
"""simple docstring"""




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

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer


if TYPE_CHECKING:
   from transformers.pipelines.conversational import Conversation


_A     :			Any  						=			logging.get_logger(__name__)

_A     :			Any  						=			{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

_A     :			Optional[Any]  						=			{
    """vocab_file""": {
        """gpt2""": """https://huggingface.co/gpt2/resolve/main/vocab.json""",
        """gpt2-medium""": """https://huggingface.co/gpt2-medium/resolve/main/vocab.json""",
        """gpt2-large""": """https://huggingface.co/gpt2-large/resolve/main/vocab.json""",
        """gpt2-xl""": """https://huggingface.co/gpt2-xl/resolve/main/vocab.json""",
        """distilgpt2""": """https://huggingface.co/distilgpt2/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """gpt2""": """https://huggingface.co/gpt2/resolve/main/merges.txt""",
        """gpt2-medium""": """https://huggingface.co/gpt2-medium/resolve/main/merges.txt""",
        """gpt2-large""": """https://huggingface.co/gpt2-large/resolve/main/merges.txt""",
        """gpt2-xl""": """https://huggingface.co/gpt2-xl/resolve/main/merges.txt""",
        """distilgpt2""": """https://huggingface.co/distilgpt2/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """gpt2""": """https://huggingface.co/gpt2/resolve/main/tokenizer.json""",
        """gpt2-medium""": """https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json""",
        """gpt2-large""": """https://huggingface.co/gpt2-large/resolve/main/tokenizer.json""",
        """gpt2-xl""": """https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json""",
        """distilgpt2""": """https://huggingface.co/distilgpt2/resolve/main/tokenizer.json""",
    },
}

_A     :			int  						=			{
    """gpt2""": 10_24,
    """gpt2-medium""": 10_24,
    """gpt2-large""": 10_24,
    """gpt2-xl""": 10_24,
    """distilgpt2""": 10_24,
}

class 						a__		( a_		):
   __lowerCAmelCase				=							VOCAB_FILES_NAMES
   __lowerCAmelCase				=							PRETRAINED_VOCAB_FILES_MAP
   __lowerCAmelCase				=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __lowerCAmelCase				=							["""input_ids""", """attention_mask"""]
   __lowerCAmelCase				=							GPTaTokenizer
   def __init__(     self      ,       _a=None      ,       _a=None      ,       _a=None      ,       _a="<|endoftext|>"      ,       _a="<|endoftext|>"      ,       _a="<|endoftext|>"      ,       _a=False      ,       **_a      ,       ):
          super().__init__(
              _a      ,       _a      ,       tokenizer_file=_a      ,       unk_token=_a      ,       bos_token=_a      ,       eos_token=_a      ,       add_prefix_space=_a      ,       **_a      ,       )

          lowercase       :							int									=				kwargs.pop("add_bos_token"      ,       _a							)

          lowercase       :							int									=				json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()							)
          if pre_tok_state.get("add_prefix_space"      ,       _a							) != add_prefix_space:
                 lowercase       :							List[str]									=				getattr(_a      ,       pre_tok_state.pop("type"							)							)
                 lowercase       :							List[str]									=				add_prefix_space
                 lowercase       :							Union[str, Any]									=				pre_tok_class(**_a							)

          lowercase       :							Optional[Any]									=				add_prefix_space
   def      __magic_name__     (     self      ,       *_a      ,       **_a							):
          lowercase       :							str									=				kwargs.get("is_split_into_words"      ,       _a							)
          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(*_a      ,       **_a							)
   def      __magic_name__     (     self      ,       *_a      ,       **_a							):
          lowercase       :							Union[str, Any]									=				kwargs.get("is_split_into_words"      ,       _a							)

          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(*_a      ,       **_a							)
   def      __magic_name__     (     self      ,       _a      ,       _a = None							):
          lowercase       :							Optional[int]									=				self._tokenizer.model.save(_a      ,       name=_a							)
          return tuple(_a							)

   def      __magic_name__     (     self      ,       _a							):
          lowercase       :							Union[str, Any]									=				[]
          for is_user, text in conversation.iter_texts():
                 input_ids.extend(self.encode(_a      ,       add_special_tokens=_a							) + [self.eos_token_id]							)

          if len(_a							) > self.model_max_length:
                 lowercase       :							List[str]									=				input_ids[-self.model_max_length :]
          return input_ids




"""simple docstring"""




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_A     :			str  						=			logging.get_logger(__name__)

_A     :			Tuple  						=			{
    """google/bigbird-roberta-base""": """https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json""",
    """google/bigbird-roberta-large""": """https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json""",
    """google/bigbird-base-trivia-itc""": """https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json""",
    # See all BigBird models at https://huggingface.co/models?filter=big_bird
}

class 						a__		( a_		):
   __lowerCAmelCase				=							"""big_bird"""
   def __init__(     self      ,       _a=50_358      ,       _a=768      ,       _a=12      ,       _a=12      ,       _a=3_072      ,       _a="gelu_new"      ,       _a=0.1      ,       _a=0.1      ,       _a=4_096      ,       _a=2      ,       _a=0.0_2      ,       _a=1E-12      ,       _a=True      ,       _a=0      ,       _a=1      ,       _a=2      ,       _a=66      ,       _a="block_sparse"      ,       _a=True      ,       _a=False      ,       _a=64      ,       _a=3      ,       _a=None      ,       **_a      ,       ):
          super().__init__(
              pad_token_id=_a      ,       bos_token_id=_a      ,       eos_token_id=_a      ,       sep_token_id=_a      ,       **_a      ,       )

          lowercase       :							List[Any]									=				vocab_size
          lowercase       :							Optional[Any]									=				max_position_embeddings
          lowercase       :							str									=				hidden_size
          lowercase       :							int									=				num_hidden_layers
          lowercase       :							Optional[Any]									=				num_attention_heads
          lowercase       :							List[Any]									=				intermediate_size
          lowercase       :							int									=				hidden_act
          lowercase       :							str									=				hidden_dropout_prob
          lowercase       :							List[Any]									=				attention_probs_dropout_prob
          lowercase       :							Tuple									=				initializer_range
          lowercase       :							Optional[int]									=				type_vocab_size
          lowercase       :							str									=				layer_norm_eps
          lowercase       :							Tuple									=				use_cache

          lowercase       :							Any									=				rescale_embeddings
          lowercase       :							List[str]									=				attention_type
          lowercase       :							int									=				use_bias
          lowercase       :							Dict									=				block_size
          lowercase       :							List[str]									=				num_random_blocks
          lowercase       :							int									=				classifier_dropout


class 						a__		( a_		):
   @property
   def      __magic_name__     (     self							):
          if self.task == "multiple-choice":
                 lowercase       :							Union[str, Any]									=				{0: "batch", 1: "choice", 2: "sequence"}
          else:
                 lowercase       :							Dict									=				{0: "batch", 1: "sequence"}
          return OrderedDict(
              [
                  ("input_ids", dynamic_axis),
                  ("attention_mask", dynamic_axis),
              ]							)


