





from dataclasses import asdict, dataclass
from typing import Optional

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


__lowercase  :   str						      =logging.get_logger(__name__)

# TODO Update this
__lowercase  :   str						      ={
    """facebook/esm-1b""": """https://huggingface.co/facebook/esm-1b/resolve/main/config.json""",
    # See all ESM models at https://huggingface.co/models?filter=esm
}

class      A       (	__lowercase						):
      _snake_case							     ='''esm'''



      def __init__(	self:						Dict		,  _lowerCAmelCase:						Dict=None		,  _lowerCAmelCase:						str=None		,  _lowerCAmelCase:						str=None		,  _lowerCAmelCase:						Tuple=768		,  _lowerCAmelCase:						int=12		,  _lowerCAmelCase:						List[Any]=12		,  _lowerCAmelCase:						Dict=3072		,  _lowerCAmelCase:						Union[str, Any]=0.1		,  _lowerCAmelCase:						Optional[Any]=0.1		,  _lowerCAmelCase:						int=1026		,  _lowerCAmelCase:						Union[str, Any]=0.02		,  _lowerCAmelCase:						Optional[int]=1e-12		,  _lowerCAmelCase:						int="absolute"		,  _lowerCAmelCase:						List[str]=True		,  _lowerCAmelCase:						Dict=None		,  _lowerCAmelCase:						Optional[int]=False		,  _lowerCAmelCase:						Tuple=False		,  _lowerCAmelCase:						List[str]=None		,  _lowerCAmelCase:						Tuple=None		,  **_lowerCAmelCase:						Union[str, Any]		,  )						->    List[str]:
         '''simple docstring'''

         super().__init__(pad_token_id=_lowerCAmelCase		,  mask_token_id=_lowerCAmelCase		,  **_lowerCAmelCase       )

         UpperCAmelCase_											=vocab_size
         UpperCAmelCase_											=hidden_size
         UpperCAmelCase_											=num_hidden_layers
         UpperCAmelCase_											=num_attention_heads
         UpperCAmelCase_											=intermediate_size
         UpperCAmelCase_											=hidden_dropout_prob
         UpperCAmelCase_											=attention_probs_dropout_prob
         UpperCAmelCase_											=max_position_embeddings
         UpperCAmelCase_											=initializer_range
         UpperCAmelCase_											=layer_norm_eps
         UpperCAmelCase_											=position_embedding_type
         UpperCAmelCase_											=use_cache
         UpperCAmelCase_											=emb_layer_norm_before
         UpperCAmelCase_											=token_dropout
         UpperCAmelCase_											=is_folding_model
         if is_folding_model:
            if esmfold_config is None:
               logger.info("No esmfold_config supplied for folding model, using default values."       )
               UpperCAmelCase_											=EsmFoldConfig()
            elif isinstance(_lowerCAmelCase		,  _lowerCAmelCase       ):
               UpperCAmelCase_											=EsmFoldConfig(**_lowerCAmelCase       )
            UpperCAmelCase_											=esmfold_config
            if vocab_list is None:
               logger.warning("No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!"       )
               UpperCAmelCase_											=get_default_vocab_list()
            else:
               UpperCAmelCase_											=vocab_list
         else:
            UpperCAmelCase_											=None
            UpperCAmelCase_											=None
         if self.esmfold_config is not None and getattr(self.esmfold_config		,  "use_esm_attn_map"		,  _lowerCAmelCase       ):
            raise ValueError("The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!"       )






      def 	lowerCAmelCase__    (	self:						List[Any]       )						->    List[str]:
         '''simple docstring'''

         UpperCAmelCase_											=super().to_dict()
         if isinstance(self.esmfold_config		,  _lowerCAmelCase       ):
            UpperCAmelCase_											=self.esmfold_config.to_dict()
         return output

@dataclass
class      A       :
      _snake_case							     =None
      _snake_case							     =True
      _snake_case							     =False
      _snake_case							     =False
      _snake_case							     =False
      _snake_case							     =0

      _snake_case							     =True
      _snake_case							     =False

      _snake_case							     =128
      _snake_case							     =None



      def 	lowerCAmelCase__    (	self:						int       )						->    Union[str, Any]:
         '''simple docstring'''

         if self.trunk is None:
            UpperCAmelCase_											=TrunkConfig()
         elif isinstance(self.trunk		,  _lowerCAmelCase       ):
            UpperCAmelCase_											=TrunkConfig(**self.trunk       )






      def 	lowerCAmelCase__    (	self:						Optional[Any]       )						->    List[str]:
         '''simple docstring'''

