






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

def      __lowercase					( snake_case,  snake_case ):







    """simple docstring"""






    __magic_name__		:int        =      '''\n'''.join(snake_case )
    Path(snake_case ).open('''w''' ).writelines(snake_case )

def      __lowercase					( snake_case ):







    """simple docstring"""






    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 						lowerCamelCase_				(      lowerCamelCase ):







    @parameterized.expand(
        [
            MBART_TINY,
            MARIAN_TINY,
            T5_TINY,
            BART_TINY,
            PEGASUS_XSUM,
        ]       ,					)
    @slow
    def 			A	(						self       ,					__lowerCAmelCase     ):
        """simple docstring"""
        __magic_name__		:Any        =      AutoTokenizer.from_pretrained(__lowerCAmelCase     )
        __magic_name__		:Any        =      make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     )
        __magic_name__		:List[str]        =      max(len(tokenizer.encode(__lowerCAmelCase     )     ) for a in ARTICLES     )
        __magic_name__		:Dict        =      max(len(tokenizer.encode(__lowerCAmelCase     )     ) for a in SUMMARIES     )
        __magic_name__		:Dict        =      4
        __magic_name__		:Dict        =      8
        assert max_len_target > max_src_len  # Will be truncated
        assert max_len_source > max_src_len  # Will be truncated
        __magic_name__		:Optional[int]        =      '''ro_RO''', '''de_DE'''  # ignored for all but mbart, but never causes error.
        __magic_name__		:Optional[int]        =      SeqaSeqDataset(
            __lowerCAmelCase       ,					data_dir=__lowerCAmelCase       ,					type_path='''train'''       ,					max_source_length=__lowerCAmelCase       ,					max_target_length=__lowerCAmelCase       ,					src_lang=__lowerCAmelCase       ,					tgt_lang=__lowerCAmelCase       ,					)
        __magic_name__		:str        =      DataLoader(__lowerCAmelCase       ,					batch_size=2       ,					collate_fn=train_dataset.collate_fn     )
        for batch in dataloader:
            assert isinstance(__lowerCAmelCase       ,					__lowerCAmelCase     )
            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
            __magic_name__		: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       ,					__lowerCAmelCase     ):
        """simple docstring"""
        __magic_name__		:Optional[int]        =      AutoTokenizer.from_pretrained(__lowerCAmelCase     )
        __magic_name__		:str        =      make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     )
        __magic_name__		:Optional[int]        =      max(len(tokenizer.encode(__lowerCAmelCase     )     ) for a in ARTICLES     )
        __magic_name__		:Union[str, Any]        =      max(len(tokenizer.encode(__lowerCAmelCase     )     ) for a in SUMMARIES     )
        __magic_name__		:str        =      4
        __magic_name__		:Optional[Any]        =      LegacySeqaSeqDataset(
            __lowerCAmelCase       ,					data_dir=__lowerCAmelCase       ,					type_path='''train'''       ,					max_source_length=2_0       ,					max_target_length=__lowerCAmelCase       ,					)
        __magic_name__		:Dict        =      DataLoader(__lowerCAmelCase       ,					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 2_0 >= 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     ):
        """simple docstring"""
        __magic_name__		:Union[str, Any]        =      AutoTokenizer.from_pretrained('''facebook/mbart-large-cc25'''     )

        __magic_name__		:List[str]        =      Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     )     )
        __magic_name__		:int        =      tmp_dir.joinpath('''train.source'''     ).open().readlines()
        __magic_name__		:Union[str, Any]        =      Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()     )     )
        pack_data_dir(__lowerCAmelCase       ,					__lowerCAmelCase       ,					1_2_8       ,					__lowerCAmelCase     )
        __magic_name__		:Optional[Any]        =      {x.name for x in tmp_dir.iterdir()}
        __magic_name__		:Union[str, Any]        =      {x.name for x in save_dir.iterdir()}
        __magic_name__		:Optional[int]        =      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(__lowerCAmelCase     ) < len(__lowerCAmelCase     )
        assert len(__lowerCAmelCase     ) == 1
        assert len(packed_examples[0]     ) == sum(len(__lowerCAmelCase     ) for x in orig_examples     )
        assert orig_paths == new_paths







    @pytest.mark.skipif(not FAIRSEQ_AVAILABLE       ,					reason='''This test requires fairseq'''     )
    def 			A	(						self     ):
        """simple docstring"""
        if not FAIRSEQ_AVAILABLE:
            return
        __magic_name__		:str        =      self._get_dataset(max_len=6_4     )
        __magic_name__		:Union[str, Any]        =      6_4
        __magic_name__		:Optional[Any]        =      ds.make_dynamic_sampler(__lowerCAmelCase       ,					required_batch_size_multiple=__lowerCAmelCase     )
        __magic_name__		:Union[str, Any]        =      [len(__lowerCAmelCase     ) for x in batch_sampler]
        assert len(set(__lowerCAmelCase     )     ) > 1  # it's not dynamic batch size if every batch is the same length
        assert sum(__lowerCAmelCase     ) == len(__lowerCAmelCase     )  # no dropped or added examples
        __magic_name__		:int        =      DataLoader(__lowerCAmelCase       ,					batch_sampler=__lowerCAmelCase       ,					collate_fn=ds.collate_fn       ,					num_workers=2     )
        __magic_name__		:Any        =      []
        __magic_name__		:Union[str, Any]        =      []
        for batch in data_loader:
            __magic_name__		:str        =      batch['''input_ids'''].shape
            __magic_name__		:int        =      src_shape[0]
            assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
            __magic_name__		:Union[str, Any]        =      np.product(batch['''input_ids'''].shape     )
            num_src_per_batch.append(__lowerCAmelCase     )
            if num_src_tokens > (max_tokens * 1.1):
                failures.append(__lowerCAmelCase     )
        assert num_src_per_batch[0] == max(__lowerCAmelCase     )
        if failures:
            raise AssertionError(F'''too many tokens in {len(__lowerCAmelCase     )} batches'''     )







