


"""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


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







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			Any:
							snake_case_          =						"""\n""".join(_SCREAMING_SNAKE_CASE			)
							Path(_SCREAMING_SNAKE_CASE			).open("""w"""			).writelines(_SCREAMING_SNAKE_CASE			)







def   _a			( _SCREAMING_SNAKE_CASE			)      ->			Union[str, Any]:
							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       (snake_case__):





							'''simple docstring'''

							@parameterized.expand(
							    [
							        MBART_TINY,
							        MARIAN_TINY,
							        T5_TINY,
							        BART_TINY,
							        PEGASUS_XSUM,
							    ]						,    )
							@slow
							def 						lowerCAmelCase    (			self							: int						,    UpperCAmelCase_							: str    )   ->Optional[int]:



														"""simple docstring"""



														snake_case_          =						AutoTokenizer.from_pretrained(UpperCAmelCase_    )
														snake_case_          =						make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()    )
														snake_case_          =						max(len(tokenizer.encode(UpperCAmelCase_    )    ) for a in ARTICLES    )
														snake_case_          =						max(len(tokenizer.encode(UpperCAmelCase_    )    ) for a in SUMMARIES    )
														snake_case_          =						4
														snake_case_          =						8
														assert max_len_target > max_src_len  # Will be truncated
														assert max_len_source > max_src_len  # Will be truncated
														snake_case_    ,  snake_case_          =						"""ro_RO""", """de_DE"""  # ignored for all but mbart, but never causes error.
														snake_case_          =						SeqaSeqDataset(
														    UpperCAmelCase_						,    data_dir=UpperCAmelCase_						,    type_path="""train"""						,    max_source_length=UpperCAmelCase_						,    max_target_length=UpperCAmelCase_						,    src_lang=UpperCAmelCase_						,    tgt_lang=UpperCAmelCase_						,    )
														snake_case_          =						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
																					snake_case_          =						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							: List[str]						,    UpperCAmelCase_							: str    )   ->Tuple:



														"""simple docstring"""



														snake_case_          =						AutoTokenizer.from_pretrained(UpperCAmelCase_    )
														snake_case_          =						make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()    )
														snake_case_          =						max(len(tokenizer.encode(UpperCAmelCase_    )    ) for a in ARTICLES    )
														snake_case_          =						max(len(tokenizer.encode(UpperCAmelCase_    )    ) for a in SUMMARIES    )
														snake_case_          =						4
														snake_case_          =						LegacySeqaSeqDataset(
														    UpperCAmelCase_						,    data_dir=UpperCAmelCase_						,    type_path="""train"""						,    max_source_length=20						,    max_target_length=UpperCAmelCase_						,    )
														snake_case_          =						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							: Optional[int]    )   ->Union[str, Any]:



														"""simple docstring"""



														snake_case_          =						AutoTokenizer.from_pretrained("""facebook/mbart-large-cc25"""    )

														snake_case_          =						Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()    )    )
														snake_case_          =						tmp_dir.joinpath("""train.source"""    ).open().readlines()
														snake_case_          =						Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()    )    )
														pack_data_dir(UpperCAmelCase_						,    UpperCAmelCase_						,    128						,    UpperCAmelCase_    )
														snake_case_          =						{x.name for x in tmp_dir.iterdir()}
														snake_case_          =						{x.name for x in save_dir.iterdir()}
														snake_case_          =						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							: Optional[Any]    )   ->List[Any]:



														"""simple docstring"""



														if not FAIRSEQ_AVAILABLE:
																					return
														snake_case_    ,  snake_case_    ,  snake_case_          =						self._get_dataset(max_len=64    )
														snake_case_          =						64
														snake_case_          =						ds.make_dynamic_sampler(UpperCAmelCase_						,    required_batch_size_multiple=UpperCAmelCase_    )
														snake_case_          =						[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
														snake_case_          =						DataLoader(UpperCAmelCase_						,    batch_sampler=UpperCAmelCase_						,    collate_fn=ds.collate_fn						,    num_workers=2    )
														snake_case_          =						[]
														snake_case_          =						[]
														for batch in data_loader:
																					snake_case_          =						batch["""input_ids"""].shape
																					snake_case_          =						src_shape[0]
																					assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
																					snake_case_          =						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							: Optional[Any]    )   ->List[Any]:



														"""simple docstring"""



														snake_case_    ,  snake_case_    ,  snake_case_          =						self._get_dataset(max_len=512    )
														snake_case_          =						2
														snake_case_          =						ds.make_sortish_sampler(UpperCAmelCase_						,    shuffle=UpperCAmelCase_    )

														snake_case_          =						DataLoader(UpperCAmelCase_						,    batch_size=UpperCAmelCase_						,    collate_fn=ds.collate_fn						,    num_workers=2    )
														snake_case_          =						DataLoader(UpperCAmelCase_						,    batch_size=UpperCAmelCase_						,    collate_fn=ds.collate_fn						,    num_workers=2						,    sampler=UpperCAmelCase_    )

														snake_case_          =						tokenizer.pad_token_id

														def count_pad_tokens(UpperCAmelCase_							: int						,    UpperCAmelCase_							: 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 						lowerCAmelCase    (			self							: Tuple						,    UpperCAmelCase_							: Union[str, Any]=1_000						,    UpperCAmelCase_							: Optional[int]=128    )   ->Tuple:



														"""simple docstring"""



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

														snake_case_          =						AutoTokenizer.from_pretrained(UpperCAmelCase_    )
														snake_case_          =						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							: Dict    )   ->str:



														"""simple docstring"""



														snake_case_    ,  snake_case_    ,  snake_case_          =						self._get_dataset()
														snake_case_          =						set(DistributedSortishSampler(UpperCAmelCase_						,    256						,    num_replicas=2						,    rank=0						,    add_extra_examples=UpperCAmelCase_    )    )
														snake_case_          =						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							: List[Any]						,    UpperCAmelCase_							: Any    )   ->str:



														"""simple docstring"""



														snake_case_          =						AutoTokenizer.from_pretrained(UpperCAmelCase_						,    use_fast=UpperCAmelCase_    )
														if tok_name == MBART_TINY:
																					snake_case_          =						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"""						,    )
																					snake_case_          =						train_dataset.dataset_kwargs
																					assert "src_lang" in kwargs and "tgt_lang" in kwargs
														else:
																					snake_case_          =						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						,    )
																					snake_case_          =						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"""
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works

from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline  # noqa: F401


deprecate(
    'stable diffusion controlnet',
    '0.22.0',
    'Importing `FlaxStableDiffusionControlNetPipeline` from diffusers.pipelines.stable_diffusion.flax_pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import FlaxStableDiffusionControlNetPipeline` instead.',
    standard_warn=False,
    stacklevel=3,
)



