




import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


lowerCamelCase       :       Optional[Any]        =    logging.getLogger(__name__)




@dataclass
class 						__lowercase		:




      """simple docstring"""





      _snake_case							     =						42
      _snake_case							     =						42
      _snake_case							     =						42




@dataclass
class 						__lowercase		:




      """simple docstring"""





      _snake_case							     =						42
      _snake_case							     =						42
      _snake_case							     =						None
      _snake_case							     =						None




class 						__lowercase		(__snake_case							):




      """simple docstring"""





      _snake_case							     =						"""train"""
      _snake_case							     =						"""dev"""
      _snake_case							     =						"""test"""




class 						__lowercase		:




      """simple docstring"""





      @staticmethod
      def 	UpperCAmelCase      (      A				,      A	)		->				List[InputExample]:
         raise NotImplementedError

      @staticmethod
      def 	UpperCAmelCase      (      A	)		->				List[str]:
         raise NotImplementedError

      @staticmethod
      def 	UpperCAmelCase      (      A				,      A				,      A				,      A				,      A=False				,      A="[CLS]"				,      A=1				,      A="[SEP]"				,      A=False				,      A=False				,      A=0				,      A=0				,      A=-1_0_0				,      A=0				,      A=True				,      )		->				List[InputFeatures]:
         snake_case      :				Tuple				=       {label: i for i, label in enumerate(UpperCamelCase__	)}

         snake_case      :				Dict				=       []
         for ex_index, example in enumerate(UpperCamelCase__	):
            if ex_index % 1_0_0_0_0 == 0:
               logger.info("""Writing example %d of %d"""				,      UpperCamelCase__				,      len(UpperCamelCase__	)	)

            snake_case      :				List[Any]				=       []
            snake_case      :				str				=       []
            for word, label in zip(example.words				,      example.labels	):
               snake_case      :				Optional[int]				=       tokenizer.tokenize(UpperCamelCase__	)

               # bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
               if len(UpperCamelCase__	) > 0:
                  tokens.extend(UpperCamelCase__	)
                  # Use the real label id for the first token of the word, and padding ids for the remaining tokens
                  label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(UpperCamelCase__	) - 1)	)

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
            snake_case      :				str				=       tokenizer.num_special_tokens_to_add()
            if len(UpperCamelCase__	) > max_seq_length - special_tokens_count:
               snake_case      :				str				=       tokens[: (max_seq_length - special_tokens_count)]
               snake_case      :				Optional[Any]				=       label_ids[: (max_seq_length - special_tokens_count)]

            # The convention in BERT is:
            # (a) For sequence pairs:
            #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
            #  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
            # (b) For single sequences:
            #  tokens:   [CLS] the dog is hairy . [SEP]
            #  type_ids:   0   0   0   0  0     0   0
            #
            # Where "type_ids" are used to indicate whether this is the first
            # sequence or the second sequence. The embedding vectors for `type=0` and
            # `type=1` were learned during pre-training and are added to the wordpiece
            # embedding vector (and position vector). This is not *strictly* necessary
            # since the [SEP] token unambiguously separates the sequences, but it makes
            # it easier for the model to learn the concept of sequences.
            #
            # For classification tasks, the first vector (corresponding to [CLS]) is
            # used as the "sentence vector". Note that this only makes sense because
            # the entire model is fine-tuned.
            tokens += [sep_token]
            label_ids += [pad_token_label_id]
            if sep_token_extra:
               # roberta uses an extra separator b/w pairs of sentences
               tokens += [sep_token]
               label_ids += [pad_token_label_id]
            snake_case      :				Union[str, Any]				=       [sequence_a_segment_id] * len(UpperCamelCase__	)

            if cls_token_at_end:
               tokens += [cls_token]
               label_ids += [pad_token_label_id]
               segment_ids += [cls_token_segment_id]
            else:
               snake_case      :				List[Any]				=       [cls_token] + tokens
               snake_case      :				Optional[Any]				=       [pad_token_label_id] + label_ids
               snake_case      :				List[Any]				=       [cls_token_segment_id] + segment_ids

            snake_case      :				Union[str, Any]				=       tokenizer.convert_tokens_to_ids(UpperCamelCase__	)

            # The mask has 1 for real tokens and 0 for padding tokens. Only real
            # tokens are attended to.
            snake_case      :				Tuple				=       [1 if mask_padding_with_zero else 0] * len(UpperCamelCase__	)

