
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__							:							Any									=			logging.get_logger(__name__)

lowercase__							:							Optional[Any]									=			{'''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__							:							str									=			{
    '''gpt2''': 1_0_2_4,
    '''gpt2-medium''': 1_0_2_4,
    '''gpt2-large''': 1_0_2_4,
    '''gpt2-xl''': 1_0_2_4,
    '''distilgpt2''': 1_0_2_4,
}






class        SCREAMING_SNAKE_CASE__    (							UpperCamelCase__    ):

							"""simple docstring"""






							_snake_case     		=  VOCAB_FILES_NAMES
							_snake_case     		=  PRETRAINED_VOCAB_FILES_MAP
							_snake_case     		=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							_snake_case     		=  ["""input_ids""", """attention_mask"""]
							_snake_case     		=  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_     ,	)->   int:





													'''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       =			kwargs.pop('''add_bos_token'''     ,	SCREAMING_SNAKE_CASE_			)

													__UpperCamelCase       =			json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()			)
													if pre_tok_state.get('''add_prefix_space'''     ,	SCREAMING_SNAKE_CASE_			) != add_prefix_space:
																			__UpperCamelCase       =			getattr(SCREAMING_SNAKE_CASE_     ,	pre_tok_state.pop('''type'''			)			)
																			__UpperCamelCase       =			add_prefix_space
																			__UpperCamelCase       =			pre_tok_class(**SCREAMING_SNAKE_CASE_			)

													__UpperCamelCase       =			add_prefix_space



							def     A__		(  self     ,	*SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_			)->   BatchEncoding:





													'''simple docstring'''






													__UpperCamelCase       =			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     A__		(  self     ,	*SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_			)->   BatchEncoding:





													'''simple docstring'''






													__UpperCamelCase       =			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     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_ = None			)->   Tuple[str]:





													'''simple docstring'''






													__UpperCamelCase       =			self._tokenizer.model.save(SCREAMING_SNAKE_CASE_     ,	name=SCREAMING_SNAKE_CASE_			)
													return tuple(SCREAMING_SNAKE_CASE_			)



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_			)->   List[int]:





													'''simple docstring'''






													__UpperCamelCase       =			[]
													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       =			input_ids[-self.model_max_length :]
													return input_ids






from typing import TYPE_CHECKING

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


lowercase__							:							Dict									=			{"configuration_vit": ["VIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTConfig", "ViTOnnxConfig"]}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase__							:							Tuple									=			["ViTFeatureExtractor"]
				lowercase__							:							str									=			["ViTImageProcessor"]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase__							:							List[str]									=			[
				    "VIT_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "ViTForImageClassification",
				    "ViTForMaskedImageModeling",
				    "ViTModel",
				    "ViTPreTrainedModel",
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase__							:							Tuple									=			[
				    "TFViTForImageClassification",
				    "TFViTModel",
				    "TFViTPreTrainedModel",
				]

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase__							:							List[Any]									=			[
				    "FlaxViTForImageClassification",
				    "FlaxViTModel",
				    "FlaxViTPreTrainedModel",
				]

if TYPE_CHECKING:
				from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_vit import ViTFeatureExtractor
								from .image_processing_vit import ViTImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_vit import (
								    VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    ViTForImageClassification,
								    ViTForMaskedImageModeling,
								    ViTModel,
								    ViTPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel


else:
				import sys

				lowercase__							:							List[Any]									=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




