :      Any      		=	0

      SCREAMING_SNAKE_CASE__   :      Dict      		=	0
      while index < len(__lowerCAmelCase							) - 1:
            SCREAMING_SNAKE_CASE__   :      List[Any]      		=	SYMBOLS[numerals[index]]
            SCREAMING_SNAKE_CASE__   :      Dict      		=	SYMBOLS[numerals[index + 1]]
            if current_value < next_value:
                  total_value -= current_value
            else:
                  total_value += current_value
            index += 1
      total_value += SYMBOLS[numerals[index]]

      return total_value




def  _lowercase					(    __lowerCAmelCase							)       ->				str:
      SCREAMING_SNAKE_CASE__   :      Optional[int]      		=	""""""

      SCREAMING_SNAKE_CASE__   :      int      		=	num // 1000
      numerals += m_count * "M"
      num %= 1000

      SCREAMING_SNAKE_CASE__   :      List[str]      		=	num // 100
      if c_count == 9:
            numerals += "CM"
            c_count -= 9
      elif c_count == 4:
            numerals += "CD"
            c_count -= 4
      if c_count >= 5:
            numerals += "D"
            c_count -= 5
      numerals += c_count * "C"
      num %= 100

      SCREAMING_SNAKE_CASE__   :      List[Any]      		=	num // 10
      if x_count == 9:
            numerals += "XC"
            x_count -= 9
      elif x_count == 4:
            numerals += "XL"
            x_count -= 4
      if x_count >= 5:
            numerals += "L"
            x_count -= 5
      numerals += x_count * "X"
      num %= 10

      if num == 9:
            numerals += "IX"
            num -= 9
      elif num == 4:
            numerals += "IV"
            num -= 4
      if num >= 5:
            numerals += "V"
            num -= 5
      numerals += num * "I"

      return numerals




def  _lowercase					(    __lowerCAmelCase = "/p089_roman.txt"							)       ->				int:
      SCREAMING_SNAKE_CASE__   :      int      		=	0

      with open(os.path.dirname(__lowerCAmelCase							) + roman_numerals_filename							) as filea:
            SCREAMING_SNAKE_CASE__   :      str      		=	filea.readlines()

      for line in lines:
            SCREAMING_SNAKE_CASE__   :      Union[str, Any]      		=	line.strip()
            SCREAMING_SNAKE_CASE__   :      Dict      		=	parse_roman_numerals(__lowerCAmelCase							)
            SCREAMING_SNAKE_CASE__   :      str      		=	generate_roman_numerals(__lowerCAmelCase							)
            savings += len(__lowerCAmelCase							) - len(__lowerCAmelCase							)

      return savings


if __name__ == "__main__":
  print(f'{solution() = }')




