





"""simple docstring"""




from dataclasses import asdict, dataclass
from typing import Optional

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


lowerCamelCase_     :						Any						=					logging.get_logger(__name__)

# TODO Update this
lowerCamelCase_     :						Any						=					{
    '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'''
      lowercase_      : Any	    =   """esm"""

      def __init__(     self					,      snake_case_=None					,      snake_case_=None					,      snake_case_=None					,      snake_case_=7_6_8					,      snake_case_=1_2					,      snake_case_=1_2					,      snake_case_=3_0_7_2					,      snake_case_=0.1					,      snake_case_=0.1					,      snake_case_=1_0_2_6					,      snake_case_=0.02					,      snake_case_=1E-12					,      snake_case_="absolute"					,      snake_case_=True					,      snake_case_=None					,      snake_case_=False					,      snake_case_=False					,      snake_case_=None					,      snake_case_=None					,      **snake_case_					,      ):

        """simple docstring"""

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

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

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_  :       Any										=  super().to_dict()
        if isinstance(self.esmfold_config					,      snake_case_  ):
          A_  :       List[str]										=  self.esmfold_config.to_dict()
        return output


@dataclass
class  _UpperCAmelCase  :



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

      lowercase_      : bool	    =   True
      lowercase_      : bool	    =   False

      lowercase_      : int	    =   128
      lowercase_      : "TrunkConfig"	    =   None

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        if self.trunk is None:
          A_  :       Optional[Any]										=  TrunkConfig()
        elif isinstance(self.trunk					,      snake_case_  ):
          A_  :       Optional[Any]										=  TrunkConfig(**self.trunk  )

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        A_  :       str										=  asdict(self  )
        A_  :       List[Any]										=  self.trunk.to_dict()
        return output


@dataclass
class  _UpperCAmelCase  :



      '''simple docstring'''
      lowercase_      : int	    =   48
      lowercase_      : int	    =   1_024
      lowercase_      : int	    =   128
      lowercase_      : int	    =   32
      lowercase_      : int	    =   32
      lowercase_      : int	    =   32
      lowercase_      : float	    =   0
      lowercase_      : float	    =   0
      lowercase_      : bool	    =   False
      lowercase_      : int	    =   4
      lowercase_      : Optional[int]	    =   128
      lowercase_      : "StructureModuleConfig"	    =   None

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        if self.structure_module is None:
          A_  :       Dict										=  StructureModuleConfig()
        elif isinstance(self.structure_module					,      snake_case_  ):
          A_  :       Union[str, 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}."""  )

        A_  :       str										=  self.sequence_state_dim // self.sequence_head_width
        A_  :       int										=  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  ):

        """simple docstring"""

        A_  :       List[str]										=  asdict(self  )
        A_  :       Optional[Any]										=  self.structure_module.to_dict()
        return output


@dataclass
class  _UpperCAmelCase  :



      '''simple docstring'''
      lowercase_      : int	    =   384
      lowercase_      : int	    =   128
      lowercase_      : int	    =   16
      lowercase_      : int	    =   128
      lowercase_      : int	    =   12
      lowercase_      : int	    =   4
      lowercase_      : int	    =   8
      lowercase_      : float	    =   0.1
      lowercase_      : int	    =   8
      lowercase_      : int	    =   1
      lowercase_      : int	    =   2
      lowercase_      : int	    =   7
      lowercase_      : int	    =   10
      lowercase_      : float	    =   1e-8
      lowercase_      : float	    =   1e5

      def 					lowerCamelCase_			(     self  ):

        """simple docstring"""

        return asdict(self  )





def 						UpperCAmelCase__		(     ):






  """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>",
  )






"""simple docstring"""




from typing import Dict

import numpy as np

from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException


if is_tf_available():
   import tensorflow as tf

   from ..tf_utils import stable_softmax


if is_torch_available():
   import torch


lowerCamelCase_     :						str						=					logging.get_logger(__name__)

@add_end_docstrings(
    UpperCAmelCase__			,   r"""
        top_k (`int`, defaults to 5):
            The number of predictions to return.
        targets (`str` or `List[str]`, *optional*):
            When passed, the model will limit the scores to the passed targets instead of looking up in the whole
            vocab. If the provided targets are not in the model vocab, they will be tokenized and the first resulting
            token will be used (with a warning, and that might be slower).

