

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__												=						'''.'''

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



def   __UpperCAmelCase					(      __a :     Tuple						,__a :     Any						,__a :     Optional[Any]      )     ->	int:

					"""simple docstring"""

					_a      :       Optional[Any]   =						SavedModel()
					_a      :       List[str]   =						[]

					with open(os.path.join(__a						,'''utils'''						,'''tf_ops'''						,'''onnx.json'''      )      ) as f:
										_a      :       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()      )

					_a      :       Optional[int]   =						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
					_a      :       Union[str, Any]   =						sorted(__a      )
					_a      :       List[Any]   =						[]

					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__":
			a__												=						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)'''
			)
			a__												=						parser.parse_args()

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










# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re

from ..utils import cached_file


# docstyle-ignore
UpperCamelCase__           =			"\nHuman: <<task>>\n\nAssistant: "


UpperCamelCase__           =			"huggingface-tools/default-prompts"
UpperCamelCase__           =			{"chat": "chat_prompt_template.txt", "run": "run_prompt_template.txt"}





def      _UpperCamelCase  (a__ :List[Any] ,      a__ :Optional[Any] ,      a__ :List[str]="run"							):






							"""simple docstring"""





							if prompt_or_repo_id is None:
														UpperCamelCase__					  =      DEFAULT_PROMPTS_REPO

							# prompt is considered a repo ID when it does not contain any kind of space
							if re.search("""\\s""" ,      a__							) is not None:
														return prompt_or_repo_id

							UpperCamelCase__					  =      cached_file(
							    a__ ,      PROMPT_FILES[mode] ,      repo_type="""dataset""" ,      user_agent={"""agent""": agent_name}							)
							with open(a__ ,      """r""" ,      encoding="""utf-8"""							) as f:
														return f.read()




