




import json
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
 import torch

 if is_vision_available():
  from transformers import OneFormerImageProcessor
  from transformers.models.oneformer.image_processing_oneformer import binary_mask_to_rle
  from transformers.models.oneformer.modeling_oneformer import OneFormerForUniversalSegmentationOutput

if is_vision_available():
 from PIL import Image






def  snake_case(    __magic_name__			,		__magic_name__="shi-labs/oneformer_demo"   )			->							int:





     '''simple docstring'''


     with open(hf_hub_download(__magic_name__			,		__magic_name__			,		repo_type='''dataset'''   )			,		'''r'''   ) as f:
          lowercase :       Any       =   json.load(__magic_name__   )
     lowercase :       List[Any]       =   {}
     lowercase :       Any       =   []
     lowercase :       List[Any]       =   []
     for key, info in class_info.items():
          lowercase :       Tuple       =   info['''name''']
          class_names.append(info['''name''']   )
          if info["isthing"]:
               thing_ids.append(int(__magic_name__   )   )
     lowercase :       Optional[Any]       =   thing_ids
     lowercase :       Optional[Any]       =   class_names
     return metadata
class 						_A (  unittest.TestCase				):






  def __init__(  self		:     Optional[int]				,						_A		:     int				,						_A		:     str=7				,						_A		:     Tuple=3				,						_A		:     Tuple=30				,						_A		:     Optional[Any]=400				,						_A		:     Tuple=None				,						_A		:     Union[str, Any]=True				,						_A		:     Union[str, Any]=True				,						_A		:     Optional[int]=[0.5, 0.5, 0.5]				,						_A		:     Any=[0.5, 0.5, 0.5]				,						_A		:     str=10				,						_A		:     Optional[Any]=False				,						_A		:     Tuple=255				,						_A		:     Any="shi-labs/oneformer_demo"				,						_A		:     int="ade20k_panoptic.json"				,						_A		:     Union[str, Any]=10				,						)		->   List[Any]:






       """simple docstring"""







       lowercase :       List[str]       =   parent
       lowercase :       str       =   batch_size
       lowercase :       Any       =   num_channels
       lowercase :       List[Any]       =   min_resolution
       lowercase :       Optional[Any]       =   max_resolution
       lowercase :       Optional[Any]       =   do_resize
       lowercase :       Optional[int]       =   {'''shortest_edge''': 32, '''longest_edge''': 1_333} if size is None else size
       lowercase :       Union[str, Any]       =   do_normalize
       lowercase :       List[Any]       =   image_mean
       lowercase :       List[Any]       =   image_std
       lowercase :       List[Any]       =   class_info_file
       lowercase :       Any       =   prepare_metadata(__A				,						__A				)
       lowercase :       Dict       =   num_text
       lowercase :       int       =   repo_path

       # for the post_process_functions
       lowercase :       Dict       =   2
       lowercase :       List[Any]       =   10
       lowercase :       Optional[int]       =   10
       lowercase :       Optional[Any]       =   3
       lowercase :       Optional[Any]       =   4
       lowercase :       int       =   num_labels
       lowercase :       Optional[Any]       =   do_reduce_labels
       lowercase :       Union[str, Any]       =   ignore_index






  def  __a      (  self		:     List[Any]				)		->   int:






       """simple docstring"""







       return {
           "do_resize": self.do_resize,
           "size": self.size,
           "do_normalize": self.do_normalize,
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "num_labels": self.num_labels,
           "do_reduce_labels": self.do_reduce_labels,
           "ignore_index": self.ignore_index,
           "class_info_file": self.class_info_file,
           "metadata": self.metadata,
           "num_text": self.num_text,
       }






  def  __a      (  self		:     int				,						_A		:     Optional[Any]				,						_A		:     str=False				)		->   Any:






