






'''simple docstring'''




from ...configuration_utils import PretrainedConfig


__lowerCamelCase   :					Union[str, Any]   							=						{
    '''google/tapas-base-finetuned-sqa''': (
        '''https://huggingface.co/google/tapas-base-finetuned-sqa/resolve/main/config.json'''
    ),
    '''google/tapas-base-finetuned-wtq''': (
        '''https://huggingface.co/google/tapas-base-finetuned-wtq/resolve/main/config.json'''
    ),
    '''google/tapas-base-finetuned-wikisql-supervised''': (
        '''https://huggingface.co/google/tapas-base-finetuned-wikisql-supervised/resolve/main/config.json'''
    ),
    '''google/tapas-base-finetuned-tabfact''': (
        '''https://huggingface.co/google/tapas-base-finetuned-tabfact/resolve/main/config.json'''
    ),
}







class    A_	(a_							):
							"""simple docstring"""




							a__   		=	'''tapas'''

							def __init__(		self			:Tuple       ,							lowerCAmelCase__			:Union[str, Any]=30_522       ,							lowerCAmelCase__			:Tuple=768       ,							lowerCAmelCase__			:Optional[Any]=12       ,							lowerCAmelCase__			:Union[str, Any]=12       ,							lowerCAmelCase__			:Optional[int]=3_072       ,							lowerCAmelCase__			:Union[str, Any]="gelu"       ,							lowerCAmelCase__			:str=0.1       ,							lowerCAmelCase__			:Optional[int]=0.1       ,							lowerCAmelCase__			:Dict=1_024       ,							lowerCAmelCase__			:Optional[Any]=[3, 256, 256, 2, 256, 256, 10]       ,							lowerCAmelCase__			:Tuple=0.0_2       ,							lowerCAmelCase__			:int=1E-1_2       ,							lowerCAmelCase__			:Tuple=0       ,							lowerCAmelCase__			:Tuple=10.0       ,							lowerCAmelCase__			:int=0       ,							lowerCAmelCase__			:str=1.0       ,							lowerCAmelCase__			:Optional[Any]=None       ,							lowerCAmelCase__			:List[Any]=1.0       ,							lowerCAmelCase__			:Tuple=False       ,							lowerCAmelCase__			:Tuple=None       ,							lowerCAmelCase__			:Any=1.0       ,							lowerCAmelCase__			:List[Any]=1.0       ,							lowerCAmelCase__			:Dict=False       ,							lowerCAmelCase__			:int=False       ,							lowerCAmelCase__			:Optional[Any]="ratio"       ,							lowerCAmelCase__			:List[str]=None       ,							lowerCAmelCase__			:Optional[int]=None       ,							lowerCAmelCase__			:int=64       ,							lowerCAmelCase__			:Union[str, Any]=32       ,							lowerCAmelCase__			:Dict=False       ,							lowerCAmelCase__			:List[Any]=True       ,							lowerCAmelCase__			:int=False       ,							lowerCAmelCase__			:Optional[Any]=False       ,							lowerCAmelCase__			:Tuple=True       ,							lowerCAmelCase__			:Optional[Any]=False       ,							lowerCAmelCase__			:Optional[Any]=None       ,							lowerCAmelCase__			:Optional[int]=None       ,							**lowerCAmelCase__			:int       ,							)				->  Union[str, Any]:


										'''simple docstring'''




										super().__init__(pad_token_id=lowerCAmelCase__       ,							**lowerCAmelCase__				)

										# BERT hyperparameters (with updated max_position_embeddings and type_vocab_sizes)
										snake_case_				:					Tuple   =       vocab_size
										snake_case_				:					List[Any]   =       hidden_size
										snake_case_				:					str   =       num_hidden_layers
										snake_case_				:					List[Any]   =       num_attention_heads
										snake_case_				:					Optional[int]   =       hidden_act
										snake_case_				:					Dict   =       intermediate_size
										snake_case_				:					List[Any]   =       hidden_dropout_prob
										snake_case_				:					Dict   =       attention_probs_dropout_prob
										snake_case_				:					Optional[int]   =       max_position_embeddings
										snake_case_				:					Union[str, Any]   =       type_vocab_sizes
										snake_case_				:					Optional[int]   =       initializer_range
										snake_case_				:					int   =       layer_norm_eps

										# Fine-tuning task hyperparameters
										snake_case_				:					Dict   =       positive_label_weight
										snake_case_				:					List[str]   =       num_aggregation_labels
										snake_case_				:					Union[str, Any]   =       aggregation_loss_weight
										snake_case_				:					Tuple   =       use_answer_as_supervision
										snake_case_				:					Optional[Any]   =       answer_loss_importance
										snake_case_				:					Dict   =       use_normalized_answer_loss
										snake_case_				:					Any   =       huber_loss_delta
										snake_case_				:					Optional[int]   =       temperature
										snake_case_				:					int   =       aggregation_temperature
										snake_case_				:					Union[str, Any]   =       use_gumbel_for_cells
										snake_case_				:					List[Any]   =       use_gumbel_for_aggregation
										snake_case_				:					Optional[Any]   =       average_approximation_function
										snake_case_				:					Optional[int]   =       cell_selection_preference
										snake_case_				:					Union[str, Any]   =       answer_loss_cutoff
										snake_case_				:					List[str]   =       max_num_rows
										snake_case_				:					List[Any]   =       max_num_columns
										snake_case_				:					str   =       average_logits_per_cell
										snake_case_				:					List[str]   =       select_one_column
										snake_case_				:					List[str]   =       allow_empty_column_selection
										snake_case_				:					Dict   =       init_cell_selection_weights_to_zero
										snake_case_				:					int   =       reset_position_index_per_cell
										snake_case_				:					Optional[Any]   =       disable_per_token_loss

										# Aggregation hyperparameters
										snake_case_				:					List[str]   =       aggregation_labels
										snake_case_				:					List[Any]   =       no_aggregation_label_index

										if isinstance(self.aggregation_labels       ,							lowerCAmelCase__				):
													snake_case_				:					Tuple   =       {int(lowerCAmelCase__				): v for k, v in aggregation_labels.items()}












'''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
__lowerCamelCase   :					str   							=					'''.'''

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

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__     ,__magic_name__						)->    List[str]:



			"""simple docstring"""



			snake_case_				:					Tuple   =       SavedModel()
			snake_case_				:					Dict   =       []

			with open(os.path.join(__magic_name__     ,"utils"     ,"tf_ops"     ,"onnx.json"						)						) as f:
						snake_case_				:					Dict   =       json.load(__magic_name__						)["opsets"]

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

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

			snake_case_				:					Tuple   =       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_				:					str   =       sorted(__magic_name__						)
			snake_case_				:					Optional[int]   =       []

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

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


if __name__ == "__main__":
		__lowerCamelCase   :					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)'''
		)
		__lowerCamelCase   :					Dict   							=					parser.parse_args()

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




