


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__    :Any       =				logging.get_logger(__name__)


lowerCAmelCase__    :List[str]       =				{
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

lowerCAmelCase__    :str       =				{
    '''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__    :str       =				{'''facebook/blenderbot-3B''': 1_2_8}

class 						__a (							UpperCAmelCase     ):
		_a      :    str           =      VOCAB_FILES_NAMES
		_a      :    Dict           =      PRETRAINED_VOCAB_FILES_MAP
		_a      :    Optional[int]           =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		_a      :    Optional[Any]           =      ['input_ids', 'attention_mask']
		_a      :    Dict           =      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     ,       )					->			List[Any]:




									"""simple docstring"""





									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		)					->			str:




									"""simple docstring"""





									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		)					->			int:




									"""simple docstring"""





									_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		)					->			BatchEncoding:




									"""simple docstring"""





									_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		)					->			BatchEncoding:




									"""simple docstring"""





									_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		)					->			Tuple[str]:




									"""simple docstring"""





									_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		)					->			List[int]:




									"""simple docstring"""





									_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		)					->			Dict:




									"""simple docstring"""





									return token_ids_a + [self.eos_token_id]






		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE		)					->			List[int]:




									"""simple docstring"""





									_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__    :int       =				logging.get_logger(__name__)

def 			lowerCAmelCase__					(     a__:						Dict			)    ->						List[str]:


							'''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 			lowerCAmelCase__					(     a__:						Optional[int]     ,    a__:						Dict     ,    a__:						List[Any]			)    ->						str:


							'''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 			lowerCAmelCase__					(     a__:						Any     ,    a__:						Optional[Any]     ,    a__:						int=4_2			)    ->						List[str]:


							'''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__			)
