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      lowerCamelCase_					(       unittest.TestCase			):






    '''simple docstring'''






    def       UpperCamelCase__       (     self)	->     Optional[int]:
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()







    @property
    def       UpperCamelCase__       (     self)	->     str:
        __UpperCamelCase					:List[str]        =		1
        __UpperCamelCase					:Dict        =		3
        __UpperCamelCase					:List[str]        =		(32, 32)

        __UpperCamelCase					:int        =		floats_tensor((batch_size, num_channels) + sizes			,			rng=random.Random(0)).to(__lowercase)
        return image







    @property
    def       UpperCamelCase__       (     self)	->     int:
        torch.manual_seed(0)
        __UpperCamelCase					:int        =		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=__lowercase			,			only_cross_attention=(True, True, False)			,			num_class_embeds=100			,			)
        return model







    @property
    def       UpperCamelCase__       (     self)	->     Optional[int]:
        torch.manual_seed(0)
        __UpperCamelCase					:Optional[Any]        =		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       UpperCamelCase__       (     self)	->     Tuple:
        torch.manual_seed(0)
        __UpperCamelCase					:Optional[int]        =		CLIPTextConfig(
            bos_token_id=0			,			eos_token_id=2			,			hidden_size=32			,			intermediate_size=37			,			layer_norm_eps=1E-0_5			,			num_attention_heads=4			,			num_hidden_layers=5			,			pad_token_id=1			,			vocab_size=1_000			,			hidden_act='''gelu'''			,			projection_dim=512			,			)
        return CLIPTextModel(__lowercase)







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

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

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

        __UpperCamelCase					:Dict        =		'''A painting of a squirrel eating a burger'''
        __UpperCamelCase					:Dict        =		torch.Generator(device=__lowercase).manual_seed(0)
        __UpperCamelCase					:Optional[Any]        =		sd_pipe(
            [prompt]			,			image=__lowercase			,			generator=__lowercase			,			guidance_scale=6.0			,			noise_level=20			,			num_inference_steps=2			,			output_type='''np'''			,			)

        __UpperCamelCase					:Any        =		output.images

        __UpperCamelCase					:List[str]        =		torch.Generator(device=__lowercase).manual_seed(0)
        __UpperCamelCase					:Dict        =		sd_pipe(
            [prompt]			,			image=__lowercase			,			generator=__lowercase			,			guidance_scale=6.0			,			noise_level=20			,			num_inference_steps=2			,			output_type='''np'''			,			return_dict=__lowercase			,			)[0]

        __UpperCamelCase					:List[str]        =		image[0, -3:, -3:, -1]
        __UpperCamelCase					:str        =		image_from_tuple[0, -3:, -3:, -1]

        __UpperCamelCase					:Union[str, Any]        =		low_res_image.size[0] * 4
        assert image.shape == (1, expected_height_width, expected_height_width, 3)
        __UpperCamelCase					:Union[str, Any]        =		np.array([0.31_13, 0.39_10, 0.42_72, 0.48_59, 0.50_61, 0.46_52, 0.53_62, 0.57_15, 0.56_61])

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

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

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

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

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







    @unittest.skipIf(torch_device != '''cuda'''			,			'''This test requires a GPU''')
    def       UpperCamelCase__       (     self)	->     List[str]:
        __UpperCamelCase					:List[str]        =		self.dummy_cond_unet_upscale
        __UpperCamelCase					:List[Any]        =		DDPMScheduler()
        __UpperCamelCase					:Dict        =		DDIMScheduler(prediction_type='''v_prediction''')
        __UpperCamelCase					:int        =		self.dummy_vae
        __UpperCamelCase					:Any        =		self.dummy_text_encoder
        __UpperCamelCase					:int        =		CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')

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

        # put models in fp16, except vae as it overflows in fp16
        __UpperCamelCase					:Any        =		unet.half()
        __UpperCamelCase					:List[str]        =		text_encoder.half()

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

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

        __UpperCamelCase					:int        =		low_res_image.size[0] * 4
        assert image.shape == (1, expected_height_width, expected_height_width, 3)


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






    '''simple docstring'''






    def       UpperCamelCase__       (     self)	->     Optional[Any]:
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()







    def       UpperCamelCase__       (     self)	->     Optional[Any]:
        __UpperCamelCase					:int        =		load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/sd2-upscale/low_res_cat.png''')
        __UpperCamelCase					:Optional[int]        =		load_numpy(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'''
            '''/upsampled_cat.npy''')

