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
__a  :     Optional[Any]       =  """."""

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





def 				UpperCAmelCase						(    lowercase							,			lowercase							,			lowercase							):







				"""simple docstring"""



				__lowercase 			=							SavedModel()
				__lowercase 			=							[]

				with open(os.path.join(lowercase							,			'''utils'''							,			'''tf_ops'''							,			'''onnx.json'''							)							) as f:
								__lowercase 			=							json.load(lowercase							)['''opsets''']

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

				with open(lowercase							,			'''rb'''							) as f:
								saved_model.ParseFromString(f.read()							)

				__lowercase 			=							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
				__lowercase 			=							sorted(lowercase							)
				__lowercase 			=							[]

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

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


if __name__ == "__main__":
		__a  :     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=1_2, 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)"""
		)
		__a  :     int       =  parser.parse_args()

		if args.framework == "onnx":
				onnx_compliancy(args.saved_model_path, args.strict, args.opset)
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__a  :     Any       =  {
    """configuration_bloom""": ["""BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BloomConfig""", """BloomOnnxConfig"""],
}
try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__a  :     Optional[int]       =  ["""BloomTokenizerFast"""]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__a  :     str       =  [
		    """BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """BloomForCausalLM""",
		    """BloomModel""",
		    """BloomPreTrainedModel""",
		    """BloomForSequenceClassification""",
		    """BloomForTokenClassification""",
		    """BloomForQuestionAnswering""",
		]

if TYPE_CHECKING:
		from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_bloom_fast import BloomTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_bloom import (
				    BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BloomForCausalLM,
				    BloomForQuestionAnswering,
				    BloomForSequenceClassification,
				    BloomForTokenClassification,
				    BloomModel,
				    BloomPreTrainedModel,
				)

else:
		import sys

		__a  :     Dict       =  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)