


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


def     lowercase	(    a     , a ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:Optional[int]				     =						"\n".join(a )
		Path(a ).open("w" ).writelines(a )


def     lowercase	(    a ):



		'''simple docstring'''




		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      (     lowercase							):






		@parameterized.expand(
		    [
		        MBART_TINY,
		        MARIAN_TINY,
		        T5_TINY,
		        BART_TINY,
		        PEGASUS_XSUM,
		    ]   ,  )
		@slow
		def _snake_case		(						self				:     str   ,  UpperCAmelCase				:     Any):
				SCREAMING_SNAKE_CASE_				:List[str]				     =						AutoTokenizer.from_pretrained(UpperCAmelCase)
				SCREAMING_SNAKE_CASE_				:str				     =						make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						max(len(tokenizer.encode(UpperCAmelCase)) for a in ARTICLES)
				SCREAMING_SNAKE_CASE_				:int				     =						max(len(tokenizer.encode(UpperCAmelCase)) for a in SUMMARIES)
				SCREAMING_SNAKE_CASE_				:Any				     =						4
				SCREAMING_SNAKE_CASE_				: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_				:Optional[Any]				     =						"ro_RO", "de_DE"  # ignored for all but mbart, but never causes error.
				SCREAMING_SNAKE_CASE_				:Tuple				     =						SeqaSeqDataset(
				    UpperCAmelCase   ,  data_dir=UpperCAmelCase   ,  type_path="train"   ,  max_source_length=UpperCAmelCase   ,  max_target_length=UpperCAmelCase   ,  src_lang=UpperCAmelCase   ,  tgt_lang=UpperCAmelCase   ,  )
				SCREAMING_SNAKE_CASE_				:List[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
						SCREAMING_SNAKE_CASE_				: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				:     Any   ,  UpperCAmelCase				:     int):
				SCREAMING_SNAKE_CASE_				:int				     =						AutoTokenizer.from_pretrained(UpperCAmelCase)
				SCREAMING_SNAKE_CASE_				:str				     =						make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
				SCREAMING_SNAKE_CASE_				:Dict				     =						max(len(tokenizer.encode(UpperCAmelCase)) for a in ARTICLES)
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						max(len(tokenizer.encode(UpperCAmelCase)) for a in SUMMARIES)
				SCREAMING_SNAKE_CASE_				:str				     =						4
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						LegacySeqaSeqDataset(
				    UpperCAmelCase   ,  data_dir=UpperCAmelCase   ,  type_path="train"   ,  max_source_length=20   ,  max_target_length=UpperCAmelCase   ,  )
				SCREAMING_SNAKE_CASE_				:int				     =						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 _snake_case		(						self				:     Tuple):
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						AutoTokenizer.from_pretrained("facebook/mbart-large-cc25")

				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()))
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						tmp_dir.joinpath("train.source").open().readlines()
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()))
				pack_data_dir(UpperCAmelCase   ,  UpperCAmelCase   ,  1_28   ,  UpperCAmelCase)
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						{x.name for x in tmp_dir.iterdir()}
				SCREAMING_SNAKE_CASE_				:int				     =						{x.name for x in save_dir.iterdir()}
				SCREAMING_SNAKE_CASE_				: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(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 _snake_case		(						self				:     int):
				if not FAIRSEQ_AVAILABLE:
						return
				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:Tuple				     =						self._get_dataset(max_len=64)
				SCREAMING_SNAKE_CASE_				:Any				     =						64
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						ds.make_dynamic_sampler(UpperCAmelCase   ,  required_batch_size_multiple=UpperCAmelCase)
				SCREAMING_SNAKE_CASE_				:str				     =						[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
				SCREAMING_SNAKE_CASE_				:List[str]				     =						DataLoader(UpperCAmelCase   ,  batch_sampler=UpperCAmelCase   ,  collate_fn=ds.collate_fn   ,  num_workers=2)
				SCREAMING_SNAKE_CASE_				:Any				     =						[]
				SCREAMING_SNAKE_CASE_				:str				     =						[]
				for batch in data_loader:
						SCREAMING_SNAKE_CASE_				:Optional[int]				     =						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_				:int				     =						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 _snake_case		(						self				:     str):
				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						self._get_dataset(max_len=5_12)
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						2
				SCREAMING_SNAKE_CASE_				:str				     =						ds.make_sortish_sampler(UpperCAmelCase   ,  shuffle=UpperCAmelCase)

				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						DataLoader(UpperCAmelCase   ,  batch_size=UpperCAmelCase   ,  collate_fn=ds.collate_fn   ,  num_workers=2)
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						DataLoader(UpperCAmelCase   ,  batch_size=UpperCAmelCase   ,  collate_fn=ds.collate_fn   ,  num_workers=2   ,  sampler=UpperCAmelCase)

				SCREAMING_SNAKE_CASE_				:List[Any]				     =						tokenizer.pad_token_id

				def count_pad_tokens(UpperCAmelCase				:     Optional[int]   ,  UpperCAmelCase				:     int="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 _snake_case		(						self				:     Union[str, Any]   ,  UpperCAmelCase				:     List[Any]=10_00   ,  UpperCAmelCase				:     List[Any]=1_28):
				if os.getenv("USE_REAL_DATA"   ,  UpperCAmelCase):
						SCREAMING_SNAKE_CASE_				:str				     =						"examples/seq2seq/wmt_en_ro"
						SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						max_len * 2 * 64
						if not Path(UpperCAmelCase).joinpath("train.len").exists():
								save_len_file(UpperCAmelCase   ,  UpperCAmelCase)
				else:
						SCREAMING_SNAKE_CASE_				:Dict				     =						"examples/seq2seq/test_data/wmt_en_ro"
						SCREAMING_SNAKE_CASE_				:List[Any]				     =						max_len * 4
						save_len_file(UpperCAmelCase   ,  UpperCAmelCase)

				SCREAMING_SNAKE_CASE_				:List[Any]				     =						AutoTokenizer.from_pretrained(UpperCAmelCase)
				SCREAMING_SNAKE_CASE_				:List[str]				     =						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 _snake_case		(						self				:     List[Any]):
				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						self._get_dataset()
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						set(DistributedSortishSampler(UpperCAmelCase   ,  2_56   ,  num_replicas=2   ,  rank=0   ,  add_extra_examples=UpperCAmelCase))
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						set(DistributedSortishSampler(UpperCAmelCase   ,  2_56   ,  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 _snake_case		(						self				:     str   ,  UpperCAmelCase				:     Optional[int]):
				SCREAMING_SNAKE_CASE_				:Tuple				     =						AutoTokenizer.from_pretrained(UpperCAmelCase   ,  use_fast=UpperCAmelCase)
				if tok_name == MBART_TINY:
						SCREAMING_SNAKE_CASE_				: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   ,  src_lang="EN"   ,  tgt_lang="FR"   ,  )
						SCREAMING_SNAKE_CASE_				:str				     =						train_dataset.dataset_kwargs
						assert "src_lang" in kwargs and "tgt_lang" in kwargs
				else:
						SCREAMING_SNAKE_CASE_				:str				     =						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   ,  )
						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(UpperCAmelCase) == 1 if tok_name == BART_TINY else len(UpperCAmelCase) == 0










def     lowercase	(    a = 50 ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:int				     =						[1] * (length + 1)

		for row_length in range(3     , length + 1 ):
				for block_length in range(3     , row_length + 1 ):
						for block_start in range(row_length - block_length ):
								ways_number[row_length] += ways_number[
								    row_length - block_start - block_length - 1
								]

						ways_number[row_length] += 1

		return ways_number[length]


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






