






"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE						:						Optional[Any]					   =       {
    '''configuration_trocr''': ['''TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TrOCRConfig'''],
    '''processing_trocr''': ['''TrOCRProcessor'''],
}


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE						:						Optional[int]					   =       [
					    '''TROCR_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TrOCRForCausalLM''',
					    '''TrOCRPreTrainedModel''',
					]


if TYPE_CHECKING:
					from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
					from .processing_trocr import TrOCRProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel

else:
					import sys

					SCREAMING_SNAKE_CASE						:						Optional[Any]					   =       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)










"""simple docstring"""






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


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

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

SCREAMING_SNAKE_CASE						:						List[Any]					   =       {
    '''tokenizer_file''': {
        '''EleutherAI/gpt-neox-20b''': '''https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json''',
    },
}

SCREAMING_SNAKE_CASE						:						str					   =       {
    '''gpt-neox-20b''': 2_048,
}





class       snake_case_       (    _lowerCamelCase       ):



	"""simple docstring"""

	SCREAMING_SNAKE_CASE_:							Union[str, Any]   			=					VOCAB_FILES_NAMES
	SCREAMING_SNAKE_CASE_:							Any   			=					PRETRAINED_VOCAB_FILES_MAP
	SCREAMING_SNAKE_CASE_:							List[Any]   			=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	SCREAMING_SNAKE_CASE_:							Optional[Any]   			=					["""input_ids""", """attention_mask"""]


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


					"""simple docstring"""

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

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

					A__							 =       add_prefix_space


	def       _UpperCAmelCase						(      self  ,					__a  ,					__a = None						):


					"""simple docstring"""

					A__							 =       self._tokenizer.model.save(__a  ,					name=__a						)
					return tuple(__a						)






	def       _UpperCAmelCase						(      self  ,					__a						):


					"""simple docstring"""

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


