"""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_roberta import RobertaTokenizer


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

__lowerCAmelCase    :       Any					     ={"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

__lowerCAmelCase    :       Union[str, Any]					     ={
    "vocab_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/vocab.json",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/vocab.json",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/vocab.json",
        "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json",
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json"
        ),
    },
    "merges_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/merges.txt",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/merges.txt",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/merges.txt",
        "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt",
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt"
        ),
    },
    "tokenizer_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/tokenizer.json",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/tokenizer.json",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json",
        "roberta-base-openai-detector": (
            "https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json"
        ),
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json"
        ),
    },
}

__lowerCAmelCase    :       Optional[int]					     ={
    "roberta-base": 5_1_2,
    "roberta-large": 5_1_2,
    "roberta-large-mnli": 5_1_2,
    "distilroberta-base": 5_1_2,
    "roberta-base-openai-detector": 5_1_2,
    "roberta-large-openai-detector": 5_1_2,
}





class  _A						(       lowerCAmelCase_	):
      snake_case__			:   str							  =	VOCAB_FILES_NAMES
      snake_case__			:   Tuple							  =	PRETRAINED_VOCAB_FILES_MAP
      snake_case__			:   List[Any]							  =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      snake_case__			:   List[Any]							  =	['input_ids', 'attention_mask']
      snake_case__			:   Optional[Any]							  =	RobertaTokenizer

      def __init__(						self		,       __lowerCAmelCase=None		,       __lowerCAmelCase=None		,       __lowerCAmelCase=None		,       __lowerCAmelCase="replace"		,       __lowerCAmelCase="<s>"		,       __lowerCAmelCase="</s>"		,       __lowerCAmelCase="</s>"		,       __lowerCAmelCase="<s>"		,       __lowerCAmelCase="<unk>"		,       __lowerCAmelCase="<pad>"		,       __lowerCAmelCase="<mask>"		,       __lowerCAmelCase=False		,       __lowerCAmelCase=True		,       **__lowerCAmelCase		,       ):







            """simple docstring"""



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

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

            lowercase														=	add_prefix_space

            lowercase														=	'''post_processor'''
            lowercase														=	getattr(self.backend_tokenizer		,       __lowerCAmelCase		,       __lowerCAmelCase						)
            if tokenizer_component_instance:
                  lowercase														=	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														=	tuple(state["""sep"""]						)
                  if "cls" in state:
                        lowercase														=	tuple(state["""cls"""]						)

                  lowercase														=	False

                  if state.get("""add_prefix_space"""		,       __lowerCAmelCase						) != add_prefix_space:
                        lowercase														=	add_prefix_space
                        lowercase														=	True

                  if state.get("""trim_offsets"""		,       __lowerCAmelCase						) != trim_offsets:
                        lowercase														=	trim_offsets
                        lowercase														=	True

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

      @property
      def 				A__       (						self						):







            """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 				A__       (						self		,       __lowerCAmelCase						):







            """simple docstring"""



            lowercase														=	AddedToken(__lowerCAmelCase		,       lstrip=__lowerCAmelCase		,       rstrip=__lowerCAmelCase						) if isinstance(__lowerCAmelCase		,       __lowerCAmelCase						) else value
            lowercase														=	value

      def 				A__       (						self		,       *__lowerCAmelCase		,       **__lowerCAmelCase						):







            """simple docstring"""



            lowercase														=	kwargs.get("""is_split_into_words"""		,       __lowerCAmelCase						)
            assert self.add_prefix_space or not is_split_into_words, (
                f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
                "to use it with pretokenized inputs."
            )

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

      def 				A__       (						self		,       *__lowerCAmelCase		,       **__lowerCAmelCase						):







            """simple docstring"""



            lowercase														=	kwargs.get("""is_split_into_words"""		,       __lowerCAmelCase						)

            assert self.add_prefix_space or not is_split_into_words, (
                f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
                "to use it with pretokenized inputs."
            )

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

      def 				A__       (						self		,       __lowerCAmelCase		,       __lowerCAmelCase = None						):







            """simple docstring"""



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

      def 				A__       (						self		,       __lowerCAmelCase		,       __lowerCAmelCase=None						):







            """simple docstring"""



            lowercase														=	[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 				A__       (						self		,       __lowerCAmelCase		,       __lowerCAmelCase = None						):







            """simple docstring"""



            lowercase														=	[self.sep_token_id]
            lowercase														=	[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]








"""simple docstring"""






from typing import List, Optional, Tuple, Union

import torch

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


_lowercase : int      						=						logging.get_logger(__name__)  # pylint: disable=invalid-name

class        __SCREAMING_SNAKE_CASE	(      lowerCAmelCase_   ):


					'''simple docstring'''





					def __init__(       self							:  List[Any],  lowerCamelCase							:  Union[str, Any],  lowerCamelCase							:  Dict     )->					Optional[int]:
								super().__init__()
								self.register_modules(unet=lowerCamelCase,  scheduler=lowerCamelCase     )




					@torch.no_grad()
					def __call__(       self							:  Optional[int],  lowerCamelCase							:  int = 1,  lowerCamelCase							:  int = 100,  lowerCamelCase							:  Optional[Union[torch.Generator, List[torch.Generator]]] = None,  lowerCamelCase							:  Optional[float] = None,  lowerCamelCase							:  bool = True,  )->					Union[AudioPipelineOutput, Tuple]:

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

								lowerCamelCase__		:		List[str]			=audio_length_in_s * self.unet.config.sample_rate

								lowerCamelCase__		:		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}.'''     )

								lowerCamelCase__		:		Optional[int]			=int(lowerCamelCase     )
								if sample_size % down_scale_factor != 0:
											lowerCamelCase__		:		Tuple			=(
											    (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.'''     )
								lowerCamelCase__		:		int			=int(lowerCamelCase     )

								lowerCamelCase__		:		str			=next(iter(self.unet.parameters()     )     ).dtype
								lowerCamelCase__		:		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.'''     )

								lowerCamelCase__		:		str			=randn_tensor(lowerCamelCase,  generator=lowerCamelCase,  device=self.device,  dtype=lowerCamelCase     )

								# set step values
								self.scheduler.set_timesteps(lowerCamelCase,  device=audio.device     )
								lowerCamelCase__		:		Any			=self.scheduler.timesteps.to(lowerCamelCase     )

								for t in self.progress_bar(self.scheduler.timesteps     ):
											# 1. predict noise model_output
											lowerCamelCase__		:		str			=self.unet(lowerCamelCase,  lowerCamelCase     ).sample

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

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

								lowerCamelCase__		:		List[Any]			=audio[:, :, :original_sample_size]

								if not return_dict:
											return (audio,)

								return AudioPipelineOutput(audios=lowerCamelCase     )




