






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





def 					lowercase_ (    __snake_case     :							Path	,	__snake_case     :							list      )     -> Tuple:





				'''simple docstring'''


				snake_case__      :List[Any]         =   "\n".join(SCREAMING_SNAKE_CASE_      )
				Path(SCREAMING_SNAKE_CASE_      ).open("w"      ).writelines(SCREAMING_SNAKE_CASE_      )





def 					lowercase_ (    __snake_case     :							Dict      )     -> Optional[Any]:





				'''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 					_snake_case						(			_UpperCAmelCase ):






	@parameterized.expand(
	    [
	        MBART_TINY,
	        MARIAN_TINY,
	        T5_TINY,
	        BART_TINY,
	        PEGASUS_XSUM,
	    ]					,)
	@slow
	def    lowerCAmelCase_							(		self					,UpperCamelCase       )						->    List[Any]:
					snake_case__      :str         =   AutoTokenizer.from_pretrained(lowercase__       )
					snake_case__      :Optional[Any]         =   make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )
					snake_case__      :str         =   max(len(tokenizer.encode(lowercase__       )       ) for a in ARTICLES       )
					snake_case__      :Optional[int]         =   max(len(tokenizer.encode(lowercase__       )       ) for a in SUMMARIES       )
					snake_case__      :Union[str, Any]         =   4
					snake_case__      :Dict         =   8
					assert max_len_target > max_src_len  # Will be truncated
					assert max_len_source > max_src_len  # Will be truncated
					snake_case__      :Tuple         =   "ro_RO", "de_DE"  # ignored for all but mbart, but never causes error.
					snake_case__      :str         =   SeqaSeqDataset(
					    lowercase__					,data_dir=lowercase__					,type_path="train"					,max_source_length=lowercase__					,max_target_length=lowercase__					,src_lang=lowercase__					,tgt_lang=lowercase__					,)
					snake_case__      :Optional[Any]         =   DataLoader(lowercase__					,batch_size=2					,collate_fn=train_dataset.collate_fn       )
					for batch in dataloader:
									assert isinstance(lowercase__					,lowercase__       )
									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
									snake_case__      :str         =   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					,UpperCamelCase       )						->    int:
					snake_case__      :Dict         =   AutoTokenizer.from_pretrained(lowercase__       )
					snake_case__      :Union[str, Any]         =   make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )
					snake_case__      :Dict         =   max(len(tokenizer.encode(lowercase__       )       ) for a in ARTICLES       )
					snake_case__      :Any         =   max(len(tokenizer.encode(lowercase__       )       ) for a in SUMMARIES       )
					snake_case__      :Union[str, Any]         =   4
					snake_case__      :Any         =   LegacySeqaSeqDataset(
					    lowercase__					,data_dir=lowercase__					,type_path="train"					,max_source_length=20					,max_target_length=lowercase__					,)
					snake_case__      :Tuple         =   DataLoader(lowercase__					,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       )						->    int:
					snake_case__      :Optional[Any]         =   AutoTokenizer.from_pretrained("facebook/mbart-large-cc25"       )

					snake_case__      :Dict         =   Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )       )
					snake_case__      :Optional[int]         =   tmp_dir.joinpath("train.source"       ).open().readlines()
					snake_case__      :Optional[int]         =   Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()       )       )
					pack_data_dir(lowercase__					,lowercase__					,128					,lowercase__       )
					snake_case__      :Union[str, Any]         =   {x.name for x in tmp_dir.iterdir()}
					snake_case__      :List[Any]         =   {x.name for x in save_dir.iterdir()}
					snake_case__      :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(lowercase__       ) < len(lowercase__       )
					assert len(lowercase__       ) == 1
					assert len(packed_examples[0]       ) == sum(len(lowercase__       ) for x in orig_examples       )
					assert orig_paths == new_paths






	@pytest.mark.skipif(not FAIRSEQ_AVAILABLE					,reason="This test requires fairseq"       )
	def    lowerCAmelCase_							(		self       )						->    Any:






					if not FAIRSEQ_AVAILABLE:
									return
					snake_case__      :List[Any]         =   self._get_dataset(max_len=64       )
					snake_case__      :Optional[Any]         =   64
					snake_case__      :Dict         =   ds.make_dynamic_sampler(lowercase__					,required_batch_size_multiple=lowercase__       )
					snake_case__      :str         =   [len(lowercase__       ) for x in batch_sampler]
					assert len(set(lowercase__       )       ) > 1  # it's not dynamic batch size if every batch is the same length
					assert sum(lowercase__       ) == len(lowercase__       )  # no dropped or added examples
					snake_case__      :Optional[int]         =   DataLoader(lowercase__					,batch_sampler=lowercase__					,collate_fn=ds.collate_fn					,num_workers=2       )
					snake_case__      :Any         =   []
					snake_case__      :List[Any]         =   []
					for batch in data_loader:
									snake_case__      :List[str]         =   batch["input_ids"].shape
									snake_case__      :Union[str, Any]         =   src_shape[0]
									assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
									snake_case__      :List[str]         =   np.product(batch["input_ids"].shape       )
									num_src_per_batch.append(lowercase__       )
									if num_src_tokens > (max_tokens * 1.1):
													failures.append(lowercase__       )
					assert num_src_per_batch[0] == max(lowercase__       )
					if failures:
									raise AssertionError(f'too many tokens in {len(lowercase__       )} batches'       )






	def    lowerCAmelCase_							(		self       )						->    List[Any]:
					snake_case__      :int         =   self._get_dataset(max_len=512       )
					snake_case__      :str         =   2
					snake_case__      :Dict         =   ds.make_sortish_sampler(lowercase__					,shuffle=lowercase__       )

