


from typing import List, Optional, Tuple, Union

import torch

from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline


SCREAMING_SNAKE_CASE       :      Union[str, Any]												= logging.get_logger(__name__)  # pylint: disable=invalid-name






class _lowerCamelCase(						_a   ):
       def __init__(					self,      lowerCamelCase,      lowerCamelCase)       ->   Tuple:




           """simple docstring"""

           super().__init__()
           self.register_modules(unet=lowerCamelCase,      scheduler=lowerCamelCase)




       @torch.no_grad()
       def __call__(					self,      lowerCamelCase = 1,      lowerCamelCase = 1_00,      lowerCamelCase = None,      lowerCamelCase = None,      lowerCamelCase = True,      )       ->   Union[AudioPipelineOutput, Tuple]:




           """simple docstring"""

           if audio_length_in_s is None:
               _lowercase       :							int      		=		self.unet.config.sample_size / self.unet.config.sample_rate

           _lowercase       :							str      		=		audio_length_in_s * self.unet.config.sample_rate

           _lowercase       :							Optional[Any]      		=		2 ** len(self.unet.up_blocks)
           if sample_size < 3 * down_scale_factor:
               raise ValueError(
                   F'''{audio_length_in_s} is too small. Make sure it\'s bigger or equal to'''
                   F''' {3 * down_scale_factor / self.unet.config.sample_rate}.''')

           _lowercase       :							List[Any]      		=		int(lowerCamelCase)
           if sample_size % down_scale_factor != 0:
               _lowercase       :							Optional[int]      		=		(
                   (audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
               ) * down_scale_factor
               logger.info(
                   F'''{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled'''
                   F''' by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising'''
                   ' process.')
           _lowercase       :							Dict      		=		int(lowerCamelCase)

           _lowercase       :							List[Any]      		=		next(iter(self.unet.parameters())).dtype
           _lowercase       :							Union[str, Any]      		=		(batch_size, self.unet.config.in_channels, sample_size)
           if isinstance(lowerCamelCase,      lowerCamelCase) and len(lowerCamelCase) != batch_size:
               raise ValueError(
                   F'''You have passed a list of generators of length {len(lowerCamelCase)}, but requested an effective batch'''
                   F''' size of {batch_size}. Make sure the batch size matches the length of the generators.''')

           _lowercase       :							Tuple      		=		randn_tensor(lowerCamelCase,      generator=lowerCamelCase,      device=self.device,      dtype=lowerCamelCase)

           # set step values
           self.scheduler.set_timesteps(lowerCamelCase,      device=audio.device)
           _lowercase       :							Optional[Any]      		=		self.scheduler.timesteps.to(lowerCamelCase)

           for t in self.progress_bar(self.scheduler.timesteps):
               # 1. predict noise model_output
               _lowercase       :							Union[str, Any]      		=		self.unet(lowerCamelCase,      lowerCamelCase).sample

               # 2. compute previous image: x_t -> t_t-1
               _lowercase       :							str      		=		self.scheduler.step(lowerCamelCase,      lowerCamelCase,      lowerCamelCase).prev_sample

           _lowercase       :							Tuple      		=		audio.clamp(-1,      1).float().cpu().numpy()

           _lowercase       :							Dict      		=		audio[:, :, :original_sample_size]

           if not return_dict:
               return (audio,)

           return AudioPipelineOutput(audios=lowerCamelCase)









'''simple docstring'''


import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp import MvpTokenizer


lowerCAmelCase					:     Tuple												=logging.get_logger(__name__)


lowerCAmelCase					:     List[str]												={'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

# See all MVP models at https://huggingface.co/models?filter=mvp
lowerCAmelCase					:     Optional[int]												={
    '''vocab_file''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json''',
    },
    '''added_tokens.json''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json''',
    },
    '''merges_file''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/tokenizer.json''',
    },
}

lowerCAmelCase					:     List[Any]												={
    '''RUCAIBox/mvp''': 1_024,
}
class       a_   (				_lowerCAmelCase							):
     __A      =			VOCAB_FILES_NAMES
     __A      =			PRETRAINED_VOCAB_FILES_MAP
     __A      =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __A      =			["input_ids", "attention_mask"]
     __A      =			MvpTokenizer





     def __init__( self		:					Optional[Any]	,      lowercase		:					Any=None	,      lowercase		:					List[Any]=None	,      lowercase		:					Dict=None	,      lowercase		:					int="replace"	,      lowercase		:					int="<s>"	,      lowercase		:					List[str]="</s>"	,      lowercase		:					Optional[Any]="</s>"	,      lowercase		:					List[str]="<s>"	,      lowercase		:					List[str]="<unk>"	,      lowercase		:					List[str]="<pad>"	,      lowercase		:					Tuple="<mask>"	,      lowercase		:					Tuple=False	,      lowercase		:					Dict=True	,      **lowercase		:					List[str]	,      ):





