





'''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


lowerCAmelCase :					Union[str, Any] 							='''bert-base-cased'''
lowerCAmelCase :					Optional[Any] 							='''google/pegasus-xsum'''
lowerCAmelCase :					Optional[int] 							=[''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
lowerCAmelCase :					str 							=['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
lowerCAmelCase :					List[str] 							='''patrickvonplaten/t5-tiny-random'''
lowerCAmelCase :					Union[str, Any] 							='''sshleifer/bart-tiny-random'''
lowerCAmelCase :					Union[str, Any] 							='''sshleifer/tiny-mbart'''
lowerCAmelCase :					Any 							='''sshleifer/tiny-marian-en-de'''
def 							UpperCAmelCase_  (						__lowerCamelCase : Path     ,__lowerCamelCase : list			):
   lowercase_      :Optional[int]         =			"\n".join(__lowerCamelCase			)
   Path(__lowerCamelCase			).open("w"			).writelines(__lowerCamelCase			)
def 							UpperCAmelCase_  (						__lowerCamelCase : List[Any]			):
   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 				a_       (    _lowerCAmelCase							):





     @parameterized.expand(
         [
             MBART_TINY,
             MARIAN_TINY,
             T5_TINY,
             BART_TINY,
             PEGASUS_XSUM,
         ]     ,				)
     @slow
     def 							lowercase__				(			self		: Optional[int]     ,				lowercase		: str						):





        """simple docstring"""




        lowercase_      :Any         =			AutoTokenizer.from_pretrained(lowercase						)
        lowercase_      :Union[str, Any]         =			make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()						)
        lowercase_      :Optional[int]         =			max(len(tokenizer.encode(lowercase						)						) for a in ARTICLES						)
        lowercase_      :Tuple         =			max(len(tokenizer.encode(lowercase						)						) for a in SUMMARIES						)
        lowercase_      :Any         =			4
        lowercase_      :Any         =			8
        assert max_len_target > max_src_len  # Will be truncated
        assert max_len_source > max_src_len  # Will be truncated
        lowercase_      ,							lowercase_      :str         =			"ro_RO", "de_DE"  # ignored for all but mbart, but never causes error.
        lowercase_      :Dict         =			SeqaSeqDataset(
            lowercase     ,				data_dir=lowercase     ,				type_path="train"     ,				max_source_length=lowercase     ,				max_target_length=lowercase     ,				src_lang=lowercase     ,				tgt_lang=lowercase     ,				)
        lowercase_      :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
           lowercase_      :Optional[Any]         =			shift_tokens_right(batch["labels"]     ,				tokenizer.pad_token_id						)
           assert batch["decoder_input_ids"][0, 0].item() == tokenizer.lang_code_to_id[tgt_lang]
           assert batch["decoder_input_ids"][0, -1].item() == tokenizer.eos_token_id
           assert batch["input_ids"][0, -2].item() == tokenizer.eos_token_id
           assert batch["input_ids"][0, -1].item() == tokenizer.lang_code_to_id[src_lang]

           break  # No need to test every batch





     @parameterized.expand([BART_TINY, BERT_BASE_CASED]						)
     def 							lowercase__				(			self		: Tuple     ,				lowercase		: Any						):





        """simple docstring"""




        lowercase_      :int         =			AutoTokenizer.from_pretrained(lowercase						)
        lowercase_      :List[str]         =			make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()						)
        lowercase_      :Tuple         =			max(len(tokenizer.encode(lowercase						)						) for a in ARTICLES						)
        lowercase_      :Dict         =			max(len(tokenizer.encode(lowercase						)						) for a in SUMMARIES						)
        lowercase_      :Optional[Any]         =			4
        lowercase_      :Union[str, Any]         =			LegacySeqaSeqDataset(
            lowercase     ,				data_dir=lowercase     ,				type_path="train"     ,				max_source_length=20     ,				max_target_length=lowercase     ,				)
        lowercase_      :Tuple         =			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 							lowercase__				(			self		: List[Any]						):





        """simple docstring"""




        lowercase_      :Any         =			AutoTokenizer.from_pretrained("facebook/mbart-large-cc25"						)

        lowercase_      :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_      :Optional[int]         =			Path(make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()						)						)
        pack_data_dir(lowercase     ,				lowercase     ,				128     ,				lowercase						)
        lowercase_      :Optional[int]         =			{x.name for x in tmp_dir.iterdir()}
        lowercase_      :Optional[int]         =			{x.name for x in save_dir.iterdir()}
        lowercase_      :Union[str, Any]         =			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 							lowercase__				(			self		: Optional[int]						):





        """simple docstring"""




