


"""simple docstring"""


class snake_case      :







		def __init__(					self      :       Optional[int]		)       ->						Tuple:





						'''simple docstring'''







						_A					  =			0
						_A					  =			0
						_A					  =			{}







		def  a_ (					self      :       List[str]    ,   a__      :       List[Any]		)       ->						Any:





						'''simple docstring'''







						if vertex not in self.adjacency:
										_A					  =			{}
										self.num_vertices += 1







		def  a_ (					self      :       Union[str, Any]    ,   a__      :       List[Any]    ,   a__      :       Optional[Any]    ,   a__      :       Dict		)       ->						Optional[int]:





						'''simple docstring'''







						self.add_vertex(a__		)
						self.add_vertex(a__		)

						if head == tail:
										return

						_A					  =			weight
						_A					  =			weight







		def  a_ (					self      :       Optional[Any]		)       ->						List[Any]:





						'''simple docstring'''







						_A					  =			self.get_edges()
						for edge in edges:
										_A  ,		_A  ,		_A					  =			edge
										edges.remove((tail, head, weight)		)
						for i in range(len(a__		)		):
										_A					  =			list(edges[i]		)

						edges.sort(key=lambda a__		: e[2]		)
						for i in range(len(a__		) - 1		):
										if edges[i][2] >= edges[i + 1][2]:
														_A					  =			edges[i][2] + 1
						for edge in edges:
										_A  ,		_A  ,		_A					  =			edge
										_A					  =			weight
										_A					  =			weight







		def __str__(					self      :       Union[str, Any]		)       ->						List[str]:





						'''simple docstring'''







						_A					  =			""
						for tail in self.adjacency:
										for head in self.adjacency[tail]:
														_A					  =			self.adjacency[head][tail]
														string += F"""{head} -> {tail} == {weight}\n"""
						return string.rstrip("\n"		)







		def  a_ (					self      :       List[str]		)       ->						Optional[Any]:





						'''simple docstring'''







						_A					  =			[]
						for tail in self.adjacency:
										for head in self.adjacency[tail]:
														output.append((tail, head, self.adjacency[head][tail])		)
						return output







		def  a_ (					self      :       List[str]		)       ->						Optional[int]:





						'''simple docstring'''







						return self.adjacency.keys()







		@staticmethod
		def  a_ (					a__      :       List[Any]=None    ,   a__      :       List[str]=None		)       ->						List[str]:





						'''simple docstring'''







						_A					  =			Graph()
						if vertices is None:
										_A					  =			[]
						if edges is None:
										_A					  =			[]
						for vertex in vertices:
										g.add_vertex(a__		)
						for edge in edges:
										g.add_edge(*a__		)
						return g
		class snake_case      :







				def __init__(					self      :       Any		)       ->						Optional[int]:





								'''simple docstring'''







								_A					  =			{}
								_A					  =			{}







				def __len__(					self      :       List[str]		)       ->						Dict:





								'''simple docstring'''







								return len(self.parent		)







				def  a_ (					self      :       List[Any]    ,   a__      :       Any		)       ->						List[Any]:





								'''simple docstring'''







								if item in self.parent:
												return self.find(a__		)

								_A					  =			item
								_A					  =			0
								return item







				def  a_ (					self      :       str    ,   a__      :       List[str]		)       ->						str:





								'''simple docstring'''







								if item not in self.parent:
												return self.make_set(a__		)
								if item != self.parent[item]:
												_A					  =			self.find(self.parent[item]		)
								return self.parent[item]







				def  a_ (					self      :       str    ,   a__      :       Optional[int]    ,   a__      :       Union[str, Any]		)       ->						List[str]:





								'''simple docstring'''







								_A					  =			self.find(a__		)
								_A					  =			self.find(a__		)

								if roota == roota:
												return roota

								if self.rank[roota] > self.rank[roota]:
												_A					  =			roota
												return roota

								if self.rank[roota] < self.rank[roota]:
												_A					  =			roota
												return roota

								if self.rank[roota] == self.rank[roota]:
												self.rank[roota] += 1
												_A					  =			roota
												return roota
								return None







		@staticmethod
		def  a_ (					a__      :       Dict		)       ->						Union[str, Any]:





						'''simple docstring'''







						_A					  =			graph.num_vertices

						_A					  =			Graph.UnionFind()
						_A					  =			[]
						while num_components > 1:
										_A					  =			{}
										for vertex in graph.get_vertices():
														_A					  =			-1

										_A					  =			graph.get_edges()
										for edge in edges:
														_A  ,		_A  ,		_A					  =			edge
														edges.remove((tail, head, weight)		)
										for edge in edges:
														_A  ,		_A  ,		_A					  =			edge
														_A					  =			union_find.find(a__		)
														_A					  =			union_find.find(a__		)
														if seta != seta:
																		if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
																						_A					  =			[head, tail, weight]

																		if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
																						_A					  =			[head, tail, weight]
										for vertex in cheap_edge:
														if cheap_edge[vertex] != -1:
																		_A  ,		_A  ,		_A					  =			cheap_edge[vertex]
																		if union_find.find(a__		) != union_find.find(a__		):
																						union_find.union(a__    ,   a__		)
																						mst_edges.append(cheap_edge[vertex]		)
																						_A					  =			num_components - 1
						_A					  =			Graph.build(edges=a__		)
						return mst



"""simple docstring"""


import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
		a_       							=    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")
		a_       							=    parser.parse_args()

		if args.model_type == "roberta":
				a_       							=    RobertaForMaskedLM.from_pretrained(args.model_name)
				a_       							=    "roberta"
		elif args.model_type == "gpt2":
				a_       							=    GPTaLMHeadModel.from_pretrained(args.model_name)
				a_       							=    "transformer"

		a_       							=    model.state_dict()
		a_       							=    {}

		# Embeddings #
		if args.model_type == "gpt2":
				for param_name in ["wte.weight", "wpe.weight"]:
						a_       							=    state_dict[f'''{prefix}.{param_name}''']
		else:
				for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
						a_       							=    f'''{prefix}.embeddings.{w}.weight'''
						a_       							=    state_dict[param_name]
				for w in ["weight", "bias"]:
						a_       							=    f'''{prefix}.embeddings.LayerNorm.{w}'''
						a_       							=    state_dict[param_name]

    # Transformer Blocks #
		a_       							=    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"]:
										a_       							=    state_dict[
										    f'''{prefix}.h.{teacher_idx}.{layer}.{w}'''
										]
						a_       							=    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"]:
										a_       							=    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"]:
						a_       							=    state_dict[f'''{layer}''']
				if args.vocab_transform:
						for w in ["weight", "bias"]:
								a_       							=    state_dict[f'''lm_head.dense.{w}''']
								a_       							=    state_dict[f'''lm_head.layer_norm.{w}''']
		elif args.model_type == "gpt2":
				for w in ["weight", "bias"]:
						a_       							=    state_dict[f'''{prefix}.ln_f.{w}''']
				a_       							=    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)