


'''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


__snake_case											=					logging.get_logger(__name__)

__snake_case											=					{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

__snake_case											=					{
    '''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											=					{
    '''gpt2''': 1024,
    '''gpt2-medium''': 1024,
    '''gpt2-large''': 1024,
    '''gpt2-xl''': 1024,
    '''distilgpt2''': 1024,
}
class        lowercase     (							SCREAMING_SNAKE_CASE__ ):

		"""simple docstring"""

		_a   			=    VOCAB_FILES_NAMES
		_a   			=    PRETRAINED_VOCAB_FILES_MAP
		_a   			=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		_a   			=    ['input_ids', 'attention_mask']
		_a   			=    GPTaTokenizer




		def __init__(		self   ,       UpperCamelCase_=None   ,       UpperCamelCase_=None   ,       UpperCamelCase_=None   ,       UpperCamelCase_="<|endoftext|>"   ,       UpperCamelCase_="<|endoftext|>"   ,       UpperCamelCase_="<|endoftext|>"   ,       UpperCamelCase_=False   ,       **UpperCamelCase_   ,       ):

							'''simple docstring'''


							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   ,       )

							UpperCamelCase__       :Tuple							 =			kwargs.pop('''add_bos_token'''   ,       _SCREAMING_SNAKE_CASE							)

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

							UpperCamelCase__       :Tuple							 =			add_prefix_space




		def    lowerCAmelCase__    (		self   ,       *UpperCamelCase_   ,       **UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__       :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()._batch_encode_plus(*_SCREAMING_SNAKE_CASE   ,       **_SCREAMING_SNAKE_CASE							)




		def    lowerCAmelCase__    (		self   ,       *UpperCamelCase_   ,       **UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__       :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   ,       UpperCamelCase_   ,       UpperCamelCase_ = None							):

							'''simple docstring'''


							UpperCamelCase__       :str							 =			self._tokenizer.model.save(_SCREAMING_SNAKE_CASE   ,       name=_SCREAMING_SNAKE_CASE							)
							return tuple(_SCREAMING_SNAKE_CASE							)




		def    lowerCAmelCase__    (		self   ,       UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__       :List[str]							 =			[]
							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:
												UpperCamelCase__       :Dict							 =			input_ids[-self.model_max_length :]
							return input_ids



'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__snake_case											=					{'''configuration_vit_msn''': ['''VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMSNConfig''']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case											=					[
		    '''VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''ViTMSNModel''',
		    '''ViTMSNForImageClassification''',
		    '''ViTMSNPreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_vit_msn import VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMSNConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_vit_msn import (
				    VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ViTMSNForImageClassification,
				    ViTMSNModel,
				    ViTMSNPreTrainedModel,
				)

else:
		import sys

		__snake_case											=					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)