


import os
from typing import List, Optional, Union

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


__a	 =      logging.get_logger(__name__)

__a	 =      {'''vocab_file''': '''vocab.txt'''}

__a	 =      {
    '''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	 =      {
    '''facebook/esm2_t6_8M_UR50D''': 1024,
    '''facebook/esm2_t12_35M_UR50D''': 1024,
}
def 	_UpperCamelCase					(    lowerCAmelCase_ )    ->List[Any]:
   with open(__snake_case	,    """r""" ) as f:
      UpperCAmelCase      					=  f.read().splitlines()
      return [l.strip() for l in lines]







class 	__lowercase					(	a_	):
      UpperCamelCase										=							VOCAB_FILES_NAMES
      UpperCamelCase										=							PRETRAINED_VOCAB_FILES_MAP
      UpperCamelCase										=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCamelCase										=							['''input_ids''', '''attention_mask''']





      def __init__(		self    :       List[str]       ,							__lowerCamelCase    :       str       ,							__lowerCamelCase    :       Dict="<unk>"       ,							__lowerCamelCase    :       List[Any]="<cls>"       ,							__lowerCamelCase    :       Optional[int]="<pad>"       ,							__lowerCamelCase    :       Optional[Any]="<mask>"       ,							__lowerCamelCase    :       str="<eos>"       ,							**__lowerCamelCase    :       Tuple       ,							)     -> Tuple:






         """simple docstring"""







         super().__init__(**__UpperCamelCase							)
         UpperCAmelCase      					=  load_vocab_file(__UpperCamelCase							)
         UpperCAmelCase      					=  dict(enumerate(self.all_tokens							)							)
         UpperCAmelCase      					=  {tok: ind for ind, tok in enumerate(self.all_tokens							)}
         UpperCAmelCase      					=  unk_token
         UpperCAmelCase      					=  cls_token
         UpperCAmelCase      					=  pad_token
         UpperCAmelCase      					=  mask_token
         UpperCAmelCase      					=  eos_token
         UpperCAmelCase      					=  self.all_tokens
         self._create_trie(self.unique_no_split_tokens							)





      def 							_lowercase					(		self    :       int       ,							__lowerCamelCase    :       List[str]							)     -> str:






         """simple docstring"""







         return self._id_to_token.get(__UpperCamelCase       ,							self.unk_token							)





      def 							_lowercase					(		self    :       List[Any]       ,							__lowerCamelCase    :       str							)     -> int:






         """simple docstring"""







         return self._token_to_id.get(__UpperCamelCase       ,							self._token_to_id.get(self.unk_token							)							)





      def 							_lowercase					(		self    :       Union[str, Any]       ,							__lowerCamelCase    :       Union[str, Any]       ,							**__lowerCamelCase    :       Optional[int]							)     -> Tuple:






         """simple docstring"""







         return text.split()





      def 							_lowercase					(		self    :       List[Any]       ,							__lowerCamelCase    :       List[Any]=False							)     -> Dict:






         """simple docstring"""







         return len(self._id_to_token							)





      def 							_lowercase					(		self    :       Optional[Any]							)     -> int:






         """simple docstring"""







         return {token: i for i, token in enumerate(self.all_tokens							)}





      def 							_lowercase					(		self    :       Any       ,							__lowerCamelCase    :       Dict							)     -> int:






         """simple docstring"""







         return self._token_to_id.get(__UpperCamelCase       ,							self._token_to_id.get(self.unk_token							)							)





      def 							_lowercase					(		self    :       List[str]       ,							__lowerCamelCase    :       Dict							)     -> str:






         """simple docstring"""







         return self._id_to_token.get(__UpperCamelCase       ,							self.unk_token							)





      def 							_lowercase					(		self    :       Union[str, Any]       ,							__lowerCamelCase    :       Optional[Any]       ,							__lowerCamelCase    :       List[str] = None							)     -> List[int]:






