



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_								=     logging.get_logger(__name__)

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

lowercase_								=     {
    """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_								=     {
    """gpt2""": 1024,
    """gpt2-medium""": 1024,
    """gpt2-large""": 1024,
    """gpt2-xl""": 1024,
    """distilgpt2""": 1024,
}
class 				_snake_case			(					lowercase__):
				UpperCamelCase__				:					int     		=VOCAB_FILES_NAMES
				UpperCamelCase__				:					str     		=PRETRAINED_VOCAB_FILES_MAP
				UpperCamelCase__				:					int     		=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				UpperCamelCase__				:					List[str]     		=["""input_ids""", """attention_mask"""]
				UpperCamelCase__				:					List[Any]     		=GPTaTokenizer




				def __init__(						self					:						Tuple,					__lowercase					:						Any=None,					__lowercase					:						Dict=None,					__lowercase					:						Union[str, Any]=None,					__lowercase					:						int="<|endoftext|>",					__lowercase					:						Tuple="<|endoftext|>",					__lowercase					:						Dict="<|endoftext|>",					__lowercase					:						Dict=False,					**__lowercase					:						Optional[int],					):
							super().__init__(
							    __lowercase,					__lowercase,					tokenizer_file=__lowercase,					unk_token=__lowercase,					bos_token=__lowercase,					eos_token=__lowercase,					add_prefix_space=__lowercase,					**__lowercase,					)

							lowercase__					  =				kwargs.pop("add_bos_token",					__lowercase						)

							lowercase__					  =				json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()						)
							if pre_tok_state.get("add_prefix_space",					__lowercase						) != add_prefix_space:
										lowercase__					  =				getattr(__lowercase,					pre_tok_state.pop("type"						)						)
										lowercase__					  =				add_prefix_space
										lowercase__					  =				pre_tok_class(**__lowercase						)

							lowercase__					  =				add_prefix_space




				def 							A__      (						self					:						List[str],					*__lowercase					:						Dict,					**__lowercase					:						Tuple						):
							lowercase__					  =				kwargs.get("is_split_into_words",					__lowercase						)
							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(*__lowercase,					**__lowercase						)




				def 							A__      (						self					:						Any,					*__lowercase					:						int,					**__lowercase					:						Optional[Any]						):
							lowercase__					  =				kwargs.get("is_split_into_words",					__lowercase						)

							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(*__lowercase,					**__lowercase						)




				def 							A__      (						self					:						List[str],					__lowercase					:						str,					__lowercase					:						Optional[str] = None						):
							lowercase__					  =				self._tokenizer.model.save(__lowercase,					name=__lowercase						)
							return tuple(__lowercase						)




				def 							A__      (						self					:						List[Any],					__lowercase					:						"Conversation"						):
							lowercase__					  =				[]
							for is_user, text in conversation.iter_texts():
										input_ids.extend(self.encode(__lowercase,					add_special_tokens=__lowercase						) + [self.eos_token_id]						)

							if len(__lowercase						) > self.model_max_length:
										lowercase__					  =				input_ids[-self.model_max_length :]
							return input_ids









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase_								=     {
    """configuration_timesformer""": ["""TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TimesformerConfig"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase_								=     [
							    """TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TimesformerModel""",
							    """TimesformerForVideoClassification""",
							    """TimesformerPreTrainedModel""",
							]

if TYPE_CHECKING:
							from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_timesformer import (
														    TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TimesformerForVideoClassification,
														    TimesformerModel,
														    TimesformerPreTrainedModel,
														)

else:
							import sys

							lowercase_								=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




