




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


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




def 			__magic_name__				( A		:					Path,  A		:					list   ):






			'''simple docstring'''







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




def 			__magic_name__				( A		:					List[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     snake_case__							(_UpperCamelCase   ):
				"""simple docstring"""



				@parameterized.expand(
				    [
				        MBART_TINY,
				        MARIAN_TINY,
				        T5_TINY,
				        BART_TINY,
				        PEGASUS_XSUM,
				    ]					,      )
				@slow
				def 							__UpperCAmelCase							(      self				:     Dict					,      __lowerCamelCase				:     Union[str, Any]							)					->     int:
							a    =			AutoTokenizer.from_pretrained(__lowerCamelCase							)
							a    =			make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)
							a    =			max(len(tokenizer.encode(__lowerCamelCase							)							) for a in ARTICLES							)
							a    =			max(len(tokenizer.encode(__lowerCamelCase							)							) 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    ,		a    =			"ro_RO", "de_DE"  # ignored for all but mbart, but never causes error.
							a    =			SeqaSeqDataset(
							    __lowerCamelCase					,      data_dir=__lowerCamelCase					,      type_path="train"					,      max_source_length=__lowerCamelCase					,      max_target_length=__lowerCamelCase					,      src_lang=__lowerCamelCase					,      tgt_lang=__lowerCamelCase					,      )
							a    =			DataLoader(__lowerCamelCase					,      batch_size=2					,      collate_fn=train_dataset.collate_fn							)
							for batch in dataloader:
										assert isinstance(__lowerCamelCase					,      __lowerCamelCase							)
										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				:     Optional[Any]					,      __lowerCamelCase				:     Any							)					->     Optional[Any]:
							a    =			AutoTokenizer.from_pretrained(__lowerCamelCase							)
							a    =			make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()							)
							a    =			max(len(tokenizer.encode(__lowerCamelCase							)							) for a in ARTICLES							)
							a    =			max(len(tokenizer.encode(__lowerCamelCase							)							) for a in SUMMARIES							)
							a    =			4
							a    =			LegacySeqaSeqDataset(
							    __lowerCamelCase					,      data_dir=__lowerCamelCase					,      type_path="train"					,      max_source_length=20					,      max_target_length=__lowerCamelCase					,      )
							a    =			DataLoader(__lowerCamelCase					,      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				:     Union[str, Any]							)					->     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(__lowerCamelCase					,      __lowerCamelCase					,      1_28					,      __lowerCamelCase							)
							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(__lowerCamelCase							) < len(__lowerCamelCase							)
							assert len(__lowerCamelCase							) == 1
							assert len(packed_examples[0]							) == sum(len(__lowerCamelCase							) 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							)					->     Union[str, Any]:
							if not FAIRSEQ_AVAILABLE:
										return
							a    ,		a    ,		a    =			self._get_dataset(max_len=64							)
							a    =			64
							a    =			ds.make_dynamic_sampler(__lowerCamelCase					,      required_batch_size_multiple=__lowerCamelCase							)
							a    =			[len(__lowerCamelCase							) for x in batch_sampler]
							assert len(set(__lowerCamelCase							)							) > 1  # it's not dynamic batch size if every batch is the same length
							assert sum(__lowerCamelCase							) == len(__lowerCamelCase							)  # no dropped or added examples
							a    =			DataLoader(__lowerCamelCase					,      batch_sampler=__lowerCamelCase					,      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(__lowerCamelCase							)
										if num_src_tokens > (max_tokens * 1.1):
													failures.append(__lowerCamelCase							)
							assert num_src_per_batch[0] == max(__lowerCamelCase							)
							if failures:
										raise AssertionError(f"""too many tokens in {len(__lowerCamelCase							)} batches"""							)





				def 							__UpperCAmelCase							(      self				:     str							)					->     List[str]:
							a    ,		a    ,		a    =			self._get_dataset(max_len=5_12							)
							a    =			2
							a    =			ds.make_sortish_sampler(__lowerCamelCase					,      shuffle=__lowerCamelCase							)

							a    =			DataLoader(__lowerCamelCase					,      batch_size=__lowerCamelCase					,      collate_fn=ds.collate_fn					,      num_workers=2							)
							a    =			DataLoader(__lowerCamelCase					,      batch_size=__lowerCamelCase					,      collate_fn=ds.collate_fn					,      num_workers=2					,      sampler=__lowerCamelCase							)

							a    =			tokenizer.pad_token_id

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

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





				def 							__UpperCAmelCase							(      self				:     Union[str, Any]					,      __lowerCamelCase				:     Optional[int]=10_00					,      __lowerCamelCase				:     Union[str, Any]=1_28							)					->     Optional[Any]:
							if os.getenv("USE_REAL_DATA"					,      __lowerCamelCase							):
										a    =			"examples/seq2seq/wmt_en_ro"
										a    =			max_len * 2 * 64
										if not Path(__lowerCamelCase							).joinpath("train.len"							).exists():
													save_len_file(__lowerCamelCase					,      __lowerCamelCase							)
							else:
										a    =			"examples/seq2seq/test_data/wmt_en_ro"
										a    =			max_len * 4
										save_len_file(__lowerCamelCase					,      __lowerCamelCase							)

							a    =			AutoTokenizer.from_pretrained(__lowerCamelCase							)
							a    =			SeqaSeqDataset(
							    __lowerCamelCase					,      data_dir=__lowerCamelCase					,      type_path="train"					,      max_source_length=__lowerCamelCase					,      max_target_length=__lowerCamelCase					,      n_obs=__lowerCamelCase					,      )
							return ds, max_tokens, tokenizer





				def 							__UpperCAmelCase							(      self				:     Optional[Any]							)					->     List[str]:
							a    ,		a    ,		a    =			self._get_dataset()
							a    =			set(DistributedSortishSampler(__lowerCamelCase					,      2_56					,      num_replicas=2					,      rank=0					,      add_extra_examples=__lowerCamelCase							)							)
							a    =			set(DistributedSortishSampler(__lowerCamelCase					,      2_56					,      num_replicas=2					,      rank=1					,      add_extra_examples=__lowerCamelCase							)							)
							assert idsa.intersection(__lowerCamelCase							) == set()





				@parameterized.expand(
				    [
				        MBART_TINY,
				        MARIAN_TINY,
				        T5_TINY,
				        BART_TINY,
				        PEGASUS_XSUM,
				    ]					,      )
				def 							__UpperCAmelCase							(      self				:     List[Any]					,      __lowerCamelCase				:     str							)					->     int:
							a    =			AutoTokenizer.from_pretrained(__lowerCamelCase					,      use_fast=__lowerCamelCase							)
							if tok_name == MBART_TINY:
										a    =			SeqaSeqDataset(
										    __lowerCamelCase					,      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(
										    __lowerCamelCase					,      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(__lowerCamelCase							) == 1 if tok_name == BART_TINY else len(__lowerCamelCase							) == 0












import warnings

from diffusers import StableDiffusionImgaImgPipeline  # noqa F401


warnings.warn(
    'The `image_to_image.py` script is outdated. Please use directly `from diffusers import'
    ' StableDiffusionImg2ImgPipeline` instead.'
)






