

import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Optional[Any])      ->					List[str]:






							'''simple docstring'''





							__UpperCamelCase			:     Union[str, Any]         =      [
							    "decoder.version",
							    "decoder.output_projection.weight",
							    "_float_tensor",
							    "decoder.embed_positions._float_tensor",
							]
							for k in ignore_keys:
														state_dict.pop(_lowerCamelCase		,						_lowerCamelCase)




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    str)      ->					List[Any]:






							'''simple docstring'''





							__UpperCamelCase			:     List[str]         =      emb.weight.shape
							__UpperCamelCase			:     Optional[int]         =      nn.Linear(_lowerCamelCase		,						_lowerCamelCase		,						bias=_lowerCamelCase)
							__UpperCamelCase			:     List[Any]         =      emb.weight.data
							return lin_layer




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Optional[int])      ->					Any:






							'''simple docstring'''





							__UpperCamelCase			:     List[Any]         =      torch.load(_lowerCamelCase		,						map_location="cpu")
							__UpperCamelCase			:     Dict         =      Namespace(**checkpoint["cfg"]["model"])
							__UpperCamelCase			:     List[Any]         =      checkpoint["model"]
							remove_ignore_keys_(_lowerCamelCase)
							__UpperCamelCase			:     Optional[Any]         =      state_dict["decoder.embed_tokens.weight"].shape[0]

							__UpperCamelCase			:     Tuple         =      {key.replace("decoder"		,						"model"): val for key, val in state_dict.items()}

							__UpperCamelCase			:     str         =      XGLMConfig(
							    vocab_size=_lowerCamelCase		,						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		,						)

							__UpperCamelCase			:     Any         =      XGLMForCausalLM(_lowerCamelCase)
							__UpperCamelCase			:     Any         =      model.load_state_dict(_lowerCamelCase		,						strict=_lowerCamelCase)
							print(_lowerCamelCase)
							__UpperCamelCase			:     Union[str, Any]         =      make_linear_from_emb(model.model.embed_tokens)

							return model


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

import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
		lowercase						:      List[str]									= argparse.ArgumentParser()

		parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')

		parser.add_argument(
		    '--txt2img_unclip',
		    default='kakaobrain/karlo-v1-alpha',
		    type=str,
		    required=False,
		    help='The pretrained txt2img unclip.',
		)

		lowercase						:      int									= parser.parse_args()

		lowercase						:      str									= UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

		lowercase						:      int									= CLIPImageProcessor()
		lowercase						:      Optional[int]									= CLIPVisionModelWithProjection.from_pretrained('openai/clip-vit-large-patch14')

		lowercase						:      int									= UnCLIPImageVariationPipeline(
		    decoder=txtaimg.decoder,
		    text_encoder=txtaimg.text_encoder,
		    tokenizer=txtaimg.tokenizer,
		    text_proj=txtaimg.text_proj,
		    feature_extractor=feature_extractor,
		    image_encoder=image_encoder,
		    super_res_first=txtaimg.super_res_first,
		    super_res_last=txtaimg.super_res_last,
		    decoder_scheduler=txtaimg.decoder_scheduler,
		    super_res_scheduler=txtaimg.super_res_scheduler,
		)

		imgaimg.save_pretrained(args.dump_path)