					snake_case__      :Dict         =   DataLoader(lowercase__					,batch_size=lowercase__					,collate_fn=ds.collate_fn					,num_workers=2       )
					snake_case__      :str         =   DataLoader(lowercase__					,batch_size=lowercase__					,collate_fn=ds.collate_fn					,num_workers=2					,sampler=lowercase__       )

					snake_case__      :Dict         =   tokenizer.pad_token_id

					def count_pad_tokens(UpperCamelCase					,UpperCamelCase="input_ids"       ):
									return [batch[k].eq(lowercase__       ).sum().item() for batch in data_loader]

					assert sum(count_pad_tokens(lowercase__					,k="labels"       )       ) < sum(count_pad_tokens(lowercase__					,k="labels"       )       )
					assert sum(count_pad_tokens(lowercase__       )       ) < sum(count_pad_tokens(lowercase__       )       )
					assert len(lowercase__       ) == len(lowercase__       )






	def    lowerCAmelCase_							(		self					,UpperCamelCase=1_000					,UpperCamelCase=128       )						->    Dict:






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

					snake_case__      :List[str]         =   AutoTokenizer.from_pretrained(lowercase__       )
					snake_case__      :str         =   SeqaSeqDataset(
					    lowercase__					,data_dir=lowercase__					,type_path="train"					,max_source_length=lowercase__					,max_target_length=lowercase__					,n_obs=lowercase__					,)
					return ds, max_tokens, tokenizer






	def    lowerCAmelCase_							(		self       )						->    str:
					snake_case__      :int         =   self._get_dataset()
					snake_case__      :Union[str, Any]         =   set(DistributedSortishSampler(lowercase__					,256					,num_replicas=2					,rank=0					,add_extra_examples=lowercase__       )       )
					snake_case__      :Any         =   set(DistributedSortishSampler(lowercase__					,256					,num_replicas=2					,rank=1					,add_extra_examples=lowercase__       )       )
					assert idsa.intersection(lowercase__       ) == set()






	@parameterized.expand(
	    [
	        MBART_TINY,
	        MARIAN_TINY,
	        T5_TINY,
	        BART_TINY,
	        PEGASUS_XSUM,
	    ]					,)
	def    lowerCAmelCase_							(		self					,UpperCamelCase       )						->    Dict:
					snake_case__      :Any         =   AutoTokenizer.from_pretrained(lowercase__					,use_fast=lowercase__       )
					if tok_name == MBART_TINY:
									snake_case__      :Tuple         =   SeqaSeqDataset(
									    lowercase__					,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"					,)
									snake_case__      :str         =   train_dataset.dataset_kwargs
									assert "src_lang" in kwargs and "tgt_lang" in kwargs
					else:
									snake_case__      :str         =   SeqaSeqDataset(
									    lowercase__					,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					,)
									snake_case__      :Tuple         =   train_dataset.dataset_kwargs
									assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
									assert len(lowercase__       ) == 1 if tok_name == BART_TINY else len(lowercase__       ) == 0









import pytest


__UpperCAmelCase							:   int							=  "__dummy_dataset1__"

__UpperCAmelCase							:   int							=  "\nimport json\nimport os\n\nimport datasets\n\n\nREPO_URL = \"https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/\"\nURLS = {\"train\": REPO_URL + \"wikiann-bn-train.jsonl\", \"validation\": REPO_URL + \"wikiann-bn-validation.jsonl\"}\n\n\nclass __DummyDataset1__(datasets.GeneratorBasedBuilder):\n\n    def _info(self):\n        features = datasets.Features(\n            {\n                \"tokens\": datasets.Sequence(datasets.Value(\"string\")),\n                \"ner_tags\": datasets.Sequence(\n                    datasets.features.ClassLabel(\n                        names=[\n                            \"O\",\n                            \"B-PER\",\n                            \"I-PER\",\n                            \"B-ORG\",\n                            \"I-ORG\",\n                            \"B-LOC\",\n                            \"I-LOC\",\n                        ]\n                    )\n                ),\n                \"langs\": datasets.Sequence(datasets.Value(\"string\")),\n                \"spans\": datasets.Sequence(datasets.Value(\"string\")),\n            }\n        )\n        return datasets.DatasetInfo(features=features)\n\n    def _split_generators(self, dl_manager):\n        dl_path = dl_manager.download(URLS)\n        return [\n            datasets.SplitGenerator(datasets.Split.TRAIN, gen_kwargs={\"filepath\": dl_path[\"train\"]}),\n            datasets.SplitGenerator(datasets.Split.VALIDATION, gen_kwargs={\"filepath\": dl_path[\"validation\"]}),\n        ]\n\n    def _generate_examples(self, filepath):\n        with open(filepath, \"r\", encoding=\"utf-8\") as f:\n            for i, line in enumerate(f):\n                yield i, json.loads(line)\n"





@pytest.fixture
def 					lowercase_ (    )     -> Optional[Any]:





				'''simple docstring'''


				return DATASET_LOADING_SCRIPT_NAME





@pytest.fixture
def 					lowercase_ (    )     -> Optional[int]:





				'''simple docstring'''


				return DATASET_LOADING_SCRIPT_CODE





@pytest.fixture
def 					lowercase_ (    __snake_case     :							Optional[int]	,	__snake_case     :							List[Any]	,	__snake_case     :							Any      )     -> Dict:





				'''simple docstring'''


				snake_case__      :Optional[Any]         =   dataset_loading_script_name
				snake_case__      :Optional[Any]         =   tmp_path / "datasets" / script_name
				script_dir.mkdir(parents=__snake_case      )
				snake_case__      :List[Any]         =   script_dir / F'{script_name}.py'
				with open(__snake_case	,	"w"      ) as f:
								f.write(__snake_case      )
				return str(__snake_case      )