
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 				_lowerCamelCase      (       lowerCamelCase_:	Path					,						lowerCamelCase_:	list		):



				'''simple docstring'''







				A  :	Dict				=					"""\n""".join(_SCREAMING_SNAKE_CASE		)
				Path(_SCREAMING_SNAKE_CASE		).open('''w'''		).writelines(_SCREAMING_SNAKE_CASE		)





def 				_lowerCamelCase      (       lowerCamelCase_:	Optional[int]		):



				'''simple docstring'''







				for split in ["train", "val", "test"]:
								_dump_articles(os.path.join(_SCREAMING_SNAKE_CASE					,						f"""{split}.source"""		)					,						_SCREAMING_SNAKE_CASE		)
								_dump_articles(os.path.join(_SCREAMING_SNAKE_CASE					,						f"""{split}.target"""		)					,						_SCREAMING_SNAKE_CASE		)
				return tmp_dir






class      _SCREAMING_SNAKE_CASE				(		__a				):





				@parameterized.expand(
				    [
				        MBART_TINY,
				        MARIAN_TINY,
				        T5_TINY,
				        BART_TINY,
				        PEGASUS_XSUM,
				    ]       ,   )
				@slow
				def 							_UpperCAmelCase    (		self			:		Any       ,   snake_case_			:		Optional[Any]				):


								"""simple docstring"""

								A  :	str				=					AutoTokenizer.from_pretrained(__UpperCamelCase				)
								A  :	Dict				=					make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)
								A  :	str				=					max(len(tokenizer.encode(__UpperCamelCase				)				) for a in ARTICLES				)
								A  :	Any				=					max(len(tokenizer.encode(__UpperCamelCase				)				) for a in SUMMARIES				)
								A  :	Dict				=					4
								A  :	str				=					8
								assert max_len_target > max_src_len  # Will be truncated
								assert max_len_source > max_src_len  # Will be truncated
								A  :	List[str]				=					"""ro_RO""", """de_DE"""  # ignored for all but mbart, but never causes error.
								A  :	List[str]				=					SeqaSeqDataset(
								    __UpperCamelCase       ,   data_dir=__UpperCamelCase       ,   type_path='''train'''       ,   max_source_length=__UpperCamelCase       ,   max_target_length=__UpperCamelCase       ,   src_lang=__UpperCamelCase       ,   tgt_lang=__UpperCamelCase       ,   )
								A  :	Dict				=					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
												A  :	List[Any]				=					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			:		Optional[Any]       ,   snake_case_			:		List[Any]				):


								"""simple docstring"""

								A  :	str				=					AutoTokenizer.from_pretrained(__UpperCamelCase				)
								A  :	Any				=					make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)
								A  :	Tuple				=					max(len(tokenizer.encode(__UpperCamelCase				)				) for a in ARTICLES				)
								A  :	Union[str, Any]				=					max(len(tokenizer.encode(__UpperCamelCase				)				) for a in SUMMARIES				)
								A  :	List[Any]				=					4
								A  :	str				=					LegacySeqaSeqDataset(
								    __UpperCamelCase       ,   data_dir=__UpperCamelCase       ,   type_path='''train'''       ,   max_source_length=20       ,   max_target_length=__UpperCamelCase       ,   )
								A  :	Dict				=					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 							_UpperCAmelCase    (		self			:		List[str]				):


								"""simple docstring"""

								A  :	List[Any]				=					AutoTokenizer.from_pretrained('''facebook/mbart-large-cc25'''				)

								A  :	Optional[Any]				=					Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)				)
								A  :	List[Any]				=					tmp_dir.joinpath('''train.source'''				).open().readlines()
								A  :	Tuple				=					Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)				)
								pack_data_dir(__UpperCamelCase       ,   __UpperCamelCase       ,   128       ,   __UpperCamelCase				)
								A  :	Optional[int]				=					{x.name for x in tmp_dir.iterdir()}
								A  :	Dict				=					{x.name for x in save_dir.iterdir()}
								A  :	List[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 							_UpperCAmelCase    (		self			:		str				):


								"""simple docstring"""

								if not FAIRSEQ_AVAILABLE:
												return
								A  :	str				=					self._get_dataset(max_len=64				)
								A  :	Any				=					64
								A  :	List[str]				=					ds.make_dynamic_sampler(__UpperCamelCase       ,   required_batch_size_multiple=__UpperCamelCase				)
								A  :	Optional[Any]				=					[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
								A  :	str				=					DataLoader(__UpperCamelCase       ,   batch_sampler=__UpperCamelCase       ,   collate_fn=ds.collate_fn       ,   num_workers=2				)
								A  :	Optional[int]				=					[]
								A  :	Dict				=					[]
								for batch in data_loader:
												A  :	Optional[int]				=					batch["""input_ids"""].shape
												A  :	Optional[int]				=					src_shape[0]
												assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
												A  :	Union[str, Any]				=					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 							_UpperCAmelCase    (		self			:		Optional[int]				):