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





        """simple docstring"""




        lowercase_      ,							lowercase_      ,							lowercase_      :int         =			self._get_dataset(max_len=512						)
        lowercase_      :Tuple         =			2
        lowercase_      :Optional[int]         =			ds.make_sortish_sampler(lowercase     ,				shuffle=lowercase						)

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

        lowercase_      :int         =			tokenizer.pad_token_id

        def count_pad_tokens(lowercase		: Union[str, Any]     ,				lowercase		: int="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 							lowercase__				(			self		: Any     ,				lowercase		: Optional[int]=1_000     ,				lowercase		: List[str]=128						):





        """simple docstring"""




        if os.getenv("USE_REAL_DATA"     ,				lowercase						):
           lowercase_      :Dict         =			"examples/seq2seq/wmt_en_ro"
           lowercase_      :str         =			max_len * 2 * 64
           if not Path(lowercase						).joinpath("train.len"						).exists():
              save_len_file(lowercase     ,				lowercase						)
        else:
           lowercase_      :str         =			"examples/seq2seq/test_data/wmt_en_ro"
           lowercase_      :int         =			max_len * 4
           save_len_file(lowercase     ,				lowercase						)

        lowercase_      :Any         =			AutoTokenizer.from_pretrained(lowercase						)
        lowercase_      :List[str]         =			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 							lowercase__				(			self		: List[Any]						):





        """simple docstring"""




        lowercase_      ,							lowercase_      ,							lowercase_      :str         =			self._get_dataset()
        lowercase_      :Optional[Any]         =			set(DistributedSortishSampler(lowercase     ,				256     ,				num_replicas=2     ,				rank=0     ,				add_extra_examples=lowercase						)						)
        lowercase_      :Any         =			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 							lowercase__				(			self		: str     ,				lowercase		: List[Any]						):





        """simple docstring"""




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









'''simple docstring'''


import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest
class 				a_       (    _lowerCAmelCase							):
     __A								=      (UniPCMultistepScheduler,)
     __A								=      (("num_inference_steps", 25),)





     def 							lowercase__				(			self		: Tuple     ,				**lowercase		: Optional[Any]						):





        """simple docstring"""




        lowercase_      :Any         =			{
            "num_train_timesteps": 1_000,
            "beta_start": 0.00_01,
            "beta_end": 0.02,
            "beta_schedule": "linear",
            "solver_order": 2,
            "solver_type": "bh2",
        }

        config.update(**lowercase						)
        return config





     def 							lowercase__				(			self		: Optional[Any]     ,				lowercase		: str=0     ,				**lowercase		: Union[str, Any]						):





        """simple docstring"""




        lowercase_      :Tuple         =			dict(self.forward_default_kwargs						)
        lowercase_      :Tuple         =			kwargs.pop("num_inference_steps"     ,				lowercase						)
        lowercase_      :Union[str, Any]         =			self.dummy_sample
        lowercase_      :Union[str, Any]         =			0.1 * sample
        lowercase_      :Any         =			[residual + 0.2, residual + 0.15, residual + 0.10]

        for scheduler_class in self.scheduler_classes:
           lowercase_      :Tuple         =			self.get_scheduler_config(**lowercase						)
           lowercase_      :Tuple         =			scheduler_class(**lowercase						)
           scheduler.set_timesteps(lowercase						)
           # copy over dummy past residuals
           lowercase_      :str         =			dummy_past_residuals[: scheduler.config.solver_order]

           with tempfile.TemporaryDirectory() as tmpdirname:
              scheduler.save_config(lowercase						)
              lowercase_      :str         =			scheduler_class.from_pretrained(lowercase						)
              new_scheduler.set_timesteps(lowercase						)
              # copy over dummy past residuals
              lowercase_      :List[Any]         =			dummy_past_residuals[: new_scheduler.config.solver_order]

           lowercase_      ,							lowercase_      :Tuple         =			sample, sample
           for t in range(lowercase     ,				time_step + scheduler.config.solver_order + 1						):
              lowercase_      :Optional[int]         =			scheduler.step(lowercase     ,				lowercase     ,				lowercase     ,				**lowercase						).prev_sample
              lowercase_      :Union[str, Any]         =			new_scheduler.step(lowercase     ,				lowercase     ,				lowercase     ,				**lowercase						).prev_sample

              assert torch.sum(torch.abs(output - new_output						)						) < 1e-5, "Scheduler outputs are not identical"





     def 							lowercase__				(			self		: str     ,				lowercase		: Any=0     ,				**lowercase		: str						):





