





import os
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple

import sentencepiece as spm

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


if TYPE_CHECKING:
  from transformers.pipelines.conversational import Conversation

UpperCAmelCase							   =      logging.get_logger(__name__)

UpperCAmelCase							   =      {"""vocab_file""": """tokenizer.model"""}

UpperCAmelCase							   =      {
    """vocab_file""": {
        """hf-internal-testing/llama-tokenizer""": """https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer.model""",
    },
    """tokenizer_file""": {
        """hf-internal-testing/llama-tokenizer""": """https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer_config.json""",
    },
}
UpperCAmelCase							   =      {
    """hf-internal-testing/llama-tokenizer""": 2048,
}
UpperCAmelCase							   =      """▁"""

UpperCAmelCase   ,  UpperCAmelCase							   =      """[INST]""", """[/INST]"""
UpperCAmelCase   ,  UpperCAmelCase							   =      """<<SYS>>\n""", """\n<</SYS>>\n\n"""

# fmt: off
UpperCAmelCase							   =      """You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your\
answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\
that your responses are socially unbiased and positive in nature.

If a question does not make any sense, or is not factually coherent, explain why instead of answering something not\
correct. If you don't know the answer to a question, please don't share false information."""






class 							_SCREAMING_SNAKE_CASE(   snake_case__	):
   UpperCamelCase_				:						List[Any] 			=   VOCAB_FILES_NAMES
   UpperCamelCase_				:						List[Any] 			=   PRETRAINED_VOCAB_FILES_MAP
   UpperCamelCase_				:						Optional[Any] 			=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   UpperCamelCase_				:						Optional[Any] 			=   ['input_ids', 'attention_mask']
   def __init__(    self							,     lowerCAmelCase_							,     lowerCAmelCase_="<unk>"							,     lowerCAmelCase_="<s>"							,     lowerCAmelCase_="</s>"							,     lowerCAmelCase_=None							,     lowerCAmelCase_ = None							,     lowerCAmelCase_=True							,     lowerCAmelCase_=False							,     lowerCAmelCase_=False							,     lowerCAmelCase_=True							,     **lowerCAmelCase_							,     )	->Union[str, Any]:
        a  :    Union[str, Any]				=						{} if sp_model_kwargs is None else sp_model_kwargs
        a  :    Union[str, Any]				=						AddedToken(lowerCAmelCase_							,     lstrip=lowerCAmelCase_							,     rstrip=lowerCAmelCase_    ) if isinstance(lowerCAmelCase_							,     lowerCAmelCase_    ) else bos_token
        a  :    Optional[Any]				=						AddedToken(lowerCAmelCase_							,     lstrip=lowerCAmelCase_							,     rstrip=lowerCAmelCase_    ) if isinstance(lowerCAmelCase_							,     lowerCAmelCase_    ) else eos_token
        a  :    str				=						AddedToken(lowerCAmelCase_							,     lstrip=lowerCAmelCase_							,     rstrip=lowerCAmelCase_    ) if isinstance(lowerCAmelCase_							,     lowerCAmelCase_    ) else unk_token
        a  :    Tuple				=						AddedToken(lowerCAmelCase_							,     lstrip=lowerCAmelCase_							,     rstrip=lowerCAmelCase_    ) if isinstance(lowerCAmelCase_							,     lowerCAmelCase_    ) else pad_token
        super().__init__(
            bos_token=lowerCAmelCase_							,     eos_token=lowerCAmelCase_							,     unk_token=lowerCAmelCase_							,     pad_token=lowerCAmelCase_							,     add_bos_token=lowerCAmelCase_							,     add_eos_token=lowerCAmelCase_							,     sp_model_kwargs=self.sp_model_kwargs							,     clean_up_tokenization_spaces=lowerCAmelCase_							,     legacy=lowerCAmelCase_							,     **lowerCAmelCase_							,     )
        if legacy:
             logger.warning_once(
                 F"""You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to"""
                 ''' read the related pull request available at https://github.com/huggingface/transformers/pull/24565'''    )
        a  :    Optional[int]				=						legacy
        a  :    List[Any]				=						vocab_file
        a  :    List[str]				=						add_bos_token
        a  :    Optional[Any]				=						add_eos_token
        a  :    Tuple				=						spm.SentencePieceProcessor(**self.sp_model_kwargs    )
        self.sp_model.Load(lowerCAmelCase_    )
   def __getstate__(    self    )	->Optional[Any]:
        a  :    Any				=						self.__dict__.copy()
        a  :    List[Any]				=						None
        a  :    List[str]				=						self.sp_model.serialized_model_proto()
        return state
   def __setstate__(    self							,     lowerCAmelCase_    )	->Tuple:
        a  :    Tuple				=						d
        a  :    Union[str, Any]				=						spm.SentencePieceProcessor(**self.sp_model_kwargs    )
        self.sp_model.LoadFromSerializedProto(self.sp_model_proto    )
   @property
   def _lowercase(    self    )	->List[str]:
        return self.sp_model.get_piece_size()
   def _lowercase(    self    )	->Dict:
        a  :    List[Any]				=						{self.convert_ids_to_tokens(lowerCAmelCase_    ): i for i in range(self.vocab_size    )}
        vocab.update(self.added_tokens_encoder    )
        return vocab
   def _lowercase(    self							,     lowerCAmelCase_							,     **lowerCAmelCase_    )	->List[str]:
        # Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
        # the beginning of the text
        if not self.legacy:
             a  :    Dict				=						SPIECE_UNDERLINE + text.replace(lowerCAmelCase_							,     ''' '''    )
        return super().tokenize(lowerCAmelCase_							,     **lowerCAmelCase_    )
   def _lowercase(    self							,     lowerCAmelCase_    )	->str:
        if not self.legacy:
             a  :    List[Any]				=						text.startswith(lowerCAmelCase_    )
             if is_first:
                  a  :    str				=						text[1:]

