




import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()
class 						_A (  unittest.TestCase				):






  def  __a      (  self		:     int				)		->   int:






       """simple docstring"""







       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()






  @property
  def  __a      (  self		:     str				)		->   str:






       """simple docstring"""







       lowercase :       Optional[Any]       =			1
       lowercase :       str       =			3
       lowercase :       int       =			(32, 32)

       lowercase :       Optional[int]       =			floats_tensor((batch_size, num_channels) + sizes				,						rng=random.Random(0				)				).to(_A				)
       return image






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






       """simple docstring"""







       torch.manual_seed(0				)
       lowercase :       List[str]       =			UNetaDConditionModel(
           block_out_channels=(32, 32, 64)				,						layers_per_block=2				,						sample_size=32				,						in_channels=7				,						out_channels=4				,						down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''', '''CrossAttnDownBlock2D''')				,						up_block_types=('''CrossAttnUpBlock2D''', '''CrossAttnUpBlock2D''', '''UpBlock2D''')				,						cross_attention_dim=32				,						attention_head_dim=8				,						use_linear_projection=_A				,						only_cross_attention=(True, True, False)				,						num_class_embeds=100				,						)
       return model






  @property
  def  __a      (  self		:     Optional[int]				)		->   int:






       """simple docstring"""







       torch.manual_seed(0				)
       lowercase :       str       =			AutoencoderKL(
           block_out_channels=[32, 32, 64]				,						in_channels=3				,						out_channels=3				,						down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''', '''DownEncoderBlock2D''']				,						up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''', '''UpDecoderBlock2D''']				,						latent_channels=4				,						)
       return model






  @property
  def  __a      (  self		:     Any				)		->   Any:






       """simple docstring"""







       torch.manual_seed(0				)
       lowercase :       Optional[int]       =			CLIPTextConfig(
           bos_token_id=0				,						eos_token_id=2				,						hidden_size=32				,						intermediate_size=37				,						layer_norm_eps=1E-05				,						num_attention_heads=4				,						num_hidden_layers=5				,						pad_token_id=1				,						vocab_size=1_000				,						hidden_act='''gelu'''				,						projection_dim=512				,						)
       return CLIPTextModel(_A				)






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






       """simple docstring"""







       lowercase :       Tuple       =			'''cpu'''  # ensure determinism for the device-dependent torch.Generator
       lowercase :       int       =			self.dummy_cond_unet_upscale
       lowercase :       Tuple       =			DDPMScheduler()
       lowercase :       Any       =			DDIMScheduler(prediction_type='''v_prediction'''				)
       lowercase :       Dict       =			self.dummy_vae
       lowercase :       Any       =			self.dummy_text_encoder
       lowercase :       Any       =			CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				)

       lowercase :       str       =			self.dummy_image.cpu().permute(0				,						2				,						3				,						1				)[0]
       lowercase :       int       =			Image.fromarray(np.uinta(_A				)				).convert('''RGB'''				).resize((64, 64)				)

       # make sure here that pndm scheduler skips prk
       lowercase :       str       =			StableDiffusionUpscalePipeline(
           unet=_A				,						low_res_scheduler=_A				,						scheduler=_A				,						vae=_A				,						text_encoder=_A				,						tokenizer=_A				,						max_noise_level=350				,						)
       lowercase :       Optional[int]       =			sd_pipe.to(_A				)
       sd_pipe.set_progress_bar_config(disable=_A				)

       lowercase :       str       =			'''A painting of a squirrel eating a burger'''
       lowercase :       int       =			torch.Generator(device=_A				).manual_seed(0				)
       lowercase :       List[str]       =			sd_pipe(
           [prompt]				,						image=_A				,						generator=_A				,						guidance_scale=6.0				,						noise_level=20				,						num_inference_steps=2				,						output_type='''np'''				,						)

       lowercase :       Tuple       =			output.images

       lowercase :       Union[str, Any]       =			torch.Generator(device=_A				).manual_seed(0				)
       lowercase :       Any       =			sd_pipe(
           [prompt]				,						image=_A				,						generator=_A				,						guidance_scale=6.0				,						noise_level=20				,						num_inference_steps=2				,						output_type='''np'''				,						return_dict=_A				,						)[0]

