






'''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


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





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase   )   -> Union[str, Any]:
							lowerCamelCase__      :			Optional[Any] 						= """\n""".join(UpperCamelCase   )
							Path(UpperCamelCase   ).open("""w"""   ).writelines(UpperCamelCase   )





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase   )   -> Union[str, Any]:
							for split in ["train", "val", "test"]:
														_dump_articles(os.path.join(UpperCamelCase       ,    f'''{split}.source'''   )       ,    UpperCamelCase   )
														_dump_articles(os.path.join(UpperCamelCase       ,    f'''{split}.target'''   )       ,    UpperCamelCase   )
							return tmp_dir



class _lowercase			(       _lowercase	):




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

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

														lowerCamelCase__      :			Any 						= DataLoader(UpperCamelCase__    ,							batch_size=UpperCamelCase__    ,							collate_fn=ds.collate_fn    ,							num_workers=2  )
														lowerCamelCase__      :			int 						= DataLoader(UpperCamelCase__    ,							batch_size=UpperCamelCase__    ,							collate_fn=ds.collate_fn    ,							num_workers=2    ,							sampler=UpperCamelCase__  )

														lowerCamelCase__      :			Optional[Any] 						= tokenizer.pad_token_id

														def count_pad_tokens(UpperCamelCase__:       List[str]    ,							UpperCamelCase__:       Optional[int]="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:       Optional[int]    ,							UpperCamelCase__:       Dict=1_000    ,							UpperCamelCase__:       List[Any]=128  ):
														if os.getenv("""USE_REAL_DATA"""    ,							UpperCamelCase__  ):
																					lowerCamelCase__      :			str 						= """examples/seq2seq/wmt_en_ro"""
																					lowerCamelCase__      :			Any 						= max_len * 2 * 64
																					if not Path(UpperCamelCase__  ).joinpath("""train.len"""  ).exists():
																												save_len_file(UpperCamelCase__    ,							UpperCamelCase__  )
														else:
																					lowerCamelCase__      :			List[str] 						= """examples/seq2seq/test_data/wmt_en_ro"""
																					lowerCamelCase__      :			Optional[Any] 						= max_len * 4
																					save_len_file(UpperCamelCase__    ,							UpperCamelCase__  )

														lowerCamelCase__      :			Any 						= AutoTokenizer.from_pretrained(UpperCamelCase__  )
														lowerCamelCase__      :			Union[str, Any] 						= 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:       Optional[int]  ):
														lowerCamelCase__      :			int 						= self._get_dataset()
														lowerCamelCase__      :			Optional[Any] 						= set(DistributedSortishSampler(UpperCamelCase__    ,							256    ,							num_replicas=2    ,							rank=0    ,							add_extra_examples=UpperCamelCase__  )  )
														lowerCamelCase__      :			str 						= 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:       Union[str, Any]    ,							UpperCamelCase__:       str  ):
														lowerCamelCase__      :			List[Any] 						= AutoTokenizer.from_pretrained(UpperCamelCase__    ,							use_fast=UpperCamelCase__  )
														if tok_name == MBART_TINY:
																					lowerCamelCase__      :			Dict 						= 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"""    ,							)
																					lowerCamelCase__      :			Optional[Any] 						= train_dataset.dataset_kwargs
																					assert "src_lang" in kwargs and "tgt_lang" in kwargs
														else:
																					lowerCamelCase__      :			Dict 						= 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    ,							)
																					lowerCamelCase__      :			Optional[int] 						= 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 ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
			from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline

