





'''simple docstring'''




from itertools import product

from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros






def 	_lowerCAmelCase      (  __snake_case  :			List[str]			,							__snake_case  :			Optional[int]      )					->					int:
     __A						:			str								=	k_size // 2
     __A			,__A						:			str								=	mgrid[0 - center : k_size - center, 0 - center : k_size - center]
     __A						:			str								=	1 / (2 * pi * sigma) * exp(-(square(__snake_case      ) + square(__snake_case      )) / (2 * square(__snake_case      ))      )
     return g






def 	_lowerCAmelCase      (  __snake_case  :			Any			,							__snake_case  :			Optional[int]			,							__snake_case  :			Tuple      )					->					Tuple:
     __A			,__A						:			Any								=	image.shape[0], image.shape[1]
     # dst image height and width
     __A						:			Tuple								=	height - k_size + 1
     __A						:			Any								=	width - k_size + 1

     # im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
     __A						:			Tuple								=	zeros((dst_height * dst_width, k_size * k_size)      )
     __A						:			str								=	0
     for i, j in product(range(__snake_case      )			,							range(__snake_case      )      ):
          __A						:			int								=	ravel(image[i : i + k_size, j : j + k_size]      )
          __A						:			int								=	window
          row += 1

     #  turn the kernel into shape(k*k, 1)
     __A						:			Union[str, Any]								=	gen_gaussian_kernel(__snake_case			,							__snake_case      )
     __A						:			Optional[int]								=	ravel(__snake_case      )

     # reshape and get the dst image
     __A						:			List[Any]								=	dot(__snake_case			,							__snake_case      ).reshape(__snake_case			,							__snake_case      ).astype(__snake_case      )

     return dst


if __name__ == "__main__":
  # read original image
  lowercase__   :				int      =   imread(r'''../image_data/lena.jpg''')
  # turn image in gray scale value
  lowercase__   :				Optional[int]      =   cvtColor(img, COLOR_BGR2GRAY)

  # get values with two different mask size
  lowercase__   :				int      =   gaussian_filter(gray, 3, sigma=1)
  lowercase__   :				str      =   gaussian_filter(gray, 5, sigma=0.8)

  # show result images
  imshow('''gaussian filter with 3x3 mask''', gaussianaxa)
  imshow('''gaussian filter with 5x5 mask''', gaussianaxa)
  waitKey()






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






def 	_lowerCAmelCase      (  __snake_case  :			Path			,							__snake_case  :			list      )					->					str:
     __A						:			Any								=	'\n'.join(__snake_case      )
     Path(__snake_case      ).open('w'      ).writelines(__snake_case      )






def 	_lowerCAmelCase      (  __snake_case  :			Optional[int]      )					->					Tuple:
     for split in ["train", "val", "test"]:
          _dump_articles(os.path.join(__snake_case			,							f'{split}.source'      )			,							__snake_case      )
          _dump_articles(os.path.join(__snake_case			,							f'{split}.target'      )			,							__snake_case      )
     return tmp_dir







class 			SCREAMING_SNAKE_CASE   (a__							):


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

          '''simple docstring'''






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

          '''simple docstring'''






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

          '''simple docstring'''






          __A						:			Dict								=	AutoTokenizer.from_pretrained('facebook/mbart-large-cc25')

          __A						:			int								=	Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()))
          __A						:			List[str]								=	tmp_dir.joinpath('train.source').open().readlines()
          __A						:			Optional[Any]								=	Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()))
          pack_data_dir(_UpperCAmelCase						,     _UpperCAmelCase						,     128						,     _UpperCAmelCase)
          __A						:			Dict								=	{x.name for x in tmp_dir.iterdir()}
          __A						:			Dict								=	{x.name for x in save_dir.iterdir()}
          __A						:			str								=	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 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          if not FAIRSEQ_AVAILABLE:
               return
          __A			,__A			,__A						:			List[Any]								=	self._get_dataset(max_len=64)
          __A						:			Union[str, Any]								=	64
          __A						:			List[Any]								=	ds.make_dynamic_sampler(_UpperCAmelCase						,     required_batch_size_multiple=_UpperCAmelCase)
          __A						:			Union[str, Any]								=	[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
          __A						:			List[Any]								=	DataLoader(_UpperCAmelCase						,     batch_sampler=_UpperCAmelCase						,     collate_fn=ds.collate_fn						,     num_workers=2)
          __A						:			Optional[int]								=	[]
          __A						:			Tuple								=	[]
          for batch in data_loader:
               __A						:			Optional[int]								=	batch['input_ids'].shape
               __A						:			Any								=	src_shape[0]
               assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
               __A						:			Tuple								=	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 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






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

          __A						:			Tuple								=	DataLoader(_UpperCAmelCase						,     batch_size=_UpperCAmelCase						,     collate_fn=ds.collate_fn						,     num_workers=2)
          __A						:			Union[str, Any]								=	DataLoader(_UpperCAmelCase						,     batch_size=_UpperCAmelCase						,     collate_fn=ds.collate_fn						,     num_workers=2						,     sampler=_UpperCAmelCase)

          __A						:			str								=	tokenizer.pad_token_id

          def count_pad_tokens(_UpperCAmelCase						,     _UpperCAmelCase="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 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase=1000						,     _UpperCAmelCase=128):

          '''simple docstring'''






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

          __A						:			Tuple								=	AutoTokenizer.from_pretrained(_UpperCAmelCase)
          __A						:			Optional[int]								=	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 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A			,__A			,__A						:			Tuple								=	self._get_dataset()
          __A						:			Optional[int]								=	set(DistributedSortishSampler(_UpperCAmelCase						,     256						,     num_replicas=2						,     rank=0						,     add_extra_examples=_UpperCAmelCase))
          __A						:			List[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 							SCREAMING_SNAKE_CASE  (						self						,     _UpperCAmelCase):

          '''simple docstring'''






          __A						:			Union[str, Any]								=	AutoTokenizer.from_pretrained(_UpperCAmelCase						,     use_fast=_UpperCAmelCase)
          if tok_name == MBART_TINY:
               __A						:			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'						,     )
               __A						:			List[Any]								=	train_dataset.dataset_kwargs
               assert "src_lang" in kwargs and "tgt_lang" in kwargs
          else:
               __A						:			Any								=	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						,     )
               __A						:			List[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(_UpperCAmelCase) == 1 if tok_name == BART_TINY else len(_UpperCAmelCase) == 0