
"""simple docstring"""



import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple

import numpy as np

from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
 from transformers.pipelines.conversational import Conversation


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

a					:	Any							=		{'''vocab_file''': '''vocab.txt''', '''emoji_file''': '''emoji.json'''}

a					:	Union[str, Any]							=		{
    '''vocab_file''': {
        '''abeja/gpt-neox-japanese-2.7b''': '''https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/vocab.txt''',
    },
    '''emoji_file''': {
        '''abeja/gpt-neox-japanese-2.7b''': '''https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/emoji.json''',
    },
}

a					:	Optional[Any]							=		{
    '''abeja/gpt-neox-japanese-2.7b''': 2_0_4_8,
}


def  _UpperCamelCase	(						_A					,      _A      )    ->  str:





    """simple docstring"""

    with open(_A					,      """r"""					,      encoding="""utf-8"""      ) as f:
        _UpperCAmelCase          =   json.loads(f.read()      )

    _UpperCAmelCase          =   collections.OrderedDict()
    _UpperCAmelCase          =   collections.OrderedDict()
    _UpperCAmelCase          =   collections.OrderedDict()
    with open(_A					,      """r"""					,      encoding="""utf-8"""      ) as f:
        _UpperCAmelCase          =   f.readlines()
    _UpperCAmelCase          =   [[t.rstrip("""\n"""      )] if (t == """,""" or """,""" not in t) else t.rstrip("""\n"""      ).split(""","""      ) for t in token]
    for idx, b in enumerate(_A      ):
        _UpperCAmelCase          =   b
        _UpperCAmelCase          =   idx
        for wd in b:
            _UpperCAmelCase          =   idx

    return vocab, raw_vocab, ids_to_tokens, emoji




class 		a_						( _UpperCAmelCase	):
    a      :				int     =				VOCAB_FILES_NAMES
    a      :				Any     =				PRETRAINED_VOCAB_FILES_MAP
    a      :				List[str]     =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    a      :				Optional[Any]     =				['input_ids', 'attention_mask']







    def __init__(			self			:       Any     ,					__UpperCamelCase			:       Any     ,					__UpperCamelCase			:       Dict     ,					__UpperCamelCase			:       Optional[Any]="<|endoftext|>"     ,					__UpperCamelCase			:       str="<|endoftext|>"     ,					__UpperCamelCase			:       str="<|startoftext|>"     ,					__UpperCamelCase			:       Any="<|endoftext|>"     ,					__UpperCamelCase			:       Optional[int]=False     ,					**__UpperCamelCase			:       Optional[Any]     ,					)		->List[Any]:






        '''simple docstring'''






        super().__init__(
            unk_token=__UpperCamelCase     ,					pad_token=__UpperCamelCase     ,					bos_token=__UpperCamelCase     ,					eos_token=__UpperCamelCase     ,					do_clean_text=__UpperCamelCase     ,					**__UpperCamelCase     ,					)
        if not os.path.isfile(__UpperCamelCase     ):
            raise ValueError(
                f"""Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained"""
                """ model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"""     )
        if not os.path.isfile(__UpperCamelCase     ):
            raise ValueError(
                f"""Can't find a emoji file at path '{emoji_file}'. To load the emoji information from a Google"""
                """ pretrained model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"""     )
        _UpperCAmelCase          =   do_clean_text
        _UpperCAmelCase    ,_UpperCAmelCase    ,_UpperCAmelCase    ,_UpperCAmelCase          =   load_vocab_and_emoji(__UpperCamelCase     ,					__UpperCamelCase     )
        _UpperCAmelCase          =   SubWordJapaneseTokenizer(
            vocab=self.vocab     ,					ids_to_tokens=self.ids_to_tokens     ,					emoji=self.emoji     )







    @property
    def      _snake_case					(			self			:       Union[str, Any]     )		->Tuple:






        '''simple docstring'''






        return len(self.raw_vocab     )







    def      _snake_case					(			self			:       Any     )		->List[str]:






        '''simple docstring'''






        return dict(self.raw_vocab     ,					**self.added_tokens_encoder     )







    def      _snake_case					(			self			:       Optional[int]     ,					__UpperCamelCase			:       Tuple     )		->List[str]:






        '''simple docstring'''






        return self.subword_tokenizer.tokenize(__UpperCamelCase     ,					clean=self.do_clean_text     )







    def      _snake_case					(			self			:       Any     ,					__UpperCamelCase			:       List[str]     )		->Any:






        '''simple docstring'''






        return self.vocab.get(__UpperCamelCase     ,					self.vocab.get(self.unk_token     )     )







    def      _snake_case					(			self			:       int     ,					__UpperCamelCase			:       Union[str, Any]     )		->str:






        '''simple docstring'''






        return self.subword_tokenizer.convert_id_to_token(__UpperCamelCase     )







    def      _snake_case					(			self			:       Optional[Any]     ,					__UpperCamelCase			:       Optional[int]     )		->int:






        '''simple docstring'''






        _UpperCAmelCase          =   """""".join(__UpperCamelCase     ).strip()
        return out_string







    def      _snake_case					(			self			:       Union[str, Any]     ,					__UpperCamelCase			:       "Conversation"     )		->List[int]:






        '''simple docstring'''






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

        if len(__UpperCamelCase     ) > self.model_max_length:
            _UpperCAmelCase          =   input_ids[-self.model_max_length :]
        return input_ids







    def      _snake_case					(			self			:       Tuple     ,					__UpperCamelCase			:       str     ,					__UpperCamelCase			:       Optional[str] = None     )		->Tuple[str]:






        '''simple docstring'''






        _UpperCAmelCase          =   0
        if os.path.isdir(__UpperCamelCase     ):
            _UpperCAmelCase          =   os.path.join(
                __UpperCamelCase     ,					(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]     )
            _UpperCAmelCase          =   os.path.join(
                __UpperCamelCase     ,					(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""emoji_file"""]     )
        else:
            _UpperCAmelCase          =   (
                (filename_prefix + """-""" if filename_prefix else """""") + save_directory + VOCAB_FILES_NAMES["""vocab_file"""]
            )
            _UpperCAmelCase          =   (
                (filename_prefix + """-""" if filename_prefix else """""") + save_directory + VOCAB_FILES_NAMES["""emoji_file"""]
            )
        with open(__UpperCamelCase     ,					"""w"""     ,					encoding="""utf-8"""     ) as writer:
            for token_index, token in self.ids_to_tokens.items():
                if index != token_index:
                    logger.warning(
                        f"""Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."""
                        """ Please check that the vocabulary is not corrupted!"""     )
                    _UpperCAmelCase          =   token_index
                writer.write(""",""".join(__UpperCamelCase     ) + """\n"""     )
                index += 1
        with open(__UpperCamelCase     ,					"""w"""     ,					encoding="""utf-8"""     ) as writer:
            json.dump(self.emoji     ,					__UpperCamelCase     )
        return vocab_file, emoji_file







class 		a_						( _UpperCAmelCase	):







