


"""simple docstring"""


from __future__ import annotations

a_       							=    {
    "A": ["B", "C", "E"],
    "B": ["A", "D", "E"],
    "C": ["A", "F", "G"],
    "D": ["B"],
    "E": ["A", "B", "D"],
    "F": ["C"],
    "G": ["C"],
}
class snake_case      :







		def __init__(					self      :       Optional[int]    ,   a__      :       dict[str, list[str]]    ,   a__      :       str		)       ->						None:





						'''simple docstring'''







						_A					  =			graph
						# mapping node to its parent in resulting breadth first tree
						_A					  =			{}
						_A					  =			source_vertex







		def  a_ (					self      :       List[str]		)       ->						None:





						'''simple docstring'''







						_A					  =			{self.source_vertex}
						_A					  =			None
						_A					  =			[self.source_vertex]  # first in first out queue

						while queue:
										_A					  =			queue.pop(0		)
										for adjacent_vertex in self.graph[vertex]:
														if adjacent_vertex not in visited:
																		visited.add(a__		)
																		_A					  =			vertex
																		queue.append(a__		)







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





						'''simple docstring'''







						if target_vertex == self.source_vertex:
										return self.source_vertex

						_A					  =			self.parent.get(a__		)
						if target_vertex_parent is None:
										_A					  =			(
										    F"""No path from vertex: {self.source_vertex} to vertex: {target_vertex}"""
										)
										raise ValueError(a__		)

						return self.shortest_path(a__		) + F"""->{target_vertex}"""


if __name__ == "__main__":
		a_       							=    Graph(graph, "G")
		g.breath_first_search()
		print(g.shortest_path("D"))
		print(g.shortest_path("G"))
		print(g.shortest_path("Foo"))



"""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)