


'''simple docstring'''





import torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest






class   __lowercase				(  _UpperCAmelCase  ):
      UpperCamelCase								=       (DDPMScheduler,)





      def        _lowercase				(		self						:					List[str]					,       **__lowerCamelCase						:					List[Any]       )    ->   str:






         """simple docstring"""







         UpperCAmelCase							=     {
             """num_train_timesteps""": 1_0_0_0,
             """beta_start""": 0.0_001,
             """beta_end""": 0.02,
             """beta_schedule""": """linear""",
             """variance_type""": """fixed_small""",
             """clip_sample""": True,
         }

         config.update(**__UpperCamelCase       )
         return config





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






         """simple docstring"""







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





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






         """simple docstring"""







         for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]					,       [0.002, 0.02, 0.2, 2]       ):
            self.check_over_configs(beta_start=__UpperCamelCase					,       beta_end=__UpperCamelCase       )





      def        _lowercase				(		self						:					int       )    ->   Tuple:






         """simple docstring"""







         for schedule in ["linear", "squaredcos_cap_v2"]:
            self.check_over_configs(beta_schedule=__UpperCamelCase       )





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






         """simple docstring"""







         for variance in ["fixed_small", "fixed_large", "other"]:
            self.check_over_configs(variance_type=__UpperCamelCase       )





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






         """simple docstring"""







         for clip_sample in [True, False]:
            self.check_over_configs(clip_sample=__UpperCamelCase       )





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






         """simple docstring"""







         self.check_over_configs(thresholding=__UpperCamelCase       )
         for threshold in [0.5, 1.0, 2.0]:
            for prediction_type in ["epsilon", "sample", "v_prediction"]:
               self.check_over_configs(
                   thresholding=__UpperCamelCase					,       prediction_type=__UpperCamelCase					,       sample_max_value=__UpperCamelCase					,       )





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






         """simple docstring"""







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





      def        _lowercase				(		self						:					int       )    ->   int:






         """simple docstring"""







         for t in [0, 5_0_0, 9_9_9]:
            self.check_over_forward(time_step=__UpperCamelCase       )





      def        _lowercase				(		self						:					List[str]       )    ->   int:






         """simple docstring"""







         UpperCAmelCase							=     self.scheduler_classes[0]
         UpperCAmelCase							=     self.get_scheduler_config()
         UpperCAmelCase							=     scheduler_class(**__UpperCamelCase       )

         assert torch.sum(torch.abs(scheduler._get_variance(0       ) - 0.0       )       ) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(4_8_7       ) - 0.00_979       )       ) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(9_9_9       ) - 0.02       )       ) < 1e-5





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






         """simple docstring"""







         UpperCAmelCase							=     self.scheduler_classes[0]
         UpperCAmelCase							=     self.get_scheduler_config()
         UpperCAmelCase							=     scheduler_class(**__UpperCamelCase       )

         UpperCAmelCase							=     len(__UpperCamelCase       )

         UpperCAmelCase							=     self.dummy_model()
         UpperCAmelCase							=     self.dummy_sample_deter
         UpperCAmelCase							=     torch.manual_seed(0       )

         for t in reversed(range(__UpperCamelCase       )       ):
            # 1. predict noise residual
            UpperCAmelCase							=     model(__UpperCamelCase					,       __UpperCamelCase       )

            # 2. predict previous mean of sample x_t-1
            UpperCAmelCase							=     scheduler.step(__UpperCamelCase					,       __UpperCamelCase					,       __UpperCamelCase					,       generator=__UpperCamelCase       ).prev_sample

            # if t > 0:
            #     noise = self.dummy_sample_deter
            #     variance = scheduler.get_variance(t) ** (0.5) * noise
            #
            # sample = pred_prev_sample + variance
            UpperCAmelCase							=     pred_prev_sample

         UpperCAmelCase							=     torch.sum(torch.abs(__UpperCamelCase       )       )
         UpperCAmelCase							=     torch.mean(torch.abs(__UpperCamelCase       )       )

         assert abs(result_sum.item() - 2_5_8.9_6_0_6       ) < 1e-2
         assert abs(result_mean.item() - 0.3_372       ) < 1e-3





      def        _lowercase				(		self						:					Optional[int]       )    ->   Dict:






         """simple docstring"""







         UpperCAmelCase							=     self.scheduler_classes[0]
         UpperCAmelCase							=     self.get_scheduler_config(prediction_type="""v_prediction"""       )
         UpperCAmelCase							=     scheduler_class(**__UpperCamelCase       )

