
import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot import BlenderbotTokenizer


if TYPE_CHECKING:
    from transformers.pipelines.conversational import Conversation

lowerCamelCase_						     =					logging.get_logger(__name__)


lowerCamelCase_						     =					{
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

lowerCamelCase_						     =					{
    '''vocab_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json'''},
    '''merges_file''': {'''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt'''},
    '''tokenizer_config_file''': {
        '''facebook/blenderbot-3B''': '''https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json'''
    },
}

lowerCamelCase_						     =					{'''facebook/blenderbot-3B''': 1_28}




class      __A(    __lowerCamelCase			):

  """simple docstring"""
  SCREAMING_SNAKE_CASE__         =      VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE__         =      PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE__         =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  SCREAMING_SNAKE_CASE__         =      ["""input_ids""", """attention_mask"""]
  SCREAMING_SNAKE_CASE__         =      BlenderbotTokenizer







  def __init__(self	,      SCREAMING_SNAKE_CASE_=None	,      SCREAMING_SNAKE_CASE_=None	,      SCREAMING_SNAKE_CASE_=None	,      SCREAMING_SNAKE_CASE_="replace"	,      SCREAMING_SNAKE_CASE_="<s>"	,      SCREAMING_SNAKE_CASE_="</s>"	,      SCREAMING_SNAKE_CASE_="</s>"	,      SCREAMING_SNAKE_CASE_="<s>"	,      SCREAMING_SNAKE_CASE_="<unk>"	,      SCREAMING_SNAKE_CASE_="<pad>"	,      SCREAMING_SNAKE_CASE_="<mask>"	,      SCREAMING_SNAKE_CASE_=False	,      SCREAMING_SNAKE_CASE_=True	,      **SCREAMING_SNAKE_CASE_	,      ):
       super().__init__(
           SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      tokenizer_file=SCREAMING_SNAKE_CASE_	,      errors=SCREAMING_SNAKE_CASE_	,      bos_token=SCREAMING_SNAKE_CASE_	,      eos_token=SCREAMING_SNAKE_CASE_	,      sep_token=SCREAMING_SNAKE_CASE_	,      cls_token=SCREAMING_SNAKE_CASE_	,      unk_token=SCREAMING_SNAKE_CASE_	,      pad_token=SCREAMING_SNAKE_CASE_	,      mask_token=SCREAMING_SNAKE_CASE_	,      add_prefix_space=SCREAMING_SNAKE_CASE_	,      trim_offsets=SCREAMING_SNAKE_CASE_	,      **SCREAMING_SNAKE_CASE_	,      )

       UpperCamelCase__				=    json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()						)
       if pre_tok_state.get("""add_prefix_space"""	,      SCREAMING_SNAKE_CASE_						) != add_prefix_space:
            UpperCamelCase__				=    getattr(SCREAMING_SNAKE_CASE_	,      pre_tok_state.pop("""type"""						)						)
            UpperCamelCase__				=    add_prefix_space
            UpperCamelCase__				=    pre_tok_class(**SCREAMING_SNAKE_CASE_						)

       UpperCamelCase__				=    add_prefix_space

       UpperCamelCase__				=    """post_processor"""
       UpperCamelCase__				=    getattr(self.backend_tokenizer	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)
       if tokenizer_component_instance:
            UpperCamelCase__				=    json.loads(tokenizer_component_instance.__getstate__()						)

            # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
            if "sep" in state:
                 UpperCamelCase__				=    tuple(state["""sep"""]						)
            if "cls" in state:
                 UpperCamelCase__				=    tuple(state["""cls"""]						)

            UpperCamelCase__				=    False

            if state.get("""add_prefix_space"""	,      SCREAMING_SNAKE_CASE_						) != add_prefix_space:
                 UpperCamelCase__				=    add_prefix_space
                 UpperCamelCase__				=    True

            if state.get("""trim_offsets"""	,      SCREAMING_SNAKE_CASE_						) != trim_offsets:
                 UpperCamelCase__				=    trim_offsets
                 UpperCamelCase__				=    True

            if changes_to_apply:
                 UpperCamelCase__				=    getattr(SCREAMING_SNAKE_CASE_	,      state.pop("""type"""						)						)
                 UpperCamelCase__				=    component_class(**SCREAMING_SNAKE_CASE_						)
                 setattr(self.backend_tokenizer	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)







