






'''simple docstring'''







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'''





		_UpperCamelCase				  =					['vqvae']






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






		def 			UpperCamelCase_					(      self       ):
								return 50 if isinstance(self.scheduler   ,_lowerCAmelCase       ) else 10_00






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

								if audio_file is not None or raw_audio is not None:
														self.mel.load_audio(_lowerCAmelCase   ,_lowerCAmelCase       )
														lowerCamelCase__         =  self.mel.audio_slice_to_image(_lowerCAmelCase       )
														lowerCamelCase__         =  np.frombuffer(input_image.tobytes()   ,dtype="""uint8"""       ).reshape(
														    (input_image.height, input_image.width)       )
														lowerCamelCase__         =  (input_image / 2_55) * 2 - 1
														lowerCamelCase__         =  torch.tensor(input_image[np.newaxis, :, :]   ,dtype=torch.float       ).to(self.device       )

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

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

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

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

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

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

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

								lowerCamelCase__         =  (images / 2 + 0.5).clamp(0   ,1       )
								lowerCamelCase__         =  images.cpu().permute(0   ,2   ,3   ,1       ).numpy()
								lowerCamelCase__         =  (images * 2_55).round().astype("""uint8"""       )
								lowerCamelCase__         =  list(
								    (Image.fromarray(_[:, :, 0]       ) for _ in images)
								    if images.shape[3] == 1
								    else (Image.fromarray(_lowerCAmelCase   ,mode="""RGB"""       ).convert("""L"""       ) for _ in images)       )

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

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






		@torch.no_grad()
		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,_lowerCAmelCase = 50       ):
								assert isinstance(self.scheduler   ,_lowerCAmelCase       )
								self.scheduler.set_timesteps(_lowerCAmelCase       )
								lowerCamelCase__         =  np.array(
								    [np.frombuffer(image.tobytes()   ,dtype="""uint8"""       ).reshape((1, image.height, image.width)       ) for image in images]       )
								lowerCamelCase__         =  (sample / 2_55) * 2 - 1
								lowerCamelCase__         =  torch.Tensor(_lowerCAmelCase       ).to(self.device       )

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

								return sample






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









'''simple docstring'''







import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def 						A__			(				__lowerCAmelCase		:			Union[str, Any]						):
						lowerCamelCase__         =  [
						    """encoder.version""",
						    """decoder.version""",
						    """model.encoder.version""",
						    """model.decoder.version""",
						    """decoder.output_projection.weight""",
						    """_float_tensor""",
						    """encoder.embed_positions._float_tensor""",
						    """decoder.embed_positions._float_tensor""",
						]
						for k in ignore_keys:
												state_dict.pop(__lowerCAmelCase   ,    __lowerCAmelCase						)
def 						A__			(				__lowerCAmelCase		:			Tuple						):
						lowerCamelCase__       ,			lowerCamelCase__         =  emb.weight.shape
						lowerCamelCase__         =  nn.Linear(__lowerCAmelCase   ,    __lowerCAmelCase   ,    bias=__lowerCAmelCase						)
						lowerCamelCase__         =  emb.weight.data
						return lin_layer
def 						A__			(				__lowerCAmelCase		:			Dict						):
						lowerCamelCase__         =  torch.load(__lowerCAmelCase   ,    map_location="""cpu"""						)
						lowerCamelCase__         =  mam_aaa["""args"""] or mam_aaa["""cfg"""]["""model"""]
						lowerCamelCase__         =  mam_aaa["""model"""]
						remove_ignore_keys_(__lowerCAmelCase						)
						lowerCamelCase__         =  state_dict["""encoder.embed_tokens.weight"""].shape[0]

						lowerCamelCase__         =  MaMaaaConfig(
						    vocab_size=__lowerCAmelCase   ,    max_position_embeddings=1024   ,    encoder_layers=args.encoder_layers   ,    decoder_layers=args.decoder_layers   ,    encoder_attention_heads=args.encoder_attention_heads   ,    decoder_attention_heads=args.decoder_attention_heads   ,    encoder_ffn_dim=args.encoder_ffn_embed_dim   ,    decoder_ffn_dim=args.decoder_ffn_embed_dim   ,    d_model=args.encoder_embed_dim   ,    encoder_layerdrop=args.encoder_layerdrop   ,    decoder_layerdrop=args.decoder_layerdrop   ,    dropout=args.dropout   ,    attention_dropout=args.attention_dropout   ,    activation_dropout=args.activation_dropout   ,    activation_function="""relu"""   ,    )

						lowerCamelCase__         =  state_dict["""decoder.embed_tokens.weight"""]
						lowerCamelCase__         =  MaMaaaForConditionalGeneration(__lowerCAmelCase						)
						model.model.load_state_dict(__lowerCAmelCase   ,    strict=__lowerCAmelCase						)
						lowerCamelCase__         =  make_linear_from_emb(model.model.shared						)

						return model


if __name__ == "__main__":
							UpperCamelCase    :      Optional[Any]  			=		argparse.ArgumentParser()
							# Required parameters
							parser.add_argument('fairseq_path', type=str, help='path to a model.pt on local filesystem.')
							parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
							UpperCamelCase    :      Tuple  			=		parser.parse_args()
							UpperCamelCase    :      List[Any]  			=		convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
							model.save_pretrained(args.pytorch_dump_folder_path)