         UpperCAmelCase_											=asdict(self       )
         UpperCAmelCase_											=self.trunk.to_dict()
         return output

@dataclass
class      A       :
      _snake_case							     =48
      _snake_case							     =1024
      _snake_case							     =128
      _snake_case							     =32
      _snake_case							     =32
      _snake_case							     =32
      _snake_case							     =0
      _snake_case							     =0
      _snake_case							     =False
      _snake_case							     =4
      _snake_case							     =128
      _snake_case							     =None



      def 	lowerCAmelCase__    (	self:						int       )						->    Dict:
         '''simple docstring'''

         if self.structure_module is None:
            UpperCAmelCase_											=StructureModuleConfig()
         elif isinstance(self.structure_module		,  _lowerCAmelCase       ):
            UpperCAmelCase_											=StructureModuleConfig(**self.structure_module       )

         if self.max_recycles <= 0:
            raise ValueError(F'`max_recycles` should be positive, got {self.max_recycles}.'       )
         if self.sequence_state_dim % self.sequence_state_dim != 0:
            raise ValueError(
                "`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got"
                F' {self.sequence_state_dim} and {self.sequence_state_dim}.'       )
         if self.pairwise_state_dim % self.pairwise_state_dim != 0:
            raise ValueError(
                "`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got"
                F' {self.pairwise_state_dim} and {self.pairwise_state_dim}.'       )

         UpperCAmelCase_											=self.sequence_state_dim // self.sequence_head_width
         UpperCAmelCase_											=self.pairwise_state_dim // self.pairwise_head_width

         if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width:
            raise ValueError(
                "`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got"
                F' {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}.'       )
         if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width:
            raise ValueError(
                "`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got"
                F' {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}.'       )
         if self.pairwise_state_dim % 2 != 0:
            raise ValueError(F'`pairwise_state_dim` should be even, got {self.pairwise_state_dim}.'       )

         if self.dropout >= 0.4:
            raise ValueError(F'`dropout` should not be greater than 0.4, got {self.dropout}.'       )






      def 	lowerCAmelCase__    (	self:						List[Any]       )						->    Union[str, Any]:
         '''simple docstring'''

         UpperCAmelCase_											=asdict(self       )
         UpperCAmelCase_											=self.structure_module.to_dict()
         return output

@dataclass
class      A       :
      _snake_case							     =384
      _snake_case							     =128
      _snake_case							     =16
      _snake_case							     =128
      _snake_case							     =12
      _snake_case							     =4
      _snake_case							     =8
      _snake_case							     =0.1
      _snake_case							     =8
      _snake_case							     =1
      _snake_case							     =2
      _snake_case							     =7
      _snake_case							     =10
      _snake_case							     =1E-8
      _snake_case							     =1E5



      def 	lowerCAmelCase__    (	self:						Optional[Any]       )						->    Tuple:
         '''simple docstring'''

         return asdict(self       )




def     a__ (					):






   '''simple docstring'''






   return (
       "<cls>",
       "<pad>",
       "<eos>",
       "<unk>",
       "L",
       "A",
       "G",
       "V",
       "S",
       "E",
       "R",
       "T",
       "I",
       "D",
       "P",
       "K",
       "Q",
       "N",
       "F",
       "Y",
       "M",
       "H",
       "W",
       "C",
       "X",
       "B",
       "U",
       "Z",
       "O",
       ".",
       "-",
       "<null_1>",
       "<mask>",
   )











import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest

class      A       (	__lowercase						):
      _snake_case							     =(DDIMParallelScheduler,)
      _snake_case							     =(('''eta''', 0.0), ('''num_inference_steps''', 50))



      def 	lowerCAmelCase__    (	self:						str		,  **_lowerCAmelCase:						str       )						->    str:
         '''simple docstring'''

         UpperCAmelCase_											={
             "num_train_timesteps": 1000,
             "beta_start": 0.00_01,
             "beta_end": 0.02,
             "beta_schedule": "linear",
             "clip_sample": True,
         }

         config.update(**_lowerCAmelCase       )
         return config



      def 	lowerCAmelCase__    (	self:						int		,  **_lowerCAmelCase:						Tuple       )						->    str:
         '''simple docstring'''