         """simple docstring"""







         UpperCAmelCase      					=  [self.cls_token_id]
         UpperCAmelCase      					=  [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[str]       ,							__lowerCamelCase    :       Tuple       ,							__lowerCamelCase    :       str = None       ,							__lowerCamelCase    :       str = False							)     -> List[int]:






         """simple docstring"""







         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]
         UpperCAmelCase      					=  [1] + ([0] * len(__UpperCamelCase							)) + [1]
         if token_ids_a is not None:
            mask += [0] * len(__UpperCamelCase							) + [1]
         return mask





      def 							_lowercase					(		self    :       Any       ,							__lowerCamelCase    :       Tuple       ,							__lowerCamelCase    :       Any							)     -> Union[str, Any]:






         """simple docstring"""







         UpperCAmelCase      					=  os.path.join(__UpperCamelCase       ,							(filename_prefix + """-""" if filename_prefix else """""") + """vocab.txt"""							)
         with open(__UpperCamelCase       ,							"""w"""							) as f:
            f.write("""\n""".join(self.all_tokens							)							)
         return (vocab_file,)





      @property
      def 							_lowercase					(		self    :       int							)     -> int:






         """simple docstring"""







         return self.get_vocab_size(with_added_tokens=__UpperCamelCase							)





      def 							_lowercase					(		self    :       str       ,							__lowerCamelCase    :       Dict       ,							__lowerCamelCase    :       Optional[int] = False							)     -> int:






         """simple docstring"""







         return super()._add_tokens(__UpperCamelCase       ,							special_tokens=__UpperCamelCase							)











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


lowerCamelCase      :       int    =						logging.get_logger(__name__)





def    __lowerCAmelCase   (    __snake_case							):
     __lowerCAmelCase             =       r"\w+[.]\d+"
     __lowerCAmelCase             =       re.findall(__snake_case       ,			__snake_case							)
     for pat in pats:
          __lowerCAmelCase             =       key.replace(__snake_case       ,			"_".join(pat.split("."							)							)							)
     return key





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case       ,			__snake_case							):
     __lowerCAmelCase             =       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)
     ):
          __lowerCAmelCase             =       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:
          __lowerCAmelCase             =       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:
          __lowerCAmelCase             =       pt_tuple_key[:-1] + ("embedding",)
          return renamed_pt_tuple_key, pt_tensor

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

     # linear layer
     __lowerCAmelCase             =       pt_tuple_key[:-1] + ("kernel",)
     if pt_tuple_key[-1] == "weight":
          __lowerCAmelCase             =       pt_tensor.T
          return renamed_pt_tuple_key, pt_tensor

     # old PyTorch layer norm weight
     __lowerCAmelCase             =       pt_tuple_key[:-1] + ("weight",)
     if pt_tuple_key[-1] == "gamma":
          return renamed_pt_tuple_key, pt_tensor

     # old PyTorch layer norm bias
     __lowerCAmelCase             =       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    __lowerCAmelCase   (    __snake_case       ,			__snake_case       ,			__snake_case=42							):
     # Step 1: Convert pytorch tensor to numpy
     __lowerCAmelCase             =       {k: v.numpy() for k, v in pt_state_dict.items()}

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

     __lowerCAmelCase             =       flatten_dict(__snake_case							)
     __lowerCAmelCase             =       {}

     # Need to change some parameters name to match Flax names
     for pt_key, pt_tensor in pt_state_dict.items():
          __lowerCAmelCase             =       rename_key(__snake_case							)
          __lowerCAmelCase             =       tuple(renamed_pt_key.split("."							)							)

          # Correctly rename weight parameters
          __lowerCAmelCase      ,			__lowerCAmelCase             =       rename_key_and_reshape_tensor(__snake_case       ,			__snake_case       ,			__snake_case							)

          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
          __lowerCAmelCase             =       jnp.asarray(__snake_case							)

     return unflatten_dict(__snake_case							)






