
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


snake_case :		Union[str, Any]							      =  logging.get_logger(__name__)

snake_case :		List[str]							      =  {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

snake_case :		List[str]							      =  {
    "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",
    },
}

snake_case :		Optional[int]							      =  {
    "gpt2": 1_024,
    "gpt2-medium": 1_024,
    "gpt2-large": 1_024,
    "gpt2-xl": 1_024,
    "distilgpt2": 1_024,
}





class       _snake_case					(     lowerCAmelCase_				):
			UpperCamelCase__          =    VOCAB_FILES_NAMES
			UpperCamelCase__          =    PRETRAINED_VOCAB_FILES_MAP
			UpperCamelCase__          =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			UpperCamelCase__          =    ["input_ids", "attention_mask"]
			UpperCamelCase__          =    GPTaTokenizer







			def __init__(				self						,   _a=None						,   _a=None						,   _a=None						,   _a="<|endoftext|>"						,   _a="<|endoftext|>"						,   _a="<|endoftext|>"						,   _a=False						,   **_a						,   ):
				super().__init__(
				    lowerCamelCase__						,   lowerCamelCase__						,   tokenizer_file=lowerCamelCase__						,   unk_token=lowerCamelCase__						,   bos_token=lowerCamelCase__						,   eos_token=lowerCamelCase__						,   add_prefix_space=lowerCamelCase__						,   **lowerCamelCase__						,   )

				__magic_name__		:		str	   =     kwargs.pop("add_bos_token"						,   lowerCamelCase__		)

				__magic_name__		:		Any	   =     json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()		)
				if pre_tok_state.get("add_prefix_space"						,   lowerCamelCase__		) != add_prefix_space:
					__magic_name__		:		Optional[int]	   =     getattr(lowerCamelCase__						,   pre_tok_state.pop("type"		)		)
					__magic_name__		:		Dict	   =     add_prefix_space
					__magic_name__		:		int	   =     pre_tok_class(**lowerCamelCase__		)

				__magic_name__		:		Optional[Any]	   =     add_prefix_space







			def 			SCREAMING_SNAKE_CASE    (				self						,   *_a						,   **_a		):
				__magic_name__		:		Dict	   =     kwargs.get("is_split_into_words"						,   lowerCamelCase__		)
				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(*lowerCamelCase__						,   **lowerCamelCase__		)







			def 			SCREAMING_SNAKE_CASE    (				self						,   *_a						,   **_a		):
				__magic_name__		:		List[str]	   =     kwargs.get("is_split_into_words"						,   lowerCamelCase__		)

				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(*lowerCamelCase__						,   **lowerCamelCase__		)







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a = None		):
				__magic_name__		:		int	   =     self._tokenizer.model.save(lowerCamelCase__						,   name=lowerCamelCase__		)
				return tuple(lowerCamelCase__		)







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

				if len(lowerCamelCase__		) > self.model_max_length:
					__magic_name__		:		int	   =     input_ids[-self.model_max_length :]
				return input_ids







from typing import Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream

class 		UpperCamelCase__ (      lowerCAmelCase_   ):



     '''simple docstring'''

     def __init__(							self						:    Union[str, Any]						,lowerCamelCase__						:    Callable						,lowerCamelCase__						:    Optional[Features] = None						,lowerCamelCase__						:    str = None						,lowerCamelCase__						:    bool = False						,lowerCamelCase__						:    bool = False						,lowerCamelCase__						:    Optional[dict] = None						,lowerCamelCase__						:    Optional[int] = None						,**lowerCamelCase__						:    Optional[Any]						,)     ->		List[str]:




           '''simple docstring'''


           super().__init__(
               features=lowerCamelCase__						,cache_dir=lowerCamelCase__						,keep_in_memory=lowerCamelCase__						,streaming=lowerCamelCase__						,num_proc=lowerCamelCase__						,**lowerCamelCase__						,)
           SCREAMING_SNAKE_CASE		=						Generator(
               cache_dir=lowerCamelCase__						,features=lowerCamelCase__						,generator=lowerCamelCase__						,gen_kwargs=lowerCamelCase__						,**lowerCamelCase__						,)






     def 				SCREAMING_SNAKE_CASE__  (							self						:    Union[str, Any]       )     ->		Optional[Any]:




           '''simple docstring'''


           if self.streaming:
                 SCREAMING_SNAKE_CASE		=						self.builder.as_streaming_dataset(split="""train"""       )
           # Build regular (map-style) dataset
           else:
                 SCREAMING_SNAKE_CASE		=						None
                 SCREAMING_SNAKE_CASE		=						None
                 SCREAMING_SNAKE_CASE		=						None
                 SCREAMING_SNAKE_CASE		=						None

                 self.builder.download_and_prepare(
                     download_config=lowerCamelCase__						,download_mode=lowerCamelCase__						,verification_mode=lowerCamelCase__						,base_path=lowerCamelCase__						,num_proc=self.num_proc						,)
                 SCREAMING_SNAKE_CASE		=						self.builder.as_dataset(
                     split="""train"""						,verification_mode=lowerCamelCase__						,in_memory=self.keep_in_memory       )
           return dataset


