


import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class  _lowercase   (   _A						):
      _a							:						Optional[Any]										=							(EulerDiscreteScheduler,)
      _a							:						Tuple										=							10
      def 						lowercase__					(     self				,			**a   ):
       snake_case__  :    Dict							       ={
           """num_train_timesteps""": 1_1_0_0,
           """beta_start""": 0.0001,
           """beta_end""": 0.02,
           """beta_schedule""": """linear""",
       }

       config.update(**a   )
       return config
      def 						lowercase__					(     self   ):
       for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
        self.check_over_configs(num_train_timesteps=a   )
      def 						lowercase__					(     self   ):
       for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]				,			[0.0002, 0.002, 0.02]   ):
        self.check_over_configs(beta_start=a				,			beta_end=a   )
      def 						lowercase__					(     self   ):
       for schedule in ["linear", "scaled_linear"]:
        self.check_over_configs(beta_schedule=a   )
      def 						lowercase__					(     self   ):
       for prediction_type in ["epsilon", "v_prediction"]:
        self.check_over_configs(prediction_type=a   )
      def 						lowercase__					(     self   ):
       snake_case__  :    Union[str, Any]							       =self.scheduler_classes[0]
       snake_case__  :    Tuple							       =self.get_scheduler_config()
       snake_case__  :    Optional[int]							       =scheduler_class(**a   )

       scheduler.set_timesteps(self.num_inference_steps   )

       snake_case__  :    Optional[Any]							       =torch.manual_seed(0   )

       snake_case__  :    Tuple							       =self.dummy_model()
       snake_case__  :    str							       =self.dummy_sample_deter * scheduler.init_noise_sigma
       snake_case__  :    List[Any]							       =sample.to(a   )

       for i, t in enumerate(scheduler.timesteps   ):
        snake_case__  :    Optional[Any]							       =scheduler.scale_model_input(a				,			a   )

        snake_case__  :    int							       =model(a				,			a   )

        snake_case__  :    Any							       =scheduler.step(a				,			a				,			a				,			generator=a   )
        snake_case__  :    int							       =output.prev_sample

       snake_case__  :    Any							       =torch.sum(torch.abs(a   )   )
       snake_case__  :    Any							       =torch.mean(torch.abs(a   )   )

       assert abs(result_sum.item() - 10.0807   ) < 1e-2
       assert abs(result_mean.item() - 0.0131   ) < 1e-3
      def 						lowercase__					(     self   ):
       snake_case__  :    List[Any]							       =self.scheduler_classes[0]
       snake_case__  :    Union[str, Any]							       =self.get_scheduler_config(prediction_type="""v_prediction"""   )
       snake_case__  :    int							       =scheduler_class(**a   )

       scheduler.set_timesteps(self.num_inference_steps   )

       snake_case__  :    List[str]							       =torch.manual_seed(0   )

       snake_case__  :    int							       =self.dummy_model()
       snake_case__  :    List[Any]							       =self.dummy_sample_deter * scheduler.init_noise_sigma
       snake_case__  :    int							       =sample.to(a   )

       for i, t in enumerate(scheduler.timesteps   ):
        snake_case__  :    Union[str, Any]							       =scheduler.scale_model_input(a				,			a   )

        snake_case__  :    Any							       =model(a				,			a   )

        snake_case__  :    Union[str, Any]							       =scheduler.step(a				,			a				,			a				,			generator=a   )
        snake_case__  :    Optional[Any]							       =output.prev_sample

       snake_case__  :    List[Any]							       =torch.sum(torch.abs(a   )   )
       snake_case__  :    Tuple							       =torch.mean(torch.abs(a   )   )

       assert abs(result_sum.item() - 0.0002   ) < 1e-2
       assert abs(result_mean.item() - 2.26_76e-06   ) < 1e-3
      def 						lowercase__					(     self   ):
       snake_case__  :    List[Any]							       =self.scheduler_classes[0]
       snake_case__  :    Optional[int]							       =self.get_scheduler_config()
       snake_case__  :    int							       =scheduler_class(**a   )

       scheduler.set_timesteps(self.num_inference_steps				,			device=a   )

       snake_case__  :    Optional[int]							       =torch.manual_seed(0   )

       snake_case__  :    Tuple							       =self.dummy_model()
       snake_case__  :    str							       =self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
       snake_case__  :    Union[str, Any]							       =sample.to(a   )

       for t in scheduler.timesteps:
        snake_case__  :    int							       =scheduler.scale_model_input(a				,			a   )

        snake_case__  :    Any							       =model(a				,			a   )

        snake_case__  :    str							       =scheduler.step(a				,			a				,			a				,			generator=a   )
        snake_case__  :    Optional[Any]							       =output.prev_sample

       snake_case__  :    Dict							       =torch.sum(torch.abs(a   )   )
       snake_case__  :    Optional[int]							       =torch.mean(torch.abs(a   )   )

       assert abs(result_sum.item() - 10.0807   ) < 1e-2
       assert abs(result_mean.item() - 0.0131   ) < 1e-3



      def 						lowercase__					(     self   ):
       snake_case__  :    List[str]							       =self.scheduler_classes[0]
       snake_case__  :    Optional[int]							       =self.get_scheduler_config()
       snake_case__  :    List[str]							       =scheduler_class(**a				,			use_karras_sigmas=a   )

       scheduler.set_timesteps(self.num_inference_steps				,			device=a   )

       snake_case__  :    Tuple							       =torch.manual_seed(0   )

       snake_case__  :    Dict							       =self.dummy_model()
       snake_case__  :    Optional[int]							       =self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
       snake_case__  :    List[str]							       =sample.to(a   )

       for t in scheduler.timesteps:
        snake_case__  :    List[str]							       =scheduler.scale_model_input(a				,			a   )

        snake_case__  :    Any							       =model(a				,			a   )

        snake_case__  :    str							       =scheduler.step(a				,			a				,			a				,			generator=a   )
        snake_case__  :    Optional[Any]							       =output.prev_sample

       snake_case__  :    Optional[int]							       =torch.sum(torch.abs(a   )   )
       snake_case__  :    Union[str, Any]							       =torch.mean(torch.abs(a   )   )

       assert abs(result_sum.item() - 124.52299499511719   ) < 1e-2
       assert abs(result_mean.item() - 0.16213932633399963   ) < 1e-3













from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__lowerCamelCase	:		Dict										=   logging.get_logger(__name__)

