


import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel




class        lowerCamelCase      (     pl.LightningModule   ):


	def __init__(						self							,    lowercase__):
							super().__init__()
							__UpperCAmelCase :       List[str]  						=  model
							__UpperCAmelCase :       List[str]  						=  2
							__UpperCAmelCase :       int  						=  nn.Linear(self.model.config.hidden_size							,    self.num_labels)


	def    A(						self):
							pass






def 						__SCREAMING_SNAKE_CASE       (					lowercase_			,					lowercase_			,					lowercase_			)		->    str:




						'''simple docstring'''







						__UpperCAmelCase :       Optional[int]  						=  LongformerModel.from_pretrained(lowercase_			)
						__UpperCAmelCase :       List[Any]  						=  LightningModel(lowercase_			)

						__UpperCAmelCase :       Any  						=  torch.load(lowercase_			,					map_location=torch.device('''cpu'''			)			)
						lightning_model.load_state_dict(ckpt['''state_dict''']			)

						# init longformer question answering model
						__UpperCAmelCase :       Optional[Any]  						=  LongformerForQuestionAnswering.from_pretrained(lowercase_			)

						# transfer weights
						longformer_for_qa.longformer.load_state_dict(lightning_model.model.state_dict()			)
						longformer_for_qa.qa_outputs.load_state_dict(lightning_model.qa_outputs.state_dict()			)
						longformer_for_qa.eval()

						# save model
						longformer_for_qa.save_pretrained(lowercase_			)

						print(f"Conversion successful. Model saved under {pytorch_dump_folder_path}"			)


if __name__ == "__main__":
				lowerCAmelCase							=						argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    """--longformer_model""",
				    default=None,
				    type=str,
				    required=True,
				    help="""model identifier of longformer. Should be either `longformer-base-4096` or `longformer-large-4096`.""",
				)
				parser.add_argument(
				    """--longformer_question_answering_ckpt_path""",
				    default=None,
				    type=str,
				    required=True,
				    help="""Path the official PyTorch Lightning Checkpoint.""",
				)
				parser.add_argument(
				    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
				)
				lowerCAmelCase							=						parser.parse_args()
				convert_longformer_qa_checkpoint_to_pytorch(
				    args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
				)





from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class        lowerCamelCase      (     _UpperCamelCase   ):


	def __init__(						self							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__ = None							,    ):
							super().__init__()
							self.register_modules(transformer=lowercase__							,    vae=lowercase__							,    scheduler=lowercase__)

							# create a imagenet -> id dictionary for easier use
							__UpperCAmelCase :       List[str]  						=  {}
							if idalabel is not None:
													for key, value in idalabel.items():
																			for label in value.split(''','''):
																									__UpperCAmelCase :       Dict  						=  int(lowercase__)
													__UpperCAmelCase :       Tuple  						=  dict(sorted(self.labels.items()))


	def    A(						self							,    lowercase__):

							if not isinstance(lowercase__							,    lowercase__):
													__UpperCAmelCase :       Dict  						=  list(lowercase__)

							for l in label:
													if l not in self.labels:
																			raise ValueError(
																			    F"{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}.")

							return [self.labels[l] for l in label]


	@torch.no_grad()
	def __call__(						self							,    lowercase__							,    lowercase__ = 4.0							,    lowercase__ = None							,    lowercase__ = 5_0							,    lowercase__ = "pil"							,    lowercase__ = True							,    ):
							__UpperCAmelCase :       List[str]  						=  len(lowercase__)
							__UpperCAmelCase :       str  						=  self.transformer.config.sample_size
							__UpperCAmelCase :       List[str]  						=  self.transformer.config.in_channels

							__UpperCAmelCase :       Union[str, Any]  						=  randn_tensor(
							    shape=(batch_size, latent_channels, latent_size, latent_size)							,    generator=lowercase__							,    device=self.device							,    dtype=self.transformer.dtype							,    )
							__UpperCAmelCase :       Optional[Any]  						=  torch.cat([latents] * 2) if guidance_scale > 1 else latents

