





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


A_												=     ["bart.large", "bart.large.mnli", "bart.large.cnn", "bart_xsum/model.pt"]
A_												=     {"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()
A_												=     logging.get_logger(__name__)

A_												=     " Hello world! cécé herlolip"

A_												=     [
    ("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) ->Tuple:
					lowerCamelCase__       =       [
					    "encoder.version",
					    "decoder.version",
					    "model.encoder.version",
					    "model.decoder.version",
					    "_float_tensor",
					]
					for k in ignore_keys:
										state_dict.pop(a,     a)





def    __UpperCamelCase      (    a,     a,     a) ->Optional[int]:
					lowerCamelCase__       =       dct.pop(a)
					lowerCamelCase__       =       val





def    __UpperCamelCase      (    a) ->Optional[int]:
					lowerCamelCase__       =       torch.load(a,     map_location="cpu")
					lowerCamelCase__       =       torch.hub.load("pytorch/fairseq",     "bart.large.cnn").eval()
					hub_interface.model.load_state_dict(sd["model"])
					return hub_interface





def    __UpperCamelCase      (    a) ->str:
					lowerCamelCase__    ,		lowerCamelCase__       =       emb.weight.shape
					lowerCamelCase__       =       nn.Linear(a,     a,     bias=a)
					lowerCamelCase__       =       emb.weight.data
					return lin_layer





@torch.no_grad()
def    __UpperCamelCase      (    a,     a,     a=None) ->Any:
					if not os.path.exists(a):
										lowerCamelCase__       =       torch.hub.load("pytorch/fairseq",     a).eval()
					else:
										lowerCamelCase__       =       load_xsum_checkpoint(a)

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

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


if __name__ == "__main__":
		A_												=     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"
		)
		A_												=     parser.parse_args()
		convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)








import importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel





def    __UpperCamelCase      (    a,     a=False) ->Optional[Any]:
					lowerCamelCase__       =       OmegaConf.load(a)
					if display:
										print(yaml.dump(OmegaConf.to_container(a)))
					return config





def    __UpperCamelCase      (    a,     a=None,     a=None) ->List[Any]:
					if conf_path is None:
										lowerCamelCase__       =       "./model_checkpoints/vqgan_only.yaml"
					lowerCamelCase__       =       load_config(a,     display=a)
					lowerCamelCase__       =       VQModel(**config.model.params)
					if ckpt_path is None:
										lowerCamelCase__       =       "./model_checkpoints/vqgan_only.pt"
					lowerCamelCase__       =       torch.load(a,     map_location=a)
					if ".ckpt" in ckpt_path:
										lowerCamelCase__       =       sd["state_dict"]
					model.load_state_dict(a,     strict=a)
					model.to(a)
					del sd
					return model





def    __UpperCamelCase      (    a,     a) ->int:
					lowerCamelCase__    ,		lowerCamelCase__    ,		lowerCamelCase__       =       model.encode(a)
					print(f"VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}")
					lowerCamelCase__       =       model.decode(a)
					return xrec





def    __UpperCamelCase      (    a,     a=False) ->Dict:
					lowerCamelCase__    ,		lowerCamelCase__       =       string.rsplit(".",     1)
					if reload:
										lowerCamelCase__       =       importlib.import_module(a)
										importlib.reload(a)
					return getattr(importlib.import_module(a,     package=a),     cls)





def    __UpperCamelCase      (    a) ->int:
					if "target" not in config:
										raise KeyError("Expected key `target` to instantiate.")
					return get_obj_from_str(config["target"])(**config.get("params",     {}))





def    __UpperCamelCase      (    a,     a,     a=True,     a=True) ->Optional[Any]:
					lowerCamelCase__       =       instantiate_from_config(a)
					if sd is not None:
										model.load_state_dict(a)
					if gpu:
										model.cuda()
					if eval_mode:
										model.eval()
					return {"model": model}







def    __UpperCamelCase      (    a,     a,     a,     a) ->Dict:
					# load the specified checkpoint
					if ckpt:
										lowerCamelCase__       =       torch.load(a,     map_location="cpu")
										lowerCamelCase__       =       pl_sd["global_step"]
										print(f"loaded model from global step {global_step}.")
					else:
										lowerCamelCase__       =       {"state_dict": None}
										lowerCamelCase__       =       None
					lowerCamelCase__       =       load_model_from_config(config.model,     pl_sd["state_dict"],     gpu=a,     eval_mode=a)["model"]
					return model, global_step