         UpperCAmelCase_											=self.scheduler_classes[0]
         UpperCAmelCase_											=self.get_scheduler_config(**_lowerCAmelCase       )
         UpperCAmelCase_											=scheduler_class(**_lowerCAmelCase       )

         UpperCAmelCase_						,       UpperCAmelCase_											=10, 0.0

         UpperCAmelCase_											=self.dummy_model()
         UpperCAmelCase_											=self.dummy_sample_deter

         scheduler.set_timesteps(_lowerCAmelCase       )

         for t in scheduler.timesteps:
            UpperCAmelCase_											=model(_lowerCAmelCase		,  _lowerCAmelCase       )
            UpperCAmelCase_											=scheduler.step(_lowerCAmelCase		,  _lowerCAmelCase		,  _lowerCAmelCase		,  _lowerCAmelCase       ).prev_sample

         return sample



      def 	lowerCAmelCase__    (	self:						Optional[int]       )						->    Optional[int]:
         '''simple docstring'''

         for timesteps in [100, 500, 1000]:
            self.check_over_configs(num_train_timesteps=_lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						int       )						->    List[str]:
         '''simple docstring'''

         for steps_offset in [0, 1]:
            self.check_over_configs(steps_offset=_lowerCAmelCase       )

         UpperCAmelCase_											=self.scheduler_classes[0]
         UpperCAmelCase_											=self.get_scheduler_config(steps_offset=1       )
         UpperCAmelCase_											=scheduler_class(**_lowerCAmelCase       )
         scheduler.set_timesteps(5       )
         assert torch.equal(scheduler.timesteps		,  torch.LongTensor([801, 601, 401, 201, 1]       )       )



      def 	lowerCAmelCase__    (	self:						int       )						->    Dict:
         '''simple docstring'''

         for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]		,  [0.0_02, 0.02, 0.2, 2]       ):
            self.check_over_configs(beta_start=_lowerCAmelCase		,  beta_end=_lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						int       )						->    Any:
         '''simple docstring'''

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



      def 	lowerCAmelCase__    (	self:						Union[str, Any]       )						->    Optional[Any]:
         '''simple docstring'''

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



      def 	lowerCAmelCase__    (	self:						Optional[int]       )						->    Tuple:
         '''simple docstring'''

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



      def 	lowerCAmelCase__    (	self:						Union[str, Any]       )						->    List[Any]:
         '''simple docstring'''

         for timestep_spacing in ["trailing", "leading"]:
            self.check_over_configs(timestep_spacing=_lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						Union[str, Any]       )						->    Any:
         '''simple docstring'''

         for rescale_betas_zero_snr in [True, False]:
            self.check_over_configs(rescale_betas_zero_snr=_lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						Union[str, Any]       )						->    Optional[Any]:
         '''simple docstring'''

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



      def 	lowerCAmelCase__    (	self:						Union[str, Any]       )						->    List[str]:
         '''simple docstring'''

         for t in [1, 10, 49]:
            self.check_over_forward(time_step=_lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						Any       )						->    List[str]:
         '''simple docstring'''

         for t, num_inference_steps in zip([1, 10, 50]		,  [10, 50, 500]       ):
            self.check_over_forward(time_step=_lowerCAmelCase		,  num_inference_steps=_lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						int       )						->    Tuple:
         '''simple docstring'''

         for t, eta in zip([1, 10, 49]		,  [0.0, 0.5, 1.0]       ):
            self.check_over_forward(time_step=_lowerCAmelCase		,  eta=_lowerCAmelCase       )



      def 	lowerCAmelCase__    (	self:						Optional[Any]       )						->    Optional[Any]:
         '''simple docstring'''

         UpperCAmelCase_											=self.scheduler_classes[0]
         UpperCAmelCase_											=self.get_scheduler_config()
         UpperCAmelCase_											=scheduler_class(**_lowerCAmelCase       )

         assert torch.sum(torch.abs(scheduler._get_variance(0		,  0       ) - 0.0       )       ) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(420		,  400       ) - 0.1_47_71       )       ) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(980		,  960       ) - 0.3_24_60       )       ) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(0		,  0       ) - 0.0       )       ) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(487		,  486       ) - 0.0_09_79       )       ) < 1e-5
         assert torch.sum(torch.abs(scheduler._get_variance(999		,  998       ) - 0.02       )       ) < 1e-5



      def 	lowerCAmelCase__    (	self:						int       )						->    Optional[Any]:
         '''simple docstring'''