    def __init__(			self			:       Optional[int]     ,					__UpperCamelCase			:       Union[str, Any]     ,					__UpperCamelCase			:       List[Any]     ,					__UpperCamelCase			:       Dict     )		->Union[str, Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   vocab  # same as swe
        _UpperCAmelCase          =   ids_to_tokens  # same as bpe
        _UpperCAmelCase          =   emoji
        _UpperCAmelCase          =   np.max([len(__UpperCamelCase     ) for w in self.vocab.keys()]     )
        _UpperCAmelCase          =   re.compile(r"""(https?|ftp)(:\/\/[-_\.!~*\'()a-zA-Z0-9;\/?:\@&=\+$,%#]+)"""     )
        _UpperCAmelCase          =   re.compile(r"""[A-Za-z0-9\._+]*@[\-_0-9A-Za-z]+(\.[A-Za-z]+)*"""     )
        _UpperCAmelCase          =   re.compile(r"""[\(]{0,1}[0-9]{2,4}[\)\-\(]{0,1}[0-9]{2,4}[\)\-]{0,1}[0-9]{3,4}"""     )
        _UpperCAmelCase          =   re.compile(
            r"""([12]\d{3}[/\-年])*(0?[1-9]|1[0-2])[/\-月]((0?[1-9]|[12][0-9]|3[01])日?)*(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*"""     )
        _UpperCAmelCase          =   re.compile(
            r"""(明治|大正|昭和|平成|令和|㍾|㍽|㍼|㍻|\u32ff)\d{1,2}年(0?[1-9]|1[0-2])月(0?[1-9]|[12][0-9]|3[01])日(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*"""     )
        _UpperCAmelCase          =   re.compile(
            r"""((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*億)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*万)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*千)*(0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*(千円|万円|千万円|円|千ドル|万ドル|千万ドル|ドル|千ユーロ|万ユーロ|千万ユーロ|ユーロ)+(\(税込\)|\(税抜\)|\+tax)*"""     )
        _UpperCAmelCase          =   """─━│┃┄┅┆┇┈┉┊┋┌┍┎┏┐┑┒┓└┕┖┗┘┙┚┛├┝┞┟┠┡┢┣┤┥┦┧┨┩┪┫┬┭┮┯┰┱┲┳┴┵┶┷┸┹┺┻┼┽┾┿╀╁╂╃╄╅╆╇╈╉╊╋╌╍╎╏═║╒╓╔╕╖╗╘╙╚╛╜╝╞╟╠╡╢╣╤╥╦╧╨╩╪╫╬╭╮╯╰╱╲╳╴╵╶╷╸╹╺╻╼╽╾╿"""
        _UpperCAmelCase          =   """▀▁▂▃▄▅▆▇█▉▊▋▌▍▎▏▐░▒▓▔▕▖▗▘▙▚▛▜▝▞▟"""
        _UpperCAmelCase          =   str.maketrans({k: """<BLOCK>""" for k in keisen + blocks}     )







    def __len__(			self			:       str     )		->List[Any]:






        '''simple docstring'''






        return len(self.ids_to_tokens     )







    def      _snake_case					(			self			:       Optional[Any]     ,					__UpperCamelCase			:       str     )		->str:






        '''simple docstring'''






        _UpperCAmelCase          =   self.content_repattera.sub("""<URL>"""     ,					__UpperCamelCase     )
        _UpperCAmelCase          =   self.content_repattera.sub("""<EMAIL>"""     ,					__UpperCamelCase     )
        _UpperCAmelCase          =   self.content_repattera.sub("""<TEL>"""     ,					__UpperCamelCase     )
        _UpperCAmelCase          =   self.content_repattera.sub("""<DATE>"""     ,					__UpperCamelCase     )
        _UpperCAmelCase          =   self.content_repattera.sub("""<DATE>"""     ,					__UpperCamelCase     )
        _UpperCAmelCase          =   self.content_repattera.sub("""<PRICE>"""     ,					__UpperCamelCase     )
        _UpperCAmelCase          =   content.translate(self.content_transa     )
        while "<BLOCK><BLOCK>" in content:
            _UpperCAmelCase          =   content.replace("""<BLOCK><BLOCK>"""     ,					"""<BLOCK>"""     )
        return content







    def      _snake_case					(			self			:       Optional[int]     ,					__UpperCamelCase			:       List[Any]     ,					__UpperCamelCase			:       Optional[Any]=False     )		->List[Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   text.replace(""" """     ,					"""<SP>"""     )
        _UpperCAmelCase          =   text.replace("""　"""     ,					"""<SP>"""     )
        _UpperCAmelCase          =   text.replace("""\r\n"""     ,					"""<BR>"""     )
        _UpperCAmelCase          =   text.replace("""\n"""     ,					"""<BR>"""     )
        _UpperCAmelCase          =   text.replace("""\r"""     ,					"""<BR>"""     )
        _UpperCAmelCase          =   text.replace("""\t"""     ,					"""<TAB>"""     )
        _UpperCAmelCase          =   text.replace("""—"""     ,					"""ー"""     )
        _UpperCAmelCase          =   text.replace("""−"""     ,					"""ー"""     )
        for k, v in self.emoji["emoji"].items():
            if k in text:
                _UpperCAmelCase          =   text.replace(__UpperCamelCase     ,					__UpperCamelCase     )
        if clean:
            _UpperCAmelCase          =   self.clean_text(__UpperCamelCase     )

