

import copy
import tempfile
import unittest

from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import MaMaaaForConditionalGeneration, MaMaaaModel, MaMaaaTokenizer
     from transformers.models.mam_aaa.modeling_mam_aaa import MaMaaaDecoder, MaMaaaEncoder
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Optional[Any]    ,			SCREAMING_SNAKE_CASE__			:	str    ,			SCREAMING_SNAKE_CASE__			:	Optional[Any]    ,			SCREAMING_SNAKE_CASE__			:	Dict=None    ,			SCREAMING_SNAKE_CASE__			:	Optional[Any]=None    ,			SCREAMING_SNAKE_CASE__			:	Tuple=None    ,			SCREAMING_SNAKE_CASE__			:	int=None    ,			SCREAMING_SNAKE_CASE__			:	Optional[int]=None    ,			)							->      Tuple:





    '''simple docstring'''






    if attention_mask is None:
        A__          =   input_ids.ne(config.pad_token_id      )
    if decoder_attention_mask is None:
        A__          =   decoder_input_ids.ne(config.pad_token_id      )
    if head_mask is None:
        A__          =   torch.ones(config.encoder_layers    ,			config.encoder_attention_heads    ,			device=SCREAMING_SNAKE_CASE__      )
    if decoder_head_mask is None:
        A__          =   torch.ones(config.decoder_layers    ,			config.decoder_attention_heads    ,			device=SCREAMING_SNAKE_CASE__      )
    if cross_attn_head_mask is None:
        A__          =   torch.ones(config.decoder_layers    ,			config.decoder_attention_heads    ,			device=SCREAMING_SNAKE_CASE__      )
    return {
        "input_ids": input_ids,
        "decoder_input_ids": decoder_input_ids,
        "attention_mask": attention_mask,
        "decoder_attention_mask": attention_mask,
        "head_mask": head_mask,
        "decoder_head_mask": decoder_head_mask,
        "cross_attn_head_mask": cross_attn_head_mask,
    }


class  A						:







    """simple docstring"""





    def __init__(      self :       int,lowercase_ :       List[Any],lowercase_ :       List[Any]=1_3,lowercase_ :       Any=7,lowercase_ :       Union[str, Any]=True,lowercase_ :       Union[str, Any]=False,lowercase_ :       List[Any]=9_9,lowercase_ :       int=1_6,lowercase_ :       str=2,lowercase_ :       int=4,lowercase_ :       Union[str, Any]=4,lowercase_ :       str="relu",lowercase_ :       int=0.1,lowercase_ :       Union[str, Any]=0.1,lowercase_ :       Any=0.0,lowercase_ :       List[Any]=0.0,lowercase_ :       int=2_0,lowercase_ :       Optional[int]=2,lowercase_ :       List[Any]=1,lowercase_ :       int=0,)->    str:





        '''simple docstring'''







        A__          =   parent
        A__          =   batch_size
        A__          =   seq_length
        A__          =   is_training
        A__          =   use_labels
        A__          =   vocab_size
        A__          =   hidden_size
        A__          =   num_hidden_layers
        A__          =   num_attention_heads
        A__          =   intermediate_size
        A__          =   hidden_act
        A__          =   hidden_dropout_prob
        A__          =   attention_probs_dropout_prob
        A__          =   encoder_layerdrop
        A__          =   decoder_layerdrop
        A__          =   max_position_embeddings
        A__          =   eos_token_id
        A__          =   pad_token_id
        A__          =   bos_token_id


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





        '''simple docstring'''







        A__          =   ids_tensor([self.batch_size, self.seq_length],self.vocab_size		)
        A__          =   self.eos_token_id  # Eos Token
        A__          =   ids_tensor([self.batch_size, self.seq_length],self.vocab_size		)

        # we need to clamp the input ids here to avoid having pad token in between
        # this is because for M2M100 the position_ids are prepared such that
        # all pad tokens have pos id = 2 and rest are between 2..seq_length
        # and the seq_length here is seq_length - num_pad_tokens
        # but when using past, there is no way of knowing if the past input ids had
        # pad tokens in them, which results in incorrect seq_lenth and which in turn results in
        # position_ids being off by num_pad_tokens in past input
        A__          =   input_ids.clamp(self.pad_token_id + 1		)
        A__          =   decoder_input_ids.clamp(self.pad_token_id + 1		)

        A__          =   self.get_config()
        A__          =   prepare_mam_aaa_inputs_dict(lowercase_,lowercase_,lowercase_		)
        return config, inputs_dict


    def 						snake_case__     (      self :       int		)->    Tuple:





