"""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   					=    '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 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase       )							->   Any:
				SCREAMING_SNAKE_CASE__		  =				"\n".join(__UpperCAmelCase       )
				Path(__UpperCAmelCase       ).open("w"       ).writelines(__UpperCAmelCase       )




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase       )							->   Any:
				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 lowerCamelCase		(_SCREAMING_SNAKE_CASE      ):






					'''simple docstring'''


					@parameterized.expand(
					    [
					        MBART_TINY,
					        MARIAN_TINY,
					        T5_TINY,
					        BART_TINY,
					        PEGASUS_XSUM,
					    ]   , )
					@slow
					def 			lowerCAmelCase_					(						self			:				Dict   , _snake_case			:				List[str]		)							->       Dict:
									SCREAMING_SNAKE_CASE__		  =				AutoTokenizer.from_pretrained(_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)
									SCREAMING_SNAKE_CASE__		  =				max(len(tokenizer.encode(_snake_case		)		) for a in ARTICLES		)
									SCREAMING_SNAKE_CASE__		  =				max(len(tokenizer.encode(_snake_case		)		) for a in SUMMARIES		)
									SCREAMING_SNAKE_CASE__		  =				4
									SCREAMING_SNAKE_CASE__		  =				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__		  =				"ro_RO", "de_DE"  # ignored for all but mbart, but never causes error.
									SCREAMING_SNAKE_CASE__		  =				SeqaSeqDataset(
									    _snake_case   , data_dir=_snake_case   , type_path="train"   , max_source_length=_snake_case   , max_target_length=_snake_case   , src_lang=_snake_case   , tgt_lang=_snake_case   , )
									SCREAMING_SNAKE_CASE__		  =				DataLoader(_snake_case   , batch_size=2   , collate_fn=train_dataset.collate_fn		)
									for batch in dataloader:
													assert isinstance(_snake_case   , _snake_case		)
													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__		  =				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 			lowerCAmelCase_					(						self			:				List[str]   , _snake_case			:				Union[str, Any]		)							->       Optional[int]:
									SCREAMING_SNAKE_CASE__		  =				AutoTokenizer.from_pretrained(_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)
									SCREAMING_SNAKE_CASE__		  =				max(len(tokenizer.encode(_snake_case		)		) for a in ARTICLES		)
									SCREAMING_SNAKE_CASE__		  =				max(len(tokenizer.encode(_snake_case		)		) for a in SUMMARIES		)
									SCREAMING_SNAKE_CASE__		  =				4
									SCREAMING_SNAKE_CASE__		  =				LegacySeqaSeqDataset(
									    _snake_case   , data_dir=_snake_case   , type_path="train"   , max_source_length=20   , max_target_length=_snake_case   , )
									SCREAMING_SNAKE_CASE__		  =				DataLoader(_snake_case   , 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 			lowerCAmelCase_					(						self			:				Optional[int]		)							->       Optional[int]:
									SCREAMING_SNAKE_CASE__		  =				AutoTokenizer.from_pretrained("facebook/mbart-large-cc25"		)

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



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



					def 			lowerCAmelCase_					(						self			:				int		)							->       Any:
									SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				self._get_dataset(max_len=512		)
									SCREAMING_SNAKE_CASE__		  =				2
									SCREAMING_SNAKE_CASE__		  =				ds.make_sortish_sampler(_snake_case   , shuffle=_snake_case		)

									SCREAMING_SNAKE_CASE__		  =				DataLoader(_snake_case   , batch_size=_snake_case   , collate_fn=ds.collate_fn   , num_workers=2		)
									SCREAMING_SNAKE_CASE__		  =				DataLoader(_snake_case   , batch_size=_snake_case   , collate_fn=ds.collate_fn   , num_workers=2   , sampler=_snake_case		)

									SCREAMING_SNAKE_CASE__		  =				tokenizer.pad_token_id

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

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



					def 			lowerCAmelCase_					(						self			:				List[str]   , _snake_case			:				List[str]=1000   , _snake_case			:				Optional[int]=128		)							->       Optional[int]:
									if os.getenv("USE_REAL_DATA"   , _snake_case		):
													SCREAMING_SNAKE_CASE__		  =				"examples/seq2seq/wmt_en_ro"
													SCREAMING_SNAKE_CASE__		  =				max_len * 2 * 64
													if not Path(_snake_case		).joinpath("train.len"		).exists():
																	save_len_file(_snake_case   , _snake_case		)
									else:
													SCREAMING_SNAKE_CASE__		  =				"examples/seq2seq/test_data/wmt_en_ro"
													SCREAMING_SNAKE_CASE__		  =				max_len * 4
													save_len_file(_snake_case   , _snake_case		)

									SCREAMING_SNAKE_CASE__		  =				AutoTokenizer.from_pretrained(_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				SeqaSeqDataset(
									    _snake_case   , data_dir=_snake_case   , type_path="train"   , max_source_length=_snake_case   , max_target_length=_snake_case   , n_obs=_snake_case   , )
									return ds, max_tokens, tokenizer



					def 			lowerCAmelCase_					(						self			:				Union[str, Any]		)							->       Union[str, Any]:
									SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__  ,       SCREAMING_SNAKE_CASE__		  =				self._get_dataset()
									SCREAMING_SNAKE_CASE__		  =				set(DistributedSortishSampler(_snake_case   , 256   , num_replicas=2   , rank=0   , add_extra_examples=_snake_case		)		)
									SCREAMING_SNAKE_CASE__		  =				set(DistributedSortishSampler(_snake_case   , 256   , num_replicas=2   , rank=1   , add_extra_examples=_snake_case		)		)
									assert idsa.intersection(_snake_case		) == set()



					@parameterized.expand(
					    [
					        MBART_TINY,
					        MARIAN_TINY,
					        T5_TINY,
					        BART_TINY,
					        PEGASUS_XSUM,
					    ]   , )
					def 			lowerCAmelCase_					(						self			:				Optional[int]   , _snake_case			:				Union[str, Any]		)							->       Any:
									SCREAMING_SNAKE_CASE__		  =				AutoTokenizer.from_pretrained(_snake_case   , use_fast=_snake_case		)
									if tok_name == MBART_TINY:
													SCREAMING_SNAKE_CASE__		  =				SeqaSeqDataset(
													    _snake_case   , 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__		  =				train_dataset.dataset_kwargs
													assert "src_lang" in kwargs and "tgt_lang" in kwargs
									else:
													SCREAMING_SNAKE_CASE__		  =				SeqaSeqDataset(
													    _snake_case   , 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__		  =				train_dataset.dataset_kwargs
													assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
													assert len(_snake_case		) == 1 if tok_name == BART_TINY else len(_snake_case		) == 0





"""simple docstring"""






from math import pi




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase       )							->   float:
				return 2 * pi * radius * (angle / 360)


if __name__ == "__main__":
						print(arc_length(9_0, 1_0))




