






import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest




class     _lowerCAmelCase(	UpperCAmelCase_ ):





       """simple docstring"""







       a    :		Dict									=(UnCLIPScheduler,)





       def       _a   ( self  ,      **_lowerCamelCase							):
         UpperCamelCase_: Dict   				=    {
             'num_train_timesteps': 1_0_0_0,
             'variance_type': 'fixed_small_log',
             'clip_sample': True,
             'clip_sample_range': 1.0,
             'prediction_type': 'epsilon',
         }

         config.update(**_lowerCamelCase							)
         return config





       def       _a   ( self							):
         for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
           self.check_over_configs(num_train_timesteps=_lowerCamelCase							)





       def       _a   ( self							):
         for variance in ["fixed_small_log", "learned_range"]:
           self.check_over_configs(variance_type=_lowerCamelCase							)





       def       _a   ( self							):
         for clip_sample in [True, False]:
           self.check_over_configs(clip_sample=_lowerCamelCase							)





       def       _a   ( self							):
         for clip_sample_range in [1, 5, 1_0, 2_0]:
           self.check_over_configs(clip_sample_range=_lowerCamelCase							)





       def       _a   ( self							):
         for prediction_type in ["epsilon", "sample"]:
           self.check_over_configs(prediction_type=_lowerCamelCase							)





       def       _a   ( self							):
         for time_step in [0, 5_0_0, 9_9_9]:
           for prev_timestep in [None, 5, 1_0_0, 2_5_0, 5_0_0, 7_5_0]:
             if prev_timestep is not None and prev_timestep >= time_step:
               continue

             self.check_over_forward(time_step=_lowerCamelCase  ,      prev_timestep=_lowerCamelCase							)





       def       _a   ( self							):
         UpperCamelCase_: Any   				=    self.scheduler_classes[0]
         UpperCamelCase_: Optional[Any]   				=    self.get_scheduler_config(variance_type='fixed_small_log'							)
         UpperCamelCase_: Any   				=    scheduler_class(**_lowerCamelCase							)

         assert torch.sum(torch.abs(scheduler._get_variance(0							) - 1.0000e-10							)							) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(4_8_7							) - 0.0_5_4_9_6_2_5							)							) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(9_9_9							) - 0.9_9_9_4_9_8_7							)							) < 1e-5





       def       _a   ( self							):
         UpperCamelCase_: Tuple   				=    self.scheduler_classes[0]
         UpperCamelCase_: str   				=    self.get_scheduler_config(variance_type='learned_range'							)
         UpperCamelCase_: Tuple   				=    scheduler_class(**_lowerCamelCase							)

         UpperCamelCase_: Optional[Any]   				=    0.5

         assert scheduler._get_variance(1  ,      predicted_variance=_lowerCamelCase							) - -1_0.1_7_1_2_7_9_0 < 1e-5
         assert scheduler._get_variance(4_8_7  ,      predicted_variance=_lowerCamelCase							) - -5.7_9_9_8_0_5_2 < 1e-5
         assert scheduler._get_variance(9_9_9  ,      predicted_variance=_lowerCamelCase							) - -0.0_0_1_0_0_1_1 < 1e-5





       def       _a   ( self							):
         UpperCamelCase_: Optional[int]   				=    self.scheduler_classes[0]
         UpperCamelCase_: str   				=    self.get_scheduler_config()
         UpperCamelCase_: Union[str, Any]   				=    scheduler_class(**_lowerCamelCase							)

         UpperCamelCase_: Tuple   				=    scheduler.timesteps

         UpperCamelCase_: Any   				=    self.dummy_model()
         UpperCamelCase_: Any   				=    self.dummy_sample_deter
         UpperCamelCase_: Optional[Any]   				=    torch.manual_seed(0							)

         for i, t in enumerate(_lowerCamelCase							):
           # 1. predict noise residual
           UpperCamelCase_: List[Any]   				=    model(_lowerCamelCase  ,      _lowerCamelCase							)

           # 2. predict previous mean of sample x_t-1
           UpperCamelCase_: Tuple   				=    scheduler.step(_lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      generator=_lowerCamelCase							).prev_sample

           UpperCamelCase_: Optional[int]   				=    pred_prev_sample

         UpperCamelCase_: int   				=    torch.sum(torch.abs(_lowerCamelCase							)							)
         UpperCamelCase_: Optional[int]   				=    torch.mean(torch.abs(_lowerCamelCase							)							)

         assert abs(result_sum.item() - 2_5_2.2_6_8_2_4_9_5							) < 1e-2
         assert abs(result_mean.item() - 0.3_2_8_4_7_4_3							) < 1e-3





       def       _a   ( self							):
         UpperCamelCase_: Any   				=    self.scheduler_classes[0]
         UpperCamelCase_: List[Any]   				=    self.get_scheduler_config()
         UpperCamelCase_: Dict   				=    scheduler_class(**_lowerCamelCase							)

         scheduler.set_timesteps(2_5							)

