


'''simple docstring'''






import os
from typing import List, Optional, Union

from ...tokenization_utils import PreTrainedTokenizer
from ...tokenization_utils_base import AddedToken
from ...utils import logging


__A					:  Any											=      logging.get_logger(__name__)

__A					:  List[Any]											=      {"vocab_file": "vocab.txt"}

__A					:  str											=      {
    "vocab_file": {
        "facebook/esm2_t6_8M_UR50D": "https://huggingface.co/facebook/esm2_t6_8M_UR50D/resolve/main/vocab.txt",
        "facebook/esm2_t12_35M_UR50D": "https://huggingface.co/facebook/esm2_t12_35M_UR50D/resolve/main/vocab.txt",
    },
}

__A					:  int											=      {
    "facebook/esm2_t6_8M_UR50D": 1024,
    "facebook/esm2_t12_35M_UR50D": 1024,
}







def 				UpperCamelCase_			(    A__					:   Optional[int]					):
  '''simple docstring'''





  with open(lowerCAmelCase__							,    """r"""					) as f:
    lowerCAmelCase_			:      List[Any]			=   f.read().splitlines()
    return [l.strip() for l in lines]






class    __snake_case       (   __a):







    """simple docstring"""



    lowercase    =					VOCAB_FILES_NAMES
    lowercase    =					PRETRAINED_VOCAB_FILES_MAP
    lowercase    =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowercase    =					['input_ids', 'attention_mask']

    def __init__(   self    :  Optional[Any]    ,						lowerCamelCase    :  str    ,						lowerCamelCase    :  List[Any]="<unk>"    ,						lowerCamelCase    :  str="<cls>"    ,						lowerCamelCase    :  Tuple="<pad>"    ,						lowerCamelCase    :  Any="<mask>"    ,						lowerCamelCase    :  Any="<eos>"    ,						**lowerCamelCase    :  List[Any]    ,						)   ->  Union[str, Any]:
      super().__init__(**_A		)
      lowerCAmelCase_			:      List[Any]			=   load_vocab_file(_A		)
      lowerCAmelCase_			:      Tuple			=   dict(enumerate(self.all_tokens		)		)
      lowerCAmelCase_			:      Optional[int]			=   {tok: ind for ind, tok in enumerate(self.all_tokens		)}
      lowerCAmelCase_			:      Tuple			=   unk_token
      lowerCAmelCase_			:      Optional[Any]			=   cls_token
      lowerCAmelCase_			:      Any			=   pad_token
      lowerCAmelCase_			:      str			=   mask_token
      lowerCAmelCase_			:      Tuple			=   eos_token
      lowerCAmelCase_			:      List[str]			=   self.all_tokens
      self._create_trie(self.unique_no_split_tokens		)

    def __lowercase   (   self    :  Any    ,						lowerCamelCase    :  int		)   ->  Union[str, Any]:
      return self._id_to_token.get(_A    ,						self.unk_token		)

    def __lowercase   (   self    :  List[Any]    ,						lowerCamelCase    :  str		)   ->  List[Any]:
      return self._token_to_id.get(_A    ,						self._token_to_id.get(self.unk_token		)		)

    def __lowercase   (   self    :  Dict    ,						lowerCamelCase    :  Optional[int]    ,						**lowerCamelCase    :  Optional[Any]		)   ->  Tuple:
      return text.split()

    def __lowercase   (   self    :  Optional[int]    ,						lowerCamelCase    :  Union[str, Any]=False		)   ->  Optional[Any]:
      return len(self._id_to_token		)

    def __lowercase   (   self    :  str		)   ->  int:
      return {token: i for i, token in enumerate(self.all_tokens		)}

    def __lowercase   (   self    :  Optional[Any]    ,						lowerCamelCase    :  str		)   ->  Any:
      return self._token_to_id.get(_A    ,						self._token_to_id.get(self.unk_token		)		)

    def __lowercase   (   self    :  Dict    ,						lowerCamelCase    :  int		)   ->  List[Any]:
      return self._id_to_token.get(_A    ,						self.unk_token		)

    def __lowercase   (   self    :  int    ,						lowerCamelCase    :  List[int]    ,						lowerCamelCase    :  Optional[List[int]] = None		)   ->  Any:
      lowerCAmelCase_			:      Optional[Any]			=   [self.cls_token_id]
      lowerCAmelCase_			:      List[str]			=   [self.eos_token_id]  # No sep token in ESM vocabulary
      if token_ids_a is None:
        if self.eos_token_id is None:
          return cls + token_ids_a
        else:
          return cls + token_ids_a + sep
      elif self.eos_token_id is None:
        raise ValueError("""Cannot tokenize multiple sequences when EOS token is not set!"""		)
      return cls + token_ids_a + sep + token_ids_a + sep  # Multiple inputs always have an EOS token

    def __lowercase   (   self    :  List[Any]    ,						lowerCamelCase    :  List    ,						lowerCamelCase    :  Optional[List] = None    ,						lowerCamelCase    :  bool = False		)   ->  int:


      if already_has_special_tokens:
        if token_ids_a is not None:
          raise ValueError(
              """You should not supply a second sequence if the provided sequence of """
              """ids is already formatted with special tokens for the model."""		)

        return [1 if token in self.all_special_ids else 0 for token in token_ids_a]
      lowerCAmelCase_			:      List[Any]			=   [1] + ([0] * len(_A		)) + [1]
      if token_ids_a is not None:
        mask += [0] * len(_A		) + [1]
      return mask

    def __lowercase   (   self    :  List[str]    ,						lowerCamelCase    :  str    ,						lowerCamelCase    :  int		)   ->  List[Any]:
      lowerCAmelCase_			:      int			=   os.path.join(_A    ,						(filename_prefix + """-""" if filename_prefix else """""") + """vocab.txt"""		)
      with open(_A    ,						"""w"""		) as f:
        f.write("""\n""".join(self.all_tokens		)		)
      return (vocab_file,)

