


'''simple docstring'''





from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class 				UpperCAmelCase			(			_snake_case						):


 def __init__(      self							:	int			,       __lowerCamelCase							:	List[Any]			,       __lowerCamelCase							:	Optional[int] ):
    super().__init__()

    # make sure scheduler can always be converted to DDIM
    UpperCAmelCase__	:Optional[int]      =      DDIMScheduler.from_config(scheduler.config )

    self.register_modules(unet=__lowerCamelCase			,       scheduler=__lowerCamelCase )





 @torch.no_grad()
 def __call__(      self							:	str			,       __lowerCamelCase							:	int = 1			,       __lowerCamelCase							:	Optional[Union[torch.Generator, List[torch.Generator]]] = None			,       __lowerCamelCase							:	float = 0.0			,       __lowerCamelCase							:	int = 5_0			,       __lowerCamelCase							:	Optional[bool] = None			,       __lowerCamelCase							:	Optional[str] = "pil"			,       __lowerCamelCase							:	bool = True			,       ):

    # Sample gaussian noise to begin loop
    if isinstance(self.unet.config.sample_size			,       __lowerCamelCase ):
       UpperCAmelCase__	:Optional[int]      =      (
           batch_size,
           self.unet.config.in_channels,
           self.unet.config.sample_size,
           self.unet.config.sample_size,
       )
    else:
       UpperCAmelCase__	:int      =      (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

    if isinstance(__lowerCamelCase			,       __lowerCamelCase ) and len(__lowerCamelCase ) != batch_size:
       raise ValueError(
           f'''You have passed a list of generators of length {len(__lowerCamelCase )}, but requested an effective batch'''
           f''' size of {batch_size}. Make sure the batch size matches the length of the generators.''' )

    UpperCAmelCase__	:Dict      =      randn_tensor(__lowerCamelCase			,       generator=__lowerCamelCase			,       device=self.device			,       dtype=self.unet.dtype )

    # set step values
    self.scheduler.set_timesteps(__lowerCamelCase )

    for t in self.progress_bar(self.scheduler.timesteps ):
       # 1. predict noise model_output
       UpperCAmelCase__	:List[str]      =      self.unet(__lowerCamelCase			,       __lowerCamelCase ).sample

       # 2. predict previous mean of image x_t-1 and add variance depending on eta
       # eta corresponds to η in paper and should be between [0, 1]
       # do x_t -> x_t-1
       UpperCAmelCase__	:Optional[int]      =      self.scheduler.step(
           __lowerCamelCase			,       __lowerCamelCase			,       __lowerCamelCase			,       eta=__lowerCamelCase			,       use_clipped_model_output=__lowerCamelCase			,       generator=__lowerCamelCase ).prev_sample

    UpperCAmelCase__	:Union[str, Any]      =      (image / 2 + 0.5).clamp(0			,       1 )
    UpperCAmelCase__	:List[str]      =      image.cpu().permute(0			,       2			,       3			,       1 ).numpy()
    if output_type == "pil":
       UpperCAmelCase__	:str      =      self.numpy_to_pil(__lowerCamelCase )

    if not return_dict:
       return (image,)

    return ImagePipelineOutput(images=__lowerCamelCase )







'''simple docstring'''





import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration


def        a__     (					UpperCamelCase_						:      Tuple						):
   UpperCAmelCase__	:Dict      =      [
       '''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        a__     (					UpperCamelCase_						:      Union[str, Any]						):
   UpperCAmelCase__   ,						UpperCAmelCase__	:Optional[Any]      =      emb.weight.shape
   UpperCAmelCase__	:int      =      nn.Linear(UpperCamelCase_,   UpperCamelCase_,   bias=UpperCamelCase_						)
   UpperCAmelCase__	:Tuple      =      emb.weight.data
   return lin_layer


def        a__     (					UpperCamelCase_						:      str						):
   UpperCAmelCase__	:List[str]      =      torch.load(UpperCamelCase_,   map_location='''cpu'''						)
   UpperCAmelCase__	:List[Any]      =      mam_aaa['''args'''] or mam_aaa['''cfg''']['''model''']
   UpperCAmelCase__	:List[Any]      =      mam_aaa['''model''']
   remove_ignore_keys_(UpperCamelCase_						)
   UpperCAmelCase__	:Tuple      =      state_dict['''encoder.embed_tokens.weight'''].shape[0]

   UpperCAmelCase__	:Union[str, Any]      =      MaMaaaConfig(
       vocab_size=UpperCamelCase_,   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__	:str      =      state_dict['''decoder.embed_tokens.weight''']
   UpperCAmelCase__	:Tuple      =      MaMaaaForConditionalGeneration(UpperCamelCase_						)
   model.model.load_state_dict(UpperCamelCase_,   strict=UpperCamelCase_						)
   UpperCAmelCase__	:Optional[int]      =      make_linear_from_emb(model.model.shared						)

   return model


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