         UpperCAmelCase_											=self.scheduler_classes[0]
         UpperCAmelCase_											=self.get_scheduler_config()
         UpperCAmelCase_											=scheduler_class(**_lowerCAmelCase       )

         UpperCAmelCase_						,       UpperCAmelCase_											=10, 0.0
         scheduler.set_timesteps(_lowerCAmelCase       )

         UpperCAmelCase_											=self.dummy_model()
         UpperCAmelCase_											=self.dummy_sample_deter
         UpperCAmelCase_											=self.dummy_sample_deter + 0.1
         UpperCAmelCase_											=self.dummy_sample_deter - 0.1

         UpperCAmelCase_											=samplea.shape[0]
         UpperCAmelCase_											=torch.stack([samplea, samplea, samplea]		,  dim=0       )
         UpperCAmelCase_											=torch.arange(_lowerCAmelCase       )[0:3, None].repeat(1		,  _lowerCAmelCase       )

         UpperCAmelCase_											=model(samples.flatten(0		,  1       )		,  timesteps.flatten(0		,  1       )       )
         UpperCAmelCase_											=scheduler.batch_step_no_noise(_lowerCAmelCase		,  timesteps.flatten(0		,  1       )		,  samples.flatten(0		,  1       )		,  _lowerCAmelCase       )

         UpperCAmelCase_											=torch.sum(torch.abs(_lowerCAmelCase       )       )
         UpperCAmelCase_											=torch.mean(torch.abs(_lowerCAmelCase       )       )

         assert abs(result_sum.item() - 11_47.79_04       ) < 1e-2
         assert abs(result_mean.item() - 0.49_82       ) < 1e-3



      def 	lowerCAmelCase__    (	self:						Optional[int]       )						->    Optional[Any]:
         '''simple docstring'''

         UpperCAmelCase_											=self.full_loop()

         UpperCAmelCase_											=torch.sum(torch.abs(_lowerCAmelCase       )       )
         UpperCAmelCase_											=torch.mean(torch.abs(_lowerCAmelCase       )       )

         assert abs(result_sum.item() - 1_72.00_67       ) < 1e-2
         assert abs(result_mean.item() - 0.22_39_67       ) < 1e-3



      def 	lowerCAmelCase__    (	self:						int       )						->    Union[str, Any]:
         '''simple docstring'''

         UpperCAmelCase_											=self.full_loop(prediction_type="v_prediction"       )

         UpperCAmelCase_											=torch.sum(torch.abs(_lowerCAmelCase       )       )
         UpperCAmelCase_											=torch.mean(torch.abs(_lowerCAmelCase       )       )

         assert abs(result_sum.item() - 52.53_02       ) < 1e-2
         assert abs(result_mean.item() - 0.06_84       ) < 1e-3



      def 	lowerCAmelCase__    (	self:						Optional[Any]       )						->    Tuple:
         '''simple docstring'''

         UpperCAmelCase_											=self.full_loop(set_alpha_to_one=_lowerCAmelCase		,  beta_start=0.01       )
         UpperCAmelCase_											=torch.sum(torch.abs(_lowerCAmelCase       )       )
         UpperCAmelCase_											=torch.mean(torch.abs(_lowerCAmelCase       )       )

         assert abs(result_sum.item() - 1_49.82_95       ) < 1e-2
         assert abs(result_mean.item() - 0.19_51       ) < 1e-3






      def 	lowerCAmelCase__    (	self:						Optional[Any]       )						->    str:
         '''simple docstring'''

         UpperCAmelCase_											=self.full_loop(set_alpha_to_one=_lowerCAmelCase		,  beta_start=0.01       )
         UpperCAmelCase_											=torch.sum(torch.abs(_lowerCAmelCase       )       )
         UpperCAmelCase_											=torch.mean(torch.abs(_lowerCAmelCase       )       )

         assert abs(result_sum.item() - 1_49.07_84       ) < 1e-2
         assert abs(result_mean.item() - 0.19_41       ) < 1e-3




