






import importlib

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

def      lowerCAmelCase__	(    _a       :			Optional[Any]  ,      _a       :			List[Any]=False    ):
		snake_case_				:							Dict						 =       OmegaConf.load(_a    )
		if display:
				print(yaml.dump(OmegaConf.to_container(_a    )    )    )
		return config

def      lowerCAmelCase__	(    _a       :			int  ,      _a       :			List[str]=None  ,      _a       :			List[str]=None    ):
		if conf_path is None:
				snake_case_				:							Tuple						 =       "./model_checkpoints/vqgan_only.yaml"
		snake_case_				:							Dict						 =       load_config(_a  ,      display=_a    )
		snake_case_				:							Union[str, Any]						 =       VQModel(**config.model.params    )
		if ckpt_path is None:
				snake_case_				:							Union[str, Any]						 =       "./model_checkpoints/vqgan_only.pt"
		snake_case_				:							Dict						 =       torch.load(_a  ,      map_location=_a    )
		if ".ckpt" in ckpt_path:
				snake_case_				:							Optional[Any]						 =       sd["state_dict"]
		model.load_state_dict(_a  ,      strict=_a    )
		model.to(_a    )
		del sd
		return model

def      lowerCAmelCase__	(    _a       :			str  ,      _a       :			Union[str, Any]    ):
		snake_case_ ,       snake_case_ ,       snake_case_				:							Optional[Any]						 =       model.encode(_a    )
		print(F'''VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}'''    )
		snake_case_				:							Union[str, Any]						 =       model.decode(_a    )
		return xrec

def      lowerCAmelCase__	(    _a       :			Dict  ,      _a       :			Dict=False    ):
		snake_case_ ,       snake_case_				:							Any						 =       string.rsplit("."  ,      1    )
		if reload:
				snake_case_				:							str						 =       importlib.import_module(_a    )
				importlib.reload(_a    )
		return getattr(importlib.import_module(_a  ,      package=_a    )  ,      cls    )

def      lowerCAmelCase__	(    _a       :			Dict    ):
		if "target" not in config:
				raise KeyError("Expected key `target` to instantiate."    )
		return get_obj_from_str(config["target"]    )(**config.get("params"  ,      {}    )    )

def      lowerCAmelCase__	(    _a       :			Any  ,      _a       :			str  ,      _a       :			Dict=True  ,      _a       :			str=True    ):
		snake_case_				:							Any						 =       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      lowerCAmelCase__	(    _a       :			List[str]  ,      _a       :			int  ,      _a       :			str  ,      _a       :			List[Any]    ):
		# load the specified checkpoint
		if ckpt:
				snake_case_				:							Dict						 =       torch.load(_a  ,      map_location="cpu"    )
				snake_case_				:							int						 =       pl_sd["global_step"]
				print(F'''loaded model from global step {global_step}.'''    )
		else:
				snake_case_				:							List[Any]						 =       {"state_dict": None}
				snake_case_				:							Optional[int]						 =       None
		snake_case_				:							List[str]						 =       load_model_from_config(config.model  ,      pl_sd["state_dict"]  ,      gpu=_a  ,      eval_mode=_a    )["model"]
		return model, global_step








import argparse
import json
import os

from tensorflow.core.protobuf.saved_model_pba import SavedModel


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
lowercase  :							Tuple			  =    '''.'''

# Internal TensorFlow ops that can be safely ignored (mostly specific to a saved model)
lowercase  :							Union[str, Any]			  =    [
    '''Assert''',
    '''AssignVariableOp''',
    '''EmptyTensorList''',
    '''MergeV2Checkpoints''',
    '''ReadVariableOp''',
    '''ResourceGather''',
    '''RestoreV2''',
    '''SaveV2''',
    '''ShardedFilename''',
    '''StatefulPartitionedCall''',
    '''StaticRegexFullMatch''',
    '''VarHandleOp''',
]

def      lowerCAmelCase__	(    _a       :			Union[str, Any]  ,      _a       :			Dict  ,      _a       :			List[str]    ):
		snake_case_				:							Optional[int]						 =       SavedModel()
		snake_case_				:							Any						 =       []

		with open(os.path.join(_a  ,      "utils"  ,      "tf_ops"  ,      "onnx.json"    )    ) as f:
				snake_case_				:							int						 =       json.load(_a    )["opsets"]

		for i in range(1  ,      opset + 1    ):
				onnx_ops.extend(onnx_opsets[str(_a    )]    )

		with open(_a  ,      "rb"    ) as f:
				saved_model.ParseFromString(f.read()    )

		snake_case_				:							str						 =       set()

		# Iterate over every metagraph in case there is more than one (a saved model can contain multiple graphs)
		for meta_graph in saved_model.meta_graphs:
				# Add operations in the graph definition
				model_op_names.update(node.op for node in meta_graph.graph_def.node    )

				# Go through the functions in the graph definition
				for func in meta_graph.graph_def.library.function:
						# Add operations in each function
						model_op_names.update(node.op for node in func.node_def    )

    # Convert to list, sorted if you want
		snake_case_				:							int						 =       sorted(_a    )
		snake_case_				:							Optional[int]						 =       []

		for op in model_op_names:
				if op not in onnx_ops and op not in INTERNAL_OPS:
						incompatible_ops.append(_a    )

		if strict and len(_a    ) > 0:
				raise Exception(F'''Found the following incompatible ops for the opset {opset}:\n''' + incompatible_ops    )
		elif len(_a    ) > 0:
				print(F'''Found the following incompatible ops for the opset {opset}:'''    )
				print(*_a  ,      sep="\n"    )
		else:
				print(F'''The saved model {saved_model_path} can properly be converted with ONNX.'''    )


if __name__ == "__main__":
							lowercase  :							List[Any]			  =    argparse.ArgumentParser()
							parser.add_argument('''--saved_model_path''', help='''Path of the saved model to check (the .pb file).''')
							parser.add_argument(
							    '''--opset''', default=12, type=int, help='''The ONNX opset against which the model has to be tested.'''
							)
							parser.add_argument(
							    '''--framework''', choices=['''onnx'''], default='''onnx''', help='''Frameworks against which to test the saved model.'''
							)
							parser.add_argument(
							    '''--strict''', action='''store_true''', help='''Whether make the checking strict (raise errors) or not (raise warnings)'''
							)
							lowercase  :							int			  =    parser.parse_args()

							if args.framework == "onnx":
														onnx_compliancy(args.saved_model_path, args.strict, args.opset)