        def check_simbol(__UpperCamelCase			:       Any     ):
            _UpperCAmelCase          =   x.encode()
            if len(__UpperCamelCase     ) == 1 and len(__UpperCamelCase     ) == 2:
                _UpperCAmelCase          =   (int(e[0]     ) << 8) + int(e[1]     )
                if (
                    (c >= 0xc2a1 and c <= 0xc2bf)
                    or (c >= 0xc780 and c <= 0xc783)
                    or (c >= 0xcab9 and c <= 0xcbbf)
                    or (c >= 0xcc80 and c <= 0xcda2)
                ):
                    return True
            return False

        def checkuae(__UpperCamelCase			:       List[Any]     ):
            _UpperCAmelCase          =   x.encode()
            if len(__UpperCamelCase     ) == 1 and len(__UpperCamelCase     ) == 3:
                _UpperCAmelCase          =   (int(e[0]     ) << 16) + (int(e[1]     ) << 8) + int(e[2]     )
                if c >= 0xe28080 and c <= 0xe2b07f:
                    return True
            return False

        _UpperCAmelCase          =   0
        _UpperCAmelCase          =   []
        while pos < len(__UpperCamelCase     ):
            _UpperCAmelCase          =   min(len(__UpperCamelCase     )     ,					pos + self.maxlen + 1     ) if text[pos] == """<""" else pos + 3
            _UpperCAmelCase          =   []  # (token_id, token, pos)
            for e in range(__UpperCamelCase     ,					__UpperCamelCase     ,					-1     ):
                _UpperCAmelCase          =   text[pos:e]
                if wd in self.vocab:
                    if wd[0] == "<" and len(__UpperCamelCase     ) > 2:
                        _UpperCAmelCase          =   [(self.vocab[wd], wd, e)]
                        break
                    else:
                        candidates.append((self.vocab[wd], wd, e)     )
            if len(__UpperCamelCase     ) > 0:
                # the smallest token_id is adopted
                _UpperCAmelCase    ,_UpperCAmelCase    ,_UpperCAmelCase          =   sorted(__UpperCamelCase     ,					key=lambda __UpperCamelCase     : x[0]     )[0]
                result.append(__UpperCamelCase     )
                _UpperCAmelCase          =   e
            else:
                _UpperCAmelCase          =   pos + 1
                _UpperCAmelCase          =   text[pos:end]
                if check_simbol(__UpperCamelCase     ):
                    result.append("""<KIGOU>"""     )
                elif checkuae(__UpperCamelCase     ):
                    result.append("""<U2000U2BFF>"""     )
                else:
                    for i in wd.encode("""utf-8"""     ):
                        result.append("""<|byte%d|>""" % i     )
                _UpperCAmelCase          =   end
        return result







    def      _snake_case					(			self			:       int     ,					__UpperCamelCase			:       Tuple     ,					__UpperCamelCase			:       Optional[int]="\n"     )		->Any:






        '''simple docstring'''






        _UpperCAmelCase          =   []
        _UpperCAmelCase          =   []
        _UpperCAmelCase          =   self.ids_to_tokens[index][0]
        if word[:6] == "<|byte" and word[-2:] == "|>":
            byte_tokens.append(int(word[6:-2]     )     )
        else:
            if len(__UpperCamelCase     ) > 0:
                words.append(bytearray(__UpperCamelCase     ).decode("""utf-8"""     ,					errors="""replace"""     )     )
                _UpperCAmelCase          =   []
            if word[:7] == "<|emoji" and word[-2:] == "|>":
                words.append(self.emoji["""emoji_inv"""][word]     )
            elif word == "<SP>":
                words.append(""" """     )
            elif word == "<BR>":
                words.append(__UpperCamelCase     )
            elif word == "<TAB>":
                words.append("""\t"""     )
            elif word == "<BLOCK>":
                words.append("""▀"""     )
            elif word == "<KIGOU>":
                words.append("""ǀ"""     )
            elif word == "<U2000U2BFF>":
                words.append("""‖"""     )
            else:
                words.append(__UpperCamelCase     )
        if len(__UpperCamelCase     ) > 0:
            words.append(bytearray(__UpperCamelCase     ).decode("""utf-8"""     ,					errors="""replace"""     )     )
        _UpperCAmelCase          =   """""".join(__UpperCamelCase     )
        return text