        '''simple docstring'''







        return MaMaaaConfig(
            vocab_size=self.vocab_size,d_model=self.hidden_size,encoder_layers=self.num_hidden_layers,decoder_layers=self.num_hidden_layers,encoder_attention_heads=self.num_attention_heads,decoder_attention_heads=self.num_attention_heads,encoder_ffn_dim=self.intermediate_size,decoder_ffn_dim=self.intermediate_size,dropout=self.hidden_dropout_prob,attention_dropout=self.attention_probs_dropout_prob,encoder_layerdrop=self.encoder_layerdrop,decoder_layerdrop=self.decoder_layerdrop,max_position_embeddings=self.max_position_embeddings,eos_token_id=self.eos_token_id,bos_token_id=self.bos_token_id,pad_token_id=self.pad_token_id,)


    def 						snake_case__     (      self :       int		)->    Optional[Any]:





        '''simple docstring'''







        A__      ,						A__          =   self.prepare_config_and_inputs()
        return config, inputs_dict


    def 						snake_case__     (      self :       str,lowercase_ :       List[Any],lowercase_ :       Optional[Any]		)->    List[Any]:





        '''simple docstring'''







        A__          =   MaMaaaModel(config=lowercase_		).get_decoder().to(lowercase_		).eval()
        A__          =   inputs_dict['input_ids']
        A__          =   inputs_dict['attention_mask']
        A__          =   inputs_dict['head_mask']

        # first forward pass
        A__          =   model(lowercase_,attention_mask=lowercase_,head_mask=lowercase_,use_cache=lowercase_		)

        A__      ,						A__          =   outputs.to_tuple()

        # create hypothetical multiple next token and extent to next_input_ids
        A__          =   ids_tensor((self.batch_size, 3),config.vocab_size		)
        A__          =   ids_tensor((self.batch_size, 3),2		)

        # append to next input_ids and
        A__          =   torch.cat([input_ids, next_tokens],dim=-1		)
        A__          =   torch.cat([attention_mask, next_attn_mask],dim=-1		)

        A__          =   model(lowercase_,attention_mask=lowercase_		)['last_hidden_state']
        A__          =   model(lowercase_,attention_mask=lowercase_,past_key_values=lowercase_		)[
            'last_hidden_state'
        ]

        # select random slice
        A__          =   ids_tensor((1,),output_from_past.shape[-1]		).item()
        A__          =   output_from_no_past[:, -3:, random_slice_idx].detach()
        A__          =   output_from_past[:, :, random_slice_idx].detach()

        self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]		)

        # test that outputs are equal for slice
        self.parent.assertTrue(torch.allclose(lowercase_,lowercase_,atol=1E-2		)		)


    def 						snake_case__     (      self :       Tuple,lowercase_ :       List[str],lowercase_ :       str		)->    Tuple:





        '''simple docstring'''







        A__          =   MaMaaaModel(config=lowercase_		).to(lowercase_		).eval()
        A__          =   model(**lowercase_		)

        A__          =   outputs.encoder_last_hidden_state
        A__          =   outputs.last_hidden_state

        with tempfile.TemporaryDirectory() as tmpdirname:
            A__          =   model.get_encoder()
            encoder.save_pretrained(lowercase_		)
            A__          =   MaMaaaEncoder.from_pretrained(lowercase_		).to(lowercase_		)

        A__          =   encoder(inputs_dict['input_ids'],attention_mask=inputs_dict['attention_mask']		)[
            0
        ]

        self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1E-3		)

        with tempfile.TemporaryDirectory() as tmpdirname:
            A__          =   model.get_decoder()
            decoder.save_pretrained(lowercase_		)
            A__          =   MaMaaaDecoder.from_pretrained(lowercase_		).to(lowercase_		)

        A__          =   decoder(
            input_ids=inputs_dict['decoder_input_ids'],attention_mask=inputs_dict['decoder_attention_mask'],encoder_hidden_states=lowercase_,encoder_attention_mask=inputs_dict['attention_mask'],)[0]

        self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1E-3		)




@require_torch
class  A						(							_UpperCAmelCase    ,							_UpperCAmelCase    ,							_UpperCAmelCase    ,							unittest.TestCase ):







