

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




from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase_       		=  logging.get_logger(__name__)

lowercase_       		=  {
    "s-JoL/Open-Llama-V1": "https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json",
}


class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    lowerCamelCase			=							'open-llama'


    def __init__(      self :       Any,lowercase_ :       Optional[int]=1_0_0_0_0_0,lowercase_ :       Union[str, Any]=4_0_9_6,lowercase_ :       Dict=1_1_0_0_8,lowercase_ :       Dict=3_2,lowercase_ :       Optional[int]=3_2,lowercase_ :       Dict="silu",lowercase_ :       Union[str, Any]=2_0_4_8,lowercase_ :       Optional[int]=0.02,lowercase_ :       Dict=1E-6,lowercase_ :       Dict=True,lowercase_ :       List[Any]=0,lowercase_ :       Optional[int]=1,lowercase_ :       str=2,lowercase_ :       str=False,lowercase_ :       str=True,lowercase_ :       int=0.1,lowercase_ :       List[Any]=0.1,lowercase_ :       List[Any]=True,lowercase_ :       Union[str, Any]=True,lowercase_ :       Any=None,**lowercase_ :       List[Any],)->    Tuple:





        '''simple docstring'''







        A__          =   vocab_size
        A__          =   max_position_embeddings
        A__          =   hidden_size
        A__          =   intermediate_size
        A__          =   num_hidden_layers
        A__          =   num_attention_heads
        A__          =   hidden_act
        A__          =   initializer_range
        A__          =   rms_norm_eps
        A__          =   use_cache
        A__          =   kwargs.pop(
            'use_memorry_efficient_attention',lowercase_		)
        A__          =   hidden_dropout_prob
        A__          =   attention_dropout_prob
        A__          =   use_stable_embedding
        A__          =   shared_input_output_embedding
        A__          =   rope_scaling
        self._rope_scaling_validation()

        super().__init__(
            pad_token_id=lowercase_,bos_token_id=lowercase_,eos_token_id=lowercase_,tie_word_embeddings=lowercase_,**lowercase_,)


    def 						snake_case__     (      self :       str		)->    str:





        '''simple docstring'''







        if self.rope_scaling is None:
            return

        if not isinstance(self.rope_scaling,lowercase_		) or len(self.rope_scaling		) != 2:
            raise ValueError(
                '`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '
                F'got {self.rope_scaling}'		)
        A__          =   self.rope_scaling.get('type',lowercase_		)
        A__          =   self.rope_scaling.get('factor',lowercase_		)
        if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
            raise ValueError(
                F'`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}'		)
        if rope_scaling_factor is None or not isinstance(lowercase_,lowercase_		) or rope_scaling_factor <= 1.0:
            raise ValueError(F'`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'		)