"""simple docstring"""



import enum
import warnings

from ..tokenization_utils import TruncationStrategy
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

 from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

if is_torch_available():
 from ..models.auto.modeling_auto import MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

a					:	List[str]							=		logging.get_logger(__name__)




class 		a_						( enum.Enum	):
    a      :				Optional[Any]     =				0
    a      :				Dict     =				1







@add_end_docstrings(_UpperCAmelCase	)
class 		a_						( _UpperCAmelCase	):
    a      :				List[Any]     =				'generated'







    def __init__(			self			:       int     ,					*__UpperCamelCase			:       Optional[int]     ,					**__UpperCamelCase			:       str     )		->Any:






        '''simple docstring'''






        super().__init__(*__UpperCamelCase     ,					**__UpperCamelCase     )

        self.check_model_type(
            TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
            if self.framework == """tf"""
            else MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING     )







    def      _snake_case					(			self			:       Optional[int]     ,					__UpperCamelCase			:       List[Any]=None     ,					__UpperCamelCase			:       Union[str, Any]=None     ,					__UpperCamelCase			:       Any=None     ,					__UpperCamelCase			:       int=None     ,					__UpperCamelCase			:       Tuple=None     ,					__UpperCamelCase			:       Dict=None     ,					**__UpperCamelCase			:       Any     ,					)		->Tuple:






        '''simple docstring'''






        _UpperCAmelCase          =   {}
        if truncation is not None:
            _UpperCAmelCase          =   truncation

        _UpperCAmelCase          =   generate_kwargs

        _UpperCAmelCase          =   {}
        if return_tensors is not None and return_type is None:
            _UpperCAmelCase          =   ReturnType.TENSORS if return_tensors else ReturnType.TEXT
        if return_type is not None:
            _UpperCAmelCase          =   return_type

        if clean_up_tokenization_spaces is not None:
            _UpperCAmelCase          =   clean_up_tokenization_spaces

        if stop_sequence is not None:
            _UpperCAmelCase          =   self.tokenizer.encode(__UpperCamelCase     ,					add_special_tokens=__UpperCamelCase     )
            if len(__UpperCamelCase     ) > 1:
                warnings.warn(
                    """Stopping on a multiple token sequence is not yet supported on transformers. The first token of"""
                    """ the stop sequence will be used as the stop sequence string in the interim."""     )
            _UpperCAmelCase          =   stop_sequence_ids[0]

        return preprocess_params, forward_params, postprocess_params







    def      _snake_case					(			self			:       int     ,					__UpperCamelCase			:       int     ,					__UpperCamelCase			:       int     ,					__UpperCamelCase			:       int     )		->Any:






        '''simple docstring'''






        return True







    def      _snake_case					(			self			:       Optional[Any]     ,					*__UpperCamelCase			:       Any     ,					__UpperCamelCase			:       Dict     )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase          =   self.model.config.prefix if self.model.config.prefix is not None else """"""
        if isinstance(args[0]     ,					__UpperCamelCase     ):
            if self.tokenizer.pad_token_id is None:
                raise ValueError("""Please make sure that the tokenizer has a pad_token_id when using a batch input"""     )
            _UpperCAmelCase          =   ([prefix + arg for arg in args[0]],)
            _UpperCAmelCase          =   True

