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


def 		UpperCAmelCase     (      a_						,  a_		)						->      Optional[int]:







				"""simple docstring"""



				__A       =					"\n".join(__A		)
				Path(__A		).open("w"		).writelines(__A		)


def 		UpperCAmelCase     (      a_		)						->      Union[str, Any]:







				"""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 				UpperCAmelCase (    __UpperCamelCase    ):




	'''simple docstring'''





	@parameterized.expand(
	    [
	        MBART_TINY,
	        MARIAN_TINY,
	        T5_TINY,
	        BART_TINY,
	        PEGASUS_XSUM,
	    ]				,)
	@slow
	def 							UpperCamelCase_    (			self   :					Optional[Any]				,A   :					List[str]					):
					__A       =					AutoTokenizer.from_pretrained(__UpperCAmelCase					)
					__A       =					make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)
					__A       =					max(len(tokenizer.encode(__UpperCAmelCase					)					) for a in ARTICLES					)
					__A       =					max(len(tokenizer.encode(__UpperCAmelCase					)					) 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       =					"ro_RO", "de_DE"  # ignored for all but mbart, but never causes error.
					__A       =					SeqaSeqDataset(
					    __UpperCAmelCase				,data_dir=__UpperCAmelCase				,type_path="train"				,max_source_length=__UpperCAmelCase				,max_target_length=__UpperCAmelCase				,src_lang=__UpperCAmelCase				,tgt_lang=__UpperCAmelCase				,)
					__A       =					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       =					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   :					List[str]				,A   :					Optional[Any]					):
					__A       =					AutoTokenizer.from_pretrained(__UpperCAmelCase					)
					__A       =					make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)
					__A       =					max(len(tokenizer.encode(__UpperCAmelCase					)					) for a in ARTICLES					)
					__A       =					max(len(tokenizer.encode(__UpperCAmelCase					)					) for a in SUMMARIES					)
					__A       =					4
					__A       =					LegacySeqaSeqDataset(
					    __UpperCAmelCase				,data_dir=__UpperCAmelCase				,type_path="train"				,max_source_length=20				,max_target_length=__UpperCAmelCase				,)
					__A       =					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   :					Tuple					):
					__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(__UpperCAmelCase				,__UpperCAmelCase				,1_28				,__UpperCAmelCase					)
					__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(__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   :					int					):



					if not FAIRSEQ_AVAILABLE:
									return
					__A       =					self._get_dataset(max_len=64					)
					__A       =					64
					__A       =					ds.make_dynamic_sampler(__UpperCAmelCase				,required_batch_size_multiple=__UpperCAmelCase					)
					__A       =					[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       =					DataLoader(__UpperCAmelCase				,batch_sampler=__UpperCAmelCase				,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(__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   :					List[str]					):
					__A       =					self._get_dataset(max_len=5_12					)
					__A       =					2
					__A       =					ds.make_sortish_sampler(__UpperCAmelCase				,shuffle=__UpperCAmelCase					)

					__A       =					DataLoader(__UpperCAmelCase				,batch_size=__UpperCAmelCase				,collate_fn=ds.collate_fn				,num_workers=2					)
					__A       =					DataLoader(__UpperCAmelCase				,batch_size=__UpperCAmelCase				,collate_fn=ds.collate_fn				,num_workers=2				,sampler=__UpperCAmelCase					)

					__A       =					tokenizer.pad_token_id

					def count_pad_tokens(A   :					List[Any]				,A   :					Any="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   :					Dict				,A   :					str=10_00				,A   :					Optional[int]=1_28					):



					if os.getenv("USE_REAL_DATA"				,__UpperCAmelCase					):
									__A       =					"examples/seq2seq/wmt_en_ro"
									__A       =					max_len * 2 * 64
									if not Path(__UpperCAmelCase					).joinpath("train.len"					).exists():
													save_len_file(__UpperCAmelCase				,__UpperCAmelCase					)
					else:
									__A       =					"examples/seq2seq/test_data/wmt_en_ro"
									__A       =					max_len * 4
									save_len_file(__UpperCAmelCase				,__UpperCAmelCase					)

					__A       =					AutoTokenizer.from_pretrained(__UpperCAmelCase					)
					__A       =					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]					):
					__A       =					self._get_dataset()
					__A       =					set(DistributedSortishSampler(__UpperCAmelCase				,2_56				,num_replicas=2				,rank=0				,add_extra_examples=__UpperCAmelCase					)					)
					__A       =					set(DistributedSortishSampler(__UpperCAmelCase				,2_56				,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   :					List[str]				,A   :					Any					):
					__A       =					AutoTokenizer.from_pretrained(__UpperCAmelCase				,use_fast=__UpperCAmelCase					)
					if tok_name == MBART_TINY:
									__A       =					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       =					train_dataset.dataset_kwargs
									assert "src_lang" in kwargs and "tgt_lang" in kwargs
					else:
									__A       =					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       =					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








UpperCAmelCase__      			=						'''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''







def     a_   ()				->						None:



					"""simple docstring"""
					__a					:						Dict      =					input("Enter message: "      )
					__a					:						Dict      =					input("Enter key [alphanumeric]: "      )
					__a					:						str      =					input("Encrypt/Decrypt [e/d]: "      )

					if mode.lower().startswith("e"      ):
										__a					:						List[str]      =					"encrypt"
										__a					:						Union[str, Any]      =					encrypt_message(__A				,       __A      )
					elif mode.lower().startswith("d"      ):
										__a					:						int      =					"decrypt"
										__a					:						List[str]      =					decrypt_message(__A				,       __A      )

					print(f'\n{mode.title()}ed message:'      )
					print(__A      )







def     a_   (__A				,       __A      )				->						str:



					"""simple docstring"""
					return translate_message(__A				,       __A				,       "encrypt"      )







def     a_   (__A				,       __A      )				->						str:



					"""simple docstring"""
					return translate_message(__A				,       __A				,       "decrypt"      )







def     a_   (__A				,       __A				,       __A      )				->						str:



					"""simple docstring"""
					__a					:						Union[str, Any]      =					[]
					__a					:						Tuple      =					0
					__a					:						Dict      =					key.upper()

					for symbol in message:
										__a					:						Dict      =					LETTERS.find(symbol.upper()      )
										if num != -1:
															if mode == "encrypt":
																				num += LETTERS.find(key[key_index]      )
															elif mode == "decrypt":
																				num -= LETTERS.find(key[key_index]      )

															num %= len(__A      )

															if symbol.isupper():
																				translated.append(LETTERS[num]      )
															elif symbol.islower():
																				translated.append(LETTERS[num].lower()      )

															key_index += 1
															if key_index == len(__A      ):
																				__a					:						List[str]      =					0
										else:
															translated.append(__A      )
					return "".join(__A      )


if __name__ == "__main__":
			main()





