






"""simple docstring"""



import json
from typing import Dict, List, Optional, Tuple, Union

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer


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


__A		:       Optional[Any]  				=		{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

__A		:       Optional[Any]  				=		{
    '''vocab_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json''',
    },
}

__A		:       int  				=		{
    '''allenai/led-base-16384''': 16_384,
}





class 					_UpperCAmelCase       ( _A							):
					SCREAMING_SNAKE_CASE_		:				Optional[Any]							    =   VOCAB_FILES_NAMES
					SCREAMING_SNAKE_CASE_		:				Optional[Any]							    =   PRETRAINED_VOCAB_FILES_MAP
					SCREAMING_SNAKE_CASE_		:				Tuple							    =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					SCREAMING_SNAKE_CASE_		:				List[str]							    =   LEDTokenizer
					SCREAMING_SNAKE_CASE_		:				Optional[Any]							    =   ["input_ids", "attention_mask"]




					def __init__(				self     :						int    ,						A     :						Any=None    ,						A     :						Optional[Any]=None    ,						A     :						List[Any]=None    ,						A     :						Optional[int]="replace"    ,						A     :						Tuple="<s>"    ,						A     :						List[str]="</s>"    ,						A     :						Optional[int]="</s>"    ,						A     :						List[Any]="<s>"    ,						A     :						Optional[Any]="<unk>"    ,						A     :						Optional[int]="<pad>"    ,						A     :						Optional[Any]="<mask>"    ,						A     :						Any=False    ,						A     :						Union[str, Any]=True    ,						**A     :						Union[str, Any]    ,						)  ->  int:
							super().__init__(
							    A    ,						A    ,						tokenizer_file=A    ,						errors=A    ,						bos_token=A    ,						eos_token=A    ,						sep_token=A    ,						cls_token=A    ,						unk_token=A    ,						pad_token=A    ,						mask_token=A    ,						add_prefix_space=A    ,						trim_offsets=A    ,						**A    ,						)

							lowercase_ : List[Any]							       =   json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()   )
							if pre_tok_state.get('''add_prefix_space'''    ,						A   ) != add_prefix_space:
									lowercase_ : List[str]							       =   getattr(A    ,						pre_tok_state.pop('''type'''   )   )
									lowercase_ : int							       =   add_prefix_space
									lowercase_ : Tuple							       =   pre_tok_class(**A   )

							lowercase_ : int							       =   add_prefix_space

							# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
							lowercase_ : List[str]							       =   '''post_processor'''
							lowercase_ : List[Any]							       =   getattr(self.backend_tokenizer    ,						A    ,						A   )
							if tokenizer_component_instance:
									lowercase_ : Tuple							       =   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_ : Dict							       =   tuple(state['''sep''']   )
									if "cls" in state:
											lowercase_ : Union[str, Any]							       =   tuple(state['''cls''']   )

									lowercase_ : Dict							       =   False

									if state.get('''add_prefix_space'''    ,						A   ) != add_prefix_space:
											lowercase_ : Union[str, Any]							       =   add_prefix_space
											lowercase_ : Union[str, Any]							       =   True

									if state.get('''trim_offsets'''    ,						A   ) != trim_offsets:
											lowercase_ : Union[str, Any]							       =   trim_offsets
											lowercase_ : str							       =   True

									if changes_to_apply:
											lowercase_ : int							       =   getattr(A    ,						state.pop('''type'''   )   )
											lowercase_ : Tuple							       =   component_class(**A   )
											setattr(self.backend_tokenizer    ,						A    ,						A   )




					@property
					# Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
					def   A  (				self     :						Optional[Any]   )  ->  str:
							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     :						Optional[int]    ,						A     :						int   )  ->  str:
							lowercase_ : str							       =   AddedToken(A    ,						lstrip=A    ,						rstrip=A   ) if isinstance(A    ,						A   ) else value
							lowercase_ : Optional[Any]							       =   value




					def   A  (				self     :						Optional[Any]    ,						*A     :						str    ,						**A     :						Optional[Any]   )  ->  BatchEncoding:
							lowercase_ : int							       =   kwargs.get('''is_split_into_words'''    ,						A   )

							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(*A    ,						**A   )




					def   A  (				self     :						List[Any]    ,						*A     :						int    ,						**A     :						List[str]   )  ->  BatchEncoding:
							lowercase_ : str							       =   kwargs.get('''is_split_into_words'''    ,						A   )

							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(*A    ,						**A   )




					def   A  (				self     :						Any    ,						A     :						str    ,						A     :						Optional[str] = None   )  ->  Tuple[str]:
							lowercase_ : str							       =   self._tokenizer.model.save(A    ,						name=A   )
							return tuple(A   )




					def   A  (				self     :						Any    ,						A     :						str    ,						A     :						Union[str, Any]=None   )  ->  Any:
							lowercase_ : Any							       =   [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     :						Union[str, Any]    ,						A     :						List[int]    ,						A     :						Optional[List[int]] = None   )  ->  List[int]:
							lowercase_ : Optional[Any]							       =   [self.sep_token_id]
							lowercase_ : Optional[int]							       =   [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]






					def   A  (				self     :						Optional[Any]    ,						A     :						Union[Dict[str, EncodedInput], BatchEncoding]    ,						A     :						Optional[int] = None    ,						A     :						PaddingStrategy = PaddingStrategy.DO_NOT_PAD    ,						A     :						Optional[int] = None    ,						A     :						Optional[bool] = None    ,						)  ->  dict:
							lowercase_ : Any							       =   super()._pad(
							    encoded_inputs=A    ,						max_length=A    ,						padding_strategy=A    ,						pad_to_multiple_of=A    ,						return_attention_mask=A    ,						)

							# Load from model defaults
							if return_attention_mask is None:
									lowercase_ : Optional[Any]							       =   '''attention_mask''' in self.model_input_names

							if return_attention_mask and "global_attention_mask" in encoded_inputs:
									lowercase_ : Optional[int]							       =   encoded_inputs[self.model_input_names[0]]
									# `global_attention_mask` need to have the same length as other (sequential) inputs.
									lowercase_ : Tuple							       =   len(encoded_inputs['''global_attention_mask''']   ) != len(A   )

									if needs_to_be_padded:
											lowercase_ : Union[str, Any]							       =   len(A   ) - len(encoded_inputs['''global_attention_mask''']   )

											if self.padding_side == "right":
													# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
													lowercase_ : Dict							       =   (
													    encoded_inputs['''global_attention_mask'''] + [-1] * difference
													)
											elif self.padding_side == "left":
													lowercase_ : str							       =   [-1] * difference + encoded_inputs[
													    '''global_attention_mask'''
													]
											else:
													raise ValueError('''Invalid padding strategy:''' + str(self.padding_side   )   )

							return encoded_inputs










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_   )