       lowercase :       List[Any]       =			image[0, -3:, -3:, -1]
       lowercase :       Dict       =			image_from_tuple[0, -3:, -3:, -1]

       lowercase :       List[Any]       =			low_res_image.size[0] * 4
       assert image.shape == (1, expected_height_width, expected_height_width, 3)
       lowercase :       List[str]       =			np.array([0.3_113, 0.3_910, 0.4_272, 0.4_859, 0.5_061, 0.4_652, 0.5_362, 0.5_715, 0.5_661]				)

       assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2
       assert np.abs(image_from_tuple_slice.flatten() - expected_slice				).max() < 1E-2






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






       """simple docstring"""







       lowercase :       Any       =			'''cpu'''  # ensure determinism for the device-dependent torch.Generator
       lowercase :       int       =			self.dummy_cond_unet_upscale
       lowercase :       Optional[Any]       =			DDPMScheduler()
       lowercase :       Any       =			DDIMScheduler(prediction_type='''v_prediction'''				)
       lowercase :       List[Any]       =			self.dummy_vae
       lowercase :       List[Any]       =			self.dummy_text_encoder
       lowercase :       Dict       =			CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				)

       lowercase :       str       =			self.dummy_image.cpu().permute(0				,						2				,						3				,						1				)[0]
       lowercase :       Any       =			Image.fromarray(np.uinta(_A				)				).convert('''RGB'''				).resize((64, 64)				)

       # make sure here that pndm scheduler skips prk
       lowercase :       Optional[Any]       =			StableDiffusionUpscalePipeline(
           unet=_A				,						low_res_scheduler=_A				,						scheduler=_A				,						vae=_A				,						text_encoder=_A				,						tokenizer=_A				,						max_noise_level=350				,						)
       lowercase :       Tuple       =			sd_pipe.to(_A				)
       sd_pipe.set_progress_bar_config(disable=_A				)

       lowercase :       Optional[int]       =			'''A painting of a squirrel eating a burger'''
       lowercase :       Union[str, Any]       =			sd_pipe(
           2 * [prompt]				,						image=2 * [low_res_image]				,						guidance_scale=6.0				,						noise_level=20				,						num_inference_steps=2				,						output_type='''np'''				,						)
       lowercase :       Dict       =			output.images
       assert image.shape[0] == 2

       lowercase :       Optional[int]       =			torch.Generator(device=_A				).manual_seed(0				)
       lowercase :       Any       =			sd_pipe(
           [prompt]				,						image=_A				,						generator=_A				,						num_images_per_prompt=2				,						guidance_scale=6.0				,						noise_level=20				,						num_inference_steps=2				,						output_type='''np'''				,						)
       lowercase :       Union[str, Any]       =			output.images
       assert image.shape[0] == 2






  @unittest.skipIf(torch_device != '''cuda'''				,						'''This test requires a GPU'''				)
  def  __a      (  self		:     List[str]				)		->   List[Any]:






       """simple docstring"""







       lowercase :       str       =			self.dummy_cond_unet_upscale
       lowercase :       Tuple       =			DDPMScheduler()
       lowercase :       int       =			DDIMScheduler(prediction_type='''v_prediction'''				)
       lowercase :       Optional[int]       =			self.dummy_vae
       lowercase :       Optional[int]       =			self.dummy_text_encoder
       lowercase :       Union[str, Any]       =			CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				)

       lowercase :       str       =			self.dummy_image.cpu().permute(0				,						2				,						3				,						1				)[0]
       lowercase :       Union[str, Any]       =			Image.fromarray(np.uinta(_A				)				).convert('''RGB'''				).resize((64, 64)				)

       # put models in fp16, except vae as it overflows in fp16
       lowercase :       Any       =			unet.half()
       lowercase :       Dict       =			text_encoder.half()