								"""simple docstring"""

								A  :	Union[str, Any]				=					self._get_dataset(max_len=512				)
								A  :	str				=					2
								A  :	Tuple				=					ds.make_sortish_sampler(__UpperCamelCase       ,   shuffle=__UpperCamelCase				)

								A  :	str				=					DataLoader(__UpperCamelCase       ,   batch_size=__UpperCamelCase       ,   collate_fn=ds.collate_fn       ,   num_workers=2				)
								A  :	Optional[Any]				=					DataLoader(__UpperCamelCase       ,   batch_size=__UpperCamelCase       ,   collate_fn=ds.collate_fn       ,   num_workers=2       ,   sampler=__UpperCamelCase				)

								A  :	Union[str, Any]				=					tokenizer.pad_token_id

								def count_pad_tokens(snake_case_			:		Optional[int]       ,   snake_case_			:		Tuple="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 							_UpperCAmelCase    (		self			:		List[Any]       ,   snake_case_			:		Tuple=1000       ,   snake_case_			:		List[str]=128				):


								"""simple docstring"""

								if os.getenv('''USE_REAL_DATA'''       ,   __UpperCamelCase				):
												A  :	Union[str, Any]				=					"""examples/seq2seq/wmt_en_ro"""
												A  :	List[Any]				=					max_len * 2 * 64
												if not Path(__UpperCamelCase				).joinpath('''train.len'''				).exists():
																save_len_file(__UpperCamelCase       ,   __UpperCamelCase				)
								else:
												A  :	Tuple				=					"""examples/seq2seq/test_data/wmt_en_ro"""
												A  :	int				=					max_len * 4
												save_len_file(__UpperCamelCase       ,   __UpperCamelCase				)

								A  :	Any				=					AutoTokenizer.from_pretrained(__UpperCamelCase				)
								A  :	Optional[int]				=					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 							_UpperCAmelCase    (		self			:		List[Any]				):


								"""simple docstring"""

								A  :	Any				=					self._get_dataset()
								A  :	List[str]				=					set(DistributedSortishSampler(__UpperCamelCase       ,   256       ,   num_replicas=2       ,   rank=0       ,   add_extra_examples=__UpperCamelCase				)				)
								A  :	Tuple				=					set(DistributedSortishSampler(__UpperCamelCase       ,   256       ,   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 							_UpperCAmelCase    (		self			:		int       ,   snake_case_			:		Union[str, Any]				):


								"""simple docstring"""

								A  :	List[str]				=					AutoTokenizer.from_pretrained(__UpperCamelCase       ,   use_fast=__UpperCamelCase				)
								if tok_name == MBART_TINY:
												A  :	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'''       ,   )
												A  :	List[str]				=					train_dataset.dataset_kwargs
												assert "src_lang" in kwargs and "tgt_lang" in kwargs
								else:
												A  :	List[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       ,   )
												A  :	Union[str, Any]				=					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
from __future__ import annotations






def     lowerCAmelCase_			(   _SCREAMING_SNAKE_CASE     :				float		, _SCREAMING_SNAKE_CASE     :				float		, _SCREAMING_SNAKE_CASE     :				float		, ):
						if (electron_conc, hole_conc, intrinsic_conc).count(0			) != 1:
												raise ValueError("""You cannot supply more or less than 2 values"""			)
						elif electron_conc < 0:
												raise ValueError("""Electron concentration cannot be negative in a semiconductor"""			)
						elif hole_conc < 0:
												raise ValueError("""Hole concentration cannot be negative in a semiconductor"""			)
						elif intrinsic_conc < 0:
												raise ValueError(
												    """Intrinsic concentration cannot be negative in a semiconductor"""			)
						elif electron_conc == 0:
												return (
												    "electron_conc",
												    intrinsic_conc**2 / hole_conc,
												)
						elif hole_conc == 0:
												return (
												    "hole_conc",
												    intrinsic_conc**2 / electron_conc,
												)
						elif intrinsic_conc == 0:
												return (
												    "intrinsic_conc",
												    (electron_conc * hole_conc) ** 0.5,
												)
						else:
												return (-1, -1)


if __name__ == "__main__":
	import doctest

	doctest.testmod()






