





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_ 							=       """bert-base-cased"""
UpperCAmelCase_ 							=       """google/pegasus-xsum"""
UpperCAmelCase_ 							=       [""" Sam ate lunch today.""", """Sams lunch ingredients."""]
UpperCAmelCase_ 							=       ["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
UpperCAmelCase_ 							=       """patrickvonplaten/t5-tiny-random"""
UpperCAmelCase_ 							=       """sshleifer/bart-tiny-random"""
UpperCAmelCase_ 							=       """sshleifer/tiny-mbart"""
UpperCAmelCase_ 							=       """sshleifer/tiny-marian-en-de"""





def   lowerCamelCase__       (       UpperCamelCase__     :    Path    ,      UpperCamelCase__     :    list  )     ->	Dict:



					'''simple docstring'''




					_snake_case													=			'\n'.join(UpperCamelCase__  )
					Path(UpperCamelCase__  ).open('w'  ).writelines(UpperCamelCase__  )





def   lowerCamelCase__       (       UpperCamelCase__     :    Optional[int]  )     ->	Union[str, Any]:



					'''simple docstring'''




					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      UpperCamelCase_						(					_lowerCamelCase   ):




					@parameterized.expand(
					    [
					        MBART_TINY,
					        MARIAN_TINY,
					        T5_TINY,
					        BART_TINY,
					        PEGASUS_XSUM,
					    ]			,  )
					@slow
					def 		lowerCAmelCase     (						self			,  lowerCAmelCase_					)						->							int:
										_snake_case													=			AutoTokenizer.from_pretrained(lowerCAmelCase_					)
										_snake_case													=			make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)
										_snake_case													=			max(len(tokenizer.encode(lowerCAmelCase_					)					) for a in ARTICLES					)
										_snake_case													=			max(len(tokenizer.encode(lowerCAmelCase_					)					) 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(
										    lowerCAmelCase_			,  data_dir=lowerCAmelCase_			,  type_path='train'			,  max_source_length=lowerCAmelCase_			,  max_target_length=lowerCAmelCase_			,  src_lang=lowerCAmelCase_			,  tgt_lang=lowerCAmelCase_			,  )
										_snake_case													=			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
															_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			,  lowerCAmelCase_					)						->							Optional[int]:
										_snake_case													=			AutoTokenizer.from_pretrained(lowerCAmelCase_					)
										_snake_case													=			make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)
										_snake_case													=			max(len(tokenizer.encode(lowerCAmelCase_					)					) for a in ARTICLES					)
										_snake_case													=			max(len(tokenizer.encode(lowerCAmelCase_					)					) for a in SUMMARIES					)
										_snake_case													=			4
										_snake_case													=			LegacySeqaSeqDataset(
										    lowerCAmelCase_			,  data_dir=lowerCAmelCase_			,  type_path='train'			,  max_source_length=20			,  max_target_length=lowerCAmelCase_			,  )
										_snake_case													=			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 		lowerCAmelCase     (						self					)						->							List[str]:
										_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(lowerCAmelCase_			,  lowerCAmelCase_			,  128			,  lowerCAmelCase_					)
										_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(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 		lowerCAmelCase     (						self					)						->							Any:
										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(lowerCAmelCase_			,  required_batch_size_multiple=lowerCAmelCase_					)
										_snake_case													=			[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
										_snake_case													=			DataLoader(lowerCAmelCase_			,  batch_sampler=lowerCAmelCase_			,  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(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 		lowerCAmelCase     (						self					)						->							Tuple:
										_snake_case							,       _snake_case							,       _snake_case													=			self._get_dataset(max_len=512					)
										_snake_case													=			2
										_snake_case													=			ds.make_sortish_sampler(lowerCAmelCase_			,  shuffle=lowerCAmelCase_					)

										_snake_case													=			DataLoader(lowerCAmelCase_			,  batch_size=lowerCAmelCase_			,  collate_fn=ds.collate_fn			,  num_workers=2					)
										_snake_case													=			DataLoader(lowerCAmelCase_			,  batch_size=lowerCAmelCase_			,  collate_fn=ds.collate_fn			,  num_workers=2			,  sampler=lowerCAmelCase_					)

										_snake_case													=			tokenizer.pad_token_id

										def count_pad_tokens(lowerCAmelCase_			,  lowerCAmelCase_="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 		lowerCAmelCase     (						self			,  lowerCAmelCase_=1000			,  lowerCAmelCase_=128					)						->							List[Any]:
										if os.getenv('USE_REAL_DATA'			,  lowerCAmelCase_					):
															_snake_case													=			'examples/seq2seq/wmt_en_ro'
															_snake_case													=			max_len * 2 * 64
															if not Path(lowerCAmelCase_					).joinpath('train.len'					).exists():
																				save_len_file(lowerCAmelCase_			,  lowerCAmelCase_					)
										else:
															_snake_case													=			'examples/seq2seq/test_data/wmt_en_ro'
															_snake_case													=			max_len * 4
															save_len_file(lowerCAmelCase_			,  lowerCAmelCase_					)

										_snake_case													=			AutoTokenizer.from_pretrained(lowerCAmelCase_					)
										_snake_case													=			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 		lowerCAmelCase     (						self					)						->							int:
										_snake_case							,       _snake_case							,       _snake_case													=			self._get_dataset()
										_snake_case													=			set(DistributedSortishSampler(lowerCAmelCase_			,  256			,  num_replicas=2			,  rank=0			,  add_extra_examples=lowerCAmelCase_					)					)
										_snake_case													=			set(DistributedSortishSampler(lowerCAmelCase_			,  256			,  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 		lowerCAmelCase     (						self			,  lowerCAmelCase_					)						->							Union[str, Any]:
										_snake_case													=			AutoTokenizer.from_pretrained(lowerCAmelCase_			,  use_fast=lowerCAmelCase_					)
										if tok_name == MBART_TINY:
															_snake_case													=			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'			,  )
															_snake_case													=			train_dataset.dataset_kwargs
															assert "src_lang" in kwargs and "tgt_lang" in kwargs
										else:
															_snake_case													=			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			,  )
															_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(lowerCAmelCase_					) == 1 if tok_name == BART_TINY else len(lowerCAmelCase_					) == 0










import warnings

from ...utils import logging
from .image_processing_flava import FlavaImageProcessor


UpperCAmelCase_ 							=       logging.get_logger(__name__)
class      UpperCamelCase_						(					_lowerCamelCase   ):




					def __init__(						self			,  *lowerCAmelCase_			,  **lowerCAmelCase_					)						->							None:
										warnings.warn(
										    'The class FlavaFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
										    ' use FlavaImageProcessor instead.'			,  lowerCAmelCase_			,  )
										super().__init__(*lowerCAmelCase_			,  **lowerCAmelCase_					)



