




"""simple docstring"""

from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput

class        lowercase      (    __UpperCAmelCase):







				def __init__(							self   :					List[str]      ,     _lowerCamelCase   :					Dict      ,     _lowerCamelCase   :					Tuple       ):



					"""simple docstring"""

					super().__init__()
					self.register_modules(unet=_lowerCamelCase      ,     scheduler=_lowerCamelCase       )







				@torch.no_grad()
				def __call__(							self   :					Tuple      ,     _lowerCamelCase   :					int = 1      ,     _lowerCamelCase   :					Optional[torch.Generator] = None      ,     _lowerCamelCase   :					int = 50      ,     _lowerCamelCase   :					Optional[str] = "pil"      ,     _lowerCamelCase   :					bool = True      ,     **_lowerCamelCase   :					str      ,     ):



					"""simple docstring"""

					A_  :				Tuple	   =		torch.randn(
					    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)      ,     generator=_lowerCamelCase      ,     )
					A_  :				List[str]	   =		image.to(self.device       )

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

					for t in self.progress_bar(self.scheduler.timesteps       ):
						# 1. predict noise model_output
						A_  :				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
						A_  :				str	   =		self.scheduler.step(_lowerCamelCase      ,     _lowerCamelCase      ,     _lowerCamelCase       ).prev_sample

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

					if not return_dict:
						return (image,), "This is a local test"

					return ImagePipelineOutput(images=_lowerCamelCase       ), "This is a local test"












"""simple docstring"""

import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM

def 		lowercase_      (		_UpperCAmelCase	):




	"""simple docstring"""






	A_  :				Optional[Any]	   =		[
	    '''decoder.version''',
	    '''decoder.output_projection.weight''',
	    '''_float_tensor''',
	    '''decoder.embed_positions._float_tensor''',
	]
	for k in ignore_keys:
		state_dict.pop(_UpperCAmelCase  ,      _UpperCAmelCase	)

def 		lowercase_      (		_UpperCAmelCase	):




	"""simple docstring"""






	A_   , A_  :				List[Any]	   =		emb.weight.shape
	A_  :				List[Any]	   =		nn.Linear(_UpperCAmelCase  ,      _UpperCAmelCase  ,      bias=_UpperCAmelCase	)
	A_  :				Any	   =		emb.weight.data
	return lin_layer

def 		lowercase_      (		_UpperCAmelCase	):




	"""simple docstring"""






	A_  :				int	   =		torch.load(_UpperCAmelCase  ,      map_location='''cpu'''	)
	A_  :				Any	   =		Namespace(**checkpoint['''cfg''']['''model''']	)
	A_  :				List[str]	   =		checkpoint['''model''']
	remove_ignore_keys_(_UpperCAmelCase	)
	A_  :				Union[str, Any]	   =		state_dict['''decoder.embed_tokens.weight'''].shape[0]

	A_  :				List[str]	   =		{key.replace('''decoder'''  ,      '''model'''	): val for key, val in state_dict.items()}

	A_  :				int	   =		XGLMConfig(
	    vocab_size=_UpperCAmelCase  ,      max_position_embeddings=args.max_target_positions  ,      num_layers=args.decoder_layers  ,      attention_heads=args.decoder_attention_heads  ,      ffn_dim=args.decoder_ffn_embed_dim  ,      d_model=args.decoder_embed_dim  ,      layerdrop=args.decoder_layerdrop  ,      dropout=args.dropout  ,      attention_dropout=args.attention_dropout  ,      activation_dropout=args.activation_dropout  ,      activation_function='''gelu'''  ,      scale_embedding=not args.no_scale_embedding  ,      tie_word_embeddings=args.share_decoder_input_output_embed  ,      )

	A_  :				Any	   =		XGLMForCausalLM(_UpperCAmelCase	)
	A_  :				int	   =		model.load_state_dict(_UpperCAmelCase  ,      strict=_UpperCAmelCase	)
	print(_UpperCAmelCase	)
	A_  :				int	   =		make_linear_from_emb(model.model.embed_tokens	)

	return model


if __name__ == "__main__":
		_lowerCamelCase  :			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.')
		_lowerCamelCase  :			int        =       parser.parse_args()
		_lowerCamelCase  :			Optional[Any]        =       convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
		model.save_pretrained(args.pytorch_dump_folder_path)