        a  :    str				=						self.sp_model.encode(lowerCAmelCase_							,     out_type=lowerCAmelCase_    )

        if not self.legacy and not is_first and not text.startswith(''' '''    ) and tokens[0].startswith(lowerCAmelCase_    ):
             a  :    int				=						([tokens[0][1:]] if len(tokens[0]    ) > 1 else []) + tokens[1:]
        return tokens
   def _lowercase(    self							,     lowerCAmelCase_    )	->Tuple:
        return self.sp_model.piece_to_id(lowerCAmelCase_    )
   def _lowercase(    self							,     lowerCAmelCase_    )	->Union[str, Any]:
        a  :    int				=						self.sp_model.IdToPiece(lowerCAmelCase_    )
        return token
   def _lowercase(    self							,     lowerCAmelCase_    )	->int:
        a  :    List[str]				=						[]
        a  :    Any				=						''''''
        a  :    List[Any]				=						False
        for i, token in enumerate(lowerCAmelCase_    ):
             # make sure that special tokens are not decoded using sentencepiece model
             if token in self.all_special_tokens:
                  if not prev_is_special and i != 0:
                       out_string += " "
                  out_string += self.sp_model.decode(lowerCAmelCase_    ) + token
                  a  :    Tuple				=						True
                  a  :    Tuple				=						[]
             else:
                  current_sub_tokens.append(lowerCAmelCase_    )
                  a  :    List[Any]				=						False
        out_string += self.sp_model.decode(lowerCAmelCase_    )
        return out_string
   def _lowercase(    self							,     lowerCAmelCase_							,     lowerCAmelCase_ = None    )	->Tuple[str]:
        if not os.path.isdir(lowerCAmelCase_    ):
             logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""    )
             return
        a  :    Tuple				=						os.path.join(
            lowerCAmelCase_							,     (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']    )

        if os.path.abspath(self.vocab_file    ) != os.path.abspath(lowerCAmelCase_    ) and os.path.isfile(self.vocab_file    ):
             copyfile(self.vocab_file							,     lowerCAmelCase_    )
        elif not os.path.isfile(self.vocab_file    ):
             with open(lowerCAmelCase_							,     '''wb'''    ) as fi:
                  a  :    List[str]				=						self.sp_model.serialized_model_proto()
                  fi.write(lowerCAmelCase_    )

