




"""simple docstring"""

from dataclasses import asdict, dataclass
from typing import Optional

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


a							      =     logging.get_logger(__name__)

# TODO Update this
a							      =     {
    '''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 							lowercase_			(	__lowerCAmelCase				):



 '''simple docstring'''
 UpperCAmelCase       :	Dict     =      '''esm'''

 def __init__(					self			:			Union[str, Any]       ,				_UpperCAmelCase			:			List[str]=None       ,				_UpperCAmelCase			:			Optional[int]=None       ,				_UpperCAmelCase			:			List[Any]=None       ,				_UpperCAmelCase			:			int=768       ,				_UpperCAmelCase			:			Union[str, Any]=12       ,				_UpperCAmelCase			:			Optional[Any]=12       ,				_UpperCAmelCase			:			int=3_072       ,				_UpperCAmelCase			:			Optional[Any]=0.1       ,				_UpperCAmelCase			:			Any=0.1       ,				_UpperCAmelCase			:			int=1_026       ,				_UpperCAmelCase			:			Dict=0.02       ,				_UpperCAmelCase			:			Optional[int]=1E-1_2       ,				_UpperCAmelCase			:			str="absolute"       ,				_UpperCAmelCase			:			Dict=True       ,				_UpperCAmelCase			:			Any=None       ,				_UpperCAmelCase			:			Optional[Any]=False       ,				_UpperCAmelCase			:			Union[str, Any]=False       ,				_UpperCAmelCase			:			Tuple=None       ,				_UpperCAmelCase			:			Tuple=None       ,				**_UpperCAmelCase			:			List[str]       ,				):
       super().__init__(pad_token_id=_UpperCAmelCase       ,				mask_token_id=_UpperCAmelCase       ,				**_UpperCAmelCase						)

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



 def      lowerCAmelCase_				(					self			:			Optional[int]						):
       _A     =  super().to_dict()
       if isinstance(self.esmfold_config       ,				_UpperCAmelCase						):
             _A     =  self.esmfold_config.to_dict()
       return output





@dataclass
class 							lowercase_			:



 '''simple docstring'''
 UpperCAmelCase       :	str     =      None
 UpperCAmelCase       :	bool     =      True
 UpperCAmelCase       :	bool     =      False
 UpperCAmelCase       :	bool     =      False
 UpperCAmelCase       :	bool     =      False
 UpperCAmelCase       :	float     =      0

 UpperCAmelCase       :	bool     =      True
 UpperCAmelCase       :	bool     =      False

 UpperCAmelCase       :	int     =      128
 UpperCAmelCase       :	"TrunkConfig"     =      None

 def      lowerCAmelCase_				(					self			:			Optional[int]						):
       if self.trunk is None:
             _A     =  TrunkConfig()
       elif isinstance(self.trunk       ,				_UpperCAmelCase						):
             _A     =  TrunkConfig(**self.trunk						)



 def      lowerCAmelCase_				(					self			:			Dict						):
       _A     =  asdict(self						)
       _A     =  self.trunk.to_dict()
       return output





@dataclass
class 							lowercase_			:



 '''simple docstring'''
 UpperCAmelCase       :	int     =      48
 UpperCAmelCase       :	int     =      1024
 UpperCAmelCase       :	int     =      128
 UpperCAmelCase       :	int     =      32
 UpperCAmelCase       :	int     =      32
 UpperCAmelCase       :	int     =      32
 UpperCAmelCase       :	float     =      0
 UpperCAmelCase       :	float     =      0
 UpperCAmelCase       :	bool     =      False
 UpperCAmelCase       :	int     =      4
 UpperCAmelCase       :	Optional[int]     =      128
 UpperCAmelCase       :	"StructureModuleConfig"     =      None

 def      lowerCAmelCase_				(					self			:			str						):
       if self.structure_module is None:
             _A     =  StructureModuleConfig()
       elif isinstance(self.structure_module       ,				_UpperCAmelCase						):
             _A     =  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}.'''						)

       _A     =  self.sequence_state_dim // self.sequence_head_width
       _A     =  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			:			Union[str, Any]						):
       _A     =  asdict(self						)
       _A     =  self.structure_module.to_dict()
       return output





@dataclass
class 							lowercase_			:



