


"""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


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






     """simple docstring"""

     _a					= """\n""".join(_lowerCAmelCase				)
     Path(_lowerCAmelCase				).open('''w'''				).writelines(_lowerCAmelCase				)
def 					A_      (      _lowerCAmelCase   :    Optional[int]				):






     """simple docstring"""

     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 			__lowerCamelCase     (    __SCREAMING_SNAKE_CASE ):

     '''simple docstring'''


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

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

          _a					= DataLoader(__UpperCAmelCase			, batch_size=__UpperCAmelCase			, collate_fn=ds.collate_fn			, num_workers=2							)
          _a					= DataLoader(__UpperCAmelCase			, batch_size=__UpperCAmelCase			, collate_fn=ds.collate_fn			, num_workers=2			, sampler=__UpperCAmelCase							)

          _a					= 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 							_UpperCAmelCase					(					self			, __UpperCAmelCase=1000			, __UpperCAmelCase=128							)				->							str:
          if os.getenv('''USE_REAL_DATA'''			, __UpperCAmelCase							):
               _a					= """examples/seq2seq/wmt_en_ro"""
               _a					= max_len * 2 * 64
               if not Path(__UpperCAmelCase							).joinpath('''train.len'''							).exists():
                    save_len_file(__UpperCAmelCase			, __UpperCAmelCase							)
          else:
               _a					= """examples/seq2seq/test_data/wmt_en_ro"""
               _a					= max_len * 4
               save_len_file(__UpperCAmelCase			, __UpperCAmelCase							)

          _a					= AutoTokenizer.from_pretrained(__UpperCAmelCase							)
          _a					= 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 							_UpperCAmelCase					(					self							)				->							List[Any]:
          _a					= self._get_dataset()
          _a					= set(DistributedSortishSampler(__UpperCAmelCase			, 256			, num_replicas=2			, rank=0			, add_extra_examples=__UpperCAmelCase							)							)
          _a					= 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 							_UpperCAmelCase					(					self			, __UpperCAmelCase							)				->							str:
          _a					= AutoTokenizer.from_pretrained(__UpperCAmelCase			, use_fast=__UpperCAmelCase							)
          if tok_name == MBART_TINY:
               _a					= 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					= train_dataset.dataset_kwargs
               assert "src_lang" in kwargs and "tgt_lang" in kwargs
          else:
               _a					= 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					= 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

from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
      import PIL


_lowerCamelCase					   =logging.get_logger(__name__)





class 							A__		(	__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase				:    Union[str, Any]           =			["""pixel_values"""]
      def __init__(      self    ,  __magic_name__ = True    ,  __magic_name__ = None    ,  __magic_name__ = None    ,  __magic_name__ = PILImageResampling.BILINEAR    ,  __magic_name__ = True    ,  __magic_name__ = 1 / 2_5_5    ,  __magic_name__ = True    ,  __magic_name__ = None    ,  __magic_name__ = None    ,  **__magic_name__    ,  ):
       super().__init__(**__magic_name__      )
       lowerCamelCase		:	Dict							=      size if size is not None else {"""shortest_edge""": 3_8_4}
       lowerCamelCase		:	Tuple							=      get_size_dict(__magic_name__    ,  default_to_square=__magic_name__      )

       lowerCamelCase		:	Dict							=      do_resize
       lowerCamelCase		:	List[Any]							=      size
       # Default value set here for backwards compatibility where the value in config is None
       lowerCamelCase		:	Any							=      crop_pct if crop_pct is not None else 2_2_4 / 2_5_6
       lowerCamelCase		:	Union[str, Any]							=      resample
       lowerCamelCase		:	str							=      do_rescale
       lowerCamelCase		:	Union[str, Any]							=      rescale_factor
       lowerCamelCase		:	Tuple							=      do_normalize
       lowerCamelCase		:	Optional[Any]							=      image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       lowerCamelCase		:	int							=      image_std if image_std is not None else IMAGENET_STANDARD_STD
      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__ = PILImageResampling.BICUBIC    ,  __magic_name__ = None    ,  **__magic_name__    ,  ):
       lowerCamelCase		:	Union[str, Any]							=      get_size_dict(__magic_name__    ,  default_to_square=__magic_name__      )
       if "shortest_edge" not in size:
        raise ValueError(F'''Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'''      )
       lowerCamelCase		:	str							=      size["""shortest_edge"""]