        return (out_vocab_file,)
   def _lowercase(    self							,     lowerCAmelCase_							,     lowerCAmelCase_=None    )	->str:
        a  :    Dict				=						[self.bos_token_id] if self.add_bos_token else []
        a  :    Union[str, Any]				=						[self.eos_token_id] if self.add_eos_token else []

        a  :    int				=						bos_token_id + token_ids_a + eos_token_id

        if token_ids_a is not None:
             a  :    Optional[Any]				=						output + bos_token_id + token_ids_a + eos_token_id

        return output
   def _lowercase(    self							,     lowerCAmelCase_							,     lowerCAmelCase_ = None							,     lowerCAmelCase_ = False    )	->List[int]:
        if already_has_special_tokens:
             return super().get_special_tokens_mask(
                 token_ids_a=lowerCAmelCase_							,     token_ids_a=lowerCAmelCase_							,     already_has_special_tokens=lowerCAmelCase_    )

        a  :    List[Any]				=						[1] if self.add_bos_token else []
        a  :    List[str]				=						[1] if self.add_eos_token else []

        if token_ids_a is None:
             return bos_token_id + ([0] * len(lowerCAmelCase_    )) + eos_token_id
        return (
            bos_token_id
            + ([0] * len(lowerCAmelCase_    ))
            + eos_token_id
            + bos_token_id
            + ([0] * len(lowerCAmelCase_    ))
            + eos_token_id
        )
   def _lowercase(    self							,     lowerCAmelCase_							,     lowerCAmelCase_ = None    )	->List[int]:
        a  :    Any				=						[self.bos_token_id] if self.add_bos_token else []
        a  :    Tuple				=						[self.eos_token_id] if self.add_eos_token else []

        a  :    List[Any]				=						[0] * len(bos_token_id + token_ids_a + eos_token_id    )

        if token_ids_a is not None:
             output += [1] * len(bos_token_id + token_ids_a + eos_token_id    )

        return output


   def _lowercase(    self							,     lowerCAmelCase_    )	->List[int]:
        a  :    str				=						list(conversation.iter_texts()    )
        if not all([is_user for is_user, msg in dialogue[::2]]    ) or not all(
            [not is_user for is_user, msg in dialogue[1::2]]    ):
             raise ValueError(
                 '''The model only supports \'user\' and \'assistant\' roles, starting with user and alternating (u/a/u/a/u...)'''    )

        a  :    List[int]				=						[]
        if len(conversation.past_user_inputs    ) > 0:
             if not conversation.past_user_inputs[0].startswith(lowerCAmelCase_    ) or E_SYS not in conversation.past_user_inputs[0]:
                  a  :    int				=						(
                      B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
                  )
        elif not dialogue[0][1].startswith(lowerCAmelCase_    ) or E_SYS not in dialogue[0][1]:
             a  :    Optional[Any]				=						(dialogue[0][0], B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + dialogue[0][1])

        dialog_tokens += sum(
            [
                [self.bos_token_id]
                + self.encode(
                    F"""{B_INST} {(prompt[1]).strip()} {E_INST} {(answer[1]).strip()} """							,     add_special_tokens=lowerCAmelCase_    )
                + [self.eos_token_id]
                for prompt, answer in zip(dialogue[::2]							,     dialogue[1::2]    )
            ]							,     []							,     )
        if not (dialogue[-1][0]):
             raise ValueError(F"""Last message must be from user, got {dialogue[-1]['role']}"""    )
        dialog_tokens += [self.bos_token_id] + self.encode(
            F"""{B_INST} {(dialogue[-1][1]).strip()} {E_INST}"""							,     add_special_tokens=lowerCAmelCase_    )
        return dialog_tokens
from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase							   =      logging.get_logger(__name__)