    """			,   )
class  _UpperCAmelCase  (   UpperCAmelCase__		):



      '''simple docstring'''

      def 					lowerCamelCase_			(     self					,      snake_case_  ):

        """simple docstring"""

        if self.framework == "tf":
          A_  :       str										=  tf.where(input_ids == self.tokenizer.mask_token_id  ).numpy()
        elif self.framework == "pt":
          A_  :       List[str]										=  torch.nonzero(input_ids == self.tokenizer.mask_token_id					,      as_tuple=snake_case_  )
        else:
          raise ValueError('Unsupported framework'  )
        return masked_index

      def 					lowerCamelCase_			(     self					,      snake_case_  ):

        """simple docstring"""

        A_  :       List[str]										=  self.get_masked_index(snake_case_  )
        A_  :       str										=  np.prod(masked_index.shape  )
        if numel < 1:
          raise PipelineException(
              'fill-mask'					,      self.model.base_model_prefix					,      F"""No mask_token ({self.tokenizer.mask_token}) found on the input"""					,      )

      def 					lowerCamelCase_			(     self					,      snake_case_  ):

        """simple docstring"""

        if isinstance(snake_case_					,      snake_case_  ):
          for model_input in model_inputs:
            self._ensure_exactly_one_mask_token(model_input['input_ids'][0]  )
        else:
          for input_ids in model_inputs["input_ids"]:
            self._ensure_exactly_one_mask_token(snake_case_  )

      def 					lowerCamelCase_			(     self					,      snake_case_					,      snake_case_=None					,      **snake_case_  ):

        """simple docstring"""

        if return_tensors is None:
          A_  :       Any										=  self.framework
        A_  :       Dict										=  self.tokenizer(snake_case_					,      return_tensors=snake_case_  )
        self.ensure_exactly_one_mask_token(snake_case_  )
        return model_inputs

      def 					lowerCamelCase_			(     self					,      snake_case_  ):

        """simple docstring"""

        A_  :       Dict										=  self.model(**snake_case_  )
        A_  :       Optional[int]										=  model_inputs['input_ids']
        return model_outputs

      def 					lowerCamelCase_			(     self					,      snake_case_					,      snake_case_=5					,      snake_case_=None  ):

        """simple docstring"""

        if target_ids is not None and target_ids.shape[0] < top_k:
          A_  :       str										=  target_ids.shape[0]
        A_  :       Optional[Any]										=  model_outputs['input_ids'][0]
        A_  :       List[Any]										=  model_outputs['logits']

        if self.framework == "tf":
          A_  :       List[str]										=  tf.where(input_ids == self.tokenizer.mask_token_id  ).numpy()[:, 0]

          A_  :       Union[str, Any]										=  outputs.numpy()

          A_  :       Optional[int]										=  outputs[0, masked_index, :]
          A_  :       Optional[Any]										=  stable_softmax(snake_case_					,      axis=-1  )
          if target_ids is not None:
            A_  :       Union[str, Any]										=  tf.gather_nd(tf.squeeze(snake_case_					,      0  )					,      target_ids.reshape(-1					,      1  )  )
            A_  :       Optional[int]										=  tf.expand_dims(snake_case_					,      0  )

          A_  :       Any										=  tf.math.top_k(snake_case_					,      k=snake_case_  )
          A_						,       A_  :       str										=  topk.values.numpy(), topk.indices.numpy()
        else:
          A_  :       int										=  torch.nonzero(input_ids == self.tokenizer.mask_token_id					,      as_tuple=snake_case_  ).squeeze(-1  )
          # Fill mask pipeline supports only one ${mask_token} per sample

          A_  :       Tuple										=  outputs[0, masked_index, :]
          A_  :       List[str]										=  logits.softmax(dim=-1  )
          if target_ids is not None:
            A_  :       str										=  probs[..., target_ids]