    def 			A	(						self     ):
        """simple docstring"""
        __magic_name__		:Optional[int]        =      self._get_dataset(max_len=5_1_2     )
        __magic_name__		:Union[str, Any]        =      2
        __magic_name__		:List[str]        =      ds.make_sortish_sampler(__lowerCAmelCase       ,					shuffle=__lowerCAmelCase     )

        __magic_name__		:str        =      DataLoader(__lowerCAmelCase       ,					batch_size=__lowerCAmelCase       ,					collate_fn=ds.collate_fn       ,					num_workers=2     )
        __magic_name__		:Dict        =      DataLoader(__lowerCAmelCase       ,					batch_size=__lowerCAmelCase       ,					collate_fn=ds.collate_fn       ,					num_workers=2       ,					sampler=__lowerCAmelCase     )

        __magic_name__		:Dict        =      tokenizer.pad_token_id

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

        assert sum(count_pad_tokens(__lowerCAmelCase       ,					k='''labels'''     )     ) < sum(count_pad_tokens(__lowerCAmelCase       ,					k='''labels'''     )     )
        assert sum(count_pad_tokens(__lowerCAmelCase     )     ) < sum(count_pad_tokens(__lowerCAmelCase     )     )
        assert len(__lowerCAmelCase     ) == len(__lowerCAmelCase     )







    def 			A	(						self       ,					__lowerCAmelCase=1_0_0_0       ,					__lowerCAmelCase=1_2_8     ):
        """simple docstring"""
        if os.getenv('''USE_REAL_DATA'''       ,					__lowerCAmelCase     ):
            __magic_name__		:Any        =      '''examples/seq2seq/wmt_en_ro'''
            __magic_name__		:Dict        =      max_len * 2 * 6_4
            if not Path(__lowerCAmelCase     ).joinpath('''train.len'''     ).exists():
                save_len_file(__lowerCAmelCase       ,					__lowerCAmelCase     )
        else:
            __magic_name__		:Optional[int]        =      '''examples/seq2seq/test_data/wmt_en_ro'''
            __magic_name__		:Tuple        =      max_len * 4
            save_len_file(__lowerCAmelCase       ,					__lowerCAmelCase     )

        __magic_name__		:Optional[Any]        =      AutoTokenizer.from_pretrained(__lowerCAmelCase     )
        __magic_name__		:Dict        =      SeqaSeqDataset(
            __lowerCAmelCase       ,					data_dir=__lowerCAmelCase       ,					type_path='''train'''       ,					max_source_length=__lowerCAmelCase       ,					max_target_length=__lowerCAmelCase       ,					n_obs=__lowerCAmelCase       ,					)
        return ds, max_tokens, tokenizer







    def 			A	(						self     ):
        """simple docstring"""
        __magic_name__		:Tuple        =      self._get_dataset()
        __magic_name__		:str        =      set(DistributedSortishSampler(__lowerCAmelCase       ,					2_5_6       ,					num_replicas=2       ,					rank=0       ,					add_extra_examples=__lowerCAmelCase     )     )
        __magic_name__		:List[str]        =      set(DistributedSortishSampler(__lowerCAmelCase       ,					2_5_6       ,					num_replicas=2       ,					rank=1       ,					add_extra_examples=__lowerCAmelCase     )     )
        assert idsa.intersection(__lowerCAmelCase     ) == set()







    @parameterized.expand(
        [
            MBART_TINY,
            MARIAN_TINY,
            T5_TINY,
            BART_TINY,
            PEGASUS_XSUM,
        ]       ,					)
    def 			A	(						self       ,					__lowerCAmelCase     ):
        """simple docstring"""
        __magic_name__		:List[Any]        =      AutoTokenizer.from_pretrained(__lowerCAmelCase       ,					use_fast=__lowerCAmelCase     )
        if tok_name == MBART_TINY:
            __magic_name__		:Any        =      SeqaSeqDataset(
                __lowerCAmelCase       ,					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'''       ,					)
            __magic_name__		:Union[str, Any]        =      train_dataset.dataset_kwargs
            assert "src_lang" in kwargs and "tgt_lang" in kwargs
        else:
            __magic_name__		:str        =      SeqaSeqDataset(
                __lowerCAmelCase       ,					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       ,					)
            __magic_name__		: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(__lowerCAmelCase     ) == 1 if tok_name == BART_TINY else len(__lowerCAmelCase     ) == 0














from __future__ import annotations

from math import pi, sqrt

def      __lowercase					( snake_case,  snake_case ):







				"""simple docstring"""






				if inductance <= 0:
								raise ValueError('''Inductance cannot be 0 or negative''' )

				elif capacitance <= 0:
								raise ValueError('''Capacitance cannot be 0 or negative''' )

				else:
								return (
								    "Resonant frequency",
								    float(1 / (2 * pi * (sqrt(inductance * capacitance ))) ),
								)


if __name__ == "__main__":
					import doctest

					doctest.testmod()






