
'''simple docstring'''
import uuid
from typing import Any, Dict, List, Optional, Union

from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
						import tensorflow as tf

if is_torch_available():
						import torch


lowerCAmelCase					:    Optional[Any]           = logging.get_logger(__name__)


class 			SCREAMING_SNAKE_CASE__      :







				def __init__(       self						,				A_ = None						,				A_ = None						,				A_=None						,				A_=None     )->		Optional[Any]:







											'''simple docstring'''






											if not conversation_id:
																		UpperCamelCase   			=     uuid.uuida()
											if past_user_inputs is None:
																		UpperCamelCase   			=     []
											if generated_responses is None:
																		UpperCamelCase   			=     []

											UpperCamelCase   			=     conversation_id
											UpperCamelCase   			=     past_user_inputs
											UpperCamelCase   			=     generated_responses
											UpperCamelCase   			=     text







				def __eq__(       self						,				A_     )->		List[Any]:







											'''simple docstring'''






											if not isinstance(A_						,				A_     ):
																		return False
											if self.uuid == other.uuid:
																		return True
											return (
											    self.new_user_input == other.new_user_input
											    and self.past_user_inputs == other.past_user_inputs
											    and self.generated_responses == other.generated_responses
											)







				def 						UpperCAmelCase_    (       self						,				A_						,				A_ = False     )->		int:







											'''simple docstring'''






											if self.new_user_input:
																		if overwrite:
																									logger.warning(
																									    F'''User input added while unprocessed input was existing: "{self.new_user_input}" was overwritten '''
																									    F'''with: "{text}".'''     )
																									UpperCamelCase   			=     text
																		else:
																									logger.warning(
																									    F'''User input added while unprocessed input was existing: "{self.new_user_input}" new input '''
																									    F'''ignored: "{text}". Set `overwrite` to True to overwrite unprocessed user input'''     )
											else:
																		UpperCamelCase   			=     text







				def 						UpperCAmelCase_    (       self     )->		Any:







											'''simple docstring'''






											if self.new_user_input:
																		self.past_user_inputs.append(self.new_user_input     )
											UpperCamelCase   			=     None







				def 						UpperCAmelCase_    (       self						,				A_     )->		int:







											'''simple docstring'''






											self.generated_responses.append(A_     )







				def 						UpperCAmelCase_    (       self     )->		List[str]:







											'''simple docstring'''






											for user_input, generated_response in zip(self.past_user_inputs						,				self.generated_responses     ):
																		yield True, user_input
																		yield False, generated_response
											if self.new_user_input:
																		yield True, self.new_user_input







				def __repr__(       self     )->		Any:







											'''simple docstring'''






											UpperCamelCase   			=     F'''Conversation id: {self.uuid} \n'''
											for is_user, text in self.iter_texts():
																		UpperCamelCase   			=     'user' if is_user else 'bot'
																		output += F'''{name} >> {text} \n'''
											return output


@add_end_docstrings(
    snake_case_							,       R"""
        min_length_for_response (`int`, *optional*, defaults to 32):
            The minimum length (in number of tokens) for a response.
        minimum_tokens (`int`, *optional*, defaults to 10):
            The minimum length of tokens to leave for a response.
    """							,       )
class 			SCREAMING_SNAKE_CASE__      (     snake_case_):







				def __init__(       self						,				*A_						,				**A_     )->		Any:







											'''simple docstring'''






											super().__init__(*A_						,				**A_     )
											if self.tokenizer.pad_token_id is None:
																		UpperCamelCase   			=     self.tokenizer.eos_token







				def 						UpperCAmelCase_    (       self						,				A_=None						,				A_=None						,				A_=None						,				**A_     )->		Union[str, Any]:







											'''simple docstring'''






											UpperCamelCase   			=     {}
											UpperCamelCase   			=     {}
											UpperCamelCase   			=     {}

											if min_length_for_response is not None:
																		UpperCamelCase   			=     min_length_for_response
											if minimum_tokens is not None:
																		UpperCamelCase   			=     minimum_tokens

											if "max_length" in generate_kwargs:
																		UpperCamelCase   			=     generate_kwargs['max_length']
																		# self.max_length = generate_kwargs.get("max_length", self.model.config.max_length)
											if clean_up_tokenization_spaces is not None:
																		UpperCamelCase   			=     clean_up_tokenization_spaces

											if generate_kwargs:
																		forward_params.update(A_     )
											return preprocess_params, forward_params, postprocess_params







				def __call__(       self						,				A_						,				A_=0						,				**A_     )->		Any:







											'''simple docstring'''






