





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


def       UpperCamelCase__(			UpperCamelCase__     :			Path					,	UpperCamelCase__     :			list    )->Union[str, Any]:
       A__		   =		'''\n'''.join(UpperCamelCase__    )
       Path(UpperCamelCase__    ).open('''w'''    ).writelines(UpperCamelCase__    )


def       UpperCamelCase__(			UpperCamelCase__     :			str    )->List[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        SCREAMING_SNAKE_CASE__					(		UpperCamelCase__ ):



 @parameterized.expand(
     [
         MBART_TINY,
         MARIAN_TINY,
         T5_TINY,
         BART_TINY,
         PEGASUS_XSUM,
     ],)
 @slow
 def 							UpperCamelCase     (				self,__lowerCamelCase					):
        A__		   =		AutoTokenizer.from_pretrained(__lowerCamelCase					)
        A__		   =		make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)
        A__		   =		max(len(tokenizer.encode(__lowerCamelCase					)					) for a in ARTICLES					)
        A__		   =		max(len(tokenizer.encode(__lowerCamelCase					)					) for a in SUMMARIES					)
        A__		   =		4
        A__		   =		8
        assert max_len_target > max_src_len  # Will be truncated
        assert max_len_source > max_src_len  # Will be truncated
        A__   ,      A__		   =		'''ro_RO''', '''de_DE'''  # ignored for all but mbart, but never causes error.
        A__		   =		SeqaSeqDataset(
            __lowerCamelCase,data_dir=__lowerCamelCase,type_path='''train''',max_source_length=__lowerCamelCase,max_target_length=__lowerCamelCase,src_lang=__lowerCamelCase,tgt_lang=__lowerCamelCase,)
        A__		   =		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
               A__		   =		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,__lowerCamelCase					):
        A__		   =		AutoTokenizer.from_pretrained(__lowerCamelCase					)
        A__		   =		make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)
        A__		   =		max(len(tokenizer.encode(__lowerCamelCase					)					) for a in ARTICLES					)
        A__		   =		max(len(tokenizer.encode(__lowerCamelCase					)					) for a in SUMMARIES					)
        A__		   =		4
        A__		   =		LegacySeqaSeqDataset(
            __lowerCamelCase,data_dir=__lowerCamelCase,type_path='''train''',max_source_length=20,max_target_length=__lowerCamelCase,)
        A__		   =		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 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					):
        A__		   =		AutoTokenizer.from_pretrained('''facebook/mbart-large-cc25'''					)

        A__		   =		Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)					)
        A__		   =		tmp_dir.joinpath('''train.source'''					).open().readlines()
        A__		   =		Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)					)
        pack_data_dir(__lowerCamelCase,__lowerCamelCase,128,__lowerCamelCase					)
        A__		   =		{x.name for x in tmp_dir.iterdir()}
        A__		   =		{x.name for x in save_dir.iterdir()}
        A__		   =		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 							UpperCamelCase     (				self					):
        if not FAIRSEQ_AVAILABLE:
               return
        A__   ,      A__   ,      A__		   =		self._get_dataset(max_len=64					)
        A__		   =		64
        A__		   =		ds.make_dynamic_sampler(__lowerCamelCase,required_batch_size_multiple=__lowerCamelCase					)
        A__		   =		[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
        A__		   =		DataLoader(__lowerCamelCase,batch_sampler=__lowerCamelCase,collate_fn=ds.collate_fn,num_workers=2					)
        A__		   =		[]
        A__		   =		[]
        for batch in data_loader:
               A__		   =		batch['''input_ids'''].shape
               A__		   =		src_shape[0]
               assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
               A__		   =		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 							UpperCamelCase     (				self					):
        A__   ,      A__   ,      A__		   =		self._get_dataset(max_len=512					)
        A__		   =		2
        A__		   =		ds.make_sortish_sampler(__lowerCamelCase,shuffle=__lowerCamelCase					)

        A__		   =		DataLoader(__lowerCamelCase,batch_size=__lowerCamelCase,collate_fn=ds.collate_fn,num_workers=2					)
        A__		   =		DataLoader(__lowerCamelCase,batch_size=__lowerCamelCase,collate_fn=ds.collate_fn,num_workers=2,sampler=__lowerCamelCase					)

        A__		   =		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 							UpperCamelCase     (				self,__lowerCamelCase=1000,__lowerCamelCase=128					):
        if os.getenv('''USE_REAL_DATA''',__lowerCamelCase					):
               A__		   =		'''examples/seq2seq/wmt_en_ro'''
               A__		   =		max_len * 2 * 64
               if not Path(__lowerCamelCase					).joinpath('''train.len'''					).exists():
                      save_len_file(__lowerCamelCase,__lowerCamelCase					)
        else:
               A__		   =		'''examples/seq2seq/test_data/wmt_en_ro'''
               A__		   =		max_len * 4
               save_len_file(__lowerCamelCase,__lowerCamelCase					)

        A__		   =		AutoTokenizer.from_pretrained(__lowerCamelCase					)
        A__		   =		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 							UpperCamelCase     (				self					):
        A__   ,      A__   ,      A__		   =		self._get_dataset()
        A__		   =		set(DistributedSortishSampler(__lowerCamelCase,256,num_replicas=2,rank=0,add_extra_examples=__lowerCamelCase					)					)
        A__		   =		set(DistributedSortishSampler(__lowerCamelCase,256,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 							UpperCamelCase     (				self,__lowerCamelCase					):
        A__		   =		AutoTokenizer.from_pretrained(__lowerCamelCase,use_fast=__lowerCamelCase					)
        if tok_name == MBART_TINY:
               A__		   =		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''',)
               A__		   =		train_dataset.dataset_kwargs
               assert "src_lang" in kwargs and "tgt_lang" in kwargs
        else:
               A__		   =		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,)
               A__		   =		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

import math
from collections.abc import Callable


def       UpperCamelCase__(			UpperCamelCase__     :			Callable[[int | float], int | float]					,	UpperCamelCase__     :			int | float					,	UpperCamelCase__     :			int | float					,	UpperCamelCase__     :			int = 1_00					,	)->float:
       A__		   =		x_start
       A__		   =		fnc(UpperCamelCase__    )
       A__		   =		0.0

       for _ in range(UpperCamelCase__    ):
              # Approximates curve as a sequence of linear lines and sums their length
              A__		   =		(x_end - x_start) / steps + xa
              A__		   =		fnc(UpperCamelCase__    )
              length += math.hypot(xa - xa					,	fxa - fxa    )

              # Increment step
              A__		   =		xa
              A__		   =		fxa

       return length


if __name__ == "__main__":


       def       UpperCamelCase__(			UpperCamelCase__     :			Union[str, Any]    )->Dict:
              return math.sin(10 * x    )

       print('f(x) = sin(10 * x)')
       print('The length of the curve from x = -10 to x = 10 is:')
       a__: str							   =						10
       while i <= 100_000:
              print(F"With {i} steps: {line_length(f, -10, 10, i)}")
              i *= 10