         UpperCamelCase_: Any   				=    scheduler.timesteps

         UpperCamelCase_: int   				=    self.dummy_model()
         UpperCamelCase_: int   				=    self.dummy_sample_deter
         UpperCamelCase_: Union[str, Any]   				=    torch.manual_seed(0							)

         for i, t in enumerate(_lowerCamelCase							):
           # 1. predict noise residual
           UpperCamelCase_: List[str]   				=    model(_lowerCamelCase  ,      _lowerCamelCase							)

           if i + 1 == timesteps.shape[0]:
             UpperCamelCase_: Optional[int]   				=    None
           else:
             UpperCamelCase_: List[Any]   				=    timesteps[i + 1]

           # 2. predict previous mean of sample x_t-1
           UpperCamelCase_: Optional[Any]   				=    scheduler.step(
               _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      prev_timestep=_lowerCamelCase  ,      generator=_lowerCamelCase							).prev_sample

           UpperCamelCase_: int   				=    pred_prev_sample

         UpperCamelCase_: Optional[Any]   				=    torch.sum(torch.abs(_lowerCamelCase							)							)
         UpperCamelCase_: Dict   				=    torch.mean(torch.abs(_lowerCamelCase							)							)

         assert abs(result_sum.item() - 2_5_8.2_0_4_4_9_8_3							) < 1e-2
         assert abs(result_mean.item() - 0.3_3_6_2_0_3_8							) < 1e-3





       def       _a   ( self							):
         pass





       def       _a   ( self							):
         pass







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


A_		:		Tuple		     =			logging.get_logger(__name__)

A_		:		Dict		     =			{
    'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/config.json',
    # See all XGLM models at https://huggingface.co/models?filter=xglm
}




class     _lowerCAmelCase(	UpperCAmelCase_ ):





       """simple docstring"""







       a    :		Tuple									='''xglm'''
       a    :		List[Any]									=['''past_key_values''']

       a    :		Union[str, Any]									={
           '''num_attention_heads''': '''attention_heads''',
           '''hidden_size''': '''d_model''',
           '''num_hidden_layers''': '''num_layers''',
       }





       def __init__( self  ,      _lowerCamelCase=2_5_6_0_0_8  ,      _lowerCamelCase=2_0_4_8  ,      _lowerCamelCase=1_0_2_4  ,      _lowerCamelCase=4_0_9_6  ,      _lowerCamelCase=2_4  ,      _lowerCamelCase=1_6  ,      _lowerCamelCase="gelu"  ,      _lowerCamelCase=0.1  ,      _lowerCamelCase=0.1  ,      _lowerCamelCase=0.0  ,      _lowerCamelCase=0.0  ,      _lowerCamelCase=0.0_2  ,      _lowerCamelCase=True  ,      _lowerCamelCase=True  ,      _lowerCamelCase=2  ,      _lowerCamelCase=1  ,      _lowerCamelCase=0  ,      _lowerCamelCase=2  ,      **_lowerCamelCase  ,      ):
         UpperCamelCase_: Optional[Any]   				=    vocab_size
         UpperCamelCase_: Optional[int]   				=    max_position_embeddings
         UpperCamelCase_: List[str]   				=    d_model
         UpperCamelCase_: List[Any]   				=    ffn_dim
         UpperCamelCase_: List[Any]   				=    num_layers
         UpperCamelCase_: List[Any]   				=    attention_heads
         UpperCamelCase_: Tuple   				=    activation_function
         UpperCamelCase_: Tuple   				=    dropout
         UpperCamelCase_: Tuple   				=    attention_dropout
         UpperCamelCase_: Optional[Any]   				=    activation_dropout
         UpperCamelCase_: List[str]   				=    layerdrop
         UpperCamelCase_: Any   				=    init_std
         UpperCamelCase_: Any   				=    scale_embedding  # scale factor will be sqrt(d_model) if True
         UpperCamelCase_: Union[str, Any]   				=    use_cache

         super().__init__(
             pad_token_id=_lowerCamelCase  ,      bos_token_id=_lowerCamelCase  ,      eos_token_id=_lowerCamelCase  ,      decoder_start_token_id=_lowerCamelCase  ,      **_lowerCamelCase  ,      )