



from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class 					snake_case__						(       __UpperCamelCase     ):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE						=	42
     _SCREAMING_SNAKE_CASE						=	42






     def __init__(       self  :  Any, _snake_case  :  UNetaDModel, _snake_case  :  KarrasVeScheduler					)   ->List[str]:
            super().__init__()
            self.register_modules(unet=_snake_case, scheduler=_snake_case					)






     @torch.no_grad()
     def __call__(       self  :  List[Any], _snake_case  :  int = 1, _snake_case  :  int = 5_0, _snake_case  :  Optional[Union[torch.Generator, List[torch.Generator]]] = None, _snake_case  :  Optional[str] = "pil", _snake_case  :  bool = True, **_snake_case  :  Tuple, )   ->Union[Tuple, ImagePipelineOutput]:
            snake_case__							:	Any   	=	self.unet.config.sample_size
            snake_case__							:	Any   	=	(batch_size, 3, img_size, img_size)

            snake_case__							:	Dict   	=	self.unet

            # sample x_0 ~ N(0, sigma_0^2 * I)
            snake_case__							:	Dict   	=	randn_tensor(_snake_case, generator=_snake_case, device=self.device					) * self.scheduler.init_noise_sigma

            self.scheduler.set_timesteps(_snake_case					)

            for t in self.progress_bar(self.scheduler.timesteps					):
                   # here sigma_t == t_i from the paper
                   snake_case__							:	List[Any]   	=	self.scheduler.schedule[t]
                   snake_case__							:	Any   	=	self.scheduler.schedule[t - 1] if t > 0 else 0

                   # 1. Select temporarily increased noise level sigma_hat
                   # 2. Add new noise to move from sample_i to sample_hat
                   snake_case__							:	Any   	=	self.scheduler.add_noise_to_input(_snake_case, _snake_case, generator=_snake_case					)

                   # 3. Predict the noise residual given the noise magnitude `sigma_hat`
                   # The model inputs and output are adjusted by following eq. (213) in [1].
                   snake_case__							:	Optional[Any]   	=	(sigma_hat / 2) * model((sample_hat + 1) / 2, sigma_hat / 2					).sample

                   # 4. Evaluate dx/dt at sigma_hat
                   # 5. Take Euler step from sigma to sigma_prev
                   snake_case__							:	Dict   	=	self.scheduler.step(_snake_case, _snake_case, _snake_case, _snake_case					)

                   if sigma_prev != 0:
                          # 6. Apply 2nd order correction
                          # The model inputs and output are adjusted by following eq. (213) in [1].
                          snake_case__							:	Optional[Any]   	=	(sigma_prev / 2) * model((step_output.prev_sample + 1) / 2, sigma_prev / 2					).sample
                          snake_case__							:	Optional[int]   	=	self.scheduler.step_correct(
                              _snake_case, _snake_case, _snake_case, _snake_case, step_output.prev_sample, step_output['derivative'], )
                   snake_case__							:	Union[str, Any]   	=	step_output.prev_sample

            snake_case__							:	Optional[int]   	=	(sample / 2 + 0.5).clamp(0, 1					)
            snake_case__							:	Union[str, Any]   	=	sample.cpu().permute(0, 2, 3, 1					).numpy()
            if output_type == "pil":
                   snake_case__							:	List[Any]   	=	self.numpy_to_pil(_snake_case					)

            if not return_dict:
                   return (image,)

            return ImagePipelineOutput(images=_snake_case					)













import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     str:
    lowerCamelCase__    :      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(_UpperCAmelCase						,	_UpperCAmelCase )


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     List[Any]:
    lowerCamelCase__ ,							lowerCamelCase__    :      List[str]					 =		emb.weight.shape
    lowerCamelCase__    :      Tuple					 =		nn.Linear(_UpperCAmelCase						,	_UpperCAmelCase						,	bias=_UpperCAmelCase )
    lowerCamelCase__    :      Dict					 =		emb.weight.data
    return lin_layer


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase )			->     str:
    lowerCamelCase__    :      Tuple					 =		torch.load(_UpperCAmelCase						,	map_location='cpu' )
    lowerCamelCase__    :      List[str]					 =		mam_aaa['args'] or mam_aaa['cfg']['model']
    lowerCamelCase__    :      Optional[int]					 =		mam_aaa['model']
    remove_ignore_keys_(_UpperCAmelCase )
    lowerCamelCase__    :      str					 =		state_dict['encoder.embed_tokens.weight'].shape[0]

    lowerCamelCase__    :      Union[str, Any]					 =		MaMaaaConfig(
        vocab_size=_UpperCAmelCase						,	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__    :      Optional[Any]					 =		state_dict['decoder.embed_tokens.weight']
    lowerCamelCase__    :      Union[str, Any]					 =		MaMaaaForConditionalGeneration(_UpperCAmelCase )
    model.model.load_state_dict(_UpperCAmelCase						,	strict=_UpperCAmelCase )
    lowerCamelCase__    :      List[str]					 =		make_linear_from_emb(model.model.shared )

    return model


if __name__ == "__main__":
    _UpperCAmelCase				:       Tuple	   =				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				:       str	   =				parser.parse_args()
    _UpperCAmelCase				:       Optional[Any]	   =				convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
    model.save_pretrained(args.pytorch_dump_folder_path)





