





import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer


__lowerCamelCase       :     List[Any]   =     {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

__lowerCamelCase       :     Any   =     {
    """vocab_file""": {
        """unc-nlp/lxmert-base-uncased""": """https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/vocab.txt""",
    },
    """tokenizer_file""": {
        """unc-nlp/lxmert-base-uncased""": (
            """https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/tokenizer.json"""
        ),
    },
}

__lowerCamelCase       :     Optional[int]   =     {
    """unc-nlp/lxmert-base-uncased""": 512,
}

__lowerCamelCase       :     int   =     {
    """unc-nlp/lxmert-base-uncased""": {"""do_lower_case""": True},
}



class 					A__   (       __snake_case			):
      _UpperCAmelCase	:List[str]						    =   VOCAB_FILES_NAMES
      _UpperCAmelCase	:Dict						    =   PRETRAINED_VOCAB_FILES_MAP
      _UpperCAmelCase	:int						    =   PRETRAINED_INIT_CONFIGURATION
      _UpperCAmelCase	:str						    =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _UpperCAmelCase	:Any						    =   LxmertTokenizer





      def __init__( self       ,							A_=None       ,							A_=None       ,							A_=True       ,							A_="[UNK]"       ,							A_="[SEP]"       ,							A_="[PAD]"       ,							A_="[CLS]"       ,							A_="[MASK]"       ,							A_=True       ,							A_=None       ,							**A_       ,							):
       '''simple docstring'''




       super().__init__(
           A_       ,							tokenizer_file=A_       ,							do_lower_case=A_       ,							unk_token=A_       ,							sep_token=A_       ,							pad_token=A_       ,							cls_token=A_       ,							mask_token=A_       ,							tokenize_chinese_chars=A_       ,							strip_accents=A_       ,							**A_       ,							)

       UpperCamelCase	:					List[str]       	=   json.loads(self.backend_tokenizer.normalizer.__getstate__()   )
       if (
           normalizer_state.get("lowercase"       ,							A_   ) != do_lower_case
           or normalizer_state.get("strip_accents"       ,							A_   ) != strip_accents
           or normalizer_state.get("handle_chinese_chars"       ,							A_   ) != tokenize_chinese_chars
       ):
        UpperCamelCase	:					Union[str, Any]       	=   getattr(A_       ,							normalizer_state.pop("type"   )   )
        UpperCamelCase	:					Optional[int]       	=   do_lower_case
        UpperCamelCase	:					Any       	=   strip_accents
        UpperCamelCase	:					int       	=   tokenize_chinese_chars
        UpperCamelCase	:					List[str]       	=   normalizer_class(**A_   )

       UpperCamelCase	:					Tuple       	=   do_lower_case





      def        __UpperCamelCase( self       ,							A_       ,							A_=None   ):
       '''simple docstring'''




       UpperCamelCase	:					str       	=   [self.cls_token_id] + token_ids_a + [self.sep_token_id]

       if token_ids_a:
        output += token_ids_a + [self.sep_token_id]

       return output





      def        __UpperCamelCase( self       ,							A_       ,							A_ = None   ):
       '''simple docstring'''




       UpperCamelCase	:					int       	=   [self.sep_token_id]
       UpperCamelCase	:					Union[str, 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        __UpperCamelCase( self       ,							A_       ,							A_ = None   ):
       '''simple docstring'''




       UpperCamelCase	:					Tuple       	=   self._tokenizer.model.save(A_       ,							name=A_   )
       return tuple(A_   )







from typing import List, Optional, Tuple, Union

import torch

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


__lowerCamelCase       :     str   =     logging.get_logger(__name__)  # pylint: disable=invalid-name



class 					A__   (       __snake_case			):





      def __init__( self       ,							A_       ,							A_   ):
       '''simple docstring'''




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





      @torch.no_grad()
      def __call__( self       ,							A_ = 1       ,							A_ = 100       ,							A_ = None       ,							A_ = None       ,							A_ = True       ,							):
       '''simple docstring'''




       if audio_length_in_s is None:
        UpperCamelCase	:					str       	=   self.unet.config.sample_size / self.unet.config.sample_rate

       UpperCamelCase	:					Optional[Any]       	=   audio_length_in_s * self.unet.config.sample_rate

       UpperCamelCase	:					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}."""   )

       UpperCamelCase	:					Union[str, Any]       	=   int(A_   )
       if sample_size % down_scale_factor != 0:
        UpperCamelCase	:					List[str]       	=   (
            (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."   )
       UpperCamelCase	:					Any       	=   int(A_   )

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

       UpperCamelCase	:					Optional[Any]       	=   randn_tensor(A_       ,							generator=A_       ,							device=self.device       ,							dtype=A_   )

       # set step values
       self.scheduler.set_timesteps(A_       ,							device=audio.device   )
       UpperCamelCase	:					Optional[int]       	=   self.scheduler.timesteps.to(A_   )

       for t in self.progress_bar(self.scheduler.timesteps   ):
        # 1. predict noise model_output
        UpperCamelCase	:					Dict       	=   self.unet(A_       ,							A_   ).sample

        # 2. compute previous image: x_t -> t_t-1
        UpperCamelCase	:					int       	=   self.scheduler.step(A_       ,							A_       ,							A_   ).prev_sample

       UpperCamelCase	:					Optional[Any]       	=   audio.clamp(-1       ,							1   ).float().cpu().numpy()

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

       if not return_dict:
        return (audio,)

       return AudioPipelineOutput(audios=A_   )
