




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 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							a__						  =     ['''vqvae''']





							def __init__(					self       :	List[Any]       ,     a       :	AutoencoderKL       ,     a       :	UNetaDConditionModel       ,     a       :	Mel       ,     a       :	Union[DDIMScheduler, DDPMScheduler]       ,     )			->					Optional[int]:
										super().__init__()
										self.register_modules(unet=a       ,     scheduler=a       ,     mel=a       ,     vqvae=a			)





							def    _UpperCAmelCase     (					self       :	List[Any]			)			->					int:
										return 50 if isinstance(self.scheduler       ,     a			) else 1_000





							@torch.no_grad()
							def __call__(					self       :	Union[str, Any]       ,     a       :	int = 1       ,     a       :	str = None       ,     a       :	np.ndarray = None       ,     a       :	int = 0       ,     a       :	int = 0       ,     a       :	int = None       ,     a       :	torch.Generator = None       ,     a       :	float = 0       ,     a       :	float = 0       ,     a       :	torch.Generator = None       ,     a       :	float = 0       ,     a       :	torch.Tensor = None       ,     a       :	torch.Tensor = None       ,     a       :	Optional[Any]=True       ,     )			->					Union[
							    Union[AudioPipelineOutput, ImagePipelineOutput],
							    Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
							]:
										SCREAMING_SNAKE_CASE           =					steps or self.get_default_steps()
										self.scheduler.set_timesteps(a			)
										SCREAMING_SNAKE_CASE           =					step_generator or generator
										# For backwards compatibility
										if type(self.unet.config.sample_size			) == int:
													SCREAMING_SNAKE_CASE           =					(self.unet.config.sample_size, self.unet.config.sample_size)
										if noise is None:
													SCREAMING_SNAKE_CASE           =					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       ,     )
										SCREAMING_SNAKE_CASE           =					noise
										SCREAMING_SNAKE_CASE           =					None

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

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

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

													SCREAMING_SNAKE_CASE           =					(
													    self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
													)
													SCREAMING_SNAKE_CASE           =					int(mask_start_secs * pixels_per_second			)
													SCREAMING_SNAKE_CASE           =					int(mask_end_secs * pixels_per_second			)
													SCREAMING_SNAKE_CASE           =					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			):
																SCREAMING_SNAKE_CASE           =					self.unet(a       ,     a       ,     a			)["""sample"""]
													else:
																SCREAMING_SNAKE_CASE           =					self.unet(a       ,     a			)["""sample"""]

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

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

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

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

										SCREAMING_SNAKE_CASE           =					[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    _UpperCAmelCase     (					self       :	List[str]       ,     a       :	List[Image.Image]       ,     a       :	int = 50			)			->					np.ndarray:
										assert isinstance(self.scheduler       ,     a			)
										self.scheduler.set_timesteps(a			)
										SCREAMING_SNAKE_CASE           =					np.array(
										    [np.frombuffer(image.tobytes()       ,     dtype="""uint8"""			).reshape((1, image.height, image.width)			) for image in images]			)
										SCREAMING_SNAKE_CASE           =					(sample / 255) * 2 - 1
										SCREAMING_SNAKE_CASE           =					torch.Tensor(a			).to(self.device			)

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

										return sample







							@staticmethod
							def    _UpperCAmelCase     (					a       :	torch.Tensor       ,     a       :	torch.Tensor       ,     a       :	float			)			->					torch.Tensor:
										SCREAMING_SNAKE_CASE           =					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
from shutil import copyfile
from typing import List, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_mbart import MBartTokenizer
else:
		__A						:							Tuple							=  None


__A						:							Any							=  logging.get_logger(__name__)


__A						:							Union[str, Any]							=  {"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

__A						:							List[Any]							=  {
    """vocab_file""": {
        """facebook/mbart-large-en-ro""": (
            """https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model"""
        ),
        """facebook/mbart-large-cc25""": (
            """https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model"""
        ),
    },
    """tokenizer_file""": {
        """facebook/mbart-large-en-ro""": """https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json""",
        """facebook/mbart-large-cc25""": """https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json""",
    },
}

