

import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class  __lowerCamelCase				(	__snake_case	):
      lowerCamelCase_	:      Any          =					(LMSDiscreteScheduler,)
      lowerCamelCase_	:      List[str]          =					10







      def 	lowerCAmelCase_    (     self							,  **lowerCamelCase   )						->       Any:
        snake_case_       						=      {
            """num_train_timesteps""": 1100,
            """beta_start""": 0.0001,
            """beta_end""": 0.02,
            """beta_schedule""": """linear""",
        }

        config.update(**lowerCamelCase   )
        return config







      def 	lowerCAmelCase_    (     self   )						->       Optional[Any]:
        for timesteps in [10, 50, 100, 1000]:
          self.check_over_configs(num_train_timesteps=lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       List[str]:
        for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]							,  [0.0002, 0.002, 0.02]   ):
          self.check_over_configs(beta_start=lowerCamelCase							,  beta_end=lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       Optional[Any]:
        for schedule in ["linear", "scaled_linear"]:
          self.check_over_configs(beta_schedule=lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       Optional[int]:
        for prediction_type in ["epsilon", "v_prediction"]:
          self.check_over_configs(prediction_type=lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       Optional[int]:
        for t in [0, 500, 800]:
          self.check_over_forward(time_step=lowerCamelCase   )







      def 	lowerCAmelCase_    (     self   )						->       str:
        snake_case_       						=      self.scheduler_classes[0]
        snake_case_       						=      self.get_scheduler_config()
        snake_case_       						=      scheduler_class(**lowerCamelCase   )

        scheduler.set_timesteps(self.num_inference_steps   )

        snake_case_       						=      self.dummy_model()
        snake_case_       						=      self.dummy_sample_deter * scheduler.init_noise_sigma

        for i, t in enumerate(scheduler.timesteps   ):
          snake_case_       						=      scheduler.scale_model_input(lowerCamelCase							,  lowerCamelCase   )

          snake_case_       						=      model(lowerCamelCase							,  lowerCamelCase   )

          snake_case_       						=      scheduler.step(lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase   )
          snake_case_       						=      output.prev_sample

        snake_case_       						=      torch.sum(torch.abs(lowerCamelCase   )   )
        snake_case_       						=      torch.mean(torch.abs(lowerCamelCase   )   )

        assert abs(result_sum.item() - 1006.388   ) < 1e-2
        assert abs(result_mean.item() - 1.31   ) < 1e-3







      def 	lowerCAmelCase_    (     self   )						->       List[str]:
        snake_case_       						=      self.scheduler_classes[0]
        snake_case_       						=      self.get_scheduler_config(prediction_type="""v_prediction"""   )
        snake_case_       						=      scheduler_class(**lowerCamelCase   )

        scheduler.set_timesteps(self.num_inference_steps   )

        snake_case_       						=      self.dummy_model()
        snake_case_       						=      self.dummy_sample_deter * scheduler.init_noise_sigma

        for i, t in enumerate(scheduler.timesteps   ):
          snake_case_       						=      scheduler.scale_model_input(lowerCamelCase							,  lowerCamelCase   )

          snake_case_       						=      model(lowerCamelCase							,  lowerCamelCase   )

          snake_case_       						=      scheduler.step(lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase   )
          snake_case_       						=      output.prev_sample

        snake_case_       						=      torch.sum(torch.abs(lowerCamelCase   )   )
        snake_case_       						=      torch.mean(torch.abs(lowerCamelCase   )   )

        assert abs(result_sum.item() - 0.0017   ) < 1e-2
        assert abs(result_mean.item() - 2.26_76e-06   ) < 1e-3







      def 	lowerCAmelCase_    (     self   )						->       List[Any]:
        snake_case_       						=      self.scheduler_classes[0]
        snake_case_       						=      self.get_scheduler_config()
        snake_case_       						=      scheduler_class(**lowerCamelCase   )

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

        snake_case_       						=      self.dummy_model()
        snake_case_       						=      self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
        snake_case_       						=      sample.to(lowerCamelCase   )

        for i, t in enumerate(scheduler.timesteps   ):
          snake_case_       						=      scheduler.scale_model_input(lowerCamelCase							,  lowerCamelCase   )

          snake_case_       						=      model(lowerCamelCase							,  lowerCamelCase   )

          snake_case_       						=      scheduler.step(lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase   )
          snake_case_       						=      output.prev_sample

        snake_case_       						=      torch.sum(torch.abs(lowerCamelCase   )   )
        snake_case_       						=      torch.mean(torch.abs(lowerCamelCase   )   )

        assert abs(result_sum.item() - 1006.388   ) < 1e-2
        assert abs(result_mean.item() - 1.31   ) < 1e-3







      def 	lowerCAmelCase_    (     self   )						->       Optional[Any]:
        snake_case_       						=      self.scheduler_classes[0]
        snake_case_       						=      self.get_scheduler_config()
        snake_case_       						=      scheduler_class(**lowerCamelCase							,  use_karras_sigmas=lowerCamelCase   )

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

        snake_case_       						=      self.dummy_model()
        snake_case_       						=      self.dummy_sample_deter.to(lowerCamelCase   ) * scheduler.init_noise_sigma
        snake_case_       						=      sample.to(lowerCamelCase   )

        for t in scheduler.timesteps:
          snake_case_       						=      scheduler.scale_model_input(lowerCamelCase							,  lowerCamelCase   )

          snake_case_       						=      model(lowerCamelCase							,  lowerCamelCase   )

          snake_case_       						=      scheduler.step(lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase   )
          snake_case_       						=      output.prev_sample

        snake_case_       						=      torch.sum(torch.abs(lowerCamelCase   )   )
        snake_case_       						=      torch.mean(torch.abs(lowerCamelCase   )   )

        assert abs(result_sum.item() - 3812.9927   ) < 2e-2
        assert abs(result_mean.item() - 4.9648   ) < 1e-3





