





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   A			(SCREAMING_SNAKE_CASE    ):
			'''simple docstring'''




			__lowerCamelCase	:       UNetaDModel
			__lowerCamelCase	:       KarrasVeScheduler






			def __init__(			self   :    Optional[Any]							,	__lowerCAmelCase   :    UNetaDModel							,	__lowerCAmelCase   :    KarrasVeScheduler   )	->	Optional[Any]:







					"""simple docstring"""






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







			@torch.no_grad()
			def __call__(			self   :    Optional[int]							,	__lowerCAmelCase   :    int = 1							,	__lowerCAmelCase   :    int = 50							,	__lowerCAmelCase   :    Optional[Union[torch.Generator, List[torch.Generator]]] = None							,	__lowerCAmelCase   :    Optional[str] = "pil"							,	__lowerCAmelCase   :    bool = True							,	**__lowerCAmelCase   :    Union[str, Any]							,	)	->	Union[Tuple, ImagePipelineOutput]:







					"""simple docstring"""






					A__        =   self.unet.config.sample_size
					A__        =   (batch_size, 3, img_size, img_size)

					A__        =   self.unet

					# sample x_0 ~ N(0, sigma_0^2 * I)
					A__        =   randn_tensor(__lowerCAmelCase							,	generator=__lowerCAmelCase							,	device=self.device   ) * self.scheduler.init_noise_sigma

					self.scheduler.set_timesteps(__lowerCAmelCase   )

					for t in self.progress_bar(self.scheduler.timesteps   ):
							# here sigma_t == t_i from the paper
							A__        =   self.scheduler.schedule[t]
							A__        =   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
							A__      ,			A__        =   self.scheduler.add_noise_to_input(__lowerCAmelCase							,	__lowerCAmelCase							,	generator=__lowerCAmelCase   )

							# 3. Predict the noise residual given the noise magnitude `sigma_hat`
							# The model inputs and output are adjusted by following eq. (213) in [1].
							A__        =   (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
							A__        =   self.scheduler.step(__lowerCAmelCase							,	__lowerCAmelCase							,	__lowerCAmelCase							,	__lowerCAmelCase   )

							if sigma_prev != 0:
									# 6. Apply 2nd order correction
									# The model inputs and output are adjusted by following eq. (213) in [1].
									A__        =   (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2							,	sigma_prev / 2   ).sample
									A__        =   self.scheduler.step_correct(
									    __lowerCAmelCase							,	__lowerCAmelCase							,	__lowerCAmelCase							,	__lowerCAmelCase							,	step_output.prev_sample							,	step_output["""derivative"""]							,	)
							A__        =   step_output.prev_sample

					A__        =   (sample / 2 + 0.5).clamp(0							,	1   )
					A__        =   sample.cpu().permute(0							,	2							,	3							,	1   ).numpy()
					if output_type == "pil":
							A__        =   self.numpy_to_pil(__lowerCAmelCase   )

					if not return_dict:
							return (image,)

					return ImagePipelineOutput(images=__lowerCAmelCase   )










import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM







def    __lowerCamelCase					(							__a   :Dict    )		->				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(__a    ,     __a    )







def    __lowerCamelCase					(							__a   :str    )		->				Union[str, Any]:






		"""simple docstring"""

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







def    __lowerCamelCase					(							__a   :str    )		->				List[str]:






		"""simple docstring"""

		A__        =   torch.load(__a    ,     map_location="""cpu"""    )
		A__        =   Namespace(**checkpoint["""cfg"""]["""model"""]    )
		A__        =   checkpoint["""model"""]
		remove_ignore_keys_(__a    )
		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=__a    ,     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(__a    )
		A__        =   model.load_state_dict(__a    ,     strict=__a    )
		print(__a    )
		A__        =   make_linear_from_emb(model.model.embed_tokens    )

		return model


if __name__ == "__main__":
					A    :					int =      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.''')
					A    :					str =      parser.parse_args()
					A    :					str =      convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
					model.save_pretrained(args.pytorch_dump_folder_path)