											UpperCamelCase   			=     super().__call__(A_						,				num_workers=A_						,				**A_     )
											if isinstance(A_						,				A_     ) and len(A_     ) == 1:
																		return outputs[0]
											return outputs







				def 						UpperCAmelCase_    (       self						,				A_						,				A_=32     )->		Dict[str, Any]:







											'''simple docstring'''






											if not isinstance(A_						,				A_     ):
																		raise ValueError('ConversationalPipeline, expects Conversation as inputs'     )
											if conversation.new_user_input is None:
																		raise ValueError(
																		    F'''Conversation with UUID {type(conversation.uuid     )} does not contain new user input to process. '''
																		    'Add user inputs with the conversation\'s `add_user_input` method'     )
											if hasattr(self.tokenizer						,				'_build_conversation_input_ids'     ):
																		UpperCamelCase   			=     self.tokenizer._build_conversation_input_ids(A_     )
											else:
																		# If the tokenizer cannot handle conversations, we default to only the old version
																		UpperCamelCase   			=     self._legacy_parse_and_tokenize(A_     )

											if self.framework == "pt":
																		UpperCamelCase   			=     torch.LongTensor([input_ids]     )
											elif self.framework == "tf":
																		UpperCamelCase   			=     tf.constant([input_ids]     )
											return {"input_ids": input_ids, "conversation": conversation}







				def 						UpperCAmelCase_    (       self						,				A_						,				A_=10						,				**A_     )->		Optional[Any]:







											'''simple docstring'''






											UpperCamelCase   			=     generate_kwargs.get('max_length'						,				self.model.config.max_length     )

											UpperCamelCase   			=     model_inputs['input_ids'].shape[1]
											if max_length - minimum_tokens < n:
																		logger.warning(F'''Conversation input is to long ({n}), trimming it to ({max_length} - {minimum_tokens})'''     )
																		UpperCamelCase   			=     max_length - minimum_tokens
																		UpperCamelCase   			=     model_inputs['input_ids'][:, -trim:]
																		if "attention_mask" in model_inputs:
																									UpperCamelCase   			=     model_inputs['attention_mask'][:, -trim:]
											UpperCamelCase   			=     model_inputs.pop('conversation'     )
											UpperCamelCase   			=     max_length
											UpperCamelCase   			=     self.model.generate(**A_						,				**A_     )
											if self.model.config.is_encoder_decoder:
																		UpperCamelCase   			=     1
											else:
																		UpperCamelCase   			=     n
											return {"output_ids": output_ids[:, start_position:], "conversation": conversation}







				def 						UpperCAmelCase_    (       self						,				A_						,				A_=True     )->		Tuple:







											'''simple docstring'''






											UpperCamelCase   			=     model_outputs['output_ids']
											UpperCamelCase   			=     self.tokenizer.decode(
											    output_ids[0]						,				skip_special_tokens=A_						,				clean_up_tokenization_spaces=A_						,				)
											UpperCamelCase   			=     model_outputs['conversation']
											conversation.mark_processed()
											conversation.append_response(A_     )
											return conversation







				def 						UpperCAmelCase_    (       self						,				A_     )->		Dict:







											'''simple docstring'''






											UpperCamelCase   			=     self.tokenizer.eos_token_id
											UpperCamelCase   			=     []
											for is_user, text in conversation.iter_texts():
																		if eos_token_id is not None:
																									input_ids.extend(self.tokenizer.encode(A_						,				add_special_tokens=A_     ) + [eos_token_id]     )
																		else:
																									input_ids.extend(self.tokenizer.encode(A_						,				add_special_tokens=A_     )     )

											if len(A_     ) > self.tokenizer.model_max_length:
																		UpperCamelCase   			=     input_ids[-self.tokenizer.model_max_length :]
											return input_ids












import os
from shutil import copyfile
from typing import List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCamelCase__       		=  logging.get_logger(__name__)

lowerCamelCase__       		=  {"""vocab_file""": """sentencepiece.model"""}

lowerCamelCase__       		=  {
    """vocab_file""": {
        """google/rembert""": """https://huggingface.co/google/rembert/resolve/main/sentencepiece.model""",
    },
}

lowerCamelCase__       		=  {
    """google/rembert""": 256,
}




class       snake_case__ (				lowercase_):

