
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


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




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Path		,   SCREAMING_SNAKE_CASE_	:	list       )  ->     int:
						_lowercase					       =					"""\n""".join(SCREAMING_SNAKE_CASE_       )
						Path(SCREAMING_SNAKE_CASE_       ).open("""w"""       ).writelines(SCREAMING_SNAKE_CASE_       )




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Tuple       )  ->     List[str]:
						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    a_ (			_a    ):






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

									_lowercase					       =					Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()  )  )
									_lowercase					       =					tmp_dir.joinpath("""train.source"""  ).open().readlines()
									_lowercase					       =					Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()  )  )
									pack_data_dir(__UpperCamelCase				,			__UpperCamelCase				,			128				,			__UpperCamelCase  )
									_lowercase					       =					{x.name for x in tmp_dir.iterdir()}
									_lowercase					       =					{x.name for x in save_dir.iterdir()}
									_lowercase					       =					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  ):
									if not FAIRSEQ_AVAILABLE:
															return
									_lowercase       ,						_lowercase       ,						_lowercase					       =					self._get_dataset(max_len=64  )
									_lowercase					       =					64
									_lowercase					       =					ds.make_dynamic_sampler(__UpperCamelCase				,			required_batch_size_multiple=__UpperCamelCase  )
									_lowercase					       =					[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					       =					DataLoader(__UpperCamelCase				,			batch_sampler=__UpperCamelCase				,			collate_fn=ds.collate_fn				,			num_workers=2  )
									_lowercase					       =					[]
									_lowercase					       =					[]
									for batch in data_loader:
															_lowercase					       =					batch["""input_ids"""].shape
															_lowercase					       =					src_shape[0]
															assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
															_lowercase					       =					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  ):
									_lowercase       ,						_lowercase       ,						_lowercase					       =					self._get_dataset(max_len=512  )
									_lowercase					       =					2
									_lowercase					       =					ds.make_sortish_sampler(__UpperCamelCase				,			shuffle=__UpperCamelCase  )

									_lowercase					       =					DataLoader(__UpperCamelCase				,			batch_size=__UpperCamelCase				,			collate_fn=ds.collate_fn				,			num_workers=2  )
									_lowercase					       =					DataLoader(__UpperCamelCase				,			batch_size=__UpperCamelCase				,			collate_fn=ds.collate_fn				,			num_workers=2				,			sampler=__UpperCamelCase  )

									_lowercase					       =					tokenizer.pad_token_id

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

									_lowercase					       =					AutoTokenizer.from_pretrained(__UpperCamelCase  )
									_lowercase					       =					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  ):
									_lowercase       ,						_lowercase       ,						_lowercase					       =					self._get_dataset()
									_lowercase					       =					set(DistributedSortishSampler(__UpperCamelCase				,			256				,			num_replicas=2				,			rank=0				,			add_extra_examples=__UpperCamelCase  )  )
									_lowercase					       =					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				,			__UpperCamelCase  ):
									_lowercase					       =					AutoTokenizer.from_pretrained(__UpperCamelCase				,			use_fast=__UpperCamelCase  )
									if tok_name == MBART_TINY:
															_lowercase					       =					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					       =					train_dataset.dataset_kwargs
															assert "src_lang" in kwargs and "tgt_lang" in kwargs
									else:
															_lowercase					       =					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					       =					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 argparse import ArgumentParser

from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
from datasets.utils.logging import set_verbosity_info




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	List[Any]       )  ->     Optional[int]:
						return {key.lstrip("""-"""       ): value for key, value in zip(unknown_args[::2]		,   unknown_args[1::2]       )}




def 	UpperCamelCase__  (     )  ->     Dict:
						_lowercase					       =					ArgumentParser(
						    """HuggingFace Datasets CLI tool"""		,   usage="""datasets-cli <command> [<args>]"""		,   allow_abbrev=SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					parser.add_subparsers(help="""datasets-cli command helpers"""       )
						set_verbosity_info()

						# Register commands
						ConvertCommand.register_subcommand(SCREAMING_SNAKE_CASE_       )
						EnvironmentCommand.register_subcommand(SCREAMING_SNAKE_CASE_       )
						TestCommand.register_subcommand(SCREAMING_SNAKE_CASE_       )
						RunBeamCommand.register_subcommand(SCREAMING_SNAKE_CASE_       )
						DummyDataCommand.register_subcommand(SCREAMING_SNAKE_CASE_       )

						# Parse args
						_lowercase       ,						_lowercase					       =					parser.parse_known_args()
						if not hasattr(SCREAMING_SNAKE_CASE_		,   """func"""       ):
												parser.print_help()
												exit(1       )
						_lowercase					       =					parse_unknown_args(SCREAMING_SNAKE_CASE_       )

						# Run
						_lowercase					       =					args.func(SCREAMING_SNAKE_CASE_		,   **SCREAMING_SNAKE_CASE_       )
						service.run()


if __name__ == "__main__":
		main()