




'''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


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

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

__lowerCamelCase   :						Dict				=			{
    '''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''',
    },
}

__lowerCamelCase   :						List[Any]				=			{
    '''gpt2''': 10_24,
    '''gpt2-medium''': 10_24,
    '''gpt2-large''': 10_24,
    '''gpt2-xl''': 10_24,
    '''distilgpt2''': 10_24,
}


class    lowerCamelCase    (		_lowerCamelCase						):






			'''simple docstring'''





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

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

								__magic_name__							:	str    							=							kwargs.pop('''add_bos_token'''       ,						lowerCamelCase_  )

								__magic_name__							:	int    							=							json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()  )
								if pre_tok_state.get('''add_prefix_space'''       ,						lowerCamelCase_  ) != add_prefix_space:
													__magic_name__							:	List[Any]    							=							getattr(lowerCamelCase_       ,						pre_tok_state.pop('''type'''  )  )
													__magic_name__							:	str    							=							add_prefix_space
													__magic_name__							:	Dict    							=							pre_tok_class(**lowerCamelCase_  )

								__magic_name__							:	List[str]    							=							add_prefix_space

			def 							UpperCAmelCase__       ( self			:   List[Any]       ,						*lowerCamelCase_			:   Optional[Any]       ,						**lowerCamelCase_			:   Any  )						->   BatchEncoding:
								__magic_name__							:	Optional[int]    							=							kwargs.get('''is_split_into_words'''       ,						lowerCamelCase_  )
								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(*lowerCamelCase_       ,						**lowerCamelCase_  )

			def 							UpperCAmelCase__       ( self			:   Any       ,						*lowerCamelCase_			:   List[str]       ,						**lowerCamelCase_			:   List[str]  )						->   BatchEncoding:
								__magic_name__							:	List[Any]    							=							kwargs.get('''is_split_into_words'''       ,						lowerCamelCase_  )

								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(*lowerCamelCase_       ,						**lowerCamelCase_  )

			def 							UpperCAmelCase__       ( self			:   Any       ,						lowerCamelCase_			:   str       ,						lowerCamelCase_			:   Optional[str] = None  )						->   Tuple[str]:
								__magic_name__							:	List[str]    							=							self._tokenizer.model.save(lowerCamelCase_       ,						name=lowerCamelCase_  )
								return tuple(lowerCamelCase_  )


			def 							UpperCAmelCase__       ( self			:   Tuple       ,						lowerCamelCase_			:   "Conversation"  )						->   List[int]:
								__magic_name__							:	List[Any]    							=							[]
								for is_user, text in conversation.iter_texts():
													input_ids.extend(self.encode(lowerCamelCase_       ,						add_special_tokens=lowerCamelCase_  ) + [self.eos_token_id]  )

								if len(lowerCamelCase_  ) > self.model_max_length:
													__magic_name__							:	List[Any]    							=							input_ids[-self.model_max_length :]
								return input_ids









from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput


@dataclass
class    lowerCamelCase    (		_lowerCamelCase						):






			'''simple docstring'''





			UpperCamelCase__								=None
			UpperCamelCase__								=None
			UpperCamelCase__								=None
			UpperCamelCase__								=None






class    lowerCamelCase    (		_lowerCamelCase						):






			'''simple docstring'''





			def __init__( self			:   List[Any]       ,						lowerCamelCase_			:   Optional[Any]=1       ,						lowerCamelCase_			:   Any=0       ,						lowerCamelCase_			:   Dict=2       ,						lowerCamelCase_			:   Dict=512       ,						lowerCamelCase_			:   int="cls"       ,						lowerCamelCase_			:   Dict=False       ,						lowerCamelCase_			:   List[Any]=True       ,						**lowerCamelCase_			:   Optional[Any]       ,						)						->   Optional[int]:
								super().__init__(pad_token_id=lowerCamelCase_       ,						bos_token_id=lowerCamelCase_       ,						eos_token_id=lowerCamelCase_       ,						**lowerCamelCase_  )
								__magic_name__							:	Dict    							=							project_dim
								__magic_name__							:	int    							=							pooler_fn
								__magic_name__							:	str    							=							learn_encoder
								__magic_name__							:	Optional[int]    							=							use_attention_mask






class    lowerCamelCase    (		_lowerCamelCase						):