          A_						,       A_  :       List[str]										=  probs.topk(snake_case_  )

        A_  :       List[Any]										=  []
        A_  :       int										=  values.shape[0] == 1
        for i, (_values, _predictions) in enumerate(zip(values.tolist()					,      predictions.tolist()  )  ):
          A_  :       str										=  []
          for v, p in zip(_values					,      _predictions  ):
            # Copy is important since we're going to modify this array in place
            A_  :       Union[str, Any]										=  input_ids.numpy().copy()
            if target_ids is not None:
              A_  :       str										=  target_ids[p].tolist()

            A_  :       Union[str, Any]										=  p
            # Filter padding out:
            A_  :       Any										=  tokens[np.where(tokens != self.tokenizer.pad_token_id  )]
            # Originally we skip special tokens to give readable output.
            # For multi masks though, the other [MASK] would be removed otherwise
            # making the output look odd, so we add them back
            A_  :       Any										=  self.tokenizer.decode(snake_case_					,      skip_special_tokens=snake_case_  )
            A_  :       Any										=  {'score': v, 'token': p, 'token_str': self.tokenizer.decode([p]  ), 'sequence': sequence}
            row.append(snake_case_  )
          result.append(snake_case_  )
        if single_mask:
          return result[0]
        return result

      def 					lowerCamelCase_			(     self					,      snake_case_					,      snake_case_=None  ):

        """simple docstring"""

        if isinstance(snake_case_					,      snake_case_  ):
          A_  :       List[str]										=  [targets]
        try:
          A_  :       Optional[int]										=  self.tokenizer.get_vocab()
        except Exception:
          A_  :       int										=  {}
        A_  :       Tuple										=  []
        for target in targets:
          A_  :       int										=  vocab.get(snake_case_					,      snake_case_  )
          if id_ is None:
            A_  :       Tuple										=  self.tokenizer(
                snake_case_					,      add_special_tokens=snake_case_					,      return_attention_mask=snake_case_					,      return_token_type_ids=snake_case_					,      max_length=1					,      truncation=snake_case_					,      )['input_ids']
            if len(snake_case_  ) == 0:
              logger.warning(
                  F"""The specified target token `{target}` does not exist in the model vocabulary. """
                  'We cannot replace it with anything meaningful, ignoring it'  )
              continue
            A_  :       str										=  input_ids[0]
            # XXX: If users encounter this pass
            # it becomes pretty slow, so let's make sure
            # The warning enables them to fix the input to
            # get faster performance.
            logger.warning(
                F"""The specified target token `{target}` does not exist in the model vocabulary. """
                F"""Replacing with `{self.tokenizer.convert_ids_to_tokens(id_  )}`."""  )
          target_ids.append(id_  )
        A_  :       Tuple										=  list(set(snake_case_  )  )
        if len(snake_case_  ) == 0:
          raise ValueError('At least one target must be provided when passed.'  )
        A_  :       Optional[Any]										=  np.array(snake_case_  )
        return target_ids

      def 					lowerCamelCase_			(     self					,      snake_case_=None					,      snake_case_=None  ):

        """simple docstring"""

        A_  :       List[str]										=  {}

        if targets is not None:
          A_  :       Any										=  self.get_target_ids(snake_case_					,      snake_case_  )
          A_  :       Optional[Any]										=  target_ids

        if top_k is not None:
          A_  :       int										=  top_k

        if self.tokenizer.mask_token_id is None:
          raise PipelineException(
              'fill-mask'					,      self.model.base_model_prefix					,      'The tokenizer does not define a `mask_token`.'  )
        return {}, {}, postprocess_params

      def __call__(     self					,      snake_case_					,      *snake_case_					,      **snake_case_  ):

        """simple docstring"""

        A_  :       List[str]										=  super().__call__(snake_case_					,      **snake_case_  )
        if isinstance(snake_case_					,      snake_case_  ) and len(snake_case_  ) == 1:
          return outputs[0]
        return outputs