       """simple docstring"""







       if not batched:
            lowercase :       int       =   image_inputs[0]
            if isinstance(__A				,						Image.Image				):
                 lowercase :       Tuple       =   image.size
            else:
                 lowercase :       int       =   image.shape[1], image.shape[2]
            if w < h:
                 lowercase :       Tuple       =   int(self.size['''shortest_edge'''] * h / w				)
                 lowercase :       int       =   self.size['''shortest_edge''']
            elif w > h:
                 lowercase :       Optional[int]       =   self.size['''shortest_edge''']
                 lowercase :       Union[str, Any]       =   int(self.size['''shortest_edge'''] * w / h				)
            else:
                 lowercase :       List[str]       =   self.size['''shortest_edge''']
                 lowercase :       Any       =   self.size['''shortest_edge''']

       else:
            lowercase :       Tuple       =   []
            for image in image_inputs:
                 lowercase :       str       =   self.get_expected_values([image]				)
                 expected_values.append((expected_height, expected_width)				)
            lowercase :       Dict       =   max(__A				,						key=lambda _A				: item[0]				)[0]
            lowercase :       Tuple       =   max(__A				,						key=lambda _A				: item[1]				)[1]

       return expected_height, expected_width






  def  __a      (  self		:     str				)		->   List[Any]:






       """simple docstring"""







       return OneFormerForUniversalSegmentationOutput(
           # +1 for null class
           class_queries_logits=torch.randn((self.batch_size, self.num_queries, self.num_classes + 1)				)				,						masks_queries_logits=torch.randn((self.batch_size, self.num_queries, self.height, self.width)				)				,						)


@require_torch
@require_vision
class 						_A (  lowerCamelCase__      ,       unittest.TestCase				):
  _UpperCamelCase       :      Dict			=      OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
  # only for test_image_processing_common.test_image_proc_to_json_string
  _UpperCamelCase       :      Tuple			=      image_processing_class






  def  __a      (  self		:     Dict				)		->   Optional[Any]:






       """simple docstring"""







       lowercase :       List[Any]       =   OneFormerImageProcessorTester(self				)






  @property
  def  __a      (  self		:     Any				)		->   Union[str, Any]:






       """simple docstring"""







       return self.image_processing_tester.prepare_image_processor_dict()






  def  __a      (  self		:     str				)		->   Optional[Any]:






       """simple docstring"""







       lowercase :       int       =   self.image_processing_class(**self.image_processor_dict				)
       self.assertTrue(hasattr(__A				,						'''image_mean'''				)				)
       self.assertTrue(hasattr(__A				,						'''image_std'''				)				)
       self.assertTrue(hasattr(__A				,						'''do_normalize'''				)				)
       self.assertTrue(hasattr(__A				,						'''do_resize'''				)				)
       self.assertTrue(hasattr(__A				,						'''size'''				)				)
       self.assertTrue(hasattr(__A				,						'''ignore_index'''				)				)
       self.assertTrue(hasattr(__A				,						'''class_info_file'''				)				)
       self.assertTrue(hasattr(__A				,						'''num_text'''				)				)
       self.assertTrue(hasattr(__A				,						'''repo_path'''				)				)
       self.assertTrue(hasattr(__A				,						'''metadata'''				)				)
       self.assertTrue(hasattr(__A				,						'''do_reduce_labels'''				)				)






  def  __a      (  self		:     List[Any]				)		->   List[str]:






       """simple docstring"""







       pass






  def  __a      (  self		:     Optional[Any]				)		->   Any:






       """simple docstring"""







       lowercase :       Union[str, Any]       =   self.image_processing_class(**self.image_processor_dict				)
       # create random PIL images
       lowercase :       Dict       =   prepare_image_inputs(self.image_processing_tester				,						equal_resolution=__A				)
       for image in image_inputs:
            self.assertIsInstance(__A				,						Image.Image				)

       # Test not batched input
       lowercase :       List[str]       =   image_processor(image_inputs[0]				,						['''semantic''']				,						return_tensors='''pt'''				).pixel_values

       lowercase :       int       =   self.image_processing_tester.get_expected_values(__A				)

       self.assertEqual(
           encoded_images.shape				,						(1, self.image_processing_tester.num_channels, expected_height, expected_width)				,						)

       # Test batched
       lowercase :       Tuple       =   self.image_processing_tester.get_expected_values(__A				,						batched=__A				)

       lowercase :       List[Any]       =   image_processor(
           __A				,						['''semantic'''] * len(__A				)				,						return_tensors='''pt'''				).pixel_values
       self.assertEqual(
           encoded_images.shape				,						(
               self.image_processing_tester.batch_size,
               self.image_processing_tester.num_channels,
               expected_height,
               expected_width,
           )				,						)






  def  __a      (  self		:     List[str]				)		->   List[Any]:






       """simple docstring"""







       lowercase :       Any       =   self.image_processing_class(**self.image_processor_dict				)
       # create random numpy tensors
       lowercase :       List[str]       =   prepare_image_inputs(self.image_processing_tester				,						equal_resolution=__A				,						numpify=__A				)
       for image in image_inputs:
            self.assertIsInstance(__A				,						np.ndarray				)

