


'''simple docstring'''



from typing import List, Optional, Tuple, Union

import torch

from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline


A__						:   Optional[Any]				=    logging.get_logger(__name__)  # pylint: disable=invalid-name





class   UpperCAmelCase_    (_UpperCAmelCase    ):





    """simple docstring"""






    def __init__(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )							->	Optional[int]:
         super().__init__()
         self.register_modules(unet=SCREAMING_SNAKE_CASE_     ,	scheduler=SCREAMING_SNAKE_CASE_      )







    @torch.no_grad()
    def __call__(				self     ,	SCREAMING_SNAKE_CASE_ = 1     ,	SCREAMING_SNAKE_CASE_ = 1_00     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = True     ,	)							->	Union[AudioPipelineOutput, Tuple]:

         if audio_length_in_s is None:
              __lowerCamelCase  :      int														=						self.unet.config.sample_size / self.unet.config.sample_rate

         __lowerCamelCase  :      Any														=						audio_length_in_s * self.unet.config.sample_rate

         __lowerCamelCase  :      Optional[Any]														=						2 ** len(self.unet.up_blocks      )
         if sample_size < 3 * down_scale_factor:
              raise ValueError(
                  f'{audio_length_in_s} is too small. Make sure it\'s bigger or equal to'
                  f' {3 * down_scale_factor / self.unet.config.sample_rate}.'      )

         __lowerCamelCase  :      Tuple														=						int(SCREAMING_SNAKE_CASE_      )
         if sample_size % down_scale_factor != 0:
              __lowerCamelCase  :      Dict														=						(
                  (audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
              ) * down_scale_factor
              logger.info(
                  f'{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled'
                  f' by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising'
                  ' process.'      )
         __lowerCamelCase  :      List[Any]														=						int(SCREAMING_SNAKE_CASE_      )

         __lowerCamelCase  :      Optional[int]														=						next(iter(self.unet.parameters()      )      ).dtype
         __lowerCamelCase  :      str														=						(batch_size, self.unet.config.in_channels, sample_size)
         if isinstance(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      ) and len(SCREAMING_SNAKE_CASE_      ) != batch_size:
              raise ValueError(
                  f'You have passed a list of generators of length {len(SCREAMING_SNAKE_CASE_      )}, but requested an effective batch'
                  f' size of {batch_size}. Make sure the batch size matches the length of the generators.'      )

         __lowerCamelCase  :      str														=						randn_tensor(SCREAMING_SNAKE_CASE_     ,	generator=SCREAMING_SNAKE_CASE_     ,	device=self.device     ,	dtype=SCREAMING_SNAKE_CASE_      )

         # set step values
         self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE_     ,	device=audio.device      )
         __lowerCamelCase  :      Tuple														=						self.scheduler.timesteps.to(SCREAMING_SNAKE_CASE_      )

         for t in self.progress_bar(self.scheduler.timesteps      ):
              # 1. predict noise model_output
              __lowerCamelCase  :      Optional[Any]														=						self.unet(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      ).sample

              # 2. compute previous image: x_t -> t_t-1
              __lowerCamelCase  :      Optional[Any]														=						self.scheduler.step(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      ).prev_sample

         __lowerCamelCase  :      List[str]														=						audio.clamp(-1     ,	1      ).float().cpu().numpy()

         __lowerCamelCase  :      Tuple														=						audio[:, :, :original_sample_size]

         if not return_dict:
              return (audio,)

         return AudioPipelineOutput(audios=SCREAMING_SNAKE_CASE_      )










'''simple docstring'''



from dataclasses import asdict, dataclass
from typing import Optional

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


A__						:   List[str]				=    logging.get_logger(__name__)

# TODO Update this
A__						:   Tuple				=    {
    """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   UpperCAmelCase_    (_UpperCAmelCase    ):





    """simple docstring"""






    lowerCamelCase    :   Tuple					=     'esm'







    def __init__(				self     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=7_68     ,	SCREAMING_SNAKE_CASE_=12     ,	SCREAMING_SNAKE_CASE_=12     ,	SCREAMING_SNAKE_CASE_=30_72     ,	SCREAMING_SNAKE_CASE_=0.1     ,	SCREAMING_SNAKE_CASE_=0.1     ,	SCREAMING_SNAKE_CASE_=10_26     ,	SCREAMING_SNAKE_CASE_=0.0_2     ,	SCREAMING_SNAKE_CASE_=1E-12     ,	SCREAMING_SNAKE_CASE_="absolute"     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=False     ,	SCREAMING_SNAKE_CASE_=False     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=None     ,	**SCREAMING_SNAKE_CASE_     ,	)							->	List[str]:
         super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_     ,	mask_token_id=SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_      )

