





'''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


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







def  a__    (    lowercase :					Path, lowercase :					list				)		->		Union[str, Any]:





     """simple docstring"""

     _UpperCamelCase       =	'''\n'''.join(lowercase				)
     Path(lowercase				).open('''w'''				).writelines(lowercase				)







def  a__    (    lowercase :					Optional[Any]				)		->		Dict:





     """simple docstring"""

     for split in ["train", "val", "test"]:
          _dump_articles(os.path.join(lowercase, F"""{split}.source"""				), lowercase				)
          _dump_articles(os.path.join(lowercase, F"""{split}.target"""				), lowercase				)
     return tmp_dir




class     __lowerCAmelCase					(   __magic_name__			):






       """simple docstring"""





       @parameterized.expand(
           [
               MBART_TINY,
               MARIAN_TINY,
               T5_TINY,
               BART_TINY,
               PEGASUS_XSUM,
           ]	,			)
       @slow
       def snake_case__						(							self       :		Tuple	,			lowerCAmelCase__       :		Union[str, Any]					)   ->					str:

            '''simple docstring'''




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

            '''simple docstring'''




            _UpperCamelCase       =	AutoTokenizer.from_pretrained(lowerCAmelCase__					)
            _UpperCamelCase       =	make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)
            _UpperCamelCase       =	max(len(tokenizer.encode(lowerCAmelCase__					)					) for a in ARTICLES					)
            _UpperCamelCase       =	max(len(tokenizer.encode(lowerCAmelCase__					)					) for a in SUMMARIES					)
            _UpperCamelCase       =	4
            _UpperCamelCase       =	LegacySeqaSeqDataset(
                lowerCAmelCase__	,			data_dir=lowerCAmelCase__	,			type_path='''train'''	,			max_source_length=20	,			max_target_length=lowerCAmelCase__	,			)
            _UpperCamelCase       =	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 snake_case__						(							self       :		Any					)   ->					Tuple:

            '''simple docstring'''




            _UpperCamelCase       =	AutoTokenizer.from_pretrained('''facebook/mbart-large-cc25'''					)

            _UpperCamelCase       =	Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)					)
            _UpperCamelCase       =	tmp_dir.joinpath('''train.source'''					).open().readlines()
            _UpperCamelCase       =	Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)					)
            pack_data_dir(lowerCAmelCase__	,			lowerCAmelCase__	,			128	,			lowerCAmelCase__					)
            _UpperCamelCase       =	{x.name for x in tmp_dir.iterdir()}
            _UpperCamelCase       =	{x.name for x in save_dir.iterdir()}
            _UpperCamelCase       =	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 snake_case__						(							self       :		Any					)   ->					str:

            '''simple docstring'''




            if not FAIRSEQ_AVAILABLE:
                 return
            _UpperCamelCase   ,    _UpperCamelCase   ,    _UpperCamelCase       =	self._get_dataset(max_len=64					)
            _UpperCamelCase       =	64
            _UpperCamelCase       =	ds.make_dynamic_sampler(lowerCAmelCase__	,			required_batch_size_multiple=lowerCAmelCase__					)
            _UpperCamelCase       =	[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
            _UpperCamelCase       =	DataLoader(lowerCAmelCase__	,			batch_sampler=lowerCAmelCase__	,			collate_fn=ds.collate_fn	,			num_workers=2					)
            _UpperCamelCase       =	[]
            _UpperCamelCase       =	[]
            for batch in data_loader:
                 _UpperCamelCase       =	batch['''input_ids'''].shape
                 _UpperCamelCase       =	src_shape[0]
                 assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
                 _UpperCamelCase       =	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 snake_case__						(							self       :		Dict					)   ->					List[str]:

            '''simple docstring'''




            _UpperCamelCase   ,    _UpperCamelCase   ,    _UpperCamelCase       =	self._get_dataset(max_len=512					)
            _UpperCamelCase       =	2
            _UpperCamelCase       =	ds.make_sortish_sampler(lowerCAmelCase__	,			shuffle=lowerCAmelCase__					)

            _UpperCamelCase       =	DataLoader(lowerCAmelCase__	,			batch_size=lowerCAmelCase__	,			collate_fn=ds.collate_fn	,			num_workers=2					)
            _UpperCamelCase       =	DataLoader(lowerCAmelCase__	,			batch_size=lowerCAmelCase__	,			collate_fn=ds.collate_fn	,			num_workers=2	,			sampler=lowerCAmelCase__					)

            _UpperCamelCase       =	tokenizer.pad_token_id

            def count_pad_tokens(lowerCAmelCase__       :		int	,			lowerCAmelCase__       :		List[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 snake_case__						(							self       :		int	,			lowerCAmelCase__       :		List[str]=1000	,			lowerCAmelCase__       :		Union[str, Any]=128					)   ->					List[Any]:

            '''simple docstring'''




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

            _UpperCamelCase       =	AutoTokenizer.from_pretrained(lowerCAmelCase__					)
            _UpperCamelCase       =	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 snake_case__						(							self       :		List[Any]					)   ->					Dict:

            '''simple docstring'''




            _UpperCamelCase   ,    _UpperCamelCase   ,    _UpperCamelCase       =	self._get_dataset()
            _UpperCamelCase       =	set(DistributedSortishSampler(lowerCAmelCase__	,			256	,			num_replicas=2	,			rank=0	,			add_extra_examples=lowerCAmelCase__					)					)
            _UpperCamelCase       =	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 snake_case__						(							self       :		Tuple	,			lowerCAmelCase__       :		Optional[int]					)   ->					Dict:

            '''simple docstring'''




            _UpperCamelCase       =	AutoTokenizer.from_pretrained(lowerCAmelCase__	,			use_fast=lowerCAmelCase__					)
            if tok_name == MBART_TINY:
                 _UpperCamelCase       =	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'''	,			)
                 _UpperCamelCase       =	train_dataset.dataset_kwargs
                 assert "src_lang" in kwargs and "tgt_lang" in kwargs
            else:
                 _UpperCamelCase       =	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	,			)
                 _UpperCamelCase       =	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










'''simple docstring'''

from cva import destroyAllWindows, imread, imshow, waitKey







def  a__    (    lowercase :					str				)		->		Optional[int]:





     """simple docstring"""

     _UpperCamelCase   ,    _UpperCamelCase       =	img.shape[0], img.shape[1]

     # converting each pixel's color to its negative
     for i in range(lowercase				):
          for j in range(lowercase				):
               _UpperCamelCase       =	[255, 255, 255] - img[i][j]

     return img


if __name__ == "__main__":
   # read original image
   lowercase__						:      Optional[int]	=    imread('image_data/lena.jpg', 1)

   # convert to its negative
   lowercase__						:      Union[str, Any]	=    convert_to_negative(img)

   # show result image
   imshow('negative of original image', img)
   waitKey(0)
   destroyAllWindows()



