






import os
from typing import List, Union

import tensorflow as tf
from tensorflow_text import BertTokenizer as BertTokenizerLayer
from tensorflow_text import FastBertTokenizer, ShrinkLongestTrimmer, case_fold_utfa, combine_segments, pad_model_inputs

from .tokenization_bert import BertTokenizer
class    a   (			tf.keras.layers.Layer       ):



      """simple docstring"""


      def __init__(				self :      Dict		,				__lowercase :      List		,				__lowercase :      bool		,				__lowercase :      int = None		,				__lowercase :      int = None		,				__lowercase :      int = None		,				__lowercase :      str = "longest"		,				__lowercase :      bool = True		,				__lowercase :      int = 512		,				__lowercase :      int = None		,				__lowercase :      bool = True		,				__lowercase :      bool = True		,				__lowercase :      bool = True		,				**__lowercase :      Dict		,				)							->				Tuple:
        super().__init__()
        if use_fast_bert_tokenizer:
          __UpperCAmelCase							:     Tuple									=  FastBertTokenizer(
              __lowercase		,				token_out_type=tf.intaa		,				lower_case_nfd_strip_accents=__lowercase		,				**__lowercase )
        else:
          __UpperCAmelCase							:     Optional[int]									=  tf.lookup.StaticVocabularyTable(
              tf.lookup.KeyValueTensorInitializer(
                  keys=__lowercase		,				key_dtype=tf.string		,				values=tf.range(tf.size(__lowercase		,				out_type=tf.intaa )		,				dtype=tf.intaa )		,				value_dtype=tf.intaa		,				)		,				num_oov_buckets=1		,				)
          __UpperCAmelCase							:     str									=  BertTokenizerLayer(
              __lowercase		,				token_out_type=tf.intaa		,				lower_case=__lowercase		,				**__lowercase )

        __UpperCAmelCase							:     Any									=  vocab_list
        __UpperCAmelCase							:     Dict									=  do_lower_case
        __UpperCAmelCase							:     Optional[int]									=  cls_token_id or vocab_list.index("""[CLS]""" )
        __UpperCAmelCase							:     List[str]									=  sep_token_id or vocab_list.index("""[SEP]""" )
        __UpperCAmelCase							:     Any									=  pad_token_id or vocab_list.index("""[PAD]""" )
        __UpperCAmelCase							:     str									=  ShrinkLongestTrimmer(max_length - 3		,				axis=1 )  # Allow room for special tokens
        __UpperCAmelCase							:     Optional[int]									=  max_length
        __UpperCAmelCase							:     Dict									=  padding
        __UpperCAmelCase							:     Dict									=  truncation
        __UpperCAmelCase							:     Tuple									=  pad_to_multiple_of
        __UpperCAmelCase							:     Union[str, Any]									=  return_token_type_ids
        __UpperCAmelCase							:     str									=  return_attention_mask
      @classmethod
      def      UpperCAmelCase				(				cls :      Any		,				__lowercase :      "PreTrainedTokenizerBase"		,				**__lowercase :      Dict )							->				List[Any]:  # noqa: F821
        __UpperCAmelCase							:     Union[str, Any]									=  kwargs.pop("""do_lower_case"""		,				__lowercase )
        __UpperCAmelCase							:     Optional[Any]									=  tokenizer.do_lower_case if do_lower_case is None else do_lower_case
        __UpperCAmelCase							:     List[Any]									=  kwargs.pop("""cls_token_id"""		,				__lowercase )
        __UpperCAmelCase							:     Any									=  tokenizer.cls_token_id if cls_token_id is None else cls_token_id
        __UpperCAmelCase							:     Any									=  kwargs.pop("""sep_token_id"""		,				__lowercase )
        __UpperCAmelCase							:     Optional[int]									=  tokenizer.sep_token_id if sep_token_id is None else sep_token_id
        __UpperCAmelCase							:     List[str]									=  kwargs.pop("""pad_token_id"""		,				__lowercase )
        __UpperCAmelCase							:     Any									=  tokenizer.pad_token_id if pad_token_id is None else pad_token_id

        __UpperCAmelCase							:     str									=  tokenizer.get_vocab()
        __UpperCAmelCase							:     Union[str, Any]									=  sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()]		,				key=lambda __lowercase : x[1] )
        __UpperCAmelCase							:     Dict									=  [entry[0] for entry in vocab]
        return cls(
            vocab_list=__lowercase		,				do_lower_case=__lowercase		,				cls_token_id=__lowercase		,				sep_token_id=__lowercase		,				pad_token_id=__lowercase		,				**__lowercase		,				)
      @classmethod
      def      UpperCAmelCase				(				cls :      int		,				__lowercase :      Union[str, os.PathLike]		,				*__lowercase :      Tuple		,				**__lowercase :      Optional[int] )							->				str:
        try:
          __UpperCAmelCase							:     Any									=  BertTokenizer.from_pretrained(__lowercase		,				*__lowercase		,				**__lowercase )
        except:  # noqa: E722
          from .tokenization_bert_fast import BertTokenizerFast