       # Test not batched input
       lowercase :       List[str]       =   image_processor(image_inputs[0]				,						['''semantic''']				,						return_tensors='''pt'''				).pixel_values

       lowercase :       Any       =   self.image_processing_tester.get_expected_values(__A				)

       self.assertEqual(
           encoded_images.shape				,						(1, self.image_processing_tester.num_channels, expected_height, expected_width)				,						)

       # Test batched
       lowercase :       List[Any]       =   self.image_processing_tester.get_expected_values(__A				,						batched=__A				)

       lowercase :       str       =   image_processor(
           __A				,						['''semantic'''] * len(__A				)				,						return_tensors='''pt'''				).pixel_values
       self.assertEqual(
           encoded_images.shape				,						(
               self.image_processing_tester.batch_size,
               self.image_processing_tester.num_channels,
               expected_height,
               expected_width,
           )				,						)






  def  __a      (  self		:     Any				)		->   List[str]:






       """simple docstring"""







       lowercase :       Optional[int]       =   self.image_processing_class(**self.image_processor_dict				)
       # create random PyTorch tensors
       lowercase :       Dict       =   prepare_image_inputs(self.image_processing_tester				,						equal_resolution=__A				,						torchify=__A				)
       for image in image_inputs:
            self.assertIsInstance(__A				,						torch.Tensor				)

       # Test not batched input
       lowercase :       Dict       =   image_processor(image_inputs[0]				,						['''semantic''']				,						return_tensors='''pt'''				).pixel_values

       lowercase :       int       =   self.image_processing_tester.get_expected_values(__A				)

       self.assertEqual(
           encoded_images.shape				,						(1, self.image_processing_tester.num_channels, expected_height, expected_width)				,						)

       # Test batched
       lowercase :       Dict       =   self.image_processing_tester.get_expected_values(__A				,						batched=__A				)

       lowercase :       Dict       =   image_processor(
           __A				,						['''semantic'''] * len(__A				)				,						return_tensors='''pt'''				).pixel_values
       self.assertEqual(
           encoded_images.shape				,						(
               self.image_processing_tester.batch_size,
               self.image_processing_tester.num_channels,
               expected_height,
               expected_width,
           )				,						)






  def  __a      (  self		:     Any				,						_A		:     str=False				,						_A		:     Union[str, Any]=False				,						_A		:     Union[str, Any]="np"				)		->   Any:






       """simple docstring"""







       lowercase :       List[str]       =   self.image_processing_class(**self.image_processor_dict				)
       # prepare image and target
       lowercase :       Tuple       =   self.image_processing_tester.num_labels
       lowercase :       List[Any]       =   None
       lowercase :       Any       =   None
       lowercase :       int       =   prepare_image_inputs(self.image_processing_tester				,						equal_resolution=__A				)
       if with_segmentation_maps:
            lowercase :       Any       =   num_labels
            if is_instance_map:
                 lowercase :       Any       =   list(range(__A				)				) * 2
                 lowercase :       Any       =   dict(enumerate(__A				)				)
            lowercase :       Optional[Any]       =   [
                np.random.randint(0				,						high * 2				,						(img.size[1], img.size[0])				).astype(np.uinta				) for img in image_inputs
            ]
            if segmentation_type == "pil":
                 lowercase :       Tuple       =   [Image.fromarray(__A				) for annotation in annotations]

       lowercase :       Tuple       =   image_processor(
           __A				,						['''semantic'''] * len(__A				)				,						__A				,						return_tensors='''pt'''				,						instance_id_to_semantic_id=__A				,						pad_and_return_pixel_mask=__A				,						)

       return inputs






  def  __a      (  self		:     Tuple				)		->   List[str]:






       """simple docstring"""







       pass






  def  __a      (  self		:     Optional[Any]				)		->   Any:






       """simple docstring"""







       def common(_A		:     Any=False				,						_A		:     int=None				):
            lowercase :       List[str]       =   self.comm_get_image_processor_inputs(
                with_segmentation_maps=__A				,						is_instance_map=__A				,						segmentation_type=__A				)

            lowercase :       List[Any]       =   inputs['''mask_labels''']
            lowercase :       Tuple       =   inputs['''class_labels''']
            lowercase :       Any       =   inputs['''pixel_values''']
            lowercase :       Optional[Any]       =   inputs['''text_inputs''']

