





import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
							lowercase  :							str			  =    argparse.ArgumentParser(
							    description=(
							        '''Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned'''
							        ''' Distillation'''
							    )
							)
							parser.add_argument('''--model_type''', default='''roberta''', choices=['''roberta''', '''gpt2'''])
							parser.add_argument('''--model_name''', default='''roberta-large''', type=str)
							parser.add_argument('''--dump_checkpoint''', default='''serialization_dir/tf_roberta_048131723.pth''', type=str)
							parser.add_argument('''--vocab_transform''', action='''store_true''')
							lowercase  :							Any			  =    parser.parse_args()

							if args.model_type == "roberta":
														lowercase  :							str			  =    RobertaForMaskedLM.from_pretrained(args.model_name)
														lowercase  :							str			  =    '''roberta'''
							elif args.model_type == "gpt2":
														lowercase  :							List[Any]			  =    GPTaLMHeadModel.from_pretrained(args.model_name)
														lowercase  :							Optional[int]			  =    '''transformer'''

							lowercase  :							str			  =    model.state_dict()
							lowercase  :							Any			  =    {}

							# Embeddings #
							if args.model_type == "gpt2":
														for param_name in ["wte.weight", "wpe.weight"]:
																					lowercase  :							Union[str, Any]			  =    state_dict[F"""{prefix}.{param_name}"""]
							else:
														for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
																					lowercase  :							List[Any]			  =    F"""{prefix}.embeddings.{w}.weight"""
																					lowercase  :							int			  =    state_dict[param_name]
														for w in ["weight", "bias"]:
																					lowercase  :							List[Any]			  =    F"""{prefix}.embeddings.LayerNorm.{w}"""
																					lowercase  :							Tuple			  =    state_dict[param_name]

    # Transformer Blocks #
							lowercase  :							Union[str, Any]			  =    0
							for teacher_idx in [0, 2, 4, 7, 9, 11]:
														if args.model_type == "gpt2":
																					for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
																												for w in ["weight", "bias"]:
																																			lowercase  :							Union[str, Any]			  =    state_dict[
																																			    F"""{prefix}.h.{teacher_idx}.{layer}.{w}"""
																																			]
																					lowercase  :							List[str]			  =    state_dict[F"""{prefix}.h.{teacher_idx}.attn.bias"""]
														else:
																					for layer in [
																					    "attention.self.query",
																					    "attention.self.key",
																					    "attention.self.value",
																					    "attention.output.dense",
																					    "attention.output.LayerNorm",
																					    "intermediate.dense",
																					    "output.dense",
																					    "output.LayerNorm",
																					]:
																												for w in ["weight", "bias"]:
																																			lowercase  :							List[str]			  =    state_dict[
																																			    F"""{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}"""
																																			]
														std_idx += 1

							# Language Modeling Head ###s
							if args.model_type == "roberta":
														for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
																					lowercase  :							int			  =    state_dict[F"""{layer}"""]
														if args.vocab_transform:
																					for w in ["weight", "bias"]:
																												lowercase  :							Dict			  =    state_dict[F"""lm_head.dense.{w}"""]
																												lowercase  :							Optional[Any]			  =    state_dict[F"""lm_head.layer_norm.{w}"""]
							elif args.model_type == "gpt2":
														for w in ["weight", "bias"]:
																					lowercase  :							List[Any]			  =    state_dict[F"""{prefix}.ln_f.{w}"""]
														lowercase  :							Optional[int]			  =    state_dict['''lm_head.weight''']

							print(F"""N layers selected for distillation: {std_idx}""")
							print(F"""Number of params transferred for distillation: {len(compressed_sd.keys())}""")

							print(F"""Save transferred checkpoint to {args.dump_checkpoint}.""")
							torch.save(compressed_sd, args.dump_checkpoint)








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)