          __UpperCAmelCase							:     Dict									=  BertTokenizerFast.from_pretrained(__lowercase		,				*__lowercase		,				**__lowercase )
        return cls.from_tokenizer(__lowercase		,				**__lowercase )
      def      UpperCAmelCase				(				self :      Optional[Any]		,				__lowercase :      Dict )							->				Dict:
        if self.do_lower_case:
          __UpperCAmelCase							:     Dict									=  case_fold_utfa(__lowercase )
        __UpperCAmelCase							:     Optional[Any]									=  self.tf_tokenizer.tokenize(__lowercase )
        return tokens.merge_dims(1		,				-1 )
      def      UpperCAmelCase				(				self :      Optional[Any]		,				__lowercase :      List[str]		,				__lowercase :      int=None		,				__lowercase :      Optional[int]=None		,				__lowercase :      List[Any]=None		,				__lowercase :      int=None		,				__lowercase :      List[str]=None		,				__lowercase :      Union[str, Any]=None		,				__lowercase :      List[str]=None		,				)							->				List[str]:
        if padding is None:
          __UpperCAmelCase							:     int									=  self.padding
        if padding not in ("longest", "max_length"):
          raise ValueError("""Padding must be either 'longest' or 'max_length'!""" )
        if max_length is not None and text_pair is not None:
          # Because we have to instantiate a Trimmer to do it properly
          raise ValueError("""max_length cannot be overridden at call time when truncating paired texts!""" )
        if max_length is None:
          __UpperCAmelCase							:     Optional[Any]									=  self.max_length
        if truncation is None:
          __UpperCAmelCase							:     Optional[int]									=  self.truncation
        if pad_to_multiple_of is None:
          __UpperCAmelCase							:     Optional[int]									=  self.pad_to_multiple_of
        if return_token_type_ids is None:
          __UpperCAmelCase							:     int									=  self.return_token_type_ids
        if return_attention_mask is None:
          __UpperCAmelCase							:     Union[str, Any]									=  self.return_attention_mask
        if not isinstance(__lowercase		,				tf.Tensor ):
          __UpperCAmelCase							:     str									=  tf.convert_to_tensor(__lowercase )
        if text_pair is not None and not isinstance(__lowercase		,				tf.Tensor ):
          __UpperCAmelCase							:     int									=  tf.convert_to_tensor(__lowercase )
        if text_pair is not None:
          if text.shape.rank > 1:
            raise ValueError("""text argument should not be multidimensional when a text pair is supplied!""" )
          if text_pair.shape.rank > 1:
            raise ValueError("""text_pair should not be multidimensional!""" )
        if text.shape.rank == 2:
          __UpperCAmelCase				,			__UpperCAmelCase							:     Optional[int]									=  text[:, 0], text[:, 1]
        __UpperCAmelCase							:     str									=  self.unpaired_tokenize(__lowercase )
        if text_pair is None:  # Unpaired text
          if truncation:
            __UpperCAmelCase							:     Optional[int]									=  text[:, : max_length - 2]  # Allow room for special tokens
          __UpperCAmelCase				,			__UpperCAmelCase							:     Dict									=  combine_segments(
              (text,)		,				start_of_sequence_id=self.cls_token_id		,				end_of_segment_id=self.sep_token_id )
        else:  # Paired text
          __UpperCAmelCase							:     List[str]									=  self.unpaired_tokenize(__lowercase )
          if truncation:
            __UpperCAmelCase				,			__UpperCAmelCase							:     Dict									=  self.paired_trimmer.trim([text, text_pair] )
          __UpperCAmelCase				,			__UpperCAmelCase							:     List[str]									=  combine_segments(
              (text, text_pair)		,				start_of_sequence_id=self.cls_token_id		,				end_of_segment_id=self.sep_token_id )
        if padding == "longest":
          __UpperCAmelCase							:     Tuple									=  input_ids.bounding_shape(axis=1 )
          if pad_to_multiple_of is not None:
            # No ceiling division in tensorflow, so we negate floordiv instead
            __UpperCAmelCase							:     Tuple									=  pad_to_multiple_of * (-tf.math.floordiv(-pad_length		,				__lowercase ))
        else:
          __UpperCAmelCase							:     Union[str, Any]									=  max_length

        __UpperCAmelCase				,			__UpperCAmelCase							:     List[str]									=  pad_model_inputs(__lowercase		,				max_seq_length=__lowercase		,				pad_value=self.pad_token_id )
        __UpperCAmelCase							:     str									=  {"""input_ids""": input_ids}
        if return_attention_mask:
          __UpperCAmelCase							:     Optional[Any]									=  attention_mask
        if return_token_type_ids:
          __UpperCAmelCase				,			__UpperCAmelCase							:     List[Any]									=  pad_model_inputs(
              __lowercase		,				max_seq_length=__lowercase		,				pad_value=self.pad_token_id )
          __UpperCAmelCase							:     int									=  token_type_ids
        return output




      def      UpperCAmelCase				(				self :      Optional[int] )							->				Dict:
        return {
            "vocab_list": self.vocab_list,
            "do_lower_case": self.do_lower_case,
            "cls_token_id": self.cls_token_id,
            "sep_token_id": self.sep_token_id,
            "pad_token_id": self.pad_token_id,
        }










import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


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

a		:	int      					=			{
    "facebook/musicgen-small": "https://huggingface.co/facebook/musicgen-small/resolve/main/config.json",
    # See all Musicgen models at https://huggingface.co/models?filter=musicgen
}
class    a   (			lowercase__       ):



