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      lowerCamelCase_					(       UpperCamelCase__			):






    '''simple docstring'''






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







    def __init__(     self			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase			,			)	->     Optional[Any]:
        super().__init__()
        self.register_modules(unet=UpperCamelCase_			,			scheduler=UpperCamelCase_			,			mel=UpperCamelCase_			,			vqvae=UpperCamelCase_)







    def       UpperCamelCase__       (     self)	->     int:
        return 50 if isinstance(self.scheduler			,			UpperCamelCase_) else 1_000







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

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

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

            if start_step > 0:
                __UpperCamelCase					:Union[str, Any]        =		self.scheduler.add_noise(UpperCamelCase_			,			UpperCamelCase_			,			self.scheduler.timesteps[start_step - 1])

            __UpperCamelCase					:List[Any]        =		(
                self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
            )
            __UpperCamelCase					:Optional[Any]        =		int(mask_start_secs * pixels_per_second)
            __UpperCamelCase					:Union[str, Any]        =		int(mask_end_secs * pixels_per_second)
            __UpperCamelCase					:Optional[int]        =		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_):
                __UpperCamelCase					:str        =		self.unet(UpperCamelCase_			,			UpperCamelCase_			,			UpperCamelCase_)['''sample''']
            else:
                __UpperCamelCase					:List[Any]        =		self.unet(UpperCamelCase_			,			UpperCamelCase_)['''sample''']

            if isinstance(self.scheduler			,			UpperCamelCase_):
                __UpperCamelCase					:Any        =		self.scheduler.step(
                    model_output=UpperCamelCase_			,			timestep=UpperCamelCase_			,			sample=UpperCamelCase_			,			eta=UpperCamelCase_			,			generator=UpperCamelCase_			,			)['''prev_sample''']
            else:
                __UpperCamelCase					:Optional[Any]        =		self.scheduler.step(
                    model_output=UpperCamelCase_			,			timestep=UpperCamelCase_			,			sample=UpperCamelCase_			,			generator=UpperCamelCase_			,			)['''prev_sample''']

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

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

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

        __UpperCamelCase					:int        =		[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       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase = 50)	->     np.ndarray:
        assert isinstance(self.scheduler			,			UpperCamelCase_)
        self.scheduler.set_timesteps(UpperCamelCase_)
        __UpperCamelCase					:Tuple        =		np.array(
            [np.frombuffer(image.tobytes()			,			dtype='''uint8''').reshape((1, image.height, image.width)) for image in images])
        __UpperCamelCase					:Optional[int]        =		(sample / 255) * 2 - 1
        __UpperCamelCase					:List[str]        =		torch.Tensor(UpperCamelCase_).to(self.device)

        for t in self.progress_bar(torch.flip(self.scheduler.timesteps			,			(0,))):
            __UpperCamelCase					:int        =		t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
            __UpperCamelCase					:Optional[int]        =		self.scheduler.alphas_cumprod[t]
            __UpperCamelCase					:int        =		(
                self.scheduler.alphas_cumprod[prev_timestep]
                if prev_timestep >= 0
                else self.scheduler.final_alpha_cumprod
            )
            __UpperCamelCase					:str        =		1 - alpha_prod_t
            __UpperCamelCase					:List[Any]        =		self.unet(UpperCamelCase_			,			UpperCamelCase_)['''sample''']
            __UpperCamelCase					:Dict        =		(1 - alpha_prod_t_prev) ** 0.5 * model_output
            __UpperCamelCase					:Dict        =		(sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
            __UpperCamelCase					:List[str]        =		sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

        return sample







    @staticmethod
    def       UpperCamelCase__       (     __lowercase			,			__lowercase			,			__lowercase)	->     torch.Tensor:
        __UpperCamelCase					:Optional[Any]        =		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 argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration



def lowerCamelCase  (       SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    __UpperCamelCase					:Optional[int]        =		[
        '''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(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )



def lowerCamelCase  (       SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    __UpperCamelCase  ,	__UpperCamelCase					:List[str]        =		emb.weight.shape
    __UpperCamelCase					:str        =		nn.Linear(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      bias=SCREAMING_SNAKE_CASE     )
    __UpperCamelCase					:Any        =		emb.weight.data
    return lin_layer



def lowerCamelCase  (       SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    __UpperCamelCase					:Dict        =		torch.load(SCREAMING_SNAKE_CASE  ,      map_location='''cpu'''     )
    __UpperCamelCase					:Tuple        =		mam_aaa['''args'''] or mam_aaa['''cfg''']['''model''']
    __UpperCamelCase					:Dict        =		mam_aaa['''model''']
    remove_ignore_keys_(SCREAMING_SNAKE_CASE     )
    __UpperCamelCase					:Dict        =		state_dict['''encoder.embed_tokens.weight'''].shape[0]

    __UpperCamelCase					:Dict        =		MaMaaaConfig(
        vocab_size=SCREAMING_SNAKE_CASE  ,      max_position_embeddings=1_024  ,      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'''  ,      )

    __UpperCamelCase					:Tuple        =		state_dict['''decoder.embed_tokens.weight''']
    __UpperCamelCase					:int        =		MaMaaaForConditionalGeneration(SCREAMING_SNAKE_CASE     )
    model.model.load_state_dict(SCREAMING_SNAKE_CASE  ,      strict=SCREAMING_SNAKE_CASE     )
    __UpperCamelCase					:Optional[Any]        =		make_linear_from_emb(model.model.shared     )

    return model


if __name__ == "__main__":
    __lowercase										= 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.''')
    __lowercase										= parser.parse_args()
    __lowercase										= convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
    model.save_pretrained(args.pytorch_dump_folder_path)





