




'''simple docstring'''






from typing import Mapping

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


A_			      =		logging.get_logger(__name__)

A_			      =		{
    "google/umt5-small": "https://huggingface.co/google/umt5-small/resolve/main/config.json",
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}






class        lowercase_      (						lowerCAmelCase_							):
     A_											=			"umt5"
     A_											=			["past_key_values"]





     def __init__(     self     :					int     ,							__lowerCamelCase     :					Any=250112     ,							__lowerCamelCase     :					Dict=512     ,							__lowerCamelCase     :					Optional[int]=64     ,							__lowerCamelCase     :					Dict=1024     ,							__lowerCamelCase     :					Any=8     ,							__lowerCamelCase     :					Union[str, Any]=None     ,							__lowerCamelCase     :					str=6     ,							__lowerCamelCase     :					List[Any]=32     ,							__lowerCamelCase     :					int=128     ,							__lowerCamelCase     :					Optional[int]=0.1     ,							__lowerCamelCase     :					Union[str, Any]=1E-6     ,							__lowerCamelCase     :					List[str]=1.0     ,							__lowerCamelCase     :					Optional[Any]="gated-gelu"     ,							__lowerCamelCase     :					Optional[Any]=True     ,							__lowerCamelCase     :					Dict=True     ,							__lowerCamelCase     :					List[Any]="T5Tokenizer"     ,							__lowerCamelCase     :					str=True     ,							__lowerCamelCase     :					List[Any]=0     ,							__lowerCamelCase     :					List[str]=1     ,							__lowerCamelCase     :					int=0     ,							**__lowerCamelCase     :					int     ,							):
           super().__init__(
               is_encoder_decoder=__lowerCamelCase     ,							tokenizer_class=__lowerCamelCase     ,							tie_word_embeddings=__lowerCamelCase     ,							pad_token_id=__lowerCamelCase     ,							eos_token_id=__lowerCamelCase     ,							decoder_start_token_id=__lowerCamelCase     ,							**__lowerCamelCase     ,							)
           snake_case__       :   Optional[int]	      =    vocab_size
           snake_case__       :   Tuple	      =    d_model
           snake_case__       :   List[str]	      =    d_kv
           snake_case__       :   str	      =    d_ff
           snake_case__       :   Dict	      =    num_layers
           snake_case__       :   Optional[Any]	      =    (
               num_decoder_layers if num_decoder_layers is not None else self.num_layers
           )  # default = symmetry
           snake_case__       :   Dict	      =    num_heads
           snake_case__       :   int	      =    relative_attention_num_buckets
           snake_case__       :   Optional[int]	      =    relative_attention_max_distance
           snake_case__       :   List[str]	      =    dropout_rate
           snake_case__       :   Tuple	      =    layer_norm_epsilon
           snake_case__       :   Optional[Any]	      =    initializer_factor
           snake_case__       :   List[str]	      =    feed_forward_proj
           snake_case__       :   Dict	      =    use_cache

           snake_case__       :   int	      =    self.feed_forward_proj.split('-'			)
           snake_case__       :   Optional[int]	      =    act_info[-1]
           snake_case__       :   Optional[Any]	      =    act_info[0] == 'gated'

           if len(__lowerCamelCase			) > 1 and act_info[0] != "gated" or len(__lowerCamelCase			) > 2:
                 raise ValueError(
                     F"`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."
                     'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
                     '\'gated-gelu\' or \'relu\''			)

           if feed_forward_proj == "gated-gelu":
                 snake_case__       :   Optional[int]	      =    'gelu_new'





     @property
     def 			_lowerCAmelCase      (     self     :					Any			):
           return self.d_model





     @property
     def 			_lowerCAmelCase      (     self     :					Any			):
           return self.num_heads





     @property
     def 			_lowerCAmelCase      (     self     :					str			):
           return self.num_layers






class        lowercase_      (						lowerCAmelCase_							):





     @property
     # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
     def 			_lowerCAmelCase      (     self     :					List[str]			):
           snake_case__       :   Any	      =    {
               'input_ids': {0: 'batch', 1: 'encoder_sequence'},
               'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
           }
           if self.use_past:
                 snake_case__       :   Union[str, Any]	      =    'past_encoder_sequence + sequence'
                 snake_case__       :   Union[str, Any]	      =    {0: 'batch'}
                 snake_case__       :   List[str]	      =    {0: 'batch', 1: 'past_decoder_sequence + sequence'}
           else:
                 snake_case__       :   Any	      =    {0: 'batch', 1: 'decoder_sequence'}
                 snake_case__       :   List[Any]	      =    {0: 'batch', 1: 'decoder_sequence'}

           if self.use_past:
                 self.fill_with_past_key_values_(__lowerCamelCase     ,							direction='inputs'			)

           return common_inputs





     @property
     # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
     def 			_lowerCAmelCase      (     self     :					Optional[int]			):
           return 13





     @property
     def 			_lowerCAmelCase      (     self     :					Tuple			):
           return 5E-4






'''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_			      =		"."

# 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__      (		__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		)    ->   Union[str, Any]:
      snake_case__       :   str	      =    SavedModel()
      snake_case__       :   Union[str, Any]	      =    []

      with open(os.path.join(__SCREAMING_SNAKE_CASE				,      'utils'				,      'tf_ops'				,      'onnx.json'		)		) as f:
            snake_case__       :   Optional[Any]	      =    json.load(__SCREAMING_SNAKE_CASE		)['opsets']

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

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

      snake_case__       :   List[str]	      =    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
      snake_case__       :   Optional[int]	      =    sorted(__SCREAMING_SNAKE_CASE		)
      snake_case__       :   Optional[Any]	      =    []

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

      if strict and len(__SCREAMING_SNAKE_CASE		) > 0:
            raise Exception(f"Found the following incompatible ops for the opset {opset}:\n" + incompatible_ops		)
      elif len(__SCREAMING_SNAKE_CASE		) > 0:
            print(f"Found the following incompatible ops for the opset {opset}:"		)
            print(*__SCREAMING_SNAKE_CASE				,      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)