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


_lowerCamelCase					:     Optional[Any]					  =  logging.get_logger(__name__)

_lowerCamelCase					:     str					  =  {
    "google/reformer-crime-and-punishment": (
        "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/config.json"
    ),
    "google/reformer-enwik8": "https://huggingface.co/google/reformer-enwik8/resolve/main/config.json",
}
class 				__snake_case     (_a						):
				lowerCAmelCase__			=   "reformer"
				lowerCAmelCase__			=   ["past_buckets_states"]
				lowerCAmelCase__			=   {}



				def __init__(					self					:						Optional[int]						,		_UpperCAmelCase					:						str=64						,		_UpperCAmelCase					:						Tuple=["local", "lsh", "local", "lsh", "local", "lsh"]						,		_UpperCAmelCase					:						Optional[int]=1.0						,		_UpperCAmelCase					:						str=True						,		_UpperCAmelCase					:						List[Any]=[64, 64]						,		_UpperCAmelCase					:						str=[64, 192]						,		_UpperCAmelCase					:						List[Any]=0						,		_UpperCAmelCase					:						Optional[Any]=2						,		_UpperCAmelCase					:						Dict=512						,		_UpperCAmelCase					:						Optional[int]=None						,		_UpperCAmelCase					:						Tuple="relu"						,		_UpperCAmelCase					:						Tuple=0.05						,		_UpperCAmelCase					:						int=256						,		_UpperCAmelCase					:						List[str]=0.02						,		_UpperCAmelCase					:						Any=False						,		_UpperCAmelCase					:						Any=1E-12						,		_UpperCAmelCase					:						List[Any]=1						,		_UpperCAmelCase					:						Optional[Any]=0						,		_UpperCAmelCase					:						Union[str, Any]=0.05						,		_UpperCAmelCase					:						int=64						,		_UpperCAmelCase					:						List[str]=64						,		_UpperCAmelCase					:						Any=0.0						,		_UpperCAmelCase					:						Any=1						,		_UpperCAmelCase					:						Optional[Any]=0						,		_UpperCAmelCase					:						Union[str, Any]=4096						,		_UpperCAmelCase					:						Dict=12						,		_UpperCAmelCase					:						int=None						,		_UpperCAmelCase					:						List[Any]=1						,		_UpperCAmelCase					:						Union[str, Any]=0						,		_UpperCAmelCase					:						Any=320						,		_UpperCAmelCase					:						Any=False						,		_UpperCAmelCase					:						List[str]=True						,		_UpperCAmelCase					:						Optional[int]=None						,		**_UpperCAmelCase					:						Tuple						,		)     ->			List[str]:





								'''simple docstring'''




								_lowerCAmelCase			:			Union[str, Any]        =					hash_seed
								_lowerCAmelCase			:			int        =					vocab_size
								_lowerCAmelCase			:			Optional[Any]        =					attention_head_size
								_lowerCAmelCase			:			Dict        =					hidden_size
								_lowerCAmelCase			:			Union[str, Any]        =					num_attention_heads
								_lowerCAmelCase			:			int        =					num_hashes
								_lowerCAmelCase			:			Union[str, Any]        =					len(_UpperCAmelCase					)
								_lowerCAmelCase			:			int        =					tuple(_UpperCAmelCase					) if isinstance(_UpperCAmelCase						,		_UpperCAmelCase					) else num_buckets
								_lowerCAmelCase			:			Union[str, Any]        =					lsh_attn_chunk_length
								_lowerCAmelCase			:			List[str]        =					local_attn_chunk_length
								_lowerCAmelCase			:			int        =					lsh_num_chunks_after
								_lowerCAmelCase			:			Union[str, Any]        =					lsh_num_chunks_before
								_lowerCAmelCase			:			str        =					local_num_chunks_after
								_lowerCAmelCase			:			Dict        =					local_num_chunks_before
								_lowerCAmelCase			:			Any        =					hidden_act
								_lowerCAmelCase			:			List[str]        =					feed_forward_size
								_lowerCAmelCase			:			str        =					hidden_dropout_prob
								_lowerCAmelCase			:			Any        =					lsh_attention_probs_dropout_prob
								_lowerCAmelCase			:			Dict        =					local_attention_probs_dropout_prob
								_lowerCAmelCase			:			Tuple        =					max_position_embeddings
								_lowerCAmelCase			:			Any        =					initializer_range
								_lowerCAmelCase			:			List[str]        =					layer_norm_eps
								_lowerCAmelCase			:			Dict        =					axial_pos_embds
								_lowerCAmelCase			:			Union[str, Any]        =					tuple(_UpperCAmelCase					)
								_lowerCAmelCase			:			int        =					tuple(_UpperCAmelCase					)
								_lowerCAmelCase			:			List[str]        =					axial_norm_std
								_lowerCAmelCase			:			List[str]        =					chunk_size_lm_head
								_lowerCAmelCase			:			int        =					attn_layers
								_lowerCAmelCase			:			List[Any]        =					use_cache
								_lowerCAmelCase			:			Optional[int]        =					classifier_dropout
								super().__init__(
								    pad_token_id=_UpperCAmelCase						,		eos_token_id=_UpperCAmelCase						,		is_decoder=_UpperCAmelCase						,		tie_word_embeddings=_UpperCAmelCase						,		**_UpperCAmelCase						,		)