        elif isinstance(args[0]     ,					__UpperCamelCase     ):
            _UpperCAmelCase          =   (prefix + args[0],)
            _UpperCAmelCase          =   False
        else:
            raise ValueError(
                f""" `args[0]`: {args[0]} have the wrong format. The should be either of type `str` or type `list`"""     )
        _UpperCAmelCase          =   self.tokenizer(*__UpperCamelCase     ,					padding=__UpperCamelCase     ,					truncation=__UpperCamelCase     ,					return_tensors=self.framework     )
        # This is produced by tokenizers but is an invalid generate kwargs
        if "token_type_ids" in inputs:
            del inputs["token_type_ids"]
        return inputs







    def __call__(			self			:       Dict     ,					*__UpperCamelCase			:       str     ,					**__UpperCamelCase			:       Dict     )		->Optional[Any]:






        '''simple docstring'''






        _UpperCAmelCase          =   super().__call__(*__UpperCamelCase     ,					**__UpperCamelCase     )
        if (
            isinstance(args[0]     ,					__UpperCamelCase     )
            and all(isinstance(__UpperCamelCase     ,					__UpperCamelCase     ) for el in args[0]     )
            and all(len(__UpperCamelCase     ) == 1 for res in result     )
        ):
            return [res[0] for res in result]
        return result







    def      _snake_case					(			self			:       List[str]     ,					__UpperCamelCase			:       Any     ,					__UpperCamelCase			:       str=TruncationStrategy.DO_NOT_TRUNCATE     ,					**__UpperCamelCase			:       Optional[int]     )		->Optional[int]:






        '''simple docstring'''






        _UpperCAmelCase          =   self._parse_and_tokenize(__UpperCamelCase     ,					truncation=__UpperCamelCase     ,					**__UpperCamelCase     )
        return inputs







    def      _snake_case					(			self			:       str     ,					__UpperCamelCase			:       Dict     ,					**__UpperCamelCase			:       Dict     )		->Tuple:






        '''simple docstring'''






        if self.framework == "pt":
            _UpperCAmelCase    ,_UpperCAmelCase          =   model_inputs["""input_ids"""].shape
        elif self.framework == "tf":
            _UpperCAmelCase    ,_UpperCAmelCase          =   tf.shape(model_inputs["""input_ids"""]     ).numpy()

        _UpperCAmelCase          =   generate_kwargs.get("""min_length"""     ,					self.model.config.min_length     )
        _UpperCAmelCase          =   generate_kwargs.get("""max_length"""     ,					self.model.config.max_length     )
        self.check_inputs(__UpperCamelCase     ,					generate_kwargs["""min_length"""]     ,					generate_kwargs["""max_length"""]     )
        _UpperCAmelCase          =   self.model.generate(**__UpperCamelCase     ,					**__UpperCamelCase     )
        _UpperCAmelCase          =   output_ids.shape[0]
        if self.framework == "pt":
            _UpperCAmelCase          =   output_ids.reshape(__UpperCamelCase     ,					out_b // in_b     ,					*output_ids.shape[1:]     )
        elif self.framework == "tf":
            _UpperCAmelCase          =   tf.reshape(__UpperCamelCase     ,					(in_b, out_b // in_b, *output_ids.shape[1:])     )
        return {"output_ids": output_ids}







    def      _snake_case					(			self			:       Tuple     ,					__UpperCamelCase			:       str     ,					__UpperCamelCase			:       Union[str, Any]=ReturnType.TEXT     ,					__UpperCamelCase			:       int=False     )		->Any:






        '''simple docstring'''






        _UpperCAmelCase          =   []
        for output_ids in model_outputs["output_ids"][0]:
            if return_type == ReturnType.TENSORS:
                _UpperCAmelCase          =   {f"""{self.return_name}_token_ids""": output_ids}
            elif return_type == ReturnType.TEXT:
                _UpperCAmelCase          =   {
                    f"""{self.return_name}_text""": self.tokenizer.decode(
                        __UpperCamelCase     ,					skip_special_tokens=__UpperCamelCase     ,					clean_up_tokenization_spaces=__UpperCamelCase     ,					)
                }
            records.append(__UpperCamelCase     )
        return records