            # check the batch_size
            for mask_label, class_label, text_input in zip(__A				,						__A				,						__A				):
                 self.assertEqual(mask_label.shape[0]				,						class_label.shape[0]				)
                 # this ensure padding has happened
                 self.assertEqual(mask_label.shape[1:]				,						pixel_values.shape[2:]				)
                 self.assertEqual(len(__A				)				,						self.image_processing_tester.num_text				)

       common()
       common(is_instance_map=__A				)
       common(is_instance_map=__A				,						segmentation_type='''pil'''				)
       common(is_instance_map=__A				,						segmentation_type='''pil'''				)






  def  __a      (  self		:     int				)		->   str:






       """simple docstring"""







       lowercase :       int       =   np.zeros((20, 50)				)
       lowercase :       Optional[int]       =   1
       lowercase :       List[str]       =   1
       lowercase :       str       =   1

       lowercase :       List[Any]       =   binary_mask_to_rle(__A				)
       self.assertEqual(len(__A				)				,						4				)
       self.assertEqual(rle[0]				,						21				)
       self.assertEqual(rle[1]				,						45				)






  def  __a      (  self		:     List[Any]				)		->   List[str]:






       """simple docstring"""







       lowercase :       Tuple       =   self.image_processing_class(
           num_labels=self.image_processing_tester.num_classes				,						max_seq_length=77				,						task_seq_length=77				,						class_info_file='''ade20k_panoptic.json'''				,						num_text=self.image_processing_tester.num_text				,						repo_path='''shi-labs/oneformer_demo'''				,						)
       lowercase :       str       =   self.image_processing_tester.get_fake_oneformer_outputs()

       lowercase :       Optional[int]       =   fature_extractor.post_process_semantic_segmentation(__A				)

       self.assertEqual(len(__A				)				,						self.image_processing_tester.batch_size				)
       self.assertEqual(
           segmentation[0].shape				,						(
               self.image_processing_tester.height,
               self.image_processing_tester.width,
           )				,						)

       lowercase :       Union[str, Any]       =   [(1, 4) for i in range(self.image_processing_tester.batch_size				)]
       lowercase :       int       =   fature_extractor.post_process_semantic_segmentation(__A				,						target_sizes=__A				)

       self.assertEqual(segmentation[0].shape				,						target_sizes[0]				)






  def  __a      (  self		:     Any				)		->   Dict:






       """simple docstring"""







       lowercase :       List[Any]       =   self.image_processing_class(
           num_labels=self.image_processing_tester.num_classes				,						max_seq_length=77				,						task_seq_length=77				,						class_info_file='''ade20k_panoptic.json'''				,						num_text=self.image_processing_tester.num_text				,						repo_path='''shi-labs/oneformer_demo'''				,						)
       lowercase :       Optional[int]       =   self.image_processing_tester.get_fake_oneformer_outputs()
       lowercase :       Tuple       =   image_processor.post_process_instance_segmentation(__A				,						threshold=0				)

       self.assertTrue(len(__A				) == self.image_processing_tester.batch_size				)
       for el in segmentation:
            self.assertTrue('''segmentation''' in el				)
            self.assertTrue('''segments_info''' in el				)
            self.assertEqual(type(el['''segments_info''']				)				,						__A				)
            self.assertEqual(
                el['''segmentation'''].shape				,						(self.image_processing_tester.height, self.image_processing_tester.width)				)






  def  __a      (  self		:     Any				)		->   Tuple:






       """simple docstring"""







       lowercase :       Tuple       =   self.image_processing_class(
           num_labels=self.image_processing_tester.num_classes				,						max_seq_length=77				,						task_seq_length=77				,						class_info_file='''ade20k_panoptic.json'''				,						num_text=self.image_processing_tester.num_text				,						repo_path='''shi-labs/oneformer_demo'''				,						)
       lowercase :       Dict       =   self.image_processing_tester.get_fake_oneformer_outputs()
       lowercase :       Optional[Any]       =   image_processor.post_process_panoptic_segmentation(__A				,						threshold=0				)

       self.assertTrue(len(__A				) == self.image_processing_tester.batch_size				)
       for el in segmentation:
            self.assertTrue('''segmentation''' in el				)
            self.assertTrue('''segments_info''' in el				)
            self.assertEqual(type(el['''segments_info''']				)				,						__A				)
            self.assertEqual(
                el['''segmentation'''].shape				,						(self.image_processing_tester.height, self.image_processing_tester.width)				)





