

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

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					Path					, lowerCAmelCase:					list	)				->    List[str]:
				_UpperCAmelCase    :						str							=      "\n".join(lowerCAmelCase	)
				Path(lowerCAmelCase	).open("w"	).writelines(lowerCAmelCase	)

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					List[str]	)				->    str:
				for split in ["train", "val", "test"]:
								_dump_articles(os.path.join(lowerCAmelCase					, F'{split}.source'	)					, lowerCAmelCase	)
								_dump_articles(os.path.join(lowerCAmelCase					, F'{split}.target'	)					, lowerCAmelCase	)
				return tmp_dir



class 			a					(		UpperCAmelCase			):


						@parameterized.expand(
						    [
						        MBART_TINY,
						        MARIAN_TINY,
						        T5_TINY,
						        BART_TINY,
						        PEGASUS_XSUM,
						    ]  ,   )
						@slow
						def     _UpperCAmelCase			(    self  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[Any]							=      AutoTokenizer.from_pretrained(A_		)
										_UpperCAmelCase    :						Dict							=      make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)
										_UpperCAmelCase    :						List[str]							=      max(len(tokenizer.encode(A_		)		) for a in ARTICLES		)
										_UpperCAmelCase    :						int							=      max(len(tokenizer.encode(A_		)		) for a in SUMMARIES		)
										_UpperCAmelCase    :						Any							=      4
										_UpperCAmelCase    :						List[Any]							=      8
										assert max_len_target > max_src_len  # Will be truncated
										assert max_len_source > max_src_len  # Will be truncated
										_UpperCAmelCase    :						Optional[Any]							=      "ro_RO", "de_DE"  # ignored for all but mbart, but never causes error.
										_UpperCAmelCase    :						List[str]							=      SeqaSeqDataset(
										    A_  ,   data_dir=A_  ,   type_path="train"  ,   max_source_length=A_  ,   max_target_length=A_  ,   src_lang=A_  ,   tgt_lang=A_  ,   )
										_UpperCAmelCase    :						List[Any]							=      DataLoader(A_  ,   batch_size=2  ,   collate_fn=train_dataset.collate_fn		)
										for batch in dataloader:
														assert isinstance(A_  ,   A_		)
														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
														_UpperCAmelCase    :						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     _UpperCAmelCase			(    self  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						Tuple							=      AutoTokenizer.from_pretrained(A_		)
										_UpperCAmelCase    :						int							=      make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)
										_UpperCAmelCase    :						Optional[Any]							=      max(len(tokenizer.encode(A_		)		) for a in ARTICLES		)
										_UpperCAmelCase    :						List[str]							=      max(len(tokenizer.encode(A_		)		) for a in SUMMARIES		)
										_UpperCAmelCase    :						Dict							=      4
										_UpperCAmelCase    :						int							=      LegacySeqaSeqDataset(
										    A_  ,   data_dir=A_  ,   type_path="train"  ,   max_source_length=20  ,   max_target_length=A_  ,   )
										_UpperCAmelCase    :						Optional[Any]							=      DataLoader(A_  ,   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		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[str]							=      AutoTokenizer.from_pretrained("facebook/mbart-large-cc25"		)

										_UpperCAmelCase    :						Tuple							=      Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)		)
										_UpperCAmelCase    :						Optional[int]							=      tmp_dir.joinpath("train.source"		).open().readlines()
										_UpperCAmelCase    :						int							=      Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()		)		)
										pack_data_dir(A_  ,   A_  ,   128  ,   A_		)
										_UpperCAmelCase    :						Optional[Any]							=      {x.name for x in tmp_dir.iterdir()}
										_UpperCAmelCase    :						Union[str, Any]							=      {x.name for x in save_dir.iterdir()}
										_UpperCAmelCase    :						Union[str, Any]							=      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(A_		) < len(A_		)
										assert len(A_		) == 1
										assert len(packed_examples[0]		) == sum(len(A_		) for x in orig_examples		)
										assert orig_paths == new_paths