 '''simple docstring'''
 UpperCAmelCase       :	int     =      384
 UpperCAmelCase       :	int     =      128
 UpperCAmelCase       :	int     =      16
 UpperCAmelCase       :	int     =      128
 UpperCAmelCase       :	int     =      12
 UpperCAmelCase       :	int     =      4
 UpperCAmelCase       :	int     =      8
 UpperCAmelCase       :	float     =      0.1
 UpperCAmelCase       :	int     =      8
 UpperCAmelCase       :	int     =      1
 UpperCAmelCase       :	int     =      2
 UpperCAmelCase       :	int     =      7
 UpperCAmelCase       :	int     =      10
 UpperCAmelCase       :	float     =      1E-8
 UpperCAmelCase       :	float     =      1E5

 def      lowerCAmelCase_				(					self			:			Optional[int]						):
       return asdict(self						)






def 		_snake_case	(   )       ->   List[Any]:






      '''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>",
      )



from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class 			lowercase_		(      A       ):
       __lowerCamelCase				       =						42
       __lowerCamelCase				       =						42


       def __init__(    self						,       __A						,       __A				)						->    List[str]:
              super().__init__()
              self.register_modules(unet=__A						,       scheduler=__A				)





       @torch.no_grad()
       def __call__(    self						,       __A = 1						,       __A = 50						,       __A = None						,       __A = "pil"						,       __A = True						,       **__A						,       )						->    Union[Tuple, ImagePipelineOutput]:
              SCREAMING_SNAKE_CASE_							:				Dict		=self.unet.config.sample_size
              SCREAMING_SNAKE_CASE_							:				Tuple		=(batch_size, 3, img_size, img_size)

              SCREAMING_SNAKE_CASE_							:				int		=self.unet

              # sample x_0 ~ N(0, sigma_0^2 * I)
              SCREAMING_SNAKE_CASE_							:				Tuple		=randn_tensor(__A						,       generator=__A						,       device=self.device				) * self.scheduler.init_noise_sigma

              self.scheduler.set_timesteps(__A				)

              for t in self.progress_bar(self.scheduler.timesteps				):
                     # here sigma_t == t_i from the paper
                     SCREAMING_SNAKE_CASE_							:				Union[str, Any]		=self.scheduler.schedule[t]
                     SCREAMING_SNAKE_CASE_							:				List[Any]		=self.scheduler.schedule[t - 1] if t > 0 else 0

                     # 1. Select temporarily increased noise level sigma_hat
                     # 2. Add new noise to move from sample_i to sample_hat
                     SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_							:				Dict		=self.scheduler.add_noise_to_input(__A						,       __A						,       generator=__A				)

                     # 3. Predict the noise residual given the noise magnitude `sigma_hat`
                     # The model inputs and output are adjusted by following eq. (213) in [1].
                     SCREAMING_SNAKE_CASE_							:				Tuple		=(sigma_hat / 2) * model((sample_hat + 1) / 2						,       sigma_hat / 2				).sample

                     # 4. Evaluate dx/dt at sigma_hat
                     # 5. Take Euler step from sigma to sigma_prev
                     SCREAMING_SNAKE_CASE_							:				List[Any]		=self.scheduler.step(__A						,       __A						,       __A						,       __A				)

                     if sigma_prev != 0:
                            # 6. Apply 2nd order correction
                            # The model inputs and output are adjusted by following eq. (213) in [1].
                            SCREAMING_SNAKE_CASE_							:				int		=(sigma_prev / 2) * model((step_output.prev_sample + 1) / 2						,       sigma_prev / 2				).sample
                            SCREAMING_SNAKE_CASE_							:				Dict		=self.scheduler.step_correct(
                                __A						,       __A						,       __A						,       __A						,       step_output.prev_sample						,       step_output['''derivative''']						,       )
                     SCREAMING_SNAKE_CASE_							:				Any		=step_output.prev_sample

              SCREAMING_SNAKE_CASE_							:				List[Any]		=(sample / 2 + 0.5).clamp(0						,       1				)
              SCREAMING_SNAKE_CASE_							:				Any		=sample.cpu().permute(0						,       2						,       3						,       1				).numpy()
              if output_type == "pil":
                     SCREAMING_SNAKE_CASE_							:				Optional[Any]		=self.numpy_to_pil(__A				)

              if not return_dict:
                     return (image,)

              return ImagePipelineOutput(images=__A				)
