


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__			:					Optional[Any]			,     SCREAMING_SNAKE_CASE__			:					List[str]			,     SCREAMING_SNAKE_CASE__			:					int				):
     __UpperCamelCase    							=SavedModel()
     __UpperCamelCase    							=[]

     with open(os.path.join(SCREAMING_SNAKE_CASE__			,     'utils'			,     'tf_ops'			,     'onnx.json'				)				) as f:
          __UpperCamelCase    							=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()				)

     __UpperCamelCase    							=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    							=sorted(SCREAMING_SNAKE_CASE__				)
     __UpperCamelCase    							=[]

     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)









import warnings

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A		       =						logging.get_logger(__name__)

_A		       =						{
    'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json',
}






class     UpperCAmelCase__       (					A_		):



    """simple docstring"""






    UpperCAmelCase__						:		Optional[int]      =  "mvp"
    UpperCAmelCase__						:		Tuple      =  ["past_key_values"]
    UpperCAmelCase__						:		Union[str, Any]      =  {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}






    def __init__(			self					,   A_=50267					,   A_=1024					,   A_=12					,   A_=4096					,   A_=16					,   A_=12					,   A_=4096					,   A_=16					,   A_=0.0					,   A_=0.0					,   A_="gelu"					,   A_=1024					,   A_=0.1					,   A_=0.0					,   A_=0.0					,   A_=0.02					,   A_=0.0					,   A_=False					,   A_=True					,   A_=1					,   A_=0					,   A_=2					,   A_=True					,   A_=2					,   A_=2					,   A_=False					,   A_=100					,   A_=800					,   **A_					,   )  ->       Union[str, Any]:
         __UpperCamelCase    							=vocab_size
         __UpperCamelCase    							=max_position_embeddings
         __UpperCamelCase    							=d_model
         __UpperCamelCase    							=encoder_ffn_dim
         __UpperCamelCase    							=encoder_layers
         __UpperCamelCase    							=encoder_attention_heads
         __UpperCamelCase    							=decoder_ffn_dim
         __UpperCamelCase    							=decoder_layers
         __UpperCamelCase    							=decoder_attention_heads
         __UpperCamelCase    							=dropout
         __UpperCamelCase    							=attention_dropout
         __UpperCamelCase    							=activation_dropout
         __UpperCamelCase    							=activation_function
         __UpperCamelCase    							=init_std
         __UpperCamelCase    							=encoder_layerdrop
         __UpperCamelCase    							=decoder_layerdrop
         __UpperCamelCase    							=classifier_dropout
         __UpperCamelCase    							=use_cache
         __UpperCamelCase    							=encoder_layers
         __UpperCamelCase    							=scale_embedding  # scale factor will be sqrt(d_model) if True
         __UpperCamelCase    							=use_prompt
         __UpperCamelCase    							=prompt_length
         __UpperCamelCase    							=prompt_mid_dim

         super().__init__(
             pad_token_id=A_					,   bos_token_id=A_					,   eos_token_id=A_					,   is_encoder_decoder=A_					,   decoder_start_token_id=A_					,   forced_eos_token_id=A_					,   **A_					,   )

         if self.forced_bos_token_id is None and kwargs.get('force_bos_token_to_be_generated'					,   A_    ):
              __UpperCamelCase    							=self.bos_token_id
              warnings.warn(
                  f'Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '
                  'The config can simply be saved and uploaded again to be fixed.'    )





