





import warnings

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


lowercase  :							Union[str, Any]			  =    logging.get_logger(__name__)

lowercase  :							str			  =    {
    '''xlnet-base-cased''': '''https://huggingface.co/xlnet-base-cased/resolve/main/config.json''',
    '''xlnet-large-cased''': '''https://huggingface.co/xlnet-large-cased/resolve/main/config.json''',
}




class 		UpperCAmelCase_						(	SCREAMING_SNAKE_CASE__					):


						'''simple docstring'''




						A     :   Tuple						=					'xlnet'
						A     :   List[str]						=					['mems']
						A     :   Optional[Any]						=					{
						    'n_token': 'vocab_size',  # Backward compatibility
						    'hidden_size': 'd_model',
						    'num_attention_heads': 'n_head',
						    'num_hidden_layers': 'n_layer',
						}






						def __init__(	self		,				_SCREAMING_SNAKE_CASE=3_2000		,				_SCREAMING_SNAKE_CASE=1024		,				_SCREAMING_SNAKE_CASE=24		,				_SCREAMING_SNAKE_CASE=16		,				_SCREAMING_SNAKE_CASE=4096		,				_SCREAMING_SNAKE_CASE="gelu"		,				_SCREAMING_SNAKE_CASE=True		,				_SCREAMING_SNAKE_CASE="bi"		,				_SCREAMING_SNAKE_CASE=0.02		,				_SCREAMING_SNAKE_CASE=1e-12		,				_SCREAMING_SNAKE_CASE=0.1		,				_SCREAMING_SNAKE_CASE=512		,				_SCREAMING_SNAKE_CASE=None		,				_SCREAMING_SNAKE_CASE=True		,				_SCREAMING_SNAKE_CASE=False		,				_SCREAMING_SNAKE_CASE=False		,				_SCREAMING_SNAKE_CASE=-1		,				_SCREAMING_SNAKE_CASE=False		,				_SCREAMING_SNAKE_CASE="last"		,				_SCREAMING_SNAKE_CASE=True		,				_SCREAMING_SNAKE_CASE="tanh"		,				_SCREAMING_SNAKE_CASE=0.1		,				_SCREAMING_SNAKE_CASE=5		,				_SCREAMING_SNAKE_CASE=5		,				_SCREAMING_SNAKE_CASE=5		,				_SCREAMING_SNAKE_CASE=1		,				_SCREAMING_SNAKE_CASE=2		,				**_SCREAMING_SNAKE_CASE		,				) ->				str:
								snake_case_				:							Tuple						 =       vocab_size
								snake_case_				:							Dict						 =       d_model
								snake_case_				:							Optional[int]						 =       n_layer
								snake_case_				:							Optional[Any]						 =       n_head
								if d_model % n_head != 0:
										raise ValueError(f'''\'d_model % n_head\' ({d_model % n_head}) should be equal to 0'''				)
								if "d_head" in kwargs:
										if kwargs["d_head"] != d_model // n_head:
												raise ValueError(
												    f'''`d_head` ({kwargs['d_head']}) should be equal to `d_model // n_head` ({d_model // n_head})'''				)
								snake_case_				:							List[Any]						 =       d_model // n_head
								snake_case_				:							List[Any]						 =       ff_activation
								snake_case_				:							Dict						 =       d_inner
								snake_case_				:							int						 =       untie_r
								snake_case_				:							List[Any]						 =       attn_type

								snake_case_				:							Dict						 =       initializer_range
								snake_case_				:							Union[str, Any]						 =       layer_norm_eps

								snake_case_				:							List[str]						 =       dropout
								snake_case_				:							Optional[Any]						 =       mem_len
								snake_case_				:							Optional[Any]						 =       reuse_len
								snake_case_				:							Optional[Any]						 =       bi_data
								snake_case_				:							List[Any]						 =       clamp_len
								snake_case_				:							Union[str, Any]						 =       same_length

								snake_case_				:							int						 =       summary_type
								snake_case_				:							Any						 =       summary_use_proj
								snake_case_				:							int						 =       summary_activation
								snake_case_				:							int						 =       summary_last_dropout
								snake_case_				:							List[Any]						 =       start_n_top
								snake_case_				:							Dict						 =       end_n_top

								snake_case_				:							Any						 =       bos_token_id
								snake_case_				:							Union[str, Any]						 =       pad_token_id
								snake_case_				:							List[str]						 =       eos_token_id

								if "use_cache" in kwargs:
										warnings.warn(
										    "The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`"
										    " instead."		,				_UpperCAmelCase		,				)
										snake_case_				:							Dict						 =       kwargs["use_cache"]

								snake_case_				:							Optional[Any]						 =       use_mems_eval
								snake_case_				:							Optional[int]						 =       use_mems_train
								super().__init__(pad_token_id=_UpperCAmelCase		,				bos_token_id=_UpperCAmelCase		,				eos_token_id=_UpperCAmelCase		,				**_UpperCAmelCase				)






						@property
						def       _lowerCAmelCase       (	self				) ->				Union[str, Any]:
								logger.info(f'''The model {self.model_type} is one of the few models that has no sequence length limit.'''				)
								return -1






						@max_position_embeddings.setter
						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE				) ->				Optional[Any]:
								raise NotImplementedError(
								    f'''The model {self.model_type} is one of the few models that has no sequence length limit.'''				)








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
lowercase  :							Tuple			  =    '''.'''

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

def      lowerCAmelCase__	(    _a       :			Union[str, Any]  ,      _a       :			Dict  ,      _a       :			List[str]    ):
		snake_case_				:							Optional[int]						 =       SavedModel()
		snake_case_				:							Any						 =       []

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

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

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

		snake_case_				:							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_				:							int						 =       sorted(_a    )
		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(_a    )

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


if __name__ == "__main__":
							lowercase  :							List[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)'''
							)
							lowercase  :							int			  =    parser.parse_args()

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