






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


lowercase  :							Optional[int]			  =    logging.get_logger(__name__)

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

lowercase  :							Tuple			  =    {
    '''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''',
    },
}

lowercase  :							List[Any]			  =    {
    '''gpt2''': 10_24,
    '''gpt2-medium''': 10_24,
    '''gpt2-large''': 10_24,
    '''gpt2-xl''': 10_24,
    '''distilgpt2''': 10_24,
}




class 		UpperCAmelCase_						(	SCREAMING_SNAKE_CASE__					):


						'''simple docstring'''




						A     :   Any						=					VOCAB_FILES_NAMES
						A     :   List[Any]						=					PRETRAINED_VOCAB_FILES_MAP
						A     :   Union[str, Any]						=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						A     :   Any						=					['input_ids', 'attention_mask']
						A     :   Any						=					GPTaTokenizer






						def __init__(	self		,				_SCREAMING_SNAKE_CASE=None		,				_SCREAMING_SNAKE_CASE=None		,				_SCREAMING_SNAKE_CASE=None		,				_SCREAMING_SNAKE_CASE="<|endoftext|>"		,				_SCREAMING_SNAKE_CASE="<|endoftext|>"		,				_SCREAMING_SNAKE_CASE="<|endoftext|>"		,				_SCREAMING_SNAKE_CASE=False		,				**_SCREAMING_SNAKE_CASE		,				) ->				Optional[Any]:
								super().__init__(
								    _SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				tokenizer_file=_SCREAMING_SNAKE_CASE		,				unk_token=_SCREAMING_SNAKE_CASE		,				bos_token=_SCREAMING_SNAKE_CASE		,				eos_token=_SCREAMING_SNAKE_CASE		,				add_prefix_space=_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE		,				)

								snake_case_				:							str						 =       kwargs.pop("add_bos_token"		,				_SCREAMING_SNAKE_CASE				)

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

								snake_case_				:							Union[str, Any]						 =       add_prefix_space






						def       _lowerCAmelCase       (	self		,				*_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE				) ->				BatchEncoding:
								snake_case_				:							Union[str, Any]						 =       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       _lowerCAmelCase       (	self		,				*_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE				) ->				BatchEncoding:
								snake_case_				:							Dict						 =       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       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE = None				) ->				Tuple[str]:
								snake_case_				:							List[str]						 =       self._tokenizer.model.save(_SCREAMING_SNAKE_CASE		,				name=_SCREAMING_SNAKE_CASE				)
								return tuple(_SCREAMING_SNAKE_CASE				)






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE				) ->				List[int]:
								snake_case_				:							Any						 =       []
								for is_user, text in conversation.iter_texts():
										input_ids.extend(self.encode(_SCREAMING_SNAKE_CASE		,				add_special_tokens=_SCREAMING_SNAKE_CASE				) + [self.eos_token_id]				)

								if len(_SCREAMING_SNAKE_CASE				) > self.model_max_length:
										snake_case_				:							Any						 =       input_ids[-self.model_max_length :]
								return input_ids








from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase  :							Union[str, Any]			  =    {'''configuration_xglm''': ['''XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XGLMConfig''']}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase  :							Optional[int]			  =    ['''XGLMTokenizer''']

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase  :							Tuple			  =    ['''XGLMTokenizerFast''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase  :							str			  =    [
							    '''XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''XGLMForCausalLM''',
							    '''XGLMModel''',
							    '''XGLMPreTrainedModel''',
							]


try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase  :							str			  =    [
							    '''FlaxXGLMForCausalLM''',
							    '''FlaxXGLMModel''',
							    '''FlaxXGLMPreTrainedModel''',
							]


try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase  :							Tuple			  =    [
							    '''TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''TFXGLMForCausalLM''',
							    '''TFXGLMModel''',
							    '''TFXGLMPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_xglm import XGLMTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_xglm_fast import XGLMTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_xglm import (
														    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFXGLMForCausalLM,
														    TFXGLMModel,
														    TFXGLMPreTrainedModel,
														)


else:
							import sys

							lowercase  :							Dict			  =    _LazyModule(__name__, globals()['''__file__'''], _import_structure)