    """simple docstring"""





    lowerCamelCase			=							(
        (
            MaMaaaModel,
            MaMaaaForConditionalGeneration,
        )
        if is_torch_available()
        else ()
    )
    lowerCamelCase			=							(MaMaaaForConditionalGeneration,) if is_torch_available() else ()
    lowerCamelCase			=							(
        {
            'conversational': MaMaaaForConditionalGeneration,
            'feature-extraction': MaMaaaModel,
            'summarization': MaMaaaForConditionalGeneration,
            'text2text-generation': MaMaaaForConditionalGeneration,
            'translation': MaMaaaForConditionalGeneration,
        }
        if is_torch_available()
        else {}
    )
    lowerCamelCase			=							True
    lowerCamelCase			=							True
    lowerCamelCase			=							False
    lowerCamelCase			=							False


    def 						snake_case__     (      self :       Dict,lowercase_ :       Union[str, Any],lowercase_ :       str,lowercase_ :       List[str],lowercase_ :       List[str],lowercase_ :       int		)->    Optional[int]:





        '''simple docstring'''







        if pipeline_test_casse_name == "TranslationPipelineTests":
            # Get `ValueError: Translation requires a `src_lang` and a `tgt_lang` for this model`.
            # `M2M100Config` was never used in pipeline tests: cannot create a simple tokenizer.
            return True

        return False


    def 						snake_case__     (      self :       Any		)->    Dict:





        '''simple docstring'''







        A__          =   MaMaaaModelTester(self		)
        A__          =   ConfigTester(self,config_class=lowercase_		)


    def 						snake_case__     (      self :       str		)->    int:





        '''simple docstring'''







        self.config_tester.run_common_tests()


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





        '''simple docstring'''







        A__      ,						A__          =   self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
            A__          =   model_class(lowercase_		)

            with tempfile.TemporaryDirectory() as tmpdirname:
                model.save_pretrained(lowercase_		)
                A__      ,						A__          =   model_class.from_pretrained(lowercase_,output_loading_info=lowercase_		)
            self.assertEqual(info['missing_keys'],[]		)


    def 						snake_case__     (      self :       int		)->    Optional[int]:





        '''simple docstring'''







        A__          =   self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_decoder_model_past_large_inputs(*lowercase_		)


    def 						snake_case__     (      self :       Optional[int]		)->    Union[str, Any]:





        '''simple docstring'''







        A__          =   self.model_tester.prepare_config_and_inputs_for_common()
        self.model_tester.check_encoder_decoder_model_standalone(*lowercase_		)


    def 						snake_case__     (      self :       int		)->    int:





        '''simple docstring'''







        A__      ,						A__          =   self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
            A__          =   model_class(lowercase_		)
            model.to(lowercase_		)
            model.eval()

            A__          =   copy.deepcopy(self._prepare_for_class(lowercase_,lowercase_		)		)

            if not self.is_encoder_decoder:
                A__          =   inputs['input_ids']
                del inputs["input_ids"]
            else:
                A__          =   inputs['input_ids']
                A__          =   inputs.get('decoder_input_ids',lowercase_		)
                del inputs["input_ids"]
                inputs.pop('decoder_input_ids',lowercase_		)

            A__          =   model.get_input_embeddings()
            if not self.is_encoder_decoder:
                A__          =   wte(lowercase_		)
            else:
                A__          =   wte(lowercase_		)
                A__          =   wte(lowercase_		)

            with torch.no_grad():
                model(**lowercase_		)[0]


    def 						snake_case__     (      self :       str		)->    Tuple:





        '''simple docstring'''







        A__      ,						A__          =   self.model_tester.prepare_config_and_inputs()
        A__          =   input_dict['input_ids']
        A__          =   input_ids.ne(1		).to(lowercase_		)
        A__          =   MaMaaaForConditionalGeneration(lowercase_		).eval().to(lowercase_		)
        if torch_device == "cuda":
            model.half()
        model.generate(lowercase_,attention_mask=lowercase_		)
        model.generate(num_beams=4,do_sample=lowercase_,early_stopping=lowercase_,num_return_sequences=3		)




def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	Union[str, Any]      )							->      List[Any]:





    '''simple docstring'''






    return torch.tensor(SCREAMING_SNAKE_CASE__    ,			dtype=torch.long    ,			device=SCREAMING_SNAKE_CASE__      )


lowercase_       		=  1e-4


@require_torch
@require_sentencepiece
@require_tokenizers
@slow
class  A						(							unittest.TestCase ):







    """simple docstring"""





    @cached_property
    def 						snake_case__     (      self :       Any		)->    Any:





        '''simple docstring'''







        return MaMaaaTokenizer.from_pretrained('facebook/m2m100_418M'		)


    def 						snake_case__     (      self :       Optional[Any]		)->    int:





        '''simple docstring'''







        A__          =   MaMaaaModel.from_pretrained('facebook/m2m100_418M'		).to(lowercase_		)
        A__          =   _long_tensor([[1_2_8_0_2_8, 9_8, 1_2, 3_0_5_2_7, 2_7_3_2, 1_5_9, 7_7_5_5, 6_1_9_0_4, 3_9_1_4_4, 3_8, 2]]		)
        A__          =   _long_tensor([[2, 1_2_8_0_2_8, 9_8, 1_2, 3_0_5_2_7, 2_7_3_2, 1_5_9, 7_7_5_5, 6_1_9_0_4, 3_9_1_4_4, 3_8]]		)
        A__          =   prepare_mam_aaa_inputs_dict(model.config,lowercase_,lowercase_		)
        with torch.no_grad():
            A__          =   model(**lowercase_		)[0]
        A__          =   torch.Size((1, 1_1, 1_0_2_4)		)
        self.assertEqual(output.shape,lowercase_		)
        # change to expected output here
        A__          =   torch.tensor(
            [[-0.7_780, -0.1_676, 0.1_038], [-6.7_556, -1.3_992, 0.0_567], [-7.5_383, -0.5_920, -0.2_779]],device=lowercase_		)
        self.assertTrue(torch.allclose(output[:, :3, :3],lowercase_,atol=lowercase_		)		)


    def 						snake_case__     (      self :       Optional[int]		)->    Optional[Any]:





        '''simple docstring'''







        A__          =   MaMaaaForConditionalGeneration.from_pretrained('facebook/m2m100_418M'		).to(lowercase_		)

        # change to intended input
        A__          =   _long_tensor([[1_2_8_0_2_8, 9_8, 1_2, 3_0_5_2_7, 2_7_3_2, 1_5_9, 7_7_5_5, 6_1_9_0_4, 3_9_1_4_4, 3_8, 2]]		)
        A__          =   _long_tensor([[2, 1_2_8_0_2_8, 9_8, 1_2, 3_0_5_2_7, 2_7_3_2, 1_5_9, 7_7_5_5, 6_1_9_0_4, 3_9_1_4_4, 3_8]]		)
        A__          =   prepare_mam_aaa_inputs_dict(model.config,lowercase_,lowercase_		)
        with torch.no_grad():
            A__          =   model(**lowercase_		)[0]
        A__          =   torch.Size((1, 1_1, model.config.vocab_size)		)
        self.assertEqual(output.shape,lowercase_		)
        # change to expected output here
        A__          =   torch.tensor(
            [[-1.0_448, -1.0_411, 3.7_992], [-3.2_191, -3.2_386, -1.3_451], [-3.6_210, -3.5_993, 0.4_925]],device=lowercase_		)
        self.assertTrue(torch.allclose(output[:, :3, :3],lowercase_,atol=lowercase_		)		)


    def 						snake_case__     (      self :       List[Any]		)->    List[Any]:





        '''simple docstring'''







        A__          =   MaMaaaForConditionalGeneration.from_pretrained('facebook/m2m100_418M'		).to(lowercase_		)
        A__          =   MaMaaaTokenizer.from_pretrained('facebook/m2m100_418M',src_lang='fr',tgt_lang='en'		)

        A__          =   [
            'L\'affaire NSA souligne l\'absence totale de débat sur le renseignement',
            'Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.',
            'Lorsque François Hollande téléphone à Barack Obama ou quand le ministre des affaires étrangères Laurent'
            ' Fabius convoque l\'ambassadeur des Etats-Unis, ils réagissent à une vraie découverte, qui est celle de'
            ' l\'ampleur de la surveillance américaine sur l\'ensemble des communications en France.',
        ]

        # The below article tests that we don't add any hypotheses outside of the top n_beams
        A__          =   tokenizer(lowercase_,padding=lowercase_,return_tensors='pt'		)

        A__          =   model.generate(
            input_ids=dct['input_ids'].to(lowercase_		),attention_mask=dct['attention_mask'].to(lowercase_		),num_beams=5,forced_bos_token_id=tokenizer.get_lang_id('en'		),)

        A__          =   [
            'The NSA case highlights the total absence of intelligence debate',
            'I think there are two levels of response from the French government.',
            'When François Hollande calls Barack Obama or when Foreign Minister Laurent Fabius calls the U.S.'
            ' Ambassador, they respond to a real discovery, which is that of the scale of U.S. surveillance on all'
            ' communications in France.',
        ]