  @property
  # Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
  def 	UpperCAmelCase_ (self						):
       if self._mask_token is None:
            if self.verbose:
                 logger.error("""Using mask_token, but it is not set yet."""						)
            return None
       return str(self._mask_token						)







  @mask_token.setter
  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    AddedToken(SCREAMING_SNAKE_CASE_	,      lstrip=SCREAMING_SNAKE_CASE_	,      rstrip=SCREAMING_SNAKE_CASE_						) if isinstance(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						) else value
       UpperCamelCase__				=    value







  def 	UpperCAmelCase_ (self	,      *SCREAMING_SNAKE_CASE_	,      **SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    kwargs.get("""is_split_into_words"""	,      SCREAMING_SNAKE_CASE_						)
       assert self.add_prefix_space or not is_split_into_words, (
           F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
           "to use it with pretokenized inputs."
       )

       return super()._batch_encode_plus(*SCREAMING_SNAKE_CASE_	,      **SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self	,      *SCREAMING_SNAKE_CASE_	,      **SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    kwargs.get("""is_split_into_words"""	,      SCREAMING_SNAKE_CASE_						)

       assert self.add_prefix_space or not is_split_into_words, (
           F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
           "to use it with pretokenized inputs."
       )

       return super()._encode_plus(*SCREAMING_SNAKE_CASE_	,      **SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_ = None						):
       UpperCamelCase__				=    self._tokenizer.model.save(SCREAMING_SNAKE_CASE_	,      name=SCREAMING_SNAKE_CASE_						)
       return tuple(SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_ = None						):
       UpperCamelCase__				=    [self.sep_token_id]
       UpperCamelCase__				=    [self.cls_token_id]

       if token_ids_a is None:
            return len(cls + token_ids_a + sep						) * [0]
       return len(cls + token_ids_a + sep + sep + token_ids_a + sep						) * [0]







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_ = None						):
       return token_ids_a + [self.eos_token_id]







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    []
       for is_user, text in conversation.iter_texts():
            if is_user:
                 # We need to space prefix as it's being done within blenderbot
                 inputs.append(""" """ + text						)
            else:
                 # Generated responses should contain them already.
                 inputs.append(SCREAMING_SNAKE_CASE_						)

       UpperCamelCase__				=    """  """.join(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    self.encode(SCREAMING_SNAKE_CASE_						)
       if len(SCREAMING_SNAKE_CASE_						) > self.model_max_length:
            UpperCamelCase__				=    input_ids[-self.model_max_length :]
            logger.warning(F"Trimmed input from conversation as it was longer than {self.model_max_length} tokens."						)
       return input_ids







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_						     =					logging.get_logger(__name__)






def 						__magic_name__			(   __a      :	Optional[int]						):



     '''simple docstring'''




     UpperCamelCase__				=    R"""\w+[.]\d+"""
     UpperCamelCase__				=    re.findall(__a	,		__a						)
     for pat in pats:
          UpperCamelCase__				=    key.replace(__a	,		"""_""".join(pat.split("""."""						)						)						)
     return key






def 						__magic_name__			(   __a      :	str	,		__a      :	Dict	,		__a      :	int						):



     '''simple docstring'''




     UpperCamelCase__				=    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__				=    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__				=    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__				=    pt_tuple_key[:-1] + ("""embedding""",)
          return renamed_pt_tuple_key, pt_tensor

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

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

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

     # old PyTorch layer norm bias
     UpperCamelCase__				=    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 						__magic_name__			(   __a      :	List[Any]	,		__a      :	List[Any]	,		__a      :	Optional[int]=42						):



     '''simple docstring'''




     UpperCamelCase__				=    {k: v.numpy() for k, v in pt_state_dict.items()}

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

     UpperCamelCase__				=    flatten_dict(__a						)
     UpperCamelCase__				=    {}

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

          # Correctly rename weight parameters
          UpperCamelCase__		,	UpperCamelCase__				=    rename_key_and_reshape_tensor(__a	,		__a	,		__a						)

          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__				=    jnp.asarray(__a						)

     return unflatten_dict(__a						)





