






'''simple docstring'''





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							:							Any  							=		"""."""

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






def    lowerCamelCase_     (    lowercase__				,     lowercase__				,     lowercase__):
 lowerCamelCase__  =     SavedModel()
 lowerCamelCase__  =     []

 with open(os.path.join(lowercase__				,     "utils"				,     "tf_ops"				,     "onnx.json")) as f:
  lowerCamelCase__  =     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())

 lowerCamelCase__  =     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
 lowerCamelCase__  =     sorted(lowercase__)
 lowerCamelCase__  =     []

 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							:							Union[str, 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)"""
 )
 __A							:							Any  							=		parser.parse_args()

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









'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__A							:							Optional[int]  							=		logging.get_logger(__name__)

__A							:							Tuple  							=		{
    """google/mobilenet_v1_1.0_224""": """https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json""",
    """google/mobilenet_v1_0.75_192""": """https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json""",
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}
class 				lowercase       (							_lowerCamelCase      ):


    '''simple docstring'''

    lowerCAmelCase__      				=							"mobilenet_v1"

    def __init__(					self     :     Union[str, Any]				,     __lowerCamelCase     :     Tuple=3				,     __lowerCamelCase     :     Any=224				,     __lowerCamelCase     :     Tuple=1.0				,     __lowerCamelCase     :     Optional[int]=8				,     __lowerCamelCase     :     str="relu6"				,     __lowerCamelCase     :     Any=True				,     __lowerCamelCase     :     Union[str, Any]=0.9_9_9				,     __lowerCamelCase     :     List[Any]=0.0_2				,     __lowerCamelCase     :     str=0.0_0_1				,     **__lowerCamelCase     :     str				,     )							->							Dict:

     '''simple docstring'''




     super().__init__(**__lowerCamelCase     )

     if depth_multiplier <= 0:
      raise ValueError("depth_multiplier must be greater than zero."     )

     lowerCamelCase__  =     num_channels
     lowerCamelCase__  =     image_size
     lowerCamelCase__  =     depth_multiplier
     lowerCamelCase__  =     min_depth
     lowerCamelCase__  =     hidden_act
     lowerCamelCase__  =     tf_padding
     lowerCamelCase__  =     classifier_dropout_prob
     lowerCamelCase__  =     initializer_range
     lowerCamelCase__  =     layer_norm_eps
class 				lowercase       (							_lowerCamelCase      ):


    '''simple docstring'''

    lowerCAmelCase__      				=							version.parse("1.11"      )

    @property
    def    a__				(					self     :     List[str]     )							->							Mapping[str, Mapping[int, str]]:

     '''simple docstring'''




     return OrderedDict([("pixel_values", {0: "batch"})]     )

    @property
    def    a__				(					self     :     str     )							->							Mapping[str, Mapping[int, str]]:

     '''simple docstring'''




     if self.task == "image-classification":
      return OrderedDict([("logits", {0: "batch"})]     )
     else:
      return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})]     )




    @property
    def    a__				(					self     :     List[Any]     )							->							float:

     '''simple docstring'''




     return 1E-4

