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

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



def 							a__ (				lowerCAmelCase	:       List[Any]       ,    lowerCAmelCase	:       Tuple       ,    lowerCAmelCase	:       List[str]		):






     '''simple docstring'''
     UpperCAmelCase__							: int 							=						SavedModel()
     UpperCAmelCase__							: Any 							=						[]

     with open(os.path.join(_lowerCAmelCase       ,    "utils"       ,    "tf_ops"       ,    "onnx.json"		)		) as f:
          UpperCAmelCase__							: Optional[Any] 							=						json.load(_lowerCAmelCase		)["opsets"]

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

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

     UpperCAmelCase__							: 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
     UpperCAmelCase__							: Union[str, Any] 							=						sorted(_lowerCAmelCase		)
     UpperCAmelCase__							: Any 							=						[]

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

     if strict and len(_lowerCAmelCase		) > 0:
          raise Exception(F"Found the following incompatible ops for the opset {opset}:\n" + incompatible_ops		)
     elif len(_lowerCAmelCase		) > 0:
          print(F"Found the following incompatible ops for the opset {opset}:"		)
          print(*_lowerCAmelCase       ,    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__      :	str   		=    parser.parse_args()

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





"""simple docstring"""




import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD



def 							a__ (				lowerCAmelCase	:       List[str]		):






     '''simple docstring'''
     def wrapper(*lowerCAmelCase	:       Any       ,    **lowerCAmelCase	:       Tuple		):
          UpperCAmelCase__							: Optional[int] 							=						timeit.default_timer()
          UpperCAmelCase__							: int 							=						func(*lowerCAmelCase       ,    **lowerCAmelCase		)
          UpperCAmelCase__							: List[Any] 							=						timeit.default_timer() - starttime
          return delta

     UpperCAmelCase__							: int 							=						func.__name__

     return wrapper



def 							a__ (				lowerCAmelCase	:       dict       ,    lowerCAmelCase	:       Optional[int]=100       ,    lowerCAmelCase	:       List[str]=None		):






     '''simple docstring'''
     UpperCAmelCase__							: str 							=						[]
     UpperCAmelCase__							: Optional[Any] 							=						seq_shapes or {}
     for i in range(lowerCAmelCase		):
          UpperCAmelCase__							: int 							=						{}
          for col_id, (k, v) in enumerate(features.items()		):
               if isinstance(lowerCAmelCase       ,    _ArrayXD		):
                    UpperCAmelCase__							: List[str] 							=						np.random.rand(*v.shape		).astype(v.dtype		)
               elif isinstance(lowerCAmelCase       ,    datasets.Value		):
                    if v.dtype == "string":
                         UpperCAmelCase__							: Dict 							=						"The small grey turtle was surprisingly fast when challenged."
                    else:
                         UpperCAmelCase__							: str 							=						np.random.randint(10       ,    size=1		).astype(v.dtype		).item()
               elif isinstance(lowerCAmelCase       ,    datasets.Sequence		):
                    while isinstance(lowerCAmelCase       ,    datasets.Sequence		):
                         UpperCAmelCase__							: List[str] 							=						v.feature
                    UpperCAmelCase__							: Optional[int] 							=						seq_shapes[k]
                    UpperCAmelCase__							: Optional[int] 							=						np.random.rand(*lowerCAmelCase		).astype(v.dtype		)
               UpperCAmelCase__							: Union[str, Any] 							=						data

          dummy_data.append((i, example)		)

     return dummy_data



def 							a__ (				lowerCAmelCase	:       List[str]       ,    lowerCAmelCase	:       Tuple       ,    lowerCAmelCase	:       List[str]=100       ,    lowerCAmelCase	:       Optional[int]=None		):






     '''simple docstring'''
     UpperCAmelCase__							: int 							=						generate_examples(lowerCAmelCase       ,    num_examples=lowerCAmelCase       ,    seq_shapes=lowerCAmelCase		)

     with ArrowWriter(features=lowerCAmelCase       ,    path=lowerCAmelCase		) as writer:
          for key, record in dummy_data:
               UpperCAmelCase__							: List[Any] 							=						features.encode_example(lowerCAmelCase		)
               writer.write(lowerCAmelCase		)

          UpperCAmelCase__							,	UpperCAmelCase__							: Optional[Any] 							=						writer.finalize()

     if not num_final_examples == num_examples:
          raise ValueError(
              F"Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}."		)

     UpperCAmelCase__							: Optional[int] 							=						datasets.Dataset.from_file(filename=lowerCAmelCase       ,    info=datasets.DatasetInfo(features=lowerCAmelCase		)		)

     return dataset



