

from math import acos, sin
from typing import List, Tuple, Union

import numpy as np
import torch
from PIL import Image

from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel



class        _a		(__magic_name__							):


					'''simple docstring'''






					UpperCAmelCase__:  List[Any]    				=			['''vqvae''']






					def __init__(					self       ,			A__       ,			A__       ,			A__       ,			A__       ,			):
						super().__init__()
						self.register_modules(unet=A__       ,			scheduler=A__       ,			mel=A__       ,			vqvae=A__     )






					def     __A     (					self     ):
						return 50 if isinstance(self.scheduler       ,			A__     ) else 1000






					@torch.no_grad()
					def __call__(					self       ,			A__ = 1       ,			A__ = None       ,			A__ = None       ,			A__ = 0       ,			A__ = 0       ,			A__ = None       ,			A__ = None       ,			A__ = 0       ,			A__ = 0       ,			A__ = None       ,			A__ = 0       ,			A__ = None       ,			A__ = None       ,			A__=True       ,			):
						A__				:       Union[str, Any]	  =						steps or self.get_default_steps()
						self.scheduler.set_timesteps(A__     )
						A__				:       Union[str, Any]	  =						step_generator or generator
						# For backwards compatibility
						if type(self.unet.config.sample_size     ) == int:
							A__				:       List[Any]	  =						(self.unet.config.sample_size, self.unet.config.sample_size)
						if noise is None:
							A__				:       Optional[Any]	  =						randn_tensor(
							    (
							        batch_size,
							        self.unet.config.in_channels,
							        self.unet.config.sample_size[0],
							        self.unet.config.sample_size[1],
							    )       ,			generator=A__       ,			device=self.device       ,			)
						A__				:       List[Any]	  =						noise
						A__				:       int	  =						None

						if audio_file is not None or raw_audio is not None:
							self.mel.load_audio(A__       ,			A__     )
							A__				:       Optional[int]	  =						self.mel.audio_slice_to_image(A__     )
							A__				:       Tuple	  =						np.frombuffer(input_image.tobytes()       ,			dtype="""uint8"""     ).reshape(
							    (input_image.height, input_image.width)     )
							A__				:       List[Any]	  =						(input_image / 255) * 2 - 1
							A__				:       Any	  =						torch.tensor(input_image[np.newaxis, :, :]       ,			dtype=torch.float     ).to(self.device     )

							if self.vqvae is not None:
								A__				:       int	  =						self.vqvae.encode(torch.unsqueeze(A__       ,			0     )     ).latent_dist.sample(
								    generator=A__     )[0]
								A__				:       Optional[int]	  =						self.vqvae.config.scaling_factor * input_images

							if start_step > 0:
								A__				:       str	  =						self.scheduler.add_noise(A__       ,			A__       ,			self.scheduler.timesteps[start_step - 1]     )

							A__				:       Any	  =						(
							    self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
							)
							A__				:       Tuple	  =						int(mask_start_secs * pixels_per_second     )
							A__				:       Optional[Any]	  =						int(mask_end_secs * pixels_per_second     )
							A__				:       List[str]	  =						self.scheduler.add_noise(A__       ,			A__       ,			torch.tensor(self.scheduler.timesteps[start_step:]     )     )

						for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]     )     ):
							if isinstance(self.unet       ,			A__     ):
								A__				:       List[str]	  =						self.unet(A__       ,			A__       ,			A__     )["""sample"""]
							else:
								A__				:       Tuple	  =						self.unet(A__       ,			A__     )["""sample"""]

							if isinstance(self.scheduler       ,			A__     ):
								A__				:       List[Any]	  =						self.scheduler.step(
								    model_output=A__       ,			timestep=A__       ,			sample=A__       ,			eta=A__       ,			generator=A__       ,			)["""prev_sample"""]
							else:
								A__				:       List[Any]	  =						self.scheduler.step(
								    model_output=A__       ,			timestep=A__       ,			sample=A__       ,			generator=A__       ,			)["""prev_sample"""]

							if mask is not None:
								if mask_start > 0:
									A__				:       Union[str, Any]	  =						mask[:, step, :, :mask_start]
								if mask_end > 0:
									A__				:       Union[str, Any]	  =						mask[:, step, :, -mask_end:]

						if self.vqvae is not None:
							# 0.18215 was scaling factor used in training to ensure unit variance
							A__				:       Any	  =						1 / self.vqvae.config.scaling_factor * images
							A__				:       int	  =						self.vqvae.decode(A__     )["""sample"""]

						A__				:       Optional[Any]	  =						(images / 2 + 0.5).clamp(0       ,			1     )
						A__				:       List[str]	  =						images.cpu().permute(0       ,			2       ,			3       ,			1     ).numpy()
						A__				:       List[str]	  =						(images * 255).round().astype("""uint8"""     )
						A__				:       Tuple	  =						list(
						    (Image.fromarray(_[:, :, 0]     ) for _ in images)
						    if images.shape[3] == 1
						    else (Image.fromarray(A__       ,			mode="""RGB"""     ).convert("""L"""     ) for _ in images)     )

						A__				:       List[str]	  =						[self.mel.image_to_audio(A__     ) for _ in images]
						if not return_dict:
							return images, (self.mel.get_sample_rate(), audios)

						return BaseOutput(**AudioPipelineOutput(np.array(A__     )[:, np.newaxis, :]     )       ,			**ImagePipelineOutput(A__     )     )






					@torch.no_grad()
					def     __A     (					self       ,			A__       ,			A__ = 50     ):
						assert isinstance(self.scheduler       ,			A__     )
						self.scheduler.set_timesteps(A__     )
						A__				:       Any	  =						np.array(
						    [np.frombuffer(image.tobytes()       ,			dtype="""uint8"""     ).reshape((1, image.height, image.width)     ) for image in images]     )
						A__				:       Optional[int]	  =						(sample / 255) * 2 - 1
						A__				:       int	  =						torch.Tensor(A__     ).to(self.device     )

						for t in self.progress_bar(torch.flip(self.scheduler.timesteps       ,			(0,)     )     ):
							A__				:       List[Any]	  =						t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
							A__				:       List[Any]	  =						self.scheduler.alphas_cumprod[t]
							A__				:       Optional[int]	  =						(
							    self.scheduler.alphas_cumprod[prev_timestep]
							    if prev_timestep >= 0
							    else self.scheduler.final_alpha_cumprod
							)
							A__				:       Tuple	  =						1 - alpha_prod_t
							A__				:       List[str]	  =						self.unet(A__       ,			A__     )["""sample"""]
							A__				:       Tuple	  =						(1 - alpha_prod_t_prev) ** 0.5 * model_output
							A__				:       Any	  =						(sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
							A__				:       str	  =						sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

						return sample






					@staticmethod
					def     __A     (					A__       ,			A__       ,			A__     ):
						A__				:       str	  =						acos(torch.dot(torch.flatten(A__     )       ,			torch.flatten(A__     )     ) / torch.norm(A__     ) / torch.norm(A__     )     )
						return sin((1 - alpha) * theta     ) * xa / sin(A__     ) + sin(alpha * theta     ) * xa / sin(A__     )






import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


A_			:					Tuple    			=     logging.get_logger(__name__)

A_			:					Tuple    			=     {'vocab_file': 'spiece.model'}

A_			:					Optional[Any]    			=     {
    'vocab_file': {
        'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model',
        'google/bigbird-roberta-large': (
            'https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'
        ),
        'google/bigbird-base-trivia-itc': (
            'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'
        ),
    }
}

A_			:					Tuple    			=     {
    'google/bigbird-roberta-base': 4096,
    'google/bigbird-roberta-large': 4096,
    'google/bigbird-base-trivia-itc': 4096,
}



class        _a		(__magic_name__							):


					'''simple docstring'''






					UpperCAmelCase__:  Optional[int]    				=			VOCAB_FILES_NAMES
					UpperCAmelCase__:  str    				=			PRETRAINED_VOCAB_FILES_MAP
					UpperCAmelCase__:  List[str]    				=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					UpperCAmelCase__:  str    				=			['''input_ids''', '''attention_mask''']
					UpperCAmelCase__:  List[int]    				=			[]






					def __init__(					self       ,			A__       ,			A__="<unk>"       ,			A__="<s>"       ,			A__="</s>"       ,			A__="<pad>"       ,			A__="[SEP]"       ,			A__="[MASK]"       ,			A__="[CLS]"       ,			A__ = None       ,			**A__       ,			):
						A__				:       Tuple	  =						AddedToken(A__       ,			lstrip=A__       ,			rstrip=A__     ) if isinstance(A__       ,			A__     ) else bos_token
						A__				:       int	  =						AddedToken(A__       ,			lstrip=A__       ,			rstrip=A__     ) if isinstance(A__       ,			A__     ) else eos_token
						A__				:       Tuple	  =						AddedToken(A__       ,			lstrip=A__       ,			rstrip=A__     ) if isinstance(A__       ,			A__     ) else unk_token
						A__				:       str	  =						AddedToken(A__       ,			lstrip=A__       ,			rstrip=A__     ) if isinstance(A__       ,			A__     ) else pad_token
						A__				:       Dict	  =						AddedToken(A__       ,			lstrip=A__       ,			rstrip=A__     ) if isinstance(A__       ,			A__     ) else cls_token
						A__				:       Any	  =						AddedToken(A__       ,			lstrip=A__       ,			rstrip=A__     ) if isinstance(A__       ,			A__     ) else sep_token

						# Mask token behave like a normal word, i.e. include the space before it
						A__				:       Optional[Any]	  =						AddedToken(A__       ,			lstrip=A__       ,			rstrip=A__     ) if isinstance(A__       ,			A__     ) else mask_token

						A__				:       Optional[Any]	  =						{} if sp_model_kwargs is None else sp_model_kwargs

						super().__init__(
						    bos_token=A__       ,			eos_token=A__       ,			unk_token=A__       ,			pad_token=A__       ,			sep_token=A__       ,			mask_token=A__       ,			cls_token=A__       ,			sp_model_kwargs=self.sp_model_kwargs       ,			**A__       ,			)

						A__				:       Any	  =						vocab_file

						A__				:       Optional[int]	  =						spm.SentencePieceProcessor(**self.sp_model_kwargs     )
						self.sp_model.Load(A__     )






					@property
					def     __A     (					self     ):
						return self.sp_model.get_piece_size()






					def     __A     (					self     ):
						A__				:       List[str]	  =						{self.convert_ids_to_tokens(A__     ): i for i in range(self.vocab_size     )}
						vocab.update(self.added_tokens_encoder     )
						return vocab






					def __getstate__(					self     ):
						A__				:       Optional[Any]	  =						self.__dict__.copy()
						A__				:       Optional[Any]	  =						None
						return state






					def __setstate__(					self       ,			A__     ):
						A__				:       Dict	  =						d

						# for backward compatibility
						if not hasattr(self       ,			"""sp_model_kwargs"""     ):
							A__				:       str	  =						{}

						A__				:       Tuple	  =						spm.SentencePieceProcessor(**self.sp_model_kwargs     )
						self.sp_model.Load(self.vocab_file     )






					def     __A     (					self       ,			A__     ):
						return self.sp_model.encode(A__       ,			out_type=A__     )






					def     __A     (					self       ,			A__     ):
						return self.sp_model.piece_to_id(A__     )






					def     __A     (					self       ,			A__     ):
						A__				:       Dict	  =						self.sp_model.IdToPiece(A__     )
						return token






					def     __A     (					self       ,			A__     ):
						A__				:       str	  =						[]
						A__				:       Optional[int]	  =						""""""
						A__				:       Any	  =						False
						for token in tokens:
							# make sure that special tokens are not decoded using sentencepiece model
							if token in self.all_special_tokens:
								if not prev_is_special:
									out_string += " "
								out_string += self.sp_model.decode(A__     ) + token
								A__				:       Union[str, Any]	  =						True
								A__				:       Union[str, Any]	  =						[]
							else:
								current_sub_tokens.append(A__     )
								A__				:       Any	  =						False
						out_string += self.sp_model.decode(A__     )
						return out_string.strip()






					def     __A     (					self       ,			A__       ,			A__ = False       ,			A__ = None       ,			A__ = True       ,			**A__       ,			):
						A__				:       List[str]	  =						kwargs.pop("""use_source_tokenizer"""       ,			A__     )

						A__				:       Union[str, Any]	  =						self.convert_ids_to_tokens(A__       ,			skip_special_tokens=A__     )

						# To avoid mixing byte-level and unicode for byte-level BPT
						# we need to build string separately for added tokens and byte-level tokens
						# cf. https://github.com/huggingface/transformers/issues/1133
						A__				:       Dict	  =						[]
						A__				:       List[Any]	  =						[]
						for token in filtered_tokens:
							if skip_special_tokens and token in self.all_special_ids:
								continue
							if token in self.added_tokens_encoder:
								if current_sub_text:
									sub_texts.append(self.convert_tokens_to_string(A__     )     )
									A__				:       Optional[Any]	  =						[]
								sub_texts.append(A__     )
							else:
								current_sub_text.append(A__     )
						if current_sub_text:
							sub_texts.append(self.convert_tokens_to_string(A__     )     )

						# Mimic the behavior of the Rust tokenizer:
						# No space before [MASK] and [SEP]
						if spaces_between_special_tokens:
							A__				:       List[Any]	  =						re.sub(r""" (\[(MASK|SEP)\])"""       ,			r"""\1"""       ,			""" """.join(A__     )     )
						else:
							A__				:       Optional[Any]	  =						"""""".join(A__     )

						A__				:       Union[str, Any]	  =						(
						    clean_up_tokenization_spaces
						    if clean_up_tokenization_spaces is not None
						    else self.clean_up_tokenization_spaces
						)
						if clean_up_tokenization_spaces:
							A__				:       Union[str, Any]	  =						self.clean_up_tokenization(A__     )
							return clean_text
						else:
							return text






					def     __A     (					self       ,			A__       ,			A__ = None     ):
						if not os.path.isdir(A__     ):
							logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""     )
							return
						A__				:       str	  =						os.path.join(
						    A__       ,			(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]     )

						if os.path.abspath(self.vocab_file     ) != os.path.abspath(A__     ) and os.path.isfile(self.vocab_file     ):
							copyfile(self.vocab_file       ,			A__     )
						elif not os.path.isfile(self.vocab_file     ):
							with open(A__       ,			"""wb"""     ) as fi:
								A__				:       Dict	  =						self.sp_model.serialized_model_proto()
								fi.write(A__     )

						return (out_vocab_file,)






					def     __A     (					self       ,			A__       ,			A__ = None     ):
						if token_ids_a is None:
							return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
						A__				:       List[Any]	  =						[self.cls_token_id]
						A__				:       List[Any]	  =						[self.sep_token_id]
						return cls + token_ids_a + sep + token_ids_a + sep






					def     __A     (					self       ,			A__       ,			A__ = None       ,			A__ = False     ):
						if already_has_special_tokens:
							return super().get_special_tokens_mask(
							    token_ids_a=A__       ,			token_ids_a=A__       ,			already_has_special_tokens=A__     )

						if token_ids_a is None:
							return [1] + ([0] * len(A__     )) + [1]
						return [1] + ([0] * len(A__     )) + [1] + ([0] * len(A__     )) + [1]






					def     __A     (					self       ,			A__       ,			A__ = None     ):
						A__				:       Any	  =						[self.sep_token_id]
						A__				:       List[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]