        """simple docstring"""




        lowercase_      :Tuple         =			dict(self.forward_default_kwargs						)
        lowercase_      :Dict         =			kwargs.pop("num_inference_steps"     ,				lowercase						)
        lowercase_      :str         =			self.dummy_sample
        lowercase_      :Optional[int]         =			0.1 * sample
        lowercase_      :Optional[int]         =			[residual + 0.2, residual + 0.15, residual + 0.10]

        for scheduler_class in self.scheduler_classes:
           lowercase_      :Any         =			self.get_scheduler_config()
           lowercase_      :Tuple         =			scheduler_class(**lowercase						)
           scheduler.set_timesteps(lowercase						)

           # copy over dummy past residuals (must be after setting timesteps)
           lowercase_      :str         =			dummy_past_residuals[: scheduler.config.solver_order]

           with tempfile.TemporaryDirectory() as tmpdirname:
              scheduler.save_config(lowercase						)
              lowercase_      :int         =			scheduler_class.from_pretrained(lowercase						)
              # copy over dummy past residuals
              new_scheduler.set_timesteps(lowercase						)

              # copy over dummy past residual (must be after setting timesteps)
              lowercase_      :Optional[Any]         =			dummy_past_residuals[: new_scheduler.config.solver_order]

           lowercase_      :Dict         =			scheduler.step(lowercase     ,				lowercase     ,				lowercase     ,				**lowercase						).prev_sample
           lowercase_      :str         =			new_scheduler.step(lowercase     ,				lowercase     ,				lowercase     ,				**lowercase						).prev_sample

           assert torch.sum(torch.abs(output - new_output						)						) < 1e-5, "Scheduler outputs are not identical"





     def 							lowercase__				(			self		: List[str]     ,				lowercase		: Optional[int]=None     ,				**lowercase		: int						):





        """simple docstring"""




        if scheduler is None:
           lowercase_      :int         =			self.scheduler_classes[0]
           lowercase_      :Optional[Any]         =			self.get_scheduler_config(**lowercase						)
           lowercase_      :Optional[Any]         =			scheduler_class(**lowercase						)

        lowercase_      :str         =			self.scheduler_classes[0]
        lowercase_      :Optional[int]         =			self.get_scheduler_config(**lowercase						)
        lowercase_      :List[Any]         =			scheduler_class(**lowercase						)

        lowercase_      :str         =			10
        lowercase_      :List[str]         =			self.dummy_model()
        lowercase_      :Optional[int]         =			self.dummy_sample_deter
        scheduler.set_timesteps(lowercase						)

        for i, t in enumerate(scheduler.timesteps						):
           lowercase_      :int         =			model(lowercase     ,				lowercase						)
           lowercase_      :Dict         =			scheduler.step(lowercase     ,				lowercase     ,				lowercase						).prev_sample

        return sample





     def 							lowercase__				(			self		: int						):





        """simple docstring"""




        lowercase_      :str         =			dict(self.forward_default_kwargs						)

        lowercase_      :Union[str, Any]         =			kwargs.pop("num_inference_steps"     ,				lowercase						)

        for scheduler_class in self.scheduler_classes:
           lowercase_      :Optional[Any]         =			self.get_scheduler_config()
           lowercase_      :Optional[int]         =			scheduler_class(**lowercase						)

           lowercase_      :Union[str, Any]         =			self.dummy_sample
           lowercase_      :str         =			0.1 * sample

           if num_inference_steps is not None and hasattr(lowercase     ,				"set_timesteps"						):
              scheduler.set_timesteps(lowercase						)
           elif num_inference_steps is not None and not hasattr(lowercase     ,				"set_timesteps"						):
              lowercase_      :str         =			num_inference_steps

           # copy over dummy past residuals (must be done after set_timesteps)
           lowercase_      :Dict         =			[residual + 0.2, residual + 0.15, residual + 0.10]
           lowercase_      :Any         =			dummy_past_residuals[: scheduler.config.solver_order]

           lowercase_      :Optional[int]         =			scheduler.timesteps[5]
           lowercase_      :List[Any]         =			scheduler.timesteps[6]

           lowercase_      :Optional[Any]         =			scheduler.step(lowercase     ,				lowercase     ,				lowercase     ,				**lowercase						).prev_sample
           lowercase_      :List[str]         =			scheduler.step(lowercase     ,				lowercase     ,				lowercase     ,				**lowercase						).prev_sample

           self.assertEqual(output_a.shape     ,				sample.shape						)
           self.assertEqual(output_a.shape     ,				output_a.shape						)





     def 							lowercase__				(			self		: List[str]						):