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







    def  lowercase_							(				self      )							->	Any:
         __lowerCamelCase  :      Any														=						super().to_dict()
         if isinstance(self.esmfold_config     ,	SCREAMING_SNAKE_CASE_      ):
              __lowerCamelCase  :      int														=						self.esmfold_config.to_dict()
         return output







@dataclass
class   UpperCAmelCase_    :





    """simple docstring"""






    lowerCamelCase    :   str					=     None
    lowerCamelCase    :   bool					=     True
    lowerCamelCase    :   bool					=     False
    lowerCamelCase    :   bool					=     False
    lowerCamelCase    :   bool					=     False
    lowerCamelCase    :   float					=     0

    lowerCamelCase    :   bool					=     True
    lowerCamelCase    :   bool					=     False

    lowerCamelCase    :   int					=     1_2_8
    lowerCamelCase    :   "TrunkConfig"					=     None







    def  lowercase_							(				self      )							->	Any:
         if self.trunk is None:
              __lowerCamelCase  :      List[str]														=						TrunkConfig()
         elif isinstance(self.trunk     ,	SCREAMING_SNAKE_CASE_      ):
              __lowerCamelCase  :      Any														=						TrunkConfig(**self.trunk      )







    def  lowercase_							(				self      )							->	int:
         __lowerCamelCase  :      Optional[int]														=						asdict(self      )
         __lowerCamelCase  :      str														=						self.trunk.to_dict()
         return output







@dataclass
class   UpperCAmelCase_    :





    """simple docstring"""






    lowerCamelCase    :   int					=     4_8
    lowerCamelCase    :   int					=     1_0_2_4
    lowerCamelCase    :   int					=     1_2_8
    lowerCamelCase    :   int					=     3_2
    lowerCamelCase    :   int					=     3_2
    lowerCamelCase    :   int					=     3_2
    lowerCamelCase    :   float					=     0
    lowerCamelCase    :   float					=     0
    lowerCamelCase    :   bool					=     False
    lowerCamelCase    :   int					=     4
    lowerCamelCase    :   Optional[int]					=     1_2_8
    lowerCamelCase    :   "StructureModuleConfig"					=     None







    def  lowercase_							(				self      )							->	Optional[int]:
         if self.structure_module is None:
              __lowerCamelCase  :      Dict														=						StructureModuleConfig()
         elif isinstance(self.structure_module     ,	SCREAMING_SNAKE_CASE_      ):
              __lowerCamelCase  :      Optional[Any]														=						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}.'      )

         __lowerCamelCase  :      Tuple														=						self.sequence_state_dim // self.sequence_head_width
         __lowerCamelCase  :      str														=						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  lowercase_							(				self      )							->	List[Any]:
         __lowerCamelCase  :      List[str]														=						asdict(self      )
         __lowerCamelCase  :      int														=						self.structure_module.to_dict()
         return output







@dataclass
class   UpperCAmelCase_    :





    """simple docstring"""






    lowerCamelCase    :   int					=     3_8_4
    lowerCamelCase    :   int					=     1_2_8
    lowerCamelCase    :   int					=     1_6
    lowerCamelCase    :   int					=     1_2_8
    lowerCamelCase    :   int					=     1_2
    lowerCamelCase    :   int					=     4
    lowerCamelCase    :   int					=     8
    lowerCamelCase    :   float					=     0.1
    lowerCamelCase    :   int					=     8
    lowerCamelCase    :   int					=     1
    lowerCamelCase    :   int					=     2
    lowerCamelCase    :   int					=     7
    lowerCamelCase    :   int					=     1_0
    lowerCamelCase    :   float					=     1e-8
    lowerCamelCase    :   float					=     1e5







    def  lowercase_							(				self      )							->	Any:
         return asdict(self      )







def      UpperCAmelCase__   (      )	->						Optional[Any]:
     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>",
     )