       if shortest_edge < 3_8_4:
        # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
        lowerCamelCase		:	List[str]							=      int(shortest_edge / crop_pct      )
        lowerCamelCase		:	Optional[Any]							=      get_resize_output_image_size(__magic_name__    ,  size=__magic_name__    ,  default_to_square=__magic_name__      )
        lowerCamelCase		:	Optional[int]							=      resize(image=__magic_name__    ,  size=__magic_name__    ,  resample=__magic_name__    ,  data_format=__magic_name__    ,  **__magic_name__      )
        # then crop to (shortest_edge, shortest_edge)
        return center_crop(image=__magic_name__    ,  size=(shortest_edge, shortest_edge)    ,  data_format=__magic_name__    ,  **__magic_name__      )
       else:
        # warping (no cropping) when evaluated at 384 or larger
        return resize(
            __magic_name__    ,  size=(shortest_edge, shortest_edge)    ,  resample=__magic_name__    ,  data_format=__magic_name__    ,  **__magic_name__      )
      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__ = None    ,  **__magic_name__    ,  ):
       return rescale(__magic_name__    ,  scale=__magic_name__    ,  data_format=__magic_name__    ,  **__magic_name__      )
      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__ = None    ,  **__magic_name__    ,  ):
       return normalize(__magic_name__    ,  mean=__magic_name__    ,  std=__magic_name__    ,  data_format=__magic_name__    ,  **__magic_name__      )






      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__ = None    ,  __magic_name__ = None    ,  __magic_name__ = None    ,  __magic_name__ = None    ,  __magic_name__ = None    ,  __magic_name__ = None    ,  __magic_name__ = None    ,  __magic_name__ = None    ,  __magic_name__ = None    ,  __magic_name__ = None    ,  __magic_name__ = ChannelDimension.FIRST    ,  **__magic_name__    ,  ):
       lowerCamelCase		:	str							=      do_resize if do_resize is not None else self.do_resize
       lowerCamelCase		:	Optional[Any]							=      crop_pct if crop_pct is not None else self.crop_pct
       lowerCamelCase		:	Optional[int]							=      resample if resample is not None else self.resample
       lowerCamelCase		:	Optional[int]							=      do_rescale if do_rescale is not None else self.do_rescale
       lowerCamelCase		:	List[str]							=      rescale_factor if rescale_factor is not None else self.rescale_factor
       lowerCamelCase		:	Tuple							=      do_normalize if do_normalize is not None else self.do_normalize
       lowerCamelCase		:	Optional[Any]							=      image_mean if image_mean is not None else self.image_mean
       lowerCamelCase		:	Tuple							=      image_std if image_std is not None else self.image_std

       lowerCamelCase		:	Dict							=      size if size is not None else self.size
       lowerCamelCase		:	str							=      get_size_dict(__magic_name__    ,  default_to_square=__magic_name__      )

       lowerCamelCase		:	List[str]							=      make_list_of_images(__magic_name__      )

       if not valid_images(__magic_name__      ):
        raise ValueError(
            """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
            """torch.Tensor, tf.Tensor or jax.ndarray."""      )

       if do_resize and size is None or resample is None:
        raise ValueError("""Size and resample must be specified if do_resize is True."""      )

       if do_resize and size["shortest_edge"] < 3_8_4 and crop_pct is None:
        raise ValueError("""crop_pct must be specified if size < 384."""      )

       if do_rescale and rescale_factor is None:
        raise ValueError("""Rescale factor must be specified if do_rescale is True."""      )

       if do_normalize and (image_mean is None or image_std is None):
        raise ValueError("""Image mean and std must be specified if do_normalize is True."""      )

       # All transformations expect numpy arrays.
       lowerCamelCase		:	Optional[Any]							=      [to_numpy_array(__magic_name__      ) for image in images]

       if do_resize:
        lowerCamelCase		:	List[Any]							=      [self.resize(image=__magic_name__    ,  size=__magic_name__    ,  crop_pct=__magic_name__    ,  resample=__magic_name__      ) for image in images]

       if do_rescale:
        lowerCamelCase		:	Union[str, Any]							=      [self.rescale(image=__magic_name__    ,  scale=__magic_name__      ) for image in images]

       if do_normalize:
        lowerCamelCase		:	List[Any]							=      [self.normalize(image=__magic_name__    ,  mean=__magic_name__    ,  std=__magic_name__      ) for image in images]

       lowerCamelCase		:	Optional[int]							=      [to_channel_dimension_format(__magic_name__    ,  __magic_name__      ) for image in images]

       lowerCamelCase		:	List[str]							=      {"""pixel_values""": images}
       return BatchFeature(data=__magic_name__    ,  tensor_type=__magic_name__      )