        """simple docstring"""




        super().__init__(
            lowercase	,      lowercase	,      tokenizer_file=lowercase	,      errors=lowercase	,      bos_token=lowercase	,      eos_token=lowercase	,      sep_token=lowercase	,      cls_token=lowercase	,      unk_token=lowercase	,      pad_token=lowercase	,      mask_token=lowercase	,      add_prefix_space=lowercase	,      trim_offsets=lowercase	,      **lowercase	,      )

        lowercase_			:Dict    			= json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()						)
        if pre_tok_state.get("add_prefix_space"	,      lowercase						) != add_prefix_space:
           lowercase_			:List[str]    			= getattr(lowercase	,      pre_tok_state.pop("type"						)						)
           lowercase_			:int    			= add_prefix_space
           lowercase_			:Optional[int]    			= pre_tok_class(**lowercase						)

        lowercase_			:Any    			= add_prefix_space

        # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
        lowercase_			:List[Any]    			= "post_processor"
        lowercase_			:str    			= getattr(self.backend_tokenizer	,      lowercase	,      lowercase						)
        if tokenizer_component_instance:
           lowercase_			:Any    			= json.loads(tokenizer_component_instance.__getstate__()						)

           # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
           if "sep" in state:
              lowercase_			:int    			= tuple(state["sep"]						)
           if "cls" in state:
              lowercase_			:Any    			= tuple(state["cls"]						)

           lowercase_			:int    			= False

           if state.get("add_prefix_space"	,      lowercase						) != add_prefix_space:
              lowercase_			:Union[str, Any]    			= add_prefix_space
              lowercase_			:int    			= True

           if state.get("trim_offsets"	,      lowercase						) != trim_offsets:
              lowercase_			:Any    			= trim_offsets
              lowercase_			:int    			= True

           if changes_to_apply:
              lowercase_			:Tuple    			= getattr(lowercase	,      state.pop("type"						)						)
              lowercase_			:Any    			= component_class(**lowercase						)
              setattr(self.backend_tokenizer	,      lowercase	,      lowercase						)





     @property
     def 							lowercase__      ( self		:					Optional[int]						):





        """simple docstring"""




        if self._mask_token is None:
           if self.verbose:
              logger.error("Using mask_token, but it is not set yet."						)
           return None
        return str(self._mask_token						)





     @mask_token.setter
     def 							lowercase__      ( self		:					int	,      lowercase		:					Dict						):





        """simple docstring"""




        lowercase_			:List[str]    			= AddedToken(lowercase	,      lstrip=lowercase	,      rstrip=lowercase						) if isinstance(lowercase	,      lowercase						) else value
        lowercase_			:Union[str, Any]    			= value





     def 							lowercase__      ( self		:					Optional[Any]	,      *lowercase		:					List[Any]	,      **lowercase		:					Any						):





        """simple docstring"""




        lowercase_			:Any    			= kwargs.get("is_split_into_words"	,      lowercase						)

        if is_split_into_words and not self.add_prefix_space:
           raise ValueError(
               F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
               "to use it with pretokenized inputs."						)

        return super()._batch_encode_plus(*lowercase	,      **lowercase						)





     def 							lowercase__      ( self		:					Optional[Any]	,      *lowercase		:					Optional[int]	,      **lowercase		:					int						):





        """simple docstring"""




        lowercase_			:Any    			= kwargs.get("is_split_into_words"	,      lowercase						)

        if is_split_into_words and not self.add_prefix_space:
           raise ValueError(
               F'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
               "to use it with pretokenized inputs."						)

        return super()._encode_plus(*lowercase	,      **lowercase						)





     def 							lowercase__      ( self		:					Dict	,      lowercase		:					str	,      lowercase		:					Optional[str] = None						):





        """simple docstring"""




        lowercase_			:str    			= self._tokenizer.model.save(lowercase	,      name=lowercase						)
        return tuple(lowercase						)





     def 							lowercase__      ( self		:					Tuple	,      lowercase		:					Dict	,      lowercase		:					int=None						):





        """simple docstring"""




        lowercase_			:List[str]    			= [self.bos_token_id] + token_ids_a + [self.eos_token_id]
        if token_ids_a is None:
           return output

        return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







     def 							lowercase__      ( self		:					int	,      lowercase		:					List[int]	,      lowercase		:					Optional[List[int]] = None						):





        """simple docstring"""




        lowercase_			:Union[str, Any]    			= [self.sep_token_id]
        lowercase_			:Tuple    			= [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 + sep + token_ids_a + sep						) * [0]