__lowerCamelCase	:		Union[str, Any]										=   {
    """xlm-mlm-en-2048""": """https://huggingface.co/xlm-mlm-en-2048/resolve/main/config.json""",
    """xlm-mlm-ende-1024""": """https://huggingface.co/xlm-mlm-ende-1024/resolve/main/config.json""",
    """xlm-mlm-enfr-1024""": """https://huggingface.co/xlm-mlm-enfr-1024/resolve/main/config.json""",
    """xlm-mlm-enro-1024""": """https://huggingface.co/xlm-mlm-enro-1024/resolve/main/config.json""",
    """xlm-mlm-tlm-xnli15-1024""": """https://huggingface.co/xlm-mlm-tlm-xnli15-1024/resolve/main/config.json""",
    """xlm-mlm-xnli15-1024""": """https://huggingface.co/xlm-mlm-xnli15-1024/resolve/main/config.json""",
    """xlm-clm-enfr-1024""": """https://huggingface.co/xlm-clm-enfr-1024/resolve/main/config.json""",
    """xlm-clm-ende-1024""": """https://huggingface.co/xlm-clm-ende-1024/resolve/main/config.json""",
    """xlm-mlm-17-1280""": """https://huggingface.co/xlm-mlm-17-1280/resolve/main/config.json""",
    """xlm-mlm-100-1280""": """https://huggingface.co/xlm-mlm-100-1280/resolve/main/config.json""",
}







class  _lowercase   (   _A						):
      _a							:						int										=							'xlm'
      _a							:						List[Any]										=							{
          'hidden_size': 'emb_dim',
          'num_attention_heads': 'n_heads',
          'num_hidden_layers': 'n_layers',
          'n_words': 'vocab_size',  # For backward compatibility
      }
      def __init__(     self				,			a=3_0_1_4_5				,			a=2_0_4_8				,			a=1_2				,			a=1_6				,			a=0.1				,			a=0.1				,			a=True				,			a=False				,			a=False				,			a=False				,			a=1				,			a=True				,			a=5_1_2				,			a=2_0_4_8**-0.5				,			a=1e-12				,			a=0.02				,			a=0				,			a=1				,			a=2				,			a=3				,			a=5				,			a=True				,			a="first"				,			a=True				,			a=None				,			a=True				,			a=0.1				,			a=5				,			a=5				,			a=0				,			a=0				,			a=2				,			a=0				,			**a				,			):
       snake_case__  :    str							       =vocab_size
       snake_case__  :    List[Any]							       =emb_dim
       snake_case__  :    List[str]							       =n_layers
       snake_case__  :    Any							       =n_heads
       snake_case__  :    Any							       =dropout
       snake_case__  :    Optional[int]							       =attention_dropout
       snake_case__  :    Dict							       =gelu_activation
       snake_case__  :    Tuple							       =sinusoidal_embeddings
       snake_case__  :    Optional[int]							       =causal
       snake_case__  :    Optional[int]							       =asm
       snake_case__  :    Optional[int]							       =n_langs
       snake_case__  :    Union[str, Any]							       =use_lang_emb
       snake_case__  :    Optional[Any]							       =layer_norm_eps
       snake_case__  :    Union[str, Any]							       =bos_index
       snake_case__  :    List[Any]							       =eos_index
       snake_case__  :    Optional[Any]							       =pad_index
       snake_case__  :    Union[str, Any]							       =unk_index
       snake_case__  :    Dict							       =mask_index
       snake_case__  :    str							       =is_encoder
       snake_case__  :    Optional[Any]							       =max_position_embeddings
       snake_case__  :    Any							       =embed_init_std
       snake_case__  :    Optional[Any]							       =init_std
       snake_case__  :    str							       =summary_type
       snake_case__  :    List[Any]							       =summary_use_proj
       snake_case__  :    str							       =summary_activation
       snake_case__  :    Optional[Any]							       =summary_proj_to_labels
       snake_case__  :    Optional[int]							       =summary_first_dropout
       snake_case__  :    Optional[Any]							       =start_n_top
       snake_case__  :    List[Any]							       =end_n_top
       snake_case__  :    Union[str, Any]							       =mask_token_id
       snake_case__  :    Any							       =lang_id

       if "n_words" in kwargs:
        snake_case__  :    int							       =kwargs["""n_words"""]

       super().__init__(pad_token_id=a				,			bos_token_id=a				,			**a   )







class  _lowercase   (   _A						):
      @property
      def 						lowercase__					(     self   ):
       if self.task == "multiple-choice":
        snake_case__  :    Any							       ={0: """batch""", 1: """choice""", 2: """sequence"""}
       else:
        snake_case__  :    Union[str, Any]							       ={0: """batch""", 1: """sequence"""}
       return OrderedDict(
           [
               ("""input_ids""", dynamic_axis),
               ("""attention_mask""", dynamic_axis),
               ("""token_type_ids""", dynamic_axis),
           ]   )





