



'''simple docstring'''




from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy



class    lowerCamelCase__				(			__lowerCamelCase						):






      """simple docstring"""
      UpperCamelCase__			 =     '''EncodecFeatureExtractor'''
      UpperCamelCase__			 =     ('''T5Tokenizer''', '''T5TokenizerFast''')




      def __init__(       self							:     Optional[Any]			,a__							:     Optional[Any]			,a__							:     Any   ):
             super().__init__(__lowercase			,__lowercase   )
             a__								= self.feature_extractor
             a__								= False




      def lowerCAmelCase_					(       self							:     Tuple			,a__							:     Any=None			,a__							:     Union[str, Any]=None			,a__							:     Dict=True   ):
             return self.tokenizer.get_decoder_prompt_ids(task=__lowercase			,language=__lowercase			,no_timestamps=__lowercase   )




      def __call__(       self							:     Any			,*a__							:     Union[str, Any]			,**a__							:     Optional[Any]   ):







             if self._in_target_context_manager:
                    return self.current_processor(*__lowercase			,**__lowercase   )

             a__								= kwargs.pop("audio"			,__lowercase   )
             a__								= kwargs.pop("sampling_rate"			,__lowercase   )
             a__								= kwargs.pop("text"			,__lowercase   )
             if len(__lowercase   ) > 0:
                    a__								= args[0]
                    a__								= args[1:]

             if audio is None and text is None:
                    raise ValueError("You need to specify either an `audio` or `text` input to process."   )

             if text is not None:
                    a__								= self.tokenizer(__lowercase			,**__lowercase   )

             if audio is not None:
                    a__								= self.feature_extractor(__lowercase			,*__lowercase			,sampling_rate=__lowercase			,**__lowercase   )

             if audio is None:
                    return inputs

             elif text is None:
                    return audio_inputs

             else:
                    a__								= audio_inputs["input_values"]
                    if "padding_mask" in audio_inputs:
                           a__								= audio_inputs["padding_mask"]
                    return inputs




      def lowerCAmelCase_					(       self							:     Optional[Any]			,*a__							:     Dict			,**a__							:     Optional[Any]   ):
             a__								= kwargs.pop("audio"			,__lowercase   )
             a__								= kwargs.pop("padding_mask"			,__lowercase   )

             if len(__lowercase   ) > 0:
                    a__								= args[0]
                    a__								= args[1:]

             if audio_values is not None:
                    return self._decode_audio(__lowercase			,padding_mask=__lowercase   )
             else:
                    return self.tokenizer.batch_decode(*__lowercase			,**__lowercase   )




      def lowerCAmelCase_					(       self							:     Tuple			,*a__							:     int			,**a__							:     int   ):
             return self.tokenizer.decode(*__lowercase			,**__lowercase   )




      def lowerCAmelCase_					(       self							:     Tuple			,a__							:     Optional[int]			,a__							:     Optional = None   ):
             a__								= to_numpy(__lowercase   )
             a__						,					a__						,					a__								= audio_values.shape

             if padding_mask is None:
                    return list(__lowercase   )

             a__								= to_numpy(__lowercase   )

             # match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
             # token (so that the generated audio values are **not** treated as padded tokens)
             a__								= seq_len - padding_mask.shape[-1]
             a__								= 1 - self.feature_extractor.padding_value
             a__								= np.pad(__lowercase			,((0, 0), (0, difference))			,"constant"			,constant_values=__lowercase   )

             a__								= audio_values.tolist()
             for i in range(__lowercase   ):
                    a__								= np.asarray(audio_values[i]   )[
                        padding_mask[i][None, :] != self.feature_extractor.padding_value
                    ]
                    a__								= sliced_audio.reshape(__lowercase			,-1   )

             return audio_values



'''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


UpperCAmelCase					 =	logging.get_logger(__name__)

UpperCAmelCase					 =	{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

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

UpperCAmelCase					 =	{
    '''gpt-neox-20b''': 2048,
}


class 	lowerCAmelCase  (   A ):
       lowerCAmelCase_												=			VOCAB_FILES_NAMES
       lowerCAmelCase_												=			PRETRAINED_VOCAB_FILES_MAP
       lowerCAmelCase_												=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowerCAmelCase_												=			["input_ids", "attention_mask"]







       def __init__(				self      :			Tuple    ,   __lowercase      :			Union[str, Any]=None    ,   __lowercase      :			Union[str, Any]=None    ,   __lowercase      :			str=None    ,   __lowercase      :			Optional[int]="<|endoftext|>"    ,   __lowercase      :			List[Any]="<|endoftext|>"    ,   __lowercase      :			Tuple="<|endoftext|>"    ,   __lowercase      :			Optional[int]=False    ,   **__lowercase      :			List[Any]    ,   ):







            """simple docstring"""







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

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

            __lowercase      		=add_prefix_space







       def       snake_case (				self      :			Optional[Any]    ,   __lowercase      :			str    ,   __lowercase      :			Optional[str] = None   ):







            """simple docstring"""







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







       def       snake_case (				self      :			Optional[Any]    ,   __lowercase      :			"Conversation"   ):







            """simple docstring"""







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

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


