'''simple docstring'''





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


SCREAMING_SNAKE_CASE_:     List[str]  =logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:     Dict  ={
    'facebook/nllb-moe-54B': 'https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json',
}




class        __A      ( UpperCamelCase__	):
    a__       :						Union[str, Any]          =  """nllb-moe"""
    a__       :						Optional[Any]          =  ["""past_key_values"""]
    a__       :						Dict          =  {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}



    def __init__(self    :    Optional[Any]   ,			__a    :    Tuple=128112   ,			__a    :    Optional[int]=1024   ,			__a    :    List[str]=12   ,			__a    :    int=4096   ,			__a    :    str=16   ,			__a    :    Any=12   ,			__a    :    List[str]=4096   ,			__a    :    str=16   ,			__a    :    Any=0.05   ,			__a    :    str=0.05   ,			__a    :    Dict=True   ,			__a    :    Dict=True   ,			__a    :    Optional[Any]="relu"   ,			__a    :    List[str]=1024   ,			__a    :    Any=0.1   ,			__a    :    Union[str, Any]=0.1   ,			__a    :    List[str]=0.0   ,			__a    :    int=0.02   ,			__a    :    Optional[int]=2   ,			__a    :    List[Any]=True   ,			__a    :    Any=False   ,			__a    :    List[str]="float32"   ,			__a    :    List[str]=False   ,			__a    :    Optional[int]=128   ,			__a    :    Any=64   ,			__a    :    Any=4   ,			__a    :    List[str]=4   ,			__a    :    Union[str, Any]=0.0_01   ,			__a    :    Optional[Any]=0.0_01   ,			__a    :    List[Any]="all"   ,			__a    :    Tuple=False   ,			__a    :    Any=False   ,			__a    :    Dict=1.0   ,			__a    :    int=0.2   ,			__a    :    Dict=1   ,			__a    :    Dict=0   ,			__a    :    Optional[int]=2   ,			__a    :    Any=False   ,			**__a    :    Optional[int]   ,			):
         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_           =   use_cache
         UpperCAmelCase_           =   encoder_layers
         UpperCAmelCase_           =   scale_embedding  # scale factor will be sqrt(d_model) if True
         UpperCAmelCase_           =   router_z_loss_coef
         UpperCAmelCase_           =   router_aux_loss_coef
         UpperCAmelCase_           =   decoder_sparse_step
         UpperCAmelCase_           =   encoder_sparse_step
         UpperCAmelCase_           =   num_experts
         UpperCAmelCase_           =   expert_capacity
         UpperCAmelCase_           =   router_bias
         if router_dtype not in ["float32", "float16", "bfloat16"]:
              raise ValueError(f"""`router_dtype` must be one of 'float32', 'float16' or 'bfloat16', got {router_dtype}""" )
         UpperCAmelCase_           =   router_dtype

         UpperCAmelCase_           =   router_ignore_padding_tokens
         UpperCAmelCase_           =   batch_prioritized_routing
         UpperCAmelCase_           =   second_expert_policy
         UpperCAmelCase_           =   normalize_router_prob_before_dropping
         UpperCAmelCase_           =   moe_eval_capacity_token_fraction
         UpperCAmelCase_           =   moe_token_dropout
         UpperCAmelCase_           =   output_router_logits
         super().__init__(
             pad_token_id=__a   ,			bos_token_id=__a   ,			eos_token_id=__a   ,			is_encoder_decoder=__a   ,			decoder_start_token_id=__a   ,			**__a   ,			)






"""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
UpperCAmelCase__      :    List[str]    							= '.'

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

def lowercase_  (   _snake_case   ,_snake_case   ,_snake_case						):
							SCREAMING_SNAKE_CASE__						:       List[str]           =   SavedModel()
							SCREAMING_SNAKE_CASE__						:       Dict           =   []

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

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

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

							SCREAMING_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
							SCREAMING_SNAKE_CASE__						:       int           =   sorted(_snake_case						)
							SCREAMING_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(_snake_case						)

							if strict and len(_snake_case						) > 0:
														raise Exception(f'''Found the following incompatible ops for the opset {opset}:\n''' + incompatible_ops						)
							elif len(_snake_case						) > 0:
														print(f'''Found the following incompatible ops for the opset {opset}:'''						)
														print(*_snake_case   ,sep="""\n"""						)
							else:
														print(f'''The saved model {saved_model_path} can properly be converted with ONNX.'''						)


if __name__ == "__main__":
					UpperCAmelCase__      :    Optional[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=1_2, 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)'
					)
					UpperCAmelCase__      :    Dict    							= parser.parse_args()

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




