






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    __lowercase   (						lowercase_	):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE     =					["vqvae"]




     def __init__(					self							:      Optional[Any]			,						UpperCamelCase_							:      AutoencoderKL			,						UpperCamelCase_							:      UNetaDConditionModel			,						UpperCamelCase_							:      Mel			,						UpperCamelCase_							:      Union[DDIMScheduler, DDPMScheduler]			,						):


         """simple docstring"""



         super().__init__()
         self.register_modules(unet=UpperCamelCase_			,						scheduler=UpperCamelCase_			,						mel=UpperCamelCase_			,						vqvae=UpperCamelCase_					)




     def 		lowerCAmelCase_  (					self							:      Optional[Any]					):


         """simple docstring"""



         return 50 if isinstance(self.scheduler			,						UpperCamelCase_					) else 1_000




     @torch.no_grad()
     def __call__(					self							:      int			,						UpperCamelCase_							:      int = 1			,						UpperCamelCase_							:      str = None			,						UpperCamelCase_							:      np.ndarray = None			,						UpperCamelCase_							:      int = 0			,						UpperCamelCase_							:      int = 0			,						UpperCamelCase_							:      int = None			,						UpperCamelCase_							:      torch.Generator = None			,						UpperCamelCase_							:      float = 0			,						UpperCamelCase_							:      float = 0			,						UpperCamelCase_							:      torch.Generator = None			,						UpperCamelCase_							:      float = 0			,						UpperCamelCase_							:      torch.Tensor = None			,						UpperCamelCase_							:      torch.Tensor = None			,						UpperCamelCase_							:      Dict=True			,						):


         """simple docstring"""



         __A       =						steps or self.get_default_steps()
         self.scheduler.set_timesteps(UpperCamelCase_					)
         __A       =						step_generator or generator
         # For backwards compatibility
         if type(self.unet.config.sample_size					) == int:
             __A       =						(self.unet.config.sample_size, self.unet.config.sample_size)
         if noise is None:
             __A       =						randn_tensor(
                 (
                     batch_size,
                     self.unet.config.in_channels,
                     self.unet.config.sample_size[0],
                     self.unet.config.sample_size[1],
                 )			,						generator=UpperCamelCase_			,						device=self.device			,						)
         __A       =						noise
         __A       =						None

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

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

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

             __A       =						(
                 self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
             )
             __A       =						int(mask_start_secs * pixels_per_second					)
             __A       =						int(mask_end_secs * pixels_per_second					)
             __A       =						self.scheduler.add_noise(UpperCamelCase_			,						UpperCamelCase_			,						torch.tensor(self.scheduler.timesteps[start_step:]					)					)

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

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

             if mask is not None:
                 if mask_start > 0:
                     __A       =						mask[:, step, :, :mask_start]
                 if mask_end > 0:
                     __A       =						mask[:, step, :, -mask_end:]

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

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

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

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




     @torch.no_grad()
     def 		lowerCAmelCase_  (					self							:      List[str]			,						UpperCamelCase_							:      List[Image.Image]			,						UpperCamelCase_							:      int = 50					):


         """simple docstring"""



         assert isinstance(self.scheduler			,						UpperCamelCase_					)
         self.scheduler.set_timesteps(UpperCamelCase_					)
         __A       =						np.array(
             [np.frombuffer(image.tobytes()			,						dtype="""uint8"""					).reshape((1, image.height, image.width)					) for image in images]					)
         __A       =						(sample / 255) * 2 - 1
         __A       =						torch.Tensor(UpperCamelCase_					).to(self.device					)

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

         return sample





     @staticmethod
     def 		lowerCAmelCase_  (					UpperCamelCase_							:      torch.Tensor			,						UpperCamelCase_							:      torch.Tensor			,						UpperCamelCase_							:      float					):


         """simple docstring"""



         __A       =						acos(torch.dot(torch.flatten(UpperCamelCase_					)			,						torch.flatten(UpperCamelCase_					)					) / torch.norm(UpperCamelCase_					) / torch.norm(UpperCamelCase_					)					)
         return sin((1 - alpha) * theta					) * xa / sin(UpperCamelCase_					) + sin(alpha * theta					) * xa / sin(UpperCamelCase_					)












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


__a   :							Union[str, Any]						= logging.get_logger(__name__)

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

__a   :							Dict						= {
    "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"
        ),
    },
}

__a   :							List[str]						= {
    "roberta-base": 512,
    "roberta-large": 512,
    "roberta-large-mnli": 512,
    "distilroberta-base": 512,
    "roberta-base-openai-detector": 512,
    "roberta-large-openai-detector": 512,
}


class    __lowercase   (						lowercase_	):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE     =					VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE     =					PRETRAINED_VOCAB_FILES_MAP
     SCREAMING_SNAKE_CASE     =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     SCREAMING_SNAKE_CASE     =					["input_ids", "attention_mask"]
     SCREAMING_SNAKE_CASE     =					RobertaTokenizer




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


         """simple docstring"""



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

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

         __A       =						add_prefix_space

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

             __A       =						False

             if state.get("""add_prefix_space"""			,						UpperCamelCase_					) != add_prefix_space:
                 __A       =						add_prefix_space
                 __A       =						True

             if state.get("""trim_offsets"""			,						UpperCamelCase_					) != trim_offsets:
                 __A       =						trim_offsets
                 __A       =						True

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




     @property
     def 		lowerCAmelCase_  (					self							:      str					):


         """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 		lowerCAmelCase_  (					self							:      str			,						UpperCamelCase_							:      Tuple					):


         """simple docstring"""



         __A       =						AddedToken(UpperCamelCase_			,						lstrip=UpperCamelCase_			,						rstrip=UpperCamelCase_					) if isinstance(UpperCamelCase_			,						UpperCamelCase_					) else value
         __A       =						value




     def 		lowerCAmelCase_  (					self							:      Tuple			,						*UpperCamelCase_							:      Optional[Any]			,						**UpperCamelCase_							:      Dict					):


         """simple docstring"""



         __A       =						kwargs.get("""is_split_into_words"""			,						UpperCamelCase_					)
         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(*UpperCamelCase_			,						**UpperCamelCase_					)




     def 		lowerCAmelCase_  (					self							:      Union[str, Any]			,						*UpperCamelCase_							:      Optional[int]			,						**UpperCamelCase_							:      Tuple					):


         """simple docstring"""



         __A       =						kwargs.get("""is_split_into_words"""			,						UpperCamelCase_					)

         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(*UpperCamelCase_			,						**UpperCamelCase_					)




     def 		lowerCAmelCase_  (					self							:      int			,						UpperCamelCase_							:      str			,						UpperCamelCase_							:      Optional[str] = None					):


         """simple docstring"""



         __A       =						self._tokenizer.model.save(UpperCamelCase_			,						name=UpperCamelCase_					)
         return tuple(UpperCamelCase_					)




     def 		lowerCAmelCase_  (					self							:      Union[str, Any]			,						UpperCamelCase_							:      Any			,						UpperCamelCase_							:      int=None					):


         """simple docstring"""



         __A       =						[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 		lowerCAmelCase_  (					self							:      int			,						UpperCamelCase_							:      List[int]			,						UpperCamelCase_							:      Optional[List[int]] = None					):


         """simple docstring"""



         __A       =						[self.sep_token_id]
         __A       =						[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]




