





import argparse
import os
from pathlib import Path

import fairseq
import torch
from packaging import version
from torch import nn

from transformers import (
    BartConfig,
    BartForConditionalGeneration,
    BartForSequenceClassification,
    BartModel,
    BartTokenizer,
)
from transformers.utils import logging


UpperCamelCase__											= ['bart.large', 'bart.large.mnli', 'bart.large.cnn', 'bart_xsum/model.pt']
UpperCamelCase__											= {'bart.large': BartModel, 'bart.large.mnli': BartForSequenceClassification}
if version.parse(fairseq.__version__) < version.parse("0.9.0"):
					raise Exception("requires fairseq >= 0.9.0")


logging.set_verbosity_info()
UpperCamelCase__											= logging.get_logger(__name__)

UpperCamelCase__											= ' Hello world! cécé herlolip'

UpperCamelCase__											= [
    ('model.classification_heads.mnli.dense.weight', 'classification_head.dense.weight'),
    ('model.classification_heads.mnli.dense.bias', 'classification_head.dense.bias'),
    ('model.classification_heads.mnli.out_proj.weight', 'classification_head.out_proj.weight'),
    ('model.classification_heads.mnli.out_proj.bias', 'classification_head.out_proj.bias'),
]





def     _UpperCamelCase     (a__   :Union[str, Any]       ):






							"""simple docstring"""





							UpperCamelCase__				     =			[
							    "encoder.version",
							    "decoder.version",
							    "model.encoder.version",
							    "model.decoder.version",
							    "_float_tensor",
							]
							for k in ignore_keys:
														state_dict.pop(_lowerCamelCase   ,			_lowerCamelCase       )





def     _UpperCamelCase     (a__   :Optional[int]   ,			a__   :Any   ,			a__   :Union[str, Any]       ):






							"""simple docstring"""





							UpperCamelCase__				     =			dct.pop(_lowerCamelCase       )
							UpperCamelCase__				     =			val





def     _UpperCamelCase     (a__   :List[Any]       ):






							"""simple docstring"""





							UpperCamelCase__				     =			torch.load(_lowerCamelCase   ,			map_location="""cpu"""       )
							UpperCamelCase__				     =			torch.hub.load("""pytorch/fairseq"""   ,			"""bart.large.cnn"""       ).eval()
							hub_interface.model.load_state_dict(sd["""model"""]       )
							return hub_interface





def     _UpperCamelCase     (a__   :Optional[int]       ):






							"""simple docstring"""





							UpperCamelCase__				     =			emb.weight.shape
							UpperCamelCase__				     =			nn.Linear(_lowerCamelCase   ,			_lowerCamelCase   ,			bias=_lowerCamelCase       )
							UpperCamelCase__				     =			emb.weight.data
							return lin_layer





@torch.no_grad()
def     _UpperCamelCase     (a__   :Any   ,			a__   :int   ,			a__   :Optional[int]=None       ):






							"""simple docstring"""





							if not os.path.exists(_lowerCamelCase       ):
														UpperCamelCase__				     =			torch.hub.load("""pytorch/fairseq"""   ,			_lowerCamelCase       ).eval()
							else:
														UpperCamelCase__				     =			load_xsum_checkpoint(_lowerCamelCase       )

							bart.model.upgrade_state_dict(bart.model.state_dict()       )
							if hf_checkpoint_name is None:
														UpperCamelCase__				     =			checkpoint_path.replace("""."""   ,			"""-"""       )
							UpperCamelCase__				     =			BartConfig.from_pretrained(_lowerCamelCase       )
							UpperCamelCase__				     =			bart.encode(_lowerCamelCase       ).unsqueeze(0       )
							UpperCamelCase__				     =			BartTokenizer.from_pretrained(_lowerCamelCase       ).encode(_lowerCamelCase   ,			return_tensors="""pt"""       ).unsqueeze(0       )
							if not torch.eq(_lowerCamelCase   ,			_lowerCamelCase       ).all():
														raise ValueError(
														    f"""converted tokenizer and pretrained tokenizer returned different output: {tokens} != {tokensa}"""       )

							if checkpoint_path == "bart.large.mnli":
														UpperCamelCase__				     =			bart.state_dict()
														remove_ignore_keys_(_lowerCamelCase       )
														UpperCamelCase__				     =			state_dict["model.decoder.embed_tokens.weight"]
														for src, dest in mnli_rename_keys:
																					rename_key(_lowerCamelCase   ,			_lowerCamelCase   ,			_lowerCamelCase       )
														UpperCamelCase__				     =			BartForSequenceClassification(_lowerCamelCase       ).eval()
														model.load_state_dict(_lowerCamelCase       )
														UpperCamelCase__				     =			bart.predict("""mnli"""   ,			_lowerCamelCase   ,			return_logits=_lowerCamelCase       )
														UpperCamelCase__				     =			model(_lowerCamelCase       )[0]  # logits
							else:  # no classification heads to worry about
														UpperCamelCase__				     =			bart.model.state_dict()
														remove_ignore_keys_(_lowerCamelCase       )
														UpperCamelCase__				     =			state_dict["decoder.embed_tokens.weight"]
														UpperCamelCase__				     =			bart.extract_features(_lowerCamelCase       )
														if hf_checkpoint_name == "facebook/bart-large":
																					UpperCamelCase__				     =			BartModel(_lowerCamelCase       ).eval()
																					model.load_state_dict(_lowerCamelCase       )
																					UpperCamelCase__				     =			model(_lowerCamelCase       ).model[0]
														else:
																					UpperCamelCase__				     =			BartForConditionalGeneration(_lowerCamelCase       ).eval()  # an existing summarization ckpt
																					model.model.load_state_dict(_lowerCamelCase       )
																					if hasattr(_lowerCamelCase   ,			"""lm_head"""       ):
																												UpperCamelCase__				     =			make_linear_from_emb(model.model.shared       )
																					UpperCamelCase__				     =			model.model(_lowerCamelCase       )[0]

    # Check results
							if fairseq_output.shape != new_model_outputs.shape:
														raise ValueError(
														    f"""`fairseq_output` shape and `new_model_output` shape are different: {fairseq_output.shape=}, {new_model_outputs.shape}"""       )
							if (fairseq_output != new_model_outputs).any().item():
														raise ValueError("""Some values in `fairseq_output` are different from `new_model_outputs`"""       )
							Path(_lowerCamelCase       ).mkdir(exist_ok=_lowerCamelCase       )
							model.save_pretrained(_lowerCamelCase       )


if __name__ == "__main__":
					UpperCamelCase__											= argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    "fairseq_path", type=str, help="bart.large, bart.large.cnn or a 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.")
					parser.add_argument(
					    "--hf_config", default=None, type=str, help="Which huggingface architecture to use: bart-large-xsum"
					)
					UpperCamelCase__											= parser.parse_args()
					convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)











import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
					UpperCamelCase__											= 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.",
					)

					UpperCamelCase__											= parser.parse_args()

					UpperCamelCase__											= UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

					UpperCamelCase__											= CLIPImageProcessor()
					UpperCamelCase__											= CLIPVisionModelWithProjection.from_pretrained("openai/clip-vit-large-patch14")

					UpperCamelCase__											= 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)




