

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  lowerCamelCase__ (					_a							,			_a):
	SCREAMING_SNAKE_CASE   :      List[Any]				     =       "\n".join(_a)
	Path(_a).open("w").writelines(_a)

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






class _UpperCamelCase							(							__A   ):
			'''simple docstring'''







			@parameterized.expand(
			    [
			        MBART_TINY,
			        MARIAN_TINY,
			        T5_TINY,
			        BART_TINY,
			        PEGASUS_XSUM,
			    ]      ,      )
			@slow
			def 							__UpperCamelCase (			self							: Union[str, Any]      ,      a							: Optional[int]	)							->       Union[str, Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE   :      List[str]				     =       AutoTokenizer.from_pretrained(a	)
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()	)
				SCREAMING_SNAKE_CASE   :      Any				     =       max(len(tokenizer.encode(a	)	) for a in ARTICLES	)
				SCREAMING_SNAKE_CASE   :      List[str]				     =       max(len(tokenizer.encode(a	)	) for a in SUMMARIES	)
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       4
				SCREAMING_SNAKE_CASE   :      Union[str, 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   :      Union[str, Any]				     =       "ro_RO", "de_DE"  # ignored for all but mbart, but never causes error.
				SCREAMING_SNAKE_CASE   :      Tuple				     =       SeqaSeqDataset(
				    a      ,      data_dir=a      ,      type_path="train"      ,      max_source_length=a      ,      max_target_length=a      ,      src_lang=a      ,      tgt_lang=a      ,      )
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       DataLoader(a      ,      batch_size=2      ,      collate_fn=train_dataset.collate_fn	)
				for batch in dataloader:
					assert isinstance(a      ,      a	)
					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[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 							__UpperCamelCase (			self							: Union[str, Any]      ,      a							: str	)							->       List[Any]:

				"""simple docstring"""

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

				"""simple docstring"""

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

				SCREAMING_SNAKE_CASE   :      Dict				     =       Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()	)	)
				SCREAMING_SNAKE_CASE   :      Tuple				     =       tmp_dir.joinpath("train.source"	).open().readlines()
				SCREAMING_SNAKE_CASE   :      str				     =       Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()	)	)
				pack_data_dir(a      ,      a      ,      128      ,      a	)
				SCREAMING_SNAKE_CASE   :      str				     =       {x.name for x in tmp_dir.iterdir()}
				SCREAMING_SNAKE_CASE   :      Any				     =       {x.name for x in save_dir.iterdir()}
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       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(a	) < len(a	)
				assert len(a	) == 1
				assert len(packed_examples[0]	) == sum(len(a	) for x in orig_examples	)
				assert orig_paths == new_paths
			@pytest.mark.skipif(not FAIRSEQ_AVAILABLE      ,      reason="This test requires fairseq"	)
			def 							__UpperCamelCase (			self							: Optional[Any]	)							->       List[str]:

				"""simple docstring"""

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

				"""simple docstring"""

				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      List[str]				     =       self._get_dataset(max_len=512	)
				SCREAMING_SNAKE_CASE   :      List[str]				     =       2
				SCREAMING_SNAKE_CASE   :      Tuple				     =       ds.make_sortish_sampler(a      ,      shuffle=a	)

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

				SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       tokenizer.pad_token_id

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

				assert sum(count_pad_tokens(a      ,      k="labels"	)	) < sum(count_pad_tokens(a      ,      k="labels"	)	)
				assert sum(count_pad_tokens(a	)	) < sum(count_pad_tokens(a	)	)
				assert len(a	) == len(a	)
			def 							__UpperCamelCase (			self							: str      ,      a							: Any=1000      ,      a							: str=128	)							->       List[str]:

				"""simple docstring"""

				if os.getenv("USE_REAL_DATA"      ,      a	):
					SCREAMING_SNAKE_CASE   :      Dict				     =       "examples/seq2seq/wmt_en_ro"
					SCREAMING_SNAKE_CASE   :      Dict				     =       max_len * 2 * 64
					if not Path(a	).joinpath("train.len"	).exists():
						save_len_file(a      ,      a	)
				else:
					SCREAMING_SNAKE_CASE   :      Dict				     =       "examples/seq2seq/test_data/wmt_en_ro"
					SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       max_len * 4
					save_len_file(a      ,      a	)

				SCREAMING_SNAKE_CASE   :      int				     =       AutoTokenizer.from_pretrained(a	)
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       SeqaSeqDataset(
				    a      ,      data_dir=a      ,      type_path="train"      ,      max_source_length=a      ,      max_target_length=a      ,      n_obs=a      ,      )
				return ds, max_tokens, tokenizer
			def 							__UpperCamelCase (			self							: Any	)							->       Optional[Any]:

				"""simple docstring"""

				SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Tuple				     =       self._get_dataset()
				SCREAMING_SNAKE_CASE   :      Any				     =       set(DistributedSortishSampler(a      ,      256      ,      num_replicas=2      ,      rank=0      ,      add_extra_examples=a	)	)
				SCREAMING_SNAKE_CASE   :      Dict				     =       set(DistributedSortishSampler(a      ,      256      ,      num_replicas=2      ,      rank=1      ,      add_extra_examples=a	)	)
				assert idsa.intersection(a	) == set()






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

				"""simple docstring"""

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


from __future__ import annotations

def  lowerCamelCase__ (					_a							,			_a):
	if b == 0:
		return (1, 0)
	((SCREAMING_SNAKE_CASE)     ,(SCREAMING_SNAKE_CASE))   :      Tuple				     =       extended_euclid(_a							,			a % b)
	SCREAMING_SNAKE_CASE   :      Dict				     =       a // b
	return (y, x - k * y)

def  lowerCamelCase__ (					_a							,			_a							,			_a							,			_a):
	((SCREAMING_SNAKE_CASE)     ,(SCREAMING_SNAKE_CASE))   :      str				     =       extended_euclid(_a							,			_a)
	SCREAMING_SNAKE_CASE   :      str				     =       na * na
	SCREAMING_SNAKE_CASE   :      int				     =       ra * x * na + ra * y * na
	return (n % m + m) % m

def  lowerCamelCase__ (					_a							,			_a):
	((SCREAMING_SNAKE_CASE)     ,(SCREAMING_SNAKE_CASE))   :      List[str]				     =       extended_euclid(_a							,			_a)
	if b < 0:
		SCREAMING_SNAKE_CASE   :      int				     =       (b % n + n) % n
	return b

def  lowerCamelCase__ (					_a							,			_a							,			_a							,			_a):
	SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Dict				     =       invert_modulo(_a							,			_a), invert_modulo(_a							,			_a)
	SCREAMING_SNAKE_CASE   :      Optional[int]				     =       na * na
	SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       ra * x * na + ra * y * na
	return (n % m + m) % m


if __name__ == "__main__":
			from doctest import testmod

			testmod(name='chinese_remainder_theorem', verbose=True)
			testmod(name='chinese_remainder_theorem2', verbose=True)
			testmod(name='invert_modulo', verbose=True)
			testmod(name='extended_euclid', verbose=True)