import inspect
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput



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







    __UpperCamelCase  ,	__UpperCamelCase					:Optional[Any]        =		image.size
    __UpperCamelCase  ,	__UpperCamelCase					:List[str]        =		(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
    __UpperCamelCase					:Tuple        =		image.resize((w, h)  ,      resample=PIL_INTERPOLATION['''lanczos''']     )
    __UpperCamelCase					:List[str]        =		np.array(SCREAMING_SNAKE_CASE     ).astype(np.floataa     ) / 255.0
    __UpperCamelCase					:List[Any]        =		image[None].transpose(0  ,      3  ,      1  ,      2     )
    __UpperCamelCase					:Optional[Any]        =		torch.from_numpy(SCREAMING_SNAKE_CASE     )
    return 2.0 * image - 1.0


class      lowerCamelCase_					(       UpperCAmelCase_			):






    '''simple docstring'''






    def __init__(     self			,			__lowercase			,			__lowercase			,			__lowercase			,			)	->     Any:
        super().__init__()
        self.register_modules(vqvae=__lowercase			,			unet=__lowercase			,			scheduler=__lowercase)







    @torch.no_grad()
    def __call__(     self			,			__lowercase = None			,			__lowercase = 1			,			__lowercase = 100			,			__lowercase = 0.0			,			__lowercase = None			,			__lowercase = "pil"			,			__lowercase = True			,			)	->     Union[Tuple, ImagePipelineOutput]:
        if isinstance(__lowercase			,			PIL.Image.Image):
            __UpperCamelCase					:int        =		1
        elif isinstance(__lowercase			,			torch.Tensor):
            __UpperCamelCase					:Any        =		image.shape[0]
        else:
            raise ValueError(f"""`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(__lowercase)}""")

        if isinstance(__lowercase			,			PIL.Image.Image):
            __UpperCamelCase					:List[str]        =		preprocess(__lowercase)

        __UpperCamelCase  ,	__UpperCamelCase					:Dict        =		image.shape[-2:]

        # in_channels should be 6: 3 for latents, 3 for low resolution image
        __UpperCamelCase					:Optional[int]        =		(batch_size, self.unet.config.in_channels // 2, height, width)
        __UpperCamelCase					:Dict        =		next(self.unet.parameters()).dtype

        __UpperCamelCase					:Dict        =		randn_tensor(__lowercase			,			generator=__lowercase			,			device=self.device			,			dtype=__lowercase)

        __UpperCamelCase					:Dict        =		image.to(device=self.device			,			dtype=__lowercase)

        # set timesteps and move to the correct device
        self.scheduler.set_timesteps(__lowercase			,			device=self.device)
        __UpperCamelCase					:int        =		self.scheduler.timesteps

        # scale the initial noise by the standard deviation required by the scheduler
        __UpperCamelCase					:Tuple        =		latents * self.scheduler.init_noise_sigma

        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
        # and should be between [0, 1]
        __UpperCamelCase					:Tuple        =		'''eta''' in set(inspect.signature(self.scheduler.step).parameters.keys())
        __UpperCamelCase					:List[str]        =		{}
        if accepts_eta:
            __UpperCamelCase					:int        =		eta

        for t in self.progress_bar(__lowercase):
            # concat latents and low resolution image in the channel dimension.
            __UpperCamelCase					:Optional[int]        =		torch.cat([latents, image]			,			dim=1)
            __UpperCamelCase					:int        =		self.scheduler.scale_model_input(__lowercase			,			__lowercase)
            # predict the noise residual
            __UpperCamelCase					:str        =		self.unet(__lowercase			,			__lowercase).sample
            # compute the previous noisy sample x_t -> x_t-1
            __UpperCamelCase					:Any        =		self.scheduler.step(__lowercase			,			__lowercase			,			__lowercase			,			**__lowercase).prev_sample

        # decode the image latents with the VQVAE
        __UpperCamelCase					:str        =		self.vqvae.decode(__lowercase).sample
        __UpperCamelCase					:Any        =		torch.clamp(__lowercase			,			-1.0			,			1.0)
        __UpperCamelCase					:Optional[Any]        =		image / 2 + 0.5
        __UpperCamelCase					:Optional[Any]        =		image.cpu().permute(0			,			2			,			3			,			1).numpy()

        if output_type == "pil":
            __UpperCamelCase					:int        =		self.numpy_to_pil(__lowercase)

        if not return_dict:
            return (image,)

        return ImagePipelineOutput(images=__lowercase)






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)