        __UpperCamelCase					:Optional[Any]        =		'''stabilityai/stable-diffusion-x4-upscaler'''
        __UpperCamelCase					:Optional[int]        =		StableDiffusionUpscalePipeline.from_pretrained(__lowercase)
        pipe.to(__lowercase)
        pipe.set_progress_bar_config(disable=__lowercase)
        pipe.enable_attention_slicing()

        __UpperCamelCase					:Optional[int]        =		'''a cat sitting on a park bench'''

        __UpperCamelCase					:Optional[Any]        =		torch.manual_seed(0)
        __UpperCamelCase					:Tuple        =		pipe(
            prompt=__lowercase			,			image=__lowercase			,			generator=__lowercase			,			output_type='''np'''			,			)
        __UpperCamelCase					:Optional[Any]        =		output.images[0]

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







    def       UpperCamelCase__       (     self)	->     Optional[Any]:
        __UpperCamelCase					:List[str]        =		load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/sd2-upscale/low_res_cat.png''')
        __UpperCamelCase					:Any        =		load_numpy(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'''
            '''/upsampled_cat_fp16.npy''')

        __UpperCamelCase					:Any        =		'''stabilityai/stable-diffusion-x4-upscaler'''
        __UpperCamelCase					:str        =		StableDiffusionUpscalePipeline.from_pretrained(
            __lowercase			,			torch_dtype=torch.floataa			,			)
        pipe.to(__lowercase)
        pipe.set_progress_bar_config(disable=__lowercase)
        pipe.enable_attention_slicing()

        __UpperCamelCase					:Union[str, Any]        =		'''a cat sitting on a park bench'''

        __UpperCamelCase					:int        =		torch.manual_seed(0)
        __UpperCamelCase					:List[str]        =		pipe(
            prompt=__lowercase			,			image=__lowercase			,			generator=__lowercase			,			output_type='''np'''			,			)
        __UpperCamelCase					:Tuple        =		output.images[0]

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







    def       UpperCamelCase__       (     self)	->     Optional[Any]:
        torch.cuda.empty_cache()
        torch.cuda.reset_max_memory_allocated()
        torch.cuda.reset_peak_memory_stats()

        __UpperCamelCase					:Optional[Any]        =		load_image(
            '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
            '''/sd2-upscale/low_res_cat.png''')

        __UpperCamelCase					:Optional[Any]        =		'''stabilityai/stable-diffusion-x4-upscaler'''
        __UpperCamelCase					:str        =		StableDiffusionUpscalePipeline.from_pretrained(
            __lowercase			,			torch_dtype=torch.floataa			,			)
        pipe.to(__lowercase)
        pipe.set_progress_bar_config(disable=__lowercase)
        pipe.enable_attention_slicing(1)
        pipe.enable_sequential_cpu_offload()

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

        __UpperCamelCase					:List[Any]        =		torch.manual_seed(0)
        __UpperCamelCase					:List[str]        =		pipe(
            prompt=__lowercase			,			image=__lowercase			,			generator=__lowercase			,			num_inference_steps=5			,			output_type='''np'''			,			)

        __UpperCamelCase					:List[Any]        =		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


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



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    __UpperCamelCase					:str        =		'''\n'''.join(SCREAMING_SNAKE_CASE     )
    Path(SCREAMING_SNAKE_CASE     ).open('''w'''     ).writelines(SCREAMING_SNAKE_CASE     )



def lowerCamelCase  (       SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







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


class      lowerCamelCase_					(       UpperCAmelCase_			):






    '''simple docstring'''






