



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
SCREAMING_SNAKE_CASE_:Any             =						"""."""

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




def 	__UpperCamelCase	(  _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase		)       ->  Dict:



    """simple docstring"""






    A :       Any          =   SavedModel()
    A :       Any          =   []

    with open(os.path.join(_lowerCAmelCase      ,     """utils"""      ,     """tf_ops"""      ,     """onnx.json"""		)		) as f:
        A :       Dict          =   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()		)

    A :       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 :       Optional[int]          =   sorted(_lowerCAmelCase		)
    A :       int          =   []

    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__":
     SCREAMING_SNAKE_CASE_:int             =						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)"""
     )
     SCREAMING_SNAKE_CASE_:Dict             =						parser.parse_args()

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







from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
     import tensorflow as tf

     from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM






@require_tf
@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE__     (		unittest.TestCase    ):




    '''simple docstring'''






    @slow
    def 			_lowerCAmelCase					(       self		):
        A :       Any          =   TFAutoModelForSeqaSeqLM.from_pretrained("""google/mt5-small"""		)
        A :       Dict          =   AutoTokenizer.from_pretrained("""google/mt5-small"""		)

        A :       Tuple          =   tokenizer("""Hello there""",		return_tensors="""tf"""		).input_ids
        A :       Optional[int]          =   tokenizer("""Hi I am""",		return_tensors="""tf"""		).input_ids

        A :       List[Any]          =   model(lowerCamelCase__,		labels=lowerCamelCase__		).loss
        A :       str          =   -tf.math.reduce_mean(lowerCamelCase__		).numpy()

        A :       Tuple          =   -21.22_8168
        self.assertTrue(abs(mtf_score - EXPECTED_SCORE		) < 2e-4		)