						@pytest.mark.skipif(not FAIRSEQ_AVAILABLE  ,   reason="This test requires fairseq"		)
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										if not FAIRSEQ_AVAILABLE:
														return
										_UpperCAmelCase    :						Optional[Any]							=      self._get_dataset(max_len=64		)
										_UpperCAmelCase    :						Optional[Any]							=      64
										_UpperCAmelCase    :						Optional[Any]							=      ds.make_dynamic_sampler(A_  ,   required_batch_size_multiple=A_		)
										_UpperCAmelCase    :						Tuple							=      [len(A_		) for x in batch_sampler]
										assert len(set(A_		)		) > 1  # it's not dynamic batch size if every batch is the same length
										assert sum(A_		) == len(A_		)  # no dropped or added examples
										_UpperCAmelCase    :						Dict							=      DataLoader(A_  ,   batch_sampler=A_  ,   collate_fn=ds.collate_fn  ,   num_workers=2		)
										_UpperCAmelCase    :						int							=      []
										_UpperCAmelCase    :						List[str]							=      []
										for batch in data_loader:
														_UpperCAmelCase    :						List[str]							=      batch["input_ids"].shape
														_UpperCAmelCase    :						Optional[int]							=      src_shape[0]
														assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
														_UpperCAmelCase    :						Optional[int]							=      np.product(batch["input_ids"].shape		)
														num_src_per_batch.append(A_		)
														if num_src_tokens > (max_tokens * 1.1):
																		failures.append(A_		)
										assert num_src_per_batch[0] == max(A_		)
										if failures:
														raise AssertionError(f'too many tokens in {len(A_		)} batches'		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Tuple							=      self._get_dataset(max_len=512		)
										_UpperCAmelCase    :						Tuple							=      2
										_UpperCAmelCase    :						Optional[int]							=      ds.make_sortish_sampler(A_  ,   shuffle=A_		)

										_UpperCAmelCase    :						str							=      DataLoader(A_  ,   batch_size=A_  ,   collate_fn=ds.collate_fn  ,   num_workers=2		)
										_UpperCAmelCase    :						str							=      DataLoader(A_  ,   batch_size=A_  ,   collate_fn=ds.collate_fn  ,   num_workers=2  ,   sampler=A_		)

										_UpperCAmelCase    :						List[str]							=      tokenizer.pad_token_id

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

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


						def     _UpperCAmelCase			(    self  ,   A_=1000  ,   A_=128		):







										'''simple docstring'''


										if os.getenv("USE_REAL_DATA"  ,   A_		):
														_UpperCAmelCase    :						List[Any]							=      "examples/seq2seq/wmt_en_ro"
														_UpperCAmelCase    :						Any							=      max_len * 2 * 64
														if not Path(A_		).joinpath("train.len"		).exists():
																		save_len_file(A_  ,   A_		)
										else:
														_UpperCAmelCase    :						Dict							=      "examples/seq2seq/test_data/wmt_en_ro"
														_UpperCAmelCase    :						int							=      max_len * 4
														save_len_file(A_  ,   A_		)

										_UpperCAmelCase    :						str							=      AutoTokenizer.from_pretrained(A_		)
										_UpperCAmelCase    :						str							=      SeqaSeqDataset(
										    A_  ,   data_dir=A_  ,   type_path="train"  ,   max_source_length=A_  ,   max_target_length=A_  ,   n_obs=A_  ,   )
										return ds, max_tokens, tokenizer


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[str]							=      self._get_dataset()
										_UpperCAmelCase    :						List[Any]							=      set(DistributedSortishSampler(A_  ,   256  ,   num_replicas=2  ,   rank=0  ,   add_extra_examples=A_		)		)
										_UpperCAmelCase    :						Optional[int]							=      set(DistributedSortishSampler(A_  ,   256  ,   num_replicas=2  ,   rank=1  ,   add_extra_examples=A_		)		)
										assert idsa.intersection(A_		) == set()





						@parameterized.expand(
						    [
						        MBART_TINY,
						        MARIAN_TINY,
						        T5_TINY,
						        BART_TINY,
						        PEGASUS_XSUM,
						    ]  ,   )
						def     _UpperCAmelCase			(    self  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[str]							=      AutoTokenizer.from_pretrained(A_  ,   use_fast=A_		)
										if tok_name == MBART_TINY:
														_UpperCAmelCase    :						Any							=      SeqaSeqDataset(
														    A_  ,   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"  ,   )
														_UpperCAmelCase    :						str							=      train_dataset.dataset_kwargs
														assert "src_lang" in kwargs and "tgt_lang" in kwargs
										else:
														_UpperCAmelCase    :						List[Any]							=      SeqaSeqDataset(
														    A_  ,   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  ,   )
														_UpperCAmelCase    :						str							=      train_dataset.dataset_kwargs
														assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
														assert len(A_		) == 1 if tok_name == BART_TINY else len(A_		) == 0





import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch


if is_vision_available():
					from transformers import TvltImageProcessor

if is_speech_available():
					from transformers import TvltFeatureExtractor

from transformers import TvltProcessor

@require_torch
class 			a					(		unittest.TestCase			):


