




import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin

class 		__lowerCAmelCase	(	__magic_name__      ,       __magic_name__ ):
							@register_to_config
							def __init__(						self						:int		,      *,
							    __magic_name__						:int = 4		,      __magic_name__						:int = 768		,      __magic_name__						:int		,      __magic_name__						:Tuple		,      ):







								'''simple docstring'''







								super().__init__()

								a										=							nn.Parameter(torch.zeros(__magic_name__				)				)

								# parameters for additional clip time embeddings
								a										=							nn.Linear(__magic_name__		,      __magic_name__				)
								a										=							nn.Linear(__magic_name__		,      __magic_name__				)

								# parameters for encoder hidden states
								a										=							clip_extra_context_tokens
								a										=							nn.Linear(
								    __magic_name__		,      self.clip_extra_context_tokens * cross_attention_dim				)
								a										=							nn.Linear(__magic_name__		,      __magic_name__				)
								a										=							nn.LayerNorm(__magic_name__				)





							def       lowerCamelCase__							(						self						:Union[str, Any]		,      *, __magic_name__						:Any		,      __magic_name__						:str		,      __magic_name__						:Optional[Any]		,      __magic_name__						:int				):







								'''simple docstring'''







								if do_classifier_free_guidance:
									# Add the classifier free guidance embeddings to the image embeddings
									a										=							image_embeddings.shape[0]
									a										=							self.learned_classifier_free_guidance_embeddings.unsqueeze(0				)
									a										=							classifier_free_guidance_embeddings.expand(
									    __magic_name__		,      -1				)
									a										=							torch.cat([classifier_free_guidance_embeddings, image_embeddings]		,      dim=0				)

								# The image embeddings batch size and the text embeddings batch size are equal
								assert image_embeddings.shape[0] == prompt_embeds.shape[0]

								a										=							prompt_embeds.shape[0]

								# "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
								# adding CLIP embeddings to the existing timestep embedding, ...
								a										=							self.embedding_proj(__magic_name__				)
								a										=							self.clip_image_embeddings_project_to_time_embeddings(__magic_name__				)
								a										=							time_projected_image_embeddings + time_projected_prompt_embeds

								# ... and by projecting CLIP embeddings into four
								# extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
								a										=							self.clip_extra_context_tokens_proj(__magic_name__				)
								a										=							clip_extra_context_tokens.reshape(__magic_name__		,      -1		,      self.clip_extra_context_tokens				)
								a										=							clip_extra_context_tokens.permute(0		,      2		,      1				)

								a										=							self.encoder_hidden_states_proj(__magic_name__				)
								a										=							self.text_encoder_hidden_states_norm(__magic_name__				)
								a										=							torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]		,      dim=1				)

								return text_encoder_hidden_states, additive_clip_time_embeddings







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						:Union[str, Any]		,      __magic_name__						:Optional[int]				):







								'''simple docstring'''







								super().__init__()
								a										=							model
								a										=							2
								a										=							nn.Linear(self.model.config.hidden_size		,      self.num_labels				)





							def       lowerCamelCase__							(						self						:Dict				):







								'''simple docstring'''







								pass







def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )      ->       Optional[int]:
	# load longformer model from model identifier
	a										=							LongformerModel.from_pretrained(__lowerCamelCase    )
	a										=							LightningModel(__lowerCamelCase    )

	a										=							torch.load(__lowerCamelCase							,			map_location=torch.device("""cpu"""    )    )
	lightning_model.load_state_dict(ckpt["""state_dict"""]    )

	# init longformer question answering model
	a										=							LongformerForQuestionAnswering.from_pretrained(__lowerCamelCase    )

	# 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(__lowerCamelCase    )

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


if __name__ == "__main__":
		__UpperCamelCase					:       List[Any]    					=      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."
		)
		__UpperCamelCase					:       List[Any]    					=      parser.parse_args()
		convert_longformer_qa_checkpoint_to_pytorch(
		    args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
		)