UpperCAmelCase							   =      {
    # pcqm4mv1 now deprecated
    """graphormer-base""": """https://huggingface.co/clefourrier/graphormer-base-pcqm4mv2/resolve/main/config.json""",
    # See all Graphormer models at https://huggingface.co/models?filter=graphormer
}






class 							_SCREAMING_SNAKE_CASE(   snake_case__	):
   UpperCamelCase_				:						int 			=   'graphormer'
   UpperCamelCase_				:						Tuple 			=   ['past_key_values']
   def __init__(    self							,     lowerCAmelCase_ = 1							,     lowerCAmelCase_ = 5_12 * 9							,     lowerCAmelCase_ = 5_12 * 3							,     lowerCAmelCase_ = 5_12							,     lowerCAmelCase_ = 5_12							,     lowerCAmelCase_ = 5_12							,     lowerCAmelCase_ = 1_28							,     lowerCAmelCase_ = 5							,     lowerCAmelCase_ = 10_24							,     lowerCAmelCase_ = "multi_hop"							,     lowerCAmelCase_ = 5_12							,     lowerCAmelCase_ = False							,     lowerCAmelCase_ = 12							,     lowerCAmelCase_ = 7_68							,     lowerCAmelCase_ = 7_68							,     lowerCAmelCase_ = 32							,     lowerCAmelCase_ = 0.1							,     lowerCAmelCase_ = 0.1							,     lowerCAmelCase_ = 0.0							,     lowerCAmelCase_ = False							,     lowerCAmelCase_ = False							,     lowerCAmelCase_ = False							,     lowerCAmelCase_ = "gelu"							,     lowerCAmelCase_ = None							,     lowerCAmelCase_ = False							,     lowerCAmelCase_ = 0							,     lowerCAmelCase_ = False							,     lowerCAmelCase_ = 0.0							,     lowerCAmelCase_ = 8							,     lowerCAmelCase_ = None							,     lowerCAmelCase_ = None							,     lowerCAmelCase_ = True							,     lowerCAmelCase_ = True							,     lowerCAmelCase_=0							,     lowerCAmelCase_=1							,     lowerCAmelCase_=2							,     **lowerCAmelCase_							,     )	->Optional[int]:
        a  :    List[Any]				=						num_classes
        a  :    Any				=						num_atoms
        a  :    Dict				=						num_in_degree
        a  :    int				=						num_out_degree
        a  :    Dict				=						num_edges
        a  :    Tuple				=						num_spatial
        a  :    Dict				=						num_edge_dis
        a  :    Optional[int]				=						edge_type
        a  :    List[Any]				=						multi_hop_max_dist
        a  :    Optional[int]				=						spatial_pos_max
        a  :    str				=						max_nodes
        a  :    Optional[Any]				=						num_hidden_layers
        a  :    Any				=						embedding_dim
        a  :    Dict				=						embedding_dim
        a  :    Dict				=						ffn_embedding_dim
        a  :    List[Any]				=						num_attention_heads
        a  :    List[str]				=						dropout
        a  :    int				=						attention_dropout
        a  :    Optional[Any]				=						layerdrop
        a  :    str				=						encoder_normalize_before
        a  :    Optional[int]				=						pre_layernorm
        a  :    Tuple				=						apply_graphormer_init
        a  :    int				=						activation_fn
        a  :    Any				=						embed_scale
        a  :    Optional[int]				=						freeze_embeddings
        a  :    Tuple				=						num_trans_layers_to_freeze
        a  :    List[str]				=						share_input_output_embed
        a  :    List[Any]				=						traceable
        a  :    Optional[Any]				=						q_noise
        a  :    Dict				=						qn_block_size

        # These parameters are here for future extensions
        # atm, the model only supports self attention
        a  :    List[Any]				=						kdim
        a  :    List[str]				=						vdim
        a  :    int				=						self_attention
        a  :    List[Any]				=						bias

        super().__init__(
            pad_token_id=lowerCAmelCase_							,     bos_token_id=lowerCAmelCase_							,     eos_token_id=lowerCAmelCase_							,     **lowerCAmelCase_							,     )