    @parameterized.expand(
        [
            MBART_TINY,
            MARIAN_TINY,
            T5_TINY,
            BART_TINY,
            PEGASUS_XSUM,
        ]			,			)
    @slow
    def       UpperCamelCase__       (     self			,			__lowercase)	->     List[Any]:
        __UpperCamelCase					:Dict        =		AutoTokenizer.from_pretrained(__lowercase)
        __UpperCamelCase					:Optional[Any]        =		make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
        __UpperCamelCase					:List[Any]        =		max(len(tokenizer.encode(__lowercase)) for a in ARTICLES)
        __UpperCamelCase					:Optional[int]        =		max(len(tokenizer.encode(__lowercase)) for a in SUMMARIES)
        __UpperCamelCase					:int        =		4
        __UpperCamelCase					:Any        =		8
        assert max_len_target > max_src_len  # Will be truncated
        assert max_len_source > max_src_len  # Will be truncated
        __UpperCamelCase  ,	__UpperCamelCase					:Tuple        =		'''ro_RO''', '''de_DE'''  # ignored for all but mbart, but never causes error.
        __UpperCamelCase					:str        =		SeqaSeqDataset(
            __lowercase			,			data_dir=__lowercase			,			type_path='''train'''			,			max_source_length=__lowercase			,			max_target_length=__lowercase			,			src_lang=__lowercase			,			tgt_lang=__lowercase			,			)
        __UpperCamelCase					:Any        =		DataLoader(__lowercase			,			batch_size=2			,			collate_fn=train_dataset.collate_fn)
        for batch in dataloader:
            assert isinstance(__lowercase			,			__lowercase)
            assert batch["attention_mask"].shape == batch["input_ids"].shape
            # show that articles were trimmed.
            assert batch["input_ids"].shape[1] == max_src_len
            # show that targets are the same len
            assert batch["labels"].shape[1] == max_tgt_len
            if tok_name != MBART_TINY:
                continue
            # check language codes in correct place
            __UpperCamelCase					:Optional[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       UpperCamelCase__       (     self			,			__lowercase)	->     int:
        __UpperCamelCase					:Union[str, Any]        =		AutoTokenizer.from_pretrained(__lowercase)
        __UpperCamelCase					:Union[str, Any]        =		make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())
        __UpperCamelCase					:int        =		max(len(tokenizer.encode(__lowercase)) for a in ARTICLES)
        __UpperCamelCase					:Dict        =		max(len(tokenizer.encode(__lowercase)) for a in SUMMARIES)
        __UpperCamelCase					:Union[str, Any]        =		4
        __UpperCamelCase					:List[str]        =		LegacySeqaSeqDataset(
            __lowercase			,			data_dir=__lowercase			,			type_path='''train'''			,			max_source_length=20			,			max_target_length=__lowercase			,			)
        __UpperCamelCase					:Dict        =		DataLoader(__lowercase			,			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)	->     Dict:
        __UpperCamelCase					:List[Any]        =		AutoTokenizer.from_pretrained('''facebook/mbart-large-cc25''')

        __UpperCamelCase					:Union[str, Any]        =		Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()))
        __UpperCamelCase					:str        =		tmp_dir.joinpath('''train.source''').open().readlines()
        __UpperCamelCase					:int        =		Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()))
        pack_data_dir(__lowercase			,			__lowercase			,			128			,			__lowercase)
        __UpperCamelCase					:Union[str, Any]        =		{x.name for x in tmp_dir.iterdir()}
        __UpperCamelCase					:int        =		{x.name for x in save_dir.iterdir()}
        __UpperCamelCase					:Optional[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(__lowercase) < len(__lowercase)
        assert len(__lowercase) == 1
        assert len(packed_examples[0]) == sum(len(__lowercase) for x in orig_examples)
        assert orig_paths == new_paths







    @pytest.mark.skipif(not FAIRSEQ_AVAILABLE			,			reason='''This test requires fairseq''')
    def       UpperCamelCase__       (     self)	->     List[Any]:
        if not FAIRSEQ_AVAILABLE:
            return
        __UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase					:Any        =		self._get_dataset(max_len=64)
        __UpperCamelCase					:Union[str, Any]        =		64
        __UpperCamelCase					:Tuple        =		ds.make_dynamic_sampler(__lowercase			,			required_batch_size_multiple=__lowercase)
        __UpperCamelCase					:List[str]        =		[len(__lowercase) for x in batch_sampler]
        assert len(set(__lowercase)) > 1  # it's not dynamic batch size if every batch is the same length
        assert sum(__lowercase) == len(__lowercase)  # no dropped or added examples
        __UpperCamelCase					:int        =		DataLoader(__lowercase			,			batch_sampler=__lowercase			,			collate_fn=ds.collate_fn			,			num_workers=2)
        __UpperCamelCase					:List[str]        =		[]
        __UpperCamelCase					:int        =		[]
        for batch in data_loader:
            __UpperCamelCase					:List[Any]        =		batch['''input_ids'''].shape
            __UpperCamelCase					:Dict        =		src_shape[0]
            assert bs % required_batch_size_multiple == 0 or bs < required_batch_size_multiple
            __UpperCamelCase					:Optional[int]        =		np.product(batch['''input_ids'''].shape)
            num_src_per_batch.append(__lowercase)
            if num_src_tokens > (max_tokens * 1.1):
                failures.append(__lowercase)
        assert num_src_per_batch[0] == max(__lowercase)
        if failures:
            raise AssertionError(f"""too many tokens in {len(__lowercase)} batches""")







    def       UpperCamelCase__       (     self)	->     Tuple:
        __UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase					:Optional[Any]        =		self._get_dataset(max_len=512)
        __UpperCamelCase					:Any        =		2
        __UpperCamelCase					:List[Any]        =		ds.make_sortish_sampler(__lowercase			,			shuffle=__lowercase)