      """simple docstring"""


      a  :			Optional[int]								=     'musicgen_decoder'
      a  :			Optional[int]								=     ['past_key_values']
      def __init__(				self :      Optional[Any]		,				__lowercase :      str=2048		,				__lowercase :      Optional[int]=2048		,				__lowercase :      List[Any]=24		,				__lowercase :      Optional[Any]=4096		,				__lowercase :      Dict=16		,				__lowercase :      List[Any]=0.0		,				__lowercase :      Tuple=True		,				__lowercase :      List[Any]="gelu"		,				__lowercase :      Any=1024		,				__lowercase :      Optional[Any]=0.1		,				__lowercase :      List[Any]=0.0		,				__lowercase :      int=0.0		,				__lowercase :      List[str]=0.02		,				__lowercase :      Dict=False		,				__lowercase :      List[str]=4		,				__lowercase :      List[str]=2048		,				__lowercase :      List[Any]=2048		,				__lowercase :      Union[str, Any]=None		,				__lowercase :      List[str]=False		,				**__lowercase :      int		,				)							->				str:
        __UpperCAmelCase							:     Union[str, Any]									=  vocab_size
        __UpperCAmelCase							:     Dict									=  max_position_embeddings
        __UpperCAmelCase							:     str									=  hidden_size
        __UpperCAmelCase							:     Optional[Any]									=  ffn_dim
        __UpperCAmelCase							:     List[Any]									=  num_hidden_layers
        __UpperCAmelCase							:     Any									=  num_attention_heads
        __UpperCAmelCase							:     List[Any]									=  dropout
        __UpperCAmelCase							:     List[str]									=  attention_dropout
        __UpperCAmelCase							:     List[Any]									=  activation_dropout
        __UpperCAmelCase							:     int									=  activation_function
        __UpperCAmelCase							:     Optional[int]									=  initializer_factor
        __UpperCAmelCase							:     List[Any]									=  layerdrop
        __UpperCAmelCase							:     Tuple									=  use_cache
        __UpperCAmelCase							:     Tuple									=  scale_embedding  # scale factor will be sqrt(d_model) if True
        __UpperCAmelCase							:     int									=  num_codebooks
        super().__init__(
            pad_token_id=__lowercase		,				bos_token_id=__lowercase		,				eos_token_id=__lowercase		,				tie_word_embeddings=__lowercase		,				**__lowercase		,				)


class    a   (			lowercase__       ):



      """simple docstring"""


      a  :			Tuple								=     'musicgen'
      a  :			int								=     True
      def __init__(				self :      Any		,				**__lowercase :      Tuple )							->				Union[str, Any]:
        super().__init__(**__lowercase )
        if "text_encoder" not in kwargs or "audio_encoder" not in kwargs or "decoder" not in kwargs:
          raise ValueError("""Config has to be initialized with text_encoder, audio_encoder and decoder config""" )

        __UpperCAmelCase							:     int									=  kwargs.pop("""text_encoder""" )
        __UpperCAmelCase							:     str									=  text_encoder_config.pop("""model_type""" )

        __UpperCAmelCase							:     Tuple									=  kwargs.pop("""audio_encoder""" )
        __UpperCAmelCase							:     Optional[Any]									=  audio_encoder_config.pop("""model_type""" )

        __UpperCAmelCase							:     Optional[Any]									=  kwargs.pop("""decoder""" )

        __UpperCAmelCase							:     Any									=  AutoConfig.for_model(__lowercase		,				**__lowercase )
        __UpperCAmelCase							:     Dict									=  AutoConfig.for_model(__lowercase		,				**__lowercase )
        __UpperCAmelCase							:     Any									=  MusicgenDecoderConfig(**__lowercase )
        __UpperCAmelCase							:     int									=  True
      @classmethod
      def      UpperCAmelCase				(				cls :      Optional[Any]		,				__lowercase :      PretrainedConfig		,				__lowercase :      PretrainedConfig		,				__lowercase :      MusicgenDecoderConfig		,				**__lowercase :      List[Any]		,				)							->				List[str]:
        return cls(
            text_encoder=text_encoder_config.to_dict()		,				audio_encoder=audio_encoder_config.to_dict()		,				decoder=decoder_config.to_dict()		,				**__lowercase		,				)




      def      UpperCAmelCase				(				self :      Any )							->				int:
        __UpperCAmelCase							:     Optional[Any]									=  copy.deepcopy(self.__dict__ )
        __UpperCAmelCase							:     List[Any]									=  self.text_encoder.to_dict()
        __UpperCAmelCase							:     str									=  self.audio_encoder.to_dict()
        __UpperCAmelCase							:     List[str]									=  self.decoder.to_dict()
        __UpperCAmelCase							:     List[str]									=  self.__class__.model_type
        return output