			'''simple docstring'''





			UpperCamelCase__								=[R'''pooler''', R'''logit_scale''']
			UpperCamelCase__								=[R'''position_ids''', R'''predictions.decoder.bias''']
			UpperCamelCase__								='''roberta'''
			UpperCamelCase__								=RobertaSeriesConfig

			def __init__( self			:   str       ,						lowerCamelCase_			:   Optional[Any]  )						->   Optional[Any]:
								super().__init__(lowerCamelCase_  )
								__magic_name__							:	Tuple    							=							XLMRobertaModel(lowerCamelCase_  )
								__magic_name__							:	Any    							=							nn.Linear(config.hidden_size       ,						config.project_dim  )
								__magic_name__							:	Optional[int]    							=							getattr(lowerCamelCase_       ,						'''has_pre_transformation'''       ,						lowerCamelCase_  )
								if self.has_pre_transformation:
													__magic_name__							:	List[Any]    							=							nn.Linear(config.hidden_size       ,						config.project_dim  )
													__magic_name__							:	Union[str, Any]    							=							nn.LayerNorm(config.hidden_size       ,						eps=config.layer_norm_eps  )
								self.post_init()


			def 							UpperCAmelCase__       ( self			:   Optional[Any]       ,						lowerCamelCase_			:   Optional[torch.Tensor] = None       ,						lowerCamelCase_			:   Optional[torch.Tensor] = None       ,						lowerCamelCase_			:   Optional[torch.Tensor] = None       ,						lowerCamelCase_			:   Optional[torch.Tensor] = None       ,						lowerCamelCase_			:   Optional[torch.Tensor] = None       ,						lowerCamelCase_			:   Optional[torch.Tensor] = None       ,						lowerCamelCase_			:   Optional[torch.Tensor] = None       ,						lowerCamelCase_			:   Optional[torch.Tensor] = None       ,						lowerCamelCase_			:   Optional[bool] = None       ,						lowerCamelCase_			:   Optional[bool] = None       ,						lowerCamelCase_			:   Optional[bool] = None       ,						)						->   List[Any]:
								__magic_name__							:	Optional[int]    							=							return_dict if return_dict is not None else self.config.use_return_dict

								__magic_name__							:	Any    							=							self.base_model(
								    input_ids=lowerCamelCase_       ,						attention_mask=lowerCamelCase_       ,						token_type_ids=lowerCamelCase_       ,						position_ids=lowerCamelCase_       ,						head_mask=lowerCamelCase_       ,						inputs_embeds=lowerCamelCase_       ,						encoder_hidden_states=lowerCamelCase_       ,						encoder_attention_mask=lowerCamelCase_       ,						output_attentions=lowerCamelCase_       ,						output_hidden_states=True if self.has_pre_transformation else output_hidden_states       ,						return_dict=lowerCamelCase_       ,						)

								if self.has_pre_transformation:
													__magic_name__							:	Any    							=							outputs['''hidden_states'''][-2]
													__magic_name__							:	Dict    							=							self.pre_LN(lowerCamelCase_  )
													__magic_name__							:	int    							=							self.transformation_pre(lowerCamelCase_  )

													return TransformationModelOutput(
													    projection_state=lowerCamelCase_       ,						last_hidden_state=outputs.last_hidden_state       ,						hidden_states=outputs.hidden_states       ,						attentions=outputs.attentions       ,						)
								else:
													__magic_name__							:	Union[str, Any]    							=							self.transformation(outputs.last_hidden_state  )
													return TransformationModelOutput(
													    projection_state=lowerCamelCase_       ,						last_hidden_state=outputs.last_hidden_state       ,						hidden_states=outputs.hidden_states       ,						attentions=outputs.attentions       ,						)