							__UpperCAmelCase :       Union[str, Any]  						=  torch.tensor(lowercase__							,    device=self.device).reshape(-1)
							__UpperCAmelCase :       Dict  						=  torch.tensor([1_0_0_0] * batch_size							,    device=self.device)
							__UpperCAmelCase :       int  						=  torch.cat([class_labels, class_null]							,    0) if guidance_scale > 1 else class_labels

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

							for t in self.progress_bar(self.scheduler.timesteps):
													if guidance_scale > 1:
																			__UpperCAmelCase :       List[str]  						=  latent_model_input[: len(lowercase__) // 2]
																			__UpperCAmelCase :       Optional[Any]  						=  torch.cat([half, half]							,    dim=0)
													__UpperCAmelCase :       Optional[Any]  						=  self.scheduler.scale_model_input(lowercase__							,    lowercase__)

													__UpperCAmelCase :       Any  						=  t
													if not torch.is_tensor(lowercase__):
																			# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
																			# This would be a good case for the `match` statement (Python 3.10+)
																			__UpperCAmelCase :       List[str]  						=  latent_model_input.device.type == '''mps'''
																			if isinstance(lowercase__							,    lowercase__):
																									__UpperCAmelCase :       Tuple  						=  torch.floataa if is_mps else torch.floataa
																			else:
																									__UpperCAmelCase :       Dict  						=  torch.intaa if is_mps else torch.intaa
																			__UpperCAmelCase :       List[str]  						=  torch.tensor([timesteps]							,    dtype=lowercase__							,    device=latent_model_input.device)
													elif len(timesteps.shape) == 0:
																			__UpperCAmelCase :       List[str]  						=  timesteps[None].to(latent_model_input.device)
													# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
													__UpperCAmelCase :       Optional[int]  						=  timesteps.expand(latent_model_input.shape[0])
													# predict noise model_output
													__UpperCAmelCase :       Any  						=  self.transformer(
													    lowercase__							,    timestep=lowercase__							,    class_labels=lowercase__).sample

													# perform guidance
													if guidance_scale > 1:
																			__UpperCAmelCase						,			__UpperCAmelCase :       Optional[Any]  						=  noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
																			__UpperCAmelCase						,			__UpperCAmelCase :       Union[str, Any]  						=  torch.split(lowercase__							,    len(lowercase__) // 2							,    dim=0)

																			__UpperCAmelCase :       List[str]  						=  uncond_eps + guidance_scale * (cond_eps - uncond_eps)
																			__UpperCAmelCase :       str  						=  torch.cat([half_eps, half_eps]							,    dim=0)

																			__UpperCAmelCase :       Any  						=  torch.cat([eps, rest]							,    dim=1)

													# learned sigma
													if self.transformer.config.out_channels // 2 == latent_channels:
																			__UpperCAmelCase						,			__UpperCAmelCase :       Union[str, Any]  						=  torch.split(lowercase__							,    lowercase__							,    dim=1)
													else:
																			__UpperCAmelCase :       Any  						=  noise_pred

													# compute previous image: x_t -> x_t-1
													__UpperCAmelCase :       Dict  						=  self.scheduler.step(lowercase__							,    lowercase__							,    lowercase__).prev_sample

							if guidance_scale > 1:
													__UpperCAmelCase						,			__UpperCAmelCase :       Any  						=  latent_model_input.chunk(2							,    dim=0)
							else:
													__UpperCAmelCase :       List[Any]  						=  latent_model_input

							__UpperCAmelCase :       List[str]  						=  1 / self.vae.config.scaling_factor * latents
							__UpperCAmelCase :       Optional[int]  						=  self.vae.decode(lowercase__).sample

							__UpperCAmelCase :       List[str]  						=  (samples / 2 + 0.5).clamp(0							,    1)

							# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
							__UpperCAmelCase :       str  						=  samples.cpu().permute(0							,    2							,    3							,    1).float().numpy()

							if output_type == "pil":
													__UpperCAmelCase :       Optional[int]  						=  self.numpy_to_pil(lowercase__)

							if not return_dict:
													return (samples,)

							return ImagePipelineOutput(images=lowercase__)

