



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


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

def 			A_      (	__a     :				Path ,		__a     :				list		):

				"""simple docstring"""
				a__				      =		"""\n""".join(__a		)
				Path(__a		).open("""w"""		).writelines(__a		)

def 			A_      (	__a     :				List[str]		):

				"""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  __snake_case      (   SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							@parameterized.expand(
							    [
							        MBART_TINY,
							        MARIAN_TINY,
							        T5_TINY,
							        BART_TINY,
							        PEGASUS_XSUM,
							    ]  ,  )
							@slow
							def _a      (							self  ,  a_		):
											a__				      =		AutoTokenizer.from_pretrained(a_		)
											a__				      =		make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)
											a__				      =		max(len(tokenizer.encode(a_		)		) for a in ARTICLES		)
											a__				      =		max(len(tokenizer.encode(a_		)		) 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(
											    a_  ,  data_dir=a_  ,  type_path="""train"""  ,  max_source_length=a_  ,  max_target_length=a_  ,  src_lang=a_  ,  tgt_lang=a_  ,  )
											a__				      =		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
															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 _a      (							self  ,  a_		):
											a__				      =		AutoTokenizer.from_pretrained(a_		)
											a__				      =		make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)
											a__				      =		max(len(tokenizer.encode(a_		)		) for a in ARTICLES		)
											a__				      =		max(len(tokenizer.encode(a_		)		) for a in SUMMARIES		)
											a__				      =		4
											a__				      =		LegacySeqaSeqDataset(
											    a_  ,  data_dir=a_  ,  type_path="""train"""  ,  max_source_length=20  ,  max_target_length=a_  ,  )
											a__				      =		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 _a      (							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(a_  ,  a_  ,  128  ,  a_		)
											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(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 _a      (							self		):
											if not FAIRSEQ_AVAILABLE:
															return
											a__      ,			a__      ,			a__				      =		self._get_dataset(max_len=64		)
											a__				      =		64
											a__				      =		ds.make_dynamic_sampler(a_  ,  required_batch_size_multiple=a_		)
											a__				      =		[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
											a__				      =		DataLoader(a_  ,  batch_sampler=a_  ,  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(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 _a      (							self		):
											a__      ,			a__      ,			a__				      =		self._get_dataset(max_len=512		)
											a__				      =		2
											a__				      =		ds.make_sortish_sampler(a_  ,  shuffle=a_		)

											a__				      =		DataLoader(a_  ,  batch_size=a_  ,  collate_fn=ds.collate_fn  ,  num_workers=2		)
											a__				      =		DataLoader(a_  ,  batch_size=a_  ,  collate_fn=ds.collate_fn  ,  num_workers=2  ,  sampler=a_		)

											a__				      =		tokenizer.pad_token_id

											def count_pad_tokens(a_  ,  a_="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 _a      (							self  ,  a_=1_000  ,  a_=128		):
											if os.getenv("""USE_REAL_DATA"""  ,  a_		):
															a__				      =		"""examples/seq2seq/wmt_en_ro"""
															a__				      =		max_len * 2 * 64
															if not Path(a_		).joinpath("""train.len"""		).exists():
																			save_len_file(a_  ,  a_		)
											else:
															a__				      =		"""examples/seq2seq/test_data/wmt_en_ro"""
															a__				      =		max_len * 4
															save_len_file(a_  ,  a_		)

											a__				      =		AutoTokenizer.from_pretrained(a_		)
											a__				      =		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 _a      (							self		):
											a__      ,			a__      ,			a__				      =		self._get_dataset()
											a__				      =		set(DistributedSortishSampler(a_  ,  256  ,  num_replicas=2  ,  rank=0  ,  add_extra_examples=a_		)		)
											a__				      =		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 _a      (							self  ,  a_		):
											a__				      =		AutoTokenizer.from_pretrained(a_  ,  use_fast=a_		)
											if tok_name == MBART_TINY:
															a__				      =		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"""  ,  )
															a__				      =		train_dataset.dataset_kwargs
															assert "src_lang" in kwargs and "tgt_lang" in kwargs
											else:
															a__				      =		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  ,  )
															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(a_		) == 1 if tok_name == BART_TINY else len(a_		) == 0






from __future__ import annotations

UpperCAmelCase							 =					8.988E9  # units = N * m^s * C^-2

def 			A_      (	__a     :				float ,		__a     :				float ,		__a     :				float ,		__a     :				float		):

				"""simple docstring"""
				a__				      =		abs(chargea * chargea		)

				if (force, chargea, chargea, distance).count(0		) != 1:
								raise ValueError("""One and only one argument must be 0"""		)
				if distance < 0:
								raise ValueError("""Distance cannot be negative"""		)
				if force == 0:
								a__				      =		COULOMBS_CONSTANT * charge_product / (distance**2)
								return {"force": force}
				elif chargea == 0:
								a__				      =		abs(__a		) * (distance**2) / (COULOMBS_CONSTANT * chargea)
								return {"charge1": chargea}
				elif chargea == 0:
								a__				      =		abs(__a		) * (distance**2) / (COULOMBS_CONSTANT * chargea)
								return {"charge2": chargea}
				elif distance == 0:
								a__				      =		(COULOMBS_CONSTANT * charge_product / abs(__a		)) ** 0.5
								return {"distance": distance}
				raise ValueError("""Exactly one argument must be 0"""		)


if __name__ == "__main__":
	import doctest

	doctest.testmod()