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						int							=      "ZinengTang/tvlt-base"
										_UpperCAmelCase    :						int							=      tempfile.mkdtemp()


						def     _UpperCAmelCase			(    self  ,   **A_		):







										'''simple docstring'''


										return TvltImageProcessor.from_pretrained(self.checkpoint  ,   **A_		)


						def     _UpperCAmelCase			(    self  ,   **A_		):







										'''simple docstring'''


										return TvltFeatureExtractor.from_pretrained(self.checkpoint  ,   **A_		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										shutil.rmtree(self.tmpdirname		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Dict							=      self.get_image_processor()
										_UpperCAmelCase    :						Optional[int]							=      self.get_feature_extractor()

										_UpperCAmelCase    :						Tuple							=      TvltProcessor(image_processor=A_  ,   feature_extractor=A_		)
										processor.save_pretrained(self.tmpdirname		)
										_UpperCAmelCase    :						str							=      TvltProcessor.from_pretrained(self.tmpdirname		)

										self.assertIsInstance(processor.feature_extractor  ,   A_		)
										self.assertIsInstance(processor.image_processor  ,   A_		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[Any]							=      self.get_image_processor()
										_UpperCAmelCase    :						int							=      self.get_feature_extractor()

										_UpperCAmelCase    :						Tuple							=      TvltProcessor(image_processor=A_  ,   feature_extractor=A_		)

										_UpperCAmelCase    :						List[str]							=      np.ones([12000]		)

										_UpperCAmelCase    :						int							=      feature_extractor(A_  ,   return_tensors="np"		)
										_UpperCAmelCase    :						int							=      processor(audio=A_  ,   return_tensors="np"		)

										for key in audio_dict.keys():
														self.assertAlmostEqual(audio_dict[key].sum()  ,   input_processor[key].sum()  ,   delta=1e-2		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[str]							=      self.get_image_processor()
										_UpperCAmelCase    :						int							=      self.get_feature_extractor()

										_UpperCAmelCase    :						Optional[Any]							=      TvltProcessor(image_processor=A_  ,   feature_extractor=A_		)

										_UpperCAmelCase    :						Union[str, Any]							=      np.ones([3, 224, 224]		)

										_UpperCAmelCase    :						Tuple							=      image_processor(A_  ,   return_tensors="np"		)
										_UpperCAmelCase    :						List[str]							=      processor(images=A_  ,   return_tensors="np"		)

										for key in image_dict.keys():
														self.assertAlmostEqual(image_dict[key].sum()  ,   input_processor[key].sum()  ,   delta=1e-2		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						List[str]							=      self.get_image_processor()
										_UpperCAmelCase    :						Any							=      self.get_feature_extractor()

										_UpperCAmelCase    :						Dict							=      TvltProcessor(image_processor=A_  ,   feature_extractor=A_		)

										_UpperCAmelCase    :						str							=      np.ones([12000]		)
										_UpperCAmelCase    :						Optional[Any]							=      np.ones([3, 224, 224]		)

										_UpperCAmelCase    :						List[Any]							=      processor(audio=A_  ,   images=A_		)

										self.assertListEqual(list(inputs.keys()		)  ,   ["audio_values", "audio_mask", "pixel_values", "pixel_mask"]		)

										# test if it raises when no input is passed
										with pytest.raises(A_		):
														processor()





						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										_UpperCAmelCase    :						Optional[Any]							=      self.get_image_processor()
										_UpperCAmelCase    :						int							=      self.get_feature_extractor()

										_UpperCAmelCase    :						str							=      TvltProcessor(image_processor=A_  ,   feature_extractor=A_		)

										self.assertListEqual(
										    processor.model_input_names  ,   image_processor.model_input_names + feature_extractor.model_input_names  ,   msg="`processor` and `image_processor`+`feature_extractor` model input names do not match"  ,   )