__A						:							int							=  {
    """facebook/mbart-large-en-ro""": 1_0_2_4,
    """facebook/mbart-large-cc25""": 1_0_2_4,
}

# fmt: off
__A						:							List[Any]							=  ["""ar_AR""", """cs_CZ""", """de_DE""", """en_XX""", """es_XX""", """et_EE""", """fi_FI""", """fr_XX""", """gu_IN""", """hi_IN""", """it_IT""", """ja_XX""", """kk_KZ""", """ko_KR""", """lt_LT""", """lv_LV""", """my_MM""", """ne_NP""", """nl_XX""", """ro_RO""", """ru_RU""", """si_LK""", """tr_TR""", """vi_VN""", """zh_CN"""]




class 			UpperCAmelCase_      (   A       ):




							'''simple docstring'''



							a__						  =     VOCAB_FILES_NAMES
							a__						  =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							a__						  =     PRETRAINED_VOCAB_FILES_MAP
							a__						  =     ['''input_ids''', '''attention_mask''']
							a__						  =     MBartTokenizer

							a__						  =     []
							a__						  =     []





							def __init__(					self       :	str       ,     a       :	List[Any]=None       ,     a       :	Optional[int]=None       ,     a       :	str="<s>"       ,     a       :	Tuple="</s>"       ,     a       :	Optional[int]="</s>"       ,     a       :	int="<s>"       ,     a       :	List[str]="<unk>"       ,     a       :	Tuple="<pad>"       ,     a       :	Dict="<mask>"       ,     a       :	Optional[Any]=None       ,     a       :	List[Any]=None       ,     a       :	Any=None       ,     **a       :	Optional[Any]       ,     )			->					Any:
										# Mask token behave like a normal word, i.e. include the space before it
										SCREAMING_SNAKE_CASE           =					AddedToken(a       ,     lstrip=a       ,     rstrip=a			) if isinstance(a       ,     a			) else mask_token

										super().__init__(
										    vocab_file=a       ,     tokenizer_file=a       ,     bos_token=a       ,     eos_token=a       ,     sep_token=a       ,     cls_token=a       ,     unk_token=a       ,     pad_token=a       ,     mask_token=a       ,     src_lang=a       ,     tgt_lang=a       ,     additional_special_tokens=a       ,     **a       ,     )

										SCREAMING_SNAKE_CASE           =					vocab_file
										SCREAMING_SNAKE_CASE           =					False if not self.vocab_file else True

										SCREAMING_SNAKE_CASE           =					FAIRSEQ_LANGUAGE_CODES.copy()

										if additional_special_tokens is not None:
													# Only add those special tokens if they are not already there.
													_additional_special_tokens.extend(
													    [t for t in additional_special_tokens if t not in _additional_special_tokens]			)

										self.add_special_tokens({"""additional_special_tokens""": _additional_special_tokens}			)
										SCREAMING_SNAKE_CASE           =					{
										    lang_code: self.convert_tokens_to_ids(a			) for lang_code in FAIRSEQ_LANGUAGE_CODES
										}

										SCREAMING_SNAKE_CASE           =					src_lang if src_lang is not None else """en_XX"""
										SCREAMING_SNAKE_CASE           =					self.convert_tokens_to_ids(self._src_lang			)
										SCREAMING_SNAKE_CASE           =					tgt_lang
										self.set_src_lang_special_tokens(self._src_lang			)





							@property
							def    _UpperCAmelCase     (					self       :	int			)			->					str:
										return self._src_lang