    @property
    def __lowercase   (   self    :  str		)   ->  Tuple:
      return self.get_vocab_size(with_added_tokens=_A		)





    def __lowercase   (   self    :  List[str]    ,						lowerCamelCase    :  Union[List[str], List[AddedToken]]    ,						lowerCamelCase    :  bool = False		)   ->  List[str]:
      return super()._add_tokens(_A    ,						special_tokens=_A		)







'''simple docstring'''
import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


UpperCamelCase__    					=      logging.get_logger(__name__)





def 					a__       (					lowerCAmelCase__						)    ->	Optional[Any]:
    UpperCAmelCase__			:		str													=				R'''\w+[.]\d+'''
    UpperCAmelCase__			:		List[Any]													=				re.findall(lowerCAmelCase__  ,					lowerCAmelCase__						)
    for pat in pats:
        UpperCAmelCase__			:		Union[str, Any]													=				key.replace(lowerCAmelCase__  ,					'''_'''.join(pat.split('''.'''						)						)						)
    return key





def 					a__       (					lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__						)    ->	Union[str, Any]:
    UpperCAmelCase__			:		Optional[Any]													=				pt_tuple_key[:-1] + ('''scale''',)
    if (
        any('''norm''' in str_ for str_ in pt_tuple_key						)
        and (pt_tuple_key[-1] == "bias")
        and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
        and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
    ):
        UpperCAmelCase__			:		Union[str, Any]													=				pt_tuple_key[:-1] + ('''scale''',)
        return renamed_pt_tuple_key, pt_tensor
    elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
        UpperCAmelCase__			:		Optional[int]													=				pt_tuple_key[:-1] + ('''scale''',)
        return renamed_pt_tuple_key, pt_tensor

    # embedding
    if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
        UpperCAmelCase__			:		str													=				pt_tuple_key[:-1] + ('''embedding''',)
        return renamed_pt_tuple_key, pt_tensor

    # conv layer
    UpperCAmelCase__			:		Optional[Any]													=				pt_tuple_key[:-1] + ('''kernel''',)
    if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
        UpperCAmelCase__			:		List[Any]													=				pt_tensor.transpose(2  ,					3  ,					1  ,					0						)
        return renamed_pt_tuple_key, pt_tensor

    # linear layer
    UpperCAmelCase__			:		int													=				pt_tuple_key[:-1] + ('''kernel''',)
    if pt_tuple_key[-1] == "weight":
        UpperCAmelCase__			:		Optional[Any]													=				pt_tensor.T
        return renamed_pt_tuple_key, pt_tensor

    # old PyTorch layer norm weight
    UpperCAmelCase__			:		Union[str, Any]													=				pt_tuple_key[:-1] + ('''weight''',)
    if pt_tuple_key[-1] == "gamma":
        return renamed_pt_tuple_key, pt_tensor

    # old PyTorch layer norm bias
    UpperCAmelCase__			:		Optional[Any]													=				pt_tuple_key[:-1] + ('''bias''',)
    if pt_tuple_key[-1] == "beta":
        return renamed_pt_tuple_key, pt_tensor

    return pt_tuple_key, pt_tensor







def 					a__       (					lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__=42						)    ->	Tuple:
    # Step 1: Convert pytorch tensor to numpy
    UpperCAmelCase__			:		int													=				{k: v.numpy() for k, v in pt_state_dict.items()}

    # Step 2: Since the model is stateless, get random Flax params
    UpperCAmelCase__			:		Tuple													=				flax_model.init_weights(PRNGKey(lowerCAmelCase__						)						)

    UpperCAmelCase__			:		Optional[Any]													=				flatten_dict(lowerCAmelCase__						)
    UpperCAmelCase__			:		List[str]													=				{}

    # Need to change some parameters name to match Flax names
    for pt_key, pt_tensor in pt_state_dict.items():
        UpperCAmelCase__			:		Optional[int]													=				rename_key(lowerCAmelCase__						)
        UpperCAmelCase__			:		str													=				tuple(renamed_pt_key.split('''.'''						)						)

        # Correctly rename weight parameters
        UpperCAmelCase__						,						UpperCAmelCase__			:		Optional[int]													=				rename_key_and_reshape_tensor(lowerCAmelCase__  ,					lowerCAmelCase__  ,					lowerCAmelCase__						)

        if flax_key in random_flax_state_dict:
            if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
                raise ValueError(
                    F"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
                    F"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}."""						)

        # also add unexpected weight so that warning is thrown
        UpperCAmelCase__			:		List[str]													=				jnp.asarray(lowerCAmelCase__						)

    return unflatten_dict(lowerCAmelCase__						)