       # make sure here that pndm scheduler skips prk
       lowercase :       Any       =			StableDiffusionUpscalePipeline(
           unet=_A				,						low_res_scheduler=_A				,						scheduler=_A				,						vae=_A				,						text_encoder=_A				,						tokenizer=_A				,						max_noise_level=350				,						)
       lowercase :       List[Any]       =			sd_pipe.to(_A				)
       sd_pipe.set_progress_bar_config(disable=_A				)

       lowercase :       Any       =			'''A painting of a squirrel eating a burger'''
       lowercase :       List[Any]       =			torch.manual_seed(0				)
       lowercase :       Dict       =			sd_pipe(
           [prompt]				,						image=_A				,						generator=_A				,						num_inference_steps=2				,						output_type='''np'''				,						).images

       lowercase :       List[Any]       =			low_res_image.size[0] * 4
       assert image.shape == (1, expected_height_width, expected_height_width, 3)


@slow
@require_torch_gpu
class 						_A (  unittest.TestCase				):






  def  __a      (  self		:     str				)		->   int:






       """simple docstring"""







       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()






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






       """simple docstring"""







       lowercase :       Tuple       =			load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/sd2-upscale/low_res_cat.png'''				)
       lowercase :       Dict       =			load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'''
           '''/upsampled_cat.npy'''				)

       lowercase :       Tuple       =			'''stabilityai/stable-diffusion-x4-upscaler'''
       lowercase :       int       =			StableDiffusionUpscalePipeline.from_pretrained(_A				)
       pipe.to(_A				)
       pipe.set_progress_bar_config(disable=_A				)
       pipe.enable_attention_slicing()

       lowercase :       Dict       =			'''a cat sitting on a park bench'''

       lowercase :       str       =			torch.manual_seed(0				)
       lowercase :       Union[str, Any]       =			pipe(
           prompt=_A				,						image=_A				,						generator=_A				,						output_type='''np'''				,						)
       lowercase :       str       =			output.images[0]

       assert image.shape == (512, 512, 3)
       assert np.abs(expected_image - image				).max() < 1E-3






  def  __a      (  self		:     Tuple				)		->   int:






       """simple docstring"""







       lowercase :       Tuple       =			load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/sd2-upscale/low_res_cat.png'''				)
       lowercase :       Tuple       =			load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'''
           '''/upsampled_cat_fp16.npy'''				)

       lowercase :       Optional[Any]       =			'''stabilityai/stable-diffusion-x4-upscaler'''
       lowercase :       Optional[Any]       =			StableDiffusionUpscalePipeline.from_pretrained(
           _A				,						torch_dtype=torch.floataa				,						)
       pipe.to(_A				)
       pipe.set_progress_bar_config(disable=_A				)
       pipe.enable_attention_slicing()

       lowercase :       Any       =			'''a cat sitting on a park bench'''

       lowercase :       List[str]       =			torch.manual_seed(0				)
       lowercase :       Any       =			pipe(
           prompt=_A				,						image=_A				,						generator=_A				,						output_type='''np'''				,						)
       lowercase :       int       =			output.images[0]

       assert image.shape == (512, 512, 3)
       assert np.abs(expected_image - image				).max() < 5E-1






  def  __a      (  self		:     Tuple				)		->   Optional[int]:






       """simple docstring"""







       torch.cuda.empty_cache()
       torch.cuda.reset_max_memory_allocated()
       torch.cuda.reset_peak_memory_stats()

       lowercase :       int       =			load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/sd2-upscale/low_res_cat.png'''				)

       lowercase :       Dict       =			'''stabilityai/stable-diffusion-x4-upscaler'''
       lowercase :       List[Any]       =			StableDiffusionUpscalePipeline.from_pretrained(
           _A				,						torch_dtype=torch.floataa				,						)
       pipe.to(_A				)
       pipe.set_progress_bar_config(disable=_A				)
       pipe.enable_attention_slicing(1				)
       pipe.enable_sequential_cpu_offload()

       lowercase :       Optional[Any]       =			'''a cat sitting on a park bench'''

       lowercase :       Union[str, Any]       =			torch.manual_seed(0				)
       lowercase :       Optional[Any]       =			pipe(
           prompt=_A				,						image=_A				,						generator=_A				,						num_inference_steps=5				,						output_type='''np'''				,						)

       lowercase :       Dict       =			torch.cuda.max_memory_allocated()
       # make sure that less than 2.9 GB is allocated
       assert mem_bytes < 2.9 * 10**9





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