        __UpperCamelCase					:List[Any]        =		DataLoader(__lowercase			,			batch_size=__lowercase			,			collate_fn=ds.collate_fn			,			num_workers=2)
        __UpperCamelCase					:Tuple        =		DataLoader(__lowercase			,			batch_size=__lowercase			,			collate_fn=ds.collate_fn			,			num_workers=2			,			sampler=__lowercase)

        __UpperCamelCase					:int        =		tokenizer.pad_token_id

        def count_pad_tokens(__lowercase			,			__lowercase="input_ids"):
            return [batch[k].eq(__lowercase).sum().item() for batch in data_loader]

        assert sum(count_pad_tokens(__lowercase			,			k='''labels''')) < sum(count_pad_tokens(__lowercase			,			k='''labels'''))
        assert sum(count_pad_tokens(__lowercase)) < sum(count_pad_tokens(__lowercase))
        assert len(__lowercase) == len(__lowercase)







    def       UpperCamelCase__       (     self			,			__lowercase=1_000			,			__lowercase=128)	->     List[Any]:
        if os.getenv('''USE_REAL_DATA'''			,			__lowercase):
            __UpperCamelCase					:Optional[Any]        =		'''examples/seq2seq/wmt_en_ro'''
            __UpperCamelCase					:Dict        =		max_len * 2 * 64
            if not Path(__lowercase).joinpath('''train.len''').exists():
                save_len_file(__lowercase			,			__lowercase)
        else:
            __UpperCamelCase					:Union[str, Any]        =		'''examples/seq2seq/test_data/wmt_en_ro'''
            __UpperCamelCase					:Optional[int]        =		max_len * 4
            save_len_file(__lowercase			,			__lowercase)

        __UpperCamelCase					:str        =		AutoTokenizer.from_pretrained(__lowercase)
        __UpperCamelCase					:List[Any]        =		SeqaSeqDataset(
            __lowercase			,			data_dir=__lowercase			,			type_path='''train'''			,			max_source_length=__lowercase			,			max_target_length=__lowercase			,			n_obs=__lowercase			,			)
        return ds, max_tokens, tokenizer







    def       UpperCamelCase__       (     self)	->     Tuple:
        __UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase					:List[Any]        =		self._get_dataset()
        __UpperCamelCase					:List[str]        =		set(DistributedSortishSampler(__lowercase			,			256			,			num_replicas=2			,			rank=0			,			add_extra_examples=__lowercase))
        __UpperCamelCase					:Tuple        =		set(DistributedSortishSampler(__lowercase			,			256			,			num_replicas=2			,			rank=1			,			add_extra_examples=__lowercase))
        assert idsa.intersection(__lowercase) == set()







    @parameterized.expand(
        [
            MBART_TINY,
            MARIAN_TINY,
            T5_TINY,
            BART_TINY,
            PEGASUS_XSUM,
        ]			,			)
    def       UpperCamelCase__       (     self			,			__lowercase)	->     List[Any]:
        __UpperCamelCase					:List[Any]        =		AutoTokenizer.from_pretrained(__lowercase			,			use_fast=__lowercase)
        if tok_name == MBART_TINY:
            __UpperCamelCase					:Optional[Any]        =		SeqaSeqDataset(
                __lowercase			,			data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())			,			type_path='''train'''			,			max_source_length=4			,			max_target_length=8			,			src_lang='''EN'''			,			tgt_lang='''FR'''			,			)
            __UpperCamelCase					:Tuple        =		train_dataset.dataset_kwargs
            assert "src_lang" in kwargs and "tgt_lang" in kwargs
        else:
            __UpperCamelCase					:Tuple        =		SeqaSeqDataset(
                __lowercase			,			data_dir=make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir())			,			type_path='''train'''			,			max_source_length=4			,			max_target_length=8			,			)
            __UpperCamelCase					:Optional[int]        =		train_dataset.dataset_kwargs
            assert "add_prefix_space" not in kwargs if tok_name != BART_TINY else "add_prefix_space" in kwargs
            assert len(__lowercase) == 1 if tok_name == BART_TINY else len(__lowercase) == 0