       '''simple docstring'''
       lowerCamelCase				:					str								=			VOCAB_FILES_NAMES
       lowerCamelCase				:					Tuple								=			PRETRAINED_VOCAB_FILES_MAP
       lowerCamelCase				:					Optional[int]								=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES



       def __init__(				self      ,    a__      ,    a__=False      ,    a__=True      ,    a__=True      ,    a__="[CLS]"      ,    a__="[SEP]"      ,    a__="[UNK]"      ,    a__="[SEP]"      ,    a__="[PAD]"      ,    a__="[CLS]"      ,    a__="[MASK]"      ,    **a__      ,    )			->   List[str]:






            '''simple docstring'''





            super().__init__(
                do_lower_case=a__      ,    remove_space=a__      ,    keep_accents=a__      ,    bos_token=a__      ,    eos_token=a__      ,    unk_token=a__      ,    sep_token=a__      ,    pad_token=a__      ,    cls_token=a__      ,    mask_token=a__      ,    **a__      ,    )

            __snake_case      :List[Any] 							=    do_lower_case
            __snake_case      :Dict 							=    remove_space
            __snake_case      :int 							=    keep_accents
            __snake_case      :Optional[int] 							=    vocab_file

            __snake_case      :int 							=    spm.SentencePieceProcessor()
            self.sp_model.Load(a__     )



       @property
       def        __lowercase    (				self     )			->   Tuple:






            '''simple docstring'''





            return len(self.sp_model     )



       def        __lowercase    (				self     )			->   Optional[int]:






            '''simple docstring'''





            __snake_case      :int 							=    {self.convert_ids_to_tokens(a__     ): i for i in range(self.vocab_size     )}
            vocab.update(self.added_tokens_encoder     )
            return vocab



       def __getstate__(				self     )			->   str:






            '''simple docstring'''





            __snake_case      :str 							=    self.__dict__.copy()
            __snake_case      :Dict 							=    None
            return state



       def __setstate__(				self      ,    a__     )			->   str:






            '''simple docstring'''





            __snake_case      :Dict 							=    d
            __snake_case      :Dict 							=    spm.SentencePieceProcessor()
            self.sp_model.Load(self.vocab_file     )



       def        __lowercase    (				self      ,    a__      ,    a__=False     )			->   Tuple:






            '''simple docstring'''





            __snake_case      :Union[str, Any] 							=    self.sp_model.EncodeAsPieces(a__     )
            return pieces



       def        __lowercase    (				self      ,    a__     )			->   str:






            '''simple docstring'''





            return self.sp_model.PieceToId(a__     )



       def        __lowercase    (				self      ,    a__     )			->   Any:






            '''simple docstring'''





            return self.sp_model.IdToPiece(a__     )



       def        __lowercase    (				self      ,    a__     )			->   Union[str, Any]:






            '''simple docstring'''





            __snake_case      :List[Any] 							=    self.sp_model.decode_pieces(a__     )
            return out_string



       def        __lowercase    (				self      ,    a__      ,    a__ = None     )			->   List[int]:






            '''simple docstring'''





            __snake_case      :Optional[int] 							=    [self.sep_token_id]
            __snake_case      :List[Any] 							=    [self.cls_token_id]
            if token_ids_a is None:
                 return cls + token_ids_a + sep
            return cls + token_ids_a + sep + token_ids_a + sep



       def        __lowercase    (				self      ,    a__      ,    a__ = None      ,    a__ = False     )			->   List[int]:






            '''simple docstring'''





            if already_has_special_tokens:
                 if token_ids_a is not None:
                      raise ValueError(
                          """You should not supply a second sequence if the provided sequence of """
                          """ids is already formatted with special tokens for the model."""     )
                 return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

            if token_ids_a is not None:
                 return [1] + ([0] * len(a__     )) + [1] + ([0] * len(a__     )) + [1]
            return [1] + ([0] * len(a__     )) + [1]



       def        __lowercase    (				self      ,    a__      ,    a__ = None     )			->   List[int]:






            '''simple docstring'''





            __snake_case      :Any 							=    [self.sep_token_id]
            __snake_case      :Any 							=    [self.cls_token_id]

            if token_ids_a is None:
                 return len(cls + token_ids_a + sep     ) * [0]
            return len(cls + token_ids_a + sep     ) * [0] + len(token_ids_a + sep     ) * [1]



       def        __lowercase    (				self      ,    a__      ,    a__ = None     )			->   Tuple[str]:






            '''simple docstring'''





            if not os.path.isdir(a__     ):
                 logger.error("""Vocabulary path ({}) should be a directory""".format(a__     )     )
                 return
            __snake_case      :Optional[int] 							=    os.path.join(
                a__      ,    (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]     )

            if os.path.abspath(self.vocab_file     ) != os.path.abspath(a__     ):
                 copyfile(self.vocab_file      ,    a__     )

            return (out_vocab_file,)