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


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






def  snake_case(    __magic_name__			,		__magic_name__   )			->							Optional[Any]:





     '''simple docstring'''


     lowercase :       List[str]       =			'''\n'''.join(__magic_name__   )
     Path(__magic_name__   ).open('''w'''   ).writelines(__magic_name__   )






def  snake_case(    __magic_name__   )			->							Optional[int]:





     '''simple docstring'''


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



class 						_A (  _lowerCamelCase				):






  @parameterized.expand(
      [
          MBART_TINY,
          MARIAN_TINY,
          T5_TINY,
          BART_TINY,
          PEGASUS_XSUM,
      ]				,						)
  @slow
  def  __a      (  self		:     List[str]				,						_A		:     Optional[Any]				)		->   Dict:






       """simple docstring"""







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






       """simple docstring"""







       lowercase :       int       =			AutoTokenizer.from_pretrained(_A				)
       lowercase :       int       =			make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)
       lowercase :       Optional[int]       =			max(len(tokenizer.encode(_A				)				) for a in ARTICLES				)
       lowercase :       List[Any]       =			max(len(tokenizer.encode(_A				)				) for a in SUMMARIES				)
       lowercase :       List[Any]       =			4
       lowercase :       Any       =			LegacySeqaSeqDataset(
           _A				,						data_dir=_A				,						type_path='''train'''				,						max_source_length=20				,						max_target_length=_A				,						)
       lowercase :       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  __a      (  self		:     List[str]				)		->   int:






       """simple docstring"""







       lowercase :       Tuple       =			AutoTokenizer.from_pretrained('''facebook/mbart-large-cc25'''				)

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






       """simple docstring"""







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






       """simple docstring"""







       lowercase      ,	lowercase      ,	lowercase :       Tuple       =			self._get_dataset(max_len=512				)
       lowercase :       Tuple       =			2
       lowercase :       Union[str, Any]       =			ds.make_sortish_sampler(_A				,						shuffle=_A				)

       lowercase :       List[Any]       =			DataLoader(_A				,						batch_size=_A				,						collate_fn=ds.collate_fn				,						num_workers=2				)
       lowercase :       List[Any]       =			DataLoader(_A				,						batch_size=_A				,						collate_fn=ds.collate_fn				,						num_workers=2				,						sampler=_A				)

       lowercase :       int       =			tokenizer.pad_token_id

       def count_pad_tokens(_A		:     List[Any]				,						_A		:     Union[str, Any]="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  __a      (  self		:     Any				,						_A		:     Union[str, Any]=1_000				,						_A		:     str=128				)		->   List[Any]:






       """simple docstring"""







       if os.getenv('''USE_REAL_DATA'''				,						_A				):
            lowercase :       Optional[Any]       =			'''examples/seq2seq/wmt_en_ro'''
            lowercase :       Optional[int]       =			max_len * 2 * 64
            if not Path(_A				).joinpath('''train.len'''				).exists():
                 save_len_file(_A				,						_A				)
       else:
            lowercase :       Tuple       =			'''examples/seq2seq/test_data/wmt_en_ro'''
            lowercase :       Optional[Any]       =			max_len * 4
            save_len_file(_A				,						_A				)

       lowercase :       Optional[Any]       =			AutoTokenizer.from_pretrained(_A				)
       lowercase :       Union[str, Any]       =			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  __a      (  self		:     List[str]				)		->   List[str]:






       """simple docstring"""







       lowercase      ,	lowercase      ,	lowercase :       Union[str, Any]       =			self._get_dataset()
       lowercase :       int       =			set(DistributedSortishSampler(_A				,						256				,						num_replicas=2				,						rank=0				,						add_extra_examples=_A				)				)
       lowercase :       Dict       =			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  __a      (  self		:     Union[str, Any]				,						_A		:     Tuple				)		->   Optional[int]:






       """simple docstring"""







       lowercase :       Union[str, Any]       =			AutoTokenizer.from_pretrained(_A				,						use_fast=_A				)
       if tok_name == MBART_TINY:
            lowercase :       Tuple       =			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'''				,						)
            lowercase :       Union[str, Any]       =			train_dataset.dataset_kwargs
            assert "src_lang" in kwargs and "tgt_lang" in kwargs
       else:
            lowercase :       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				,						)
            lowercase :       Dict       =			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