         UpperCAmelCase							=     len(__UpperCamelCase       )

         UpperCAmelCase							=     self.dummy_model()
         UpperCAmelCase							=     self.dummy_sample_deter
         UpperCAmelCase							=     torch.manual_seed(0       )

         for t in reversed(range(__UpperCamelCase       )       ):
            # 1. predict noise residual
            UpperCAmelCase							=     model(__UpperCamelCase					,       __UpperCamelCase       )

            # 2. predict previous mean of sample x_t-1
            UpperCAmelCase							=     scheduler.step(__UpperCamelCase					,       __UpperCamelCase					,       __UpperCamelCase					,       generator=__UpperCamelCase       ).prev_sample

            # if t > 0:
            #     noise = self.dummy_sample_deter
            #     variance = scheduler.get_variance(t) ** (0.5) * noise
            #
            # sample = pred_prev_sample + variance
            UpperCAmelCase							=     pred_prev_sample

         UpperCAmelCase							=     torch.sum(torch.abs(__UpperCamelCase       )       )
         UpperCAmelCase							=     torch.mean(torch.abs(__UpperCamelCase       )       )

         assert abs(result_sum.item() - 2_0_2.0_2_9_6       ) < 1e-2
         assert abs(result_mean.item() - 0.2_631       ) < 1e-3





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






         """simple docstring"""







         UpperCAmelCase							=     self.scheduler_classes[0]
         UpperCAmelCase							=     self.get_scheduler_config()
         UpperCAmelCase							=     scheduler_class(**__UpperCamelCase       )

         UpperCAmelCase							=     [1_0_0, 8_7, 5_0, 1, 0]

         scheduler.set_timesteps(timesteps=__UpperCamelCase       )

         UpperCAmelCase							=     scheduler.timesteps

         for i, timestep in enumerate(__UpperCamelCase       ):
            if i == len(__UpperCamelCase       ) - 1:
               UpperCAmelCase							=     -1
            else:
               UpperCAmelCase							=     timesteps[i + 1]

            UpperCAmelCase							=     scheduler.previous_timestep(__UpperCamelCase       )
            UpperCAmelCase							=     prev_t.item()

            self.assertEqual(__UpperCamelCase					,       __UpperCamelCase       )





      def        _lowercase				(		self						:					Tuple       )    ->   Tuple:






         """simple docstring"""







         UpperCAmelCase							=     self.scheduler_classes[0]
         UpperCAmelCase							=     self.get_scheduler_config()
         UpperCAmelCase							=     scheduler_class(**__UpperCamelCase       )

         UpperCAmelCase							=     [1_0_0, 8_7, 5_0, 5_1, 0]

         with self.assertRaises(__UpperCamelCase					,       msg="""`custom_timesteps` must be in descending order."""       ):
            scheduler.set_timesteps(timesteps=__UpperCamelCase       )





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






         """simple docstring"""







         UpperCAmelCase							=     self.scheduler_classes[0]
         UpperCAmelCase							=     self.get_scheduler_config()
         UpperCAmelCase							=     scheduler_class(**__UpperCamelCase       )

         UpperCAmelCase							=     [1_0_0, 8_7, 5_0, 1, 0]
         UpperCAmelCase							=     len(__UpperCamelCase       )

         with self.assertRaises(__UpperCamelCase					,       msg="""Can only pass one of `num_inference_steps` or `custom_timesteps`."""       ):
            scheduler.set_timesteps(num_inference_steps=__UpperCamelCase					,       timesteps=__UpperCamelCase       )





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






         """simple docstring"""







         UpperCAmelCase							=     self.scheduler_classes[0]
         UpperCAmelCase							=     self.get_scheduler_config()
         UpperCAmelCase							=     scheduler_class(**__UpperCamelCase       )

         UpperCAmelCase							=     [scheduler.config.num_train_timesteps]

         with self.assertRaises(
             __UpperCamelCase					,       msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"""					,       ):
            scheduler.set_timesteps(timesteps=__UpperCamelCase       )