							@src_lang.setter
							def    _UpperCAmelCase     (					self       :	List[Any]       ,     a       :	str			)			->					None:
										SCREAMING_SNAKE_CASE           =					new_src_lang
										self.set_src_lang_special_tokens(self._src_lang			)





							def    _UpperCAmelCase     (					self       :	Dict       ,     a       :	List[int]       ,     a       :	Optional[List[int]] = None			)			->					List[int]:
										if token_ids_a is None:
													return self.prefix_tokens + token_ids_a + self.suffix_tokens
										# We don't expect to process pairs, but leave the pair logic for API consistency
										return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens





							def    _UpperCAmelCase     (					self       :	Tuple       ,     a       :	List[int]       ,     a       :	Optional[List[int]] = None			)			->					List[int]:
										SCREAMING_SNAKE_CASE           =					[self.sep_token_id]
										SCREAMING_SNAKE_CASE           =					[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    _UpperCAmelCase     (					self       :	Optional[Any]       ,     a       :	Optional[int]       ,     a       :	str       ,     a       :	Optional[str]       ,     a       :	Optional[str]       ,     **a       :	Optional[Any]			)			->					Optional[Any]:
										if src_lang is None or tgt_lang is None:
													raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model"""			)
										SCREAMING_SNAKE_CASE           =					src_lang
										SCREAMING_SNAKE_CASE           =					self(a       ,     add_special_tokens=a       ,     return_tensors=a       ,     **a			)
										SCREAMING_SNAKE_CASE           =					self.convert_tokens_to_ids(a			)
										SCREAMING_SNAKE_CASE           =					tgt_lang_id
										return inputs





							def    _UpperCAmelCase     (					self       :	str       ,     a       :	List[str]       ,     a       :	str = "en_XX"       ,     a       :	Optional[List[str]] = None       ,     a       :	str = "ro_RO"       ,     **a       :	List[str]       ,     )			->					BatchEncoding:
										SCREAMING_SNAKE_CASE           =					src_lang
										SCREAMING_SNAKE_CASE           =					tgt_lang
										return super().prepare_seqaseq_batch(a       ,     a       ,     **a			)





							def    _UpperCAmelCase     (					self       :	Optional[Any]			)			->					str:
										return self.set_src_lang_special_tokens(self.src_lang			)





							def    _UpperCAmelCase     (					self       :	str			)			->					List[str]:
										return self.set_tgt_lang_special_tokens(self.tgt_lang			)





							def    _UpperCAmelCase     (					self       :	Dict       ,     a       :	List[str]			)			->					None:
										SCREAMING_SNAKE_CASE           =					self.convert_tokens_to_ids(a			)
										SCREAMING_SNAKE_CASE           =					[]
										SCREAMING_SNAKE_CASE           =					[self.eos_token_id, self.cur_lang_code]

										SCREAMING_SNAKE_CASE           =					self.convert_ids_to_tokens(self.prefix_tokens			)
										SCREAMING_SNAKE_CASE           =					self.convert_ids_to_tokens(self.suffix_tokens			)

										SCREAMING_SNAKE_CASE           =					processors.TemplateProcessing(
										    single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str       ,     pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str       ,     special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str       ,     self.prefix_tokens + self.suffix_tokens			)			)       ,     )





							def    _UpperCAmelCase     (					self       :	Dict       ,     a       :	str			)			->					None:
										SCREAMING_SNAKE_CASE           =					self.convert_tokens_to_ids(a			)
										SCREAMING_SNAKE_CASE           =					[]
										SCREAMING_SNAKE_CASE           =					[self.eos_token_id, self.cur_lang_code]

										SCREAMING_SNAKE_CASE           =					self.convert_ids_to_tokens(self.prefix_tokens			)
										SCREAMING_SNAKE_CASE           =					self.convert_ids_to_tokens(self.suffix_tokens			)

										SCREAMING_SNAKE_CASE           =					processors.TemplateProcessing(
										    single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str       ,     pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str       ,     special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str       ,     self.prefix_tokens + self.suffix_tokens			)			)       ,     )







							def    _UpperCAmelCase     (					self       :	Dict       ,     a       :	str       ,     a       :	Optional[str] = None			)			->					Tuple[str]:
										if not self.can_save_slow_tokenizer:
													raise ValueError(
													    """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
													    """tokenizer."""			)

										if not os.path.isdir(a			):
													logger.error(f"""Vocabulary path ({save_directory}) should be a directory."""			)
													return
										SCREAMING_SNAKE_CASE           =					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			):
													copyfile(self.vocab_file       ,     a			)

										return (out_vocab_file,)