            # Zero-pad up to the sequence length.
            snake_case      :				Dict				=       max_seq_length - len(UpperCamelCase__	)
            if pad_on_left:
               snake_case      :				str				=       ([pad_token] * padding_length) + input_ids
               snake_case      :				str				=       ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
               snake_case      :				List[str]				=       ([pad_token_segment_id] * padding_length) + segment_ids
               snake_case      :				Tuple				=       ([pad_token_label_id] * padding_length) + label_ids
            else:
               input_ids += [pad_token] * padding_length
               input_mask += [0 if mask_padding_with_zero else 1] * padding_length
               segment_ids += [pad_token_segment_id] * padding_length
               label_ids += [pad_token_label_id] * padding_length

            assert len(UpperCamelCase__	) == max_seq_length
            assert len(UpperCamelCase__	) == max_seq_length
            assert len(UpperCamelCase__	) == max_seq_length
            assert len(UpperCamelCase__	) == max_seq_length

            if ex_index < 5:
               logger.info("""*** Example ***"""	)
               logger.info("""guid: %s"""				,      example.guid	)
               logger.info("""tokens: %s"""				,      """ """.join([str(UpperCamelCase__	) for x in tokens]	)	)
               logger.info("""input_ids: %s"""				,      """ """.join([str(UpperCamelCase__	) for x in input_ids]	)	)
               logger.info("""input_mask: %s"""				,      """ """.join([str(UpperCamelCase__	) for x in input_mask]	)	)
               logger.info("""segment_ids: %s"""				,      """ """.join([str(UpperCamelCase__	) for x in segment_ids]	)	)
               logger.info("""label_ids: %s"""				,      """ """.join([str(UpperCamelCase__	) for x in label_ids]	)	)

            if "token_type_ids" not in tokenizer.model_input_names:
               snake_case      :				Optional[Any]				=       None

            features.append(
                InputFeatures(
                    input_ids=UpperCamelCase__				,      attention_mask=UpperCamelCase__				,      token_type_ids=UpperCamelCase__				,      label_ids=UpperCamelCase__	)	)
         return features


if is_torch_available():
       import torch
       from torch import nn
       from torch.utils.data import Dataset




       class 						__lowercase		(__snake_case							):




             """simple docstring"""





             _snake_case							     =						42
             _snake_case							     =						nn.CrossEntropyLoss().ignore_index

             def __init__(      self				,      A				,      A				,      A				,      A				,      A				,      A = None				,      A=False				,      A = Split.train				,      )		->				Optional[Any]:
                snake_case      :				Dict				=       os.path.join(
                    UpperCamelCase__				,      """cached_{}_{}_{}""".format(mode.value				,      tokenizer.__class__.__name__				,      str(UpperCamelCase__	)	)				,      )

                # Make sure only the first process in distributed training processes the dataset,
                # and the others will use the cache.
                snake_case      :				List[Any]				=       cached_features_file + ".lock"
                with FileLock(UpperCamelCase__	):
                   if os.path.exists(UpperCamelCase__	) and not overwrite_cache:
                      logger.info(f"""Loading features from cached file {cached_features_file}"""	)
                      snake_case      :				List[Any]				=       torch.load(UpperCamelCase__	)
                   else:
                      logger.info(f"""Creating features from dataset file at {data_dir}"""	)
                      snake_case      :				Optional[Any]				=       token_classification_task.read_examples_from_file(UpperCamelCase__				,      UpperCamelCase__	)
                      # TODO clean up all this to leverage built-in features of tokenizers
                      snake_case      :				List[Any]				=       token_classification_task.convert_examples_to_features(
                          UpperCamelCase__				,      UpperCamelCase__				,      UpperCamelCase__				,      UpperCamelCase__				,      cls_token_at_end=bool(model_type in ["""xlnet"""]	)				,      cls_token=tokenizer.cls_token				,      cls_token_segment_id=2 if model_type in ["""xlnet"""] else 0				,      sep_token=tokenizer.sep_token				,      sep_token_extra=UpperCamelCase__				,      pad_on_left=bool(tokenizer.padding_side == """left"""	)				,      pad_token=tokenizer.pad_token_id				,      pad_token_segment_id=tokenizer.pad_token_type_id				,      pad_token_label_id=self.pad_token_label_id				,      )
                      logger.info(f"""Saving features into cached file {cached_features_file}"""	)
                      torch.save(self.features				,      UpperCamelCase__	)

             def __len__(      self	)		->				Optional[int]:
                return len(self.features	)

             def __getitem__(      self				,      A	)		->				InputFeatures:
                return self.features[i]


if is_tf_available():
       import tensorflow as tf




       class 						__lowercase		:




             """simple docstring"""





