

from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

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


_lowerCAmelCase					:			Union[str, Any]  				=			transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)






def        UpperCamelCase_(							_snake_case   :  Dict	):




			"""simple docstring"""






			if isinstance(_snake_case       ,     torch.Tensor	):
						return image
			elif isinstance(_snake_case       ,     PIL.Image.Image	):
						__a        =[image]

			__a        =[trans(img.convert('RGB'	)	) for img in image]
			__a        =torch.stack(_snake_case	)
			return image






class 	__magic_name__    (			lowerCAmelCase_      ):





			def __init__(		self					,  __snake_case					,  __snake_case					)   -> List[Any]:




						'''simple docstring'''
						super().__init__()

						# make sure scheduler can always be converted to DDIM
						__a        =DDIMScheduler.from_config(scheduler.config					)

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





			def 		__magic_name__    (		self					,  __snake_case					)   -> int:




						'''simple docstring'''
						if strength < 0 or strength > 1:
									raise ValueError(f'The value of strength should in [0.0, 1.0] but is {strength}'					)





			def 		__magic_name__    (		self					,  __snake_case					,  __snake_case					,  __snake_case					)   -> List[str]:




						'''simple docstring'''
						# get the original timestep using init_timestep
						__a        =min(int(num_inference_steps * strength					)					,  __snake_case					)

						__a        =max(num_inference_steps - init_timestep					,  0					)
						__a        =self.scheduler.timesteps[t_start:]

						return timesteps, num_inference_steps - t_start





			def 		__magic_name__    (		self					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case					,  __snake_case=None					)   -> Any:




						'''simple docstring'''
						if not isinstance(__snake_case					,  (torch.Tensor, PIL.Image.Image, list)					):
									raise ValueError(
									    f'`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(__snake_case					)}'					)

						__a        =image.to(device=__snake_case					,  dtype=__snake_case					)

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

						__a        =init_latents.shape
						__a        =randn_tensor(__snake_case					,  generator=__snake_case					,  device=__snake_case					,  dtype=__snake_case					)

						# get latents
						print('add noise to latents at timestep'					,  __snake_case					)
						__a        =self.scheduler.add_noise(__snake_case					,  __snake_case					,  __snake_case					)
						__a        =init_latents

						return latents





			@torch.no_grad()
			def __call__(		self					,  __snake_case = None					,  __snake_case = 0.8					,  __snake_case = 1					,  __snake_case = None					,  __snake_case = 0.0					,  __snake_case = 50					,  __snake_case = None					,  __snake_case = "pil"					,  __snake_case = True					,  )   -> Union[ImagePipelineOutput, Tuple]:




						'''simple docstring'''
						self.check_inputs(__snake_case					)

						# 2. Preprocess image
						__a        =preprocess(__snake_case					)

						# 3. set timesteps
						self.scheduler.set_timesteps(__snake_case					,  device=self.device					)
						__a      ,    __a        =self.get_timesteps(__snake_case					,  __snake_case					,  self.device					)
						__a        =timesteps[:1].repeat(__snake_case					)

						# 4. Prepare latent variables
						__a        =self.prepare_latents(__snake_case					,  __snake_case					,  __snake_case					,  self.unet.dtype					,  self.device					,  __snake_case					)
						__a        =latents

						# 5. Denoising loop
						for t in self.progress_bar(__snake_case					):
									# 1. predict noise model_output
									__a        =self.unet(__snake_case					,  __snake_case					).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        =self.scheduler.step(
									    __snake_case					,  __snake_case					,  __snake_case					,  eta=__snake_case					,  use_clipped_model_output=__snake_case					,  generator=__snake_case					,  ).prev_sample

						__a        =(image / 2 + 0.5).clamp(0					,  1					)
						__a        =image.cpu().permute(0					,  2					,  3					,  1					).numpy()
						if output_type == "pil":
									__a        =self.numpy_to_pil(__snake_case					)

						if not return_dict:
									return (image, latent_timestep.item())

						return ImagePipelineOutput(images=__snake_case					)









import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM






def        UpperCamelCase_(							_snake_case   :  Union[str, Any]	):




			"""simple docstring"""






			__a        =[
			    'decoder.version',
			    'decoder.output_projection.weight',
			    '_float_tensor',
			    'decoder.embed_positions._float_tensor',
			]
			for k in ignore_keys:
						state_dict.pop(_snake_case       ,     _snake_case	)






def        UpperCamelCase_(							_snake_case   :  Any	):




			"""simple docstring"""






			__a      ,    __a        =emb.weight.shape
			__a        =nn.Linear(_snake_case       ,     _snake_case       ,     bias=_snake_case	)
			__a        =emb.weight.data
			return lin_layer






def        UpperCamelCase_(							_snake_case   :  str	):




			"""simple docstring"""






			__a        =torch.load(_snake_case       ,     map_location='cpu'	)
			__a        =Namespace(**checkpoint['cfg']['model']	)
			__a        =checkpoint['model']
			remove_ignore_keys_(_snake_case	)
			__a        =state_dict['decoder.embed_tokens.weight'].shape[0]

			__a        ={key.replace('decoder'       ,     'model'	): val for key, val in state_dict.items()}

			__a        =XGLMConfig(
			    vocab_size=_snake_case       ,     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        =XGLMForCausalLM(_snake_case	)
			__a        =model.load_state_dict(_snake_case       ,     strict=_snake_case	)
			print(_snake_case	)
			__a        =make_linear_from_emb(model.model.embed_tokens	)

			return model


if __name__ == "__main__":
						_lowerCAmelCase					:			str  				=			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					:			str  				=			parser.parse_args()
						_lowerCAmelCase					:			Union[str, Any]  				=			convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
						model.save_pretrained(args.pytorch_dump_folder_path)