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


__a     =			logging.get_logger(__name__)

__a     =			{
    """facebook/nllb-moe-54B""": """https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json""",
}






class   __lowercase				(  __snake_case  ):
      UpperCamelCase								=       '''nllb-moe'''
      UpperCamelCase								=       ['''past_key_values''']
      UpperCamelCase								=       {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}





      def __init__(		self						:					Optional[int]					,       __lowerCamelCase						:					Optional[Any]=1_2_8_1_1_2					,       __lowerCamelCase						:					Dict=1_0_2_4					,       __lowerCamelCase						:					Optional[int]=1_2					,       __lowerCamelCase						:					Union[str, Any]=4_0_9_6					,       __lowerCamelCase						:					List[str]=1_6					,       __lowerCamelCase						:					List[str]=1_2					,       __lowerCamelCase						:					int=4_0_9_6					,       __lowerCamelCase						:					Tuple=1_6					,       __lowerCamelCase						:					str=0.05					,       __lowerCamelCase						:					List[str]=0.05					,       __lowerCamelCase						:					List[Any]=True					,       __lowerCamelCase						:					Tuple=True					,       __lowerCamelCase						:					str="relu"					,       __lowerCamelCase						:					Dict=1_0_2_4					,       __lowerCamelCase						:					List[str]=0.1					,       __lowerCamelCase						:					Optional[int]=0.1					,       __lowerCamelCase						:					List[Any]=0.0					,       __lowerCamelCase						:					Optional[Any]=0.02					,       __lowerCamelCase						:					Dict=2					,       __lowerCamelCase						:					Union[str, Any]=True					,       __lowerCamelCase						:					Any=False					,       __lowerCamelCase						:					Tuple="float32"					,       __lowerCamelCase						:					Any=False					,       __lowerCamelCase						:					Optional[int]=1_2_8					,       __lowerCamelCase						:					List[str]=6_4					,       __lowerCamelCase						:					List[Any]=4					,       __lowerCamelCase						:					Tuple=4					,       __lowerCamelCase						:					str=0.001					,       __lowerCamelCase						:					Optional[int]=0.001					,       __lowerCamelCase						:					Tuple="all"					,       __lowerCamelCase						:					Any=False					,       __lowerCamelCase						:					Optional[int]=False					,       __lowerCamelCase						:					List[str]=1.0					,       __lowerCamelCase						:					Dict=0.2					,       __lowerCamelCase						:					Union[str, Any]=1					,       __lowerCamelCase						:					int=0					,       __lowerCamelCase						:					Dict=2					,       __lowerCamelCase						:					int=False					,       **__lowerCamelCase						:					str					,       )    ->   int:






         """simple docstring"""







         UpperCAmelCase							=     vocab_size
         UpperCAmelCase							=     max_position_embeddings
         UpperCAmelCase							=     d_model
         UpperCAmelCase							=     encoder_ffn_dim
         UpperCAmelCase							=     encoder_layers
         UpperCAmelCase							=     encoder_attention_heads
         UpperCAmelCase							=     decoder_ffn_dim
         UpperCAmelCase							=     decoder_layers
         UpperCAmelCase							=     decoder_attention_heads
         UpperCAmelCase							=     dropout
         UpperCAmelCase							=     attention_dropout
         UpperCAmelCase							=     activation_dropout
         UpperCAmelCase							=     activation_function
         UpperCAmelCase							=     init_std
         UpperCAmelCase							=     encoder_layerdrop
         UpperCAmelCase							=     decoder_layerdrop
         UpperCAmelCase							=     use_cache
         UpperCAmelCase							=     encoder_layers
         UpperCAmelCase							=     scale_embedding  # scale factor will be sqrt(d_model) if True
         UpperCAmelCase							=     router_z_loss_coef
         UpperCAmelCase							=     router_aux_loss_coef
         UpperCAmelCase							=     decoder_sparse_step
         UpperCAmelCase							=     encoder_sparse_step
         UpperCAmelCase							=     num_experts
         UpperCAmelCase							=     expert_capacity
         UpperCAmelCase							=     router_bias
         if router_dtype not in ["float32", "float16", "bfloat16"]:
            raise ValueError(F"""`router_dtype` must be one of 'float32', 'float16' or 'bfloat16', got {router_dtype}"""       )
         UpperCAmelCase							=     router_dtype

         UpperCAmelCase							=     router_ignore_padding_tokens
         UpperCAmelCase							=     batch_prioritized_routing
         UpperCAmelCase							=     second_expert_policy
         UpperCAmelCase							=     normalize_router_prob_before_dropping
         UpperCAmelCase							=     moe_eval_capacity_token_fraction
         UpperCAmelCase							=     moe_token_dropout
         UpperCAmelCase							=     output_router_logits
         super().__init__(
             pad_token_id=__lowerCamelCase					,       bos_token_id=__lowerCamelCase					,       eos_token_id=__lowerCamelCase					,       is_encoder_decoder=__lowerCamelCase					,       decoder_start_token_id=__lowerCamelCase					,       **__lowerCamelCase					,       )