             _snake_case							     =						42
             _snake_case							     =						-100

             def __init__(      self				,      A				,      A				,      A				,      A				,      A				,      A = None				,      A=False				,      A = Split.train				,      )		->				Optional[int]:
                snake_case      :				Optional[Any]				=       token_classification_task.read_examples_from_file(UpperCamelCase__				,      UpperCamelCase__	)
                # TODO clean up all this to leverage built-in features of tokenizers
                snake_case      :				Dict				=       token_classification_task.convert_examples_to_features(
                    UpperCamelCase__				,      UpperCamelCase__				,      UpperCamelCase__				,      UpperCamelCase__				,      cls_token_at_end=bool(model_type in ["""xlnet"""]	)				,      cls_token=tokenizer.cls_token				,      cls_token_segment_id=2 if model_type in ["""xlnet"""] else 0				,      sep_token=tokenizer.sep_token				,      sep_token_extra=UpperCamelCase__				,      pad_on_left=bool(tokenizer.padding_side == """left"""	)				,      pad_token=tokenizer.pad_token_id				,      pad_token_segment_id=tokenizer.pad_token_type_id				,      pad_token_label_id=self.pad_token_label_id				,      )

                def gen():
                   for ex in self.features:
                      if ex.token_type_ids is None:
                         yield (
                             {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
                             ex.label_ids,
                         )
                      else:
                         yield (
                             {
                                 "input_ids": ex.input_ids,
                                 "attention_mask": ex.attention_mask,
                                 "token_type_ids": ex.token_type_ids,
                             },
                             ex.label_ids,
                         )

                if "token_type_ids" not in tokenizer.model_input_names:
                   snake_case      :				Optional[Any]				=       tf.data.Dataset.from_generator(
                       UpperCamelCase__				,      ({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa}, tf.intaa)				,      (
                           {"""input_ids""": tf.TensorShape([None]	), """attention_mask""": tf.TensorShape([None]	)},
                           tf.TensorShape([None]	),
                       )				,      )
                else:
                   snake_case      :				Optional[Any]				=       tf.data.Dataset.from_generator(
                       UpperCamelCase__				,      ({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa, """token_type_ids""": tf.intaa}, tf.intaa)				,      (
                           {
                               """input_ids""": tf.TensorShape([None]	),
                               """attention_mask""": tf.TensorShape([None]	),
                               """token_type_ids""": tf.TensorShape([None]	),
                           },
                           tf.TensorShape([None]	),
                       )				,      )

             def 	UpperCAmelCase      (      self	)		->				Union[str, Any]:
                snake_case      :				Dict				=       self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features	)	)	)

                return self.dataset

             def __len__(      self	)		->				Optional[Any]:
                return len(self.features	)

             def __getitem__(      self				,      A	)		->				InputFeatures:
                return self.features[i]








"""simple docstring"""


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

__A						   =			logging.get_logger(__name__)


__A						   =			{
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

__A						   =			{
    "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"
    },
}

__A						   =			{"facebook/blenderbot-3B": 128}





class     snake_case	(				__snake_case  ):
				SCREAMING_SNAKE_CASE_   :      Dict         =  VOCAB_FILES_NAMES
				SCREAMING_SNAKE_CASE_   :      str         =  PRETRAINED_VOCAB_FILES_MAP
				SCREAMING_SNAKE_CASE_   :      Optional[Any]         =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				SCREAMING_SNAKE_CASE_   :      Optional[int]         =  ["""input_ids""", """attention_mask"""]
				SCREAMING_SNAKE_CASE_   :      List[Any]         =  BlenderbotTokenizer

				def __init__(	self							:				Any     ,  UpperCamelCase__							:				str=None     ,  UpperCamelCase__							:				Tuple=None     ,  UpperCamelCase__							:				List[Any]=None     ,  UpperCamelCase__							:				Optional[Any]="replace"     ,  UpperCamelCase__							:				int="<s>"     ,  UpperCamelCase__							:				str="</s>"     ,  UpperCamelCase__							:				List[Any]="</s>"     ,  UpperCamelCase__							:				Union[str, Any]="<s>"     ,  UpperCamelCase__							:				List[str]="<unk>"     ,  UpperCamelCase__							:				Dict="<pad>"     ,  UpperCamelCase__							:				List[Any]="<mask>"     ,  UpperCamelCase__							:				Any=False     ,  UpperCamelCase__							:				int=True     ,  **UpperCamelCase__							:				Union[str, Any]     ,  )->   int:



						'''simple docstring'''


						super().__init__(
						    UpperCamelCase__     ,  UpperCamelCase__     ,  tokenizer_file=UpperCamelCase__     ,  errors=UpperCamelCase__     ,  bos_token=UpperCamelCase__     ,  eos_token=UpperCamelCase__     ,  sep_token=UpperCamelCase__     ,  cls_token=UpperCamelCase__     ,  unk_token=UpperCamelCase__     ,  pad_token=UpperCamelCase__     ,  mask_token=UpperCamelCase__     ,  add_prefix_space=UpperCamelCase__     ,  trim_offsets=UpperCamelCase__     ,  **UpperCamelCase__     ,  )

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

						__lowerCAmelCase:       List[Any]          =			add_prefix_space

						__lowerCAmelCase:       Union[str, Any]          =			"post_processor"
						__lowerCAmelCase:       Tuple          =			getattr(self.backend_tokenizer     ,  UpperCamelCase__     ,  UpperCamelCase__)
						if tokenizer_component_instance:
								__lowerCAmelCase:       List[Any]          =			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:
										__lowerCAmelCase:       str          =			tuple(state["sep"])
								if "cls" in state:
										__lowerCAmelCase:       Any          =			tuple(state["cls"])

								__lowerCAmelCase:       int          =			False

								if state.get("add_prefix_space"     ,  UpperCamelCase__) != add_prefix_space:
										__lowerCAmelCase:       Dict          =			add_prefix_space
										__lowerCAmelCase:       Optional[Any]          =			True

								if state.get("trim_offsets"     ,  UpperCamelCase__) != trim_offsets:
										__lowerCAmelCase:       Dict          =			trim_offsets
										__lowerCAmelCase:       Tuple          =			True

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

				@property
				# Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
				def 							lowercase_     (	self							:				Optional[Any])->   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 							lowercase_     (	self							:				str     ,  UpperCamelCase__							:				Union[str, Any])->   Tuple:



						'''simple docstring'''


						__lowerCAmelCase:       int          =			AddedToken(UpperCamelCase__     ,  lstrip=UpperCamelCase__     ,  rstrip=UpperCamelCase__) if isinstance(UpperCamelCase__     ,  UpperCamelCase__) else value
						__lowerCAmelCase:       Any          =			value

				def 							lowercase_     (	self							:				Union[str, Any]     ,  *UpperCamelCase__							:				List[str]     ,  **UpperCamelCase__							:				Optional[int])->   BatchEncoding:



						'''simple docstring'''


						__lowerCAmelCase:       List[str]          =			kwargs.get("is_split_into_words"     ,  UpperCamelCase__)
						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(*UpperCamelCase__     ,  **UpperCamelCase__)

				def 							lowercase_     (	self							:				Any     ,  *UpperCamelCase__							:				Optional[int]     ,  **UpperCamelCase__							:				int)->   BatchEncoding:



						'''simple docstring'''


						__lowerCAmelCase:       List[Any]          =			kwargs.get("is_split_into_words"     ,  UpperCamelCase__)

						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(*UpperCamelCase__     ,  **UpperCamelCase__)

				def 							lowercase_     (	self							:				List[str]     ,  UpperCamelCase__							:				str     ,  UpperCamelCase__							:				Optional[str] = None)->   Tuple[str]:



						'''simple docstring'''


						__lowerCAmelCase:       Optional[int]          =			self._tokenizer.model.save(UpperCamelCase__     ,  name=UpperCamelCase__)
						return tuple(UpperCamelCase__)

				def 							lowercase_     (	self							:				str     ,  UpperCamelCase__							:				List[int]     ,  UpperCamelCase__							:				Optional[List[int]] = None)->   List[int]:



						'''simple docstring'''


						__lowerCAmelCase:       Tuple          =			[self.sep_token_id]
						__lowerCAmelCase:       Any          =			[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 							lowercase_     (	self							:				Union[str, Any]     ,  UpperCamelCase__							:				List[int]     ,  UpperCamelCase__							:				Optional[List[int]] = None)->   List[Any]:



						'''simple docstring'''


						return token_ids_a + [self.eos_token_id]




				def 							lowercase_     (	self							:				Dict     ,  UpperCamelCase__							:				"Conversation")->   List[int]:



						'''simple docstring'''


						__lowerCAmelCase:       str          =			[]
						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(UpperCamelCase__)

						__lowerCAmelCase:       Optional[int]          =			"  ".join(UpperCamelCase__)
						__lowerCAmelCase:       Tuple          =			self.encode(UpperCamelCase__)
						if len(UpperCamelCase__) > self.model_max_length:
								__lowerCAmelCase:       int          =			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


