


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
__snake_case            =   "."

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






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Dict       ,  SCREAMING_SNAKE_CASE_   :		List[Any]       ,  SCREAMING_SNAKE_CASE_   :		int	):





			"""simple docstring"""




			UpperCamelCase         =					SavedModel()
			UpperCamelCase         =					[]

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

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

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

			UpperCamelCase         =					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
			UpperCamelCase         =					sorted(_SCREAMING_SNAKE_CASE	)
			UpperCamelCase         =					[]

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

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


if __name__ == "__main__":
			__snake_case            =   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)"
			)
			__snake_case            =   parser.parse_args()

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








from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
			from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline




