

'''simple docstring'''





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			(   __magic_name__						:     Path   , __magic_name__						:     list						)				->  str:
						lowercase :							Any    		='''\n'''.join(__magic_name__						)
						Path(__magic_name__						).open('''w'''						).writelines(__magic_name__						)






def 				_lowerCAmelCase			(   __magic_name__						:     int						)				->  List[Any]:
						for split in ["train", "val", "test"]:
												_dump_articles(os.path.join(__magic_name__   , f'''{split}.source'''						)   , __magic_name__						)
												_dump_articles(os.path.join(__magic_name__   , f'''{split}.target'''						)   , __magic_name__						)
						return tmp_dir







class 		__SCREAMING_SNAKE_CASE							(			lowercase__      ):







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

										lowercase :							List[str]    		=Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()   )   )
										lowercase :							Tuple    		=tmp_dir.joinpath('''train.source'''   ).open().readlines()
										lowercase :							int    		=Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()   )   )
										pack_data_dir(UpperCAmelCase__		,					UpperCAmelCase__		,					128		,					UpperCAmelCase__   )
										lowercase :							Union[str, Any]    		={x.name for x in tmp_dir.iterdir()}
										lowercase :							Dict    		={x.name for x in save_dir.iterdir()}
										lowercase :							str    		=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  lowerCamelCase_	(       self     :		Tuple   ):
										'''simple docstring'''
										if not FAIRSEQ_AVAILABLE:
																return
										lowercase		,	lowercase		,	lowercase :							Union[str, Any]    		=self._get_dataset(max_len=64   )
										lowercase :							List[str]    		=64
										lowercase :							Dict    		=ds.make_dynamic_sampler(UpperCAmelCase__		,					required_batch_size_multiple=UpperCAmelCase__   )
										lowercase :							Union[str, 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
										lowercase :							Optional[int]    		=DataLoader(UpperCAmelCase__		,					batch_sampler=UpperCAmelCase__		,					collate_fn=ds.collate_fn		,					num_workers=2   )
										lowercase :							List[Any]    		=[]
										lowercase :							Tuple    		=[]
										for batch in data_loader:
																lowercase :							str    		=batch['''input_ids'''].shape
																lowercase :							List[Any]    		=src_shape[0]
																assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
																lowercase :							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  lowerCamelCase_	(       self     :		int   ):
										'''simple docstring'''
										lowercase		,	lowercase		,	lowercase :							Optional[Any]    		=self._get_dataset(max_len=512   )
										lowercase :							Optional[Any]    		=2
										lowercase :							List[Any]    		=ds.make_sortish_sampler(UpperCAmelCase__		,					shuffle=UpperCAmelCase__   )

										lowercase :							List[Any]    		=DataLoader(UpperCAmelCase__		,					batch_size=UpperCAmelCase__		,					collate_fn=ds.collate_fn		,					num_workers=2   )
										lowercase :							List[str]    		=DataLoader(UpperCAmelCase__		,					batch_size=UpperCAmelCase__		,					collate_fn=ds.collate_fn		,					num_workers=2		,					sampler=UpperCAmelCase__   )

										lowercase :							Any    		=tokenizer.pad_token_id

										def count_pad_tokens(UpperCAmelCase__     :		List[str]		,					UpperCAmelCase__     :		List[str]="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  lowerCamelCase_	(       self     :		str		,					UpperCAmelCase__     :		List[Any]=1000		,					UpperCAmelCase__     :		List[str]=128   ):
										'''simple docstring'''
										if os.getenv('''USE_REAL_DATA'''		,					UpperCAmelCase__   ):
																lowercase :							Any    		='''examples/seq2seq/wmt_en_ro'''
																lowercase :							Any    		=max_len * 2 * 64
																if not Path(UpperCAmelCase__   ).joinpath('''train.len'''   ).exists():
																						save_len_file(UpperCAmelCase__		,					UpperCAmelCase__   )
										else:
																lowercase :							List[Any]    		='''examples/seq2seq/test_data/wmt_en_ro'''
																lowercase :							Union[str, Any]    		=max_len * 4
																save_len_file(UpperCAmelCase__		,					UpperCAmelCase__   )

										lowercase :							Dict    		=AutoTokenizer.from_pretrained(UpperCAmelCase__   )
										lowercase :							str    		=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  lowerCamelCase_	(       self     :		int   ):
										'''simple docstring'''
										lowercase		,	lowercase		,	lowercase :							int    		=self._get_dataset()
										lowercase :							Union[str, Any]    		=set(DistributedSortishSampler(UpperCAmelCase__		,					256		,					num_replicas=2		,					rank=0		,					add_extra_examples=UpperCAmelCase__   )   )
										lowercase :							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  lowerCamelCase_	(       self     :		str		,					UpperCAmelCase__     :		Tuple   ):
										'''simple docstring'''
										lowercase :							Any    		=AutoTokenizer.from_pretrained(UpperCAmelCase__		,					use_fast=UpperCAmelCase__   )
										if tok_name == MBART_TINY:
																lowercase :							str    		=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'''		,					)
																lowercase :							Optional[int]    		=train_dataset.dataset_kwargs
																assert "src_lang" in kwargs and "tgt_lang" in kwargs
										else:
																lowercase :							str    		=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		,					)
																lowercase :							Optional[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








'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import _LazyModule


UpperCamelCase_    						=       {"""tokenization_byt5""": ["""ByT5Tokenizer"""]}


if TYPE_CHECKING:
							from .tokenization_byta import ByTaTokenizer
else:
							import sys

							UpperCamelCase_    						=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