        """simple docstring"""




        lowercase_      :List[Any]         =			UniPCMultistepScheduler(**self.get_scheduler_config()						)
        lowercase_      :Dict         =			self.full_loop(scheduler=lowercase						)
        lowercase_      :List[Any]         =			torch.mean(torch.abs(lowercase						)						)

        assert abs(result_mean.item() - 0.24_64						) < 1e-3

        lowercase_      :List[str]         =			DPMSolverSinglestepScheduler.from_config(scheduler.config						)
        lowercase_      :Tuple         =			DEISMultistepScheduler.from_config(scheduler.config						)
        lowercase_      :Optional[int]         =			DPMSolverMultistepScheduler.from_config(scheduler.config						)
        lowercase_      :Tuple         =			UniPCMultistepScheduler.from_config(scheduler.config						)

        lowercase_      :Tuple         =			self.full_loop(scheduler=lowercase						)
        lowercase_      :Tuple         =			torch.mean(torch.abs(lowercase						)						)

        assert abs(result_mean.item() - 0.24_64						) < 1e-3





     def 							lowercase__				(			self		: Tuple						):





        """simple docstring"""




        for timesteps in [25, 50, 100, 999, 1_000]:
           self.check_over_configs(num_train_timesteps=lowercase						)





     def 							lowercase__				(			self		: int						):





        """simple docstring"""




        self.check_over_configs(thresholding=lowercase						)
        for order in [1, 2, 3]:
           for solver_type in ["bh1", "bh2"]:
              for threshold in [0.5, 1.0, 2.0]:
                 for prediction_type in ["epsilon", "sample"]:
                    self.check_over_configs(
                        thresholding=lowercase     ,				prediction_type=lowercase     ,				sample_max_value=lowercase     ,				solver_order=lowercase     ,				solver_type=lowercase     ,				)





     def 							lowercase__				(			self		: List[str]						):





        """simple docstring"""




        for prediction_type in ["epsilon", "v_prediction"]:
           self.check_over_configs(prediction_type=lowercase						)





     def 							lowercase__				(			self		: Optional[int]						):





        """simple docstring"""




        for solver_type in ["bh1", "bh2"]:
           for order in [1, 2, 3]:
              for prediction_type in ["epsilon", "sample"]:
                 self.check_over_configs(
                     solver_order=lowercase     ,				solver_type=lowercase     ,				prediction_type=lowercase     ,				)
                 lowercase_      :Tuple         =			self.full_loop(
                     solver_order=lowercase     ,				solver_type=lowercase     ,				prediction_type=lowercase     ,				)
                 assert not torch.isnan(lowercase						).any(), "Samples have nan numbers"





     def 							lowercase__				(			self		: List[Any]						):





        """simple docstring"""




        self.check_over_configs(lower_order_final=lowercase						)
        self.check_over_configs(lower_order_final=lowercase						)





     def 							lowercase__				(			self		: Tuple						):





        """simple docstring"""




        for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1_000]:
           self.check_over_forward(num_inference_steps=lowercase     ,				time_step=0						)





     def 							lowercase__				(			self		: Dict						):





        """simple docstring"""




        lowercase_      :List[str]         =			self.full_loop()
        lowercase_      :List[str]         =			torch.mean(torch.abs(lowercase						)						)

        assert abs(result_mean.item() - 0.24_64						) < 1e-3





     def 							lowercase__				(			self		: Any						):





        """simple docstring"""




        lowercase_      :Dict         =			self.full_loop(prediction_type="v_prediction"						)
        lowercase_      :Any         =			torch.mean(torch.abs(lowercase						)						)

        assert abs(result_mean.item() - 0.10_14						) < 1e-3





     def 							lowercase__				(			self		: Optional[Any]						):





        """simple docstring"""




        lowercase_      :Union[str, Any]         =			self.scheduler_classes[0]
        lowercase_      :str         =			self.get_scheduler_config(thresholding=lowercase     ,				dynamic_thresholding_ratio=0						)
        lowercase_      :Tuple         =			scheduler_class(**lowercase						)

        lowercase_      :Optional[int]         =			10
        lowercase_      :Optional[int]         =			self.dummy_model()
        lowercase_      :Any         =			self.dummy_sample_deter.half()
        scheduler.set_timesteps(lowercase						)

        for i, t in enumerate(scheduler.timesteps						):
           lowercase_      :Tuple         =			model(lowercase     ,				lowercase						)
           lowercase_      :Any         =			scheduler.step(lowercase     ,				lowercase     ,				lowercase						).prev_sample

        assert sample.dtype == torch.floataa







     def 							lowercase__				(			self		: List[Any]     ,				**lowercase		: Union[str, Any]						):





        """simple docstring"""




        for scheduler_class in self.scheduler_classes:
           lowercase_      :Optional[int]         =			self.get_scheduler_config(**lowercase						)
           lowercase_      :Dict         =			scheduler_class(**lowercase						)

           scheduler.set_timesteps(scheduler.config.num_train_timesteps						)
           assert len(scheduler.timesteps.unique()						) == scheduler.num_inference_steps


