
import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
		from transformers.pipelines.conversational import Conversation


lowerCamelCase__	=							logging.get_logger(__name__)

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

lowerCamelCase__	=							{
    "tokenizer_file": {
        "EleutherAI/gpt-neox-20b": "https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json",
    },
}

lowerCamelCase__	=							{
    "gpt-neox-20b": 2048,
}







class        lowerCAmelCase__      (   __lowercase	):
						UpperCamelCase_							: Optional[Any]	=		VOCAB_FILES_NAMES
						UpperCamelCase_							: Any	=		PRETRAINED_VOCAB_FILES_MAP
						UpperCamelCase_							: Union[str, Any]	=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						UpperCamelCase_							: int	=		["input_ids", "attention_mask"]

						def __init__(			self       ,							a=None       ,							a=None       ,							a=None       ,							a="<|endoftext|>"       ,							a="<|endoftext|>"       ,							a="<|endoftext|>"       ,							a=False       ,							**a       ,							)					->      Optional[int]:


								'''simple docstring'''

								super().__init__(
								    a       ,							a       ,							tokenizer_file=a       ,							unk_token=a       ,							bos_token=a       ,							eos_token=a       ,							add_prefix_space=a       ,							**a       ,							)

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

								_UpperCamelCase						    =	add_prefix_space

						def  A_				(			self       ,							a       ,							a = None				)					->      Tuple[str]:


								'''simple docstring'''

								_UpperCamelCase						    =	self._tokenizer.model.save(a       ,							name=a				)
								return tuple(a				)







						def  A_				(			self       ,							a				)					->      List[int]:


								'''simple docstring'''

								_UpperCamelCase						    =	[]
								for is_user, text in conversation.iter_texts():
										input_ids.extend(self.encode(a       ,							add_special_tokens=a				) + [self.eos_token_id]				)

								if len(a				) > self.model_max_length:
										_UpperCamelCase						    =	input_ids[-self.model_max_length :]
								return input_ids





import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType







class        lowerCAmelCase__      (   __lowercase	):
						UpperCamelCase_							: Dict	=		["image_processor", "tokenizer"]
						UpperCamelCase_							: Union[str, Any]	=		"ViltImageProcessor"
						UpperCamelCase_							: List[str]	=		("BertTokenizer", "BertTokenizerFast")

						def __init__(			self       ,							a=None       ,							a=None       ,							**a				)					->      Optional[int]:


								'''simple docstring'''

								_UpperCamelCase						    =	None
								if "feature_extractor" in kwargs:
										warnings.warn(
										    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
										    """ instead."""       ,							a       ,							)
										_UpperCamelCase						    =	kwargs.pop("""feature_extractor"""				)

								_UpperCamelCase						    =	image_processor if image_processor is not None else feature_extractor
								if image_processor is None:
										raise ValueError("""You need to specify an `image_processor`."""				)
								if tokenizer is None:
										raise ValueError("""You need to specify a `tokenizer`."""				)

								super().__init__(a       ,							a				)
								_UpperCamelCase						    =	self.image_processor

						def __call__(			self       ,							a       ,							a = None       ,							a = True       ,							a = False       ,							a = None       ,							a = None       ,							a = 0       ,							a = None       ,							a = None       ,							a = None       ,							a = False       ,							a = False       ,							a = False       ,							a = False       ,							a = True       ,							a = None       ,							**a       ,							)					->      BatchEncoding:


								'''simple docstring'''

								_UpperCamelCase						    =	self.tokenizer(
								    text=a       ,							add_special_tokens=a       ,							padding=a       ,							truncation=a       ,							max_length=a       ,							stride=a       ,							pad_to_multiple_of=a       ,							return_token_type_ids=a       ,							return_attention_mask=a       ,							return_overflowing_tokens=a       ,							return_special_tokens_mask=a       ,							return_offsets_mapping=a       ,							return_length=a       ,							verbose=a       ,							return_tensors=a       ,							**a       ,							)
								# add pixel_values + pixel_mask
								_UpperCamelCase						    =	self.image_processor(a       ,							return_tensors=a				)
								encoding.update(a				)

								return encoding

						def  A_				(			self       ,							*a       ,							**a				)					->      Optional[Any]:


								'''simple docstring'''

								return self.tokenizer.batch_decode(*a       ,							**a				)

						def  A_				(			self       ,							*a       ,							**a				)					->      int:


								'''simple docstring'''

								return self.tokenizer.decode(*a       ,							**a				)

						@property
						def  A_				(			self				)					->      Optional[Any]:


								'''simple docstring'''

								_UpperCamelCase						    =	self.tokenizer.model_input_names
								_UpperCamelCase						    =	self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)

						@property
						def  A_				(			self				)					->      Tuple:


								'''simple docstring'''

								warnings.warn(
								    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""       ,							a       ,							)
								return self.image_processor_class







						@property
						def  A_				(			self				)					->      Dict:


								'''simple docstring'''

								warnings.warn(
								    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""       ,							a       ,							)
								return self.image_processor