@add_end_docstrings(_UpperCAmelCase	)
class 		a_						( _UpperCAmelCase	):
    a      :				List[Any]     =				'summary'







    def __call__(			self			:       Optional[Any]     ,					*__UpperCamelCase			:       Optional[Any]     ,					**__UpperCamelCase			:       Optional[int]     )		->Any:






        '''simple docstring'''






        return super().__call__(*__UpperCamelCase     ,					**__UpperCamelCase     )







    def      _snake_case					(			self			:       str     ,					__UpperCamelCase			:       int     ,					__UpperCamelCase			:       int     ,					__UpperCamelCase			:       int     )		->bool:






        '''simple docstring'''






        if max_length < min_length:
            logger.warning(f"""Your min_length={min_length} must be inferior than your max_length={max_length}."""     )

        if input_length < max_length:
            logger.warning(
                f"""Your max_length is set to {max_length}, but your input_length is only {input_length}. Since this is """
                """a summarization task, where outputs shorter than the input are typically wanted, you might """
                f"""consider decreasing max_length manually, e.g. summarizer('...', max_length={input_length//2})"""     )







@add_end_docstrings(_UpperCAmelCase	)
class 		a_						( _UpperCAmelCase	):
    a      :				Optional[int]     =				'translation'







    def      _snake_case					(			self			:       Union[str, Any]     ,					__UpperCamelCase			:       int     ,					__UpperCamelCase			:       int     ,					__UpperCamelCase			:       int     )		->Any:






        '''simple docstring'''






        if input_length > 0.9 * max_length:
            logger.warning(
                f"""Your input_length: {input_length} is bigger than 0.9 * max_length: {max_length}. You might consider """
                """increasing your max_length manually, e.g. translator('...', max_length=400)"""     )
        return True







    def      _snake_case					(			self			:       Tuple     ,					*__UpperCamelCase			:       List[str]     ,					__UpperCamelCase			:       Tuple=TruncationStrategy.DO_NOT_TRUNCATE     ,					__UpperCamelCase			:       Tuple=None     ,					__UpperCamelCase			:       Union[str, Any]=None     )		->Tuple:






        '''simple docstring'''






        if getattr(self.tokenizer     ,					"""_build_translation_inputs"""     ,					__UpperCamelCase     ):
            return self.tokenizer._build_translation_inputs(
                *__UpperCamelCase     ,					return_tensors=self.framework     ,					truncation=__UpperCamelCase     ,					src_lang=__UpperCamelCase     ,					tgt_lang=__UpperCamelCase     )
        else:
            return super()._parse_and_tokenize(*__UpperCamelCase     ,					truncation=__UpperCamelCase     )







    def      _snake_case					(			self			:       List[str]     ,					__UpperCamelCase			:       int=None     ,					__UpperCamelCase			:       int=None     ,					**__UpperCamelCase			:       Any     )		->int:






        '''simple docstring'''






        _UpperCAmelCase    ,_UpperCAmelCase    ,_UpperCAmelCase          =   super()._sanitize_parameters(**__UpperCamelCase     )
        if src_lang is not None:
            _UpperCAmelCase          =   src_lang
        if tgt_lang is not None:
            _UpperCAmelCase          =   tgt_lang
        if src_lang is None and tgt_lang is None:
            # Backward compatibility, direct arguments use is preferred.
            _UpperCAmelCase          =   kwargs.get("""task"""     ,					self.task     )
            _UpperCAmelCase          =   task.split("""_"""     )
            if task and len(__UpperCamelCase     ) == 4:
                # translation, XX, to YY
                _UpperCAmelCase          =   items[1]
                _UpperCAmelCase          =   items[3]
        return preprocess_params, forward_params, postprocess_params







    def __call__(			self			:       List[str]     ,					*__UpperCamelCase			:       str     ,					**__UpperCamelCase			:       Optional[Any]     )		->int:






        '''simple docstring'''






        return super().__call__(*__UpperCamelCase     ,					**__UpperCamelCase     )