        A__          =   tokenizer.batch_decode(
            hypotheses_batch.tolist(),clean_up_tokenization_spaces=lowercase_,skip_special_tokens=lowercase_		)
        assert generated == expected_en




import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest


class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    lowerCamelCase			=							(DPMSolverSinglestepScheduler,)
    lowerCamelCase			=							(('num_inference_steps', 25),)


    def 						snake_case__     (      self :       Tuple,**lowercase_ :       Dict		)->    Optional[int]:





        '''simple docstring'''







        A__          =   {
            'num_train_timesteps': 1_0_0_0,
            'beta_start': 0.0_001,
            'beta_end': 0.02,
            'beta_schedule': 'linear',
            'solver_order': 2,
            'prediction_type': 'epsilon',
            'thresholding': False,
            'sample_max_value': 1.0,
            'algorithm_type': 'dpmsolver++',
            'solver_type': 'midpoint',
            'lambda_min_clipped': -float('inf'		),
            'variance_type': None,
        }

        config.update(**lowercase_		)
        return config


    def 						snake_case__     (      self :       str,lowercase_ :       Optional[Any]=0,**lowercase_ :       Any		)->    List[Any]:





        '''simple docstring'''







        A__          =   dict(self.forward_default_kwargs		)
        A__          =   kwargs.pop('num_inference_steps',lowercase_		)
        A__          =   self.dummy_sample
        A__          =   0.1 * sample
        A__          =   [residual + 0.2, residual + 0.15, residual + 0.10]

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

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

            A__      ,						A__          =   sample, sample
            for t in range(lowercase_,time_step + scheduler.config.solver_order + 1		):
                A__          =   scheduler.step(lowercase_,lowercase_,lowercase_,**lowercase_		).prev_sample
                A__          =   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 						snake_case__     (      self :       List[str]		)->    List[Any]:





        '''simple docstring'''







        pass


    def 						snake_case__     (      self :       Tuple,lowercase_ :       Union[str, Any]=0,**lowercase_ :       Union[str, Any]		)->    Union[str, Any]:





        '''simple docstring'''







        A__          =   dict(self.forward_default_kwargs		)
        A__          =   kwargs.pop('num_inference_steps',lowercase_		)
        A__          =   self.dummy_sample
        A__          =   0.1 * sample
        A__          =   [residual + 0.2, residual + 0.15, residual + 0.10]

        for scheduler_class in self.scheduler_classes:
            A__          =   self.get_scheduler_config()
            A__          =   scheduler_class(**lowercase_		)
            scheduler.set_timesteps(lowercase_		)

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

            with tempfile.TemporaryDirectory() as tmpdirname:
                scheduler.save_config(lowercase_		)
                A__          =   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)
                A__          =   dummy_past_residuals[: new_scheduler.config.solver_order]

            A__          =   scheduler.step(lowercase_,lowercase_,lowercase_,**lowercase_		).prev_sample
            A__          =   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 						snake_case__     (      self :       Optional[Any],lowercase_ :       Optional[int]=None,**lowercase_ :       int		)->    int:





        '''simple docstring'''







        if scheduler is None:
            A__          =   self.scheduler_classes[0]
            A__          =   self.get_scheduler_config(**lowercase_		)
            A__          =   scheduler_class(**lowercase_		)

        A__          =   self.scheduler_classes[0]
        A__          =   self.get_scheduler_config(**lowercase_		)
        A__          =   scheduler_class(**lowercase_		)

        A__          =   1_0
        A__          =   self.dummy_model()
        A__          =   self.dummy_sample_deter
        scheduler.set_timesteps(lowercase_		)

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

        return sample


    def 						snake_case__     (      self :       Any		)->    str:





        '''simple docstring'''







        A__          =   DPMSolverSinglestepScheduler(**self.get_scheduler_config()		)
        A__          =   5_0
        A__          =   self.dummy_model()
        A__          =   self.dummy_sample_deter
        scheduler.set_timesteps(lowercase_		)

        # make sure that the first t is uneven
        for i, t in enumerate(scheduler.timesteps[3:]		):
            A__          =   model(lowercase_,lowercase_		)
            A__          =   scheduler.step(lowercase_,lowercase_,lowercase_		).prev_sample

        A__          =   torch.mean(torch.abs(lowercase_		)		)

        assert abs(result_mean.item() - 0.2_574		) < 1E-3


    def 						snake_case__     (      self :       Optional[Any]		)->    List[Any]:





        '''simple docstring'''







        for timesteps in [2_5, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
            self.check_over_configs(num_train_timesteps=lowercase_		)


    def 						snake_case__     (      self :       int		)->    Optional[Any]:





        '''simple docstring'''







        A__          =   DPMSolverSinglestepScheduler(**self.get_scheduler_config()		)
        A__          =   self.full_loop(scheduler=lowercase_		)
        A__          =   torch.mean(torch.abs(lowercase_		)		)

        assert abs(result_mean.item() - 0.2_791		) < 1E-3

        A__          =   DEISMultistepScheduler.from_config(scheduler.config		)
        A__          =   DPMSolverMultistepScheduler.from_config(scheduler.config		)
        A__          =   UniPCMultistepScheduler.from_config(scheduler.config		)
        A__          =   DPMSolverSinglestepScheduler.from_config(scheduler.config		)

        A__          =   self.full_loop(scheduler=lowercase_		)
        A__          =   torch.mean(torch.abs(lowercase_		)		)

        assert abs(result_mean.item() - 0.2_791		) < 1E-3


    def 						snake_case__     (      self :       Tuple		)->    Any:





        '''simple docstring'''







        self.check_over_configs(thresholding=lowercase_		)
        for order in [1, 2, 3]:
            for solver_type in ["midpoint", "heun"]:
                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_,algorithm_type='dpmsolver++',solver_order=lowercase_,solver_type=lowercase_,)


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





        '''simple docstring'''







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


    def 						snake_case__     (      self :       Dict		)->    List[Any]:





        '''simple docstring'''







        for algorithm_type in ["dpmsolver", "dpmsolver++"]:
            for solver_type in ["midpoint", "heun"]:
                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_,algorithm_type=lowercase_,)
                        A__          =   self.full_loop(
                            solver_order=lowercase_,solver_type=lowercase_,prediction_type=lowercase_,algorithm_type=lowercase_,)
                        assert not torch.isnan(lowercase_		).any(), "Samples have nan numbers"


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





        '''simple docstring'''







        self.check_over_configs(lower_order_final=lowercase_		)
        self.check_over_configs(lower_order_final=lowercase_		)


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





        '''simple docstring'''







        self.check_over_configs(lambda_min_clipped=-float('inf'		)		)
        self.check_over_configs(lambda_min_clipped=-5.1		)


    def 						snake_case__     (      self :       Optional[Any]		)->    Tuple:





        '''simple docstring'''







        self.check_over_configs(variance_type=lowercase_		)
        self.check_over_configs(variance_type='learned_range'		)


    def 						snake_case__     (      self :       str		)->    Any:





        '''simple docstring'''







        for num_inference_steps in [1, 2, 3, 5, 1_0, 5_0, 1_0_0, 9_9_9, 1_0_0_0]:
            self.check_over_forward(num_inference_steps=lowercase_,time_step=0		)


    def 						snake_case__     (      self :       Tuple		)->    Tuple:





        '''simple docstring'''







        A__          =   self.full_loop()
        A__          =   torch.mean(torch.abs(lowercase_		)		)

        assert abs(result_mean.item() - 0.2_791		) < 1E-3


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





        '''simple docstring'''







        A__          =   self.full_loop(use_karras_sigmas=lowercase_		)
        A__          =   torch.mean(torch.abs(lowercase_		)		)

        assert abs(result_mean.item() - 0.2_248		) < 1E-3


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





        '''simple docstring'''







        A__          =   self.full_loop(prediction_type='v_prediction'		)
        A__          =   torch.mean(torch.abs(lowercase_		)		)

        assert abs(result_mean.item() - 0.1_453		) < 1E-3


    def 						snake_case__     (      self :       Tuple		)->    int:





        '''simple docstring'''







        A__          =   self.full_loop(prediction_type='v_prediction',use_karras_sigmas=lowercase_		)
        A__          =   torch.mean(torch.abs(lowercase_		)		)

        assert abs(result_mean.item() - 0.0_649		) < 1E-3


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





        '''simple docstring'''







        A__          =   self.scheduler_classes[0]
        A__          =   self.get_scheduler_config(thresholding=lowercase_,dynamic_thresholding_ratio=0		)
        A__          =   scheduler_class(**lowercase_		)

        A__          =   1_0
        A__          =   self.dummy_model()
        A__          =   self.dummy_sample_deter.half()
        scheduler.set_timesteps(lowercase_		)

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

        assert sample.dtype == torch.floataa

